From 0e1939abd449ab05fcae86378155aef6f63a4fed Mon Sep 17 00:00:00 2001
From: Troyanov Daniil <troyanov-2018@mail.ru>
Date: Thu, 13 Nov 2025 08:41:26 +0300
Subject: [PATCH] =?UTF-8?q?=D0=9B=D0=B0=D0=B1=D0=BE=D1=80=D0=B0=D1=82?=
 =?UTF-8?q?=D0=BE=D1=80=D0=BD=D0=B0=D1=8F=20=D1=80=D0=B0=D0=B1=D0=BE=D1=82?=
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MIME-Version: 1.0
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---
 .DS_Store                                     |   Bin 6148 -> 6148 bytes
 labworks/.DS_Store                            |   Bin 6148 -> 6148 bytes
 labworks/LW2/.DS_Store                        |   Bin 6148 -> 6148 bytes
 .../LW2/__pycache__/lab02_lib.cpython-311.pyc |   Bin 35083 -> 0 bytes
 labworks/LW2/data.txt                         |  1100 +-
 labworks/LW2/lab2_brigada1.ipynb              | 11298 ++++++++++++++++
 labworks/LW2/lab2_variant1.ipynb              |   712 -
 labworks/LW2/out/.DS_Store                    |   Bin 6148 -> 0 bytes
 labworks/LW2/out/AE1.h5                       |   Bin 0 -> 35592 bytes
 labworks/LW2/out/AE1_AE2_train_def.png        |   Bin 0 -> 21531 bytes
 .../LW2/out/AE1_AE2_train_def_anomalies.png   |   Bin 0 -> 22598 bytes
 labworks/LW2/out/AE1_ire_th.txt               |     1 +
 labworks/LW2/out/AE1_train_def.png            |   Bin 0 -> 34803 bytes
 labworks/LW2/out/AE2.h5                       |   Bin 0 -> 84304 bytes
 labworks/LW2/out/AE2_ire_th.txt               |     1 +
 labworks/LW2/out/AE2_train_def.png            |   Bin 0 -> 32108 bytes
 labworks/LW2/out/IRE_testAE1.png              |   Bin 0 -> 42447 bytes
 labworks/LW2/out/IRE_testAE2.png              |   Bin 0 -> 34461 bytes
 labworks/LW2/out/IRE_testLetter_AE.png        |   Bin 0 -> 75439 bytes
 labworks/LW2/out/IRE_trainingAE1.png          |   Bin 0 -> 113181 bytes
 labworks/LW2/out/IRE_trainingAE2.png          |   Bin 0 -> 107037 bytes
 labworks/LW2/out/IRE_trainingLetter_AE.png    |   Bin 0 -> 95122 bytes
 labworks/LW2/out/Letter_AE.h5                 |   Bin 0 -> 193168 bytes
 labworks/LW2/out/Letter_AE_ire_th.txt         |     1 +
 labworks/LW2/out/Letter_AE_norm.h5            |   Bin 0 -> 874304 bytes
 labworks/LW2/out/Letter_AE_norm_ire_th.txt    |     1 +
 labworks/LW2/out/Letter_AE_raw.h5             |   Bin 0 -> 874304 bytes
 labworks/LW2/out/Letter_AE_raw_ire_th.txt     |     1 +
 labworks/LW2/out/XtXd_1.png                   |   Bin 0 -> 75709 bytes
 labworks/LW2/out/XtXd_1_metrics.png           |   Bin 0 -> 90132 bytes
 labworks/LW2/out/XtXd_2.png                   |   Bin 0 -> 64605 bytes
 labworks/LW2/out/XtXd_2_metrics.png           |   Bin 0 -> 77560 bytes
 labworks/LW2/out/ae1_results.png              |   Bin 198257 -> 0 bytes
 labworks/LW2/out/ae2_results.png              |   Bin 183978 -> 0 bytes
 labworks/LW2/out/letter_test_results.png      |   Bin 279769 -> 0 bytes
 labworks/LW2/out/letter_training_results.png  |   Bin 179050 -> 0 bytes
 labworks/LW2/out/result.txt                   |     5 +
 labworks/LW2/out/synthetic_data.png           |   Bin 177159 -> 0 bytes
 labworks/LW2/out/test_results.png             |   Bin 279341 -> 0 bytes
 labworks/LW2/out/train_set.png                |   Bin 0 -> 44892 bytes
 labworks/LW2/report_lr2_Troyanov_Chernov.md   |   435 +
 labworks/LW2/report_variant1.md               |   320 -
 labworks/LW2/requirements.txt                 |     5 -
 43 files changed, 12743 insertions(+), 1137 deletions(-)
 delete mode 100644 labworks/LW2/__pycache__/lab02_lib.cpython-311.pyc
 create mode 100644 labworks/LW2/lab2_brigada1.ipynb
 delete mode 100644 labworks/LW2/lab2_variant1.ipynb
 delete mode 100644 labworks/LW2/out/.DS_Store
 create mode 100644 labworks/LW2/out/AE1.h5
 create mode 100644 labworks/LW2/out/AE1_AE2_train_def.png
 create mode 100644 labworks/LW2/out/AE1_AE2_train_def_anomalies.png
 create mode 100644 labworks/LW2/out/AE1_ire_th.txt
 create mode 100644 labworks/LW2/out/AE1_train_def.png
 create mode 100644 labworks/LW2/out/AE2.h5
 create mode 100644 labworks/LW2/out/AE2_ire_th.txt
 create mode 100644 labworks/LW2/out/AE2_train_def.png
 create mode 100644 labworks/LW2/out/IRE_testAE1.png
 create mode 100644 labworks/LW2/out/IRE_testAE2.png
 create mode 100644 labworks/LW2/out/IRE_testLetter_AE.png
 create mode 100644 labworks/LW2/out/IRE_trainingAE1.png
 create mode 100644 labworks/LW2/out/IRE_trainingAE2.png
 create mode 100644 labworks/LW2/out/IRE_trainingLetter_AE.png
 create mode 100644 labworks/LW2/out/Letter_AE.h5
 create mode 100644 labworks/LW2/out/Letter_AE_ire_th.txt
 create mode 100644 labworks/LW2/out/Letter_AE_norm.h5
 create mode 100644 labworks/LW2/out/Letter_AE_norm_ire_th.txt
 create mode 100644 labworks/LW2/out/Letter_AE_raw.h5
 create mode 100644 labworks/LW2/out/Letter_AE_raw_ire_th.txt
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 create mode 100644 labworks/LW2/out/XtXd_1_metrics.png
 create mode 100644 labworks/LW2/out/XtXd_2.png
 create mode 100644 labworks/LW2/out/XtXd_2_metrics.png
 delete mode 100644 labworks/LW2/out/ae1_results.png
 delete mode 100644 labworks/LW2/out/ae2_results.png
 delete mode 100644 labworks/LW2/out/letter_test_results.png
 delete mode 100644 labworks/LW2/out/letter_training_results.png
 create mode 100644 labworks/LW2/out/result.txt
 delete mode 100644 labworks/LW2/out/synthetic_data.png
 delete mode 100644 labworks/LW2/out/test_results.png
 create mode 100644 labworks/LW2/out/train_set.png
 create mode 100644 labworks/LW2/report_lr2_Troyanov_Chernov.md
 delete mode 100644 labworks/LW2/report_variant1.md
 delete mode 100644 labworks/LW2/requirements.txt

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diff --git a/labworks/LW2/data.txt b/labworks/LW2/data.txt
index 4920401..0a0ebdc 100644
--- a/labworks/LW2/data.txt
+++ b/labworks/LW2/data.txt
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diff --git a/labworks/LW2/lab2_brigada1.ipynb b/labworks/LW2/lab2_brigada1.ipynb
new file mode 100644
index 0000000..e9f2124
--- /dev/null
+++ b/labworks/LW2/lab2_brigada1.ipynb
@@ -0,0 +1,11298 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Лабораторная работа №2\n",
+    "## Обнаружение аномалий с помощью автокодировщиков\n",
+    "\n",
+    "**Выполнили:** Троянов Даниил Сергеевич, Чернов Данила Евгеньевич  \n",
+    "**Группа:** А-01-22  \n",
+    "**Бригада:** 1\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Задание 1\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1) Импорт необходимых библиотек и модулей\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Библиотеки успешно импортированы\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import lab02_lib as lib\n",
+    "from unittest.mock import patch\n",
+    "import builtins\n",
+    "\n",
+    "# Создаем директорию для выходных файлов\n",
+    "os.makedirs('out', exist_ok=True)\n",
+    "\n",
+    "print('Библиотеки успешно импортированы')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2) Генерация индивидуального набора двумерных данных\n",
+    "\n",
+    "Сгенерируем набор данных с координатами центра (1, 1), где 1 – номер бригады.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Исходные данные (первые 10 строк):\n",
+      "[[1.03326723 0.99972092]\n",
+      " [1.02921365 0.91434934]\n",
+      " [0.93529125 1.1143075 ]\n",
+      " [0.95622629 0.83603272]\n",
+      " [1.09119986 0.97851408]\n",
+      " [0.96169719 0.91449577]\n",
+      " [1.12309181 0.87265742]\n",
+      " [1.02241727 0.92151295]\n",
+      " [1.03240626 0.93404089]\n",
+      " [1.06070112 1.15962338]]\n",
+      "\n",
+      "Размерность данных:\n",
+      "(1000, 2)\n",
+      "\n",
+      "Центр кластера: (1, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Генерация датасета для бригады 1\n",
+    "brigade_num = 1\n",
+    "data = lib.datagen(brigade_num, brigade_num, 1000, 2)\n",
+    "\n",
+    "# Вывод данных и размерности\n",
+    "print('Исходные данные (первые 10 строк):')\n",
+    "print(data[:10])\n",
+    "print('\\nРазмерность данных:')\n",
+    "print(data.shape)\n",
+    "print(f'\\nЦентр кластера: ({brigade_num}, {brigade_num})')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3) Создание и обучение автокодировщика AE1 простой архитектуры\n",
+    "\n",
+    "Создадим простой автокодировщик с минимальной архитектурой и небольшим количеством эпох обучения.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучение автокодировщика AE1...\n",
+      "Архитектура: 2 -> 1 -> 2 (простая)\n",
+      "Epoch 1/1000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step - loss: 1.0661\n",
+      "Epoch 2/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 1.0624\n",
+      "Epoch 3/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 1.0587\n",
+      "Epoch 4/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 1.0550\n",
+      "Epoch 5/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.0514\n",
+      "Epoch 6/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 1.0477\n",
+      "Epoch 7/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 1.0441\n",
+      "Epoch 8/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.0404\n",
+      "Epoch 9/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.0368\n",
+      "Epoch 10/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 1.0332\n",
+      "Epoch 11/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 1.0296\n",
+      "Epoch 12/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 1.0260\n",
+      "Epoch 13/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 1.0224\n",
+      "Epoch 14/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 1.0188\n",
+      "Epoch 15/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 325ms/step - loss: 1.0152\n",
+      "Epoch 16/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 1.0117\n",
+      "Epoch 17/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 1.0081\n",
+      "Epoch 18/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 1.0046\n",
+      "Epoch 19/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 1.0010\n",
+      "Epoch 20/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.9975\n",
+      "Epoch 21/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.9940\n",
+      "Epoch 22/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.9905\n",
+      "Epoch 23/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.9870\n",
+      "Epoch 24/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.9835\n",
+      "Epoch 25/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.9800\n",
+      "Epoch 26/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.9766\n",
+      "Epoch 27/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.9731\n",
+      "Epoch 28/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.9697\n",
+      "Epoch 29/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.9663\n",
+      "Epoch 30/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.9628\n",
+      "Epoch 31/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.9594\n",
+      "Epoch 32/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.9560\n",
+      "Epoch 33/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.9526\n",
+      "Epoch 34/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.9493\n",
+      "Epoch 35/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.9459\n",
+      "Epoch 36/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.9425\n",
+      "Epoch 37/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.9392\n",
+      "Epoch 38/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.9358\n",
+      "Epoch 39/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.9325\n",
+      "Epoch 40/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.9292\n",
+      "Epoch 41/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.9259\n",
+      "Epoch 42/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.9226\n",
+      "Epoch 43/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.9193\n",
+      "Epoch 44/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.9160\n",
+      "Epoch 45/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.9128\n",
+      "Epoch 46/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.9095\n",
+      "Epoch 47/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.9063\n",
+      "Epoch 48/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.9031\n",
+      "Epoch 49/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.8998\n",
+      "Epoch 50/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.8966\n",
+      "Epoch 51/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.8934\n",
+      "Epoch 52/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.8902\n",
+      "Epoch 53/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.8871\n",
+      "Epoch 54/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.8839\n",
+      "Epoch 55/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.8807\n",
+      "Epoch 56/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.8776\n",
+      "Epoch 57/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.8745\n",
+      "Epoch 58/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.8713\n",
+      "Epoch 59/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.8682\n",
+      "Epoch 60/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.8651\n",
+      "Epoch 61/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.8620\n",
+      "Epoch 62/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.8589\n",
+      "Epoch 63/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.8559\n",
+      "Epoch 64/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.8528\n",
+      "Epoch 65/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 236ms/step - loss: 0.8497\n",
+      "Epoch 66/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.8467\n",
+      "Epoch 67/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.8437\n",
+      "Epoch 68/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.8406\n",
+      "Epoch 69/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - loss: 0.8376\n",
+      "Epoch 70/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - loss: 0.8346\n",
+      "Epoch 71/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.8316\n",
+      "Epoch 72/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.8286\n",
+      "Epoch 73/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.8257\n",
+      "Epoch 74/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.8227\n",
+      "Epoch 75/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.8198\n",
+      "Epoch 76/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.8168\n",
+      "Epoch 77/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.8139\n",
+      "Epoch 78/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.8110\n",
+      "Epoch 79/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.8080\n",
+      "Epoch 80/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.8051\n",
+      "Epoch 81/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.8023\n",
+      "Epoch 82/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.7994\n",
+      "Epoch 83/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.7965\n",
+      "Epoch 84/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.7936\n",
+      "Epoch 85/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.7908\n",
+      "Epoch 86/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.7879\n",
+      "Epoch 87/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.7851\n",
+      "Epoch 88/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.7823\n",
+      "Epoch 89/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.7795\n",
+      "Epoch 90/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.7767\n",
+      "Epoch 91/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.7739\n",
+      "Epoch 92/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 211ms/step - loss: 0.7711\n",
+      "Epoch 93/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.7683\n",
+      "Epoch 94/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.7656\n",
+      "Epoch 95/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.7628\n",
+      "Epoch 96/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.7601\n",
+      "Epoch 97/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.7573\n",
+      "Epoch 98/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.7546\n",
+      "Epoch 99/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.7519\n",
+      "Epoch 100/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7492\n",
+      "Epoch 101/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.7465\n",
+      "Epoch 102/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.7438\n",
+      "Epoch 103/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.7411\n",
+      "Epoch 104/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.7385\n",
+      "Epoch 105/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.7358\n",
+      "Epoch 106/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.7332\n",
+      "Epoch 107/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7305\n",
+      "Epoch 108/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7279\n",
+      "Epoch 109/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.7253\n",
+      "Epoch 110/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.7227\n",
+      "Epoch 111/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.7201\n",
+      "Epoch 112/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7175\n",
+      "Epoch 113/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7149\n",
+      "Epoch 114/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.7123\n",
+      "Epoch 115/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.7098\n",
+      "Epoch 116/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.7072\n",
+      "Epoch 117/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.7047\n",
+      "Epoch 118/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.7021\n",
+      "Epoch 119/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.6996\n",
+      "Epoch 120/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6971\n",
+      "Epoch 121/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6946\n",
+      "Epoch 122/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6921\n",
+      "Epoch 123/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.6896\n",
+      "Epoch 124/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.6872\n",
+      "Epoch 125/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.6847\n",
+      "Epoch 126/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.6823\n",
+      "Epoch 127/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.6798\n",
+      "Epoch 128/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.6774\n",
+      "Epoch 129/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6750\n",
+      "Epoch 130/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6726\n",
+      "Epoch 131/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.6702\n",
+      "Epoch 132/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.6678\n",
+      "Epoch 133/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.6654\n",
+      "Epoch 134/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6630\n",
+      "Epoch 135/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6606\n",
+      "Epoch 136/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6583\n",
+      "Epoch 137/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6560\n",
+      "Epoch 138/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6536\n",
+      "Epoch 139/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6513\n",
+      "Epoch 140/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6490\n",
+      "Epoch 141/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6467\n",
+      "Epoch 142/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6444\n",
+      "Epoch 143/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6421\n",
+      "Epoch 144/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6398\n",
+      "Epoch 145/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6376\n",
+      "Epoch 146/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6353\n",
+      "Epoch 147/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6331\n",
+      "Epoch 148/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6308\n",
+      "Epoch 149/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6286\n",
+      "Epoch 150/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6264\n",
+      "Epoch 151/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6242\n",
+      "Epoch 152/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6220\n",
+      "Epoch 153/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6198\n",
+      "Epoch 154/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6177\n",
+      "Epoch 155/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6155\n",
+      "Epoch 156/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.6134\n",
+      "Epoch 157/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.6112\n",
+      "Epoch 158/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6091\n",
+      "Epoch 159/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6070\n",
+      "Epoch 160/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6049\n",
+      "Epoch 161/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6028\n",
+      "Epoch 162/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.6007\n",
+      "Epoch 163/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5986\n",
+      "Epoch 164/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5965\n",
+      "Epoch 165/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5945\n",
+      "Epoch 166/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5924\n",
+      "Epoch 167/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5904\n",
+      "Epoch 168/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5884\n",
+      "Epoch 169/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5863\n",
+      "Epoch 170/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5843\n",
+      "Epoch 171/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5823\n",
+      "Epoch 172/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5803\n",
+      "Epoch 173/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5784\n",
+      "Epoch 174/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.5764\n",
+      "Epoch 175/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5744\n",
+      "Epoch 176/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.5725\n",
+      "Epoch 177/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.5705\n",
+      "Epoch 178/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.5686\n",
+      "Epoch 179/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.5667\n",
+      "Epoch 180/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.5648\n",
+      "Epoch 181/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.5629\n",
+      "Epoch 182/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.5610\n",
+      "Epoch 183/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.5591\n",
+      "Epoch 184/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.5572\n",
+      "Epoch 185/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.5553\n",
+      "Epoch 186/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.5535\n",
+      "Epoch 187/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.5516\n",
+      "Epoch 188/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.5498\n",
+      "Epoch 189/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.5480\n",
+      "Epoch 190/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.5461\n",
+      "Epoch 191/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.5443\n",
+      "Epoch 192/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.5425\n",
+      "Epoch 193/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.5407\n",
+      "Epoch 194/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.5389\n",
+      "Epoch 195/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 195ms/step - loss: 0.5372\n",
+      "Epoch 196/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.5354\n",
+      "Epoch 197/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.5336\n",
+      "Epoch 198/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.5319\n",
+      "Epoch 199/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.5301\n",
+      "Epoch 200/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.5284\n",
+      "Epoch 201/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.5266\n",
+      "Epoch 202/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.5249\n",
+      "Epoch 203/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.5232\n",
+      "Epoch 204/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.5215\n",
+      "Epoch 205/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.5198\n",
+      "Epoch 206/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.5181\n",
+      "Epoch 207/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.5164\n",
+      "Epoch 208/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.5147\n",
+      "Epoch 209/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.5130\n",
+      "Epoch 210/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.5114\n",
+      "Epoch 211/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.5097\n",
+      "Epoch 212/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.5080\n",
+      "Epoch 213/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 230ms/step - loss: 0.5064\n",
+      "Epoch 214/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.5048\n",
+      "Epoch 215/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.5031\n",
+      "Epoch 216/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.5015\n",
+      "Epoch 217/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.4999\n",
+      "Epoch 218/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.4982\n",
+      "Epoch 219/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.4966\n",
+      "Epoch 220/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.4950\n",
+      "Epoch 221/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.4934\n",
+      "Epoch 222/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.4918\n",
+      "Epoch 223/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.4902\n",
+      "Epoch 224/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.4886\n",
+      "Epoch 225/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.4871\n",
+      "Epoch 226/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.4855\n",
+      "Epoch 227/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.4839\n",
+      "Epoch 228/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.4824\n",
+      "Epoch 229/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.4808\n",
+      "Epoch 230/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.4792\n",
+      "Epoch 231/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.4777\n",
+      "Epoch 232/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.4762\n",
+      "Epoch 233/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.4746\n",
+      "Epoch 234/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.4731\n",
+      "Epoch 235/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.4716\n",
+      "Epoch 236/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.4700\n",
+      "Epoch 237/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.4685\n",
+      "Epoch 238/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.4670\n",
+      "Epoch 239/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.4655\n",
+      "Epoch 240/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.4640\n",
+      "Epoch 241/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.4625\n",
+      "Epoch 242/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.4610\n",
+      "Epoch 243/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.4595\n",
+      "Epoch 244/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.4580\n",
+      "Epoch 245/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.4566\n",
+      "Epoch 246/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.4551\n",
+      "Epoch 247/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.4536\n",
+      "Epoch 248/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.4522\n",
+      "Epoch 249/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.4507\n",
+      "Epoch 250/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.4493\n",
+      "Epoch 251/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.4478\n",
+      "Epoch 252/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.4464\n",
+      "Epoch 253/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.4449\n",
+      "Epoch 254/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.4435\n",
+      "Epoch 255/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.4421\n",
+      "Epoch 256/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 218ms/step - loss: 0.4406\n",
+      "Epoch 257/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.4392\n",
+      "Epoch 258/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.4378\n",
+      "Epoch 259/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.4364\n",
+      "Epoch 260/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.4350\n",
+      "Epoch 261/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.4336\n",
+      "Epoch 262/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.4322\n",
+      "Epoch 263/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.4308\n",
+      "Epoch 264/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.4294\n",
+      "Epoch 265/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.4280\n",
+      "Epoch 266/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.4266\n",
+      "Epoch 267/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.4252\n",
+      "Epoch 268/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.4239\n",
+      "Epoch 269/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.4225\n",
+      "Epoch 270/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.4211\n",
+      "Epoch 271/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.4198\n",
+      "Epoch 272/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.4184\n",
+      "Epoch 273/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.4171\n",
+      "Epoch 274/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.4157\n",
+      "Epoch 275/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.4144\n",
+      "Epoch 276/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.4131\n",
+      "Epoch 277/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.4117\n",
+      "Epoch 278/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.4104\n",
+      "Epoch 279/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.4091\n",
+      "Epoch 280/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.4077\n",
+      "Epoch 281/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.4064\n",
+      "Epoch 282/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.4051\n",
+      "Epoch 283/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.4038\n",
+      "Epoch 284/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.4025\n",
+      "Epoch 285/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.4012\n",
+      "Epoch 286/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.3999\n",
+      "Epoch 287/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 194ms/step - loss: 0.3986\n",
+      "Epoch 288/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.3973\n",
+      "Epoch 289/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.3960\n",
+      "Epoch 290/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.3948\n",
+      "Epoch 291/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.3935\n",
+      "Epoch 292/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.3922\n",
+      "Epoch 293/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.3910\n",
+      "Epoch 294/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.3897\n",
+      "Epoch 295/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.3884\n",
+      "Epoch 296/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.3872\n",
+      "Epoch 297/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.3859\n",
+      "Epoch 298/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 233ms/step - loss: 0.3847\n",
+      "Epoch 299/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.3834\n",
+      "Epoch 300/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.3822\n",
+      "Epoch 301/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.3809\n",
+      "Epoch 302/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.3797\n",
+      "Epoch 303/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.3785\n",
+      "Epoch 304/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.3773\n",
+      "Epoch 305/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.3760\n",
+      "Epoch 306/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.3748\n",
+      "Epoch 307/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.3736\n",
+      "Epoch 308/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.3724\n",
+      "Epoch 309/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187ms/step - loss: 0.3712\n",
+      "Epoch 310/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.3700\n",
+      "Epoch 311/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.3688\n",
+      "Epoch 312/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.3676\n",
+      "Epoch 313/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.3664\n",
+      "Epoch 314/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.3652\n",
+      "Epoch 315/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.3640\n",
+      "Epoch 316/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.3629\n",
+      "Epoch 317/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.3617\n",
+      "Epoch 318/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.3605\n",
+      "Epoch 319/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.3593\n",
+      "Epoch 320/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.3582\n",
+      "Epoch 321/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.3570\n",
+      "Epoch 322/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.3558\n",
+      "Epoch 323/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.3547\n",
+      "Epoch 324/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.3535\n",
+      "Epoch 325/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.3524\n",
+      "Epoch 326/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.3512\n",
+      "Epoch 327/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.3501\n",
+      "Epoch 328/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.3490\n",
+      "Epoch 329/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.3478\n",
+      "Epoch 330/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - loss: 0.3467\n",
+      "Epoch 331/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.3456\n",
+      "Epoch 332/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.3445\n",
+      "Epoch 333/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.3433\n",
+      "Epoch 334/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.3422\n",
+      "Epoch 335/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.3411\n",
+      "Epoch 336/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.3400\n",
+      "Epoch 337/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.3389\n",
+      "Epoch 338/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - loss: 0.3378\n",
+      "Epoch 339/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.3367\n",
+      "Epoch 340/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.3356\n",
+      "Epoch 341/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.3345\n",
+      "Epoch 342/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.3334\n",
+      "Epoch 343/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 206ms/step - loss: 0.3323\n",
+      "Epoch 344/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.3312\n",
+      "Epoch 345/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.3302\n",
+      "Epoch 346/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.3291\n",
+      "Epoch 347/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.3280\n",
+      "Epoch 348/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.3269\n",
+      "Epoch 349/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.3259\n",
+      "Epoch 350/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.3248\n",
+      "Epoch 351/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.3237\n",
+      "Epoch 352/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.3227\n",
+      "Epoch 353/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.3216\n",
+      "Epoch 354/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.3206\n",
+      "Epoch 355/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.3195\n",
+      "Epoch 356/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.3185\n",
+      "Epoch 357/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.3174\n",
+      "Epoch 358/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183ms/step - loss: 0.3164\n",
+      "Epoch 359/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.3154\n",
+      "Epoch 360/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.3143\n",
+      "Epoch 361/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.3133\n",
+      "Epoch 362/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.3123\n",
+      "Epoch 363/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.3113\n",
+      "Epoch 364/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.3102\n",
+      "Epoch 365/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.3092\n",
+      "Epoch 366/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.3082\n",
+      "Epoch 367/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.3072\n",
+      "Epoch 368/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.3062\n",
+      "Epoch 369/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.3052\n",
+      "Epoch 370/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.3042\n",
+      "Epoch 371/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.3032\n",
+      "Epoch 372/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.3022\n",
+      "Epoch 373/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.3012\n",
+      "Epoch 374/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.3002\n",
+      "Epoch 375/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.2992\n",
+      "Epoch 376/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 275ms/step - loss: 0.2982\n",
+      "Epoch 377/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.2973\n",
+      "Epoch 378/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.2963\n",
+      "Epoch 379/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.2953\n",
+      "Epoch 380/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 275ms/step - loss: 0.2943\n",
+      "Epoch 381/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 243ms/step - loss: 0.2934\n",
+      "Epoch 382/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2924\n",
+      "Epoch 383/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step - loss: 0.2914\n",
+      "Epoch 384/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.2905\n",
+      "Epoch 385/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.2895\n",
+      "Epoch 386/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.2886\n",
+      "Epoch 387/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.2876\n",
+      "Epoch 388/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.2867\n",
+      "Epoch 389/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2857\n",
+      "Epoch 390/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300ms/step - loss: 0.2848\n",
+      "Epoch 391/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.2838\n",
+      "Epoch 392/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 223ms/step - loss: 0.2829\n",
+      "Epoch 393/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.2820\n",
+      "Epoch 394/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.2810\n",
+      "Epoch 395/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 341ms/step - loss: 0.2801\n",
+      "Epoch 396/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - loss: 0.2792\n",
+      "Epoch 397/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 206ms/step - loss: 0.2783\n",
+      "Epoch 398/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.2773\n",
+      "Epoch 399/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.2764\n",
+      "Epoch 400/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.2755\n",
+      "Epoch 401/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.2746\n",
+      "Epoch 402/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.2737\n",
+      "Epoch 403/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.2728\n",
+      "Epoch 404/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2719\n",
+      "Epoch 405/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.2710\n",
+      "Epoch 406/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.2701\n",
+      "Epoch 407/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.2692\n",
+      "Epoch 408/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.2683\n",
+      "Epoch 409/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.2674\n",
+      "Epoch 410/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.2665\n",
+      "Epoch 411/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 273ms/step - loss: 0.2656\n",
+      "Epoch 412/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.2647\n",
+      "Epoch 413/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2638\n",
+      "Epoch 414/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.2630\n",
+      "Epoch 415/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.2621\n",
+      "Epoch 416/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.2612\n",
+      "Epoch 417/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.2603\n",
+      "Epoch 418/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.2595\n",
+      "Epoch 419/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.2586\n",
+      "Epoch 420/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.2578\n",
+      "Epoch 421/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 197ms/step - loss: 0.2569\n",
+      "Epoch 422/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 181ms/step - loss: 0.2560\n",
+      "Epoch 423/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.2552\n",
+      "Epoch 424/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.2543\n",
+      "Epoch 425/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.2535\n",
+      "Epoch 426/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.2526\n",
+      "Epoch 427/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.2518\n",
+      "Epoch 428/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.2509\n",
+      "Epoch 429/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.2501\n",
+      "Epoch 430/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.2493\n",
+      "Epoch 431/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.2484\n",
+      "Epoch 432/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.2476\n",
+      "Epoch 433/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.2468\n",
+      "Epoch 434/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.2459\n",
+      "Epoch 435/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.2451\n",
+      "Epoch 436/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.2443\n",
+      "Epoch 437/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.2435\n",
+      "Epoch 438/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 296ms/step - loss: 0.2426\n",
+      "Epoch 439/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.2418\n",
+      "Epoch 440/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.2410\n",
+      "Epoch 441/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.2402\n",
+      "Epoch 442/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.2394\n",
+      "Epoch 443/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.2386\n",
+      "Epoch 444/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 203ms/step - loss: 0.2378\n",
+      "Epoch 445/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.2370\n",
+      "Epoch 446/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.2362\n",
+      "Epoch 447/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.2354\n",
+      "Epoch 448/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.2346\n",
+      "Epoch 449/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.2338\n",
+      "Epoch 450/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.2330\n",
+      "Epoch 451/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.2322\n",
+      "Epoch 452/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.2314\n",
+      "Epoch 453/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.2306\n",
+      "Epoch 454/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.2299\n",
+      "Epoch 455/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2291\n",
+      "Epoch 456/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.2283\n",
+      "Epoch 457/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.2275\n",
+      "Epoch 458/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.2268\n",
+      "Epoch 459/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.2260\n",
+      "Epoch 460/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.2252\n",
+      "Epoch 461/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.2244\n",
+      "Epoch 462/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.2237\n",
+      "Epoch 463/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.2229\n",
+      "Epoch 464/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.2222\n",
+      "Epoch 465/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.2214\n",
+      "Epoch 466/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.2207\n",
+      "Epoch 467/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.2199\n",
+      "Epoch 468/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.2192\n",
+      "Epoch 469/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.2184\n",
+      "Epoch 470/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.2177\n",
+      "Epoch 471/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2169\n",
+      "Epoch 472/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.2162\n",
+      "Epoch 473/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.2154\n",
+      "Epoch 474/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.2147\n",
+      "Epoch 475/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 182ms/step - loss: 0.2140\n",
+      "Epoch 476/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.2132\n",
+      "Epoch 477/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.2125\n",
+      "Epoch 478/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.2118\n",
+      "Epoch 479/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2110\n",
+      "Epoch 480/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187ms/step - loss: 0.2103\n",
+      "Epoch 481/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.2096\n",
+      "Epoch 482/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.2089\n",
+      "Epoch 483/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.2081\n",
+      "Epoch 484/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.2074\n",
+      "Epoch 485/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.2067\n",
+      "Epoch 486/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.2060\n",
+      "Epoch 487/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.2053\n",
+      "Epoch 488/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.2046\n",
+      "Epoch 489/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.2039\n",
+      "Epoch 490/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.2032\n",
+      "Epoch 491/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.2025\n",
+      "Epoch 492/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.2018\n",
+      "Epoch 493/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.2011\n",
+      "Epoch 494/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.2004\n",
+      "Epoch 495/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.1997\n",
+      "Epoch 496/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.1990\n",
+      "Epoch 497/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.1983\n",
+      "Epoch 498/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.1976\n",
+      "Epoch 499/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.1969\n",
+      "Epoch 500/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.1962\n",
+      "Epoch 501/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.1956\n",
+      "Epoch 502/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 197ms/step - loss: 0.1949\n",
+      "Epoch 503/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183ms/step - loss: 0.1942\n",
+      "Epoch 504/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.1935\n",
+      "Epoch 505/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1929\n",
+      "Epoch 506/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - loss: 0.1922\n",
+      "Epoch 507/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.1915\n",
+      "Epoch 508/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.1908\n",
+      "Epoch 509/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.1902\n",
+      "Epoch 510/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.1895\n",
+      "Epoch 511/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.1888\n",
+      "Epoch 512/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.1882\n",
+      "Epoch 513/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 288ms/step - loss: 0.1875\n",
+      "Epoch 514/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.1869\n",
+      "Epoch 515/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.1862\n",
+      "Epoch 516/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.1856\n",
+      "Epoch 517/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.1849\n",
+      "Epoch 518/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.1843\n",
+      "Epoch 519/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.1836\n",
+      "Epoch 520/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 216ms/step - loss: 0.1830\n",
+      "Epoch 521/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.1823\n",
+      "Epoch 522/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.1817\n",
+      "Epoch 523/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.1810\n",
+      "Epoch 524/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1804\n",
+      "Epoch 525/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.1798\n",
+      "Epoch 526/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1791\n",
+      "Epoch 527/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.1785\n",
+      "Epoch 528/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.1779\n",
+      "Epoch 529/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.1772\n",
+      "Epoch 530/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.1766\n",
+      "Epoch 531/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.1760\n",
+      "Epoch 532/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.1754\n",
+      "Epoch 533/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.1747\n",
+      "Epoch 534/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.1741\n",
+      "Epoch 535/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.1735\n",
+      "Epoch 536/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.1729\n",
+      "Epoch 537/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.1723\n",
+      "Epoch 538/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.1716\n",
+      "Epoch 539/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1710\n",
+      "Epoch 540/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.1704\n",
+      "Epoch 541/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.1698\n",
+      "Epoch 542/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.1692\n",
+      "Epoch 543/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187ms/step - loss: 0.1686\n",
+      "Epoch 544/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.1680\n",
+      "Epoch 545/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.1674\n",
+      "Epoch 546/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1668\n",
+      "Epoch 547/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.1662\n",
+      "Epoch 548/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.1656\n",
+      "Epoch 549/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.1650\n",
+      "Epoch 550/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1644\n",
+      "Epoch 551/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1638\n",
+      "Epoch 552/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.1632\n",
+      "Epoch 553/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.1626\n",
+      "Epoch 554/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.1621\n",
+      "Epoch 555/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.1615\n",
+      "Epoch 556/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.1609\n",
+      "Epoch 557/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.1603\n",
+      "Epoch 558/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.1597\n",
+      "Epoch 559/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1592\n",
+      "Epoch 560/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1586\n",
+      "Epoch 561/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.1580\n",
+      "Epoch 562/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 205ms/step - loss: 0.1574\n",
+      "Epoch 563/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.1569\n",
+      "Epoch 564/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1563\n",
+      "Epoch 565/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1557\n",
+      "Epoch 566/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1552\n",
+      "Epoch 567/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1546\n",
+      "Epoch 568/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.1540\n",
+      "Epoch 569/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.1535\n",
+      "Epoch 570/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1529\n",
+      "Epoch 571/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1524\n",
+      "Epoch 572/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.1518\n",
+      "Epoch 573/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1512\n",
+      "Epoch 574/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1507\n",
+      "Epoch 575/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.1501\n",
+      "Epoch 576/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.1496\n",
+      "Epoch 577/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1490\n",
+      "Epoch 578/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.1485\n",
+      "Epoch 579/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1479\n",
+      "Epoch 580/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.1474\n",
+      "Epoch 581/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.1469\n",
+      "Epoch 582/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.1463\n",
+      "Epoch 583/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.1458\n",
+      "Epoch 584/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.1452\n",
+      "Epoch 585/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.1447\n",
+      "Epoch 586/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.1442\n",
+      "Epoch 587/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.1436\n",
+      "Epoch 588/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.1431\n",
+      "Epoch 589/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1426\n",
+      "Epoch 590/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1421\n",
+      "Epoch 591/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.1415\n",
+      "Epoch 592/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1410\n",
+      "Epoch 593/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.1405\n",
+      "Epoch 594/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.1400\n",
+      "Epoch 595/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.1394\n",
+      "Epoch 596/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.1389\n",
+      "Epoch 597/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.1384\n",
+      "Epoch 598/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1379\n",
+      "Epoch 599/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 290ms/step - loss: 0.1374\n",
+      "Epoch 600/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.1369\n",
+      "Epoch 601/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1363\n",
+      "Epoch 602/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1358\n",
+      "Epoch 603/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1353\n",
+      "Epoch 604/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1348\n",
+      "Epoch 605/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1343\n",
+      "Epoch 606/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1338\n",
+      "Epoch 607/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.1333\n",
+      "Epoch 608/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1328\n",
+      "Epoch 609/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.1323\n",
+      "Epoch 610/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.1318\n",
+      "Epoch 611/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.1313\n",
+      "Epoch 612/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.1308\n",
+      "Epoch 613/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.1303\n",
+      "Epoch 614/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.1298\n",
+      "Epoch 615/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1293\n",
+      "Epoch 616/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.1289\n",
+      "Epoch 617/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.1284\n",
+      "Epoch 618/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1279\n",
+      "Epoch 619/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.1274\n",
+      "Epoch 620/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.1269\n",
+      "Epoch 621/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.1264\n",
+      "Epoch 622/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.1260\n",
+      "Epoch 623/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.1255\n",
+      "Epoch 624/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.1250\n",
+      "Epoch 625/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.1245\n",
+      "Epoch 626/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1240\n",
+      "Epoch 627/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.1236\n",
+      "Epoch 628/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1231\n",
+      "Epoch 629/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.1226\n",
+      "Epoch 630/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1222\n",
+      "Epoch 631/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1217\n",
+      "Epoch 632/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1212\n",
+      "Epoch 633/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1208\n",
+      "Epoch 634/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1203\n",
+      "Epoch 635/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.1198\n",
+      "Epoch 636/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1194\n",
+      "Epoch 637/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1189\n",
+      "Epoch 638/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1185\n",
+      "Epoch 639/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.1180\n",
+      "Epoch 640/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.1175\n",
+      "Epoch 641/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.1171\n",
+      "Epoch 642/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.1166\n",
+      "Epoch 643/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 266ms/step - loss: 0.1162\n",
+      "Epoch 644/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.1157\n",
+      "Epoch 645/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.1153\n",
+      "Epoch 646/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.1148\n",
+      "Epoch 647/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.1144\n",
+      "Epoch 648/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1139\n",
+      "Epoch 649/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.1135\n",
+      "Epoch 650/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.1131\n",
+      "Epoch 651/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.1126\n",
+      "Epoch 652/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183ms/step - loss: 0.1122\n",
+      "Epoch 653/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.1117\n",
+      "Epoch 654/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.1113\n",
+      "Epoch 655/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.1109\n",
+      "Epoch 656/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.1104\n",
+      "Epoch 657/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.1100\n",
+      "Epoch 658/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.1096\n",
+      "Epoch 659/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1091\n",
+      "Epoch 660/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.1087\n",
+      "Epoch 661/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.1083\n",
+      "Epoch 662/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.1079\n",
+      "Epoch 663/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.1074\n",
+      "Epoch 664/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1070\n",
+      "Epoch 665/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.1066\n",
+      "Epoch 666/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.1062\n",
+      "Epoch 667/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 0.1057\n",
+      "Epoch 668/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.1053\n",
+      "Epoch 669/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.1049\n",
+      "Epoch 670/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.1045\n",
+      "Epoch 671/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.1041\n",
+      "Epoch 672/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.1037\n",
+      "Epoch 673/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.1033\n",
+      "Epoch 674/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.1028\n",
+      "Epoch 675/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.1024\n",
+      "Epoch 676/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.1020\n",
+      "Epoch 677/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.1016\n",
+      "Epoch 678/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - loss: 0.1012\n",
+      "Epoch 679/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.1008\n",
+      "Epoch 680/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.1004\n",
+      "Epoch 681/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.1000\n",
+      "Epoch 682/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0996\n",
+      "Epoch 683/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0992\n",
+      "Epoch 684/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 194ms/step - loss: 0.0988\n",
+      "Epoch 685/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0984\n",
+      "Epoch 686/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0980\n",
+      "Epoch 687/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0976\n",
+      "Epoch 688/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0972\n",
+      "Epoch 689/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0968\n",
+      "Epoch 690/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0964\n",
+      "Epoch 691/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0961\n",
+      "Epoch 692/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0957\n",
+      "Epoch 693/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0953\n",
+      "Epoch 694/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0949\n",
+      "Epoch 695/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0945\n",
+      "Epoch 696/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0941\n",
+      "Epoch 697/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0937\n",
+      "Epoch 698/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0934\n",
+      "Epoch 699/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0930\n",
+      "Epoch 700/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0926\n",
+      "Epoch 701/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0922\n",
+      "Epoch 702/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0919\n",
+      "Epoch 703/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0915\n",
+      "Epoch 704/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0911\n",
+      "Epoch 705/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0907\n",
+      "Epoch 706/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0904\n",
+      "Epoch 707/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0900\n",
+      "Epoch 708/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0896\n",
+      "Epoch 709/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0893\n",
+      "Epoch 710/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0889\n",
+      "Epoch 711/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0885\n",
+      "Epoch 712/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0882\n",
+      "Epoch 713/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0878\n",
+      "Epoch 714/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0874\n",
+      "Epoch 715/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 209ms/step - loss: 0.0871\n",
+      "Epoch 716/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0867\n",
+      "Epoch 717/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0864\n",
+      "Epoch 718/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0860\n",
+      "Epoch 719/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0857\n",
+      "Epoch 720/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0853\n",
+      "Epoch 721/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0849\n",
+      "Epoch 722/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0846\n",
+      "Epoch 723/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0842\n",
+      "Epoch 724/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0839\n",
+      "Epoch 725/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0835\n",
+      "Epoch 726/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0832\n",
+      "Epoch 727/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0828\n",
+      "Epoch 728/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0825\n",
+      "Epoch 729/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0822\n",
+      "Epoch 730/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0818\n",
+      "Epoch 731/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0815\n",
+      "Epoch 732/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0811\n",
+      "Epoch 733/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0808\n",
+      "Epoch 734/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0805\n",
+      "Epoch 735/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0801\n",
+      "Epoch 736/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0798\n",
+      "Epoch 737/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0794\n",
+      "Epoch 738/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0791\n",
+      "Epoch 739/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0788\n",
+      "Epoch 740/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0784\n",
+      "Epoch 741/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0781\n",
+      "Epoch 742/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0778\n",
+      "Epoch 743/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0775\n",
+      "Epoch 744/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0771\n",
+      "Epoch 745/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0768\n",
+      "Epoch 746/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0765\n",
+      "Epoch 747/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0762\n",
+      "Epoch 748/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0758\n",
+      "Epoch 749/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0755\n",
+      "Epoch 750/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0752\n",
+      "Epoch 751/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0749\n",
+      "Epoch 752/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0746\n",
+      "Epoch 753/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0742\n",
+      "Epoch 754/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0739\n",
+      "Epoch 755/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0736\n",
+      "Epoch 756/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0733\n",
+      "Epoch 757/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0730\n",
+      "Epoch 758/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0727\n",
+      "Epoch 759/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0724\n",
+      "Epoch 760/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0720\n",
+      "Epoch 761/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 227ms/step - loss: 0.0717\n",
+      "Epoch 762/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0714\n",
+      "Epoch 763/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0711\n",
+      "Epoch 764/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.0708\n",
+      "Epoch 765/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0705\n",
+      "Epoch 766/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0702\n",
+      "Epoch 767/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0699\n",
+      "Epoch 768/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0696\n",
+      "Epoch 769/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0693\n",
+      "Epoch 770/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0690\n",
+      "Epoch 771/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0687\n",
+      "Epoch 772/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0684\n",
+      "Epoch 773/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0681\n",
+      "Epoch 774/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0678\n",
+      "Epoch 775/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0675\n",
+      "Epoch 776/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0672\n",
+      "Epoch 777/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0669\n",
+      "Epoch 778/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0667\n",
+      "Epoch 779/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0664\n",
+      "Epoch 780/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0661\n",
+      "Epoch 781/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0658\n",
+      "Epoch 782/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0655\n",
+      "Epoch 783/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0652\n",
+      "Epoch 784/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0649\n",
+      "Epoch 785/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0646\n",
+      "Epoch 786/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0644\n",
+      "Epoch 787/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0641\n",
+      "Epoch 788/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0638\n",
+      "Epoch 789/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0635\n",
+      "Epoch 790/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0632\n",
+      "Epoch 791/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.0630\n",
+      "Epoch 792/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0627\n",
+      "Epoch 793/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0624\n",
+      "Epoch 794/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0621\n",
+      "Epoch 795/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0619\n",
+      "Epoch 796/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0616\n",
+      "Epoch 797/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0613\n",
+      "Epoch 798/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0611\n",
+      "Epoch 799/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0608\n",
+      "Epoch 800/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0605\n",
+      "Epoch 801/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0603\n",
+      "Epoch 802/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0600\n",
+      "Epoch 803/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0597\n",
+      "Epoch 804/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 204ms/step - loss: 0.0595\n",
+      "Epoch 805/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0592\n",
+      "Epoch 806/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0589\n",
+      "Epoch 807/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0587\n",
+      "Epoch 808/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0584\n",
+      "Epoch 809/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0582\n",
+      "Epoch 810/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 334ms/step - loss: 0.0579\n",
+      "Epoch 811/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - loss: 0.0576\n",
+      "Epoch 812/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0574\n",
+      "Epoch 813/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0571\n",
+      "Epoch 814/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0569\n",
+      "Epoch 815/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0566\n",
+      "Epoch 816/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0564\n",
+      "Epoch 817/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0561\n",
+      "Epoch 818/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0559\n",
+      "Epoch 819/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0556\n",
+      "Epoch 820/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0554\n",
+      "Epoch 821/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0551\n",
+      "Epoch 822/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0549\n",
+      "Epoch 823/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0546\n",
+      "Epoch 824/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0544\n",
+      "Epoch 825/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0541\n",
+      "Epoch 826/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0539\n",
+      "Epoch 827/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0537\n",
+      "Epoch 828/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0534\n",
+      "Epoch 829/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0532\n",
+      "Epoch 830/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0529\n",
+      "Epoch 831/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0527\n",
+      "Epoch 832/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0525\n",
+      "Epoch 833/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0522\n",
+      "Epoch 834/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0520\n",
+      "Epoch 835/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0518\n",
+      "Epoch 836/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0515\n",
+      "Epoch 837/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0513\n",
+      "Epoch 838/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0511\n",
+      "Epoch 839/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0508\n",
+      "Epoch 840/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0506\n",
+      "Epoch 841/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0504\n",
+      "Epoch 842/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0502\n",
+      "Epoch 843/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0499\n",
+      "Epoch 844/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0497\n",
+      "Epoch 845/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0495\n",
+      "Epoch 846/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0493\n",
+      "Epoch 847/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0490\n",
+      "Epoch 848/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0488\n",
+      "Epoch 849/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0486\n",
+      "Epoch 850/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0484\n",
+      "Epoch 851/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0481\n",
+      "Epoch 852/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0479\n",
+      "Epoch 853/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0477\n",
+      "Epoch 854/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 255ms/step - loss: 0.0475\n",
+      "Epoch 855/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0473\n",
+      "Epoch 856/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - loss: 0.0471\n",
+      "Epoch 857/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0468\n",
+      "Epoch 858/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0466\n",
+      "Epoch 859/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303ms/step - loss: 0.0464\n",
+      "Epoch 860/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0462\n",
+      "Epoch 861/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 210ms/step - loss: 0.0460\n",
+      "Epoch 862/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0458\n",
+      "Epoch 863/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0456\n",
+      "Epoch 864/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0454\n",
+      "Epoch 865/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0452\n",
+      "Epoch 866/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0450\n",
+      "Epoch 867/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 264ms/step - loss: 0.0448\n",
+      "Epoch 868/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0446\n",
+      "Epoch 869/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0444\n",
+      "Epoch 870/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0441\n",
+      "Epoch 871/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0439\n",
+      "Epoch 872/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0437\n",
+      "Epoch 873/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0435\n",
+      "Epoch 874/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0433\n",
+      "Epoch 875/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0431\n",
+      "Epoch 876/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0430\n",
+      "Epoch 877/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 346ms/step - loss: 0.0428\n",
+      "Epoch 878/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0426\n",
+      "Epoch 879/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0424\n",
+      "Epoch 880/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0422\n",
+      "Epoch 881/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0420\n",
+      "Epoch 882/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0418\n",
+      "Epoch 883/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0416\n",
+      "Epoch 884/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0414\n",
+      "Epoch 885/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0412\n",
+      "Epoch 886/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0410\n",
+      "Epoch 887/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0408\n",
+      "Epoch 888/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0407\n",
+      "Epoch 889/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0405\n",
+      "Epoch 890/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0403\n",
+      "Epoch 891/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0401\n",
+      "Epoch 892/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0399\n",
+      "Epoch 893/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0397\n",
+      "Epoch 894/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0395\n",
+      "Epoch 895/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0394\n",
+      "Epoch 896/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0392\n",
+      "Epoch 897/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0390\n",
+      "Epoch 898/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0388\n",
+      "Epoch 899/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0386\n",
+      "Epoch 900/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0385\n",
+      "Epoch 901/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0383\n",
+      "Epoch 902/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187ms/step - loss: 0.0381\n",
+      "Epoch 903/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0379\n",
+      "Epoch 904/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0378\n",
+      "Epoch 905/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0376\n",
+      "Epoch 906/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0374\n",
+      "Epoch 907/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0372\n",
+      "Epoch 908/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0371\n",
+      "Epoch 909/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0369\n",
+      "Epoch 910/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0367\n",
+      "Epoch 911/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0366\n",
+      "Epoch 912/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0364\n",
+      "Epoch 913/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0362\n",
+      "Epoch 914/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0361\n",
+      "Epoch 915/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0359\n",
+      "Epoch 916/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0357\n",
+      "Epoch 917/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0356\n",
+      "Epoch 918/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 418ms/step - loss: 0.0354\n",
+      "Epoch 919/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319ms/step - loss: 0.0352\n",
+      "Epoch 920/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 227ms/step - loss: 0.0351\n",
+      "Epoch 921/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0349\n",
+      "Epoch 922/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0348\n",
+      "Epoch 923/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 182ms/step - loss: 0.0346\n",
+      "Epoch 924/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - loss: 0.0344\n",
+      "Epoch 925/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0343\n",
+      "Epoch 926/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0341\n",
+      "Epoch 927/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0340\n",
+      "Epoch 928/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0338\n",
+      "Epoch 929/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - loss: 0.0337\n",
+      "Epoch 930/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0335\n",
+      "Epoch 931/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0334\n",
+      "Epoch 932/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0332\n",
+      "Epoch 933/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0331\n",
+      "Epoch 934/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0329\n",
+      "Epoch 935/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300ms/step - loss: 0.0328\n",
+      "Epoch 936/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0326\n",
+      "Epoch 937/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0325\n",
+      "Epoch 938/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0323\n",
+      "Epoch 939/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0322\n",
+      "Epoch 940/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0320\n",
+      "Epoch 941/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0319\n",
+      "Epoch 942/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0317\n",
+      "Epoch 943/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0316\n",
+      "Epoch 944/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0314\n",
+      "Epoch 945/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0313\n",
+      "Epoch 946/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0311\n",
+      "Epoch 947/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0310\n",
+      "Epoch 948/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0309\n",
+      "Epoch 949/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0307\n",
+      "Epoch 950/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0306\n",
+      "Epoch 951/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 225ms/step - loss: 0.0304\n",
+      "Epoch 952/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0303\n",
+      "Epoch 953/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0302\n",
+      "Epoch 954/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0300\n",
+      "Epoch 955/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0299\n",
+      "Epoch 956/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0298\n",
+      "Epoch 957/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 204ms/step - loss: 0.0296\n",
+      "Epoch 958/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 363ms/step - loss: 0.0295\n",
+      "Epoch 959/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 243ms/step - loss: 0.0294\n",
+      "Epoch 960/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0292\n",
+      "Epoch 961/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0291\n",
+      "Epoch 962/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0290\n",
+      "Epoch 963/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0288\n",
+      "Epoch 964/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0287\n",
+      "Epoch 965/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 284ms/step - loss: 0.0286\n",
+      "Epoch 966/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0284\n",
+      "Epoch 967/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0283\n",
+      "Epoch 968/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0282\n",
+      "Epoch 969/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0281\n",
+      "Epoch 970/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0279\n",
+      "Epoch 971/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0278\n",
+      "Epoch 972/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0277\n",
+      "Epoch 973/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0276\n",
+      "Epoch 974/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 199ms/step - loss: 0.0274\n",
+      "Epoch 975/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0273\n",
+      "Epoch 976/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0272\n",
+      "Epoch 977/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0271\n",
+      "Epoch 978/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0269\n",
+      "Epoch 979/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0268\n",
+      "Epoch 980/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0267\n",
+      "Epoch 981/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0266\n",
+      "Epoch 982/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 209ms/step - loss: 0.0265\n",
+      "Epoch 983/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0263\n",
+      "Epoch 984/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0262\n",
+      "Epoch 985/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0261\n",
+      "Epoch 986/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0260\n",
+      "Epoch 987/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0259\n",
+      "Epoch 988/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0258\n",
+      "Epoch 989/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0257\n",
+      "Epoch 990/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0255\n",
+      "Epoch 991/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.0254\n",
+      "Epoch 992/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 326ms/step - loss: 0.0253\n",
+      "Epoch 993/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0252\n",
+      "Epoch 994/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0251\n",
+      "Epoch 995/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0250\n",
+      "Epoch 996/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0249\n",
+      "Epoch 997/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0248\n",
+      "Epoch 998/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0247\n",
+      "Epoch 999/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 196ms/step - loss: 0.0245\n",
+      "Epoch 1000/1000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0244\n",
+      "Epoch 1000/1000\n",
+      " - loss: 0.0244\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0244\n",
+      "Restoring model weights from the end of the best epoch: 992.\n",
+      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "Обучение завершено!\n",
+      "MSE_stop (приблизительно): 0.025203\n",
+      "Порог IRE: 0.560000\n",
+      "Количество скрытых слоев: 1\n",
+      "Количество нейронов в скрытых слоях: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Обучение AE1 с использованием функции из lab02_lib\n",
+    "# Для простой архитектуры используем вариант с пользовательской архитектурой\n",
+    "# Архитектура: 2 -> 1 -> 2 (1 скрытый слой с 1 нейроном)\n",
+    "\n",
+    "print('Обучение автокодировщика AE1...')\n",
+    "print('Архитектура: 2 -> 1 -> 2 (простая)')\n",
+    "\n",
+    "# Автоматизируем ввод для выбора пользовательской архитектуры\n",
+    "with patch('builtins.input', side_effect=['1', '1', '1']):\n",
+    "    ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(\n",
+    "        data, \n",
+    "        'out/AE1.h5', \n",
+    "        'out/AE1_ire_th.txt',\n",
+    "        1000,  # epochs\n",
+    "        True,  # verbose_show\n",
+    "        300,  # patience\n",
+    "        early_stopping_delta=0.001\n",
+    "    )\n",
+    "\n",
+    "# Вычисление MSE из истории обучения (приблизительно)\n",
+    "# Для точного значения нужно сохранить историю, но функция не возвращает её\n",
+    "# Используем предсказание для оценки\n",
+    "X_pred_ae1 = ae1_trained.predict(data, verbose=0)\n",
+    "mse_ae1 = np.mean((data - X_pred_ae1) ** 2)\n",
+    "\n",
+    "print(f'\\nОбучение завершено!')\n",
+    "print(f'MSE_stop (приблизительно): {mse_ae1:.6f}')\n",
+    "print(f'Порог IRE: {IREth1:.6f}')\n",
+    "print(f'Количество скрытых слоев: 1')\n",
+    "print(f'Количество нейронов в скрытых слоях: 1')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4) Построение графика ошибки реконструкции для AE1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Порог ошибки реконструкции (IREth1): 0.560000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Построение графика ошибки реконструкции\n",
+    "lib.ire_plot('training', IRE1, IREth1, 'AE1')\n",
+    "print(f'Порог ошибки реконструкции (IREth1): {IREth1:.6f}')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5) Создание и обучение автокодировщика AE2 с усложненной архитектурой\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучение автокодировщика AE2...\n",
+      "Архитектура: 2 -> 8 -> 4 -> 2 -> 1 -> 2 -> 4 -> 8 -> 2 (усложненная)\n",
+      "Epoch 1/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5s/step - loss: 0.7725\n",
+      "Epoch 2/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.7566\n",
+      "Epoch 3/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.7407\n",
+      "Epoch 4/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.7248\n",
+      "Epoch 5/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.7091\n",
+      "Epoch 6/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.6936\n",
+      "Epoch 7/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.6781\n",
+      "Epoch 8/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.6628\n",
+      "Epoch 9/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.6476\n",
+      "Epoch 10/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.6326\n",
+      "Epoch 11/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.6177\n",
+      "Epoch 12/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.6030\n",
+      "Epoch 13/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.5884\n",
+      "Epoch 14/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.5740\n",
+      "Epoch 15/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.5598\n",
+      "Epoch 16/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.5457\n",
+      "Epoch 17/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.5318\n",
+      "Epoch 18/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.5180\n",
+      "Epoch 19/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.5045\n",
+      "Epoch 20/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - loss: 0.4911\n",
+      "Epoch 21/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.4779\n",
+      "Epoch 22/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.4649\n",
+      "Epoch 23/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.4521\n",
+      "Epoch 24/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.4395\n",
+      "Epoch 25/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.4271\n",
+      "Epoch 26/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.4149\n",
+      "Epoch 27/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.4029\n",
+      "Epoch 28/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.3911\n",
+      "Epoch 29/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.3795\n",
+      "Epoch 30/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.3681\n",
+      "Epoch 31/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.3569\n",
+      "Epoch 32/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.3459\n",
+      "Epoch 33/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 0.3351\n",
+      "Epoch 34/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 196ms/step - loss: 0.3245\n",
+      "Epoch 35/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.3141\n",
+      "Epoch 36/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.3040\n",
+      "Epoch 37/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.2940\n",
+      "Epoch 38/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.2843\n",
+      "Epoch 39/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.2748\n",
+      "Epoch 40/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.2654\n",
+      "Epoch 41/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.2563\n",
+      "Epoch 42/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.2474\n",
+      "Epoch 43/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.2387\n",
+      "Epoch 44/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.2302\n",
+      "Epoch 45/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - loss: 0.2220\n",
+      "Epoch 46/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.2139\n",
+      "Epoch 47/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.2060\n",
+      "Epoch 48/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.1983\n",
+      "Epoch 49/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.1909\n",
+      "Epoch 50/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.1836\n",
+      "Epoch 51/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1765\n",
+      "Epoch 52/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1697\n",
+      "Epoch 53/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1630\n",
+      "Epoch 54/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1565\n",
+      "Epoch 55/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1502\n",
+      "Epoch 56/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.1441\n",
+      "Epoch 57/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.1382\n",
+      "Epoch 58/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.1325\n",
+      "Epoch 59/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.1270\n",
+      "Epoch 60/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.1216\n",
+      "Epoch 61/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.1164\n",
+      "Epoch 62/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.1114\n",
+      "Epoch 63/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.1066\n",
+      "Epoch 64/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.1019\n",
+      "Epoch 65/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0974\n",
+      "Epoch 66/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 283ms/step - loss: 0.0931\n",
+      "Epoch 67/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0889\n",
+      "Epoch 68/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0849\n",
+      "Epoch 69/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0810\n",
+      "Epoch 70/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0773\n",
+      "Epoch 71/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0737\n",
+      "Epoch 72/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0703\n",
+      "Epoch 73/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0670\n",
+      "Epoch 74/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0638\n",
+      "Epoch 75/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0608\n",
+      "Epoch 76/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0579\n",
+      "Epoch 77/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0552\n",
+      "Epoch 78/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0526\n",
+      "Epoch 79/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0500\n",
+      "Epoch 80/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0476\n",
+      "Epoch 81/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0454\n",
+      "Epoch 82/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0432\n",
+      "Epoch 83/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0411\n",
+      "Epoch 84/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0391\n",
+      "Epoch 85/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0373\n",
+      "Epoch 86/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0355\n",
+      "Epoch 87/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0338\n",
+      "Epoch 88/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0322\n",
+      "Epoch 89/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0307\n",
+      "Epoch 90/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0293\n",
+      "Epoch 91/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0279\n",
+      "Epoch 92/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0267\n",
+      "Epoch 93/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0255\n",
+      "Epoch 94/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0243\n",
+      "Epoch 95/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0233\n",
+      "Epoch 96/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0223\n",
+      "Epoch 97/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0213\n",
+      "Epoch 98/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0205\n",
+      "Epoch 99/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0196\n",
+      "Epoch 100/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0189\n",
+      "Epoch 101/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0181\n",
+      "Epoch 102/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0175\n",
+      "Epoch 103/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0168\n",
+      "Epoch 104/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 274ms/step - loss: 0.0163\n",
+      "Epoch 105/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0157\n",
+      "Epoch 106/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0152\n",
+      "Epoch 107/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0147\n",
+      "Epoch 108/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0143\n",
+      "Epoch 109/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0139\n",
+      "Epoch 110/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0135\n",
+      "Epoch 111/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0132\n",
+      "Epoch 112/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0128\n",
+      "Epoch 113/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0125\n",
+      "Epoch 114/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0123\n",
+      "Epoch 115/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0120\n",
+      "Epoch 116/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0118\n",
+      "Epoch 117/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0116\n",
+      "Epoch 118/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0114\n",
+      "Epoch 119/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0112\n",
+      "Epoch 120/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0110\n",
+      "Epoch 121/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0109\n",
+      "Epoch 122/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0108\n",
+      "Epoch 123/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0106\n",
+      "Epoch 124/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0105\n",
+      "Epoch 125/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0104\n",
+      "Epoch 126/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0103\n",
+      "Epoch 127/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0103\n",
+      "Epoch 128/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0102\n",
+      "Epoch 129/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0101\n",
+      "Epoch 130/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0101\n",
+      "Epoch 131/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n",
+      "Epoch 132/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0100\n",
+      "Epoch 133/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0099\n",
+      "Epoch 134/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0099\n",
+      "Epoch 135/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0098\n",
+      "Epoch 136/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0098\n",
+      "Epoch 137/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0098\n",
+      "Epoch 138/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0098\n",
+      "Epoch 139/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0097\n",
+      "Epoch 140/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0097\n",
+      "Epoch 141/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0097\n",
+      "Epoch 142/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0097\n",
+      "Epoch 143/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0097\n",
+      "Epoch 144/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0097\n",
+      "Epoch 145/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0097\n",
+      "Epoch 146/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0097\n",
+      "Epoch 147/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 225ms/step - loss: 0.0096\n",
+      "Epoch 148/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0096\n",
+      "Epoch 149/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0096\n",
+      "Epoch 150/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0096\n",
+      "Epoch 151/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0096\n",
+      "Epoch 152/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0096\n",
+      "Epoch 153/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0096\n",
+      "Epoch 154/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0096\n",
+      "Epoch 155/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0096\n",
+      "Epoch 156/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0096\n",
+      "Epoch 157/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0096\n",
+      "Epoch 158/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0096\n",
+      "Epoch 159/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0096\n",
+      "Epoch 160/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 161/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0096\n",
+      "Epoch 162/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0096\n",
+      "Epoch 163/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0096\n",
+      "Epoch 164/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0096\n",
+      "Epoch 165/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0096\n",
+      "Epoch 166/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0096\n",
+      "Epoch 167/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0096\n",
+      "Epoch 168/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0096\n",
+      "Epoch 169/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0096\n",
+      "Epoch 170/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0096\n",
+      "Epoch 171/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0096\n",
+      "Epoch 172/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0096\n",
+      "Epoch 173/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0096\n",
+      "Epoch 174/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0096\n",
+      "Epoch 175/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0096\n",
+      "Epoch 176/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0096\n",
+      "Epoch 177/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0096\n",
+      "Epoch 178/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0096\n",
+      "Epoch 179/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0096\n",
+      "Epoch 180/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0096\n",
+      "Epoch 181/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 182/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0096\n",
+      "Epoch 183/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0096\n",
+      "Epoch 184/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 185/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0096\n",
+      "Epoch 186/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0096\n",
+      "Epoch 187/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0096\n",
+      "Epoch 188/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0096\n",
+      "Epoch 189/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0096\n",
+      "Epoch 190/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0096\n",
+      "Epoch 191/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 256ms/step - loss: 0.0096\n",
+      "Epoch 192/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0096\n",
+      "Epoch 193/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0096\n",
+      "Epoch 194/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0096\n",
+      "Epoch 195/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0096\n",
+      "Epoch 196/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0096\n",
+      "Epoch 197/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0096\n",
+      "Epoch 198/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0096\n",
+      "Epoch 199/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0096\n",
+      "Epoch 200/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0096\n",
+      "Epoch 201/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0096\n",
+      "Epoch 202/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0096\n",
+      "Epoch 203/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0096\n",
+      "Epoch 204/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0096\n",
+      "Epoch 205/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0096\n",
+      "Epoch 206/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0096\n",
+      "Epoch 207/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0096\n",
+      "Epoch 208/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0096\n",
+      "Epoch 209/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0096\n",
+      "Epoch 210/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0096\n",
+      "Epoch 211/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0096\n",
+      "Epoch 212/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0096\n",
+      "Epoch 213/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0096\n",
+      "Epoch 214/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0096\n",
+      "Epoch 215/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0096\n",
+      "Epoch 216/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0096\n",
+      "Epoch 217/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0096\n",
+      "Epoch 218/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0096\n",
+      "Epoch 219/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0096\n",
+      "Epoch 220/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0096\n",
+      "Epoch 221/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0096\n",
+      "Epoch 222/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0096\n",
+      "Epoch 223/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0096\n",
+      "Epoch 224/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0096\n",
+      "Epoch 225/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0096\n",
+      "Epoch 226/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0096\n",
+      "Epoch 227/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0096\n",
+      "Epoch 228/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 229/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0096\n",
+      "Epoch 230/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0096\n",
+      "Epoch 231/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0096\n",
+      "Epoch 232/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0096\n",
+      "Epoch 233/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0096\n",
+      "Epoch 234/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0096\n",
+      "Epoch 235/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0096\n",
+      "Epoch 236/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - loss: 0.0096\n",
+      "Epoch 237/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0096\n",
+      "Epoch 238/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0096\n",
+      "Epoch 239/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0096\n",
+      "Epoch 240/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 202ms/step - loss: 0.0096\n",
+      "Epoch 241/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0096\n",
+      "Epoch 242/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0096\n",
+      "Epoch 243/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0096\n",
+      "Epoch 244/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0096\n",
+      "Epoch 245/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0096\n",
+      "Epoch 246/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0096\n",
+      "Epoch 247/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0096\n",
+      "Epoch 248/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0096\n",
+      "Epoch 249/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0096\n",
+      "Epoch 250/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0096\n",
+      "Epoch 251/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0096\n",
+      "Epoch 252/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0096\n",
+      "Epoch 253/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 254/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0096\n",
+      "Epoch 255/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0096\n",
+      "Epoch 256/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0096\n",
+      "Epoch 257/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0096\n",
+      "Epoch 258/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 259/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0096\n",
+      "Epoch 260/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0096\n",
+      "Epoch 261/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0096\n",
+      "Epoch 262/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0096\n",
+      "Epoch 263/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0096\n",
+      "Epoch 264/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0096\n",
+      "Epoch 265/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0096\n",
+      "Epoch 266/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0096\n",
+      "Epoch 267/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0096\n",
+      "Epoch 268/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0096\n",
+      "Epoch 269/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0096\n",
+      "Epoch 270/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 271/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0096\n",
+      "Epoch 272/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0096\n",
+      "Epoch 273/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0096\n",
+      "Epoch 274/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0096\n",
+      "Epoch 275/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0096\n",
+      "Epoch 276/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0096\n",
+      "Epoch 277/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0096\n",
+      "Epoch 278/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0096\n",
+      "Epoch 279/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0096\n",
+      "Epoch 280/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0096\n",
+      "Epoch 281/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0096\n",
+      "Epoch 282/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0096\n",
+      "Epoch 283/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0096\n",
+      "Epoch 284/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0096\n",
+      "Epoch 285/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 286/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0096\n",
+      "Epoch 287/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0096\n",
+      "Epoch 288/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0096\n",
+      "Epoch 289/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0096\n",
+      "Epoch 290/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0096\n",
+      "Epoch 291/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0096\n",
+      "Epoch 292/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0096\n",
+      "Epoch 293/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0096\n",
+      "Epoch 294/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0096\n",
+      "Epoch 295/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0096\n",
+      "Epoch 296/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0096\n",
+      "Epoch 297/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0096\n",
+      "Epoch 298/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0096\n",
+      "Epoch 299/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0096\n",
+      "Epoch 300/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 301/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0096\n",
+      "Epoch 302/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0096\n",
+      "Epoch 303/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0096\n",
+      "Epoch 304/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0096\n",
+      "Epoch 305/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0096\n",
+      "Epoch 306/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0096\n",
+      "Epoch 307/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0096\n",
+      "Epoch 308/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0096\n",
+      "Epoch 309/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0096\n",
+      "Epoch 310/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0096\n",
+      "Epoch 311/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0096\n",
+      "Epoch 312/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0096\n",
+      "Epoch 313/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0096\n",
+      "Epoch 314/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0096\n",
+      "Epoch 315/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0096\n",
+      "Epoch 316/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0096\n",
+      "Epoch 317/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 195ms/step - loss: 0.0096\n",
+      "Epoch 318/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0096\n",
+      "Epoch 319/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0096\n",
+      "Epoch 320/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 321/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0096\n",
+      "Epoch 322/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0096\n",
+      "Epoch 323/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0096\n",
+      "Epoch 324/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0096\n",
+      "Epoch 325/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0096\n",
+      "Epoch 326/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0096\n",
+      "Epoch 327/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0096\n",
+      "Epoch 328/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0096\n",
+      "Epoch 329/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0096\n",
+      "Epoch 330/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0096\n",
+      "Epoch 331/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 332/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0096\n",
+      "Epoch 333/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0096\n",
+      "Epoch 334/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0096\n",
+      "Epoch 335/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0096\n",
+      "Epoch 336/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0096\n",
+      "Epoch 337/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0096\n",
+      "Epoch 338/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0096\n",
+      "Epoch 339/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0096\n",
+      "Epoch 340/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0096\n",
+      "Epoch 341/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0096\n",
+      "Epoch 342/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0096\n",
+      "Epoch 343/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0096\n",
+      "Epoch 344/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0096\n",
+      "Epoch 345/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0096\n",
+      "Epoch 346/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0096\n",
+      "Epoch 347/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0096\n",
+      "Epoch 348/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0096\n",
+      "Epoch 349/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0096\n",
+      "Epoch 350/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0096\n",
+      "Epoch 351/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0096\n",
+      "Epoch 352/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0096\n",
+      "Epoch 353/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0096\n",
+      "Epoch 354/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0096\n",
+      "Epoch 355/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0096\n",
+      "Epoch 356/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0096\n",
+      "Epoch 357/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0096\n",
+      "Epoch 358/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0096\n",
+      "Epoch 359/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0096\n",
+      "Epoch 360/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0096\n",
+      "Epoch 361/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0096\n",
+      "Epoch 362/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0096\n",
+      "Epoch 363/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0096\n",
+      "Epoch 364/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0096\n",
+      "Epoch 365/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0096\n",
+      "Epoch 366/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0096\n",
+      "Epoch 367/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0096\n",
+      "Epoch 368/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0096\n",
+      "Epoch 369/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0096\n",
+      "Epoch 370/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0096\n",
+      "Epoch 371/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0096\n",
+      "Epoch 372/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0096\n",
+      "Epoch 373/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0096\n",
+      "Epoch 374/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0096\n",
+      "Epoch 375/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0096\n",
+      "Epoch 376/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0096\n",
+      "Epoch 377/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0096\n",
+      "Epoch 378/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0096\n",
+      "Epoch 379/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0096\n",
+      "Epoch 380/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0096\n",
+      "Epoch 381/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0096\n",
+      "Epoch 382/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0096\n",
+      "Epoch 383/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0096\n",
+      "Epoch 384/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0096\n",
+      "Epoch 385/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187ms/step - loss: 0.0096\n",
+      "Epoch 386/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0096\n",
+      "Epoch 387/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0096\n",
+      "Epoch 388/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0096\n",
+      "Epoch 389/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0096\n",
+      "Epoch 390/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0096\n",
+      "Epoch 391/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0096\n",
+      "Epoch 392/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0096\n",
+      "Epoch 393/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0096\n",
+      "Epoch 394/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0096\n",
+      "Epoch 395/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0096\n",
+      "Epoch 396/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0096\n",
+      "Epoch 397/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0096\n",
+      "Epoch 398/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0096\n",
+      "Epoch 399/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0096\n",
+      "Epoch 400/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0096\n",
+      "Epoch 401/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0096\n",
+      "Epoch 402/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0096\n",
+      "Epoch 403/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0096\n",
+      "Epoch 404/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0096\n",
+      "Epoch 405/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0096\n",
+      "Epoch 406/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0096\n",
+      "Epoch 407/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0096\n",
+      "Epoch 408/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0096\n",
+      "Epoch 409/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0096\n",
+      "Epoch 410/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0096\n",
+      "Epoch 411/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0096\n",
+      "Epoch 412/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0096\n",
+      "Epoch 413/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0096\n",
+      "Epoch 414/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0096\n",
+      "Epoch 415/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0096\n",
+      "Epoch 416/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0096\n",
+      "Epoch 417/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0096\n",
+      "Epoch 418/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0096\n",
+      "Epoch 419/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0096\n",
+      "Epoch 420/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0096\n",
+      "Epoch 421/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0096\n",
+      "Epoch 422/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0096\n",
+      "Epoch 423/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0096\n",
+      "Epoch 424/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0096\n",
+      "Epoch 425/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0096\n",
+      "Epoch 426/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0096\n",
+      "Epoch 427/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0096\n",
+      "Epoch 428/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0096\n",
+      "Epoch 429/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0096\n",
+      "Epoch 430/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0096\n",
+      "Epoch 431/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0096\n",
+      "Epoch 432/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0096\n",
+      "Epoch 433/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0096\n",
+      "Epoch 434/3000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0096\n",
+      "Epoch 434: early stopping\n",
+      "Restoring model weights from the end of the best epoch: 134.\n",
+      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "Обучение завершено!\n",
+      "MSE_stop (приблизительно): 0.009841\n",
+      "Порог IRE: 0.380000\n",
+      "Количество скрытых слоев: 6\n",
+      "Количество нейронов в скрытых слоях: 8-4-2-1-2-4-8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Обучение AE2 с использованием функции из lab02_lib\n",
+    "# Для усложненной архитектуры используем вариант с пользовательской архитектурой\n",
+    "# Архитектура: 2 -> 8 -> 4 -> 2 -> 1 -> 2 -> 4 -> 8 -> 2 (6 скрытых слоев)\n",
+    "\n",
+    "print('Обучение автокодировщика AE2...')\n",
+    "print('Архитектура: 2 -> 8 -> 4 -> 2 -> 1 -> 2 -> 4 -> 8 -> 2 (усложненная)')\n",
+    "\n",
+    "# Автоматизируем ввод для выбора пользовательской архитектуры\n",
+    "# 7 скрытых слоев: 8 4 2 1 2 4 8\n",
+    "with patch('builtins.input', side_effect=['1', '7', '8 4 2 1 2 4 8']):\n",
+    "    ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(\n",
+    "        data, \n",
+    "        'out/AE2.h5', \n",
+    "        'out/AE2_ire_th.txt',\n",
+    "        3000,  # epochs\n",
+    "        True,  # verbose_show\n",
+    "        300,  # patience\n",
+    "        early_stopping_delta=0.001\n",
+    "    )\n",
+    "\n",
+    "# Вычисление MSE из предсказания\n",
+    "X_pred_ae2 = ae2_trained.predict(data, verbose=0)\n",
+    "mse_ae2 = np.mean((data - X_pred_ae2) ** 2)\n",
+    "\n",
+    "print(f'\\nОбучение завершено!')\n",
+    "print(f'MSE_stop (приблизительно): {mse_ae2:.6f}')\n",
+    "print(f'Порог IRE: {IREth2:.6f}')\n",
+    "print(f'Количество скрытых слоев: 6')\n",
+    "print(f'Количество нейронов в скрытых слоях: 8-4-2-1-2-4-8')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6) Построение графика ошибки реконструкции для AE2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Порог ошибки реконструкции (IREth2): 0.380000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Построение графика ошибки реконструкции\n",
+    "lib.ire_plot('training', IRE2, IREth2, 'AE2')\n",
+    "print(f'Порог ошибки реконструкции (IREth2): {IREth2:.6f}')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7) Расчет характеристик качества обучения EDCA для AE1 и AE2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m231/231\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "amount:  20\n",
+      "amount_ae:  135\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2200x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Оценка качества AE1\n",
+      "IDEAL = 0. Excess:  5.75\n",
+      "IDEAL = 0. Deficit:  0.0\n",
+      "IDEAL = 1. Coating:  1.0\n",
+      "summa:  1.0\n",
+      "IDEAL = 1. Extrapolation precision (Approx):  0.14814814814814814\n",
+      "\n",
+      "\n",
+      "\n",
+      "Сохраненные результаты для AE1:\n",
+      "Excess: 5.75\n",
+      "Approx: 0.14814814814814814\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Расчет EDCA для AE1\n",
+    "numb_square = 20\n",
+    "xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)\n",
+    "\n",
+    "# Сохраняем результаты EDCA для AE1 сразу после расчета\n",
+    "excess_ae1 = None\n",
+    "approx_ae1 = None\n",
+    "try:\n",
+    "    with open('out/result.txt', 'r') as f:\n",
+    "        content = f.read()\n",
+    "        if 'AE1' in content:\n",
+    "            lines = content.split('\\n')\n",
+    "            for line in lines:\n",
+    "                if 'Excess' in line and excess_ae1 is None:\n",
+    "                    excess_ae1 = float(line.split('=')[1].strip())\n",
+    "                elif 'Approx' in line and approx_ae1 is None:\n",
+    "                    approx_ae1 = float(line.split('=')[1].strip())\n",
+    "except:\n",
+    "    pass\n",
+    "\n",
+    "print(f'\\nСохраненные результаты для AE1:')\n",
+    "print(f'Excess: {excess_ae1}')\n",
+    "print(f'Approx: {approx_ae1}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m231/231\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "amount:  20\n",
+      "amount_ae:  28\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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W7allAE3LnAU0JO0Za9OmTRERsaXkN7GzckPs3bM7ytb8Mna/vjX6nviReHXD0nh1w9Kjztm+5bmIiCjf9PsoWXHo/cs3/T7KNy6L7gPOiCSJKFlxa5NnHEr1jtLY/Kd7ol3HXtG9/9/HhmfvjB2vvhgREcvWv3r0AZCyoiRJknwvoiFFRUVx3333xYUXXtjoY2666aa48cYb449//GMce+yx9e4ze/bsmDNnzgHbzz///GjbNr1XoCxfvjzGjh2b2vllZDNHRnYytm3bFo8//nhq5ye7Tj311Bg8eHBq5y+E749cZeQip6amJhYvXhwVFRXRtWvX1HKaO3OWDBnZyMhVTtoZO3fujN/+9repnZ/D17ZDr+h67KhUM15d99sYM2ZM9OvXL7WMQvj+KJQMM1bjmbNkyJDRVMxYLZvns1pORlpzVkEVgD/72c/i8ssvj//+7/+OCRMmNLhffX8xNXDgwNSH2MmTJ8f999+f2vllZDNHRnYy7r777rj00kvjyjFD4oxBx6SS8ZOn18WSkq3R7+RLo3u/MalklJeuiNIXFsSQd38xOnQd2KwzdlWsjRPGfyWV89dacc8HY8GCBXHJJZekllEI3x+5yshFTmVlZXTr1s2TU4dgzpIhIxsZucpJO2PVqlUxevToWPCh0TGid5dUMn68am3ctmJtQcxAucjYXbU5Tn3/Hamcv9Yjtw02Z7WgDDNW45mzZMiQ0VRyMWPN/d2f4p4XS6Nbv7+L18tfjppd5dFr8IRo27FXk+aUb3oqdpa/FJ17j4wuvU9pcL/tW56LHVueOeR+R5NxKDWvb42t634bbdt3j2MGvzeKWr31Rxa1GUPHfjGOGXTOEWccTNW2l+PFJVfH17/+9bj22mtTyYgojO+RQslIa84qmLcA/fnPfx6f/OQn4xe/+MVBh6WIiOLi4iguLs7RyoCsOWPQMXHJKek82bJs/auxpGRrdO83JvoO/1AqGRERpS8siF6DzzmqYSYLGa9UbU71/EDTMGcBjTWid5c4vV/3VM7dv0uHiIiCmIFyMme9/KvUzg00HXMW0Bhpzli9O735c6X69c2xd091jP7QL1O55t8fH/tq7Cx/KXoNmRBDx9R/Pb+1K2+N0hcWHPE1/xqTcSi11/zr2mdkjPzAXdGmXed6M9p3GZjaLLenZmcq56XlKYgC8P/9f//fuOyyy+LnP/95fOADH8j3cgAACoY5CwAgHeYsIEt279gcp12wMJXyrzHWrrw1SpZ/94jLv6ZQW/516jm83vIvF97YvSPWPH59znMpTJkrAHfs2BEvv/zWxdBLSkpi9erV0bNnzxg0aFDMnDkzNm7cGHfddVdEvPk2CVOnTo3vf//7MW7cuNi8eXNERHTo0CG6deuWl8cAAJBF5iwAgHSYs4DmbtDpVyr/MlD+PfPAlNhZsS7n2RSmVvlewNutWLEiTjvttDjttNMiImLGjBlx2mmnxfXXv9l6l5aWxvr16+v2/8///M9444034rOf/Wz069ev7nbVVVflZf0AAFllzgIASIc5C2juOnQdnJdc5d+basu/qtfWxIln3ZDzfApT5l4BePbZZ0eSJA1+fP78+fvdf/TRR9NdEMBh2L1nb76XANAgcxYAQDrMWQCHT/n3pn3Lv1EfXBBFrVrnfA0Upsy9AhCgudpeXRMPrCnL9zIAAAAAINOUf296e/mXr7dhpTApAAGawPbqmjj/7ifj1Z27870UAAAAAMgs5d+blH+kTQEIcJRqy7//e6UyJp3YN9/LAQAAAIBMKt/0e+VfKP/IDQUgwFHYt/x7+F/OiD6divO9JAAAAADIpPKNy5R/yj9yRAEIcITeXv6NHdAj30sCAAAAgMzZvuW5iIjoPuAM5Z/yjxxRAAIcAeUfAAAAABza2pW3xo4tz0RERPf+f5eXNSj/aIkUgACHSfkHAAAAAIe2duWtUbL8u9G598i8rUH5R0ulAAQ4DMo/AAAAADi02vJv6NgvRpfep+RlDco/WjIFIEAjKf8AAAAA4ND2Lf9c80/5R34oAAEaQfkHAAAAAIem/HuT8o98UwACHILyDwAAAAAOLQvlX/WOUuUfhAIQ4KCUfwAAAABwaFko/yIiNv/pHuUfhAIQoEHKPwAAAAA4tCyUfzWvb42IiHYdeyn/IBSAAPVS/gEAAADAoWWh/KssWx1b1/02IiL6DP8n5R+EAhDgAMo/AAAAADi0rJR/q391abRt3z0iIlq1bpfzNSj/yCIFIMA+lH8AAAAAcGhZKv869Rwexwx+b17WoPwjqxSAAH+j/AMAAACAQ8ta+TfyA3dFUau2OV+D8o8sUwAChPIPAAAAABoji+Wfa/7BgRSAQIun/AMAAACAQ1P+vWnvG7uUf2Rem3wvACCflH8AAAAAcGjKv7esW3171Ox8VflHpjX6FYCbNm1Kcx11li5dGpMmTYr+/ftHUVFRLFq06JDHPProo3H66adHcXFxDBs2LObPn5/6OoHmT/kHZIU5CwCg6eVqxoowZwGFT/n3pmRvTURE7N6xWflH5jW6AHznO98ZP/vZz9JcS0REVFVVxciRI2PevHmN2r+kpCQ+8IEPxDnnnBOrV6+Oq6++Oj75yU/GQw89lPJKgeZM+QdkiTkLAKDp5WrGijBnAYVN+femN3bviFfXLYmIiEGnX6n8I/Ma/Rag3/jGN+LTn/503HffffHDH/4wevbsmcqCJk6cGBMnTmz0/rfffnsMHTo0vvvd70ZExIgRI+Lxxx+P733ve3HeeeelskagedtZs0f5B2SKOQsAoOnlasaKMGcBhUv596Y3du+IZx6YEjW7yiMiokPXwTlfAxyuRheAn/nMZ2LixInxr//6r3HyySfHj370o5g0aVKaa2uUJ598MiZMmLDftvPOOy+uvvrqBo+prq6O6urquvuVlZVpLQ/IoK/89vnYsfuN+MSowfGbP78Sv/nzK0127pWbyiMiYmflhti+5bkmO+++dlZuiIiIqm0vp3L+XGcA5iygcDz4Ulm8uHV7Kud+ZnN5RBTODGTOgvRldcaKMGcBh+fHq9ZG/y4dUjn3io3lERGxpeQ3B8wQ5Zt+H+Ubl0X3AWdEkkSUrLj1iHNqnycr3/T7KFnR+OOqd5TG5j/dE+069oru/f8+Njx7Z5NnHMrePbujbM0vY/frW6NDtyGxs/zlqNjchAFvs2u7WY6mUZQkSXK4B/3gBz+IL3zhCzFixIho02b/DnHVqlVNt7iiorjvvvviwgsvbHCf4cOHx7Rp02LmzJl12x588MH4wAc+EK+//np06HDgD8bZs2fHnDlzDth+/vnnR9u2bZtk7fVZvnx5jB07NrXzy8hmjozsZKxbty6effbZ1M5Pdp166qkxeHB6f5lVCN8fucrIRU5NTU0sXrw4KioqomvXrqnlpMWcdWQK5XtERsvLyFVO2hmlpaWxYkV6T4KQXeaslpNhxmo8c5YMGTKaiueyWrZhw4bFiBEjUjt/IXyPFEpGWnNWo18BWGvdunVx7733Ro8ePeKCCy44YGhqDmbOnBkzZsyou19ZWRkDBw6MhQsXpjrETp48Oe6///7Uzi8jmzkyspNx9913x6WXXhpD3v3F6NB1YCoZ5aUrovSFBTIykrGzckOs/cN345prrolLLrkklYyIwvj+yFVGLnIqKyujW7duqZ0/TeasI1co3yMyWl5GrnJyNWeNeO8t0anHsFQytq57JNb+4bvNfj4plAxzVsvLMGPlnzlLhoyWl3HDDTfEs88+G/3feWl06zsmlYzSP/4iyjcui869R0aX3qdExJuvpNux5Zn9th2t8k1Pxc7ylxp9zprXt8bWdb+Ntu27xzGD3xtFrQ79hw6Hm3Eoyd6aeHXdkqjZVR69Bk+Ith17NXlGfXZWlET5xmUxderUuPbaa1PJiCiM75FCyUhrzjqsiedHP/pRfPGLX4wJEybE888/H717927yBR2uvn37RllZ2X7bysrKomvXrvX+tVRERHFxcRQXF+dieUAG9Rp8Tmq/oCMiSl9YICMjGdu3PBdr//DdVM4NTc2cBRSCTj2GpfZ7vfYtM5v7fFIoGeYsmosszlgR5izg8HTrOyb6Dv9QKucuL10R5RuXRa8hE2LomM/H2pW3RukLC5r8mn9/fOyrsbP8pbqcg6m95l/XPiMP65p/h5NxKLXX/Nu7pzpGf+iX0bXPqCbPaMjmNfdF+cZlqZyblqXRBeD5558fy5cvjx/84AcxZcqUNNd0WMaPHx8PPvjgftsefvjhGD9+fJ5WBABweMxZAABNL6szVoQ5C8imtStvjZLl323y8u9w1JZ/nXoOP6zyrynVln9Vr62JUR9cUFf+QXPT6AJwz5498eyzz8Zxxx2X5npix44d8fLLb10MvaSkJFavXh09e/aMQYMGxcyZM2Pjxo1x1113RUTEFVdcET/4wQ/immuuicsuuyz+93//N/6//+//iwceeCDVdQIANBVzFgBA08vVjBVhzgKav/JNv4/yjcuUf8o/CkijC8CHH344zXXUWbFiRZxzzjl192vf23zq1Kkxf/78KC0tjfXr19d9fOjQofHAAw/EF77whfj+978fxx13XPz4xz+O8847LyfrBQA4WuYsAICml6sZK8KcBTR/yr/slH9bSh7KSy6FJ3NXPT777LMjSZIGPz5//vx6j3n66adTXBUAQPNnzgIASIc5C2iutm95LiIiug84Q/mXgfJv7cpbY+tffp2XbApPq3wvAAAAAAAAyK21K2+NHVueiYiI7v3/Li9rUP69pfYajL2On5iXfAqPAhAAAAAAAFqQ2rKpc++ReVuD8u8ttZ+PoWO/GL2HejtomoYCEAAAAAAAWoh9y6YuvU/JyxqUf2/Z9/ORr7dhpTApAAEAAAAAoAXIQtmk/HtLFj4fFC4FIAAAAAAAFLgslE3VO0qVf3+Thc8HhU0BCAAAAAAABSwrZdPmP92j/IvsfD4obApAAAAAAAAoUFkom2pe3xoREe069lL+ZeDzQcugAAQAAAAAgAKUhbKpsmx1bF3324iI6DP8n5R/yj9yRAEIAAAAAAAFJgtlU2XZ6lj9q0ujbfvuERHRqnW7nK9B+UdLpQAEAAAAAIACkoWyqbb869RzeBwz+L15WYPyj5ZMAQgAAAAAAAUiC2XTvuXfyA/cFUWt2uZ8Dco/WjoFIAAAAAAAFIAslE1vL/9c80/5R34oAAEAAAAAoJnLQtmUhfJv757dyj+IiDb5XgAAAAAAAHDkslA2ZaH8i4goW/PLeGP3duUfLZ4CEAAAAAAAmqkslE1ZKP+SvTUREbH79a1x+oW/UP7R4nkLUAAAAAAAaIayUDZlofx7Y/eOeHXdkoiI6HviR5R/EApAAAAAAABodrJQNmWl/HvmgSlRs6s8IiKKO/fL+RoisvH5gH0pAAEAAAAAoBnJQtmUpfKv6rU10WvwhJzn18rC5wPeTgEIAAAAAADNRBbKpqyVf6M+uCDaduyV8zVEZOPzAfVRAAIAAAAAQDOQhbIpi+Wfa/7BgTJZAM6bNy+GDBkS7du3j3HjxsXy5csb3Lempia+9rWvxQknnBDt27ePkSNHxuLFi3O4WgCA5sOcBQCQDnMWkLYslE3Kv7dk4fMBB5O5AnDhwoUxY8aMmDVrVqxatSpGjhwZ5513Xrzyyiv17n/ttdfGD3/4w/h//p//J1544YW44oor4kMf+lA8/fTTOV45AEC2mbMAANJhzgLSloWySfn3lvJNv8/75wMOJXMF4M033xyXX355TJs2LU4++eS4/fbbo2PHjnHnnXfWu/9Pf/rT+Pd///d4//vfH8cff3xceeWV8f73vz+++93v5njlAADZZs4CAEiHOQtI05aSh/JeNin/9le+cZnyj8xrk+8F7Gv37t2xcuXKmDlzZt22Vq1axYQJE+LJJ5+s95jq6upo3779fts6dOgQjz/+eIM51dXVUV1dXXe/srLyKFcONCdb1z0SVdteTuXc5aUrIiJSO39ExM7KDTIaKc1zQ3NjzgJywZzVcjLMWfAWcxaQtq1/+XX0On5itO8yMDavua9Jz72zoiQi/vaKthX171O9ozQ2/+meaNexV3Tv//ex4dn6/7jhYLZvee6QOQezd8/uKFvzy9j9+tboe+JH4tUNS+PVDUubNKMxtq79bUREFHcZFEkSUbLi1ibP2PHqi01+TlqmoiRJknwvotamTZtiwIAB8cQTT8T48ePrtl9zzTXx2GOPxVNPPXXAMRdffHE888wzsWjRojjhhBNiyZIlccEFF8SePXv2G4r2NXv27JgzZ84B288///xo27Zt0z2gt1m+fHmMHTs2tfPLyGaOjOxkrFu3Lp599tnUzk92nXrqqTF48ODUzl8I3x+5yshFTk1NTSxevDgqKiqia9euqeU0N+YsGTKymZGrHHMWaTFntZwMM1bDzFkyZMhIixmrZRs2bFiMGDEitfMXwvdIoWSkNWdl6hWAR+L73/9+XH755XHSSSdFUVFRnHDCCTFt2rQG32IhImLmzJkxY8aMuvuVlZUxcODAWLhwYapD7OTJk+P+++9P7fwyspkjIzsZd999d1x66aVxfPeO8Zfy1+Pd/bvHmP7dmzRj6dqt8fzWHdHv5Euje78xTXruWuWlK6L0hQUx5N1fjA5dB8o4iJ2VG2LtH74b11xzTVxyySWpZEQUxvdHrjJykVNZWRndunVL7fwtiTlLhozC+LmYi4zaOeu9Q3vF8GPSeTsoc1a2MsxZLS/DjNW0zFkyZMhojFWrVsXo0aPjIyP6Re9OxalkPLZ2a7ywdUd0H3BGdOg2dL+P1by+Nbau+220bd89jhn83ihqdeR/bFC+6anYWf5SdO49Mrr0PqXRxyV7a+LVdUuiZld59Bo8Idp27NXkGY2xfctzsWPLM9Gm/THxxq5XU8motbOiJMo3LoupU6fGtddem0pGRGF8jxRKRlpzVqYKwF69ekXr1q2jrKxsv+1lZWXRt2/feo/p3bt3LFq0KHbt2hWvvvpq9O/fP77yla/E8ccf32BOcXFxFBen8wMTyL6/lL8eXz9nRFz7Dyc2+bk/88DqeH7rjujeb0z0Hf6hJj9/rdIXFkSvweekNmgUSsb2Lc/F2j+4hgZEmLOA3Jh22uC45JR0CidzVrYyzFnwFnMWkLaZZ50Yp/frnsq5P/PA6nhh647od9JH95uxaq/517XPyCa55t8fH/tq7Cx/KXoNmRBDxzTuunm11/zbu6c6Rn/ol4e85t+RZDTG2pW3RukLC2Lo2C/Grh1lb85ZTZyxr81r7ovyjctSOTctS6t8L2Bf7dq1i9GjR8eSJUvqtu3duzeWLFmy31so1Kd9+/YxYMCAeOONN+KXv/xlXHDBBWkvF2imPjKiXyrlH0CWmbMAANJhzgIKTW3516nn8CYp/45EbflX9dqaGPXBBYcs/9KyduWtUbL8uzF07BdjyOh0Cj9IS6ZeARgRMWPGjJg6dWqMGTMmxo4dG7fccktUVVXFtGnTIiJiypQpMWDAgJg7d25ERDz11FOxcePGGDVqVGzcuDFmz54de/fujWuuuSafDwPIsAtH9M/3EgDywpwFAJAOcxZQKJR/b1H+0dxlrgC86KKLYsuWLXH99dfH5s2bY9SoUbF48eLo06dPRESsX78+WrV664WLu3btimuvvTb+8pe/ROfOneP9739//PSnP43u3bvn6REAAGSTOQsAIB3mLKAQKP/eovyjEGSuAIyImD59ekyfPr3ejz366KP73X/Pe94TL7zwQg5WBQDQ/JmzAADSYc4CmrOdletize+uU/6F8o/CkalrAAIAAAAAALm1ftVtyr/IRvm3s3JdXnIpPApAAAAAAABowdp17qv8y0D5V1m2Otavui0v2RQeBSAAAAAAALRgg0ddofzLQPm3+leXRrvOffOST+FRAAIAAAAAQAvWqk37nGcq/95SW/516jk8Bo+6Ii9roPAoAAGaUFlVdb6XAAAAAACZpvx7y77l38gP3JWXMpbCpAAEaCLLN26L//nT5nwvAwAAAAAyS/n3lreXf/l4G1YKlwIQoAks37gtzv3psjimQ7t8LwUAAAAAMmnvnt3Kv79R/pE2BSDAUaot/951bNf4wPA++V4OAAAAAGRS2ZpfKv9C+UduKAABjsK+5d/iS8ZHu9Z+rAIAAADAvpK9NRERsfv1rco/5R854plqgCP09vKvS3HbfC8JAAAAADLljd074tV1SyIiou+JH1H+Kf/IEQUgwBFQ/gEAAADAwb2xe0c888CUqNlVHhERxZ375WUdyj9aIgUgwGFS/gEAAADAwdWWf1WvrYlegyfkbR3KP1oqBSDAYVD+AQAAAMDB7Vv+jfrggmjbsVde1qH8oyVTAAI0kvIPAAAAAA7u7eWfa/4p/8gPBSBAIyj/AAAAAODglH9vUf6Rb23yvQCArFP+AQAAAMDBZaX8K9/0+yjfuEz5R4vnFYAAB6H8AwAAAICDy0r5FxHKP/gbBSBAA5R/AAAAAHBwWSn/tm95LiIiug84Q/kHoQAEqJfyDwAAAAAOLivl39qVt8aOLc9ERET3/n+XlzUo/8iaTBaA8+bNiyFDhkT79u1j3LhxsXz58oPuf8stt8SJJ54YHTp0iIEDB8YXvvCF2LVrV45WCxQa5R9QyMxZAADpMGcBLU2Wyr+S5d+Nzr1H5iU/QvlHNmWuAFy4cGHMmDEjZs2aFatWrYqRI0fGeeedF6+88kq9+//sZz+Lr3zlKzFr1qx48cUX44477oiFCxfGv//7v+d45UAhUP4BhcycBQCQDnMW0NJkrfwbOvaL0aX3KXlZg/KPrMpcAXjzzTfH5ZdfHtOmTYuTTz45br/99ujYsWPceeed9e7/xBNPxBlnnBEXX3xxDBkyJN73vvfFP//zPx/yr6wA3k75BxQ6cxYAQDrMWUBLksXyzzX/4EBt8r2Afe3evTtWrlwZM2fOrNvWqlWrmDBhQjz55JP1HvP3f//3sWDBgli+fHmMHTs2/vKXv8SDDz4Y//Iv/9JgTnV1dVRXV9fdr6ysbLoHAWTesvWvHrDtz69VxdzH18TArh1i2qhBcf+azUd07jWv7oiIiJ2VG+ouPNzUdlZuiIiIqm0vp3L+XGdsXfdIajm1GYA5C8iN+uaspmLOylZGmueG5sacBaTtxS3bUzv3lqo3f65sKflNo55H2btnd5St+WXsfn1r9D3xI/HqhqXx6oalhzyudn4r3/T7KFlxdGuuPU/5xmXRfcAZkSQRJStubfKM+tRm7Hj1xShZcUusX3VbtOvcN/qd+NHYuvbhJsmo2JzS4mlxipIkSfK9iFqbNm2KAQMGxBNPPBHjx4+v237NNdfEY489Fk899VS9x916663xb//2b5EkSbzxxhtxxRVXxG233dZgzuzZs2POnDkHbD///POjbdv0XvFTO9SlSUb2cmRkJ2Pbtm3x+OOPp3Z+suvUU0+NwYMHp3b+Qvj+yFVGLnJqampi8eLFUVFREV27dk0tp7kxZ8mQkc2MXOWYs0jLmDFjol+/fqmdvxC+Pwolw4zVMHOWDBky0rJz58747W9/m9r5yTbPZ7WcjLTmrEy9AvBIPProo/HNb34z/uM//iPGjRsXL7/8clx11VXx9a9/Pa677rp6j5k5c2bMmDGj7n5lZWUMHDgwFi5cmOoQO3ny5Lj//vtTO7+MbObIyFbGWWedFVdccUVq51+2bFncdtttMeTdX4wOXQemklFeuiJKX1hQMBlj+3eP0f27R0REWVV1/M+fNscxHdrFB4b3iXatj+6dqte8uiOWlGyNa665Ji655JImWHX9CuX7o1B+9lZWVka3bt1SO39LYs6SIaMwfi7mKsOclZ2MnZUbYu0fvhsfGdEvRvbtnkrGpu0747YVa+OHP/xhnH766alkRBTO90chZJixmpY5S4YMGY117rnnxre+9a3Uzv/jH/84brvttug+4Izo0G1og/sle2vi1XVLomZXefQaPCHadux1WDnlm56KneUvRefeI4/qWn3btzwXO7Y8U+95mirjYHZWlET5xmXx7n7dY8yA7qlkbKmqjnteLI2f/OQn5qwWkpHWnJWpArBXr17RunXrKCsr2297WVlZ9O3bt95jrrvuuviXf/mX+OQnPxkREaecckpUVVXFpz71qfjqV78arVod+ORxcXFxFBcXN/0DADKvR48eqRZBERG33XZb9Bp8TqoXHi59YUHBZEw6sV9c+w8n1l2D8d0DejTZNRjvfm5DLCnZ2gQrhebPnAWkzZyVnYztW56LtX/4blw4on9ccko6JeOq0vK4bcXaVM4NzY05C0hThw4dUi2B+vfvHxER/U76aPQd/qF696m95t/ePdUx+kO/PKJr/v3xsa/GzvKXoteQCTF0zJFdr2/tyluj9IUFDV7zrykyDqVkxS1RvnFZTD7pzeez0rCqtDzuebE0lXPTshzdSyuaWLt27WL06NGxZMmSum179+6NJUuW7PcWCvt6/fXXDxiKWrduHRERGXp3UwAOorb8e9exXZus/AP2Z84CAEiHOQsoZLXlX9Vra2LUBxccUfnXFNauvDVKln+3wfIvFyrLVsf6VQ2/VTNkTaZeARgRMWPGjJg6dWqMGTMmxo4dG7fccktUVVXFtGnTIiJiypQpMWDAgJg7d25EREyaNCluvvnmOO200+reMuG6666LSZMm1Q1OAGTXXyt3Kv8gR8xZAADpMGcBhUj595bKstWx+leXRrvOfWNXxdq8rAEOV+YKwIsuuii2bNkS119/fWzevDlGjRoVixcvjj59+kRExPr16/f7C6lrr702ioqK4tprr42NGzdG7969Y9KkSfGNb3wjXw8BgMMwf/W6GN2/6d72E2iYOQsAIB3mLKDQKP/eUlv+deo5PPqd+NH402Nfycs64HBlrgCMiJg+fXpMnz693o89+uij+91v06ZNzJo1K2bNmpWDlQHQ1I7tVKz8gxwyZwEApMOcBRQK5d9b9i3/Rn7grti69uG8rAOORKauAQhAyzN15CDlHwAAAEAGKP/e8vbyr027znlZBxwpBSAAeVXcxvUtAAAAAPJN+fcW5R+FQAEIAAAAAAAt2N43din//kb5R6HI5DUAAQAAAACA3Fi3+vao2fmq8k/5RwFRAAIAAAAAQAu2e8fmOO2Chco/5R8FxFuAAgAAAABACzbo9CuVf8o/CowCEAAAAAAAWrAOXQfnJVf5B+lRAAJQsHbW7Mn3EgAAAACoh/IP0qUABKAgba+uiZuWvZTvZQAAAADwNso/SJ8CEICCs726Js6/+8nYULkz30sBAAAAYB/KP8gNBSAABaW2/Pu/Vypj5pnD870cAAAAAP6mfNPvlX+QIwpAAArGvuXfw/9yRpzQs1O+lwQAAADA35RvXKb8gxxRAAJQEN5e/o0d0CPfSwIAAAAgIrZveS4iIroPOEP5BzmiAASg2VP+AQAAAGTT2pW3xo4tz0RERPf+f5eXNSj/aIkUgAA0a8o/AAAAgGxau/LWKFn+3ejce2Te1qD8o6VSAALQbCn/AAAAALKptvwbOvaL0aX3KXlZg/KPlkwBCECzpPwDAAAAyKZ9yz/X/IP8UAAC0Owo/wAAAACySfkH2aAABKBZUf4BAAAAZFMWyr/qHaXKPwgFIADNiPIPAAAAIJuyUP5FRGz+0z3KP4iMFoDz5s2LIUOGRPv27WPcuHGxfPnyg+5fXl4en/3sZ6Nfv35RXFwcw4cPjwcffDBHqwUgF5R/0DTMWQAA6TBnAS1ZFsq/mte3RkREu469lH8QEW3yvYC3W7hwYcyYMSNuv/32GDduXNxyyy1x3nnnxZ/+9Kc49thjD9h/9+7dce6558axxx4b99xzTwwYMCDWrVsX3bt3z/3iAUiF8g+ahjkLACAd5iygJctC+VdZtjq2rvttRET0Gf5Pyj+IDBaAN998c1x++eUxbdq0iIi4/fbb44EHHog777wzvvKVrxyw/5133hmvvfZaPPHEE9G2bduIiBgyZEgulwxAipR/0HTMWQAA6TBnAS1VVsq/1b+6NNq27x41O7dGq9bt8rIOyJpMFYC7d++OlStXxsyZM+u2tWrVKiZMmBBPPvlkvcfcf//9MX78+PjsZz8b//3f/x29e/eOiy++OL785S9H69at6z2muro6qqur6+5XVlY27QMBWryt6x6Jqm0vp3Lu8tIVBZXxzOaKuPu5DfXus7NmT9y07KXYULkzZp45PF56bUe89NqORmcsW/9qk6wVCoE5CygUac0mERE7KzcUREbtuUu2vR6rSstTyXhxy/ZUzgvNkTkLKAQVm1cc9jFbSh6KrX/5dfQ6fmK07zIwNq+5r8F9d1aURETEjldfPOh+h2tn5bpYv+q2aNe5bxR37BsVO7c2eca+av+dNm3fac4i84qSJEnyvYhamzZtigEDBsQTTzwR48ePr9t+zTXXxGOPPRZPPfXUAcecdNJJsXbt2rjkkkviM5/5TLz88svxmc98Jj7/+c/HrFmz6s2ZPXt2zJkz54Dt559/ft1fXaVh+fLlMXbs2NTOLyObOTJaVsa6devi2WefTe38HJkzzzwzevRI75WDhfC1m6uMXOTU1NTE4sWLo6KiIrp27ZpaTnNjzpIhI5sZucophIzS0tJYseLwn5giXRMmTIgOHTqkdv5C+NotlAwzVsPMWTJkyGjOGZ7Lyi5zVsvJSGvOytQrAI/E3r1749hjj43//M//jNatW8fo0aNj48aN8e1vf7vBgWnmzJkxY8aMuvuVlZUxcODAWLhwYapD7OTJk+P+++9P7fwyspkjo2Vl3H333XHppZfGe4f2iuHHHP57ja/YVB5/2FQe7+7fPcb0717vPkvXbo3nt+6Isf27x+gG9jkaZVXVcd+LpZFExOl9u8a443o2eUZExJpXd8SSkq1x5ZVXxhlnnJFKRsSbb73zu9/9LrXzRxTG126uMnKRU1lZGd26dUvt/C2JOUuGjML4uVgoGatWrYrRo0fHlWOGRP8u6TwR8szm8rjnxdJmn7Fp+864bcXaWLBgQYwYMSKVjIiIL3/5y/Hwww+ndv6IwvjaLZQMM1bTMmfJkCEjKxm1M9ZHRvSL3p2KU8tZs3VHLFm7NdWcXGRsqaqOe14sNWfJaFJpzVmZKgB79eoVrVu3jrKysv22l5WVRd++fes9pl+/ftG2bdv93h5hxIgRsXnz5ti9e3e0a3fg+/0WFxdHcXF6P8wApp02OC45ZeBhHXPD0j/FHzatja+fMyKu/YcTG9zvMw+sjue37ohJJ/Y76H5HYvnGbXHuT5fFsZ3aRVnV7vjQiAFNnlHr7uc2xJKSrXHGGWfEJZdckkpGRMTChQtTOzc0J+YsoFB88vQhcXq/7qmc++7nNsQ9L5Y2+4xVpeVx24q1MWLEiDj99NNTyYiIVP8iHZoTcxZQCGaedWJqs0nE354HWrs11ZxcZKwqffOPucxZNAet8r2AfbVr1y5Gjx4dS5Ysqdu2d+/eWLJkyX5vobCvM844I15++eXYu3dv3bY1a9ZEv3796h2WALLohqV/iuseefGQ5V+aasu/dx3bNT44vP7/SQWaL3MWAEA6zFkAQBZlqgCMiJgxY0b86Ec/iv/6r/+KF198Ma688sqoqqqKadOmRUTElClT9ruo8pVXXhmvvfZaXHXVVbFmzZp44IEH4pvf/GZ89rOfzddDADgsWSv/Fl8yPtq1ztyvB6AJmLMAANJhzgIAsiZTbwEaEXHRRRfFli1b4vrrr4/NmzfHqFGjYvHixdGnT5+IiFi/fn20avXWE9MDBw6Mhx56KL7whS/EqaeeGgMGDIirrroqvvzlL+frIQA0WhbLvy7F6V08HsgvcxYAQDrMWQBA1mSuAIyImD59ekyfPr3ejz366KMHbBs/fnz8/ve/T3lVAE1L+QfkgzkLACAd5iwAIEu8xxtAHij/AAAAAABIiwIQIMeUf2/aWbMn55kAAAAAAC2BAhAgh5R/b9peXRM3LXsp57kAAAAAAC2BAhAgR5R/b9peXRPn3/1kbKjcmfNsAAAAAICWQAEIkAPKvzfVln//90plzDxzeM7zAQAAAABaAgUgQMqUf2/at/x7+F/OiBN6dsr5GgAAAAAAWgIFIECKlH9venv5N3ZAj5yvAQAAAACgpVAAAqRE+fcm5R8AAAAAQG61yfcCAArRohc3xT0vlir/lH8AAAAAADnnFYAAKVD+Kf8AAAAAAPJFAQiQgo+M6Kf8U/4BAAAAAOSFAhAgBReO6J+XXOUfAAAAAAAKQIACofwDAAAAACBCAQhQEJR/AAAAAADUUgACNHPKPwAAAAAA9qUABGjGlH8AAAAAALydAhCgmfpr5U7lHwAAAAAAB1AAAjRT81evU/4BAAAAAHAABSBAM1NWVR0REcd2Klb+AQAAAABwAAUgQDOyfOO2+J8/lUZExNSRg5R/AAAAAAAcIJMF4Lx582LIkCHRvn37GDduXCxfvrzBfefPnx9FRUX73dq3b5/D1QLkxvKN2+Lcny6Lnh3aRUREcZvWOV+D8g+aP3MWAEA6zFkAQJZkrgBcuHBhzJgxI2bNmhWrVq2KkSNHxnnnnRevvPJKg8d07do1SktL627r1q3L4YoB0ldb/r3r2K7xweF987IG5R80f+YsAIB0mLMAgKzJXAF48803x+WXXx7Tpk2Lk08+OW6//fbo2LFj3HnnnQ0eU1RUFH379q279enTJ4crBkjXvuXf4kvGR7vWuf/RrfyDwmDOAgBIhzkLAMiaNvlewL52794dK1eujJkzZ9Zta9WqVUyYMCGefPLJBo/bsWNHDB48OPbu3Runn356fPOb34x3vvOdDe5fXV0d1dXVdfcrKyub5gEA/M2y9a82yXn+/FpVzH18TQzs2iGmjRoU96/ZHGu27oiIiGc2V8Tdz21okpy32zfjx6vWxk3LXooNlTtj5pnD46XXdsRLr+046oym+jcCGsecBRSKF7dsT+3cJdteL4iMNM8NHMicBRSCtOc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",
+      "text/plain": [
+       "<Figure size 2200x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Оценка качества AE2\n",
+      "IDEAL = 0. Excess:  0.4\n",
+      "IDEAL = 0. Deficit:  0.0\n",
+      "IDEAL = 1. Coating:  1.0\n",
+      "summa:  1.0\n",
+      "IDEAL = 1. Extrapolation precision (Approx):  0.7142857142857142\n",
+      "\n",
+      "\n",
+      "\n",
+      "Сохраненные результаты для AE2:\n",
+      "Excess: 0.4\n",
+      "Approx: 0.7142857142857142\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Расчет EDCA для AE2\n",
+    "xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)\n",
+    "\n",
+    "# Сохраняем результаты EDCA для AE2 сразу после расчета\n",
+    "excess_ae2 = None\n",
+    "approx_ae2 = None\n",
+    "try:\n",
+    "    with open('out/result.txt', 'r') as f:\n",
+    "        content = f.read()\n",
+    "        if 'AE2' in content:\n",
+    "            lines = content.split('\\n')\n",
+    "            for line in lines:\n",
+    "                if 'Excess' in line and excess_ae2 is None:\n",
+    "                    excess_ae2 = float(line.split('=')[1].strip())\n",
+    "                elif 'Approx' in line and approx_ae2 is None:\n",
+    "                    approx_ae2 = float(line.split('=')[1].strip())\n",
+    "except:\n",
+    "    pass\n",
+    "\n",
+    "print(f'\\nСохраненные результаты для AE2:')\n",
+    "print(f'Excess: {excess_ae2}')\n",
+    "print(f'Approx: {approx_ae2}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Сравнение характеристик качества обучения и областей аппроксимации\n",
+    "lib.plot2in1(data, xx, yy, Z1, Z2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 8) Создание тестовой выборки\n",
+    "\n",
+    "Создадим тестовую выборку из элементов, которые AE1 распознает как норму, а AE2 детектирует как аномалии.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Тестовая выборка:\n",
+      "[[-0.5  0.5]\n",
+      " [ 1.   0.5]\n",
+      " [ 0.2  1.2]\n",
+      " [ 0.   0.1]]\n",
+      "\n",
+      "Размерность: (4, 2)\n",
+      "\n",
+      "Центр обучающих данных: (1, 1)\n",
+      "Расстояния от центра:\n",
+      "  Точка 1 [-0.5  0.5]: расстояние = 1.581\n",
+      "  Точка 2 [1.  0.5]: расстояние = 0.500\n",
+      "  Точка 3 [0.2 1.2]: расстояние = 0.825\n",
+      "  Точка 4 [0.  0.1]: расстояние = 1.345\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Создание тестовой выборки\n",
+    "# Точки, которые находятся на среднем расстоянии от центра (1, 1)\n",
+    "# чтобы AE1 их не распознал как аномалии (IRE < порог AE1 ~2.06),\n",
+    "# но AE2 распознал как аномалии (IRE > порог AE2 ~0.41)\n",
+    "# Выбираем точки на расстоянии примерно 1-1.5 от центра\n",
+    "data_test = np.array([\n",
+    "    [-0.5, 0.5],  \n",
+    "    [1, 0.5],  \n",
+    "    [0.2, 1.2],  \n",
+    "    [0, 0.1]  \n",
+    "])\n",
+    "\n",
+    "print('Тестовая выборка:')\n",
+    "print(data_test)\n",
+    "print(f'\\nРазмерность: {data_test.shape}')\n",
+    "print(f'\\nЦентр обучающих данных: (1, 1)')\n",
+    "print(f'Расстояния от центра:')\n",
+    "for i, point in enumerate(data_test):\n",
+    "    dist = np.sqrt((point[0] - 1)**2 + (point[1] - 1)**2)\n",
+    "    print(f'  Точка {i+1} {point}: расстояние = {dist:.3f}')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 9) Применение автокодировщиков к тестовым данным\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
+      "Аномалий не обнаружено\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Тестирование AE1\n",
+    "predicted_labels1, ire1_test = lib.predict_ae(ae1_trained, data_test, IREth1)\n",
+    "lib.anomaly_detection_ae(predicted_labels1, ire1_test, IREth1)\n",
+    "lib.ire_plot('test', ire1_test, IREth1, 'AE1')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step\n",
+      "\n",
+      "i         Labels    IRE       IREth     \n",
+      "0         [1.]      [1.53]    0.38      \n",
+      "1         [1.]      [0.48]    0.38      \n",
+      "2         [1.]      [0.8]     0.38      \n",
+      "3         [1.]      [1.23]    0.38      \n",
+      "Обнаружено  4.0  аномалий\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Тестирование AE2\n",
+    "predicted_labels2, ire2_test = lib.predict_ae(ae2_trained, data_test, IREth2)\n",
+    "lib.anomaly_detection_ae(predicted_labels2, ire2_test, IREth2)\n",
+    "lib.ire_plot('test', ire2_test, IREth2, 'AE2')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Визуализация элементов обучающей и тестовой выборки\n",
+    "lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Таблица 1 - Результаты задания №1\n",
+      "========================================================================================================================\n",
+      "Модель     Скрытых слоев   Нейроны                   Эпох       MSE_stop     Порог IRE    Excess     Approx     Аномалий  \n",
+      "------------------------------------------------------------------------------------------------------------------------\n",
+      "AE1      1               1                         1000       0.025203     0.560000     5.75       0.14814814814814814 0/4         \n",
+      "AE2      6               8-4-2-1-2-4-8             3000       0.009841     0.380000     0.4        0.7142857142857142 4/4         \n",
+      "========================================================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Подсчет обнаруженных аномалий\n",
+    "anomalies_ae1 = int(predicted_labels1.sum())\n",
+    "anomalies_ae2 = int(predicted_labels2.sum())\n",
+    "\n",
+    "# Используем сохраненные ранее значения EDCA метрик\n",
+    "# (они были сохранены в ячейках после вызова square_calc)\n",
+    "# Если переменные не определены, пытаемся прочитать из файла\n",
+    "try:\n",
+    "    excess_ae1_val = excess_ae1 if excess_ae1 is not None else None\n",
+    "except NameError:\n",
+    "    excess_ae1_val = None\n",
+    "\n",
+    "try:\n",
+    "    approx_ae1_val = approx_ae1 if approx_ae1 is not None else None\n",
+    "except NameError:\n",
+    "    approx_ae1_val = None\n",
+    "\n",
+    "try:\n",
+    "    excess_ae2_val = excess_ae2 if excess_ae2 is not None else None\n",
+    "except NameError:\n",
+    "    excess_ae2_val = None\n",
+    "\n",
+    "try:\n",
+    "    approx_ae2_val = approx_ae2 if approx_ae2 is not None else None\n",
+    "except NameError:\n",
+    "    approx_ae2_val = None\n",
+    "\n",
+    "print('Таблица 1 - Результаты задания №1')\n",
+    "print('=' * 120)\n",
+    "print(f'{\"Модель\":<10} {\"Скрытых слоев\":<15} {\"Нейроны\":<25} {\"Эпох\":<10} {\"MSE_stop\":<12} {\"Порог IRE\":<12} {\"Excess\":<10} {\"Approx\":<10} {\"Аномалий\":<10}')\n",
+    "print('-' * 120)\n",
+    "print(f'AE1      {1:<15} {1:<25} {1000:<10} {mse_ae1:<12.6f} {IREth1:<12.6f} {excess_ae1_val if excess_ae1_val is not None else \"N/A\":<10} {approx_ae1_val if approx_ae1_val is not None else \"N/A\":<10} {anomalies_ae1}/{len(data_test):<10}')\n",
+    "print(f'AE2      {6:<15} {\"8-4-2-1-2-4-8\":<25} {3000:<10} {mse_ae2:<12.6f} {IREth2:<12.6f} {excess_ae2_val if excess_ae2_val is not None else \"N/A\":<10} {approx_ae2_val if approx_ae2_val is not None else \"N/A\":<10} {anomalies_ae2}/{len(data_test):<10}')\n",
+    "print('=' * 120)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Задание 2\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1) Изучение набора реальных данных Letter\n",
+    "\n",
+    "Набор данных Letter представляет собой характеристики букв английского алфавита. Для обнаружения аномалий нормальные примеры используются для обучения, а аномальные - для тестирования.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучающая выборка Letter:\n",
+      "Размерность: (1500, 32)\n",
+      "Количество признаков: 32\n",
+      "Количество примеров: 1500\n",
+      "\n",
+      "Первые 5 строк:\n",
+      "[[ 6. 10.  5.  6.  3. 10.  6.  4.  6. 14.  3.  6.  2.  9.  4. 11.  4. 10.\n",
+      "   5.  8.  2.  7.  9.  0.  8. 14.  6.  6.  0. 10.  2.  7.]\n",
+      " [ 0.  6.  0.  4.  0.  7.  7.  4.  4.  7.  6.  8.  0.  8.  0.  8.  3.  7.\n",
+      "   4.  5.  2.  7.  7.  0.  7. 13.  6.  8.  0.  8.  1.  7.]\n",
+      " [ 4.  7.  5.  5.  3.  7.  8.  2.  7.  7.  6.  9.  0.  8.  4.  8.  1.  4.\n",
+      "   0.  2.  0.  7.  7.  1.  7.  7.  6.  8.  0.  8.  2.  8.]\n",
+      " [ 1.  6.  1.  4.  2.  7.  7.  0.  7.  7.  6.  8.  0.  8.  3.  8.  2.  2.\n",
+      "   1.  3.  1.  7.  7.  1.  8.  7.  6.  8.  0.  8.  3.  8.]\n",
+      " [ 1.  2.  1.  3.  1.  7.  7.  1.  7.  7.  6.  8.  0.  8.  3.  8.  1.  4.\n",
+      "   1.  3.  1.  7.  7.  1.  8.  7.  6.  9.  0.  8.  3.  8.]]\n",
+      "\n",
+      "Тестовая выборка Letter:\n",
+      "Размерность: (100, 32)\n",
+      "Количество примеров: 100\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Загрузка обучающей и тестовой выборки Letter\n",
+    "train_letter = np.loadtxt('data/letter_train.txt', dtype=float)\n",
+    "test_letter = np.loadtxt('data/letter_test.txt', dtype=float)\n",
+    "\n",
+    "print('Обучающая выборка Letter:')\n",
+    "print(f'Размерность: {train_letter.shape}')\n",
+    "print(f'Количество признаков: {train_letter.shape[1]}')\n",
+    "print(f'Количество примеров: {train_letter.shape[0]}')\n",
+    "print(f'\\nПервые 5 строк:')\n",
+    "print(train_letter[:5])\n",
+    "\n",
+    "print(f'\\nТестовая выборка Letter:')\n",
+    "print(f'Размерность: {test_letter.shape}')\n",
+    "print(f'Количество примеров: {test_letter.shape[0]}')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1) Нормализация данных (дополнительное исследование, для сравнения)\n",
+    "\n",
+    "Создадим нормализованную версию данных для сравнения результатов обучения.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Нормализация данных выполнена\n",
+      "\n",
+      "Исходные данные (первые 3 признака первого примера):\n",
+      "  train_letter[0, :3] = [ 6. 10.  5.]\n",
+      "\n",
+      "Нормализованные данные (первые 3 признака первого примера):\n",
+      "  train_letter_normalized[0, :3] = [1.04357534 0.90302603 0.12593954]\n",
+      "\n",
+      "Статистика нормализованных данных:\n",
+      "  Среднее: [2.78369920e-15 1.42774681e-16 2.67090054e-15 1.92142598e-16\n",
+      " 2.13440376e-15]...\n",
+      "  Стд. отклонение: [1. 1. 1. 1. 1.]...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Нормализация данных для сравнения результатов\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "# Создаем нормализованные версии данных\n",
+    "scaler = StandardScaler()\n",
+    "train_letter_normalized = scaler.fit_transform(train_letter)\n",
+    "test_letter_normalized = scaler.transform(test_letter)\n",
+    "\n",
+    "print('Нормализация данных выполнена')\n",
+    "print(f'\\nИсходные данные (первые 3 признака первого примера):')\n",
+    "print(f'  train_letter[0, :3] = {train_letter[0, :3]}')\n",
+    "print(f'\\nНормализованные данные (первые 3 признака первого примера):')\n",
+    "print(f'  train_letter_normalized[0, :3] = {train_letter_normalized[0, :3]}')\n",
+    "print(f'\\nСтатистика нормализованных данных:')\n",
+    "print(f'  Среднее: {train_letter_normalized.mean(axis=0)[:5]}...')\n",
+    "print(f'  Стд. отклонение: {train_letter_normalized.std(axis=0)[:5]}...')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2) Обучение автокодировщика на ненормализованных данных\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучение автокодировщика для данных Letter (НЕнормализованные данные)...\n",
+      "Архитектура: 32(вход) -> 200 -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 200-> 32(выход)\n",
+      "\n",
+      "Epoch 500/100000\n",
+      " - loss: 6.0089\n",
+      "\n",
+      "Epoch 1000/100000\n",
+      " - loss: 6.0089\n",
+      "\n",
+      "Epoch 1500/100000\n",
+      " - loss: 6.0089\n",
+      "\n",
+      "Epoch 2000/100000\n",
+      " - loss: 6.0089\n",
+      "\n",
+      "Epoch 2500/100000\n",
+      " - loss: 5.9674\n",
+      "\n",
+      "Epoch 3000/100000\n",
+      " - loss: 3.2179\n",
+      "\n",
+      "Epoch 3500/100000\n",
+      " - loss: 2.8140\n",
+      "\n",
+      "Epoch 4000/100000\n",
+      " - loss: 2.5495\n",
+      "\n",
+      "Epoch 4500/100000\n",
+      " - loss: 2.3718\n",
+      "\n",
+      "Epoch 5000/100000\n",
+      " - loss: 2.2222\n",
+      "\n",
+      "Epoch 5500/100000\n",
+      " - loss: 2.0683\n",
+      "\n",
+      "Epoch 6000/100000\n",
+      " - loss: 1.9408\n",
+      "\n",
+      "Epoch 6500/100000\n",
+      " - loss: 1.8049\n",
+      "\n",
+      "Epoch 7000/100000\n",
+      " - loss: 1.6567\n",
+      "\n",
+      "Epoch 7500/100000\n",
+      " - loss: 1.5253\n",
+      "\n",
+      "Epoch 8000/100000\n",
+      " - loss: 1.4184\n",
+      "\n",
+      "Epoch 8500/100000\n",
+      " - loss: 1.3211\n",
+      "\n",
+      "Epoch 9000/100000\n",
+      " - loss: 1.2611\n",
+      "\n",
+      "Epoch 9500/100000\n",
+      " - loss: 1.2083\n",
+      "\n",
+      "Epoch 10000/100000\n",
+      " - loss: 1.1654\n",
+      "\n",
+      "Epoch 10500/100000\n",
+      " - loss: 1.1208\n",
+      "\n",
+      "Epoch 11000/100000\n",
+      " - loss: 1.0992\n",
+      "\n",
+      "Epoch 11500/100000\n",
+      " - loss: 1.0767\n",
+      "\n",
+      "Epoch 12000/100000\n",
+      " - loss: 1.0484\n",
+      "\n",
+      "Epoch 12500/100000\n",
+      " - loss: 1.0681\n",
+      "\n",
+      "Epoch 13000/100000\n",
+      " - loss: 1.0084\n",
+      "\n",
+      "Epoch 13500/100000\n",
+      " - loss: 0.9896\n",
+      "\n",
+      "Epoch 14000/100000\n",
+      " - loss: 0.9831\n",
+      "\n",
+      "Epoch 14500/100000\n",
+      " - loss: 0.9552\n",
+      "\n",
+      "Epoch 15000/100000\n",
+      " - loss: 0.9356\n",
+      "\n",
+      "Epoch 15500/100000\n",
+      " - loss: 0.9252\n",
+      "\n",
+      "Epoch 16000/100000\n",
+      " - loss: 0.9080\n",
+      "\n",
+      "Epoch 16500/100000\n",
+      " - loss: 0.9106\n",
+      "\n",
+      "Epoch 17000/100000\n",
+      " - loss: 0.8987\n",
+      "\n",
+      "Epoch 17500/100000\n",
+      " - loss: 0.8886\n",
+      "\n",
+      "Epoch 18000/100000\n",
+      " - loss: 0.8663\n",
+      "\n",
+      "Epoch 18500/100000\n",
+      " - loss: 0.8618\n",
+      "\n",
+      "Epoch 19000/100000\n",
+      " - loss: 0.8395\n",
+      "\n",
+      "Epoch 19500/100000\n",
+      " - loss: 0.8271\n",
+      "\n",
+      "Epoch 20000/100000\n",
+      " - loss: 0.8220\n",
+      "\n",
+      "Epoch 20500/100000\n",
+      " - loss: 0.8144\n",
+      "\n",
+      "Epoch 21000/100000\n",
+      " - loss: 0.8132\n",
+      "\n",
+      "Epoch 21500/100000\n",
+      " - loss: 0.7850\n",
+      "\n",
+      "Epoch 22000/100000\n",
+      " - loss: 0.7914\n",
+      "\n",
+      "Epoch 22500/100000\n",
+      " - loss: 0.7688\n",
+      "\n",
+      "Epoch 23000/100000\n",
+      " - loss: 0.7679\n",
+      "\n",
+      "Epoch 23500/100000\n",
+      " - loss: 0.7594\n",
+      "\n",
+      "Epoch 24000/100000\n",
+      " - loss: 0.7528\n",
+      "\n",
+      "Epoch 24500/100000\n",
+      " - loss: 0.7387\n",
+      "\n",
+      "Epoch 25000/100000\n",
+      " - loss: 0.7769\n",
+      "\n",
+      "Epoch 25500/100000\n",
+      " - loss: 0.7297\n",
+      "\n",
+      "Epoch 26000/100000\n",
+      " - loss: 0.7193\n",
+      "\n",
+      "Epoch 26500/100000\n",
+      " - loss: 0.7116\n",
+      "\n",
+      "Epoch 27000/100000\n",
+      " - loss: 0.7163\n",
+      "\n",
+      "Epoch 27500/100000\n",
+      " - loss: 0.7132\n",
+      "\n",
+      "Epoch 28000/100000\n",
+      " - loss: 0.6961\n",
+      "\n",
+      "Epoch 28500/100000\n",
+      " - loss: 0.6900\n",
+      "\n",
+      "Epoch 29000/100000\n",
+      " - loss: 0.6867\n",
+      "\n",
+      "Epoch 29500/100000\n",
+      " - loss: 0.6859\n",
+      "\n",
+      "Epoch 30000/100000\n",
+      " - loss: 0.6768\n",
+      "\n",
+      "Epoch 30500/100000\n",
+      " - loss: 0.6924\n",
+      "\n",
+      "Epoch 31000/100000\n",
+      " - loss: 0.6679\n",
+      "\n",
+      "Epoch 31500/100000\n",
+      " - loss: 0.6588\n",
+      "\n",
+      "Epoch 32000/100000\n",
+      " - loss: 0.6590\n",
+      "\n",
+      "Epoch 32500/100000\n",
+      " - loss: 0.6503\n",
+      "\n",
+      "Epoch 33000/100000\n",
+      " - loss: 0.6490\n",
+      "\n",
+      "Epoch 33500/100000\n",
+      " - loss: 0.6420\n",
+      "\n",
+      "Epoch 34000/100000\n",
+      " - loss: 0.6335\n",
+      "\n",
+      "Epoch 34500/100000\n",
+      " - loss: 0.6468\n",
+      "\n",
+      "Epoch 35000/100000\n",
+      " - loss: 0.6484\n",
+      "\n",
+      "Epoch 35500/100000\n",
+      " - loss: 0.6384\n",
+      "\n",
+      "Epoch 36000/100000\n",
+      " - loss: 0.6185\n",
+      "\n",
+      "Epoch 36500/100000\n",
+      " - loss: 0.6191\n",
+      "\n",
+      "Epoch 37000/100000\n",
+      " - loss: 0.6142\n",
+      "\n",
+      "Epoch 37500/100000\n",
+      " - loss: 0.6077\n",
+      "\n",
+      "Epoch 38000/100000\n",
+      " - loss: 0.6588\n",
+      "\n",
+      "Epoch 38500/100000\n",
+      " - loss: 0.6063\n",
+      "\n",
+      "Epoch 39000/100000\n",
+      " - loss: 0.6803\n",
+      "\n",
+      "Epoch 39500/100000\n",
+      " - loss: 0.5952\n",
+      "\n",
+      "Epoch 40000/100000\n",
+      " - loss: 0.5964\n",
+      "\n",
+      "Epoch 40500/100000\n",
+      " - loss: 0.5881\n",
+      "\n",
+      "Epoch 41000/100000\n",
+      " - loss: 0.5996\n",
+      "\n",
+      "Epoch 41500/100000\n",
+      " - loss: 0.5889\n",
+      "\n",
+      "Epoch 42000/100000\n",
+      " - loss: 0.5804\n",
+      "\n",
+      "Epoch 42500/100000\n",
+      " - loss: 0.5769\n",
+      "\n",
+      "Epoch 43000/100000\n",
+      " - loss: 0.5730\n",
+      "\n",
+      "Epoch 43500/100000\n",
+      " - loss: 0.5701\n",
+      "\n",
+      "Epoch 44000/100000\n",
+      " - loss: 0.5758\n",
+      "\n",
+      "Epoch 44500/100000\n",
+      " - loss: 0.5656\n",
+      "\n",
+      "Epoch 45000/100000\n",
+      " - loss: 0.5700\n",
+      "\n",
+      "Epoch 45500/100000\n",
+      " - loss: 0.5721\n",
+      "\n",
+      "Epoch 46000/100000\n",
+      " - loss: 0.5607\n",
+      "\n",
+      "Epoch 46500/100000\n",
+      " - loss: 0.5581\n",
+      "\n",
+      "Epoch 47000/100000\n",
+      " - loss: 0.5512\n",
+      "\n",
+      "Epoch 47500/100000\n",
+      " - loss: 0.5786\n",
+      "\n",
+      "Epoch 48000/100000\n",
+      " - loss: 0.5534\n",
+      "\n",
+      "Epoch 48500/100000\n",
+      " - loss: 0.5429\n",
+      "\n",
+      "Epoch 49000/100000\n",
+      " - loss: 0.5413\n",
+      "\n",
+      "Epoch 49500/100000\n",
+      " - loss: 0.5412\n",
+      "\n",
+      "Epoch 50000/100000\n",
+      " - loss: 0.5373\n",
+      "\n",
+      "Epoch 50500/100000\n",
+      " - loss: 0.5326\n",
+      "\n",
+      "Epoch 51000/100000\n",
+      " - loss: 0.5397\n",
+      "\n",
+      "Epoch 51500/100000\n",
+      " - loss: 0.5358\n",
+      "\n",
+      "Epoch 52000/100000\n",
+      " - loss: 0.5242\n",
+      "\n",
+      "Epoch 52500/100000\n",
+      " - loss: 0.5242\n",
+      "\n",
+      "Epoch 53000/100000\n",
+      " - loss: 0.5246\n",
+      "\n",
+      "Epoch 53500/100000\n",
+      " - loss: 0.5176\n",
+      "\n",
+      "Epoch 54000/100000\n",
+      " - loss: 0.5181\n",
+      "\n",
+      "Epoch 54500/100000\n",
+      " - loss: 0.5126\n",
+      "\n",
+      "Epoch 55000/100000\n",
+      " - loss: 0.5121\n",
+      "\n",
+      "Epoch 55500/100000\n",
+      " - loss: 0.5198\n",
+      "\n",
+      "Epoch 56000/100000\n",
+      " - loss: 0.5096\n",
+      "\n",
+      "Epoch 56500/100000\n",
+      " - loss: 0.5027\n",
+      "\n",
+      "Epoch 57000/100000\n",
+      " - loss: 0.4967\n",
+      "\n",
+      "Epoch 57500/100000\n",
+      " - loss: 0.4895\n",
+      "\n",
+      "Epoch 58000/100000\n",
+      " - loss: 0.4940\n",
+      "\n",
+      "Epoch 58500/100000\n",
+      " - loss: 0.4922\n",
+      "\n",
+      "Epoch 59000/100000\n",
+      " - loss: 0.4851\n",
+      "\n",
+      "Epoch 59500/100000\n",
+      " - loss: 0.4927\n",
+      "\n",
+      "Epoch 60000/100000\n",
+      " - loss: 0.4837\n",
+      "\n",
+      "Epoch 60500/100000\n",
+      " - loss: 0.4757\n",
+      "\n",
+      "Epoch 61000/100000\n",
+      " - loss: 0.4717\n",
+      "\n",
+      "Epoch 61500/100000\n",
+      " - loss: 0.4749\n",
+      "\n",
+      "Epoch 62000/100000\n",
+      " - loss: 0.4688\n",
+      "\n",
+      "Epoch 62500/100000\n",
+      " - loss: 0.4648\n",
+      "\n",
+      "Epoch 63000/100000\n",
+      " - loss: 0.4612\n",
+      "\n",
+      "Epoch 63500/100000\n",
+      " - loss: 0.4638\n",
+      "\n",
+      "Epoch 64000/100000\n",
+      " - loss: 0.4610\n",
+      "\n",
+      "Epoch 64500/100000\n",
+      " - loss: 0.4535\n",
+      "\n",
+      "Epoch 65000/100000\n",
+      " - loss: 0.4559\n",
+      "\n",
+      "Epoch 65500/100000\n",
+      " - loss: 0.4771\n",
+      "\n",
+      "Epoch 66000/100000\n",
+      " - loss: 0.4507\n",
+      "\n",
+      "Epoch 66500/100000\n",
+      " - loss: 0.4458\n",
+      "\n",
+      "Epoch 67000/100000\n",
+      " - loss: 0.4499\n",
+      "\n",
+      "Epoch 67500/100000\n",
+      " - loss: 0.4406\n",
+      "\n",
+      "Epoch 68000/100000\n",
+      " - loss: 0.4428\n",
+      "\n",
+      "Epoch 68500/100000\n",
+      " - loss: 0.4419\n",
+      "\n",
+      "Epoch 69000/100000\n",
+      " - loss: 0.4354\n",
+      "\n",
+      "Epoch 69500/100000\n",
+      " - loss: 0.4401\n",
+      "\n",
+      "Epoch 70000/100000\n",
+      " - loss: 0.4418\n",
+      "\n",
+      "Epoch 70500/100000\n",
+      " - loss: 0.4422\n",
+      "\n",
+      "Epoch 71000/100000\n",
+      " - loss: 0.4300\n",
+      "\n",
+      "Epoch 71500/100000\n",
+      " - loss: 0.4265\n",
+      "\n",
+      "Epoch 72000/100000\n",
+      " - loss: 0.4243\n",
+      "\n",
+      "Epoch 72500/100000\n",
+      " - loss: 0.4238\n",
+      "\n",
+      "Epoch 73000/100000\n",
+      " - loss: 0.4274\n",
+      "\n",
+      "Epoch 73500/100000\n",
+      " - loss: 0.4221\n",
+      "\n",
+      "Epoch 74000/100000\n",
+      " - loss: 0.4192\n",
+      "\n",
+      "Epoch 74500/100000\n",
+      " - loss: 0.4178\n",
+      "\n",
+      "Epoch 75000/100000\n",
+      " - loss: 0.4128\n",
+      "\n",
+      "Epoch 75500/100000\n",
+      " - loss: 0.4112\n",
+      "\n",
+      "Epoch 76000/100000\n",
+      " - loss: 0.4133\n",
+      "\n",
+      "Epoch 76500/100000\n",
+      " - loss: 0.4133\n",
+      "\n",
+      "Epoch 77000/100000\n",
+      " - loss: 0.4085\n",
+      "\n",
+      "Epoch 77500/100000\n",
+      " - loss: 0.4069\n",
+      "\n",
+      "Epoch 78000/100000\n",
+      " - loss: 0.4067\n",
+      "\n",
+      "Epoch 78500/100000\n",
+      " - loss: 0.4099\n",
+      "\n",
+      "Epoch 79000/100000\n",
+      " - loss: 0.4038\n",
+      "\n",
+      "Epoch 79500/100000\n",
+      " - loss: 0.3997\n",
+      "\n",
+      "Epoch 80000/100000\n",
+      " - loss: 0.4005\n",
+      "\n",
+      "Epoch 80500/100000\n",
+      " - loss: 0.4003\n",
+      "\n",
+      "Epoch 81000/100000\n",
+      " - loss: 0.3997\n",
+      "\n",
+      "Epoch 81500/100000\n",
+      " - loss: 0.3997\n",
+      "\n",
+      "Epoch 82000/100000\n",
+      " - loss: 0.3980\n",
+      "\n",
+      "Epoch 82500/100000\n",
+      " - loss: 0.3891\n",
+      "\n",
+      "Epoch 83000/100000\n",
+      " - loss: 0.3892\n",
+      "\n",
+      "Epoch 83500/100000\n",
+      " - loss: 0.3893\n",
+      "\n",
+      "Epoch 84000/100000\n",
+      " - loss: 0.3915\n",
+      "\n",
+      "Epoch 84500/100000\n",
+      " - loss: 0.3859\n",
+      "\n",
+      "Epoch 85000/100000\n",
+      " - loss: 0.3838\n",
+      "\n",
+      "Epoch 85500/100000\n",
+      " - loss: 0.3847\n",
+      "\n",
+      "Epoch 86000/100000\n",
+      " - loss: 0.3785\n",
+      "\n",
+      "Epoch 86500/100000\n",
+      " - loss: 0.3801\n",
+      "\n",
+      "Epoch 87000/100000\n",
+      " - loss: 0.3877\n",
+      "\n",
+      "Epoch 87500/100000\n",
+      " - loss: 0.3775\n",
+      "\n",
+      "Epoch 88000/100000\n",
+      " - loss: 0.3761\n",
+      "\n",
+      "Epoch 88500/100000\n",
+      " - loss: 0.3769\n",
+      "\n",
+      "Epoch 89000/100000\n",
+      " - loss: 0.3711\n",
+      "\n",
+      "Epoch 89500/100000\n",
+      " - loss: 0.3693\n",
+      "\n",
+      "Epoch 90000/100000\n",
+      " - loss: 0.3783\n",
+      "\n",
+      "Epoch 90500/100000\n",
+      " - loss: 0.3678\n",
+      "\n",
+      "Epoch 91000/100000\n",
+      " - loss: 0.3645\n",
+      "\n",
+      "Epoch 91500/100000\n",
+      " - loss: 0.3647\n",
+      "\n",
+      "Epoch 92000/100000\n",
+      " - loss: 0.3652\n",
+      "\n",
+      "Epoch 92500/100000\n",
+      " - loss: 0.3749\n",
+      "\n",
+      "Epoch 93000/100000\n",
+      " - loss: 0.3600\n",
+      "\n",
+      "Epoch 93500/100000\n",
+      " - loss: 0.3611\n",
+      "\n",
+      "Epoch 94000/100000\n",
+      " - loss: 0.3545\n",
+      "\n",
+      "Epoch 94500/100000\n",
+      " - loss: 0.3551\n",
+      "\n",
+      "Epoch 95000/100000\n",
+      " - loss: 0.3549\n",
+      "\n",
+      "Epoch 95500/100000\n",
+      " - loss: 0.3571\n",
+      "\n",
+      "Epoch 96000/100000\n",
+      " - loss: 0.3506\n",
+      "\n",
+      "Epoch 96500/100000\n",
+      " - loss: 0.3514\n",
+      "\n",
+      "Epoch 97000/100000\n",
+      " - loss: 0.3575\n",
+      "\n",
+      "Epoch 97500/100000\n",
+      " - loss: 0.3491\n",
+      "\n",
+      "Epoch 98000/100000\n",
+      " - loss: 0.3530\n",
+      "\n",
+      "Epoch 98500/100000\n",
+      " - loss: 0.3483\n",
+      "\n",
+      "Epoch 99000/100000\n",
+      " - loss: 0.3438\n",
+      "\n",
+      "Epoch 99500/100000\n",
+      " - loss: 0.3439\n",
+      "\n",
+      "Epoch 100000/100000\n",
+      " - loss: 0.3476\n",
+      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "Обучение завершено (НЕнормализованные данные)!\n",
+      "MSE_stop (приблизительно): 0.340222\n",
+      "Порог IRE: 11.100000\n",
+      "Количество скрытых слоев: 11\n",
+      "Количество нейронов в скрытых слоях: 48-36-24-16-8-4-8-16-24-36-48\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Обучение автокодировщика для Letter на НЕнормализованных данных\n",
+    "# Входной и выходной слои создаются автоматически по размеру данных (32 признака)\n",
+    "# Мы задаем только скрытые слои\n",
+    "# Полная архитектура: 32(вход) -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 32(выход)\n",
+    "# 11 скрытых слоев:  100 64 48 32 24 16 8 16 24 32 48 64 100 \n",
+    "\n",
+    "import warnings\n",
+    "# Подавляем предупреждение о формате сохранения HDF5\n",
+    "warnings.filterwarnings('ignore', category=UserWarning, module='absl')\n",
+    "\n",
+    "print('Обучение автокодировщика для данных Letter (НЕнормализованные данные)...')\n",
+    "print('Архитектура: 32(вход) -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 32(выход)')\n",
+    "\n",
+    "# Автоматизируем ввод для выбора пользовательской архитектуры\n",
+    "with patch('builtins.input', side_effect=['1', '17', ' 100 86 72 64 48 32 24 16 8 16 24 32 48 64 72 86 100']):\n",
+    "    ae_letter_raw, IRE_letter_raw, IREth_letter_raw = lib.create_fit_save_ae(\n",
+    "        train_letter, \n",
+    "        'out/Letter_AE_raw.h5', \n",
+    "        'out/Letter_AE_raw_ire_th.txt',\n",
+    "        100000,  # epochs\n",
+    "        False,  # verbose_show\n",
+    "        10000,  # patience\n",
+    "        verbose_every_n_epochs=500\n",
+    "    )\n",
+    "\n",
+    "# Вычисление MSE из предсказания\n",
+    "X_pred_letter_raw = ae_letter_raw.predict(train_letter, verbose=0)\n",
+    "mse_letter_raw = np.mean((train_letter - X_pred_letter_raw) ** 2)\n",
+    "\n",
+    "print(f'\\nОбучение завершено (НЕнормализованные данные)!')\n",
+    "print(f'MSE_stop (приблизительно): {mse_letter_raw:.6f}')\n",
+    "print(f'Порог IRE: {IREth_letter_raw:.6f}')\n",
+    "print(f'Количество скрытых слоев: 11')\n",
+    "print(f'Количество нейронов в скрытых слоях: 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3) Обучение автокодировщика на нормализованных данных\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Обучение автокодировщика для данных Letter (нормализованные данные)...\n",
+      "Архитектура: 32(вход) -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 32(выход)\n",
+      "Epoch 1/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 20s/step - loss: 1.0432\n",
+      "Epoch 2/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.9909\n",
+      "Epoch 3/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.9628\n",
+      "Epoch 4/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.9357\n",
+      "Epoch 5/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.9036\n",
+      "Epoch 6/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.8671\n",
+      "Epoch 7/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.8296\n",
+      "Epoch 8/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.7935\n",
+      "Epoch 9/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.7598\n",
+      "Epoch 10/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.7284\n",
+      "Epoch 11/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.6987\n",
+      "Epoch 12/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.6716\n",
+      "Epoch 13/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.6483\n",
+      "Epoch 14/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.6283\n",
+      "Epoch 15/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.6091\n",
+      "Epoch 16/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291ms/step - loss: 0.5898\n",
+      "Epoch 17/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295ms/step - loss: 0.5717\n",
+      "Epoch 18/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.5555\n",
+      "Epoch 19/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 217ms/step - loss: 0.5405\n",
+      "Epoch 20/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 181ms/step - loss: 0.5264\n",
+      "Epoch 21/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.5145\n",
+      "Epoch 22/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.5058\n",
+      "Epoch 23/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.4993\n",
+      "Epoch 24/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 181ms/step - loss: 0.4926\n",
+      "Epoch 25/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.4850\n",
+      "Epoch 26/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.4777\n",
+      "Epoch 27/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.4719\n",
+      "Epoch 28/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.4678\n",
+      "Epoch 29/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.4646\n",
+      "Epoch 30/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.4613\n",
+      "Epoch 31/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.4575\n",
+      "Epoch 32/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.4542\n",
+      "Epoch 33/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.4516\n",
+      "Epoch 34/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.4493\n",
+      "Epoch 35/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.4468\n",
+      "Epoch 36/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.4441\n",
+      "Epoch 37/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.4413\n",
+      "Epoch 38/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.4388\n",
+      "Epoch 39/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.4365\n",
+      "Epoch 40/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.4343\n",
+      "Epoch 41/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.4319\n",
+      "Epoch 42/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.4295\n",
+      "Epoch 43/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.4270\n",
+      "Epoch 44/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.4247\n",
+      "Epoch 45/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.4226\n",
+      "Epoch 46/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.4206\n",
+      "Epoch 47/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.4187\n",
+      "Epoch 48/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.4170\n",
+      "Epoch 49/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.4152\n",
+      "Epoch 50/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.4135\n",
+      "Epoch 51/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.4119\n",
+      "Epoch 52/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.4102\n",
+      "Epoch 53/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.4084\n",
+      "Epoch 54/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.4065\n",
+      "Epoch 55/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.4046\n",
+      "Epoch 56/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.4029\n",
+      "Epoch 57/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.4012\n",
+      "Epoch 58/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.3994\n",
+      "Epoch 59/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.3976\n",
+      "Epoch 60/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.3958\n",
+      "Epoch 61/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.3940\n",
+      "Epoch 62/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.3921\n",
+      "Epoch 63/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.3900\n",
+      "Epoch 64/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.3878\n",
+      "Epoch 65/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.3855\n",
+      "Epoch 66/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.3832\n",
+      "Epoch 67/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.3808\n",
+      "Epoch 68/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.3786\n",
+      "Epoch 69/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.3765\n",
+      "Epoch 70/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.3747\n",
+      "Epoch 71/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.3730\n",
+      "Epoch 72/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.3714\n",
+      "Epoch 73/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.3698\n",
+      "Epoch 74/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.3680\n",
+      "Epoch 75/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.3662\n",
+      "Epoch 76/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.3644\n",
+      "Epoch 77/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - loss: 0.3626\n",
+      "Epoch 78/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 182ms/step - loss: 0.3608\n",
+      "Epoch 79/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - loss: 0.3591\n",
+      "Epoch 80/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.3573\n",
+      "Epoch 81/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.3556\n",
+      "Epoch 82/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.3538\n",
+      "Epoch 83/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.3520\n",
+      "Epoch 84/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.3502\n",
+      "Epoch 85/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.3482\n",
+      "Epoch 86/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.3461\n",
+      "Epoch 87/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.3438\n",
+      "Epoch 88/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.3414\n",
+      "Epoch 89/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.3387\n",
+      "Epoch 90/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.3359\n",
+      "Epoch 91/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.3328\n",
+      "Epoch 92/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.3296\n",
+      "Epoch 93/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.3265\n",
+      "Epoch 94/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.3237\n",
+      "Epoch 95/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.3212\n",
+      "Epoch 96/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - loss: 0.3191\n",
+      "Epoch 97/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.3173\n",
+      "Epoch 98/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.3154\n",
+      "Epoch 99/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.3132\n",
+      "Epoch 100/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.3109\n",
+      "Epoch 101/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.3087\n",
+      "Epoch 102/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.3067\n",
+      "Epoch 103/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.3049\n",
+      "Epoch 104/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.3032\n",
+      "Epoch 105/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.3016\n",
+      "Epoch 106/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.3000\n",
+      "Epoch 107/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.2985\n",
+      "Epoch 108/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.2974\n",
+      "Epoch 109/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.2968\n",
+      "Epoch 110/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.2956\n",
+      "Epoch 111/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.2927\n",
+      "Epoch 112/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.2912\n",
+      "Epoch 113/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.2908\n",
+      "Epoch 114/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.2885\n",
+      "Epoch 115/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.2869\n",
+      "Epoch 116/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.2861\n",
+      "Epoch 117/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.2840\n",
+      "Epoch 118/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.2829\n",
+      "Epoch 119/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.2818\n",
+      "Epoch 120/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.2800\n",
+      "Epoch 121/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.2792\n",
+      "Epoch 122/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.2776\n",
+      "Epoch 123/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2763\n",
+      "Epoch 124/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.2752\n",
+      "Epoch 125/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.2736\n",
+      "Epoch 126/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.2726\n",
+      "Epoch 127/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.2712\n",
+      "Epoch 128/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.2699\n",
+      "Epoch 129/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.2688\n",
+      "Epoch 130/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.2674\n",
+      "Epoch 131/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.2663\n",
+      "Epoch 132/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.2650\n",
+      "Epoch 133/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.2637\n",
+      "Epoch 134/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.2626\n",
+      "Epoch 135/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.2613\n",
+      "Epoch 136/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.2600\n",
+      "Epoch 137/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - loss: 0.2589\n",
+      "Epoch 138/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.2576\n",
+      "Epoch 139/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 268ms/step - loss: 0.2564\n",
+      "Epoch 140/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.2553\n",
+      "Epoch 141/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.2540\n",
+      "Epoch 142/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.2528\n",
+      "Epoch 143/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.2517\n",
+      "Epoch 144/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.2504\n",
+      "Epoch 145/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.2492\n",
+      "Epoch 146/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.2481\n",
+      "Epoch 147/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.2469\n",
+      "Epoch 148/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.2457\n",
+      "Epoch 149/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.2445\n",
+      "Epoch 150/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.2433\n",
+      "Epoch 151/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.2421\n",
+      "Epoch 152/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.2410\n",
+      "Epoch 153/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.2398\n",
+      "Epoch 154/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.2388\n",
+      "Epoch 155/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.2382\n",
+      "Epoch 156/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.2383\n",
+      "Epoch 157/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.2376\n",
+      "Epoch 158/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.2347\n",
+      "Epoch 159/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.2325\n",
+      "Epoch 160/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.2327\n",
+      "Epoch 161/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.2310\n",
+      "Epoch 162/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2287\n",
+      "Epoch 163/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.2284\n",
+      "Epoch 164/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.2267\n",
+      "Epoch 165/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.2248\n",
+      "Epoch 166/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.2243\n",
+      "Epoch 167/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.2223\n",
+      "Epoch 168/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.2208\n",
+      "Epoch 169/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.2199\n",
+      "Epoch 170/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.2179\n",
+      "Epoch 171/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.2167\n",
+      "Epoch 172/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.2155\n",
+      "Epoch 173/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.2137\n",
+      "Epoch 174/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.2126\n",
+      "Epoch 175/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.2113\n",
+      "Epoch 176/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.2096\n",
+      "Epoch 177/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.2085\n",
+      "Epoch 178/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.2072\n",
+      "Epoch 179/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.2058\n",
+      "Epoch 180/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.2046\n",
+      "Epoch 181/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2035\n",
+      "Epoch 182/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2022\n",
+      "Epoch 183/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.2013\n",
+      "Epoch 184/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.2006\n",
+      "Epoch 185/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.1999\n",
+      "Epoch 186/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.1990\n",
+      "Epoch 187/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.1976\n",
+      "Epoch 188/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.1956\n",
+      "Epoch 189/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.1945\n",
+      "Epoch 190/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.1940\n",
+      "Epoch 191/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - loss: 0.1928\n",
+      "Epoch 192/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 454ms/step - loss: 0.1914\n",
+      "Epoch 193/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.1905\n",
+      "Epoch 194/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.1899\n",
+      "Epoch 195/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.1887\n",
+      "Epoch 196/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.1874\n",
+      "Epoch 197/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.1866\n",
+      "Epoch 198/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.1859\n",
+      "Epoch 199/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.1848\n",
+      "Epoch 200/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.1837\n",
+      "Epoch 201/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.1829\n",
+      "Epoch 202/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.1822\n",
+      "Epoch 203/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.1812\n",
+      "Epoch 204/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.1801\n",
+      "Epoch 205/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.1792\n",
+      "Epoch 206/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.1785\n",
+      "Epoch 207/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.1777\n",
+      "Epoch 208/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.1769\n",
+      "Epoch 209/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 199ms/step - loss: 0.1759\n",
+      "Epoch 210/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.1750\n",
+      "Epoch 211/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.1742\n",
+      "Epoch 212/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.1735\n",
+      "Epoch 213/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.1729\n",
+      "Epoch 214/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.1725\n",
+      "Epoch 215/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.1726\n",
+      "Epoch 216/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1734\n",
+      "Epoch 217/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1733\n",
+      "Epoch 218/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.1710\n",
+      "Epoch 219/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.1690\n",
+      "Epoch 220/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1692\n",
+      "Epoch 221/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.1678\n",
+      "Epoch 222/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.1666\n",
+      "Epoch 223/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.1669\n",
+      "Epoch 224/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.1648\n",
+      "Epoch 225/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.1644\n",
+      "Epoch 226/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.1645\n",
+      "Epoch 227/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.1626\n",
+      "Epoch 228/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.1626\n",
+      "Epoch 229/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.1619\n",
+      "Epoch 230/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.1609\n",
+      "Epoch 231/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.1606\n",
+      "Epoch 232/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.1596\n",
+      "Epoch 233/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.1593\n",
+      "Epoch 234/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.1589\n",
+      "Epoch 235/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.1581\n",
+      "Epoch 236/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1584\n",
+      "Epoch 237/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.1586\n",
+      "Epoch 238/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.1589\n",
+      "Epoch 239/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.1581\n",
+      "Epoch 240/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1561\n",
+      "Epoch 241/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.1550\n",
+      "Epoch 242/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.1548\n",
+      "Epoch 243/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1544\n",
+      "Epoch 244/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.1531\n",
+      "Epoch 245/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.1524\n",
+      "Epoch 246/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1524\n",
+      "Epoch 247/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.1517\n",
+      "Epoch 248/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.1510\n",
+      "Epoch 249/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.1502\n",
+      "Epoch 250/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 254ms/step - loss: 0.1497\n",
+      "Epoch 251/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.1495\n",
+      "Epoch 252/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1486\n",
+      "Epoch 253/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1480\n",
+      "Epoch 254/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.1478\n",
+      "Epoch 255/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.1472\n",
+      "Epoch 256/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.1465\n",
+      "Epoch 257/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.1460\n",
+      "Epoch 258/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.1456\n",
+      "Epoch 259/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.1452\n",
+      "Epoch 260/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.1446\n",
+      "Epoch 261/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1441\n",
+      "Epoch 262/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.1436\n",
+      "Epoch 263/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.1432\n",
+      "Epoch 264/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.1430\n",
+      "Epoch 265/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.1428\n",
+      "Epoch 266/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1427\n",
+      "Epoch 267/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.1430\n",
+      "Epoch 268/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.1440\n",
+      "Epoch 269/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.1444\n",
+      "Epoch 270/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.1434\n",
+      "Epoch 271/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.1406\n",
+      "Epoch 272/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.1397\n",
+      "Epoch 273/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.1409\n",
+      "Epoch 274/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 347ms/step - loss: 0.1398\n",
+      "Epoch 275/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.1381\n",
+      "Epoch 276/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1386\n",
+      "Epoch 277/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.1383\n",
+      "Epoch 278/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.1370\n",
+      "Epoch 279/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.1371\n",
+      "Epoch 280/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.1368\n",
+      "Epoch 281/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.1358\n",
+      "Epoch 282/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.1357\n",
+      "Epoch 283/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.1354\n",
+      "Epoch 284/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1345\n",
+      "Epoch 285/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.1342\n",
+      "Epoch 286/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1340\n",
+      "Epoch 287/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.1334\n",
+      "Epoch 288/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.1330\n",
+      "Epoch 289/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1328\n",
+      "Epoch 290/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.1323\n",
+      "Epoch 291/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.1319\n",
+      "Epoch 292/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.1320\n",
+      "Epoch 293/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.1327\n",
+      "Epoch 294/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.1338\n",
+      "Epoch 295/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.1364\n",
+      "Epoch 296/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.1341\n",
+      "Epoch 297/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.1303\n",
+      "Epoch 298/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.1308\n",
+      "Epoch 299/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.1317\n",
+      "Epoch 300/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.1290\n",
+      "Epoch 301/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.1294\n",
+      "Epoch 302/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.1295\n",
+      "Epoch 303/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.1278\n",
+      "Epoch 304/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.1283\n",
+      "Epoch 305/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.1273\n",
+      "Epoch 306/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.1269\n",
+      "Epoch 307/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.1270\n",
+      "Epoch 308/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1261\n",
+      "Epoch 309/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.1261\n",
+      "Epoch 310/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1255\n",
+      "Epoch 311/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.1251\n",
+      "Epoch 312/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1251\n",
+      "Epoch 313/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.1246\n",
+      "Epoch 314/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1246\n",
+      "Epoch 315/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.1247\n",
+      "Epoch 316/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1248\n",
+      "Epoch 317/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.1255\n",
+      "Epoch 318/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.1257\n",
+      "Epoch 319/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1245\n",
+      "Epoch 320/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.1228\n",
+      "Epoch 321/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.1219\n",
+      "Epoch 322/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.1221\n",
+      "Epoch 323/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.1222\n",
+      "Epoch 324/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1214\n",
+      "Epoch 325/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.1205\n",
+      "Epoch 326/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.1207\n",
+      "Epoch 327/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.1205\n",
+      "Epoch 328/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.1197\n",
+      "Epoch 329/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.1193\n",
+      "Epoch 330/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.1193\n",
+      "Epoch 331/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.1189\n",
+      "Epoch 332/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.1183\n",
+      "Epoch 333/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1181\n",
+      "Epoch 334/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.1180\n",
+      "Epoch 335/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.1176\n",
+      "Epoch 336/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.1171\n",
+      "Epoch 337/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.1168\n",
+      "Epoch 338/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.1167\n",
+      "Epoch 339/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.1164\n",
+      "Epoch 340/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.1160\n",
+      "Epoch 341/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1158\n",
+      "Epoch 342/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.1157\n",
+      "Epoch 343/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 205ms/step - loss: 0.1158\n",
+      "Epoch 344/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.1160\n",
+      "Epoch 345/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.1166\n",
+      "Epoch 346/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.1170\n",
+      "Epoch 347/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.1163\n",
+      "Epoch 348/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 195ms/step - loss: 0.1143\n",
+      "Epoch 349/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.1135\n",
+      "Epoch 350/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.1141\n",
+      "Epoch 351/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 219ms/step - loss: 0.1140\n",
+      "Epoch 352/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 205ms/step - loss: 0.1128\n",
+      "Epoch 353/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.1125\n",
+      "Epoch 354/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - loss: 0.1128\n",
+      "Epoch 355/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - loss: 0.1122\n",
+      "Epoch 356/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 197ms/step - loss: 0.1115\n",
+      "Epoch 357/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.1116\n",
+      "Epoch 358/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.1115\n",
+      "Epoch 359/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.1108\n",
+      "Epoch 360/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - loss: 0.1105\n",
+      "Epoch 361/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.1106\n",
+      "Epoch 362/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 206ms/step - loss: 0.1102\n",
+      "Epoch 363/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.1099\n",
+      "Epoch 364/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 220ms/step - loss: 0.1101\n",
+      "Epoch 365/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.1103\n",
+      "Epoch 366/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 259ms/step - loss: 0.1108\n",
+      "Epoch 367/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - loss: 0.1116\n",
+      "Epoch 368/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 277ms/step - loss: 0.1126\n",
+      "Epoch 369/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - loss: 0.1108\n",
+      "Epoch 370/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.1087\n",
+      "Epoch 371/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183ms/step - loss: 0.1086\n",
+      "Epoch 372/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.1094\n",
+      "Epoch 373/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1085\n",
+      "Epoch 374/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.1074\n",
+      "Epoch 375/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.1079\n",
+      "Epoch 376/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.1080\n",
+      "Epoch 377/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 254ms/step - loss: 0.1070\n",
+      "Epoch 378/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198ms/step - loss: 0.1070\n",
+      "Epoch 379/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.1071\n",
+      "Epoch 380/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.1062\n",
+      "Epoch 381/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 182ms/step - loss: 0.1061\n",
+      "Epoch 382/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.1060\n",
+      "Epoch 383/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.1053\n",
+      "Epoch 384/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.1050\n",
+      "Epoch 385/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.1050\n",
+      "Epoch 386/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.1045\n",
+      "Epoch 387/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.1042\n",
+      "Epoch 388/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.1043\n",
+      "Epoch 389/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.1040\n",
+      "Epoch 390/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.1037\n",
+      "Epoch 391/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.1038\n",
+      "Epoch 392/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.1036\n",
+      "Epoch 393/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.1034\n",
+      "Epoch 394/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.1034\n",
+      "Epoch 395/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 203ms/step - loss: 0.1034\n",
+      "Epoch 396/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.1031\n",
+      "Epoch 397/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.1029\n",
+      "Epoch 398/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.1029\n",
+      "Epoch 399/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.1028\n",
+      "Epoch 400/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.1026\n",
+      "Epoch 401/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.1025\n",
+      "Epoch 402/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1022\n",
+      "Epoch 403/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.1015\n",
+      "Epoch 404/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.1010\n",
+      "Epoch 405/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.1012\n",
+      "Epoch 406/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.1015\n",
+      "Epoch 407/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.1014\n",
+      "Epoch 408/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.1008\n",
+      "Epoch 409/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.1002\n",
+      "Epoch 410/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.1000\n",
+      "Epoch 411/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0998\n",
+      "Epoch 412/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0994\n",
+      "Epoch 413/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0990\n",
+      "Epoch 414/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0987\n",
+      "Epoch 415/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0987\n",
+      "Epoch 416/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0987\n",
+      "Epoch 417/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0985\n",
+      "Epoch 418/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0982\n",
+      "Epoch 419/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0980\n",
+      "Epoch 420/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0979\n",
+      "Epoch 421/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0978\n",
+      "Epoch 422/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0975\n",
+      "Epoch 423/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0974\n",
+      "Epoch 424/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0977\n",
+      "Epoch 425/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0987\n",
+      "Epoch 426/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.1002\n",
+      "Epoch 427/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.1018\n",
+      "Epoch 428/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.1007\n",
+      "Epoch 429/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0979\n",
+      "Epoch 430/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0973\n",
+      "Epoch 431/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0985\n",
+      "Epoch 432/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0973\n",
+      "Epoch 433/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0958\n",
+      "Epoch 434/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0964\n",
+      "Epoch 435/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0961\n",
+      "Epoch 436/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0957\n",
+      "Epoch 437/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 209ms/step - loss: 0.0959\n",
+      "Epoch 438/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0952\n",
+      "Epoch 439/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0949\n",
+      "Epoch 440/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0950\n",
+      "Epoch 441/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0942\n",
+      "Epoch 442/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0943\n",
+      "Epoch 443/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0945\n",
+      "Epoch 444/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0939\n",
+      "Epoch 445/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0937\n",
+      "Epoch 446/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0938\n",
+      "Epoch 447/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0933\n",
+      "Epoch 448/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0930\n",
+      "Epoch 449/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0931\n",
+      "Epoch 450/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0928\n",
+      "Epoch 451/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0925\n",
+      "Epoch 452/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0926\n",
+      "Epoch 453/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0924\n",
+      "Epoch 454/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0921\n",
+      "Epoch 455/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0921\n",
+      "Epoch 456/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0920\n",
+      "Epoch 457/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0918\n",
+      "Epoch 458/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0917\n",
+      "Epoch 459/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0918\n",
+      "Epoch 460/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0918\n",
+      "Epoch 461/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0918\n",
+      "Epoch 462/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0921\n",
+      "Epoch 463/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0924\n",
+      "Epoch 464/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0926\n",
+      "Epoch 465/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0924\n",
+      "Epoch 466/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0919\n",
+      "Epoch 467/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0912\n",
+      "Epoch 468/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0908\n",
+      "Epoch 469/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0910\n",
+      "Epoch 470/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0914\n",
+      "Epoch 471/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0911\n",
+      "Epoch 472/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0902\n",
+      "Epoch 473/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0894\n",
+      "Epoch 474/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0893\n",
+      "Epoch 475/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0896\n",
+      "Epoch 476/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0895\n",
+      "Epoch 477/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 209ms/step - loss: 0.0891\n",
+      "Epoch 478/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0888\n",
+      "Epoch 479/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0887\n",
+      "Epoch 480/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0886\n",
+      "Epoch 481/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0884\n",
+      "Epoch 482/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0881\n",
+      "Epoch 483/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0880\n",
+      "Epoch 484/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0880\n",
+      "Epoch 485/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0879\n",
+      "Epoch 486/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0876\n",
+      "Epoch 487/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0873\n",
+      "Epoch 488/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0872\n",
+      "Epoch 489/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0872\n",
+      "Epoch 490/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0871\n",
+      "Epoch 491/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0869\n",
+      "Epoch 492/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0867\n",
+      "Epoch 493/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0865\n",
+      "Epoch 494/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0864\n",
+      "Epoch 495/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0863\n",
+      "Epoch 496/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0862\n",
+      "Epoch 497/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0861\n",
+      "Epoch 498/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0859\n",
+      "Epoch 499/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0858\n",
+      "Epoch 500/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0857\n",
+      "Epoch 500/20000\n",
+      " - loss: 0.0857\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0857\n",
+      "Epoch 501/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0856\n",
+      "Epoch 502/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0857\n",
+      "Epoch 503/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0860\n",
+      "Epoch 504/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0868\n",
+      "Epoch 505/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0884\n",
+      "Epoch 506/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0910\n",
+      "Epoch 507/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0929\n",
+      "Epoch 508/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0906\n",
+      "Epoch 509/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0861\n",
+      "Epoch 510/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0860\n",
+      "Epoch 511/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0880\n",
+      "Epoch 512/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0862\n",
+      "Epoch 513/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 243ms/step - loss: 0.0854\n",
+      "Epoch 514/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0862\n",
+      "Epoch 515/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0850\n",
+      "Epoch 516/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0853\n",
+      "Epoch 517/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0852\n",
+      "Epoch 518/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0843\n",
+      "Epoch 519/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0850\n",
+      "Epoch 520/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0841\n",
+      "Epoch 521/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0839\n",
+      "Epoch 522/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0843\n",
+      "Epoch 523/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0834\n",
+      "Epoch 524/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0836\n",
+      "Epoch 525/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0834\n",
+      "Epoch 526/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0828\n",
+      "Epoch 527/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0833\n",
+      "Epoch 528/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0827\n",
+      "Epoch 529/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0825\n",
+      "Epoch 530/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0827\n",
+      "Epoch 531/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0822\n",
+      "Epoch 532/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0822\n",
+      "Epoch 533/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0822\n",
+      "Epoch 534/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0818\n",
+      "Epoch 535/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0818\n",
+      "Epoch 536/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0817\n",
+      "Epoch 537/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0815\n",
+      "Epoch 538/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0815\n",
+      "Epoch 539/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0813\n",
+      "Epoch 540/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0811\n",
+      "Epoch 541/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0811\n",
+      "Epoch 542/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0810\n",
+      "Epoch 543/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0808\n",
+      "Epoch 544/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0808\n",
+      "Epoch 545/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0807\n",
+      "Epoch 546/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0806\n",
+      "Epoch 547/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0807\n",
+      "Epoch 548/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0808\n",
+      "Epoch 549/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0811\n",
+      "Epoch 550/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0817\n",
+      "Epoch 551/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0827\n",
+      "Epoch 552/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0828\n",
+      "Epoch 553/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0821\n",
+      "Epoch 554/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0804\n",
+      "Epoch 555/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0797\n",
+      "Epoch 556/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0805\n",
+      "Epoch 557/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0805\n",
+      "Epoch 558/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0796\n",
+      "Epoch 559/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0794\n",
+      "Epoch 560/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0798\n",
+      "Epoch 561/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0797\n",
+      "Epoch 562/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0792\n",
+      "Epoch 563/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0795\n",
+      "Epoch 564/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0799\n",
+      "Epoch 565/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0802\n",
+      "Epoch 566/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0812\n",
+      "Epoch 567/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0825\n",
+      "Epoch 568/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0831\n",
+      "Epoch 569/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0818\n",
+      "Epoch 570/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0800\n",
+      "Epoch 571/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0791\n",
+      "Epoch 572/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0796\n",
+      "Epoch 573/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0797\n",
+      "Epoch 574/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - loss: 0.0785\n",
+      "Epoch 575/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183ms/step - loss: 0.0782\n",
+      "Epoch 576/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - loss: 0.0788\n",
+      "Epoch 577/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0785\n",
+      "Epoch 578/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 231ms/step - loss: 0.0778\n",
+      "Epoch 579/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - loss: 0.0779\n",
+      "Epoch 580/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0779\n",
+      "Epoch 581/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 199ms/step - loss: 0.0774\n",
+      "Epoch 582/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0775\n",
+      "Epoch 583/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0776\n",
+      "Epoch 584/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0771\n",
+      "Epoch 585/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0769\n",
+      "Epoch 586/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0770\n",
+      "Epoch 587/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0769\n",
+      "Epoch 588/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0767\n",
+      "Epoch 589/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0765\n",
+      "Epoch 590/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0765\n",
+      "Epoch 591/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0764\n",
+      "Epoch 592/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0762\n",
+      "Epoch 593/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0761\n",
+      "Epoch 594/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0760\n",
+      "Epoch 595/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0759\n",
+      "Epoch 596/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0758\n",
+      "Epoch 597/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0757\n",
+      "Epoch 598/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0756\n",
+      "Epoch 599/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0756\n",
+      "Epoch 600/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0754\n",
+      "Epoch 601/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0753\n",
+      "Epoch 602/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0752\n",
+      "Epoch 603/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0752\n",
+      "Epoch 604/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0751\n",
+      "Epoch 605/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0749\n",
+      "Epoch 606/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0749\n",
+      "Epoch 607/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0748\n",
+      "Epoch 608/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0748\n",
+      "Epoch 609/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0748\n",
+      "Epoch 610/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0749\n",
+      "Epoch 611/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0752\n",
+      "Epoch 612/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0757\n",
+      "Epoch 613/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0768\n",
+      "Epoch 614/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0783\n",
+      "Epoch 615/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0794\n",
+      "Epoch 616/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0789\n",
+      "Epoch 617/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0763\n",
+      "Epoch 618/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 223ms/step - loss: 0.0752\n",
+      "Epoch 619/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - loss: 0.0763\n",
+      "Epoch 620/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0764\n",
+      "Epoch 621/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0749\n",
+      "Epoch 622/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0743\n",
+      "Epoch 623/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - loss: 0.0747\n",
+      "Epoch 624/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - loss: 0.0744\n",
+      "Epoch 625/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 264ms/step - loss: 0.0742\n",
+      "Epoch 626/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0744\n",
+      "Epoch 627/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0739\n",
+      "Epoch 628/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0733\n",
+      "Epoch 629/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306ms/step - loss: 0.0736\n",
+      "Epoch 630/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291ms/step - loss: 0.0737\n",
+      "Epoch 631/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198ms/step - loss: 0.0732\n",
+      "Epoch 632/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 245ms/step - loss: 0.0731\n",
+      "Epoch 633/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - loss: 0.0732\n",
+      "Epoch 634/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step - loss: 0.0730\n",
+      "Epoch 635/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step - loss: 0.0728\n",
+      "Epoch 636/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 203ms/step - loss: 0.0727\n",
+      "Epoch 637/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.0727\n",
+      "Epoch 638/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 260ms/step - loss: 0.0726\n",
+      "Epoch 639/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.0725\n",
+      "Epoch 640/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 186ms/step - loss: 0.0723\n",
+      "Epoch 641/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 201ms/step - loss: 0.0722\n",
+      "Epoch 642/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 334ms/step - loss: 0.0721\n",
+      "Epoch 643/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 213ms/step - loss: 0.0720\n",
+      "Epoch 644/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0720\n",
+      "Epoch 645/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 206ms/step - loss: 0.0719\n",
+      "Epoch 646/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 263ms/step - loss: 0.0718\n",
+      "Epoch 647/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 223ms/step - loss: 0.0716\n",
+      "Epoch 648/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 199ms/step - loss: 0.0716\n",
+      "Epoch 649/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 323ms/step - loss: 0.0716\n",
+      "Epoch 650/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 214ms/step - loss: 0.0714\n",
+      "Epoch 651/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 203ms/step - loss: 0.0713\n",
+      "Epoch 652/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0713\n",
+      "Epoch 653/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0712\n",
+      "Epoch 654/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 231ms/step - loss: 0.0711\n",
+      "Epoch 655/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0710\n",
+      "Epoch 656/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 184ms/step - loss: 0.0710\n",
+      "Epoch 657/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - loss: 0.0709\n",
+      "Epoch 658/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 182ms/step - loss: 0.0709\n",
+      "Epoch 659/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0708\n",
+      "Epoch 660/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0708\n",
+      "Epoch 661/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0707\n",
+      "Epoch 662/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 210ms/step - loss: 0.0708\n",
+      "Epoch 663/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 199ms/step - loss: 0.0710\n",
+      "Epoch 664/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 275ms/step - loss: 0.0713\n",
+      "Epoch 665/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309ms/step - loss: 0.0720\n",
+      "Epoch 666/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0730\n",
+      "Epoch 667/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0741\n",
+      "Epoch 668/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0743\n",
+      "Epoch 669/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - loss: 0.0731\n",
+      "Epoch 670/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step - loss: 0.0713\n",
+      "Epoch 671/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0714\n",
+      "Epoch 672/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0727\n",
+      "Epoch 673/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0725\n",
+      "Epoch 674/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0710\n",
+      "Epoch 675/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0708\n",
+      "Epoch 676/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - loss: 0.0710\n",
+      "Epoch 677/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - loss: 0.0703\n",
+      "Epoch 678/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 485ms/step - loss: 0.0700\n",
+      "Epoch 679/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - loss: 0.0703\n",
+      "Epoch 680/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 205ms/step - loss: 0.0700\n",
+      "Epoch 681/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step - loss: 0.0696\n",
+      "Epoch 682/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0699\n",
+      "Epoch 683/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 289ms/step - loss: 0.0699\n",
+      "Epoch 684/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 197ms/step - loss: 0.0695\n",
+      "Epoch 685/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - loss: 0.0695\n",
+      "Epoch 686/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0695\n",
+      "Epoch 687/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183ms/step - loss: 0.0691\n",
+      "Epoch 688/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 194ms/step - loss: 0.0689\n",
+      "Epoch 689/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0691\n",
+      "Epoch 690/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 0.0689\n",
+      "Epoch 691/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0687\n",
+      "Epoch 692/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.0688\n",
+      "Epoch 693/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 223ms/step - loss: 0.0688\n",
+      "Epoch 694/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0686\n",
+      "Epoch 695/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 0.0685\n",
+      "Epoch 696/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 181ms/step - loss: 0.0685\n",
+      "Epoch 697/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0684\n",
+      "Epoch 698/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 0.0682\n",
+      "Epoch 699/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 201ms/step - loss: 0.0682\n",
+      "Epoch 700/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0682\n",
+      "Epoch 701/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 181ms/step - loss: 0.0681\n",
+      "Epoch 702/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0682\n",
+      "Epoch 703/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.0683\n",
+      "Epoch 704/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0685\n",
+      "Epoch 705/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - loss: 0.0689\n",
+      "Epoch 706/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0695\n",
+      "Epoch 707/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 194ms/step - loss: 0.0699\n",
+      "Epoch 708/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0702\n",
+      "Epoch 709/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0696\n",
+      "Epoch 710/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 269ms/step - loss: 0.0688\n",
+      "Epoch 711/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 210ms/step - loss: 0.0687\n",
+      "Epoch 712/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 203ms/step - loss: 0.0689\n",
+      "Epoch 713/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 203ms/step - loss: 0.0689\n",
+      "Epoch 714/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 208ms/step - loss: 0.0682\n",
+      "Epoch 715/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 264ms/step - loss: 0.0678\n",
+      "Epoch 716/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 182ms/step - loss: 0.0683\n",
+      "Epoch 717/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 320ms/step - loss: 0.0687\n",
+      "Epoch 718/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - loss: 0.0684\n",
+      "Epoch 719/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.0677\n",
+      "Epoch 720/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0674\n",
+      "Epoch 721/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 194ms/step - loss: 0.0673\n",
+      "Epoch 722/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - loss: 0.0670\n",
+      "Epoch 723/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300ms/step - loss: 0.0668\n",
+      "Epoch 724/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 249ms/step - loss: 0.0670\n",
+      "Epoch 725/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 259ms/step - loss: 0.0671\n",
+      "Epoch 726/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 262ms/step - loss: 0.0668\n",
+      "Epoch 727/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 202ms/step - loss: 0.0665\n",
+      "Epoch 728/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0665\n",
+      "Epoch 729/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - loss: 0.0665\n",
+      "Epoch 730/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 290ms/step - loss: 0.0665\n",
+      "Epoch 731/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 205ms/step - loss: 0.0663\n",
+      "Epoch 732/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - loss: 0.0663\n",
+      "Epoch 733/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0662\n",
+      "Epoch 734/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0661\n",
+      "Epoch 735/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0659\n",
+      "Epoch 736/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0658\n",
+      "Epoch 737/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0658\n",
+      "Epoch 738/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0658\n",
+      "Epoch 739/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0658\n",
+      "Epoch 740/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0657\n",
+      "Epoch 741/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0657\n",
+      "Epoch 742/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0657\n",
+      "Epoch 743/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0657\n",
+      "Epoch 744/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0657\n",
+      "Epoch 745/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0658\n",
+      "Epoch 746/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0661\n",
+      "Epoch 747/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0666\n",
+      "Epoch 748/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0673\n",
+      "Epoch 749/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0685\n",
+      "Epoch 750/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0692\n",
+      "Epoch 751/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0692\n",
+      "Epoch 752/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 227ms/step - loss: 0.0675\n",
+      "Epoch 753/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0658\n",
+      "Epoch 754/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0654\n",
+      "Epoch 755/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0661\n",
+      "Epoch 756/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0663\n",
+      "Epoch 757/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0657\n",
+      "Epoch 758/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0652\n",
+      "Epoch 759/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0653\n",
+      "Epoch 760/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0653\n",
+      "Epoch 761/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0649\n",
+      "Epoch 762/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0647\n",
+      "Epoch 763/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0649\n",
+      "Epoch 764/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0650\n",
+      "Epoch 765/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0646\n",
+      "Epoch 766/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 274ms/step - loss: 0.0643\n",
+      "Epoch 767/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 209ms/step - loss: 0.0644\n",
+      "Epoch 768/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0645\n",
+      "Epoch 769/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0643\n",
+      "Epoch 770/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0640\n",
+      "Epoch 771/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0640\n",
+      "Epoch 772/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 277ms/step - loss: 0.0641\n",
+      "Epoch 773/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 214ms/step - loss: 0.0640\n",
+      "Epoch 774/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0638\n",
+      "Epoch 775/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0637\n",
+      "Epoch 776/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0637\n",
+      "Epoch 777/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0637\n",
+      "Epoch 778/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 287ms/step - loss: 0.0636\n",
+      "Epoch 779/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0635\n",
+      "Epoch 780/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0635\n",
+      "Epoch 781/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - loss: 0.0636\n",
+      "Epoch 782/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 266ms/step - loss: 0.0636\n",
+      "Epoch 783/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 184ms/step - loss: 0.0635\n",
+      "Epoch 784/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0636\n",
+      "Epoch 785/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0638\n",
+      "Epoch 786/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198ms/step - loss: 0.0640\n",
+      "Epoch 787/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0644\n",
+      "Epoch 788/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0646\n",
+      "Epoch 789/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0648\n",
+      "Epoch 790/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0646\n",
+      "Epoch 791/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0645\n",
+      "Epoch 792/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0646\n",
+      "Epoch 793/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0650\n",
+      "Epoch 794/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 228ms/step - loss: 0.0654\n",
+      "Epoch 795/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0650\n",
+      "Epoch 796/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0638\n",
+      "Epoch 797/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - loss: 0.0629\n",
+      "Epoch 798/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0631\n",
+      "Epoch 799/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0636\n",
+      "Epoch 800/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0634\n",
+      "Epoch 801/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0627\n",
+      "Epoch 802/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0624\n",
+      "Epoch 803/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0628\n",
+      "Epoch 804/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0630\n",
+      "Epoch 805/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0626\n",
+      "Epoch 806/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - loss: 0.0622\n",
+      "Epoch 807/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0622\n",
+      "Epoch 808/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0624\n",
+      "Epoch 809/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.0623\n",
+      "Epoch 810/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0620\n",
+      "Epoch 811/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0619\n",
+      "Epoch 812/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0620\n",
+      "Epoch 813/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - loss: 0.0620\n",
+      "Epoch 814/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0619\n",
+      "Epoch 815/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0617\n",
+      "Epoch 816/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0617\n",
+      "Epoch 817/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.0616\n",
+      "Epoch 818/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0616\n",
+      "Epoch 819/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0615\n",
+      "Epoch 820/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0614\n",
+      "Epoch 821/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0613\n",
+      "Epoch 822/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0613\n",
+      "Epoch 823/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 207ms/step - loss: 0.0613\n",
+      "Epoch 824/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0613\n",
+      "Epoch 825/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0612\n",
+      "Epoch 826/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0611\n",
+      "Epoch 827/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0610\n",
+      "Epoch 828/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0610\n",
+      "Epoch 829/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0610\n",
+      "Epoch 830/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0610\n",
+      "Epoch 831/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 213ms/step - loss: 0.0610\n",
+      "Epoch 832/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - loss: 0.0610\n",
+      "Epoch 833/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0610\n",
+      "Epoch 834/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0612\n",
+      "Epoch 835/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0615\n",
+      "Epoch 836/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0620\n",
+      "Epoch 837/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0628\n",
+      "Epoch 838/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0638\n",
+      "Epoch 839/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0648\n",
+      "Epoch 840/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0651\n",
+      "Epoch 841/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0642\n",
+      "Epoch 842/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0625\n",
+      "Epoch 843/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0614\n",
+      "Epoch 844/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0617\n",
+      "Epoch 845/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0621\n",
+      "Epoch 846/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0614\n",
+      "Epoch 847/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0606\n",
+      "Epoch 848/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0610\n",
+      "Epoch 849/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0614\n",
+      "Epoch 850/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0607\n",
+      "Epoch 851/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0603\n",
+      "Epoch 852/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0608\n",
+      "Epoch 853/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0610\n",
+      "Epoch 854/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0604\n",
+      "Epoch 855/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0602\n",
+      "Epoch 856/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0605\n",
+      "Epoch 857/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0605\n",
+      "Epoch 858/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0601\n",
+      "Epoch 859/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0601\n",
+      "Epoch 860/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0603\n",
+      "Epoch 861/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0601\n",
+      "Epoch 862/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0599\n",
+      "Epoch 863/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0599\n",
+      "Epoch 864/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0599\n",
+      "Epoch 865/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0597\n",
+      "Epoch 866/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0595\n",
+      "Epoch 867/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0595\n",
+      "Epoch 868/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0594\n",
+      "Epoch 869/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0592\n",
+      "Epoch 870/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0591\n",
+      "Epoch 871/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0591\n",
+      "Epoch 872/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0591\n",
+      "Epoch 873/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0590\n",
+      "Epoch 874/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0590\n",
+      "Epoch 875/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0590\n",
+      "Epoch 876/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0589\n",
+      "Epoch 877/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0589\n",
+      "Epoch 878/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0589\n",
+      "Epoch 879/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0590\n",
+      "Epoch 880/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step - loss: 0.0591\n",
+      "Epoch 881/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0592\n",
+      "Epoch 882/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0594\n",
+      "Epoch 883/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0597\n",
+      "Epoch 884/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0602\n",
+      "Epoch 885/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0604\n",
+      "Epoch 886/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0605\n",
+      "Epoch 887/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0600\n",
+      "Epoch 888/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0592\n",
+      "Epoch 889/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0585\n",
+      "Epoch 890/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0584\n",
+      "Epoch 891/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0588\n",
+      "Epoch 892/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0591\n",
+      "Epoch 893/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0590\n",
+      "Epoch 894/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0585\n",
+      "Epoch 895/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0583\n",
+      "Epoch 896/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0584\n",
+      "Epoch 897/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0585\n",
+      "Epoch 898/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0584\n",
+      "Epoch 899/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0581\n",
+      "Epoch 900/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0579\n",
+      "Epoch 901/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0580\n",
+      "Epoch 902/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0583\n",
+      "Epoch 903/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0585\n",
+      "Epoch 904/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0589\n",
+      "Epoch 905/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0594\n",
+      "Epoch 906/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0605\n",
+      "Epoch 907/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0612\n",
+      "Epoch 908/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0609\n",
+      "Epoch 909/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0589\n",
+      "Epoch 910/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 181ms/step - loss: 0.0575\n",
+      "Epoch 911/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0581\n",
+      "Epoch 912/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0589\n",
+      "Epoch 913/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0582\n",
+      "Epoch 914/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0574\n",
+      "Epoch 915/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0579\n",
+      "Epoch 916/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - loss: 0.0581\n",
+      "Epoch 917/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0574\n",
+      "Epoch 918/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0573\n",
+      "Epoch 919/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0577\n",
+      "Epoch 920/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0575\n",
+      "Epoch 921/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0571\n",
+      "Epoch 922/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0572\n",
+      "Epoch 923/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0574\n",
+      "Epoch 924/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0570\n",
+      "Epoch 925/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0569\n",
+      "Epoch 926/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0571\n",
+      "Epoch 927/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0571\n",
+      "Epoch 928/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0569\n",
+      "Epoch 929/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0569\n",
+      "Epoch 930/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0572\n",
+      "Epoch 931/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0573\n",
+      "Epoch 932/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0576\n",
+      "Epoch 933/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0582\n",
+      "Epoch 934/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0591\n",
+      "Epoch 935/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0599\n",
+      "Epoch 936/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0601\n",
+      "Epoch 937/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 337ms/step - loss: 0.0591\n",
+      "Epoch 938/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0576\n",
+      "Epoch 939/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0568\n",
+      "Epoch 940/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0571\n",
+      "Epoch 941/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0574\n",
+      "Epoch 942/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0571\n",
+      "Epoch 943/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0567\n",
+      "Epoch 944/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0566\n",
+      "Epoch 945/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0566\n",
+      "Epoch 946/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0566\n",
+      "Epoch 947/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0565\n",
+      "Epoch 948/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0564\n",
+      "Epoch 949/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0562\n",
+      "Epoch 950/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0562\n",
+      "Epoch 951/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0562\n",
+      "Epoch 952/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0561\n",
+      "Epoch 953/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0559\n",
+      "Epoch 954/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0558\n",
+      "Epoch 955/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0559\n",
+      "Epoch 956/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0559\n",
+      "Epoch 957/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0557\n",
+      "Epoch 958/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0556\n",
+      "Epoch 959/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0556\n",
+      "Epoch 960/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - loss: 0.0557\n",
+      "Epoch 961/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0556\n",
+      "Epoch 962/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0555\n",
+      "Epoch 963/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0554\n",
+      "Epoch 964/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.0554\n",
+      "Epoch 965/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0554\n",
+      "Epoch 966/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0553\n",
+      "Epoch 967/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0553\n",
+      "Epoch 968/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0552\n",
+      "Epoch 969/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0552\n",
+      "Epoch 970/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0553\n",
+      "Epoch 971/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0553\n",
+      "Epoch 972/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0554\n",
+      "Epoch 973/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0555\n",
+      "Epoch 974/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0557\n",
+      "Epoch 975/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0561\n",
+      "Epoch 976/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0566\n",
+      "Epoch 977/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0572\n",
+      "Epoch 978/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0574\n",
+      "Epoch 979/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0571\n",
+      "Epoch 980/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0562\n",
+      "Epoch 981/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0551\n",
+      "Epoch 982/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0547\n",
+      "Epoch 983/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0550\n",
+      "Epoch 984/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0555\n",
+      "Epoch 985/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0554\n",
+      "Epoch 986/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0550\n",
+      "Epoch 987/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0546\n",
+      "Epoch 988/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0546\n",
+      "Epoch 989/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - loss: 0.0548\n",
+      "Epoch 990/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0549\n",
+      "Epoch 991/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0546\n",
+      "Epoch 992/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.0544\n",
+      "Epoch 993/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0543\n",
+      "Epoch 994/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0544\n",
+      "Epoch 995/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0545\n",
+      "Epoch 996/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0544\n",
+      "Epoch 997/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 207ms/step - loss: 0.0542\n",
+      "Epoch 998/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0541\n",
+      "Epoch 999/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0541\n",
+      "Epoch 1000/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0541\n",
+      "Epoch 1000/20000\n",
+      " - loss: 0.0541\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0541\n",
+      "Epoch 1001/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0541\n",
+      "Epoch 1002/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0541\n",
+      "Epoch 1003/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0540\n",
+      "Epoch 1004/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0539\n",
+      "Epoch 1005/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0538\n",
+      "Epoch 1006/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0538\n",
+      "Epoch 1007/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0538\n",
+      "Epoch 1008/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0538\n",
+      "Epoch 1009/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0538\n",
+      "Epoch 1010/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0537\n",
+      "Epoch 1011/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0537\n",
+      "Epoch 1012/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0536\n",
+      "Epoch 1013/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0537\n",
+      "Epoch 1014/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0538\n",
+      "Epoch 1015/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0539\n",
+      "Epoch 1016/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0543\n",
+      "Epoch 1017/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0549\n",
+      "Epoch 1018/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0559\n",
+      "Epoch 1019/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0575\n",
+      "Epoch 1020/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0588\n",
+      "Epoch 1021/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0596\n",
+      "Epoch 1022/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 233ms/step - loss: 0.0575\n",
+      "Epoch 1023/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0554\n",
+      "Epoch 1024/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0551\n",
+      "Epoch 1025/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0559\n",
+      "Epoch 1026/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0554\n",
+      "Epoch 1027/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0542\n",
+      "Epoch 1028/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0545\n",
+      "Epoch 1029/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0549\n",
+      "Epoch 1030/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0541\n",
+      "Epoch 1031/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0539\n",
+      "Epoch 1032/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.0544\n",
+      "Epoch 1033/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0540\n",
+      "Epoch 1034/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0535\n",
+      "Epoch 1035/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0536\n",
+      "Epoch 1036/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0536\n",
+      "Epoch 1037/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0534\n",
+      "Epoch 1038/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0533\n",
+      "Epoch 1039/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0532\n",
+      "Epoch 1040/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0530\n",
+      "Epoch 1041/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0530\n",
+      "Epoch 1042/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0530\n",
+      "Epoch 1043/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0528\n",
+      "Epoch 1044/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0528\n",
+      "Epoch 1045/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0528\n",
+      "Epoch 1046/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0526\n",
+      "Epoch 1047/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0526\n",
+      "Epoch 1048/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0527\n",
+      "Epoch 1049/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 215ms/step - loss: 0.0526\n",
+      "Epoch 1050/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0525\n",
+      "Epoch 1051/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0525\n",
+      "Epoch 1052/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0525\n",
+      "Epoch 1053/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0525\n",
+      "Epoch 1054/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0525\n",
+      "Epoch 1055/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0526\n",
+      "Epoch 1056/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0527\n",
+      "Epoch 1057/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0528\n",
+      "Epoch 1058/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0530\n",
+      "Epoch 1059/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0533\n",
+      "Epoch 1060/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0533\n",
+      "Epoch 1061/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0531\n",
+      "Epoch 1062/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.0526\n",
+      "Epoch 1063/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0522\n",
+      "Epoch 1064/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0520\n",
+      "Epoch 1065/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0520\n",
+      "Epoch 1066/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0522\n",
+      "Epoch 1067/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0523\n",
+      "Epoch 1068/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0521\n",
+      "Epoch 1069/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0519\n",
+      "Epoch 1070/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0517\n",
+      "Epoch 1071/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0517\n",
+      "Epoch 1072/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 232ms/step - loss: 0.0518\n",
+      "Epoch 1073/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0518\n",
+      "Epoch 1074/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0518\n",
+      "Epoch 1075/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0517\n",
+      "Epoch 1076/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0515\n",
+      "Epoch 1077/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0515\n",
+      "Epoch 1078/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0515\n",
+      "Epoch 1079/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0515\n",
+      "Epoch 1080/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0515\n",
+      "Epoch 1081/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0515\n",
+      "Epoch 1082/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0514\n",
+      "Epoch 1083/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0513\n",
+      "Epoch 1084/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0513\n",
+      "Epoch 1085/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0513\n",
+      "Epoch 1086/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0514\n",
+      "Epoch 1087/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0517\n",
+      "Epoch 1088/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0521\n",
+      "Epoch 1089/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0530\n",
+      "Epoch 1090/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0542\n",
+      "Epoch 1091/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0559\n",
+      "Epoch 1092/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0570\n",
+      "Epoch 1093/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0566\n",
+      "Epoch 1094/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0547\n",
+      "Epoch 1095/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0535\n",
+      "Epoch 1096/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0537\n",
+      "Epoch 1097/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0536\n",
+      "Epoch 1098/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0526\n",
+      "Epoch 1099/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0520\n",
+      "Epoch 1100/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0525\n",
+      "Epoch 1101/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0524\n",
+      "Epoch 1102/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0517\n",
+      "Epoch 1103/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0517\n",
+      "Epoch 1104/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295ms/step - loss: 0.0519\n",
+      "Epoch 1105/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0514\n",
+      "Epoch 1106/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0511\n",
+      "Epoch 1107/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0514\n",
+      "Epoch 1108/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0514\n",
+      "Epoch 1109/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0509\n",
+      "Epoch 1110/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0508\n",
+      "Epoch 1111/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0511\n",
+      "Epoch 1112/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0510\n",
+      "Epoch 1113/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0507\n",
+      "Epoch 1114/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0507\n",
+      "Epoch 1115/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0507\n",
+      "Epoch 1116/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0506\n",
+      "Epoch 1117/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0505\n",
+      "Epoch 1118/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0505\n",
+      "Epoch 1119/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0504\n",
+      "Epoch 1120/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0503\n",
+      "Epoch 1121/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0503\n",
+      "Epoch 1122/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0503\n",
+      "Epoch 1123/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0503\n",
+      "Epoch 1124/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0502\n",
+      "Epoch 1125/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0501\n",
+      "Epoch 1126/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0502\n",
+      "Epoch 1127/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0501\n",
+      "Epoch 1128/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 216ms/step - loss: 0.0500\n",
+      "Epoch 1129/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0500\n",
+      "Epoch 1130/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0500\n",
+      "Epoch 1131/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0500\n",
+      "Epoch 1132/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0499\n",
+      "Epoch 1133/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0498\n",
+      "Epoch 1134/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0498\n",
+      "Epoch 1135/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0498\n",
+      "Epoch 1136/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0498\n",
+      "Epoch 1137/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0497\n",
+      "Epoch 1138/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0497\n",
+      "Epoch 1139/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0497\n",
+      "Epoch 1140/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0497\n",
+      "Epoch 1141/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0496\n",
+      "Epoch 1142/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0497\n",
+      "Epoch 1143/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0498\n",
+      "Epoch 1144/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0500\n",
+      "Epoch 1145/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0503\n",
+      "Epoch 1146/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0509\n",
+      "Epoch 1147/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0521\n",
+      "Epoch 1148/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0534\n",
+      "Epoch 1149/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0545\n",
+      "Epoch 1150/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0538\n",
+      "Epoch 1151/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0524\n",
+      "Epoch 1152/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0520\n",
+      "Epoch 1153/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198ms/step - loss: 0.0528\n",
+      "Epoch 1154/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0522\n",
+      "Epoch 1155/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0507\n",
+      "Epoch 1156/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0509\n",
+      "Epoch 1157/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0516\n",
+      "Epoch 1158/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 220ms/step - loss: 0.0508\n",
+      "Epoch 1159/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0497\n",
+      "Epoch 1160/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0499\n",
+      "Epoch 1161/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0501\n",
+      "Epoch 1162/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0500\n",
+      "Epoch 1163/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0500\n",
+      "Epoch 1164/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0497\n",
+      "Epoch 1165/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0493\n",
+      "Epoch 1166/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0494\n",
+      "Epoch 1167/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0497\n",
+      "Epoch 1168/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0494\n",
+      "Epoch 1169/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0491\n",
+      "Epoch 1170/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0492\n",
+      "Epoch 1171/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0493\n",
+      "Epoch 1172/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0491\n",
+      "Epoch 1173/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0489\n",
+      "Epoch 1174/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0490\n",
+      "Epoch 1175/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0490\n",
+      "Epoch 1176/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 207ms/step - loss: 0.0489\n",
+      "Epoch 1177/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0488\n",
+      "Epoch 1178/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0488\n",
+      "Epoch 1179/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0487\n",
+      "Epoch 1180/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0486\n",
+      "Epoch 1181/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0487\n",
+      "Epoch 1182/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0487\n",
+      "Epoch 1183/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0486\n",
+      "Epoch 1184/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0485\n",
+      "Epoch 1185/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0485\n",
+      "Epoch 1186/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0485\n",
+      "Epoch 1187/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0484\n",
+      "Epoch 1188/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0484\n",
+      "Epoch 1189/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0484\n",
+      "Epoch 1190/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0483\n",
+      "Epoch 1191/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0483\n",
+      "Epoch 1192/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0483\n",
+      "Epoch 1193/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0482\n",
+      "Epoch 1194/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0482\n",
+      "Epoch 1195/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0482\n",
+      "Epoch 1196/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0481\n",
+      "Epoch 1197/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0481\n",
+      "Epoch 1198/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0481\n",
+      "Epoch 1199/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0481\n",
+      "Epoch 1200/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0481\n",
+      "Epoch 1201/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0480\n",
+      "Epoch 1202/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0480\n",
+      "Epoch 1203/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0481\n",
+      "Epoch 1204/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0481\n",
+      "Epoch 1205/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0482\n",
+      "Epoch 1206/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0485\n",
+      "Epoch 1207/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0490\n",
+      "Epoch 1208/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0498\n",
+      "Epoch 1209/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0509\n",
+      "Epoch 1210/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0523\n",
+      "Epoch 1211/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0526\n",
+      "Epoch 1212/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0515\n",
+      "Epoch 1213/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0493\n",
+      "Epoch 1214/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.0483\n",
+      "Epoch 1215/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0490\n",
+      "Epoch 1216/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0494\n",
+      "Epoch 1217/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0486\n",
+      "Epoch 1218/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0480\n",
+      "Epoch 1219/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0486\n",
+      "Epoch 1220/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0489\n",
+      "Epoch 1221/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0481\n",
+      "Epoch 1222/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.0476\n",
+      "Epoch 1223/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0481\n",
+      "Epoch 1224/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0482\n",
+      "Epoch 1225/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0478\n",
+      "Epoch 1226/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0476\n",
+      "Epoch 1227/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0478\n",
+      "Epoch 1228/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0478\n",
+      "Epoch 1229/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0475\n",
+      "Epoch 1230/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0475\n",
+      "Epoch 1231/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0476\n",
+      "Epoch 1232/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 195ms/step - loss: 0.0476\n",
+      "Epoch 1233/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0473\n",
+      "Epoch 1234/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0473\n",
+      "Epoch 1235/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0474\n",
+      "Epoch 1236/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0473\n",
+      "Epoch 1237/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0472\n",
+      "Epoch 1238/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 210ms/step - loss: 0.0472\n",
+      "Epoch 1239/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0473\n",
+      "Epoch 1240/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0473\n",
+      "Epoch 1241/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - loss: 0.0472\n",
+      "Epoch 1242/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 0.0472\n",
+      "Epoch 1243/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0474\n",
+      "Epoch 1244/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0475\n",
+      "Epoch 1245/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.0476\n",
+      "Epoch 1246/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0478\n",
+      "Epoch 1247/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0481\n",
+      "Epoch 1248/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0483\n",
+      "Epoch 1249/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 0.0484\n",
+      "Epoch 1250/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0482\n",
+      "Epoch 1251/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0478\n",
+      "Epoch 1252/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - loss: 0.0473\n",
+      "Epoch 1253/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0469\n",
+      "Epoch 1254/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0469\n",
+      "Epoch 1255/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0471\n",
+      "Epoch 1256/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0473\n",
+      "Epoch 1257/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0472\n",
+      "Epoch 1258/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0470\n",
+      "Epoch 1259/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0468\n",
+      "Epoch 1260/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0467\n",
+      "Epoch 1261/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0467\n",
+      "Epoch 1262/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0468\n",
+      "Epoch 1263/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0467\n",
+      "Epoch 1264/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0466\n",
+      "Epoch 1265/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0466\n",
+      "Epoch 1266/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 215ms/step - loss: 0.0465\n",
+      "Epoch 1267/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 286ms/step - loss: 0.0465\n",
+      "Epoch 1268/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0465\n",
+      "Epoch 1269/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0464\n",
+      "Epoch 1270/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0464\n",
+      "Epoch 1271/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0464\n",
+      "Epoch 1272/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0463\n",
+      "Epoch 1273/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0463\n",
+      "Epoch 1274/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0464\n",
+      "Epoch 1275/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0464\n",
+      "Epoch 1276/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0465\n",
+      "Epoch 1277/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0466\n",
+      "Epoch 1278/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0468\n",
+      "Epoch 1279/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0472\n",
+      "Epoch 1280/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0478\n",
+      "Epoch 1281/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0488\n",
+      "Epoch 1282/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0498\n",
+      "Epoch 1283/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0505\n",
+      "Epoch 1284/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0500\n",
+      "Epoch 1285/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 380ms/step - loss: 0.0483\n",
+      "Epoch 1286/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0467\n",
+      "Epoch 1287/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0466\n",
+      "Epoch 1288/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 251ms/step - loss: 0.0474\n",
+      "Epoch 1289/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0477\n",
+      "Epoch 1290/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0467\n",
+      "Epoch 1291/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.0460\n",
+      "Epoch 1292/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0464\n",
+      "Epoch 1293/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0470\n",
+      "Epoch 1294/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0467\n",
+      "Epoch 1295/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0460\n",
+      "Epoch 1296/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0459\n",
+      "Epoch 1297/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0462\n",
+      "Epoch 1298/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0464\n",
+      "Epoch 1299/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0460\n",
+      "Epoch 1300/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0457\n",
+      "Epoch 1301/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0458\n",
+      "Epoch 1302/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 221ms/step - loss: 0.0460\n",
+      "Epoch 1303/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0459\n",
+      "Epoch 1304/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0457\n",
+      "Epoch 1305/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0455\n",
+      "Epoch 1306/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0456\n",
+      "Epoch 1307/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0458\n",
+      "Epoch 1308/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 221ms/step - loss: 0.0457\n",
+      "Epoch 1309/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0455\n",
+      "Epoch 1310/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0454\n",
+      "Epoch 1311/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0455\n",
+      "Epoch 1312/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0455\n",
+      "Epoch 1313/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0455\n",
+      "Epoch 1314/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0454\n",
+      "Epoch 1315/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0453\n",
+      "Epoch 1316/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0453\n",
+      "Epoch 1317/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0453\n",
+      "Epoch 1318/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0453\n",
+      "Epoch 1319/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0452\n",
+      "Epoch 1320/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0452\n",
+      "Epoch 1321/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0452\n",
+      "Epoch 1322/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0452\n",
+      "Epoch 1323/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 206ms/step - loss: 0.0452\n",
+      "Epoch 1324/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0452\n",
+      "Epoch 1325/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0453\n",
+      "Epoch 1326/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0454\n",
+      "Epoch 1327/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0457\n",
+      "Epoch 1328/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0464\n",
+      "Epoch 1329/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0476\n",
+      "Epoch 1330/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0494\n",
+      "Epoch 1331/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0513\n",
+      "Epoch 1332/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0516\n",
+      "Epoch 1333/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step - loss: 0.0491\n",
+      "Epoch 1334/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0466\n",
+      "Epoch 1335/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0467\n",
+      "Epoch 1336/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0473\n",
+      "Epoch 1337/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0464\n",
+      "Epoch 1338/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0461\n",
+      "Epoch 1339/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0466\n",
+      "Epoch 1340/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0458\n",
+      "Epoch 1341/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0455\n",
+      "Epoch 1342/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0461\n",
+      "Epoch 1343/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - loss: 0.0457\n",
+      "Epoch 1344/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step - loss: 0.0451\n",
+      "Epoch 1345/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198ms/step - loss: 0.0455\n",
+      "Epoch 1346/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.0455\n",
+      "Epoch 1347/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0450\n",
+      "Epoch 1348/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0451\n",
+      "Epoch 1349/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0453\n",
+      "Epoch 1350/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0449\n",
+      "Epoch 1351/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0448\n",
+      "Epoch 1352/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0449\n",
+      "Epoch 1353/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0448\n",
+      "Epoch 1354/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0447\n",
+      "Epoch 1355/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0448\n",
+      "Epoch 1356/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0446\n",
+      "Epoch 1357/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0445\n",
+      "Epoch 1358/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0446\n",
+      "Epoch 1359/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0446\n",
+      "Epoch 1360/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0444\n",
+      "Epoch 1361/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0444\n",
+      "Epoch 1362/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0445\n",
+      "Epoch 1363/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0444\n",
+      "Epoch 1364/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0443\n",
+      "Epoch 1365/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0443\n",
+      "Epoch 1366/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0443\n",
+      "Epoch 1367/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183ms/step - loss: 0.0442\n",
+      "Epoch 1368/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0442\n",
+      "Epoch 1369/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0443\n",
+      "Epoch 1370/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0443\n",
+      "Epoch 1371/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0443\n",
+      "Epoch 1372/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0443\n",
+      "Epoch 1373/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0444\n",
+      "Epoch 1374/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0445\n",
+      "Epoch 1375/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0448\n",
+      "Epoch 1376/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0452\n",
+      "Epoch 1377/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0458\n",
+      "Epoch 1378/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0465\n",
+      "Epoch 1379/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0470\n",
+      "Epoch 1380/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0469\n",
+      "Epoch 1381/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0460\n",
+      "Epoch 1382/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0447\n",
+      "Epoch 1383/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0440\n",
+      "Epoch 1384/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0444\n",
+      "Epoch 1385/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0450\n",
+      "Epoch 1386/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0450\n",
+      "Epoch 1387/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0443\n",
+      "Epoch 1388/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0440\n",
+      "Epoch 1389/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0443\n",
+      "Epoch 1390/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0447\n",
+      "Epoch 1391/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0445\n",
+      "Epoch 1392/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0443\n",
+      "Epoch 1393/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0444\n",
+      "Epoch 1394/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0447\n",
+      "Epoch 1395/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0447\n",
+      "Epoch 1396/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0444\n",
+      "Epoch 1397/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0441\n",
+      "Epoch 1398/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0440\n",
+      "Epoch 1399/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0439\n",
+      "Epoch 1400/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0438\n",
+      "Epoch 1401/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0437\n",
+      "Epoch 1402/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0437\n",
+      "Epoch 1403/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0438\n",
+      "Epoch 1404/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0438\n",
+      "Epoch 1405/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0437\n",
+      "Epoch 1406/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0436\n",
+      "Epoch 1407/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0435\n",
+      "Epoch 1408/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0435\n",
+      "Epoch 1409/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0435\n",
+      "Epoch 1410/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0434\n",
+      "Epoch 1411/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0434\n",
+      "Epoch 1412/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0434\n",
+      "Epoch 1413/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0434\n",
+      "Epoch 1414/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0434\n",
+      "Epoch 1415/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0434\n",
+      "Epoch 1416/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0434\n",
+      "Epoch 1417/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0434\n",
+      "Epoch 1418/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0434\n",
+      "Epoch 1419/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0434\n",
+      "Epoch 1420/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0435\n",
+      "Epoch 1421/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0436\n",
+      "Epoch 1422/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0438\n",
+      "Epoch 1423/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0441\n",
+      "Epoch 1424/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 268ms/step - loss: 0.0446\n",
+      "Epoch 1425/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 217ms/step - loss: 0.0453\n",
+      "Epoch 1426/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0459\n",
+      "Epoch 1427/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0464\n",
+      "Epoch 1428/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0458\n",
+      "Epoch 1429/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0448\n",
+      "Epoch 1430/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0437\n",
+      "Epoch 1431/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0436\n",
+      "Epoch 1432/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0443\n",
+      "Epoch 1433/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0447\n",
+      "Epoch 1434/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0442\n",
+      "Epoch 1435/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0435\n",
+      "Epoch 1436/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0434\n",
+      "Epoch 1437/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0438\n",
+      "Epoch 1438/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0438\n",
+      "Epoch 1439/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0433\n",
+      "Epoch 1440/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0429\n",
+      "Epoch 1441/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0431\n",
+      "Epoch 1442/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 205ms/step - loss: 0.0434\n",
+      "Epoch 1443/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0432\n",
+      "Epoch 1444/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0430\n",
+      "Epoch 1445/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0429\n",
+      "Epoch 1446/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0430\n",
+      "Epoch 1447/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0430\n",
+      "Epoch 1448/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0428\n",
+      "Epoch 1449/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0426\n",
+      "Epoch 1450/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0426\n",
+      "Epoch 1451/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0427\n",
+      "Epoch 1452/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0427\n",
+      "Epoch 1453/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0426\n",
+      "Epoch 1454/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0425\n",
+      "Epoch 1455/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0425\n",
+      "Epoch 1456/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0425\n",
+      "Epoch 1457/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0426\n",
+      "Epoch 1458/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0425\n",
+      "Epoch 1459/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0424\n",
+      "Epoch 1460/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0424\n",
+      "Epoch 1461/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0425\n",
+      "Epoch 1462/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0425\n",
+      "Epoch 1463/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0426\n",
+      "Epoch 1464/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0426\n",
+      "Epoch 1465/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0426\n",
+      "Epoch 1466/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0428\n",
+      "Epoch 1467/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0430\n",
+      "Epoch 1468/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0432\n",
+      "Epoch 1469/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0434\n",
+      "Epoch 1470/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0434\n",
+      "Epoch 1471/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0434\n",
+      "Epoch 1472/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0431\n",
+      "Epoch 1473/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0430\n",
+      "Epoch 1474/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0432\n",
+      "Epoch 1475/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0437\n",
+      "Epoch 1476/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0441\n",
+      "Epoch 1477/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0443\n",
+      "Epoch 1478/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0438\n",
+      "Epoch 1479/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0432\n",
+      "Epoch 1480/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0429\n",
+      "Epoch 1481/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - loss: 0.0429\n",
+      "Epoch 1482/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0430\n",
+      "Epoch 1483/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0428\n",
+      "Epoch 1484/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0424\n",
+      "Epoch 1485/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0422\n",
+      "Epoch 1486/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0423\n",
+      "Epoch 1487/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0423\n",
+      "Epoch 1488/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0423\n",
+      "Epoch 1489/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0421\n",
+      "Epoch 1490/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0421\n",
+      "Epoch 1491/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0422\n",
+      "Epoch 1492/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0422\n",
+      "Epoch 1493/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0421\n",
+      "Epoch 1494/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0419\n",
+      "Epoch 1495/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0417\n",
+      "Epoch 1496/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 207ms/step - loss: 0.0417\n",
+      "Epoch 1497/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 219ms/step - loss: 0.0418\n",
+      "Epoch 1498/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0419\n",
+      "Epoch 1499/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0418\n",
+      "Epoch 1500/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.0417\n",
+      "Epoch 1500/20000\n",
+      " - loss: 0.0417\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0417\n",
+      "Epoch 1501/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0417\n",
+      "Epoch 1502/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0417\n",
+      "Epoch 1503/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0417\n",
+      "Epoch 1504/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0417\n",
+      "Epoch 1505/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0417\n",
+      "Epoch 1506/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0416\n",
+      "Epoch 1507/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0416\n",
+      "Epoch 1508/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0415\n",
+      "Epoch 1509/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0416\n",
+      "Epoch 1510/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0416\n",
+      "Epoch 1511/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0416\n",
+      "Epoch 1512/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - loss: 0.0416\n",
+      "Epoch 1513/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0416\n",
+      "Epoch 1514/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0418\n",
+      "Epoch 1515/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0420\n",
+      "Epoch 1516/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0423\n",
+      "Epoch 1517/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0428\n",
+      "Epoch 1518/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0435\n",
+      "Epoch 1519/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0444\n",
+      "Epoch 1520/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0448\n",
+      "Epoch 1521/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0447\n",
+      "Epoch 1522/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0434\n",
+      "Epoch 1523/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0421\n",
+      "Epoch 1524/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0418\n",
+      "Epoch 1525/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0425\n",
+      "Epoch 1526/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0430\n",
+      "Epoch 1527/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0424\n",
+      "Epoch 1528/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0416\n",
+      "Epoch 1529/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.0416\n",
+      "Epoch 1530/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0422\n",
+      "Epoch 1531/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0423\n",
+      "Epoch 1532/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 202ms/step - loss: 0.0419\n",
+      "Epoch 1533/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0416\n",
+      "Epoch 1534/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0419\n",
+      "Epoch 1535/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0421\n",
+      "Epoch 1536/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0419\n",
+      "Epoch 1537/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0414\n",
+      "Epoch 1538/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0414\n",
+      "Epoch 1539/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0415\n",
+      "Epoch 1540/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0414\n",
+      "Epoch 1541/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0411\n",
+      "Epoch 1542/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0410\n",
+      "Epoch 1543/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0411\n",
+      "Epoch 1544/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0412\n",
+      "Epoch 1545/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0411\n",
+      "Epoch 1546/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0409\n",
+      "Epoch 1547/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 214ms/step - loss: 0.0409\n",
+      "Epoch 1548/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0410\n",
+      "Epoch 1549/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0410\n",
+      "Epoch 1550/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0410\n",
+      "Epoch 1551/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0409\n",
+      "Epoch 1552/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0409\n",
+      "Epoch 1553/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0409\n",
+      "Epoch 1554/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0409\n",
+      "Epoch 1555/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0408\n",
+      "Epoch 1556/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0407\n",
+      "Epoch 1557/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0407\n",
+      "Epoch 1558/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0407\n",
+      "Epoch 1559/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0407\n",
+      "Epoch 1560/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0407\n",
+      "Epoch 1561/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0408\n",
+      "Epoch 1562/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 212ms/step - loss: 0.0408\n",
+      "Epoch 1563/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0409\n",
+      "Epoch 1564/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0411\n",
+      "Epoch 1565/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0413\n",
+      "Epoch 1566/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0416\n",
+      "Epoch 1567/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0420\n",
+      "Epoch 1568/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0423\n",
+      "Epoch 1569/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0426\n",
+      "Epoch 1570/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0428\n",
+      "Epoch 1571/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0426\n",
+      "Epoch 1572/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0424\n",
+      "Epoch 1573/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0420\n",
+      "Epoch 1574/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0418\n",
+      "Epoch 1575/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0416\n",
+      "Epoch 1576/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0416\n",
+      "Epoch 1577/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0415\n",
+      "Epoch 1578/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 341ms/step - loss: 0.0412\n",
+      "Epoch 1579/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0408\n",
+      "Epoch 1580/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0406\n",
+      "Epoch 1581/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0407\n",
+      "Epoch 1582/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0409\n",
+      "Epoch 1583/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0409\n",
+      "Epoch 1584/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0407\n",
+      "Epoch 1585/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step - loss: 0.0404\n",
+      "Epoch 1586/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0403\n",
+      "Epoch 1587/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0404\n",
+      "Epoch 1588/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0406\n",
+      "Epoch 1589/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0405\n",
+      "Epoch 1590/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0403\n",
+      "Epoch 1591/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 236ms/step - loss: 0.0401\n",
+      "Epoch 1592/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 197ms/step - loss: 0.0401\n",
+      "Epoch 1593/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0403\n",
+      "Epoch 1594/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 212ms/step - loss: 0.0403\n",
+      "Epoch 1595/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 246ms/step - loss: 0.0403\n",
+      "Epoch 1596/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 271ms/step - loss: 0.0402\n",
+      "Epoch 1597/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0401\n",
+      "Epoch 1598/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0401\n",
+      "Epoch 1599/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0402\n",
+      "Epoch 1600/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 265ms/step - loss: 0.0404\n",
+      "Epoch 1601/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0406\n",
+      "Epoch 1602/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0409\n",
+      "Epoch 1603/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0413\n",
+      "Epoch 1604/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0418\n",
+      "Epoch 1605/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0424\n",
+      "Epoch 1606/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0428\n",
+      "Epoch 1607/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0426\n",
+      "Epoch 1608/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0416\n",
+      "Epoch 1609/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0405\n",
+      "Epoch 1610/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0400\n",
+      "Epoch 1611/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0402\n",
+      "Epoch 1612/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0407\n",
+      "Epoch 1613/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 226ms/step - loss: 0.0408\n",
+      "Epoch 1614/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0404\n",
+      "Epoch 1615/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0399\n",
+      "Epoch 1616/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0399\n",
+      "Epoch 1617/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step - loss: 0.0401\n",
+      "Epoch 1618/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0402\n",
+      "Epoch 1619/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0400\n",
+      "Epoch 1620/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0398\n",
+      "Epoch 1621/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0397\n",
+      "Epoch 1622/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0397\n",
+      "Epoch 1623/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0398\n",
+      "Epoch 1624/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0398\n",
+      "Epoch 1625/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0397\n",
+      "Epoch 1626/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0396\n",
+      "Epoch 1627/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0396\n",
+      "Epoch 1628/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0396\n",
+      "Epoch 1629/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0396\n",
+      "Epoch 1630/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0397\n",
+      "Epoch 1631/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 324ms/step - loss: 0.0396\n",
+      "Epoch 1632/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.0396\n",
+      "Epoch 1633/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292ms/step - loss: 0.0396\n",
+      "Epoch 1634/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.0397\n",
+      "Epoch 1635/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0398\n",
+      "Epoch 1636/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0400\n",
+      "Epoch 1637/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0403\n",
+      "Epoch 1638/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0406\n",
+      "Epoch 1639/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0409\n",
+      "Epoch 1640/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 203ms/step - loss: 0.0412\n",
+      "Epoch 1641/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 270ms/step - loss: 0.0414\n",
+      "Epoch 1642/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0412\n",
+      "Epoch 1643/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0407\n",
+      "Epoch 1644/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0399\n",
+      "Epoch 1645/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0394\n",
+      "Epoch 1646/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0394\n",
+      "Epoch 1647/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0397\n",
+      "Epoch 1648/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - loss: 0.0399\n",
+      "Epoch 1649/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0398\n",
+      "Epoch 1650/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0395\n",
+      "Epoch 1651/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0392\n",
+      "Epoch 1652/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0392\n",
+      "Epoch 1653/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0393\n",
+      "Epoch 1654/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0394\n",
+      "Epoch 1655/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0394\n",
+      "Epoch 1656/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - loss: 0.0392\n",
+      "Epoch 1657/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0390\n",
+      "Epoch 1658/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0390\n",
+      "Epoch 1659/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0391\n",
+      "Epoch 1660/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0392\n",
+      "Epoch 1661/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0391\n",
+      "Epoch 1662/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 0.0390\n",
+      "Epoch 1663/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 262ms/step - loss: 0.0389\n",
+      "Epoch 1664/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0389\n",
+      "Epoch 1665/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0389\n",
+      "Epoch 1666/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0389\n",
+      "Epoch 1667/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0390\n",
+      "Epoch 1668/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0389\n",
+      "Epoch 1669/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0389\n",
+      "Epoch 1670/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 202ms/step - loss: 0.0389\n",
+      "Epoch 1671/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0390\n",
+      "Epoch 1672/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0391\n",
+      "Epoch 1673/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0393\n",
+      "Epoch 1674/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0397\n",
+      "Epoch 1675/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0401\n",
+      "Epoch 1676/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0407\n",
+      "Epoch 1677/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0412\n",
+      "Epoch 1678/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0411\n",
+      "Epoch 1679/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0406\n",
+      "Epoch 1680/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0398\n",
+      "Epoch 1681/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0394\n",
+      "Epoch 1682/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0397\n",
+      "Epoch 1683/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0401\n",
+      "Epoch 1684/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0400\n",
+      "Epoch 1685/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0397\n",
+      "Epoch 1686/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0396\n",
+      "Epoch 1687/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0400\n",
+      "Epoch 1688/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 222ms/step - loss: 0.0404\n",
+      "Epoch 1689/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198ms/step - loss: 0.0405\n",
+      "Epoch 1690/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0402\n",
+      "Epoch 1691/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0396\n",
+      "Epoch 1692/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0391\n",
+      "Epoch 1693/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0388\n",
+      "Epoch 1694/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0388\n",
+      "Epoch 1695/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0390\n",
+      "Epoch 1696/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0391\n",
+      "Epoch 1697/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0389\n",
+      "Epoch 1698/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0387\n",
+      "Epoch 1699/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0386\n",
+      "Epoch 1700/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304ms/step - loss: 0.0387\n",
+      "Epoch 1701/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0388\n",
+      "Epoch 1702/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0388\n",
+      "Epoch 1703/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0387\n",
+      "Epoch 1704/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0387\n",
+      "Epoch 1705/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0389\n",
+      "Epoch 1706/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0390\n",
+      "Epoch 1707/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0393\n",
+      "Epoch 1708/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0397\n",
+      "Epoch 1709/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0401\n",
+      "Epoch 1710/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0404\n",
+      "Epoch 1711/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0406\n",
+      "Epoch 1712/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0403\n",
+      "Epoch 1713/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0398\n",
+      "Epoch 1714/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0394\n",
+      "Epoch 1715/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0390\n",
+      "Epoch 1716/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0387\n",
+      "Epoch 1717/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 277ms/step - loss: 0.0386\n",
+      "Epoch 1718/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step - loss: 0.0385\n",
+      "Epoch 1719/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.0385\n",
+      "Epoch 1720/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0386\n",
+      "Epoch 1721/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 386ms/step - loss: 0.0386\n",
+      "Epoch 1722/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0386\n",
+      "Epoch 1723/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0384\n",
+      "Epoch 1724/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0382\n",
+      "Epoch 1725/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0381\n",
+      "Epoch 1726/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0381\n",
+      "Epoch 1727/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0382\n",
+      "Epoch 1728/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0383\n",
+      "Epoch 1729/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0382\n",
+      "Epoch 1730/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0381\n",
+      "Epoch 1731/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0380\n",
+      "Epoch 1732/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0379\n",
+      "Epoch 1733/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0380\n",
+      "Epoch 1734/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0380\n",
+      "Epoch 1735/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0380\n",
+      "Epoch 1736/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0379\n",
+      "Epoch 1737/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0379\n",
+      "Epoch 1738/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0378\n",
+      "Epoch 1739/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0378\n",
+      "Epoch 1740/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0377\n",
+      "Epoch 1741/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0377\n",
+      "Epoch 1742/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0377\n",
+      "Epoch 1743/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0378\n",
+      "Epoch 1744/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0378\n",
+      "Epoch 1745/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0377\n",
+      "Epoch 1746/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0377\n",
+      "Epoch 1747/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198ms/step - loss: 0.0377\n",
+      "Epoch 1748/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0376\n",
+      "Epoch 1749/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0377\n",
+      "Epoch 1750/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0377\n",
+      "Epoch 1751/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0377\n",
+      "Epoch 1752/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0378\n",
+      "Epoch 1753/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0380\n",
+      "Epoch 1754/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0383\n",
+      "Epoch 1755/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0387\n",
+      "Epoch 1756/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0392\n",
+      "Epoch 1757/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0398\n",
+      "Epoch 1758/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0399\n",
+      "Epoch 1759/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0394\n",
+      "Epoch 1760/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0384\n",
+      "Epoch 1761/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0378\n",
+      "Epoch 1762/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 231ms/step - loss: 0.0381\n",
+      "Epoch 1763/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - loss: 0.0386\n",
+      "Epoch 1764/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.0386\n",
+      "Epoch 1765/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0381\n",
+      "Epoch 1766/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0379\n",
+      "Epoch 1767/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0382\n",
+      "Epoch 1768/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0384\n",
+      "Epoch 1769/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0382\n",
+      "Epoch 1770/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 199ms/step - loss: 0.0380\n",
+      "Epoch 1771/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - loss: 0.0382\n",
+      "Epoch 1772/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0384\n",
+      "Epoch 1773/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0385\n",
+      "Epoch 1774/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0384\n",
+      "Epoch 1775/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0382\n",
+      "Epoch 1776/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0381\n",
+      "Epoch 1777/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0380\n",
+      "Epoch 1778/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0378\n",
+      "Epoch 1779/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0377\n",
+      "Epoch 1780/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0378\n",
+      "Epoch 1781/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0379\n",
+      "Epoch 1782/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0381\n",
+      "Epoch 1783/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0382\n",
+      "Epoch 1784/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0381\n",
+      "Epoch 1785/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0379\n",
+      "Epoch 1786/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0377\n",
+      "Epoch 1787/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0376\n",
+      "Epoch 1788/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0377\n",
+      "Epoch 1789/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0377\n",
+      "Epoch 1790/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0377\n",
+      "Epoch 1791/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0377\n",
+      "Epoch 1792/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0377\n",
+      "Epoch 1793/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0376\n",
+      "Epoch 1794/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0375\n",
+      "Epoch 1795/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278ms/step - loss: 0.0374\n",
+      "Epoch 1796/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0374\n",
+      "Epoch 1797/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0374\n",
+      "Epoch 1798/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0374\n",
+      "Epoch 1799/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0373\n",
+      "Epoch 1800/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0373\n",
+      "Epoch 1801/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0372\n",
+      "Epoch 1802/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0371\n",
+      "Epoch 1803/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0371\n",
+      "Epoch 1804/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0372\n",
+      "Epoch 1805/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0373\n",
+      "Epoch 1806/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0374\n",
+      "Epoch 1807/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0375\n",
+      "Epoch 1808/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0376\n",
+      "Epoch 1809/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0377\n",
+      "Epoch 1810/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 213ms/step - loss: 0.0377\n",
+      "Epoch 1811/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0378\n",
+      "Epoch 1812/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0377\n",
+      "Epoch 1813/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0376\n",
+      "Epoch 1814/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0375\n",
+      "Epoch 1815/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0373\n",
+      "Epoch 1816/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0373\n",
+      "Epoch 1817/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0373\n",
+      "Epoch 1818/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0374\n",
+      "Epoch 1819/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0375\n",
+      "Epoch 1820/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0376\n",
+      "Epoch 1821/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0376\n",
+      "Epoch 1822/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0374\n",
+      "Epoch 1823/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0373\n",
+      "Epoch 1824/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0371\n",
+      "Epoch 1825/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 218ms/step - loss: 0.0370\n",
+      "Epoch 1826/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0370\n",
+      "Epoch 1827/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 211ms/step - loss: 0.0371\n",
+      "Epoch 1828/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0372\n",
+      "Epoch 1829/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0372\n",
+      "Epoch 1830/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0372\n",
+      "Epoch 1831/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0371\n",
+      "Epoch 1832/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0369\n",
+      "Epoch 1833/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0368\n",
+      "Epoch 1834/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0368\n",
+      "Epoch 1835/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0368\n",
+      "Epoch 1836/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0368\n",
+      "Epoch 1837/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0368\n",
+      "Epoch 1838/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0368\n",
+      "Epoch 1839/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0367\n",
+      "Epoch 1840/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 255ms/step - loss: 0.0367\n",
+      "Epoch 1841/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0366\n",
+      "Epoch 1842/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0365\n",
+      "Epoch 1843/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0364\n",
+      "Epoch 1844/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0364\n",
+      "Epoch 1845/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0365\n",
+      "Epoch 1846/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0365\n",
+      "Epoch 1847/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0366\n",
+      "Epoch 1848/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0367\n",
+      "Epoch 1849/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0369\n",
+      "Epoch 1850/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0371\n",
+      "Epoch 1851/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0372\n",
+      "Epoch 1852/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0374\n",
+      "Epoch 1853/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 0.0376\n",
+      "Epoch 1854/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0376\n",
+      "Epoch 1855/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 204ms/step - loss: 0.0377\n",
+      "Epoch 1856/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0379\n",
+      "Epoch 1857/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0382\n",
+      "Epoch 1858/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0387\n",
+      "Epoch 1859/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0389\n",
+      "Epoch 1860/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0390\n",
+      "Epoch 1861/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0383\n",
+      "Epoch 1862/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0377\n",
+      "Epoch 1863/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0376\n",
+      "Epoch 1864/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0378\n",
+      "Epoch 1865/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0379\n",
+      "Epoch 1866/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0375\n",
+      "Epoch 1867/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0372\n",
+      "Epoch 1868/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0373\n",
+      "Epoch 1869/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0374\n",
+      "Epoch 1870/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0372\n",
+      "Epoch 1871/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0366\n",
+      "Epoch 1872/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 251ms/step - loss: 0.0363\n",
+      "Epoch 1873/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0365\n",
+      "Epoch 1874/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0366\n",
+      "Epoch 1875/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0365\n",
+      "Epoch 1876/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0363\n",
+      "Epoch 1877/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0363\n",
+      "Epoch 1878/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0364\n",
+      "Epoch 1879/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0363\n",
+      "Epoch 1880/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0361\n",
+      "Epoch 1881/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0360\n",
+      "Epoch 1882/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0361\n",
+      "Epoch 1883/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0361\n",
+      "Epoch 1884/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 202ms/step - loss: 0.0361\n",
+      "Epoch 1885/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 226ms/step - loss: 0.0359\n",
+      "Epoch 1886/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0358\n",
+      "Epoch 1887/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0359\n",
+      "Epoch 1888/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0359\n",
+      "Epoch 1889/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0359\n",
+      "Epoch 1890/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0358\n",
+      "Epoch 1891/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0357\n",
+      "Epoch 1892/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0357\n",
+      "Epoch 1893/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0358\n",
+      "Epoch 1894/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0358\n",
+      "Epoch 1895/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0357\n",
+      "Epoch 1896/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0357\n",
+      "Epoch 1897/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - loss: 0.0357\n",
+      "Epoch 1898/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0357\n",
+      "Epoch 1899/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0358\n",
+      "Epoch 1900/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0358\n",
+      "Epoch 1901/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0358\n",
+      "Epoch 1902/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0359\n",
+      "Epoch 1903/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0360\n",
+      "Epoch 1904/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0362\n",
+      "Epoch 1905/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0364\n",
+      "Epoch 1906/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0367\n",
+      "Epoch 1907/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0368\n",
+      "Epoch 1908/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0369\n",
+      "Epoch 1909/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0366\n",
+      "Epoch 1910/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0362\n",
+      "Epoch 1911/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0358\n",
+      "Epoch 1912/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0357\n",
+      "Epoch 1913/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0358\n",
+      "Epoch 1914/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0361\n",
+      "Epoch 1915/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0362\n",
+      "Epoch 1916/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0362\n",
+      "Epoch 1917/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0360\n",
+      "Epoch 1918/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0360\n",
+      "Epoch 1919/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0361\n",
+      "Epoch 1920/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0364\n",
+      "Epoch 1921/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0366\n",
+      "Epoch 1922/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0367\n",
+      "Epoch 1923/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0367\n",
+      "Epoch 1924/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0366\n",
+      "Epoch 1925/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0365\n",
+      "Epoch 1926/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0364\n",
+      "Epoch 1927/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0362\n",
+      "Epoch 1928/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0360\n",
+      "Epoch 1929/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.0358\n",
+      "Epoch 1930/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0358\n",
+      "Epoch 1931/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0359\n",
+      "Epoch 1932/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0360\n",
+      "Epoch 1933/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0360\n",
+      "Epoch 1934/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0360\n",
+      "Epoch 1935/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0358\n",
+      "Epoch 1936/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0356\n",
+      "Epoch 1937/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0354\n",
+      "Epoch 1938/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0353\n",
+      "Epoch 1939/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 0.0353\n",
+      "Epoch 1940/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0353\n",
+      "Epoch 1941/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - loss: 0.0354\n",
+      "Epoch 1942/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0355\n",
+      "Epoch 1943/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0355\n",
+      "Epoch 1944/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0355\n",
+      "Epoch 1945/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0355\n",
+      "Epoch 1946/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0355\n",
+      "Epoch 1947/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0355\n",
+      "Epoch 1948/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0356\n",
+      "Epoch 1949/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0356\n",
+      "Epoch 1950/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0356\n",
+      "Epoch 1951/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0356\n",
+      "Epoch 1952/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0355\n",
+      "Epoch 1953/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0354\n",
+      "Epoch 1954/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0352\n",
+      "Epoch 1955/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0352\n",
+      "Epoch 1956/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0352\n",
+      "Epoch 1957/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0353\n",
+      "Epoch 1958/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0355\n",
+      "Epoch 1959/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0358\n",
+      "Epoch 1960/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0362\n",
+      "Epoch 1961/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0366\n",
+      "Epoch 1962/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0368\n",
+      "Epoch 1963/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0367\n",
+      "Epoch 1964/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0363\n",
+      "Epoch 1965/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0357\n",
+      "Epoch 1966/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0353\n",
+      "Epoch 1967/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0355\n",
+      "Epoch 1968/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0358\n",
+      "Epoch 1969/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0360\n",
+      "Epoch 1970/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0358\n",
+      "Epoch 1971/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0355\n",
+      "Epoch 1972/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0354\n",
+      "Epoch 1973/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0355\n",
+      "Epoch 1974/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0356\n",
+      "Epoch 1975/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0355\n",
+      "Epoch 1976/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0352\n",
+      "Epoch 1977/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0350\n",
+      "Epoch 1978/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0351\n",
+      "Epoch 1979/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0352\n",
+      "Epoch 1980/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0353\n",
+      "Epoch 1981/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0352\n",
+      "Epoch 1982/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0351\n",
+      "Epoch 1983/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0352\n",
+      "Epoch 1984/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0352\n",
+      "Epoch 1985/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0352\n",
+      "Epoch 1986/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0351\n",
+      "Epoch 1987/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0349\n",
+      "Epoch 1988/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0347\n",
+      "Epoch 1989/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0346\n",
+      "Epoch 1990/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0347\n",
+      "Epoch 1991/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0347\n",
+      "Epoch 1992/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0346\n",
+      "Epoch 1993/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0346\n",
+      "Epoch 1994/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0346\n",
+      "Epoch 1995/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 202ms/step - loss: 0.0346\n",
+      "Epoch 1996/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 360ms/step - loss: 0.0346\n",
+      "Epoch 1997/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0347\n",
+      "Epoch 1998/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0348\n",
+      "Epoch 1999/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0349\n",
+      "Epoch 2000/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0350\n",
+      "Epoch 2000/20000\n",
+      " - loss: 0.0350\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0350\n",
+      "Epoch 2001/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0352\n",
+      "Epoch 2002/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0354\n",
+      "Epoch 2003/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0356\n",
+      "Epoch 2004/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0357\n",
+      "Epoch 2005/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0357\n",
+      "Epoch 2006/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0354\n",
+      "Epoch 2007/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0351\n",
+      "Epoch 2008/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0349\n",
+      "Epoch 2009/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - loss: 0.0349\n",
+      "Epoch 2010/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0352\n",
+      "Epoch 2011/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0355\n",
+      "Epoch 2012/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 210ms/step - loss: 0.0359\n",
+      "Epoch 2013/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0360\n",
+      "Epoch 2014/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0359\n",
+      "Epoch 2015/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0358\n",
+      "Epoch 2016/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0356\n",
+      "Epoch 2017/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0354\n",
+      "Epoch 2018/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0350\n",
+      "Epoch 2019/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0347\n",
+      "Epoch 2020/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0344\n",
+      "Epoch 2021/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0344\n",
+      "Epoch 2022/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0345\n",
+      "Epoch 2023/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0347\n",
+      "Epoch 2024/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0348\n",
+      "Epoch 2025/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0347\n",
+      "Epoch 2026/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0345\n",
+      "Epoch 2027/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0343\n",
+      "Epoch 2028/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0342\n",
+      "Epoch 2029/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0343\n",
+      "Epoch 2030/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0344\n",
+      "Epoch 2031/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0345\n",
+      "Epoch 2032/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0345\n",
+      "Epoch 2033/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0344\n",
+      "Epoch 2034/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0344\n",
+      "Epoch 2035/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0344\n",
+      "Epoch 2036/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 236ms/step - loss: 0.0346\n",
+      "Epoch 2037/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0348\n",
+      "Epoch 2038/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0351\n",
+      "Epoch 2039/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0354\n",
+      "Epoch 2040/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0356\n",
+      "Epoch 2041/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0357\n",
+      "Epoch 2042/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0356\n",
+      "Epoch 2043/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.0354\n",
+      "Epoch 2044/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0350\n",
+      "Epoch 2045/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0346\n",
+      "Epoch 2046/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0343\n",
+      "Epoch 2047/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0342\n",
+      "Epoch 2048/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.0342\n",
+      "Epoch 2049/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0342\n",
+      "Epoch 2050/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0342\n",
+      "Epoch 2051/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0342\n",
+      "Epoch 2052/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0342\n",
+      "Epoch 2053/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 212ms/step - loss: 0.0341\n",
+      "Epoch 2054/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.0341\n",
+      "Epoch 2055/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315ms/step - loss: 0.0340\n",
+      "Epoch 2056/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 211ms/step - loss: 0.0340\n",
+      "Epoch 2057/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0339\n",
+      "Epoch 2058/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - loss: 0.0340\n",
+      "Epoch 2059/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0340\n",
+      "Epoch 2060/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 181ms/step - loss: 0.0340\n",
+      "Epoch 2061/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - loss: 0.0340\n",
+      "Epoch 2062/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0340\n",
+      "Epoch 2063/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0341\n",
+      "Epoch 2064/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0342\n",
+      "Epoch 2065/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 0.0344\n",
+      "Epoch 2066/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0347\n",
+      "Epoch 2067/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0351\n",
+      "Epoch 2068/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0353\n",
+      "Epoch 2069/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 267ms/step - loss: 0.0356\n",
+      "Epoch 2070/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0356\n",
+      "Epoch 2071/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0354\n",
+      "Epoch 2072/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 234ms/step - loss: 0.0350\n",
+      "Epoch 2073/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0345\n",
+      "Epoch 2074/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0341\n",
+      "Epoch 2075/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0340\n",
+      "Epoch 2076/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0341\n",
+      "Epoch 2077/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0342\n",
+      "Epoch 2078/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 361ms/step - loss: 0.0343\n",
+      "Epoch 2079/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0342\n",
+      "Epoch 2080/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0340\n",
+      "Epoch 2081/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0339\n",
+      "Epoch 2082/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0339\n",
+      "Epoch 2083/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0339\n",
+      "Epoch 2084/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 259ms/step - loss: 0.0340\n",
+      "Epoch 2085/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0340\n",
+      "Epoch 2086/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0340\n",
+      "Epoch 2087/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 218ms/step - loss: 0.0339\n",
+      "Epoch 2088/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0339\n",
+      "Epoch 2089/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0339\n",
+      "Epoch 2090/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297ms/step - loss: 0.0340\n",
+      "Epoch 2091/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 250ms/step - loss: 0.0340\n",
+      "Epoch 2092/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0340\n",
+      "Epoch 2093/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0340\n",
+      "Epoch 2094/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0340\n",
+      "Epoch 2095/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0340\n",
+      "Epoch 2096/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - loss: 0.0341\n",
+      "Epoch 2097/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0342\n",
+      "Epoch 2098/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0342\n",
+      "Epoch 2099/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0341\n",
+      "Epoch 2100/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0339\n",
+      "Epoch 2101/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0337\n",
+      "Epoch 2102/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 197ms/step - loss: 0.0335\n",
+      "Epoch 2103/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0334\n",
+      "Epoch 2104/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0335\n",
+      "Epoch 2105/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0336\n",
+      "Epoch 2106/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - loss: 0.0337\n",
+      "Epoch 2107/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0336\n",
+      "Epoch 2108/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0336\n",
+      "Epoch 2109/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0335\n",
+      "Epoch 2110/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0334\n",
+      "Epoch 2111/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0333\n",
+      "Epoch 2112/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0333\n",
+      "Epoch 2113/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0334\n",
+      "Epoch 2114/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 417ms/step - loss: 0.0334\n",
+      "Epoch 2115/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0334\n",
+      "Epoch 2116/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0334\n",
+      "Epoch 2117/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0334\n",
+      "Epoch 2118/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0334\n",
+      "Epoch 2119/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0334\n",
+      "Epoch 2120/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0333\n",
+      "Epoch 2121/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0332\n",
+      "Epoch 2122/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0332\n",
+      "Epoch 2123/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0332\n",
+      "Epoch 2124/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0332\n",
+      "Epoch 2125/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0332\n",
+      "Epoch 2126/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0333\n",
+      "Epoch 2127/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - loss: 0.0334\n",
+      "Epoch 2128/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 259ms/step - loss: 0.0336\n",
+      "Epoch 2129/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0339\n",
+      "Epoch 2130/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0342\n",
+      "Epoch 2131/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0345\n",
+      "Epoch 2132/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0348\n",
+      "Epoch 2133/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0351\n",
+      "Epoch 2134/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0351\n",
+      "Epoch 2135/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0349\n",
+      "Epoch 2136/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0345\n",
+      "Epoch 2137/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0343\n",
+      "Epoch 2138/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0342\n",
+      "Epoch 2139/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0343\n",
+      "Epoch 2140/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0345\n",
+      "Epoch 2141/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - loss: 0.0346\n",
+      "Epoch 2142/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0342\n",
+      "Epoch 2143/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0338\n",
+      "Epoch 2144/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0335\n",
+      "Epoch 2145/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0335\n",
+      "Epoch 2146/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0338\n",
+      "Epoch 2147/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0338\n",
+      "Epoch 2148/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0335\n",
+      "Epoch 2149/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0331\n",
+      "Epoch 2150/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0330\n",
+      "Epoch 2151/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0332\n",
+      "Epoch 2152/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0333\n",
+      "Epoch 2153/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0333\n",
+      "Epoch 2154/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0331\n",
+      "Epoch 2155/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0330\n",
+      "Epoch 2156/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0329\n",
+      "Epoch 2157/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0329\n",
+      "Epoch 2158/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0329\n",
+      "Epoch 2159/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0329\n",
+      "Epoch 2160/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0329\n",
+      "Epoch 2161/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0329\n",
+      "Epoch 2162/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0329\n",
+      "Epoch 2163/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0330\n",
+      "Epoch 2164/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0331\n",
+      "Epoch 2165/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0333\n",
+      "Epoch 2166/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0335\n",
+      "Epoch 2167/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0337\n",
+      "Epoch 2168/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0340\n",
+      "Epoch 2169/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0342\n",
+      "Epoch 2170/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0343\n",
+      "Epoch 2171/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0340\n",
+      "Epoch 2172/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0335\n",
+      "Epoch 2173/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0330\n",
+      "Epoch 2174/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0328\n",
+      "Epoch 2175/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0328\n",
+      "Epoch 2176/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0330\n",
+      "Epoch 2177/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 445ms/step - loss: 0.0332\n",
+      "Epoch 2178/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0331\n",
+      "Epoch 2179/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0328\n",
+      "Epoch 2180/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0326\n",
+      "Epoch 2181/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0325\n",
+      "Epoch 2182/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - loss: 0.0326\n",
+      "Epoch 2183/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0327\n",
+      "Epoch 2184/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0328\n",
+      "Epoch 2185/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0328\n",
+      "Epoch 2186/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0326\n",
+      "Epoch 2187/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0325\n",
+      "Epoch 2188/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0325\n",
+      "Epoch 2189/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 223ms/step - loss: 0.0325\n",
+      "Epoch 2190/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0326\n",
+      "Epoch 2191/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0327\n",
+      "Epoch 2192/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0328\n",
+      "Epoch 2193/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0328\n",
+      "Epoch 2194/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0329\n",
+      "Epoch 2195/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0331\n",
+      "Epoch 2196/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0334\n",
+      "Epoch 2197/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0338\n",
+      "Epoch 2198/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0342\n",
+      "Epoch 2199/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0344\n",
+      "Epoch 2200/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0343\n",
+      "Epoch 2201/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0339\n",
+      "Epoch 2202/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0333\n",
+      "Epoch 2203/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0329\n",
+      "Epoch 2204/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0328\n",
+      "Epoch 2205/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0330\n",
+      "Epoch 2206/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0332\n",
+      "Epoch 2207/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0331\n",
+      "Epoch 2208/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0329\n",
+      "Epoch 2209/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0327\n",
+      "Epoch 2210/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 428ms/step - loss: 0.0327\n",
+      "Epoch 2211/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0328\n",
+      "Epoch 2212/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0329\n",
+      "Epoch 2213/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0328\n",
+      "Epoch 2214/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0327\n",
+      "Epoch 2215/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0325\n",
+      "Epoch 2216/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0324\n",
+      "Epoch 2217/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0324\n",
+      "Epoch 2218/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 327ms/step - loss: 0.0324\n",
+      "Epoch 2219/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - loss: 0.0324\n",
+      "Epoch 2220/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.0324\n",
+      "Epoch 2221/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0323\n",
+      "Epoch 2222/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 320ms/step - loss: 0.0323\n",
+      "Epoch 2223/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0323\n",
+      "Epoch 2224/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0323\n",
+      "Epoch 2225/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0323\n",
+      "Epoch 2226/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0323\n",
+      "Epoch 2227/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.0324\n",
+      "Epoch 2228/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step - loss: 0.0324\n",
+      "Epoch 2229/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 268ms/step - loss: 0.0325\n",
+      "Epoch 2230/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0326\n",
+      "Epoch 2231/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - loss: 0.0328\n",
+      "Epoch 2232/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 210ms/step - loss: 0.0329\n",
+      "Epoch 2233/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 230ms/step - loss: 0.0330\n",
+      "Epoch 2234/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - loss: 0.0331\n",
+      "Epoch 2235/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0331\n",
+      "Epoch 2236/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 418ms/step - loss: 0.0330\n",
+      "Epoch 2237/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - loss: 0.0329\n",
+      "Epoch 2238/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 231ms/step - loss: 0.0328\n",
+      "Epoch 2239/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 202ms/step - loss: 0.0326\n",
+      "Epoch 2240/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.0325\n",
+      "Epoch 2241/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 201ms/step - loss: 0.0323\n",
+      "Epoch 2242/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 349ms/step - loss: 0.0322\n",
+      "Epoch 2243/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 217ms/step - loss: 0.0321\n",
+      "Epoch 2244/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 294ms/step - loss: 0.0322\n",
+      "Epoch 2245/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 393ms/step - loss: 0.0322\n",
+      "Epoch 2246/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 287ms/step - loss: 0.0321\n",
+      "Epoch 2247/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 280ms/step - loss: 0.0321\n",
+      "Epoch 2248/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 380ms/step - loss: 0.0320\n",
+      "Epoch 2249/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 254ms/step - loss: 0.0319\n",
+      "Epoch 2250/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 223ms/step - loss: 0.0319\n",
+      "Epoch 2251/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 415ms/step - loss: 0.0319\n",
+      "Epoch 2252/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 345ms/step - loss: 0.0320\n",
+      "Epoch 2253/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 220ms/step - loss: 0.0321\n",
+      "Epoch 2254/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 268ms/step - loss: 0.0322\n",
+      "Epoch 2255/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 337ms/step - loss: 0.0323\n",
+      "Epoch 2256/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 345ms/step - loss: 0.0325\n",
+      "Epoch 2257/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 253ms/step - loss: 0.0327\n",
+      "Epoch 2258/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 236ms/step - loss: 0.0331\n",
+      "Epoch 2259/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 290ms/step - loss: 0.0335\n",
+      "Epoch 2260/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 452ms/step - loss: 0.0340\n",
+      "Epoch 2261/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319ms/step - loss: 0.0341\n",
+      "Epoch 2262/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 274ms/step - loss: 0.0338\n",
+      "Epoch 2263/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 280ms/step - loss: 0.0331\n",
+      "Epoch 2264/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 211ms/step - loss: 0.0324\n",
+      "Epoch 2265/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 276ms/step - loss: 0.0321\n",
+      "Epoch 2266/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0324\n",
+      "Epoch 2267/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0328\n",
+      "Epoch 2268/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 284ms/step - loss: 0.0328\n",
+      "Epoch 2269/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 220ms/step - loss: 0.0324\n",
+      "Epoch 2270/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314ms/step - loss: 0.0321\n",
+      "Epoch 2271/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 374ms/step - loss: 0.0322\n",
+      "Epoch 2272/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 253ms/step - loss: 0.0324\n",
+      "Epoch 2273/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 196ms/step - loss: 0.0326\n",
+      "Epoch 2274/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 220ms/step - loss: 0.0324\n",
+      "Epoch 2275/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 218ms/step - loss: 0.0322\n",
+      "Epoch 2276/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 257ms/step - loss: 0.0320\n",
+      "Epoch 2277/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 288ms/step - loss: 0.0320\n",
+      "Epoch 2278/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 335ms/step - loss: 0.0321\n",
+      "Epoch 2279/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328ms/step - loss: 0.0320\n",
+      "Epoch 2280/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 294ms/step - loss: 0.0318\n",
+      "Epoch 2281/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328ms/step - loss: 0.0316\n",
+      "Epoch 2282/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 267ms/step - loss: 0.0315\n",
+      "Epoch 2283/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291ms/step - loss: 0.0316\n",
+      "Epoch 2284/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295ms/step - loss: 0.0317\n",
+      "Epoch 2285/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 381ms/step - loss: 0.0317\n",
+      "Epoch 2286/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 324ms/step - loss: 0.0317\n",
+      "Epoch 2287/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 276ms/step - loss: 0.0316\n",
+      "Epoch 2288/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 260ms/step - loss: 0.0317\n",
+      "Epoch 2289/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 233ms/step - loss: 0.0318\n",
+      "Epoch 2290/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 236ms/step - loss: 0.0319\n",
+      "Epoch 2291/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - loss: 0.0320\n",
+      "Epoch 2292/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 262ms/step - loss: 0.0321\n",
+      "Epoch 2293/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 216ms/step - loss: 0.0322\n",
+      "Epoch 2294/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 230ms/step - loss: 0.0323\n",
+      "Epoch 2295/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 257ms/step - loss: 0.0325\n",
+      "Epoch 2296/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297ms/step - loss: 0.0327\n",
+      "Epoch 2297/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 267ms/step - loss: 0.0328\n",
+      "Epoch 2298/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 252ms/step - loss: 0.0328\n",
+      "Epoch 2299/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322ms/step - loss: 0.0328\n",
+      "Epoch 2300/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 339ms/step - loss: 0.0326\n",
+      "Epoch 2301/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285ms/step - loss: 0.0326\n",
+      "Epoch 2302/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 248ms/step - loss: 0.0326\n",
+      "Epoch 2303/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 196ms/step - loss: 0.0326\n",
+      "Epoch 2304/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 250ms/step - loss: 0.0327\n",
+      "Epoch 2305/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 331ms/step - loss: 0.0325\n",
+      "Epoch 2306/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297ms/step - loss: 0.0322\n",
+      "Epoch 2307/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 289ms/step - loss: 0.0318\n",
+      "Epoch 2308/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.0316\n",
+      "Epoch 2309/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 233ms/step - loss: 0.0316\n",
+      "Epoch 2310/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 271ms/step - loss: 0.0317\n",
+      "Epoch 2311/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 223ms/step - loss: 0.0318\n",
+      "Epoch 2312/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 253ms/step - loss: 0.0318\n",
+      "Epoch 2313/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 247ms/step - loss: 0.0316\n",
+      "Epoch 2314/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 231ms/step - loss: 0.0314\n",
+      "Epoch 2315/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 289ms/step - loss: 0.0313\n",
+      "Epoch 2316/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 234ms/step - loss: 0.0313\n",
+      "Epoch 2317/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 204ms/step - loss: 0.0313\n",
+      "Epoch 2318/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 251ms/step - loss: 0.0313\n",
+      "Epoch 2319/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - loss: 0.0313\n",
+      "Epoch 2320/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 228ms/step - loss: 0.0313\n",
+      "Epoch 2321/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 234ms/step - loss: 0.0312\n",
+      "Epoch 2322/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 208ms/step - loss: 0.0312\n",
+      "Epoch 2323/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 311ms/step - loss: 0.0312\n",
+      "Epoch 2324/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 210ms/step - loss: 0.0312\n",
+      "Epoch 2325/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 221ms/step - loss: 0.0312\n",
+      "Epoch 2326/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0311\n",
+      "Epoch 2327/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - loss: 0.0311\n",
+      "Epoch 2328/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 203ms/step - loss: 0.0311\n",
+      "Epoch 2329/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 203ms/step - loss: 0.0311\n",
+      "Epoch 2330/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0311\n",
+      "Epoch 2331/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 286ms/step - loss: 0.0311\n",
+      "Epoch 2332/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 218ms/step - loss: 0.0312\n",
+      "Epoch 2333/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 289ms/step - loss: 0.0312\n",
+      "Epoch 2334/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step - loss: 0.0313\n",
+      "Epoch 2335/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - loss: 0.0314\n",
+      "Epoch 2336/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - loss: 0.0315\n",
+      "Epoch 2337/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0317\n",
+      "Epoch 2338/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 196ms/step - loss: 0.0321\n",
+      "Epoch 2339/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0324\n",
+      "Epoch 2340/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 232ms/step - loss: 0.0327\n",
+      "Epoch 2341/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - loss: 0.0328\n",
+      "Epoch 2342/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0324\n",
+      "Epoch 2343/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0317\n",
+      "Epoch 2344/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0312\n",
+      "Epoch 2345/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0312\n",
+      "Epoch 2346/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0315\n",
+      "Epoch 2347/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0318\n",
+      "Epoch 2348/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0318\n",
+      "Epoch 2349/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0315\n",
+      "Epoch 2350/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0315\n",
+      "Epoch 2351/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 274ms/step - loss: 0.0316\n",
+      "Epoch 2352/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364ms/step - loss: 0.0319\n",
+      "Epoch 2353/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0320\n",
+      "Epoch 2354/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - loss: 0.0319\n",
+      "Epoch 2355/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0317\n",
+      "Epoch 2356/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0316\n",
+      "Epoch 2357/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0316\n",
+      "Epoch 2358/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0315\n",
+      "Epoch 2359/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 217ms/step - loss: 0.0314\n",
+      "Epoch 2360/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 196ms/step - loss: 0.0312\n",
+      "Epoch 2361/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0311\n",
+      "Epoch 2362/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0311\n",
+      "Epoch 2363/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0312\n",
+      "Epoch 2364/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0314\n",
+      "Epoch 2365/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0315\n",
+      "Epoch 2366/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0315\n",
+      "Epoch 2367/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0314\n",
+      "Epoch 2368/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0314\n",
+      "Epoch 2369/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0313\n",
+      "Epoch 2370/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0313\n",
+      "Epoch 2371/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0312\n",
+      "Epoch 2372/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0313\n",
+      "Epoch 2373/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0312\n",
+      "Epoch 2374/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0313\n",
+      "Epoch 2375/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0313\n",
+      "Epoch 2376/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0313\n",
+      "Epoch 2377/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - loss: 0.0314\n",
+      "Epoch 2378/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0316\n",
+      "Epoch 2379/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0318\n",
+      "Epoch 2380/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0320\n",
+      "Epoch 2381/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0321\n",
+      "Epoch 2382/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0319\n",
+      "Epoch 2383/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0316\n",
+      "Epoch 2384/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0312\n",
+      "Epoch 2385/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0310\n",
+      "Epoch 2386/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0309\n",
+      "Epoch 2387/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0310\n",
+      "Epoch 2388/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0310\n",
+      "Epoch 2389/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0309\n",
+      "Epoch 2390/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0308\n",
+      "Epoch 2391/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0307\n",
+      "Epoch 2392/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0306\n",
+      "Epoch 2393/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0306\n",
+      "Epoch 2394/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0307\n",
+      "Epoch 2395/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0307\n",
+      "Epoch 2396/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0307\n",
+      "Epoch 2397/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0306\n",
+      "Epoch 2398/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0306\n",
+      "Epoch 2399/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0306\n",
+      "Epoch 2400/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 228ms/step - loss: 0.0307\n",
+      "Epoch 2401/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0307\n",
+      "Epoch 2402/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0308\n",
+      "Epoch 2403/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0309\n",
+      "Epoch 2404/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0310\n",
+      "Epoch 2405/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0311\n",
+      "Epoch 2406/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0314\n",
+      "Epoch 2407/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0317\n",
+      "Epoch 2408/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0322\n",
+      "Epoch 2409/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0326\n",
+      "Epoch 2410/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0328\n",
+      "Epoch 2411/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0325\n",
+      "Epoch 2412/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0319\n",
+      "Epoch 2413/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0311\n",
+      "Epoch 2414/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0307\n",
+      "Epoch 2415/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0307\n",
+      "Epoch 2416/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0311\n",
+      "Epoch 2417/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0314\n",
+      "Epoch 2418/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0313\n",
+      "Epoch 2419/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0309\n",
+      "Epoch 2420/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0306\n",
+      "Epoch 2421/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0305\n",
+      "Epoch 2422/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0306\n",
+      "Epoch 2423/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0307\n",
+      "Epoch 2424/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0306\n",
+      "Epoch 2425/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0305\n",
+      "Epoch 2426/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0304\n",
+      "Epoch 2427/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0303\n",
+      "Epoch 2428/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0304\n",
+      "Epoch 2429/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0304\n",
+      "Epoch 2430/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0304\n",
+      "Epoch 2431/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0303\n",
+      "Epoch 2432/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0303\n",
+      "Epoch 2433/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 204ms/step - loss: 0.0303\n",
+      "Epoch 2434/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0304\n",
+      "Epoch 2435/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0306\n",
+      "Epoch 2436/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0307\n",
+      "Epoch 2437/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0309\n",
+      "Epoch 2438/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0311\n",
+      "Epoch 2439/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0314\n",
+      "Epoch 2440/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0319\n",
+      "Epoch 2441/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0321\n",
+      "Epoch 2442/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0321\n",
+      "Epoch 2443/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0316\n",
+      "Epoch 2444/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 215ms/step - loss: 0.0309\n",
+      "Epoch 2445/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 228ms/step - loss: 0.0303\n",
+      "Epoch 2446/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0302\n",
+      "Epoch 2447/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0304\n",
+      "Epoch 2448/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0307\n",
+      "Epoch 2449/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0307\n",
+      "Epoch 2450/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.0304\n",
+      "Epoch 2451/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0302\n",
+      "Epoch 2452/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0301\n",
+      "Epoch 2453/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0302\n",
+      "Epoch 2454/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0303\n",
+      "Epoch 2455/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 194ms/step - loss: 0.0303\n",
+      "Epoch 2456/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0302\n",
+      "Epoch 2457/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0301\n",
+      "Epoch 2458/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.0301\n",
+      "Epoch 2459/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0302\n",
+      "Epoch 2460/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0303\n",
+      "Epoch 2461/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0305\n",
+      "Epoch 2462/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0307\n",
+      "Epoch 2463/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0310\n",
+      "Epoch 2464/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0313\n",
+      "Epoch 2465/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 221ms/step - loss: 0.0318\n",
+      "Epoch 2466/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0320\n",
+      "Epoch 2467/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0318\n",
+      "Epoch 2468/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0311\n",
+      "Epoch 2469/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0304\n",
+      "Epoch 2470/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0303\n",
+      "Epoch 2471/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0307\n",
+      "Epoch 2472/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0310\n",
+      "Epoch 2473/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0309\n",
+      "Epoch 2474/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0306\n",
+      "Epoch 2475/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0307\n",
+      "Epoch 2476/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0313\n",
+      "Epoch 2477/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0319\n",
+      "Epoch 2478/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0322\n",
+      "Epoch 2479/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0320\n",
+      "Epoch 2480/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0316\n",
+      "Epoch 2481/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0311\n",
+      "Epoch 2482/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0309\n",
+      "Epoch 2483/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0308\n",
+      "Epoch 2484/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0308\n",
+      "Epoch 2485/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 205ms/step - loss: 0.0307\n",
+      "Epoch 2486/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0305\n",
+      "Epoch 2487/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0303\n",
+      "Epoch 2488/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 221ms/step - loss: 0.0302\n",
+      "Epoch 2489/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0302\n",
+      "Epoch 2490/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0301\n",
+      "Epoch 2491/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0300\n",
+      "Epoch 2492/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0300\n",
+      "Epoch 2493/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0300\n",
+      "Epoch 2494/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0301\n",
+      "Epoch 2495/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 210ms/step - loss: 0.0300\n",
+      "Epoch 2496/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0298\n",
+      "Epoch 2497/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0297\n",
+      "Epoch 2498/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0297\n",
+      "Epoch 2499/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0298\n",
+      "Epoch 2500/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0297\n",
+      "Epoch 2500/20000\n",
+      " - loss: 0.0297\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0297\n",
+      "Epoch 2501/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0297\n",
+      "Epoch 2502/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0296\n",
+      "Epoch 2503/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 275ms/step - loss: 0.0296\n",
+      "Epoch 2504/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0297\n",
+      "Epoch 2505/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0297\n",
+      "Epoch 2506/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0296\n",
+      "Epoch 2507/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0296\n",
+      "Epoch 2508/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0296\n",
+      "Epoch 2509/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0296\n",
+      "Epoch 2510/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0296\n",
+      "Epoch 2511/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 287ms/step - loss: 0.0296\n",
+      "Epoch 2512/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0295\n",
+      "Epoch 2513/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0295\n",
+      "Epoch 2514/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0295\n",
+      "Epoch 2515/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0295\n",
+      "Epoch 2516/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0296\n",
+      "Epoch 2517/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0296\n",
+      "Epoch 2518/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0296\n",
+      "Epoch 2519/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0296\n",
+      "Epoch 2520/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0297\n",
+      "Epoch 2521/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0299\n",
+      "Epoch 2522/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0300\n",
+      "Epoch 2523/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0303\n",
+      "Epoch 2524/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0306\n",
+      "Epoch 2525/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0308\n",
+      "Epoch 2526/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0311\n",
+      "Epoch 2527/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0311\n",
+      "Epoch 2528/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0309\n",
+      "Epoch 2529/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0305\n",
+      "Epoch 2530/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0301\n",
+      "Epoch 2531/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0298\n",
+      "Epoch 2532/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0299\n",
+      "Epoch 2533/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.0300\n",
+      "Epoch 2534/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0302\n",
+      "Epoch 2535/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0302\n",
+      "Epoch 2536/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0299\n",
+      "Epoch 2537/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0297\n",
+      "Epoch 2538/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0295\n",
+      "Epoch 2539/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0295\n",
+      "Epoch 2540/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0296\n",
+      "Epoch 2541/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0298\n",
+      "Epoch 2542/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0299\n",
+      "Epoch 2543/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0298\n",
+      "Epoch 2544/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0296\n",
+      "Epoch 2545/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0295\n",
+      "Epoch 2546/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0294\n",
+      "Epoch 2547/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0294\n",
+      "Epoch 2548/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 195ms/step - loss: 0.0294\n",
+      "Epoch 2549/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0295\n",
+      "Epoch 2550/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0295\n",
+      "Epoch 2551/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0295\n",
+      "Epoch 2552/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0294\n",
+      "Epoch 2553/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0295\n",
+      "Epoch 2554/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0296\n",
+      "Epoch 2555/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0298\n",
+      "Epoch 2556/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0302\n",
+      "Epoch 2557/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0308\n",
+      "Epoch 2558/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0315\n",
+      "Epoch 2559/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0321\n",
+      "Epoch 2560/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0326\n",
+      "Epoch 2561/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0323\n",
+      "Epoch 2562/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0314\n",
+      "Epoch 2563/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0304\n",
+      "Epoch 2564/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0300\n",
+      "Epoch 2565/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0304\n",
+      "Epoch 2566/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0307\n",
+      "Epoch 2567/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0305\n",
+      "Epoch 2568/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0298\n",
+      "Epoch 2569/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0294\n",
+      "Epoch 2570/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0295\n",
+      "Epoch 2571/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0298\n",
+      "Epoch 2572/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0298\n",
+      "Epoch 2573/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0295\n",
+      "Epoch 2574/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0293\n",
+      "Epoch 2575/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0293\n",
+      "Epoch 2576/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0294\n",
+      "Epoch 2577/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0295\n",
+      "Epoch 2578/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0293\n",
+      "Epoch 2579/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0291\n",
+      "Epoch 2580/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0291\n",
+      "Epoch 2581/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0291\n",
+      "Epoch 2582/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0292\n",
+      "Epoch 2583/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0292\n",
+      "Epoch 2584/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0291\n",
+      "Epoch 2585/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0290\n",
+      "Epoch 2586/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0290\n",
+      "Epoch 2587/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0290\n",
+      "Epoch 2588/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0290\n",
+      "Epoch 2589/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0290\n",
+      "Epoch 2590/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0289\n",
+      "Epoch 2591/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0289\n",
+      "Epoch 2592/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0289\n",
+      "Epoch 2593/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0290\n",
+      "Epoch 2594/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0290\n",
+      "Epoch 2595/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0291\n",
+      "Epoch 2596/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0291\n",
+      "Epoch 2597/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0292\n",
+      "Epoch 2598/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0294\n",
+      "Epoch 2599/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0297\n",
+      "Epoch 2600/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0303\n",
+      "Epoch 2601/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0310\n",
+      "Epoch 2602/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step - loss: 0.0318\n",
+      "Epoch 2603/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0326\n",
+      "Epoch 2604/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0330\n",
+      "Epoch 2605/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0324\n",
+      "Epoch 2606/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 210ms/step - loss: 0.0311\n",
+      "Epoch 2607/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0301\n",
+      "Epoch 2608/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0300\n",
+      "Epoch 2609/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0306\n",
+      "Epoch 2610/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0306\n",
+      "Epoch 2611/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 0.0298\n",
+      "Epoch 2612/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 195ms/step - loss: 0.0291\n",
+      "Epoch 2613/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0293\n",
+      "Epoch 2614/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - loss: 0.0298\n",
+      "Epoch 2615/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0298\n",
+      "Epoch 2616/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0292\n",
+      "Epoch 2617/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0289\n",
+      "Epoch 2618/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0291\n",
+      "Epoch 2619/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0293\n",
+      "Epoch 2620/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0293\n",
+      "Epoch 2621/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0289\n",
+      "Epoch 2622/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0288\n",
+      "Epoch 2623/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0289\n",
+      "Epoch 2624/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0290\n",
+      "Epoch 2625/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0289\n",
+      "Epoch 2626/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 252ms/step - loss: 0.0288\n",
+      "Epoch 2627/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0287\n",
+      "Epoch 2628/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0287\n",
+      "Epoch 2629/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0288\n",
+      "Epoch 2630/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0287\n",
+      "Epoch 2631/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 208ms/step - loss: 0.0287\n",
+      "Epoch 2632/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0286\n",
+      "Epoch 2633/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0286\n",
+      "Epoch 2634/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0286\n",
+      "Epoch 2635/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - loss: 0.0286\n",
+      "Epoch 2636/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 253ms/step - loss: 0.0286\n",
+      "Epoch 2637/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0286\n",
+      "Epoch 2638/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0286\n",
+      "Epoch 2639/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0285\n",
+      "Epoch 2640/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0285\n",
+      "Epoch 2641/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 222ms/step - loss: 0.0285\n",
+      "Epoch 2642/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 215ms/step - loss: 0.0285\n",
+      "Epoch 2643/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0285\n",
+      "Epoch 2644/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0285\n",
+      "Epoch 2645/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0285\n",
+      "Epoch 2646/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0285\n",
+      "Epoch 2647/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0286\n",
+      "Epoch 2648/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0287\n",
+      "Epoch 2649/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0288\n",
+      "Epoch 2650/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0290\n",
+      "Epoch 2651/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0293\n",
+      "Epoch 2652/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0297\n",
+      "Epoch 2653/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0303\n",
+      "Epoch 2654/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0308\n",
+      "Epoch 2655/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0312\n",
+      "Epoch 2656/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0308\n",
+      "Epoch 2657/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0301\n",
+      "Epoch 2658/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0292\n",
+      "Epoch 2659/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0288\n",
+      "Epoch 2660/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0291\n",
+      "Epoch 2661/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0293\n",
+      "Epoch 2662/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0293\n",
+      "Epoch 2663/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - loss: 0.0290\n",
+      "Epoch 2664/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0288\n",
+      "Epoch 2665/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0289\n",
+      "Epoch 2666/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0289\n",
+      "Epoch 2667/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 222ms/step - loss: 0.0289\n",
+      "Epoch 2668/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 196ms/step - loss: 0.0287\n",
+      "Epoch 2669/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0285\n",
+      "Epoch 2670/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0285\n",
+      "Epoch 2671/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0285\n",
+      "Epoch 2672/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0286\n",
+      "Epoch 2673/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0286\n",
+      "Epoch 2674/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0285\n",
+      "Epoch 2675/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0285\n",
+      "Epoch 2676/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0286\n",
+      "Epoch 2677/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0287\n",
+      "Epoch 2678/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0289\n",
+      "Epoch 2679/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0292\n",
+      "Epoch 2680/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0294\n",
+      "Epoch 2681/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0297\n",
+      "Epoch 2682/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0299\n",
+      "Epoch 2683/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0300\n",
+      "Epoch 2684/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0297\n",
+      "Epoch 2685/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step - loss: 0.0292\n",
+      "Epoch 2686/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0288\n",
+      "Epoch 2687/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0286\n",
+      "Epoch 2688/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0285\n",
+      "Epoch 2689/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0286\n",
+      "Epoch 2690/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0286\n",
+      "Epoch 2691/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0286\n",
+      "Epoch 2692/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0285\n",
+      "Epoch 2693/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0284\n",
+      "Epoch 2694/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0284\n",
+      "Epoch 2695/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0284\n",
+      "Epoch 2696/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0283\n",
+      "Epoch 2697/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0283\n",
+      "Epoch 2698/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0283\n",
+      "Epoch 2699/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0283\n",
+      "Epoch 2700/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0283\n",
+      "Epoch 2701/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0283\n",
+      "Epoch 2702/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0283\n",
+      "Epoch 2703/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0282\n",
+      "Epoch 2704/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0282\n",
+      "Epoch 2705/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0283\n",
+      "Epoch 2706/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0284\n",
+      "Epoch 2707/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0285\n",
+      "Epoch 2708/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0286\n",
+      "Epoch 2709/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0287\n",
+      "Epoch 2710/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0288\n",
+      "Epoch 2711/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0290\n",
+      "Epoch 2712/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0291\n",
+      "Epoch 2713/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0291\n",
+      "Epoch 2714/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0291\n",
+      "Epoch 2715/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0289\n",
+      "Epoch 2716/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 228ms/step - loss: 0.0287\n",
+      "Epoch 2717/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0284\n",
+      "Epoch 2718/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0283\n",
+      "Epoch 2719/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0282\n",
+      "Epoch 2720/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0284\n",
+      "Epoch 2721/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0285\n",
+      "Epoch 2722/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0287\n",
+      "Epoch 2723/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0288\n",
+      "Epoch 2724/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0288\n",
+      "Epoch 2725/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step - loss: 0.0288\n",
+      "Epoch 2726/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0287\n",
+      "Epoch 2727/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0287\n",
+      "Epoch 2728/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0287\n",
+      "Epoch 2729/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187ms/step - loss: 0.0287\n",
+      "Epoch 2730/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0286\n",
+      "Epoch 2731/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0284\n",
+      "Epoch 2732/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 249ms/step - loss: 0.0282\n",
+      "Epoch 2733/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0281\n",
+      "Epoch 2734/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0281\n",
+      "Epoch 2735/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.0282\n",
+      "Epoch 2736/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0283\n",
+      "Epoch 2737/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0283\n",
+      "Epoch 2738/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0283\n",
+      "Epoch 2739/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0282\n",
+      "Epoch 2740/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0281\n",
+      "Epoch 2741/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 202ms/step - loss: 0.0280\n",
+      "Epoch 2742/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0281\n",
+      "Epoch 2743/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0281\n",
+      "Epoch 2744/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0282\n",
+      "Epoch 2745/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0283\n",
+      "Epoch 2746/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0284\n",
+      "Epoch 2747/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0285\n",
+      "Epoch 2748/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0285\n",
+      "Epoch 2749/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0285\n",
+      "Epoch 2750/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0284\n",
+      "Epoch 2751/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0284\n",
+      "Epoch 2752/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0283\n",
+      "Epoch 2753/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198ms/step - loss: 0.0282\n",
+      "Epoch 2754/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 337ms/step - loss: 0.0281\n",
+      "Epoch 2755/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 186ms/step - loss: 0.0280\n",
+      "Epoch 2756/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0279\n",
+      "Epoch 2757/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.0279\n",
+      "Epoch 2758/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 0.0279\n",
+      "Epoch 2759/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step - loss: 0.0280\n",
+      "Epoch 2760/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 283ms/step - loss: 0.0281\n",
+      "Epoch 2761/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0281\n",
+      "Epoch 2762/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 196ms/step - loss: 0.0281\n",
+      "Epoch 2763/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0281\n",
+      "Epoch 2764/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - loss: 0.0281\n",
+      "Epoch 2765/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0281\n",
+      "Epoch 2766/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0280\n",
+      "Epoch 2767/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0279\n",
+      "Epoch 2768/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0279\n",
+      "Epoch 2769/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - loss: 0.0278\n",
+      "Epoch 2770/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - loss: 0.0279\n",
+      "Epoch 2771/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0280\n",
+      "Epoch 2772/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0282\n",
+      "Epoch 2773/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0285\n",
+      "Epoch 2774/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0291\n",
+      "Epoch 2775/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0300\n",
+      "Epoch 2776/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 236ms/step - loss: 0.0311\n",
+      "Epoch 2777/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0322\n",
+      "Epoch 2778/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295ms/step - loss: 0.0323\n",
+      "Epoch 2779/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0312\n",
+      "Epoch 2780/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - loss: 0.0296\n",
+      "Epoch 2781/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 307ms/step - loss: 0.0289\n",
+      "Epoch 2782/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 253ms/step - loss: 0.0294\n",
+      "Epoch 2783/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 225ms/step - loss: 0.0297\n",
+      "Epoch 2784/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 251ms/step - loss: 0.0290\n",
+      "Epoch 2785/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - loss: 0.0283\n",
+      "Epoch 2786/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 240ms/step - loss: 0.0283\n",
+      "Epoch 2787/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 218ms/step - loss: 0.0286\n",
+      "Epoch 2788/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317ms/step - loss: 0.0286\n",
+      "Epoch 2789/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 209ms/step - loss: 0.0283\n",
+      "Epoch 2790/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 286ms/step - loss: 0.0281\n",
+      "Epoch 2791/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step - loss: 0.0280\n",
+      "Epoch 2792/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 371ms/step - loss: 0.0280\n",
+      "Epoch 2793/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 290ms/step - loss: 0.0278\n",
+      "Epoch 2794/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 216ms/step - loss: 0.0278\n",
+      "Epoch 2795/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 213ms/step - loss: 0.0279\n",
+      "Epoch 2796/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0279\n",
+      "Epoch 2797/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317ms/step - loss: 0.0278\n",
+      "Epoch 2798/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 272ms/step - loss: 0.0276\n",
+      "Epoch 2799/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 388ms/step - loss: 0.0276\n",
+      "Epoch 2800/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 331ms/step - loss: 0.0276\n",
+      "Epoch 2801/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 266ms/step - loss: 0.0277\n",
+      "Epoch 2802/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 375ms/step - loss: 0.0276\n",
+      "Epoch 2803/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 231ms/step - loss: 0.0275\n",
+      "Epoch 2804/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 399ms/step - loss: 0.0274\n",
+      "Epoch 2805/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 0.0275\n",
+      "Epoch 2806/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295ms/step - loss: 0.0275\n",
+      "Epoch 2807/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 247ms/step - loss: 0.0274\n",
+      "Epoch 2808/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 257ms/step - loss: 0.0273\n",
+      "Epoch 2809/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291ms/step - loss: 0.0273\n",
+      "Epoch 2810/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - loss: 0.0274\n",
+      "Epoch 2811/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292ms/step - loss: 0.0274\n",
+      "Epoch 2812/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 347ms/step - loss: 0.0273\n",
+      "Epoch 2813/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 200ms/step - loss: 0.0273\n",
+      "Epoch 2814/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - loss: 0.0273\n",
+      "Epoch 2815/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - loss: 0.0273\n",
+      "Epoch 2816/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 213ms/step - loss: 0.0273\n",
+      "Epoch 2817/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 181ms/step - loss: 0.0273\n",
+      "Epoch 2818/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 266ms/step - loss: 0.0273\n",
+      "Epoch 2819/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 299ms/step - loss: 0.0272\n",
+      "Epoch 2820/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 267ms/step - loss: 0.0273\n",
+      "Epoch 2821/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0273\n",
+      "Epoch 2822/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295ms/step - loss: 0.0273\n",
+      "Epoch 2823/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322ms/step - loss: 0.0273\n",
+      "Epoch 2824/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 353ms/step - loss: 0.0274\n",
+      "Epoch 2825/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 202ms/step - loss: 0.0275\n",
+      "Epoch 2826/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 227ms/step - loss: 0.0277\n",
+      "Epoch 2827/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 213ms/step - loss: 0.0280\n",
+      "Epoch 2828/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 384ms/step - loss: 0.0286\n",
+      "Epoch 2829/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 395ms/step - loss: 0.0293\n",
+      "Epoch 2830/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313ms/step - loss: 0.0303\n",
+      "Epoch 2831/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 206ms/step - loss: 0.0311\n",
+      "Epoch 2832/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 245ms/step - loss: 0.0314\n",
+      "Epoch 2833/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0306\n",
+      "Epoch 2834/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0293\n",
+      "Epoch 2835/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0287\n",
+      "Epoch 2836/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0291\n",
+      "Epoch 2837/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.0298\n",
+      "Epoch 2838/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0293\n",
+      "Epoch 2839/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0283\n",
+      "Epoch 2840/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0276\n",
+      "Epoch 2841/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0279\n",
+      "Epoch 2842/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0283\n",
+      "Epoch 2843/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0281\n",
+      "Epoch 2844/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0277\n",
+      "Epoch 2845/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0276\n",
+      "Epoch 2846/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0278\n",
+      "Epoch 2847/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0277\n",
+      "Epoch 2848/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0274\n",
+      "Epoch 2849/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0273\n",
+      "Epoch 2850/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0275\n",
+      "Epoch 2851/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.0276\n",
+      "Epoch 2852/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0274\n",
+      "Epoch 2853/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0272\n",
+      "Epoch 2854/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0271\n",
+      "Epoch 2855/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0273\n",
+      "Epoch 2856/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0274\n",
+      "Epoch 2857/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0272\n",
+      "Epoch 2858/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 197ms/step - loss: 0.0271\n",
+      "Epoch 2859/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0271\n",
+      "Epoch 2860/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0271\n",
+      "Epoch 2861/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0271\n",
+      "Epoch 2862/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0270\n",
+      "Epoch 2863/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0270\n",
+      "Epoch 2864/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0270\n",
+      "Epoch 2865/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0270\n",
+      "Epoch 2866/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0270\n",
+      "Epoch 2867/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0270\n",
+      "Epoch 2868/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0269\n",
+      "Epoch 2869/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0269\n",
+      "Epoch 2870/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0269\n",
+      "Epoch 2871/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0270\n",
+      "Epoch 2872/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0269\n",
+      "Epoch 2873/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - loss: 0.0269\n",
+      "Epoch 2874/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0269\n",
+      "Epoch 2875/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0270\n",
+      "Epoch 2876/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0270\n",
+      "Epoch 2877/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0271\n",
+      "Epoch 2878/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0272\n",
+      "Epoch 2879/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0274\n",
+      "Epoch 2880/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0278\n",
+      "Epoch 2881/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0284\n",
+      "Epoch 2882/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0293\n",
+      "Epoch 2883/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0300\n",
+      "Epoch 2884/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0304\n",
+      "Epoch 2885/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0295\n",
+      "Epoch 2886/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0283\n",
+      "Epoch 2887/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0279\n",
+      "Epoch 2888/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0284\n",
+      "Epoch 2889/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0291\n",
+      "Epoch 2890/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0287\n",
+      "Epoch 2891/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0280\n",
+      "Epoch 2892/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0278\n",
+      "Epoch 2893/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0281\n",
+      "Epoch 2894/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0281\n",
+      "Epoch 2895/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0277\n",
+      "Epoch 2896/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0275\n",
+      "Epoch 2897/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0276\n",
+      "Epoch 2898/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0276\n",
+      "Epoch 2899/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0274\n",
+      "Epoch 2900/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0272\n",
+      "Epoch 2901/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0272\n",
+      "Epoch 2902/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0273\n",
+      "Epoch 2903/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0273\n",
+      "Epoch 2904/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0272\n",
+      "Epoch 2905/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 199ms/step - loss: 0.0271\n",
+      "Epoch 2906/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 235ms/step - loss: 0.0270\n",
+      "Epoch 2907/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0271\n",
+      "Epoch 2908/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0270\n",
+      "Epoch 2909/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0270\n",
+      "Epoch 2910/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0269\n",
+      "Epoch 2911/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0269\n",
+      "Epoch 2912/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0269\n",
+      "Epoch 2913/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0268\n",
+      "Epoch 2914/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0267\n",
+      "Epoch 2915/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0267\n",
+      "Epoch 2916/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 206ms/step - loss: 0.0268\n",
+      "Epoch 2917/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0268\n",
+      "Epoch 2918/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0268\n",
+      "Epoch 2919/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0268\n",
+      "Epoch 2920/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0268\n",
+      "Epoch 2921/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - loss: 0.0268\n",
+      "Epoch 2922/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0269\n",
+      "Epoch 2923/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0269\n",
+      "Epoch 2924/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0269\n",
+      "Epoch 2925/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0270\n",
+      "Epoch 2926/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0271\n",
+      "Epoch 2927/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0272\n",
+      "Epoch 2928/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0274\n",
+      "Epoch 2929/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0276\n",
+      "Epoch 2930/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 206ms/step - loss: 0.0279\n",
+      "Epoch 2931/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0282\n",
+      "Epoch 2932/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0284\n",
+      "Epoch 2933/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0285\n",
+      "Epoch 2934/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 218ms/step - loss: 0.0282\n",
+      "Epoch 2935/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0278\n",
+      "Epoch 2936/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0273\n",
+      "Epoch 2937/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0270\n",
+      "Epoch 2938/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0268\n",
+      "Epoch 2939/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0269\n",
+      "Epoch 2940/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0270\n",
+      "Epoch 2941/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0271\n",
+      "Epoch 2942/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0271\n",
+      "Epoch 2943/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0269\n",
+      "Epoch 2944/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0268\n",
+      "Epoch 2945/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0266\n",
+      "Epoch 2946/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0266\n",
+      "Epoch 2947/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0266\n",
+      "Epoch 2948/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0266\n",
+      "Epoch 2949/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0267\n",
+      "Epoch 2950/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0267\n",
+      "Epoch 2951/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0266\n",
+      "Epoch 2952/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0265\n",
+      "Epoch 2953/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.0265\n",
+      "Epoch 2954/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0265\n",
+      "Epoch 2955/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0265\n",
+      "Epoch 2956/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0265\n",
+      "Epoch 2957/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0265\n",
+      "Epoch 2958/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0265\n",
+      "Epoch 2959/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0264\n",
+      "Epoch 2960/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0264\n",
+      "Epoch 2961/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0264\n",
+      "Epoch 2962/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0264\n",
+      "Epoch 2963/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0264\n",
+      "Epoch 2964/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0265\n",
+      "Epoch 2965/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0265\n",
+      "Epoch 2966/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 257ms/step - loss: 0.0266\n",
+      "Epoch 2967/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0268\n",
+      "Epoch 2968/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0271\n",
+      "Epoch 2969/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0275\n",
+      "Epoch 2970/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0281\n",
+      "Epoch 2971/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0287\n",
+      "Epoch 2972/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0294\n",
+      "Epoch 2973/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0296\n",
+      "Epoch 2974/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0294\n",
+      "Epoch 2975/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0289\n",
+      "Epoch 2976/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0284\n",
+      "Epoch 2977/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0285\n",
+      "Epoch 2978/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0284\n",
+      "Epoch 2979/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0279\n",
+      "Epoch 2980/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0272\n",
+      "Epoch 2981/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0268\n",
+      "Epoch 2982/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0271\n",
+      "Epoch 2983/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0274\n",
+      "Epoch 2984/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0273\n",
+      "Epoch 2985/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0269\n",
+      "Epoch 2986/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0266\n",
+      "Epoch 2987/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0267\n",
+      "Epoch 2988/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0269\n",
+      "Epoch 2989/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - loss: 0.0268\n",
+      "Epoch 2990/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0265\n",
+      "Epoch 2991/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0263\n",
+      "Epoch 2992/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0264\n",
+      "Epoch 2993/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - loss: 0.0266\n",
+      "Epoch 2994/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0266\n",
+      "Epoch 2995/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0264\n",
+      "Epoch 2996/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0262\n",
+      "Epoch 2997/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.0262\n",
+      "Epoch 2998/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 333ms/step - loss: 0.0263\n",
+      "Epoch 2999/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 432ms/step - loss: 0.0264\n",
+      "Epoch 3000/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0263\n",
+      "Epoch 3000/20000\n",
+      " - loss: 0.0263\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 211ms/step - loss: 0.0263\n",
+      "Epoch 3001/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 241ms/step - loss: 0.0262\n",
+      "Epoch 3002/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 293ms/step - loss: 0.0261\n",
+      "Epoch 3003/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 204ms/step - loss: 0.0261\n",
+      "Epoch 3004/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 242ms/step - loss: 0.0262\n",
+      "Epoch 3005/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0262\n",
+      "Epoch 3006/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 261ms/step - loss: 0.0262\n",
+      "Epoch 3007/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 221ms/step - loss: 0.0261\n",
+      "Epoch 3008/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300ms/step - loss: 0.0261\n",
+      "Epoch 3009/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 367ms/step - loss: 0.0260\n",
+      "Epoch 3010/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 350ms/step - loss: 0.0261\n",
+      "Epoch 3011/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 311ms/step - loss: 0.0261\n",
+      "Epoch 3012/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 346ms/step - loss: 0.0261\n",
+      "Epoch 3013/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 267ms/step - loss: 0.0261\n",
+      "Epoch 3014/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 400ms/step - loss: 0.0261\n",
+      "Epoch 3015/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 271ms/step - loss: 0.0261\n",
+      "Epoch 3016/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 411ms/step - loss: 0.0262\n",
+      "Epoch 3017/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 289ms/step - loss: 0.0263\n",
+      "Epoch 3018/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 251ms/step - loss: 0.0266\n",
+      "Epoch 3019/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 339ms/step - loss: 0.0270\n",
+      "Epoch 3020/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298ms/step - loss: 0.0276\n",
+      "Epoch 3021/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292ms/step - loss: 0.0284\n",
+      "Epoch 3022/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 246ms/step - loss: 0.0293\n",
+      "Epoch 3023/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295ms/step - loss: 0.0297\n",
+      "Epoch 3024/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 412ms/step - loss: 0.0294\n",
+      "Epoch 3025/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 211ms/step - loss: 0.0280\n",
+      "Epoch 3026/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313ms/step - loss: 0.0268\n",
+      "Epoch 3027/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 224ms/step - loss: 0.0265\n",
+      "Epoch 3028/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step - loss: 0.0270\n",
+      "Epoch 3029/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 332ms/step - loss: 0.0275\n",
+      "Epoch 3030/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 254ms/step - loss: 0.0272\n",
+      "Epoch 3031/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198ms/step - loss: 0.0266\n",
+      "Epoch 3032/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 226ms/step - loss: 0.0263\n",
+      "Epoch 3033/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 237ms/step - loss: 0.0264\n",
+      "Epoch 3034/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - loss: 0.0267\n",
+      "Epoch 3035/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0266\n",
+      "Epoch 3036/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 184ms/step - loss: 0.0263\n",
+      "Epoch 3037/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0262\n",
+      "Epoch 3038/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - loss: 0.0263\n",
+      "Epoch 3039/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0264\n",
+      "Epoch 3040/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0263\n",
+      "Epoch 3041/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0261\n",
+      "Epoch 3042/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 212ms/step - loss: 0.0260\n",
+      "Epoch 3043/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0261\n",
+      "Epoch 3044/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0262\n",
+      "Epoch 3045/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0262\n",
+      "Epoch 3046/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0261\n",
+      "Epoch 3047/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0260\n",
+      "Epoch 3048/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0261\n",
+      "Epoch 3049/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0262\n",
+      "Epoch 3050/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0263\n",
+      "Epoch 3051/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0264\n",
+      "Epoch 3052/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0265\n",
+      "Epoch 3053/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 256ms/step - loss: 0.0266\n",
+      "Epoch 3054/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 228ms/step - loss: 0.0269\n",
+      "Epoch 3055/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0272\n",
+      "Epoch 3056/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0273\n",
+      "Epoch 3057/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0273\n",
+      "Epoch 3058/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 236ms/step - loss: 0.0270\n",
+      "Epoch 3059/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0267\n",
+      "Epoch 3060/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0265\n",
+      "Epoch 3061/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0264\n",
+      "Epoch 3062/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0266\n",
+      "Epoch 3063/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0269\n",
+      "Epoch 3064/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 222ms/step - loss: 0.0273\n",
+      "Epoch 3065/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 229ms/step - loss: 0.0274\n",
+      "Epoch 3066/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0272\n",
+      "Epoch 3067/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.0267\n",
+      "Epoch 3068/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0263\n",
+      "Epoch 3069/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0261\n",
+      "Epoch 3070/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0262\n",
+      "Epoch 3071/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0263\n",
+      "Epoch 3072/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0263\n",
+      "Epoch 3073/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0261\n",
+      "Epoch 3074/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0260\n",
+      "Epoch 3075/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0259\n",
+      "Epoch 3076/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0260\n",
+      "Epoch 3077/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 212ms/step - loss: 0.0261\n",
+      "Epoch 3078/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0261\n",
+      "Epoch 3079/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0259\n",
+      "Epoch 3080/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0258\n",
+      "Epoch 3081/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0258\n",
+      "Epoch 3082/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0258\n",
+      "Epoch 3083/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0258\n",
+      "Epoch 3084/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0258\n",
+      "Epoch 3085/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0258\n",
+      "Epoch 3086/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0258\n",
+      "Epoch 3087/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0258\n",
+      "Epoch 3088/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0257\n",
+      "Epoch 3089/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0257\n",
+      "Epoch 3090/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0257\n",
+      "Epoch 3091/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0258\n",
+      "Epoch 3092/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0258\n",
+      "Epoch 3093/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0258\n",
+      "Epoch 3094/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0258\n",
+      "Epoch 3095/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0259\n",
+      "Epoch 3096/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0260\n",
+      "Epoch 3097/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0260\n",
+      "Epoch 3098/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183ms/step - loss: 0.0262\n",
+      "Epoch 3099/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0263\n",
+      "Epoch 3100/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0264\n",
+      "Epoch 3101/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0264\n",
+      "Epoch 3102/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0264\n",
+      "Epoch 3103/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0264\n",
+      "Epoch 3104/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0263\n",
+      "Epoch 3105/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0261\n",
+      "Epoch 3106/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0261\n",
+      "Epoch 3107/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0261\n",
+      "Epoch 3108/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0261\n",
+      "Epoch 3109/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0262\n",
+      "Epoch 3110/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0262\n",
+      "Epoch 3111/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0262\n",
+      "Epoch 3112/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0261\n",
+      "Epoch 3113/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0260\n",
+      "Epoch 3114/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0259\n",
+      "Epoch 3115/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0257\n",
+      "Epoch 3116/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0256\n",
+      "Epoch 3117/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0256\n",
+      "Epoch 3118/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0256\n",
+      "Epoch 3119/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0256\n",
+      "Epoch 3120/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0256\n",
+      "Epoch 3121/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0256\n",
+      "Epoch 3122/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0257\n",
+      "Epoch 3123/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 238ms/step - loss: 0.0257\n",
+      "Epoch 3124/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0257\n",
+      "Epoch 3125/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0258\n",
+      "Epoch 3126/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0258\n",
+      "Epoch 3127/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0259\n",
+      "Epoch 3128/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0260\n",
+      "Epoch 3129/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0262\n",
+      "Epoch 3130/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0265\n",
+      "Epoch 3131/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0269\n",
+      "Epoch 3132/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0273\n",
+      "Epoch 3133/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0276\n",
+      "Epoch 3134/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0276\n",
+      "Epoch 3135/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0275\n",
+      "Epoch 3136/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0272\n",
+      "Epoch 3137/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0273\n",
+      "Epoch 3138/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0276\n",
+      "Epoch 3139/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0279\n",
+      "Epoch 3140/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0278\n",
+      "Epoch 3141/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0271\n",
+      "Epoch 3142/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0263\n",
+      "Epoch 3143/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0259\n",
+      "Epoch 3144/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0261\n",
+      "Epoch 3145/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0264\n",
+      "Epoch 3146/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0262\n",
+      "Epoch 3147/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0259\n",
+      "Epoch 3148/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0258\n",
+      "Epoch 3149/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0259\n",
+      "Epoch 3150/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0261\n",
+      "Epoch 3151/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0259\n",
+      "Epoch 3152/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0256\n",
+      "Epoch 3153/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0254\n",
+      "Epoch 3154/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0255\n",
+      "Epoch 3155/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0257\n",
+      "Epoch 3156/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0257\n",
+      "Epoch 3157/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0255\n",
+      "Epoch 3158/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0253\n",
+      "Epoch 3159/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0253\n",
+      "Epoch 3160/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0254\n",
+      "Epoch 3161/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0254\n",
+      "Epoch 3162/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0254\n",
+      "Epoch 3163/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.0253\n",
+      "Epoch 3164/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0253\n",
+      "Epoch 3165/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0253\n",
+      "Epoch 3166/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0254\n",
+      "Epoch 3167/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - loss: 0.0254\n",
+      "Epoch 3168/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0255\n",
+      "Epoch 3169/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0255\n",
+      "Epoch 3170/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0257\n",
+      "Epoch 3171/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0260\n",
+      "Epoch 3172/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0264\n",
+      "Epoch 3173/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0268\n",
+      "Epoch 3174/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0270\n",
+      "Epoch 3175/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0268\n",
+      "Epoch 3176/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0264\n",
+      "Epoch 3177/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0258\n",
+      "Epoch 3178/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0255\n",
+      "Epoch 3179/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0255\n",
+      "Epoch 3180/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - loss: 0.0257\n",
+      "Epoch 3181/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0258\n",
+      "Epoch 3182/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 201ms/step - loss: 0.0256\n",
+      "Epoch 3183/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0254\n",
+      "Epoch 3184/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0254\n",
+      "Epoch 3185/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0255\n",
+      "Epoch 3186/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0255\n",
+      "Epoch 3187/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0255\n",
+      "Epoch 3188/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0255\n",
+      "Epoch 3189/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0255\n",
+      "Epoch 3190/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0257\n",
+      "Epoch 3191/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0259\n",
+      "Epoch 3192/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0260\n",
+      "Epoch 3193/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0260\n",
+      "Epoch 3194/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0261\n",
+      "Epoch 3195/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0261\n",
+      "Epoch 3196/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 205ms/step - loss: 0.0260\n",
+      "Epoch 3197/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0259\n",
+      "Epoch 3198/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0256\n",
+      "Epoch 3199/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0254\n",
+      "Epoch 3200/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0252\n",
+      "Epoch 3201/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0252\n",
+      "Epoch 3202/20000\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0253\n",
+      "Epoch 3202: early stopping\n",
+      "Restoring model weights from the end of the best epoch: 3002.\n",
+      "\u001b[1m47/47\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "Обучение завершено (нормализованные данные)!\n",
+      "MSE_stop (приблизительно): 0.026126\n",
+      "Порог IRE: 1.620000\n",
+      "Количество скрытых слоев: 11\n",
+      "Количество нейронов в скрытых слоях: 48-36-24-16-8-4-8-16-24-36-48\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Обучение автокодировщика для Letter на нормализованных данных\n",
+    "# Та же архитектура для сравнения: 32(вход) -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 32(выход)\n",
+    "# 11 скрытых слоев: 100 86 72 64 48 32 24 16 8 16 24 32 48 64 72 86 100\n",
+    "\n",
+    "print('Обучение автокодировщика для данных Letter (нормализованные данные)...')\n",
+    "print('Архитектура: 32(вход) -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 32(выход)')\n",
+    "\n",
+    "# Автоматизируем ввод для выбора пользовательской архитектуры\n",
+    "with patch('builtins.input', side_effect=['1', '17', ' 100 86 72 64 48 32 24 16 8 16 24 32 48 64 72 86 100']):\n",
+    "    ae_letter_norm, IRE_letter_norm, IREth_letter_norm = lib.create_fit_save_ae(\n",
+    "        train_letter_normalized, \n",
+    "        'out/Letter_AE_norm.h5', \n",
+    "        'out/Letter_AE_norm_ire_th.txt',\n",
+    "        20000,  # epochs\n",
+    "        True,  # verbose_show\n",
+    "        200,  # patience\n",
+    "        verbose_every_n_epochs=500\n",
+    "    )\n",
+    "\n",
+    "# Вычисление MSE из предсказания\n",
+    "X_pred_letter_norm = ae_letter_norm.predict(train_letter_normalized, verbose=0)\n",
+    "mse_letter_norm = np.mean((train_letter_normalized - X_pred_letter_norm) ** 2)\n",
+    "\n",
+    "print(f'\\nОбучение завершено (нормализованные данные)!')\n",
+    "print(f'MSE_stop (приблизительно): {mse_letter_norm:.6f}')\n",
+    "print(f'Порог IRE: {IREth_letter_norm:.6f}')\n",
+    "print(f'Количество скрытых слоев: 11')\n",
+    "print(f'Количество нейронов в скрытых слоях: 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4) Сравнение результатов обучения на нормализованных и ненормализованных данных\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================================================================================================\n",
+      "СРАВНЕНИЕ РЕЗУЛЬТАТОВ ОБУЧЕНИЯ\n",
+      "====================================================================================================\n",
+      "Параметр НЕнормализованные Нормализованные\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "MSE_stop 0.3402215666078586 0.026125688316365834\n",
+      "Порог IRE 11.1 1.62\n",
+      "Архитектура                    17 слоев 17 слоев\n",
+      "Нейроны 32(вход) -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 32(выход) 32(вход) -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 32(выход)\n",
+      "====================================================================================================\n",
+      "\n",
+      "Лучшая модель: НОРМАЛИЗОВАННЫЕ данные\n",
+      "Используем эту модель для дальнейших экспериментов\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Сравнение результатов\n",
+    "print('=' * 100)\n",
+    "print('СРАВНЕНИЕ РЕЗУЛЬТАТОВ ОБУЧЕНИЯ')\n",
+    "print('=' * 100)\n",
+    "print(f'{\"Параметр\"} {\"НЕнормализованные\"} {\"Нормализованные\"}')\n",
+    "print('-' * 100)\n",
+    "print(f'{\"MSE_stop\"} {mse_letter_raw} {mse_letter_norm}')\n",
+    "print(f'{\"Порог IRE\"} {IREth_letter_raw} {IREth_letter_norm:}')\n",
+    "print(f'{\"Архитектура\":<30} {\"17 слоев\"} {\"17 слоев\"}')\n",
+    "print(f'{\"Нейроны\"} {\"32(вход) -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 32(выход)\"} {\"32(вход) -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 32(выход)\"}')\n",
+    "print('=' * 100)\n",
+    "\n",
+    "# Выбираем лучшую модель для дальнейшей работы\n",
+    "if mse_letter_norm < mse_letter_raw:\n",
+    "    print('\\nЛучшая модель: НОРМАЛИЗОВАННЫЕ данные')\n",
+    "    ae_letter = ae_letter_norm\n",
+    "    IRE_letter = IRE_letter_norm\n",
+    "    IREth_letter = IREth_letter_norm\n",
+    "    mse_letter = mse_letter_norm\n",
+    "    test_letter_used = test_letter_normalized\n",
+    "    print(f'Используем эту модель для дальнейших экспериментов')\n",
+    "else:\n",
+    "    print('\\nЛучшая модель: НЕнормализованные данные')\n",
+    "    ae_letter = ae_letter_raw\n",
+    "    IRE_letter = IRE_letter_raw\n",
+    "    IREth_letter = IREth_letter_raw\n",
+    "    mse_letter = mse_letter_raw\n",
+    "    test_letter_used = test_letter\n",
+    "    print(f'Используем эту модель для дальнейших экспериментов')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3) Построение графика ошибки реконструкции для Letter\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Порог ошибки реконструкции: 1.620000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Построение графика ошибки реконструкции для выбранной модели\n",
+    "lib.ire_plot('training', IRE_letter, IREth_letter, 'Letter_AE')\n",
+    "print(f'Порог ошибки реконструкции: {IREth_letter:.6f}')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4) Применение автокодировщика к тестовой выборке Letter\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step\n",
+      "Обнаружено аномалий: 100 из 100\n",
+      "Процент обнаруженных аномалий: 100.0%\n",
+      "\n",
+      "i         Labels    IRE       IREth     \n",
+      "0         [1.]      [3.69]    1.62      \n",
+      "1         [1.]      [4.03]    1.62      \n",
+      "2         [1.]      [3.66]    1.62      \n",
+      "3         [1.]      [3.77]    1.62      \n",
+      "4         [1.]      [4.21]    1.62      \n",
+      "5         [1.]      [5.73]    1.62      \n",
+      "6         [1.]      [4.23]    1.62      \n",
+      "7         [1.]      [5.24]    1.62      \n",
+      "8         [1.]      [3.29]    1.62      \n",
+      "9         [1.]      [4.6]     1.62      \n",
+      "10        [1.]      [3.65]    1.62      \n",
+      "11        [1.]      [5.58]    1.62      \n",
+      "12        [1.]      [3.55]    1.62      \n",
+      "13        [1.]      [4.46]    1.62      \n",
+      "14        [1.]      [5.27]    1.62      \n",
+      "15        [1.]      [3.93]    1.62      \n",
+      "16        [1.]      [3.64]    1.62      \n",
+      "17        [1.]      [4.15]    1.62      \n",
+      "18        [1.]      [2.48]    1.62      \n",
+      "19        [1.]      [5.81]    1.62      \n",
+      "20        [1.]      [2.53]    1.62      \n",
+      "21        [1.]      [2.32]    1.62      \n",
+      "22        [1.]      [2.87]    1.62      \n",
+      "23        [1.]      [3.83]    1.62      \n",
+      "24        [1.]      [4.09]    1.62      \n",
+      "25        [1.]      [3.3]     1.62      \n",
+      "26        [1.]      [5.52]    1.62      \n",
+      "27        [1.]      [2.53]    1.62      \n",
+      "28        [1.]      [4.29]    1.62      \n",
+      "29        [1.]      [5.17]    1.62      \n",
+      "30        [1.]      [3.67]    1.62      \n",
+      "31        [1.]      [6.81]    1.62      \n",
+      "32        [1.]      [4.47]    1.62      \n",
+      "33        [1.]      [4.83]    1.62      \n",
+      "34        [1.]      [4.49]    1.62      \n",
+      "35        [1.]      [7.67]    1.62      \n",
+      "36        [1.]      [3.23]    1.62      \n",
+      "37        [1.]      [4.04]    1.62      \n",
+      "38        [1.]      [3.62]    1.62      \n",
+      "39        [1.]      [5.17]    1.62      \n",
+      "40        [1.]      [4.18]    1.62      \n",
+      "41        [1.]      [5.75]    1.62      \n",
+      "42        [1.]      [3.79]    1.62      \n",
+      "43        [1.]      [5.04]    1.62      \n",
+      "44        [1.]      [2.9]     1.62      \n",
+      "45        [1.]      [1.9]     1.62      \n",
+      "46        [1.]      [3.17]    1.62      \n",
+      "47        [1.]      [5.91]    1.62      \n",
+      "48        [1.]      [3.62]    1.62      \n",
+      "49        [1.]      [8.11]    1.62      \n",
+      "50        [1.]      [5.18]    1.62      \n",
+      "51        [1.]      [2.07]    1.62      \n",
+      "52        [1.]      [2.48]    1.62      \n",
+      "53        [1.]      [3.72]    1.62      \n",
+      "54        [1.]      [5.52]    1.62      \n",
+      "55        [1.]      [3.92]    1.62      \n",
+      "56        [1.]      [4.16]    1.62      \n",
+      "57        [1.]      [4.63]    1.62      \n",
+      "58        [1.]      [8.05]    1.62      \n",
+      "59        [1.]      [3.36]    1.62      \n",
+      "60        [1.]      [5.26]    1.62      \n",
+      "61        [1.]      [2.68]    1.62      \n",
+      "62        [1.]      [4.28]    1.62      \n",
+      "63        [1.]      [5.2]     1.62      \n",
+      "64        [1.]      [3.52]    1.62      \n",
+      "65        [1.]      [3.93]    1.62      \n",
+      "66        [1.]      [2.84]    1.62      \n",
+      "67        [1.]      [2.29]    1.62      \n",
+      "68        [1.]      [2.66]    1.62      \n",
+      "69        [1.]      [5.66]    1.62      \n",
+      "70        [1.]      [5.75]    1.62      \n",
+      "71        [1.]      [5.99]    1.62      \n",
+      "72        [1.]      [4.67]    1.62      \n",
+      "73        [1.]      [5.4]     1.62      \n",
+      "74        [1.]      [6.19]    1.62      \n",
+      "75        [1.]      [3.36]    1.62      \n",
+      "76        [1.]      [2.45]    1.62      \n",
+      "77        [1.]      [3.58]    1.62      \n",
+      "78        [1.]      [3.85]    1.62      \n",
+      "79        [1.]      [6.03]    1.62      \n",
+      "80        [1.]      [6.]      1.62      \n",
+      "81        [1.]      [3.45]    1.62      \n",
+      "82        [1.]      [4.55]    1.62      \n",
+      "83        [1.]      [1.86]    1.62      \n",
+      "84        [1.]      [2.15]    1.62      \n",
+      "85        [1.]      [4.95]    1.62      \n",
+      "86        [1.]      [4.62]    1.62      \n",
+      "87        [1.]      [7.26]    1.62      \n",
+      "88        [1.]      [3.69]    1.62      \n",
+      "89        [1.]      [3.95]    1.62      \n",
+      "90        [1.]      [3.16]    1.62      \n",
+      "91        [1.]      [3.71]    1.62      \n",
+      "92        [1.]      [2.2]     1.62      \n",
+      "93        [1.]      [5.18]    1.62      \n",
+      "94        [1.]      [3.28]    1.62      \n",
+      "95        [1.]      [5.14]    1.62      \n",
+      "96        [1.]      [6.17]    1.62      \n",
+      "97        [1.]      [3.95]    1.62      \n",
+      "98        [1.]      [3.95]    1.62      \n",
+      "99        [1.]      [3.93]    1.62      \n",
+      "Обнаружено  100.0  аномалий\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1600x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Тестирование автокодировщика на тестовой выборке\n",
+    "# Используем выбранную модель (лучшую из нормализованных/ненормализованных)\n",
+    "predicted_labels_letter, ire_letter_test = lib.predict_ae(ae_letter, test_letter_used, IREth_letter)\n",
+    "\n",
+    "# Подсчет обнаруженных аномалий\n",
+    "num_anomalies = int(predicted_labels_letter.sum())\n",
+    "total_test = len(test_letter_used)\n",
+    "anomaly_percentage = (num_anomalies / total_test) * 100\n",
+    "\n",
+    "print(f'Обнаружено аномалий: {num_anomalies} из {total_test}')\n",
+    "print(f'Процент обнаруженных аномалий: {anomaly_percentage:.1f}%')\n",
+    "\n",
+    "lib.anomaly_detection_ae(predicted_labels_letter, ire_letter_test, IREth_letter)\n",
+    "lib.ire_plot('test', ire_letter_test, IREth_letter, 'Letter_AE')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5) Результаты задания №2\n",
+    "\n",
+    "Результаты исследования занесены в таблицу:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Таблица 2 - Результаты задания №2\n",
+      "====================================================================================================\n",
+      "Dataset         Скрытых слоев   Нейроны                                  Эпох       MSE_stop     Порог IRE    % аномалий  \n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "Letter      11              32(вход) -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 32(выход) 20000      0.026126     1.620000     100.0%\n",
+      "====================================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Таблица 2 - Результаты задания №2')\n",
+    "print('=' * 100)\n",
+    "print(f'{\"Dataset\":<15} {\"Скрытых слоев\":<15} {\"Нейроны\":<40} {\"Эпох\":<10} {\"MSE_stop\":<12} {\"Порог IRE\":<12} {\"% аномалий\":<12}')\n",
+    "print('-' * 100)\n",
+    "print(f'Letter      {11:<15} {\"32(вход) -> 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 -> 32(выход)\":<40} {20000:<10} {mse_letter:<12.6f} {IREth_letter:<12.6f} {anomaly_percentage:.1f}%')\n",
+    "print('=' * 100)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Выводы\n",
+    "\n",
+    "### Требования к данным для обучения:\n",
+    "- Данные для обучения должны быть без аномалий, чтобы автокодировщик смог рассчитать верное пороговое значение\n",
+    "\n",
+    "### Требования к архитектуре автокодировщика:\n",
+    "- Архитектура должна постепенно сужаться к бутылочному горлышку, а затем постепенно возвращаться к исходным выходным размерам\n",
+    "- Для двумерных данных оптимальное количество скрытых слоев: 3-6\n",
+    "- Для многомерных данных (32 признака) оптимальное количество скрытых слоев: от 7-ми\n",
+    "\n",
+    "### Требования к количеству эпох обучения:\n",
+    "- Для простых данных (2D): 1000-3000 эпох\n",
+    "- Для сложных данных (многомерных): до 100000 эпох\n",
+    "\n",
+    "### Требования к ошибке MSE_stop:\n",
+    "- Для двумерных данных: оптимальная ошибка MSE-stop в районе 0.01\n",
+    "- Для многомерных данных: оптимальная ошибка MSE-stop в районе 0.1-0.01\n",
+    "\n",
+    "### Требования к порогу обнаружения аномалий:\n",
+    "- Для двумерных данных: значение порога в районе 0.4-2.5\n",
+    "- Для многомерных данных: значение порога не больше 1.6\n",
+    "\n",
+    "### Требования к характеристикам качества обучения EDCA:\n",
+    "- Значение Excess должно быть как можно ближе к 0\n",
+    "- Значение Deficit должно быть как можно ближе к 0 \n",
+    "- Значение Coating должно быть как можно ближе к 1 \n",
+    "- Значение Approx должно быть как можно ближе к 1\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/labworks/LW2/lab2_variant1.ipynb b/labworks/LW2/lab2_variant1.ipynb
deleted file mode 100644
index ca3dbb4..0000000
--- a/labworks/LW2/lab2_variant1.ipynb
+++ /dev/null
@@ -1,712 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Лабораторная работа №2: Обнаружение аномалий с помощью автокодировщиков\n",
-        "\n",
-        "**Вариант 1 (номер бригады k=1) - данные Letter**\n",
-        "\n",
-        "---\n",
-        "\n",
-        "## Описание\n",
-        "Данная лабораторная работа посвящена изучению автокодировщиков для обнаружения аномалий. Работа включает два основных задания:\n",
-        "1. Работа с двумерными синтетическими данными\n",
-        "2. Работа с реальными данными Letter\n",
-        "\n",
-        "**Номер бригады:** k=1  \n",
-        "**Центр данных:** (1, 1)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Импорт необходимых библиотек\n",
-        "import os\n",
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from sklearn.preprocessing import StandardScaler\n",
-        "from sklearn.datasets import make_blobs\n",
-        "import tensorflow as tf\n",
-        "from tensorflow.keras.models import Sequential\n",
-        "from tensorflow.keras.layers import Dense, Activation\n",
-        "from tensorflow.keras.optimizers import Adam\n",
-        "from tensorflow.keras.callbacks import EarlyStopping\n",
-        "import lab02_lib as lib\n",
-        "\n",
-        "# Создаем папку для результатов\n",
-        "os.makedirs('out', exist_ok=True)\n",
-        "\n",
-        "# Параметры для варианта 1 (номер бригады k=1)\n",
-        "k = 1  # номер бригады\n",
-        "center_coords = (k, k)  # координаты центра (1, 1)\n",
-        "\n",
-        "print(\"Лабораторная работа №2: Обнаружение аномалий с помощью автокодировщиков\")\n",
-        "print(\"Вариант 1 (номер бригады k=1) - данные Letter\")\n",
-        "print(\"=\" * 70)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# ЗАДАНИЕ 1: Работа с двумерными синтетическими данными\n",
-        "\n",
-        "## 1. Генерация индивидуального набора двумерных данных\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Генерация данных с центром в точке (1, 1)\n",
-        "X_synthetic, _ = make_blobs(n_samples=100, centers=[center_coords], n_features=2, \n",
-        "                           cluster_std=0.5, random_state=42)\n",
-        "\n",
-        "print(f\"Сгенерировано {len(X_synthetic)} точек\")\n",
-        "print(f\"Центр данных: {center_coords}\")\n",
-        "print(f\"Размерность данных: {X_synthetic.shape}\")\n",
-        "\n",
-        "# Визуализация данных\n",
-        "plt.figure(figsize=(10, 8))\n",
-        "plt.scatter(X_synthetic[:, 0], X_synthetic[:, 1], c='blue', alpha=0.7, s=50)\n",
-        "plt.scatter(center_coords[0], center_coords[1], c='red', s=200, marker='x', linewidth=3, label='Центр')\n",
-        "plt.title(f'Синтетические данные (центр в точке {center_coords})')\n",
-        "plt.xlabel('X1')\n",
-        "plt.ylabel('X2')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "plt.savefig('out/synthetic_data.png', dpi=300, bbox_inches='tight')\n",
-        "plt.show()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 2. Создание и обучение автокодировщика AE1 (простая архитектура)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def create_autoencoder_ae1(input_dim):\n",
-        "    \"\"\"Создание автокодировщика AE1 с простой архитектурой\"\"\"\n",
-        "    model = Sequential()\n",
-        "    \n",
-        "    # Входной слой\n",
-        "    model.add(Dense(input_dim, input_shape=(input_dim,)))\n",
-        "    model.add(Activation('tanh'))\n",
-        "    \n",
-        "    # Скрытые слои \n",
-        "    model.add(Dense(1))  # сжатие до 1 нейрона\n",
-        "    model.add(Activation('tanh'))\n",
-        "    \n",
-        "    # Выходной слой\n",
-        "    model.add(Dense(input_dim))\n",
-        "    model.add(Activation('linear'))\n",
-        "    \n",
-        "    return model\n",
-        "\n",
-        "# Создание AE1\n",
-        "ae1 = create_autoencoder_ae1(2)\n",
-        "ae1.compile(loss='mse', optimizer=Adam(learning_rate=0.001))\n",
-        "\n",
-        "print(\"Архитектура AE1:\")\n",
-        "ae1.summary()\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Обучение AE1\n",
-        "print(\"\\nОбучение AE1 (20 эпох)...\")\n",
-        "history_ae1 = ae1.fit(X_synthetic, X_synthetic, \n",
-        "                      epochs=20, \n",
-        "                      batch_size=32, \n",
-        "                      validation_split=0.2,\n",
-        "                      verbose=1)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Анализ результатов AE1\n",
-        "print(\"\\nАнализ результатов AE1\")\n",
-        "mse_ae1 = history_ae1.history['loss'][-1]\n",
-        "print(f\"Финальная ошибка MSE AE1: {mse_ae1:.6f}\")\n",
-        "\n",
-        "# Построение графика ошибки реконструкции\n",
-        "plt.figure(figsize=(12, 4))\n",
-        "plt.subplot(1, 2, 1)\n",
-        "plt.plot(history_ae1.history['loss'], label='Training Loss')\n",
-        "plt.plot(history_ae1.history['val_loss'], label='Validation Loss')\n",
-        "plt.title('AE1: Ошибка обучения')\n",
-        "plt.xlabel('Эпоха')\n",
-        "plt.ylabel('MSE')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "\n",
-        "# Вычисление ошибок реконструкции для обучающих данных\n",
-        "X_pred_ae1 = ae1.predict(X_synthetic)\n",
-        "reconstruction_errors_ae1 = np.mean(np.square(X_synthetic - X_pred_ae1), axis=1)\n",
-        "threshold_ae1 = np.percentile(reconstruction_errors_ae1, 95)\n",
-        "\n",
-        "plt.subplot(1, 2, 2)\n",
-        "plt.hist(reconstruction_errors_ae1, bins=20, alpha=0.7, color='blue', edgecolor='black')\n",
-        "plt.axvline(threshold_ae1, color='red', linestyle='--', linewidth=2, \n",
-        "           label=f'Порог: {threshold_ae1:.6f}')\n",
-        "plt.title('AE1: Распределение ошибок реконструкции')\n",
-        "plt.xlabel('Ошибка реконструкции')\n",
-        "plt.ylabel('Частота')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.savefig('out/ae1_results.png', dpi=300, bbox_inches='tight')\n",
-        "plt.show()\n",
-        "\n",
-        "print(f\"Порог ошибки реконструкции AE1: {threshold_ae1:.6f}\")\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 3. Создание и обучение автокодировщика AE2 (усложненная архитектура)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def create_autoencoder_ae2(input_dim):\n",
-        "    \"\"\"Создание автокодировщика AE2 с усложненной архитектурой\"\"\"\n",
-        "    model = Sequential()\n",
-        "    \n",
-        "    # Входной слой\n",
-        "    model.add(Dense(input_dim, input_shape=(input_dim,)))\n",
-        "    model.add(Activation('tanh'))\n",
-        "    \n",
-        "    # Скрытые слои (усложненная архитектура)\n",
-        "    model.add(Dense(4))\n",
-        "    model.add(Activation('tanh'))\n",
-        "    model.add(Dense(2))\n",
-        "    model.add(Activation('tanh'))\n",
-        "    model.add(Dense(1))  # сжатие до 1 нейрона\n",
-        "    model.add(Activation('tanh'))\n",
-        "    model.add(Dense(2))\n",
-        "    model.add(Activation('tanh'))\n",
-        "    model.add(Dense(4))\n",
-        "    model.add(Activation('tanh'))\n",
-        "    \n",
-        "    # Выходной слой\n",
-        "    model.add(Dense(input_dim))\n",
-        "    model.add(Activation('linear'))\n",
-        "    \n",
-        "    return model\n",
-        "\n",
-        "# Создание AE2\n",
-        "ae2 = create_autoencoder_ae2(2)\n",
-        "ae2.compile(loss='mse', optimizer=Adam(learning_rate=0.001))\n",
-        "\n",
-        "print(\"Архитектура AE2:\")\n",
-        "ae2.summary()\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Обучение AE2 \n",
-        "print(\"\\nОбучение AE2 (100 эпох)...\")\n",
-        "history_ae2 = ae2.fit(X_synthetic, X_synthetic, \n",
-        "                      epochs=100, \n",
-        "                      batch_size=32, \n",
-        "                      validation_split=0.2,\n",
-        "                      verbose=1)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Анализ результатов AE2\n",
-        "print(\"\\nАнализ результатов AE2\")\n",
-        "mse_ae2 = history_ae2.history['loss'][-1]\n",
-        "print(f\"Финальная ошибка MSE AE2: {mse_ae2:.6f}\")\n",
-        "\n",
-        "# Построение графика ошибки реконструкции\n",
-        "plt.figure(figsize=(12, 4))\n",
-        "plt.subplot(1, 2, 1)\n",
-        "plt.plot(history_ae2.history['loss'], label='Training Loss')\n",
-        "plt.plot(history_ae2.history['val_loss'], label='Validation Loss')\n",
-        "plt.title('AE2: Ошибка обучения')\n",
-        "plt.xlabel('Эпоха')\n",
-        "plt.ylabel('MSE')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "\n",
-        "# Вычисление ошибок реконструкции для обучающих данных\n",
-        "X_pred_ae2 = ae2.predict(X_synthetic)\n",
-        "reconstruction_errors_ae2 = np.mean(np.square(X_synthetic - X_pred_ae2), axis=1)\n",
-        "threshold_ae2 = np.percentile(reconstruction_errors_ae2, 95)\n",
-        "\n",
-        "plt.subplot(1, 2, 2)\n",
-        "plt.hist(reconstruction_errors_ae2, bins=20, alpha=0.7, color='green', edgecolor='black')\n",
-        "plt.axvline(threshold_ae2, color='red', linestyle='--', linewidth=2, \n",
-        "           label=f'Порог: {threshold_ae2:.6f}')\n",
-        "plt.title('AE2: Распределение ошибок реконструкции')\n",
-        "plt.xlabel('Ошибка реконструкции')\n",
-        "plt.ylabel('Частота')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.savefig('out/ae2_results.png', dpi=300, bbox_inches='tight')\n",
-        "plt.show()\n",
-        "\n",
-        "print(f\"Порог ошибки реконструкции AE2: {threshold_ae2:.6f}\")\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 4. Расчет характеристик качества обучения EDCA\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Используем функции из lab02_lib для расчета EDCA\n",
-        "try:\n",
-        "    # Сохраняем данные для использования с lab02_lib\n",
-        "    np.savetxt('data.txt', X_synthetic)\n",
-        "    \n",
-        "    # Создаем и обучаем AE1 через lab02_lib\n",
-        "    ae1_lib, ire_array_ae1, ire_th_ae1 = lib.create_fit_save_ae(\n",
-        "        X_synthetic, 'out/ae1_model.h5', 'out/ire_ae1.txt', \n",
-        "        epochs=20, verbose_show=False, patience=5\n",
-        "    )\n",
-        "    \n",
-        "    # Создаем и обучаем AE2 через lab02_lib\n",
-        "    ae2_lib, ire_array_ae2, ire_th_ae2 = lib.create_fit_save_ae(\n",
-        "        X_synthetic, 'out/ae2_model.h5', 'out/ire_ae2.txt', \n",
-        "        epochs=100, verbose_show=False, patience=10\n",
-        "    )\n",
-        "    \n",
-        "    # Расчет характеристик EDCA\n",
-        "    xx, yy, Z1 = lib.square_calc(20, X_synthetic, ae1_lib, ire_th_ae1, 1, visual=True)\n",
-        "    xx, yy, Z2 = lib.square_calc(20, X_synthetic, ae2_lib, ire_th_ae2, 2, visual=True)\n",
-        "    \n",
-        "    print(\"Характеристики EDCA рассчитаны и визуализированы\")\n",
-        "    \n",
-        "except Exception as e:\n",
-        "    print(f\"Ошибка при расчете EDCA: {e}\")\n",
-        "    print(\"Продолжаем без EDCA анализа\")\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 5. Создание тестовой выборки и применение автокодировщиков\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Создаем тестовые точки, которые AE1 распознает как норму, а AE2 как аномалии\n",
-        "test_points = np.array([\n",
-        "    [1.2, 1.2],  # близко к центру\n",
-        "    [1.5, 1.5],  # немного дальше\n",
-        "    [0.8, 0.8],  # с другой стороны\n",
-        "    [1.1, 0.9]   # асимметрично\n",
-        "])\n",
-        "\n",
-        "print(\"Тестовые точки:\")\n",
-        "for i, point in enumerate(test_points):\n",
-        "    print(f\"  Точка {i+1}: {point}\")\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Применение автокодировщиков к тестовым данным\n",
-        "print(\"\\nПрименение автокодировщиков к тестовым данным\")\n",
-        "\n",
-        "# Предсказания AE1\n",
-        "test_pred_ae1 = ae1.predict(test_points)\n",
-        "test_errors_ae1 = np.mean(np.square(test_points - test_pred_ae1), axis=1)\n",
-        "\n",
-        "# Предсказания AE2\n",
-        "test_pred_ae2 = ae2.predict(test_points)\n",
-        "test_errors_ae2 = np.mean(np.square(test_points - test_pred_ae2), axis=1)\n",
-        "\n",
-        "print(\"\\nРезультаты для тестовых точек:\")\n",
-        "print(\"Точка | AE1 ошибка | AE1 статус | AE2 ошибка | AE2 статус\")\n",
-        "print(\"-\" * 55)\n",
-        "for i in range(len(test_points)):\n",
-        "    ae1_status = \"Норма\" if test_errors_ae1[i] <= threshold_ae1 else \"Аномалия\"\n",
-        "    ae2_status = \"Норма\" if test_errors_ae2[i] <= threshold_ae2 else \"Аномалия\"\n",
-        "    print(f\"{i+1:5d} | {test_errors_ae1[i]:10.6f} | {ae1_status:10s} | {test_errors_ae2[i]:10.6f} | {ae2_status:10s}\")\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Визуализация результатов\n",
-        "plt.figure(figsize=(15, 5))\n",
-        "\n",
-        "# График ошибок AE1\n",
-        "plt.subplot(1, 3, 1)\n",
-        "plt.scatter(range(len(test_errors_ae1)), test_errors_ae1, c='blue', s=100)\n",
-        "plt.axhline(threshold_ae1, color='red', linestyle='--', linewidth=2, \n",
-        "           label=f'Порог AE1: {threshold_ae1:.6f}')\n",
-        "plt.title('AE1: Ошибки тестовых точек')\n",
-        "plt.xlabel('Номер точки')\n",
-        "plt.ylabel('Ошибка реконструкции')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "\n",
-        "# График ошибок AE2\n",
-        "plt.subplot(1, 3, 2)\n",
-        "plt.scatter(range(len(test_errors_ae2)), test_errors_ae2, c='green', s=100)\n",
-        "plt.axhline(threshold_ae2, color='red', linestyle='--', linewidth=2, \n",
-        "           label=f'Порог AE2: {threshold_ae2:.6f}')\n",
-        "plt.title('AE2: Ошибки тестовых точек')\n",
-        "plt.xlabel('Номер точки')\n",
-        "plt.ylabel('Ошибка реконструкции')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "\n",
-        "# Визуализация в пространстве признаков\n",
-        "plt.subplot(1, 3, 3)\n",
-        "plt.scatter(X_synthetic[:, 0], X_synthetic[:, 1], c='lightblue', alpha=0.5, s=30, label='Обучающие данные')\n",
-        "plt.scatter(test_points[:, 0], test_points[:, 1], c='red', s=100, marker='s', label='Тестовые точки')\n",
-        "plt.scatter(center_coords[0], center_coords[1], c='black', s=200, marker='x', linewidth=3, label='Центр')\n",
-        "plt.title('Пространство признаков')\n",
-        "plt.xlabel('X1')\n",
-        "plt.ylabel('X2')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.savefig('out/test_results.png', dpi=300, bbox_inches='tight')\n",
-        "plt.show()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# ЗАДАНИЕ 2: Работа с реальными данными Letter\n",
-        "\n",
-        "## 1. Изучение и загрузка набора данных Letter\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Изучение набора данных Letter\n",
-        "print(\"Изучение набора данных Letter\")\n",
-        "print(\"Набор данных Letter содержит характеристики букв алфавита\")\n",
-        "\n",
-        "# Загрузка обучающей выборки\n",
-        "print(\"\\nЗагрузка обучающей выборки\")\n",
-        "X_letter_train = np.loadtxt('data/letter_train.txt')\n",
-        "print(f\"Размерность обучающей выборки: {X_letter_train.shape}\")\n",
-        "print(f\"Количество признаков: {X_letter_train.shape[1]}\")\n",
-        "print(f\"Количество образцов: {X_letter_train.shape[0]}\")\n",
-        "\n",
-        "# Вывод данных в консоль\n",
-        "print(\"\\nПервые 5 строк данных:\")\n",
-        "print(X_letter_train[:5])\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 2. Создание и обучение автокодировщика для Letter\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def create_letter_autoencoder(input_dim):\n",
-        "    \"\"\"Создание автокодировщика для данных Letter\"\"\"\n",
-        "    model = Sequential()\n",
-        "    \n",
-        "    # Входной слой\n",
-        "    model.add(Dense(input_dim, input_shape=(input_dim,)))\n",
-        "    model.add(Activation('tanh'))\n",
-        "    \n",
-        "    # Скрытые слои\n",
-        "    model.add(Dense(16))\n",
-        "    model.add(Activation('tanh'))\n",
-        "    model.add(Dense(8))\n",
-        "    model.add(Activation('tanh'))\n",
-        "    model.add(Dense(4))  # сжатие до 4 нейронов\n",
-        "    model.add(Activation('tanh'))\n",
-        "    model.add(Dense(8))\n",
-        "    model.add(Activation('tanh'))\n",
-        "    model.add(Dense(16))\n",
-        "    model.add(Activation('tanh'))\n",
-        "    \n",
-        "    # Выходной слой\n",
-        "    model.add(Dense(input_dim))\n",
-        "    model.add(Activation('linear'))\n",
-        "    \n",
-        "    return model\n",
-        "\n",
-        "# Нормализация данных\n",
-        "scaler_letter = StandardScaler()\n",
-        "X_letter_train_scaled = scaler_letter.fit_transform(X_letter_train)\n",
-        "\n",
-        "# Создание модели\n",
-        "ae_letter = create_letter_autoencoder(X_letter_train.shape[1])\n",
-        "ae_letter.compile(loss='mse', optimizer=Adam(learning_rate=0.001))\n",
-        "\n",
-        "print(\"Архитектура автокодировщика для Letter:\")\n",
-        "ae_letter.summary()\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Обучение автокодировщика для Letter\n",
-        "print(\"\\nОбучение автокодировщика для Letter (50 эпох)...\")\n",
-        "history_letter = ae_letter.fit(X_letter_train_scaled, X_letter_train_scaled,\n",
-        "                              epochs=50,\n",
-        "                              batch_size=32,\n",
-        "                              validation_split=0.2,\n",
-        "                              verbose=1)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Анализ результатов обучения\n",
-        "print(\"\\nАнализ результатов обучения\")\n",
-        "mse_letter = history_letter.history['loss'][-1]\n",
-        "print(f\"Финальная ошибка MSE: {mse_letter:.6f}\")\n",
-        "\n",
-        "# Построение графика ошибки обучения\n",
-        "plt.figure(figsize=(12, 4))\n",
-        "plt.subplot(1, 2, 1)\n",
-        "plt.plot(history_letter.history['loss'], label='Training Loss')\n",
-        "plt.plot(history_letter.history['val_loss'], label='Validation Loss')\n",
-        "plt.title('Letter: Ошибка обучения')\n",
-        "plt.xlabel('Эпоха')\n",
-        "plt.ylabel('MSE')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "\n",
-        "# Вычисление ошибок реконструкции\n",
-        "X_letter_pred = ae_letter.predict(X_letter_train_scaled)\n",
-        "reconstruction_errors_letter = np.mean(np.square(X_letter_train_scaled - X_letter_pred), axis=1)\n",
-        "threshold_letter = np.percentile(reconstruction_errors_letter, 95)\n",
-        "\n",
-        "plt.subplot(1, 2, 2)\n",
-        "plt.hist(reconstruction_errors_letter, bins=50, alpha=0.7, color='purple', edgecolor='black')\n",
-        "plt.axvline(threshold_letter, color='red', linestyle='--', linewidth=2, \n",
-        "           label=f'Порог: {threshold_letter:.6f}')\n",
-        "plt.title('Letter: Распределение ошибок реконструкции')\n",
-        "plt.xlabel('Ошибка реконструкции')\n",
-        "plt.ylabel('Частота')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.savefig('out/letter_training_results.png', dpi=300, bbox_inches='tight')\n",
-        "plt.show()\n",
-        "\n",
-        "print(f\"Порог ошибки реконструкции: {threshold_letter:.6f}\")\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 3. Оценка пригодности автокодировщика\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Оценка пригодности автокодировщика\n",
-        "print(\"\\nОценка пригодности автокодировщика\")\n",
-        "anomalies_train = np.sum(reconstruction_errors_letter > threshold_letter)\n",
-        "anomaly_rate_train = anomalies_train / len(reconstruction_errors_letter) * 100\n",
-        "print(f\"Обнаружено аномалий в обучающей выборке: {anomalies_train} ({anomaly_rate_train:.1f}%)\")\n",
-        "\n",
-        "if anomaly_rate_train > 10:\n",
-        "    print(\"Порог слишком высокий, требуется корректировка параметров\")\n",
-        "else:\n",
-        "    print(\"Автокодировщик подходит для обнаружения аномалий\")\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## 4. Загрузка тестовой выборки и применение к ней\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Загрузка тестовой выборки\n",
-        "print(\"\\nЗагрузка тестовой выборки\")\n",
-        "X_letter_test = np.loadtxt('data/letter_test.txt')\n",
-        "print(f\"Размерность тестовой выборки: {X_letter_test.shape}\")\n",
-        "\n",
-        "# Применение к тестовой выборке\n",
-        "print(\"\\nПрименение к тестовой выборке\")\n",
-        "X_letter_test_scaled = scaler_letter.transform(X_letter_test)\n",
-        "X_letter_test_pred = ae_letter.predict(X_letter_test_scaled)\n",
-        "test_errors_letter = np.mean(np.square(X_letter_test_scaled - X_letter_test_pred), axis=1)\n",
-        "\n",
-        "# Определение аномалий\n",
-        "test_anomalies = test_errors_letter > threshold_letter\n",
-        "n_anomalies = np.sum(test_anomalies)\n",
-        "anomaly_rate = n_anomalies / len(test_errors_letter) * 100\n",
-        "\n",
-        "print(f\"Обнаружено аномалий в тестовой выборке: {n_anomalies} из {len(test_errors_letter)} ({anomaly_rate:.1f}%)\")\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Визуализация результатов\n",
-        "plt.figure(figsize=(12, 4))\n",
-        "plt.subplot(1, 2, 1)\n",
-        "plt.hist(test_errors_letter, bins=30, alpha=0.7, color='orange', edgecolor='black')\n",
-        "plt.axvline(threshold_letter, color='red', linestyle='--', linewidth=2, \n",
-        "           label=f'Порог: {threshold_letter:.6f}')\n",
-        "plt.title('Letter: Ошибки тестовой выборки')\n",
-        "plt.xlabel('Ошибка реконструкции')\n",
-        "plt.ylabel('Частота')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "\n",
-        "plt.subplot(1, 2, 2)\n",
-        "plt.scatter(range(len(test_errors_letter)), test_errors_letter, \n",
-        "           c=test_anomalies, cmap='RdYlBu_r', alpha=0.7)\n",
-        "plt.axhline(threshold_letter, color='red', linestyle='--', linewidth=2, \n",
-        "           label=f'Порог: {threshold_letter:.6f}')\n",
-        "plt.title('Letter: Ошибки по образцам')\n",
-        "plt.xlabel('Номер образца')\n",
-        "plt.ylabel('Ошибка реконструкции')\n",
-        "plt.legend()\n",
-        "plt.grid(True, alpha=0.3)\n",
-        "\n",
-        "plt.tight_layout()\n",
-        "plt.savefig('out/letter_test_results.png', dpi=300, bbox_inches='tight')\n",
-        "plt.show()\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# ИТОГОВЫЕ РЕЗУЛЬТАТЫ\n",
-        "\n",
-        "## Таблица результатов\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "s# Итоговые результаты\n",
-        "print(\"\\n\" + \"=\"*70)\n",
-        "print(\"ИТОГОВЫЕ РЕЗУЛЬТАТЫ\")\n",
-        "print(\"=\"*70)\n",
-        "\n",
-        "print(\"\\nТаблица 1 - Результаты задания №1:\")\n",
-        "print(\"Модель | Скрытые слои | Нейроны | Эпохи | MSE_stop | Порог | Аномалии\")\n",
-        "print(\"-\" * 70)\n",
-        "print(f\"AE1    | 1            | 1       | 20    | {mse_ae1:.6f} | {threshold_ae1:.6f} | -\")\n",
-        "print(f\"AE2    | 6            | 4-2-1-2-4 | 100  | {mse_ae2:.6f} | {threshold_ae2:.6f} | -\")\n",
-        "\n",
-        "print(\"\\nТаблица 2 - Результаты задания №2:\")\n",
-        "print(\"Dataset | Скрытые слои | Нейроны | Эпохи | MSE_stop | Порог | % аномалий\")\n",
-        "print(\"-\" * 70)\n",
-        "print(f\"Letter  | 6            | 16-8-4-8-16 | 50  | {mse_letter:.6f} | {threshold_letter:.6f} | {anomaly_rate:.1f}%\")\n",
-        "\n",
-        "print(\"\\nВыводы:\")\n",
-        "print(\"1. AE2 показал лучшие результаты благодаря более сложной архитектуре\")\n",
-        "print(\"2. Для данных Letter автокодировщик успешно обнаруживает аномалии\")\n",
-        "print(\"3. Порог 95-го перцентиля обеспечивает разумный баланс между точностью и полнотой\")\n",
-        "\n",
-        "print(\"\\nЛабораторная работа завершена!\")\n"
-      ]
-    }
-  ],
-  "metadata": {
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
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diff --git a/labworks/LW2/out/AE1_ire_th.txt b/labworks/LW2/out/AE1_ire_th.txt
new file mode 100644
index 0000000..3fb437d
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+++ b/labworks/LW2/out/AE1_ire_th.txt
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+0.56
\ No newline at end of file
diff --git a/labworks/LW2/out/AE1_train_def.png b/labworks/LW2/out/AE1_train_def.png
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diff --git a/labworks/LW2/out/AE2_ire_th.txt b/labworks/LW2/out/AE2_ire_th.txt
new file mode 100644
index 0000000..df52bdc
--- /dev/null
+++ b/labworks/LW2/out/AE2_ire_th.txt
@@ -0,0 +1 @@
+0.38
\ No newline at end of file
diff --git a/labworks/LW2/out/AE2_train_def.png b/labworks/LW2/out/AE2_train_def.png
new file mode 100644
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diff --git a/labworks/LW2/out/Letter_AE_ire_th.txt b/labworks/LW2/out/Letter_AE_ire_th.txt
new file mode 100644
index 0000000..99433b0
--- /dev/null
+++ b/labworks/LW2/out/Letter_AE_ire_th.txt
@@ -0,0 +1 @@
+29.84
\ No newline at end of file
diff --git a/labworks/LW2/out/Letter_AE_norm.h5 b/labworks/LW2/out/Letter_AE_norm.h5
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diff --git a/labworks/LW2/out/Letter_AE_norm_ire_th.txt b/labworks/LW2/out/Letter_AE_norm_ire_th.txt
new file mode 100644
index 0000000..fc696a2
--- /dev/null
+++ b/labworks/LW2/out/Letter_AE_norm_ire_th.txt
@@ -0,0 +1 @@
+1.62
\ No newline at end of file
diff --git a/labworks/LW2/out/Letter_AE_raw.h5 b/labworks/LW2/out/Letter_AE_raw.h5
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diff --git a/labworks/LW2/out/Letter_AE_raw_ire_th.txt b/labworks/LW2/out/Letter_AE_raw_ire_th.txt
new file mode 100644
index 0000000..7592d5e
--- /dev/null
+++ b/labworks/LW2/out/Letter_AE_raw_ire_th.txt
@@ -0,0 +1 @@
+11.1
\ No newline at end of file
diff --git a/labworks/LW2/out/XtXd_1.png b/labworks/LW2/out/XtXd_1.png
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diff --git a/labworks/LW2/out/result.txt b/labworks/LW2/out/result.txt
new file mode 100644
index 0000000..d8c88c3
--- /dev/null
+++ b/labworks/LW2/out/result.txt
@@ -0,0 +1,5 @@
+------------Оценка качества AE2 С ПОМОЩЬЮ НОВЫХ МЕТРИК------------
+Approx = 0.7142857142857142
+Excess = 0.4
+Deficit = 0.0
+Coating = 1.0
diff --git a/labworks/LW2/out/synthetic_data.png b/labworks/LW2/out/synthetic_data.png
deleted file mode 100644
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diff --git a/labworks/LW2/report_lr2_Troyanov_Chernov.md b/labworks/LW2/report_lr2_Troyanov_Chernov.md
new file mode 100644
index 0000000..b17d460
--- /dev/null
+++ b/labworks/LW2/report_lr2_Troyanov_Chernov.md
@@ -0,0 +1,435 @@
+# Отчёт по лабораторной работе №2
+## Обнаружение аномалий с помощью автокодировщиков
+
+**Выполнили:** Троянов Даниил Сергеевич, Чернов Данила Евгеньевич  
+**Группа:** А-01-22  
+**Бригада:** 1
+
+---
+
+## Задание 1
+
+### 1) Подготовка рабочей среды и импорт библиотек
+
+В среде Jupyter Notebook был создан новый блокнот для выполнения лабораторной работы. Импортированы необходимые библиотеки и модули для работы с данными и нейронными сетями.
+
+```python
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import lab02_lib as lib
+from unittest.mock import patch
+import builtins
+
+# Создаем директорию для выходных файлов
+os.makedirs('out', exist_ok=True)
+```
+
+### 2) Генерация синтетического набора двумерных данных
+
+Сгенерирован индивидуальный набор двумерных данных в пространстве признаков с координатами центра (1, 1), где 1 – номер бригады. Данные представлены в виде кластера из 1000 точек.
+
+```python
+# Генерация датасета для бригады 1
+brigade_num = 1
+data = lib.datagen(brigade_num, brigade_num, 1000, 2)
+```
+
+Размерность данных: (1000, 2)
+
+![Обучающая выборка](out/train_set.png)
+
+### 3) Создание и обучение автокодировщика AE1 с простой архитектурой
+
+Создан первый автокодировщик AE1 с минимальной архитектурой, состоящей из одного скрытого слоя с одним нейроном. Обучение проводилось в течение 1000 эпох.
+
+```python
+# Обучение AE1
+with patch('builtins.input', side_effect=['1', '1', '1']):
+    ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(
+        data, 
+        'out/AE1.h5', 
+        'out/AE1_ire_th.txt',
+        1000,  # epochs
+        True,  # verbose_show
+        200,   # patience
+    )
+```
+
+Архитектура: 2 → 1 → 2 (1 скрытый слой с 1 нейроном)
+
+### 4) Анализ результатов обучения AE1
+
+Зафиксирована ошибка MSE, на которой завершилось обучение. Построен график ошибки реконструкции обучающей выборки. Определён порог ошибки реконструкции для обнаружения аномалий.
+
+Ошибка MSE_AE1 ≈ 0.025
+
+```python
+# Построение графика ошибки реконструкции
+lib.ire_plot('training', IRE1, IREth1, 'AE1')
+```
+
+![График ошибки реконструкции для обучающей выборки, AE1](out/IRE_trainingAE1.png)
+
+Порог ошибки реконструкции (IREth1) ≈ 0.56
+
+### 5) Создание и обучение автокодировщика AE2 с усложнённой архитектурой
+
+Создан второй автокодировщик AE2 с более сложной архитектурой, включающей 7 скрытых слоёв. Обучение проводилось в течение 3000 эпох для достижения лучшего качества аппроксимации области нормальных данных.
+
+```python
+# Обучение AE2
+with patch('builtins.input', side_effect=['1', '7', '8 4 2 1 2 4 8']):
+    ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(
+        data, 
+        'out/AE2.h5', 
+        'out/AE2_ire_th.txt',
+        3000,  # epochs
+        True,  # verbose_show
+        200,   # patience
+    )
+
+# Вычисление MSE из предсказания
+X_pred_ae2 = ae2_trained.predict(data, verbose=0)
+mse_ae2 = np.mean((data - X_pred_ae2) ** 2)
+
+print(f'\nОбучение завершено!')
+print(f'MSE_stop (приблизительно): {mse_ae2:.6f}')
+print(f'Порог IRE: {IREth2:.6f}')
+print(f'Количество скрытых слоев: 7')
+print(f'Количество нейронов в скрытых слоях: 8-4-2-1-2-4-8')
+```
+
+Архитектура: 2 → 8 → 4 → 2 → 1 → 2 → 4 → 8 → 2 (7 скрытых слоёв: 8-4-2-1-2-4-8)
+
+### 6) Анализ результатов обучения AE2
+
+Зафиксирована ошибка MSE, на которой завершилось обучение. Построен график ошибки реконструкции обучающей выборки. Определён порог ошибки реконструкции для обнаружения аномалий.
+
+Ошибка MSE_AE2 ≈ 0.0098
+
+```python
+# Построение графика ошибки реконструкции
+lib.ire_plot('training', IRE2, IREth2, 'AE2')
+```
+
+![График ошибки реконструкции для обучающей выборки, AE2](out/IRE_trainingAE2.png)
+
+Порог ошибки реконструкции (IREth2) ≈ 0.38
+
+### 7) Оценка качества обучения с помощью метрик EDCA
+
+Рассчитаны характеристики качества обучения EDCA (Excess, Deficit, Coating, Approx) для обоих автокодировщиков. Визуализированы и сравнены области пространства признаков, распознаваемые автокодировщиками AE1 и AE2.
+
+```python
+# Расчет характеристик качества обучения для AE1
+numb_square = 20
+xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)
+```
+
+![Области покрытия и границы классов для AE1](out/AE1_train_def.png)
+
+![Площади множеств Xt и Xd для AE1](out/XtXd_1.png)
+
+![Метрики EDCA для AE1](out/XtXd_1_metrics.png)
+
+Результаты для AE1:
+- Excess ≈ 5.75
+- Approx ≈ 0.148
+
+```python
+# Расчет характеристик качества обучения для AE2
+xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)
+```
+
+![Области покрытия и границы классов для AE2](out/AE2_train_def.png)
+
+![Площади множеств Xt и Xd для AE2](out/XtXd_2.png)
+
+![Метрики EDCA для AE2](out/XtXd_2_metrics.png)
+
+Результаты для AE2:
+- Excess ≈ 0.4
+- Approx ≈ 0.714
+
+```python
+# Сравнение областей аппроксимации
+lib.plot2in1(data, xx, yy, Z1, Z2)
+```
+
+![Сравнение областей аппроксимации AE1 и AE2](out/AE1_AE2_train_def.png)
+
+### 8) Оценка пригодности автокодировщиков
+
+Полученные показатели EDCA для автокодировщика AE2 демонстрируют значительно лучшее качество аппроксимации по сравнению с AE1. Значение Approx для AE2 (0.714) существенно выше, чем для AE1 (0.148), что указывает на более точную аппроксимацию области обучающих данных. Значение Excess для AE2 (0.4) значительно ниже, чем для AE1 (5.75), что подтверждает более качественное обучение модели AE2.
+
+### 9) Подготовка тестовой выборки
+
+Создана тестовая выборка, состоящая из 4 элементов, не входящих в обучающую выборку. Элементы подобраны таким образом, чтобы AE1 распознавал их как норму, а AE2 обнаруживал как аномалии.
+
+```python
+# Тестовая выборка
+data_test = np.array([[2.2, 1.2], [1.2, 2.2], [0.2, 1.2], [1.2, 0.2]])
+```
+
+### 10) Применение автокодировщиков к тестовым данным
+
+Применены обученные автокодировщики AE1 и AE2 к тестовым данным. Выведены значения ошибки реконструкции для каждого элемента тестовой выборки относительно порога на график и в консоль.
+
+```python
+# Тестирование AE1
+predicted_labels1, ire1 = lib.predict_ae(ae1_trained, data_test, IREth1)
+lib.anomaly_detection_ae(predicted_labels1, ire1, IREth1)
+lib.ire_plot('test', ire1, IREth1, 'AE1')
+```
+
+![График ошибки реконструкции для тестовой выборки, AE1](out/IRE_testAE1.png)
+
+Результат для AE1: аномалий не обнаружено (0 из 4)
+
+```python
+# Тестирование AE2
+predicted_labels2, ire2 = lib.predict_ae(ae2_trained, data_test, IREth2)
+lib.anomaly_detection_ae(predicted_labels2, ire2, IREth2)
+lib.ire_plot('test', ire2, IREth2, 'AE2')
+```
+
+![График ошибки реконструкции для тестовой выборки, AE2](out/IRE_testAE2.png)
+
+Результат для AE2: обнаружено 4 аномалии из 4
+
+### 11) Визуализация результатов тестирования
+
+Визуализированы элементы обучающей и тестовой выборки в областях пространства признаков, распознаваемых автокодировщиками AE1 и AE2.
+
+```python
+# Построение областей аппроксимации и точек тестового набора
+lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)
+```
+
+![Визуализация тестовых точек в областях аппроксимации AE1 и AE2](out/AE1_AE2_train_def_anomalies.png)
+
+### 12) Сводная таблица результатов задания №1
+
+**Табл. 1 Результаты задания №1**
+
+|      | Количество<br>скрытых слоев | Количество<br>нейронов в скрытых слоях | Количество<br>эпох обучения | Ошибка<br>MSE_stop | Порог ошибки<br>реконструкции | Значение показателя<br>Excess | Значение показателя<br>Approx | Количество обнаруженных<br>аномалий |
+|-----:|------------------------------|----------------------------------------|-----------------------------|--------------------|-------------------------------|-------------------------------|--------------------------------|-------------------------------------|
+| AE1  | 1                            | 1                                      | 1000                        | 0.025              | 0.56                         | 5.75                          | 0.148                          | 0                                   |
+| AE2  | 7                            | 8-4-2-1-2-4-8                         | 3000                        | 0.0098             | 0.38                         | 0.4                           | 0.714                          | 4                                   |
+
+### 13) Выводы по заданию 1
+
+На основе проведённых экспериментов сформированы следующие выводы о требованиях к параметрам автокодировщиков для качественного обнаружения аномалий:
+
+**Требования к данным для обучения:**
+- Данные для обучения должны быть без аномалий, чтобы автокодировщик смог рассчитать верное пороговое значение ошибки реконструкции.
+
+**Требования к архитектуре автокодировщика:**
+- Архитектура должна постепенно сужаться к бутылочному горлышку (bottleneck), а затем постепенно возвращаться к исходным выходным размерам.
+- Для двумерных данных оптимальное количество скрытых слоёв: 3-6.
+- Простая архитектура с одним скрытым слоем показывает низкое качество аппроксимации (Excess = 5.75, Approx = 0.148), что подтверждается результатами AE1.
+
+**Требования к количеству эпох обучения:**
+- Для простых данных (2D): 1000-3000 эпох.
+- При использовании ранней остановки (early stopping) обучение может завершиться раньше при достижении оптимальных значений.
+
+**Требования к ошибке MSE_stop:**
+- Для двумерных данных: оптимальная ошибка MSE-stop в районе 0.01.
+- Более высокие значения (например, 0.025) указывают на недостаточное качество обучения и требуют улучшения архитектуры или увеличения количества эпох.
+
+**Требования к порогу обнаружения аномалий:**
+- Для двумерных данных: значение порога в районе 0.4-2.5.
+- Более высокие пороги (например, 0.56) могут пропускать аномалии.
+
+**Требования к характеристикам качества обучения EDCA:**
+
+- Значение Excess должно быть как можно ближе к 0. Для AE2 получено 0.4, что является приемлемым.
+- Значение Deficit должно быть как можно ближе к 0 
+- Значение Coating должно быть как можно ближе к 1 
+- Значение Approx должно быть как можно ближе к 1. Для AE2 получено 0.714, что соответствует требованиям.
+---
+
+## Задание 2
+
+### 1) Изучение набора реальных данных Letter
+
+Бригада 1 работает с набором данных **Letter**. Это реальный набор данных, представляющий собой характеристики букв латинского алфавита, извлечённые из изображений. Каждый пример содержит 32 признака, описывающих различные геометрические и статистические характеристики буквы.
+
+| Параметр | Значение |
+|----------|----------|
+| Количество признаков | 32 |
+| Количество примеров в обучающей выборке | 1500 |
+| Количество примеров в тестовой выборке | 500 |
+
+### 2) Загрузка многомерной обучающей выборки
+
+Загружена многомерная обучающая выборка реальных данных Letter.
+
+```python
+# Загрузка обучающей и тестовой выборки Letter
+train_letter = np.loadtxt('data/letter_train.txt', dtype=float)
+test_letter = np.loadtxt('data/letter_test.txt', dtype=float)
+```
+
+### 3) Вывод данных и их размерности
+
+Выведены полученные данные и их размерность в консоль.
+
+```python
+print('Обучающая выборка Letter:')
+print(f'Размерность: {train_letter.shape}')
+print(f'Количество признаков: {train_letter.shape[1]}')
+print(f'Количество примеров: {train_letter.shape[0]}')
+```
+
+Размерность обучающей выборки: (1500, 32)  
+Размерность тестовой выборки: (500, 32)
+
+### 4) Нормализация данных (для сравнения результатов)
+
+Для сравнения результатов обучения создана нормализованная версия данных с использованием StandardScaler.
+
+```python
+from sklearn.preprocessing import StandardScaler
+
+scaler = StandardScaler()
+train_letter_normalized = scaler.fit_transform(train_letter)
+test_letter_normalized = scaler.transform(test_letter)
+```
+
+### 5) Обучение автокодировщика на ненормализованных данных
+
+Создан и обучен автокодировщик с подходящей для данных архитектурой на ненормализованных данных. Выбрано необходимое количество эпох обучения.
+
+```python
+# Обучение на ненормализованных данных
+with patch('builtins.input', side_effect=['1', '11', '48 36 24 16 8 4 8 16 24 36 48']):
+    ae_letter_raw, IRE_letter_raw, IREth_letter_raw = lib.create_fit_save_ae(
+        train_letter, 
+        'out/Letter_AE_raw.h5', 
+        'out/Letter_AE_raw_ire_th.txt',
+        20000,  # epochs
+        True,   # verbose_show
+        200,    # patience
+    )
+```
+
+Архитектура: 32 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100 → 32  
+Количество скрытых слоёв: 17  
+Нейроны в скрытых слоях: 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100
+
+### 6) Обучение автокодировщика на нормализованных данных
+
+Создан и обучен автокодировщик с той же архитектурой на нормализованных данных для сравнения результатов.
+
+```python
+# Обучение на нормализованных данных
+with patch('builtins.input', side_effect=['1', '11', '48 36 24 16 8 4 8 16 24 36 48']):
+    ae_letter_norm, IRE_letter_norm, IREth_letter_norm = lib.create_fit_save_ae(
+        train_letter_normalized, 
+        'out/Letter_AE_norm.h5', 
+        'out/Letter_AE_norm_ire_th.txt',
+        20000,  # epochs
+        True,   # verbose_show
+        200,    # patience
+    )
+```
+
+### 7) Сравнение результатов обучения
+
+Проведено сравнение результатов обучения на нормализованных и ненормализованных данных. Выбрана лучшая модель для дальнейшей работы.
+
+**Сравнение результатов:**
+
+| Параметр | Ненормализованные | Нормализованные |
+|----------|-------------------|-----------------|
+| MSE_stop | 0.340 | 0.026 |
+| Порог IRE | 11.1 | 1.62 |
+
+На основе сравнения результатов выбрана модель, обученная на нормализованных данных, так как она показала значительно лучшее качество (MSE = 0.026 против 0.340) и более низкий порог обнаружения аномалий (1.62 против 11.1).
+
+### 8) Анализ результатов обучения
+
+Зафиксирована ошибка MSE, на которой завершилось обучение. Построен график ошибки реконструкции обучающей выборки. Определён порог ошибки реконструкции для обнаружения аномалий.
+
+```python
+# Построение графика ошибки реконструкции
+lib.ire_plot('training', IRE_letter, IREth_letter, 'Letter_AE')
+```
+
+![График ошибки реконструкции для обучающей выборки Letter](out/IRE_trainingLetter_AE.png)
+
+### 9) Применение автокодировщика к тестовой выборке
+
+Применён обученный автокодировщик к тестовой выборке для обнаружения аномалий. Выведен график ошибки реконструкции элементов тестовой выборки относительно порога.
+
+```python
+# Тестирование автокодировщика
+predicted_labels_letter, ire_letter_test = lib.predict_ae(ae_letter, test_letter_used, IREth_letter)
+lib.anomaly_detection_ae(predicted_labels_letter, ire_letter_test, IREth_letter)
+lib.ire_plot('test', ire_letter_test, IREth_letter, 'Letter_AE')
+```
+
+![График ошибки реконструкции для тестовой выборки Letter](out/IRE_testLetter_AE.png)
+
+### 10) Сводная таблица результатов задания №2
+
+**Табл. 2 Результаты задания №2**
+
+| Dataset | Количество<br>скрытых слоев | Количество<br>нейронов в скрытых слоях | Количество<br>эпох обучения | Ошибка<br>MSE_stop | Порог ошибки<br>реконструкции | % обнаруженных<br>аномалий |
+|:--------|:----------------------------|:---------------------------------------|:----------------------------|:-------------------|:------------------------------|:---------------------------|
+| Letter  | 11                          | 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100          | 20000                       | 0.026              | 1.62                         | 100.0%                     |
+
+*Примечание: Результаты получены для модели, обученной на нормализованных данных.*
+
+### 11) Выводы по заданию 2
+
+На основе проведённых экспериментов с многомерными данными (32 признака) сформированы следующие выводы:
+
+**Требования к данным для обучения:**
+- Данные для обучения должны быть без аномалий, чтобы автокодировщик смог рассчитать верное пороговое значение ошибки реконструкции.
+- Для многомерных данных нормализация признаков значительно улучшает сходимость обучения и качество обнаружения аномалий. Как показали эксперименты, нормализация снизила MSE с 0.340 до 0.026 и порог IRE с 11.1 до 1.62.
+
+**Требования к архитектуре автокодировщика:**
+- Для многомерных данных требуется более сложная архитектура с большим количеством скрытых слоёв.
+- Архитектура должна постепенно сужаться к бутылочному горлышку (bottleneck), а затем постепенно возвращаться к исходным выходным размерам.
+- Широкая архитектура с большим количеством нейронов в начальных слоях (например, 100 -> 68 -> 48 -> 32 -> 24 -> 16 -> 8 -> 16 -> 24 -> 32 -> 48 -> 64 -> 100) позволяет лучше аппроксимировать сложные зависимости в многомерных данных.
+
+**Требования к количеству эпох обучения:**
+- Для многомерных данных требуется значительно большее количество эпох обучения (до 10000 эпох) по сравнению с двумерными данными (1000-3000 эпох).
+- Использование ранней остановки (early stopping) позволяет автоматически завершить обучение при достижении оптимальных значений.
+
+**Требования к ошибке MSE_stop:**
+- Для многомерных данных оптимальная ошибка MSE-stop в районе 0.1-0.01.
+- Нормализация данных может значительно улучшить качество обучения (пример: MSE снизилась с 0.340 до 0.026 при нормализации).
+
+**Требования к порогу обнаружения аномалий:**
+- Для многомерных данных значение порога не больше 1.6.
+- Нормализация данных позволяет получить более низкий и стабильный порог (пример: порог снизился с 11.1 до 1.62 при нормализации).
+
+**Требования к характеристикам качества обучения EDCA:**
+- Значение Excess должно быть как можно ближе к 0
+- Значение Deficit должно быть как можно ближе к 0 
+- Значение Coating должно быть как можно ближе к 1 
+- Значение Approx должно быть как можно ближе к 1
+
+
+---
+
+## Общие выводы
+
+В ходе выполнения лабораторной работы были исследованы возможности использования автокодировщиков для обнаружения аномалий в данных различной размерности. Показано, что качество обнаружения аномалий существенно зависит от архитектуры автокодировщика, количества эпох обучения, правильного выбора порога ошибки реконструкции и предобработки данных (нормализации).
+
+**Основные выводы:**
+
+1. **Влияние размерности данных**: Для многомерных данных требуется более сложная архитектура и большее количество эпох обучения  по сравнению с двумерными данными.
+
+2. **Архитектура**: Архитектура должна постепенно сужаться к бутылочному горлышку и постепенно возвращаться к исходным размерам. Симметричная структура обеспечивает лучшее качество аппроксимации.
+
+3. **Параметры обучения**: Оптимальные значения MSE-stop и порога ошибки реконструкции различаются для данных разной размерности. Для двумерных данных MSE-stop ≈ 0.01, порог 0.4-2.5. Для многомерных данных MSE-stop ≈ 0.1-0.01, порог ≤ 1.6.
+
+4. **Метрики качества**: Характеристики EDCA (Excess, Deficit, Coating, Approx) позволяют оценить качество аппроксимации области нормальных данных и пригодность автокодировщика для обнаружения аномалий.
+
+5. **Нормализация данных**: Для многомерных данных нормализация признаков значительно улучшает сходимость обучения и качество обнаружения аномалий. Экспериментально подтверждено, что нормализация снижает MSE с 0.340 до 0.026 и порог IRE с 11.1 до 1.62, что приводит к обнаружению 100% аномалий в тестовой выборке.
+
diff --git a/labworks/LW2/report_variant1.md b/labworks/LW2/report_variant1.md
deleted file mode 100644
index 4db65f6..0000000
--- a/labworks/LW2/report_variant1.md
+++ /dev/null
@@ -1,320 +0,0 @@
-# Лабораторная работа №2: Обнаружение аномалий с помощью автокодировщиков
-**Троянов Д.С., Чернов Д.Е. — А-01-22**
-## Вариант 1 (номер бригады k=1) - данные Letter
-
-### Цель работы
-Получить практические навыки создания, обучения и применения искусственных нейронных сетей типа автокодировщик. Исследовать влияние архитектуры автокодировщика и количества эпох обучения на области в пространстве признаков, распознаваемые автокодировщиком после обучения. Научиться оценивать качество обучения автокодировщика на основе ошибки реконструкции и новых метрик EDCA. Научиться решать актуальную задачу обнаружения аномалий в данных с помощью автокодировщика как одноклассового классификатора.
-
-### Определение варианта
-- Номер бригады: k = 1
-- N = k mod 3 = 1 mod 3 = 1
-- Вариант 1 => данные **Letter**
-
----
-
-## ЗАДАНИЕ 1: Работа с двумерными синтетическими данными
-
-### Блок 1: Импорт библиотек и настройка окружения
-```python
-import os
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.preprocessing import StandardScaler
-from sklearn.datasets import make_blobs
-import tensorflow as tf
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Dense, Activation
-from tensorflow.keras.optimizers import Adam
-from tensorflow.keras.callbacks import EarlyStopping
-import lab02_lib as lib
-
-# Параметры для варианта 1 (номер бригады k=1)
-k = 1  # номер бригады
-center_coords = (k, k)  # координаты центра (1, 1)
-```
-
-**Описание:** Импортируются необходимые библиотеки и устанавливаются параметры для варианта 1.
-
-### Блок 2: Генерация индивидуального набора двумерных данных
-```python
-# Генерируем данные с центром в точке (1, 1)
-X_synthetic, _ = make_blobs(n_samples=100, centers=[center_coords], n_features=2, 
-                           cluster_std=0.5, random_state=42)
-
-print(f"Сгенерировано {len(X_synthetic)} точек")
-print(f"Центр данных: {center_coords}")
-print(f"Размерность данных: {X_synthetic.shape}")
-```
-
-**Результат выполнения:**
-```
-Сгенерировано 100 точек
-Центр данных: (1, 1)
-Размерность данных: (100, 2)
-```
-
-![Синтетические данные](out/synthetic_data.png)
-
-### Блок 3: Создание и обучение автокодировщика AE1
-```python
-def create_autoencoder_ae1(input_dim):
-    """Создание автокодировщика AE1 с простой архитектурой"""
-    model = Sequential()
-    
-    # Входной слой
-    model.add(Dense(input_dim, input_shape=(input_dim,)))
-    model.add(Activation('tanh'))
-    
-    # Скрытые слои (простая архитектура)
-    model.add(Dense(1))  # сжатие до 1 нейрона
-    model.add(Activation('tanh'))
-    
-    # Выходной слой
-    model.add(Dense(input_dim))
-    model.add(Activation('linear'))
-    
-    return model
-
-# Обучение AE1 (20 эпох)
-history_ae1 = ae1.fit(X_synthetic, X_synthetic, 
-                      epochs=20, 
-                      batch_size=32, 
-                      validation_split=0.2,
-                      verbose=1)
-```
-
-**Описание:** Создается автокодировщик AE1 с простой архитектурой (сжатие до 1 нейрона).
-
-**Результаты обучения AE1:**
-- Финальная ошибка MSE: 0.868448
-- Порог ошибки реконструкции: 2.153999
-
-![Результаты AE1](out/ae1_results.png)
-
-### Блок 4: Создание и обучение автокодировщика AE2 
-```python
-def create_autoencoder_ae2(input_dim):
-    """Создание автокодировщика AE2 с усложненной архитектурой"""
-    model = Sequential()
-    
-    # Входной слой
-    model.add(Dense(input_dim, input_shape=(input_dim,)))
-    model.add(Activation('tanh'))
-    
-    # Скрытые слои
-    model.add(Dense(4))
-    model.add(Activation('tanh'))
-    model.add(Dense(2))
-    model.add(Activation('tanh'))
-    model.add(Dense(1))  # сжатие до 1 нейрона
-    model.add(Activation('tanh'))
-    model.add(Dense(2))
-    model.add(Activation('tanh'))
-    model.add(Dense(4))
-    model.add(Activation('tanh'))
-    
-    # Выходной слой
-    model.add(Dense(input_dim))
-    model.add(Activation('linear'))
-    
-    return model
-
-# Обучение AE2 (больше эпох)
-history_ae2 = ae2.fit(X_synthetic, X_synthetic, 
-                      epochs=100, 
-                      batch_size=32, 
-                      validation_split=0.2,
-                      verbose=1)
-```
-
-**Описание:** Создается автокодировщик AE2 с усложненной архитектурой.
-
-**Результаты обучения AE2:**
-- Финальная ошибка MSE: 0.207574
-- Порог ошибки реконструкции: 0.584772
-
-![Результаты AE2](out/ae2_results.png)
-
-### Блок 5: Создание тестовой выборки
-```python
-# Создаем тестовые точки, которые AE1 распознает как норму, а AE2 как аномалии
-test_points = np.array([
-    [1.2, 1.2],  # близко к центру
-    [1.5, 1.5],  # немного дальше
-    [0.8, 0.8],  # с другой стороны
-    [1.1, 0.9]   # асимметрично
-])
-```
-
-**Результат выполнения:**
-```
-Тестовые точки:
-  Точка 1: [1.2 1.2]
-  Точка 2: [1.5 1.5]
-  Точка 3: [0.8 0.8]
-  Точка 4: [1.1 0.9]
-```
-
-### Блок 6: Применение автокодировщиков к тестовым данным
-```python
-# Предсказания AE1
-test_pred_ae1 = ae1.predict(test_points)
-test_errors_ae1 = np.mean(np.square(test_points - test_pred_ae1), axis=1)
-
-# Предсказания AE2
-test_pred_ae2 = ae2.predict(test_points)
-test_errors_ae2 = np.mean(np.square(test_points - test_pred_ae2), axis=1)
-```
-
-**Результаты для тестовых точек:**
-```
-Точка | AE1 ошибка | AE1 статус | AE2 ошибка | AE2 статус
--------------------------------------------------------
-    1 |   0.924488 | Норма      |   0.086807 | Норма     
-    2 |   1.494785 | Норма      |   0.352019 | Норма     
-    3 |   0.393357 | Норма      |   0.012042 | Норма     
-    4 |   0.556103 | Норма      |   0.013652 | Норма     
-```
-
-![Результаты тестирования](out/test_results.png)
-
----
-
-## ЗАДАНИЕ 2: Работа с реальными данными Letter
-
-### Блок 7: Загрузка и изучение данных Letter
-```python
-# Загрузка обучающей выборки
-X_letter_train = np.loadtxt('data/letter_train.txt')
-print(f"Размерность обучающей выборки: {X_letter_train.shape}")
-print(f"Количество признаков: {X_letter_train.shape[1]}")
-print(f"Количество образцов: {X_letter_train.shape[0]}")
-```
-
-**Описание:** Загружаются данные Letter, которые содержат характеристики букв алфавита.
-
-**Результат выполнения:**
-```
-Размерность обучающей выборки: (1500, 32)
-Количество признаков: 32
-Количество образцов: 1500
-```
-
-### Блок 8: Создание и обучение автокодировщика для Letter
-```python
-def create_letter_autoencoder(input_dim):
-    """Создание автокодировщика для данных Letter"""
-    model = Sequential()
-    
-    # Входной слой
-    model.add(Dense(input_dim, input_shape=(input_dim,)))
-    model.add(Activation('tanh'))
-    
-    # Скрытые слои
-    model.add(Dense(16))
-    model.add(Activation('tanh'))
-    model.add(Dense(8))
-    model.add(Activation('tanh'))
-    model.add(Dense(4))  # сжатие до 4 нейронов
-    model.add(Activation('tanh'))
-    model.add(Dense(8))
-    model.add(Activation('tanh'))
-    model.add(Dense(16))
-    model.add(Activation('tanh'))
-    
-    # Выходной слой
-    model.add(Dense(input_dim))
-    model.add(Activation('linear'))
-    
-    return model
-
-# Обучение
-history_letter = ae_letter.fit(X_letter_train_scaled, X_letter_train_scaled,
-                              epochs=50,
-                              batch_size=32,
-                              validation_split=0.2,
-                              verbose=1)
-```
-
-**Описание:** Создается автокодировщик, подходящей для 32-мерных данных Letter.
-
-**Результаты обучения:**
-- Финальная ошибка MSE: 0.371572
-- Порог ошибки реконструкции: 0.782392
-
-![Результаты обучения Letter](out/letter_training_results.png)
-
-### Блок 9: Применение к тестовой выборке
-```python
-# Загрузка тестовой выборки
-X_letter_test = np.loadtxt('data/letter_test.txt')
-
-# Применение к тестовой выборке
-X_letter_test_scaled = scaler_letter.transform(X_letter_test)
-X_letter_test_pred = ae_letter.predict(X_letter_test_scaled)
-test_errors_letter = np.mean(np.square(X_letter_test_scaled - X_letter_test_pred), axis=1)
-
-# Определение аномалий
-test_anomalies = test_errors_letter > threshold_letter
-n_anomalies = np.sum(test_anomalies)
-anomaly_rate = n_anomalies / len(test_errors_letter) * 100
-```
-
-**Описание:** Применяется обученный автокодировщик к тестовой выборке для обнаружения аномалий.
-
-**Результаты обнаружения аномалий:**
-```
-Обнаружено аномалий в тестовой выборке: 29 из 100 (29.0%)
-```
-
-![Результаты тестирования Letter](out/letter_test_results.png)
-
----
-
-## ИТОГОВЫЕ РЕЗУЛЬТАТЫ
-
-### Таблица 1 - Результаты задания №1
-| Модель | Количество скрытых слоев | Количество нейронов в скрытых слоях | Количество эпох обучения | Ошибка MSE_stop | Порог ошибки реконструкции | Значение показателя Excess | Значение показателя Approx | Количество обнаруженных аномалий |
-|--------|--------------------------|-------------------------------------|--------------------------|-----------------|----------------------------|----------------------------|----------------------------|----------------------------------|
-| AE1    | 1                        | 1                                   | 20                       | 0.868448        | 2.153999                   | -                          | -                          | -                                |
-| AE2    | 6                        | 4-2-1-2-4                          | 100                      | 0.207574        | 0.584772                   | -                          | -                          | -                                |
-
-### Таблица 2 - Результаты задания №2
-| Dataset name | Количество скрытых слоев | Количество нейронов в скрытых слоях | Количество эпох обучения | Ошибка MSE_stop | Порог ошибки реконструкции | % обнаруженных аномалий |
-|--------------|--------------------------|-------------------------------------|--------------------------|-----------------|----------------------------|-------------------------|
-| Letter       | 6                        | 16-8-4-8-16                        | 50                       | 0.371572        | 0.782392                   | 29.0%                   |
-
----
-
-## ВЫВОДЫ
-
-### Требования к данным для обучения:
-- Данные должны быть нормализованы для стабильного обучения
-- Обучающая выборка должна содержать только нормальные (не аномальные) образцы
-- Размер выборки должен быть достаточным для обучения (минимум несколько сотен образцов)
-
-### Требования к архитектуре автокодировщика:
-- **Простая архитектура (AE1)**: подходит для простых задач, но может не улавливать сложные зависимости
-- **Сложная архитектура (AE2)**: лучше аппроксимирует данные, но требует больше времени на обучение
-- Для многомерных данных (32 признака) необходима более глубокая архитектура с постепенным сжатием
-
-### Требования к количеству эпох обучения:
-- **AE1 (20 эпох)**: недостаточно для качественного обучения
-- **AE2 (100 эпох)**: обеспечивает хорошую сходимость
-- Для реальных данных (Letter) достаточно 50 эпох
-
-### Требования к ошибке MSE_stop:
-- **AE1**: 0.868448 - слишком высокая, указывает на недообучение
-- **AE2**: 0.207574 - приемлемая для синтетических данных
-- **Letter**: 0.371572 - хорошая для реальных данных
-
-### Требования к порогу обнаружения аномалий:
-- Порог 95-го перцентиля обеспечивает разумный баланс
-- **AE1**: 2.153999 - слишком высокий, может пропускать аномалии
-- **AE2**: 0.584772 - более чувствительный к аномалиям
-- **Letter**: 0.782392 - подходящий для реальных данных
-
-### Характеристики качества обучения EDCA:
-- Более сложная архитектура (AE2) показывает лучшие результаты
-- Увеличение количества эпох обучения улучшает качество аппроксимации
-- Для качественного обнаружения аномалий необходимо тщательно подбирать параметры модели
diff --git a/labworks/LW2/requirements.txt b/labworks/LW2/requirements.txt
deleted file mode 100644
index dba9ef0..0000000
--- a/labworks/LW2/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-numpy>=1.21.0
-matplotlib>=3.5.0
-scikit-learn>=1.0.0
-tensorflow>=2.8.0
-pandas>=1.3.0