diff --git a/labworks/LW2/IS_LR2 b/labworks/LW2/IS_LR2
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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyOcz3CNVzX19HqD+LTVjpuY"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"it4uc-rdRYyh","executionInfo":{"status":"ok","timestamp":1760905404698,"user_tz":-180,"elapsed":25963,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"ec0956cf-16f6-442f-f3fe-a778e47fbd1d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","import os\n","os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab2')\n","import numpy as np\n","import lab02_lib as lib"]},{"cell_type":"code","source":["work_dir = '/content/drive/MyDrive/Colab Notebooks/is_lab2'\n","os.makedirs(work_dir, exist_ok=True)\n","os.chdir(work_dir)\n","os.makedirs('out', exist_ok=True)\n","dataset_name = 'WBC'\n","base_url = \"http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/\"\n","!wget -N {base_url}lab02_lib.py\n","!wget -N {base_url}data/{dataset_name}_train.txt\n","!wget -N {base_url}data/{dataset_name}_test.txt\n"],"metadata":{"collapsed":true,"id":"vYz3DOYLUI8y","executionInfo":{"status":"ok","timestamp":1760905408080,"user_tz":-180,"elapsed":1461,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"fc9a19b1-8092-4eeb-81e4-94957aea11a7"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["--2025-10-19 20:23:26--  http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/lab02_lib.py\n","Resolving uit.mpei.ru (uit.mpei.ru)... 193.233.68.149\n","Connecting to uit.mpei.ru (uit.mpei.ru)|193.233.68.149|:80... connected.\n","HTTP request sent, awaiting response... 304 Not Modified\n","File ‘lab02_lib.py’ not modified on server. Omitting download.\n","\n","--2025-10-19 20:23:27--  http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/data/WBC_train.txt\n","Resolving uit.mpei.ru (uit.mpei.ru)... 193.233.68.149\n","Connecting to uit.mpei.ru (uit.mpei.ru)|193.233.68.149|:80... connected.\n","HTTP request sent, awaiting response... 304 Not Modified\n","File ‘WBC_train.txt’ not modified on server. Omitting download.\n","\n","--2025-10-19 20:23:27--  http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/data/WBC_test.txt\n","Resolving uit.mpei.ru (uit.mpei.ru)... 193.233.68.149\n","Connecting to uit.mpei.ru (uit.mpei.ru)|193.233.68.149|:80... connected.\n","HTTP request sent, awaiting response... 304 Not Modified\n","File ‘WBC_test.txt’ not modified on server. Omitting download.\n","\n"]}]},{"cell_type":"code","source":["data = lib.datagen(2, 2, 1000, 2)\n","\n","print('Исходные данные:')\n","print(data)\n","print('Размерность данных:')\n","print(data.shape)"],"metadata":{"id":"NLgoZbg4wVAV","executionInfo":{"status":"ok","timestamp":1760896708134,"user_tz":-180,"elapsed":391,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/","height":899},"collapsed":true,"outputId":"b1dab394-c79e-4b3e-f185-62149dbdae0f"},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x800 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Исходные данные:\n","[[1.9863081  1.86491133]\n"," [2.04641244 1.8589354 ]\n"," [1.89688572 1.89978633]\n"," ...\n"," [1.99310837 2.06214288]\n"," [1.94695115 1.99630611]\n"," [1.79129354 1.91688919]]\n","Размерность данных:\n","(1000, 2)\n"]}]},{"cell_type":"code","source":["patience = 300\n","ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(data,'out/AE1.h5','out/AE1_ire_th.txt',\n","1000, True, patience)"],"metadata":{"collapsed":true,"id":"GpN7flLuiFY3","executionInfo":{"status":"ok","timestamp":1760897428892,"user_tz":-180,"elapsed":122434,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"ec3f3ae3-30f4-409c-c734-0cc032c7521c"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Задать архитектуру автокодировщиков или использовать архитектуру по умолчанию? (1/2): 1\n","Задайте количество скрытых слоёв (нечетное число) : 1\n","Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 1\n","Epoch 1/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step - loss: 9.2644\n","Epoch 2/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 9.2513\n","Epoch 3/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 9.2383\n","Epoch 4/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 9.2253\n","Epoch 5/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 9.2123\n","Epoch 6/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 9.1993\n","Epoch 7/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 9.1863\n","Epoch 8/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 9.1733\n","Epoch 9/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 9.1603\n","Epoch 10/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 9.1473\n","Epoch 11/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 9.1343\n","Epoch 12/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 9.1213\n","Epoch 13/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 9.1084\n","Epoch 14/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 9.0954\n","Epoch 15/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 9.0824\n","Epoch 16/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 9.0694\n","Epoch 17/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 9.0564\n","Epoch 18/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 9.0434\n","Epoch 19/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 9.0305\n","Epoch 20/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 9.0175\n","Epoch 21/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 9.0045\n","Epoch 22/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 8.9915\n","Epoch 23/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.9786\n","Epoch 24/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.9656\n","Epoch 25/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.9526\n","Epoch 26/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.9396\n","Epoch 27/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.9267\n","Epoch 28/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 8.9137\n","Epoch 29/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 8.9007\n","Epoch 30/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.8878\n","Epoch 31/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.8748\n","Epoch 32/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.8618\n","Epoch 33/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.8488\n","Epoch 34/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.8359\n","Epoch 35/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 8.8229\n","Epoch 36/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.8099\n","Epoch 37/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 8.7969\n","Epoch 38/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.7839\n","Epoch 39/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 8.7710\n","Epoch 40/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 8.7580\n","Epoch 41/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.7450\n","Epoch 42/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.7320\n","Epoch 43/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.7190\n","Epoch 44/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.7060\n","Epoch 45/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.6930\n","Epoch 46/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.6800\n","Epoch 47/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.6669\n","Epoch 48/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.6539\n","Epoch 49/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 8.6409\n","Epoch 50/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.6278\n","Epoch 51/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.6148\n","Epoch 52/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 8.6017\n","Epoch 53/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.5887\n","Epoch 54/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.5756\n","Epoch 55/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 8.5625\n","Epoch 56/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 8.5494\n","Epoch 57/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 8.5363\n","Epoch 58/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.5232\n","Epoch 59/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.5101\n","Epoch 60/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.4970\n","Epoch 61/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 8.4838\n","Epoch 62/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.4707\n","Epoch 63/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.4575\n","Epoch 64/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 8.4443\n","Epoch 65/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.4311\n","Epoch 66/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.4179\n","Epoch 67/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.4047\n","Epoch 68/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 8.3914\n","Epoch 69/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.3781\n","Epoch 70/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.3648\n","Epoch 71/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.3515\n","Epoch 72/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.3382\n","Epoch 73/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.3249\n","Epoch 74/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 8.3115\n","Epoch 75/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.2981\n","Epoch 76/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.2847\n","Epoch 77/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.2712\n","Epoch 78/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.2577\n","Epoch 79/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 8.2442\n","Epoch 80/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 8.2307\n","Epoch 81/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 8.2172\n","Epoch 82/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.2036\n","Epoch 83/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.1899\n","Epoch 84/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.1763\n","Epoch 85/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 8.1626\n","Epoch 86/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 8.1489\n","Epoch 87/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.1351\n","Epoch 88/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 8.1213\n","Epoch 89/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.1074\n","Epoch 90/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.0935\n","Epoch 91/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 8.0796\n","Epoch 92/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.0656\n","Epoch 93/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.0516\n","Epoch 94/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 8.0375\n","Epoch 95/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 8.0234\n","Epoch 96/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.0092\n","Epoch 97/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 7.9949\n","Epoch 98/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 7.9806\n","Epoch 99/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 7.9662\n","Epoch 100/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 7.9518\n","Epoch 101/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 7.9373\n","Epoch 102/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 7.9227\n","Epoch 103/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 7.9081\n","Epoch 104/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 7.8934\n","Epoch 105/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 7.8786\n","Epoch 106/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 7.8637\n","Epoch 107/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 7.8488\n","Epoch 108/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 7.8337\n","Epoch 109/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 7.8186\n","Epoch 110/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 7.8034\n","Epoch 111/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 7.7880\n","Epoch 112/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.7726\n","Epoch 113/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 7.7571\n","Epoch 114/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 7.7414\n","Epoch 115/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 7.7256\n","Epoch 116/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 7.7098\n","Epoch 117/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 7.6938\n","Epoch 118/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 7.6776\n","Epoch 119/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.6613\n","Epoch 120/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.6449\n","Epoch 121/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 7.6284\n","Epoch 122/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.6116\n","Epoch 123/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 7.5948\n","Epoch 124/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 7.5777\n","Epoch 125/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 7.5605\n","Epoch 126/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 7.5431\n","Epoch 127/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 7.5255\n","Epoch 128/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 7.5078\n","Epoch 129/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 7.4898\n","Epoch 130/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 7.4716\n","Epoch 131/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 7.4532\n","Epoch 132/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 7.4345\n","Epoch 133/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 7.4156\n","Epoch 134/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 7.3965\n","Epoch 135/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.3771\n","Epoch 136/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 7.3575\n","Epoch 137/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 7.3375\n","Epoch 138/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 7.3173\n","Epoch 139/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 7.2967\n","Epoch 140/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 7.2759\n","Epoch 141/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 7.2546\n","Epoch 142/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 7.2331\n","Epoch 143/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 7.2112\n","Epoch 144/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 7.1889\n","Epoch 145/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 7.1662\n","Epoch 146/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.1431\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: 7.1196\n","Epoch 148/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.0956\n","Epoch 149/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.0712\n","Epoch 150/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 7.0463\n","Epoch 151/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.0208\n","Epoch 152/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 6.9949\n","Epoch 153/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 6.9684\n","Epoch 154/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 6.9413\n","Epoch 155/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 6.9137\n","Epoch 156/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 6.8854\n","Epoch 157/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 6.8565\n","Epoch 158/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 6.8269\n","Epoch 159/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 6.7967\n","Epoch 160/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 6.7657\n","Epoch 161/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 6.7340\n","Epoch 162/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 6.7015\n","Epoch 163/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 6.6682\n","Epoch 164/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 6.6340\n","Epoch 165/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 6.5990\n","Epoch 166/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 6.5632\n","Epoch 167/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 6.5263\n","Epoch 168/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 6.4886\n","Epoch 169/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 6.4498\n","Epoch 170/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 6.4101\n","Epoch 171/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 6.3693\n","Epoch 172/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 6.3274\n","Epoch 173/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 6.2844\n","Epoch 174/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 6.2403\n","Epoch 175/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 6.1950\n","Epoch 176/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 6.1485\n","Epoch 177/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 6.1008\n","Epoch 178/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 6.0519\n","Epoch 179/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 6.0017\n","Epoch 180/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 5.9502\n","Epoch 181/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 5.8975\n","Epoch 182/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 5.8434\n","Epoch 183/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 5.7879\n","Epoch 184/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 5.7312\n","Epoch 185/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 5.6731\n","Epoch 186/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 5.6137\n","Epoch 187/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 5.5529\n","Epoch 188/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.4908\n","Epoch 189/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.4274\n","Epoch 190/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.3627\n","Epoch 191/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.2968\n","Epoch 192/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.2296\n","Epoch 193/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.1612\n","Epoch 194/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.0917\n","Epoch 195/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.0212\n","Epoch 196/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.9496\n","Epoch 197/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.8770\n","Epoch 198/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.8035\n","Epoch 199/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.7293\n","Epoch 200/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.6543\n","Epoch 201/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.5787\n","Epoch 202/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.5026\n","Epoch 203/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 4.4260\n","Epoch 204/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 4.3492\n","Epoch 205/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.2721\n","Epoch 206/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.1950\n","Epoch 207/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.1178\n","Epoch 208/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 4.0408\n","Epoch 209/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.9641\n","Epoch 210/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.8878\n","Epoch 211/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.8119\n","Epoch 212/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.7367\n","Epoch 213/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.6622\n","Epoch 214/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.5885\n","Epoch 215/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.5158\n","Epoch 216/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.4440\n","Epoch 217/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.3734\n","Epoch 218/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.3040\n","Epoch 219/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.2359\n","Epoch 220/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.1691\n","Epoch 221/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.1038\n","Epoch 222/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.0399\n","Epoch 223/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 2.9775\n","Epoch 224/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 2.9166\n","Epoch 225/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.8574\n","Epoch 226/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.7997\n","Epoch 227/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 2.7436\n","Epoch 228/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.6892\n","Epoch 229/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.6363\n","Epoch 230/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.5851\n","Epoch 231/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.5355\n","Epoch 232/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.4875\n","Epoch 233/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.4411\n","Epoch 234/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.3962\n","Epoch 235/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.3528\n","Epoch 236/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.3109\n","Epoch 237/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.2705\n","Epoch 238/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.2315\n","Epoch 239/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.1938\n","Epoch 240/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.1575\n","Epoch 241/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.1225\n","Epoch 242/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.0888\n","Epoch 243/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.0563\n","Epoch 244/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.0250\n","Epoch 245/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.9947\n","Epoch 246/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.9656\n","Epoch 247/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.9376\n","Epoch 248/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.9105\n","Epoch 249/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.8844\n","Epoch 250/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.8593\n","Epoch 251/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.8350\n","Epoch 252/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.8116\n","Epoch 253/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.7891\n","Epoch 254/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.7673\n","Epoch 255/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.7462\n","Epoch 256/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.7259\n","Epoch 257/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.7063\n","Epoch 258/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.6873\n","Epoch 259/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.6690\n","Epoch 260/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.6512\n","Epoch 261/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.6341\n","Epoch 262/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.6175\n","Epoch 263/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.6014\n","Epoch 264/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.5858\n","Epoch 265/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.5707\n","Epoch 266/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.5561\n","Epoch 267/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.5419\n","Epoch 268/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.5281\n","Epoch 269/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.5147\n","Epoch 270/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 1.5017\n","Epoch 271/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.4891\n","Epoch 272/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.4768\n","Epoch 273/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.4649\n","Epoch 274/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 1.4533\n","Epoch 275/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.4420\n","Epoch 276/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.4310\n","Epoch 277/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.4203\n","Epoch 278/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.4098\n","Epoch 279/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.3996\n","Epoch 280/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 1.3897\n","Epoch 281/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.3800\n","Epoch 282/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 1.3705\n","Epoch 283/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.3613\n","Epoch 284/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.3523\n","Epoch 285/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.3434\n","Epoch 286/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 1.3348\n","Epoch 287/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.3263\n","Epoch 288/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.3181\n","Epoch 289/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.3100\n","Epoch 290/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 1.3021\n","Epoch 291/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.2943\n","Epoch 292/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.2867\n","Epoch 293/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.2793\n","Epoch 294/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.2719\n","Epoch 295/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.2648\n","Epoch 296/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.2577\n","Epoch 297/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.2508\n","Epoch 298/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.2441\n","Epoch 299/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.2374\n","Epoch 300/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.2308\n","Epoch 301/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.2244\n","Epoch 302/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.2181\n","Epoch 303/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.2119\n","Epoch 304/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.2058\n","Epoch 305/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.1997\n","Epoch 306/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.1938\n","Epoch 307/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.1880\n","Epoch 308/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.1823\n","Epoch 309/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.1766\n","Epoch 310/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.1710\n","Epoch 311/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.1655\n","Epoch 312/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.1601\n","Epoch 313/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.1548\n","Epoch 314/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.1495\n","Epoch 315/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.1444\n","Epoch 316/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.1392\n","Epoch 317/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.1342\n","Epoch 318/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.1292\n","Epoch 319/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.1243\n","Epoch 320/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.1195\n","Epoch 321/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 1.1147\n","Epoch 322/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.1099\n","Epoch 323/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.1053\n","Epoch 324/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.1006\n","Epoch 325/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 1.0961\n","Epoch 326/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.0916\n","Epoch 327/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 1.0871\n","Epoch 328/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.0827\n","Epoch 329/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.0783\n","Epoch 330/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 1.0740\n","Epoch 331/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.0697\n","Epoch 332/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.0655\n","Epoch 333/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 1.0614\n","Epoch 334/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.0572\n","Epoch 335/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.0531\n","Epoch 336/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.0491\n","Epoch 337/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.0451\n","Epoch 338/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.0411\n","Epoch 339/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 1.0372\n","Epoch 340/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 1.0333\n","Epoch 341/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.0294\n","Epoch 342/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.0256\n","Epoch 343/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 1.0218\n","Epoch 344/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.0181\n","Epoch 345/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.0143\n","Epoch 346/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.0107\n","Epoch 347/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.0070\n","Epoch 348/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 1.0034\n","Epoch 349/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.9998\n","Epoch 350/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.9962\n","Epoch 351/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.9927\n","Epoch 352/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.9892\n","Epoch 353/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.9857\n","Epoch 354/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.9823\n","Epoch 355/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.9789\n","Epoch 356/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.9755\n","Epoch 357/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9721\n","Epoch 358/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.9688\n","Epoch 359/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.9655\n","Epoch 360/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.9622\n","Epoch 361/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.9589\n","Epoch 362/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9557\n","Epoch 363/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.9525\n","Epoch 364/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.9493\n","Epoch 365/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.9461\n","Epoch 366/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.9430\n","Epoch 367/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9398\n","Epoch 368/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9367\n","Epoch 369/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.9336\n","Epoch 370/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.9306\n","Epoch 371/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9275\n","Epoch 372/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9245\n","Epoch 373/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9215\n","Epoch 374/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.9185\n","Epoch 375/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.9155\n","Epoch 376/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.9126\n","Epoch 377/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9097\n","Epoch 378/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.9068\n","Epoch 379/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.9039\n","Epoch 380/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9010\n","Epoch 381/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.8981\n","Epoch 382/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.8953\n","Epoch 383/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.8925\n","Epoch 384/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.8897\n","Epoch 385/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.8869\n","Epoch 386/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.8841\n","Epoch 387/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.8813\n","Epoch 388/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.8786\n","Epoch 389/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.8758\n","Epoch 390/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.8731\n","Epoch 391/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.8704\n","Epoch 392/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.8677\n","Epoch 393/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.8651\n","Epoch 394/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.8624\n","Epoch 395/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.8598\n","Epoch 396/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.8571\n","Epoch 397/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.8545\n","Epoch 398/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.8519\n","Epoch 399/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.8493\n","Epoch 400/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.8468\n","Epoch 401/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.8442\n","Epoch 402/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.8416\n","Epoch 403/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.8391\n","Epoch 404/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.8366\n","Epoch 405/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.8341\n","Epoch 406/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.8316\n","Epoch 407/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.8291\n","Epoch 408/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.8266\n","Epoch 409/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.8241\n","Epoch 410/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.8217\n","Epoch 411/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.8192\n","Epoch 412/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.8168\n","Epoch 413/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.8144\n","Epoch 414/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.8119\n","Epoch 415/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.8095\n","Epoch 416/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.8072\n","Epoch 417/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.8048\n","Epoch 418/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.8024\n","Epoch 419/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.8000\n","Epoch 420/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.7977\n","Epoch 421/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.7954\n","Epoch 422/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.7930\n","Epoch 423/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.7907\n","Epoch 424/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.7884\n","Epoch 425/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7861\n","Epoch 426/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.7838\n","Epoch 427/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.7815\n","Epoch 428/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.7793\n","Epoch 429/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.7770\n","Epoch 430/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.7747\n","Epoch 431/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.7725\n","Epoch 432/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.7703\n","Epoch 433/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.7680\n","Epoch 434/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.7658\n","Epoch 435/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.7636\n","Epoch 436/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.7614\n","Epoch 437/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.7592\n","Epoch 438/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.7570\n","Epoch 439/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.7548\n","Epoch 440/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.7527\n","Epoch 441/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.7505\n","Epoch 442/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.7484\n","Epoch 443/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.7462\n","Epoch 444/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.7441\n","Epoch 445/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.7420\n","Epoch 446/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.7398\n","Epoch 447/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.7377\n","Epoch 448/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7356\n","Epoch 449/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.7335\n","Epoch 450/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.7314\n","Epoch 451/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.7294\n","Epoch 452/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.7273\n","Epoch 453/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.7252\n","Epoch 454/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.7232\n","Epoch 455/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.7211\n","Epoch 456/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.7191\n","Epoch 457/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.7170\n","Epoch 458/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.7150\n","Epoch 459/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.7130\n","Epoch 460/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.7109\n","Epoch 461/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7089\n","Epoch 462/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.7069\n","Epoch 463/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7049\n","Epoch 464/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.7029\n","Epoch 465/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.7010\n","Epoch 466/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.6990\n","Epoch 467/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6970\n","Epoch 468/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6950\n","Epoch 469/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6931\n","Epoch 470/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.6911\n","Epoch 471/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6892\n","Epoch 472/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.6872\n","Epoch 473/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6853\n","Epoch 474/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6834\n","Epoch 475/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.6815\n","Epoch 476/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6796\n","Epoch 477/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.6776\n","Epoch 478/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6757\n","Epoch 479/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6738\n","Epoch 480/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.6720\n","Epoch 481/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6701\n","Epoch 482/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.6682\n","Epoch 483/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.6663\n","Epoch 484/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6644\n","Epoch 485/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6626\n","Epoch 486/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.6607\n","Epoch 487/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6589\n","Epoch 488/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.6570\n","Epoch 489/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.6552\n","Epoch 490/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6534\n","Epoch 491/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6515\n","Epoch 492/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.6497\n","Epoch 493/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6479\n","Epoch 494/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.6461\n","Epoch 495/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6443\n","Epoch 496/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.6425\n","Epoch 497/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6407\n","Epoch 498/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6389\n","Epoch 499/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6371\n","Epoch 500/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6353\n","Epoch 501/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.6335\n","Epoch 502/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6318\n","Epoch 503/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.6300\n","Epoch 504/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.6282\n","Epoch 505/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6265\n","Epoch 506/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.6247\n","Epoch 507/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.6230\n","Epoch 508/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6213\n","Epoch 509/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.6195\n","Epoch 510/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6178\n","Epoch 511/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.6161\n","Epoch 512/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.6143\n","Epoch 513/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.6126\n","Epoch 514/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.6109\n","Epoch 515/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.6092\n","Epoch 516/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.6075\n","Epoch 517/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6058\n","Epoch 518/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.6041\n","Epoch 519/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6024\n","Epoch 520/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.6008\n","Epoch 521/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5991\n","Epoch 522/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5974\n","Epoch 523/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.5957\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: 0.5941\n","Epoch 525/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5924\n","Epoch 526/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5908\n","Epoch 527/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5891\n","Epoch 528/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5875\n","Epoch 529/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5858\n","Epoch 530/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5842\n","Epoch 531/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.5826\n","Epoch 532/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.5809\n","Epoch 533/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5793\n","Epoch 534/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5777\n","Epoch 535/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5761\n","Epoch 536/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5745\n","Epoch 537/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5729\n","Epoch 538/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5713\n","Epoch 539/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5697\n","Epoch 540/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5681\n","Epoch 541/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5665\n","Epoch 542/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5649\n","Epoch 543/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5633\n","Epoch 544/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.5617\n","Epoch 545/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5602\n","Epoch 546/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5586\n","Epoch 547/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5570\n","Epoch 548/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5555\n","Epoch 549/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5539\n","Epoch 550/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5524\n","Epoch 551/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5508\n","Epoch 552/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5493\n","Epoch 553/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5477\n","Epoch 554/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5462\n","Epoch 555/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5447\n","Epoch 556/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5431\n","Epoch 557/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5416\n","Epoch 558/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5401\n","Epoch 559/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5386\n","Epoch 560/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5371\n","Epoch 561/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5355\n","Epoch 562/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5340\n","Epoch 563/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5325\n","Epoch 564/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.5310\n","Epoch 565/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5296\n","Epoch 566/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5281\n","Epoch 567/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5266\n","Epoch 568/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5251\n","Epoch 569/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5236\n","Epoch 570/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5221\n","Epoch 571/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5207\n","Epoch 572/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5192\n","Epoch 573/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5177\n","Epoch 574/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5163\n","Epoch 575/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5148\n","Epoch 576/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5134\n","Epoch 577/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5119\n","Epoch 578/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5105\n","Epoch 579/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5090\n","Epoch 580/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5076\n","Epoch 581/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5062\n","Epoch 582/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5047\n","Epoch 583/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5033\n","Epoch 584/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5019\n","Epoch 585/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5005\n","Epoch 586/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4991\n","Epoch 587/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4977\n","Epoch 588/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4962\n","Epoch 589/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4948\n","Epoch 590/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4934\n","Epoch 591/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4920\n","Epoch 592/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4906\n","Epoch 593/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.4893\n","Epoch 594/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4879\n","Epoch 595/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.4865\n","Epoch 596/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.4851\n","Epoch 597/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.4837\n","Epoch 598/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.4824\n","Epoch 599/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4810\n","Epoch 600/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4796\n","Epoch 601/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4783\n","Epoch 602/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4769\n","Epoch 603/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4755\n","Epoch 604/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.4742\n","Epoch 605/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4728\n","Epoch 606/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.4715\n","Epoch 607/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4701\n","Epoch 608/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4688\n","Epoch 609/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4675\n","Epoch 610/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4661\n","Epoch 611/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.4648\n","Epoch 612/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.4635\n","Epoch 613/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.4622\n","Epoch 614/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4608\n","Epoch 615/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4595\n","Epoch 616/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4582\n","Epoch 617/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4569\n","Epoch 618/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.4556\n","Epoch 619/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.4543\n","Epoch 620/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4530\n","Epoch 621/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.4517\n","Epoch 622/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4504\n","Epoch 623/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.4491\n","Epoch 624/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4478\n","Epoch 625/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.4465\n","Epoch 626/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.4452\n","Epoch 627/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.4440\n","Epoch 628/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4427\n","Epoch 629/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.4414\n","Epoch 630/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.4401\n","Epoch 631/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4389\n","Epoch 632/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4376\n","Epoch 633/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.4364\n","Epoch 634/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.4351\n","Epoch 635/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.4338\n","Epoch 636/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4326\n","Epoch 637/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.4314\n","Epoch 638/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.4301\n","Epoch 639/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.4289\n","Epoch 640/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.4276\n","Epoch 641/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.4264\n","Epoch 642/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.4252\n","Epoch 643/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.4239\n","Epoch 644/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.4227\n","Epoch 645/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.4215\n","Epoch 646/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.4203\n","Epoch 647/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.4190\n","Epoch 648/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.4178\n","Epoch 649/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.4166\n","Epoch 650/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.4154\n","Epoch 651/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.4142\n","Epoch 652/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.4130\n","Epoch 653/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.4118\n","Epoch 654/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.4106\n","Epoch 655/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.4094\n","Epoch 656/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.4082\n","Epoch 657/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.4070\n","Epoch 658/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.4059\n","Epoch 659/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.4047\n","Epoch 660/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.4035\n","Epoch 661/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.4023\n","Epoch 662/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.4011\n","Epoch 663/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.4000\n","Epoch 664/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.3988\n","Epoch 665/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.3976\n","Epoch 666/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.3965\n","Epoch 667/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.3953\n","Epoch 668/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.3942\n","Epoch 669/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3930\n","Epoch 670/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.3919\n","Epoch 671/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.3907\n","Epoch 672/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3896\n","Epoch 673/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.3884\n","Epoch 674/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.3873\n","Epoch 675/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3862\n","Epoch 676/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.3850\n","Epoch 677/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.3839\n","Epoch 678/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3828\n","Epoch 679/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.3816\n","Epoch 680/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3805\n","Epoch 681/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.3794\n","Epoch 682/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.3783\n","Epoch 683/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3772\n","Epoch 684/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3761\n","Epoch 685/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3749\n","Epoch 686/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3738\n","Epoch 687/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3727\n","Epoch 688/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3716\n","Epoch 689/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3705\n","Epoch 690/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3694\n","Epoch 691/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.3684\n","Epoch 692/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.3673\n","Epoch 693/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.3662\n","Epoch 694/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3651\n","Epoch 695/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3640\n","Epoch 696/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.3629\n","Epoch 697/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.3619\n","Epoch 698/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3608\n","Epoch 699/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.3597\n","Epoch 700/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3586\n","Epoch 701/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3576\n","Epoch 702/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3565\n","Epoch 703/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3555\n","Epoch 704/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3544\n","Epoch 705/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3533\n","Epoch 706/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3523\n","Epoch 707/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3512\n","Epoch 708/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3502\n","Epoch 709/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3492\n","Epoch 710/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3481\n","Epoch 711/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3471\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: 0.3460\n","Epoch 713/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.3450\n","Epoch 714/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3440\n","Epoch 715/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.3429\n","Epoch 716/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3419\n","Epoch 717/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3409\n","Epoch 718/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3399\n","Epoch 719/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3389\n","Epoch 720/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3378\n","Epoch 721/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3368\n","Epoch 722/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3358\n","Epoch 723/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3348\n","Epoch 724/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3338\n","Epoch 725/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.3328\n","Epoch 726/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3318\n","Epoch 727/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.3308\n","Epoch 728/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.3298\n","Epoch 729/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3288\n","Epoch 730/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3278\n","Epoch 731/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3268\n","Epoch 732/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3258\n","Epoch 733/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3249\n","Epoch 734/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3239\n","Epoch 735/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3229\n","Epoch 736/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3219\n","Epoch 737/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.3210\n","Epoch 738/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.3200\n","Epoch 739/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3190\n","Epoch 740/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.3180\n","Epoch 741/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3171\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: 0.3161\n","Epoch 743/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3152\n","Epoch 744/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3142\n","Epoch 745/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.3133\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: 0.3123\n","Epoch 747/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.3114\n","Epoch 748/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.3104\n","Epoch 749/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.3095\n","Epoch 750/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3085\n","Epoch 751/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3076\n","Epoch 752/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3066\n","Epoch 753/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3057\n","Epoch 754/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3048\n","Epoch 755/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3038\n","Epoch 756/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3029\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: 0.3020\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: 0.3011\n","Epoch 759/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3001\n","Epoch 760/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.2992\n","Epoch 761/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.2983\n","Epoch 762/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2974\n","Epoch 763/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2965\n","Epoch 764/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2956\n","Epoch 765/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.2947\n","Epoch 766/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.2938\n","Epoch 767/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2929\n","Epoch 768/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2920\n","Epoch 769/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2911\n","Epoch 770/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2902\n","Epoch 771/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.2893\n","Epoch 772/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2884\n","Epoch 773/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2875\n","Epoch 774/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2866\n","Epoch 775/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.2857\n","Epoch 776/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2848\n","Epoch 777/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2840\n","Epoch 778/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2831\n","Epoch 779/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2822\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: 0.2813\n","Epoch 781/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2805\n","Epoch 782/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2796\n","Epoch 783/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2787\n","Epoch 784/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.2779\n","Epoch 785/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.2770\n","Epoch 786/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2762\n","Epoch 787/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2753\n","Epoch 788/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.2744\n","Epoch 789/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.2736\n","Epoch 790/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2727\n","Epoch 791/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2719\n","Epoch 792/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2710\n","Epoch 793/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2702\n","Epoch 794/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2694\n","Epoch 795/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.2685\n","Epoch 796/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2677\n","Epoch 797/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2669\n","Epoch 798/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2660\n","Epoch 799/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2652\n","Epoch 800/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2644\n","Epoch 801/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2635\n","Epoch 802/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2627\n","Epoch 803/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2619\n","Epoch 804/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.2611\n","Epoch 805/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.2603\n","Epoch 806/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2594\n","Epoch 807/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.2586\n","Epoch 808/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.2578\n","Epoch 809/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2570\n","Epoch 810/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2562\n","Epoch 811/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.2554\n","Epoch 812/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.2546\n","Epoch 813/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2538\n","Epoch 814/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2530\n","Epoch 815/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.2522\n","Epoch 816/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.2514\n","Epoch 817/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2506\n","Epoch 818/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2498\n","Epoch 819/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2490\n","Epoch 820/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2482\n","Epoch 821/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2475\n","Epoch 822/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2467\n","Epoch 823/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2459\n","Epoch 824/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2451\n","Epoch 825/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2443\n","Epoch 826/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.2436\n","Epoch 827/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2428\n","Epoch 828/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2420\n","Epoch 829/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2413\n","Epoch 830/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2405\n","Epoch 831/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.2397\n","Epoch 832/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2390\n","Epoch 833/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2382\n","Epoch 834/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2375\n","Epoch 835/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2367\n","Epoch 836/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2360\n","Epoch 837/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.2352\n","Epoch 838/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2345\n","Epoch 839/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2337\n","Epoch 840/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.2330\n","Epoch 841/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2322\n","Epoch 842/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.2315\n","Epoch 843/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.2307\n","Epoch 844/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2300\n","Epoch 845/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2293\n","Epoch 846/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2285\n","Epoch 847/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.2278\n","Epoch 848/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2271\n","Epoch 849/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2263\n","Epoch 850/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.2256\n","Epoch 851/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.2249\n","Epoch 852/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.2242\n","Epoch 853/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.2235\n","Epoch 854/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.2227\n","Epoch 855/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.2220\n","Epoch 856/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.2213\n","Epoch 857/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.2206\n","Epoch 858/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.2199\n","Epoch 859/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.2192\n","Epoch 860/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.2185\n","Epoch 861/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.2178\n","Epoch 862/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.2171\n","Epoch 863/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.2164\n","Epoch 864/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.2157\n","Epoch 865/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.2150\n","Epoch 866/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.2143\n","Epoch 867/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.2136\n","Epoch 868/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.2129\n","Epoch 869/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.2122\n","Epoch 870/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.2115\n","Epoch 871/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2108\n","Epoch 872/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.2102\n","Epoch 873/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.2095\n","Epoch 874/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.2088\n","Epoch 875/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.2081\n","Epoch 876/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2074\n","Epoch 877/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2068\n","Epoch 878/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.2061\n","Epoch 879/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.2054\n","Epoch 880/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.2048\n","Epoch 881/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.2041\n","Epoch 882/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.2034\n","Epoch 883/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.2028\n","Epoch 884/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.2021\n","Epoch 885/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.2014\n","Epoch 886/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.2008\n","Epoch 887/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.2001\n","Epoch 888/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1995\n","Epoch 889/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.1988\n","Epoch 890/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1982\n","Epoch 891/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1975\n","Epoch 892/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1969\n","Epoch 893/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1962\n","Epoch 894/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1956\n","Epoch 895/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1950\n","Epoch 896/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1943\n","Epoch 897/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1937\n","Epoch 898/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1930\n","Epoch 899/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1924\n","Epoch 900/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.1918\n","Epoch 901/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1912\n","Epoch 902/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1905\n","Epoch 903/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.1899\n","Epoch 904/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1893\n","Epoch 905/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1886\n","Epoch 906/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1880\n","Epoch 907/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1874\n","Epoch 908/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1868\n","Epoch 909/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1862\n","Epoch 910/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1856\n","Epoch 911/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1849\n","Epoch 912/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1843\n","Epoch 913/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1837\n","Epoch 914/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1831\n","Epoch 915/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1825\n","Epoch 916/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1819\n","Epoch 917/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1813\n","Epoch 918/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1807\n","Epoch 919/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1801\n","Epoch 920/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1795\n","Epoch 921/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1789\n","Epoch 922/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1783\n","Epoch 923/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1777\n","Epoch 924/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1771\n","Epoch 925/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1766\n","Epoch 926/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1760\n","Epoch 927/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1754\n","Epoch 928/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1748\n","Epoch 929/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1742\n","Epoch 930/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1736\n","Epoch 931/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1731\n","Epoch 932/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1725\n","Epoch 933/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.1719\n","Epoch 934/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1713\n","Epoch 935/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1708\n","Epoch 936/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1702\n","Epoch 937/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1696\n","Epoch 938/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1691\n","Epoch 939/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1685\n","Epoch 940/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1679\n","Epoch 941/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1674\n","Epoch 942/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1668\n","Epoch 943/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1662\n","Epoch 944/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1657\n","Epoch 945/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1651\n","Epoch 946/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1646\n","Epoch 947/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1640\n","Epoch 948/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1635\n","Epoch 949/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1629\n","Epoch 950/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1624\n","Epoch 951/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1618\n","Epoch 952/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1613\n","Epoch 953/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1607\n","Epoch 954/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1602\n","Epoch 955/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1597\n","Epoch 956/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1591\n","Epoch 957/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.1586\n","Epoch 958/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1581\n","Epoch 959/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1575\n","Epoch 960/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1570\n","Epoch 961/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1565\n","Epoch 962/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1559\n","Epoch 963/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1554\n","Epoch 964/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1549\n","Epoch 965/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1544\n","Epoch 966/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1538\n","Epoch 967/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1533\n","Epoch 968/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1528\n","Epoch 969/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1523\n","Epoch 970/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1518\n","Epoch 971/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1512\n","Epoch 972/1000\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 973/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1502\n","Epoch 974/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1497\n","Epoch 975/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1492\n","Epoch 976/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1487\n","Epoch 977/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1482\n","Epoch 978/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1477\n","Epoch 979/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1472\n","Epoch 980/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1467\n","Epoch 981/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1462\n","Epoch 982/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1457\n","Epoch 983/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1452\n","Epoch 984/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.1447\n","Epoch 985/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.1442\n","Epoch 986/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1437\n","Epoch 987/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1432\n","Epoch 988/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.1427\n","Epoch 989/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1422\n","Epoch 990/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1417\n","Epoch 991/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1413\n","Epoch 992/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1408\n","Epoch 993/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.1403\n","Epoch 994/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1398\n","Epoch 995/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.1393\n","Epoch 996/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1389\n","Epoch 997/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.1384\n","Epoch 998/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1379\n","Epoch 999/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1374\n","Epoch 1000/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 0.1370\n","Epoch 1000/1000\n"," - loss: 0.1370\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1370\n","Restoring model weights from the end of the best epoch: 987.\n","\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step\n"]},{"output_type":"stream","name":"stderr","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"]},{"output_type":"stream","name":"stdout","text":["\n","\n"]}]},{"cell_type":"code","source":["lib.ire_plot('training', IRE1, IREth1, 'AE1')"],"metadata":{"id":"KYX7-3zJjHIA","executionInfo":{"status":"ok","timestamp":1760897497177,"user_tz":-180,"elapsed":520,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/","height":744},"collapsed":true,"outputId":"a7727e7e-5942-4461-a395-e4257ef9af9c"},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(data,'out/AE2.h5','out/AE2_ire_th.txt',\n","3000, True, patience)"],"metadata":{"collapsed":true,"id":"FNBrKD13j425","executionInfo":{"status":"ok","timestamp":1760897910820,"user_tz":-180,"elapsed":82882,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"9fc78b8d-371f-4dcb-84bc-9720e1709159"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Задать архитектуру автокодировщиков или использовать архитектуру по умолчанию? (1/2): 1\n","Задайте количество скрытых слоёв (нечетное число) : 5\n","Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 3 2 1 2 3\n","Epoch 1/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3s/step - loss: 4.2929\n","Epoch 2/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.2579\n","Epoch 3/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.2233\n","Epoch 4/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.1890\n","Epoch 5/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.1553\n","Epoch 6/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 4.1219\n","Epoch 7/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.0890\n","Epoch 8/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.0566\n","Epoch 9/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.0247\n","Epoch 10/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.9932\n","Epoch 11/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.9623\n","Epoch 12/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.9318\n","Epoch 13/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.9017\n","Epoch 14/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.8720\n","Epoch 15/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.8427\n","Epoch 16/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.8136\n","Epoch 17/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.7849\n","Epoch 18/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 3.7564\n","Epoch 19/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 3.7280\n","Epoch 20/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 3.6997\n","Epoch 21/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.6716\n","Epoch 22/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 3.6435\n","Epoch 23/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 3.6155\n","Epoch 24/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 3.5875\n","Epoch 25/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 3.5595\n","Epoch 26/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 3.5315\n","Epoch 27/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 3.5035\n","Epoch 28/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 3.4755\n","Epoch 29/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 3.4474\n","Epoch 30/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 3.4194\n","Epoch 31/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 3.3914\n","Epoch 32/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.3634\n","Epoch 33/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 3.3354\n","Epoch 34/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 3.3074\n","Epoch 35/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.2795\n","Epoch 36/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 3.2516\n","Epoch 37/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.2237\n","Epoch 38/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 3.1959\n","Epoch 39/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 3.1682\n","Epoch 40/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.1405\n","Epoch 41/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 3.1129\n","Epoch 42/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 3.0853\n","Epoch 43/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 3.0579\n","Epoch 44/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.0306\n","Epoch 45/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 3.0033\n","Epoch 46/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 2.9762\n","Epoch 47/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 2.9492\n","Epoch 48/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 2.9223\n","Epoch 49/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.8955\n","Epoch 50/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 2.8688\n","Epoch 51/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.8423\n","Epoch 52/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 2.8160\n","Epoch 53/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 2.7897\n","Epoch 54/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 2.7636\n","Epoch 55/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 2.7377\n","Epoch 56/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.7119\n","Epoch 57/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.6863\n","Epoch 58/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.6609\n","Epoch 59/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.6356\n","Epoch 60/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.6105\n","Epoch 61/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 2.5855\n","Epoch 62/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.5608\n","Epoch 63/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.5362\n","Epoch 64/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.5117\n","Epoch 65/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.4875\n","Epoch 66/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 2.4634\n","Epoch 67/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 2.4396\n","Epoch 68/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.4159\n","Epoch 69/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.3923\n","Epoch 70/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.3690\n","Epoch 71/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.3459\n","Epoch 72/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.3229\n","Epoch 73/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.3001\n","Epoch 74/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.2775\n","Epoch 75/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.2551\n","Epoch 76/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.2329\n","Epoch 77/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 2.2108\n","Epoch 78/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.1890\n","Epoch 79/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.1673\n","Epoch 80/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 2.1458\n","Epoch 81/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.1245\n","Epoch 82/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.1034\n","Epoch 83/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.0825\n","Epoch 84/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.0617\n","Epoch 85/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 2.0412\n","Epoch 86/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.0208\n","Epoch 87/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 2.0005\n","Epoch 88/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.9805\n","Epoch 89/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 1.9606\n","Epoch 90/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.9409\n","Epoch 91/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.9214\n","Epoch 92/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.9021\n","Epoch 93/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.8829\n","Epoch 94/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.8639\n","Epoch 95/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.8451\n","Epoch 96/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.8264\n","Epoch 97/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.8079\n","Epoch 98/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.7896\n","Epoch 99/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.7714\n","Epoch 100/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 1.7534\n","Epoch 101/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.7356\n","Epoch 102/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.7179\n","Epoch 103/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 1.7004\n","Epoch 104/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.6830\n","Epoch 105/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.6658\n","Epoch 106/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.6487\n","Epoch 107/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.6318\n","Epoch 108/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 1.6151\n","Epoch 109/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.5985\n","Epoch 110/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.5821\n","Epoch 111/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.5658\n","Epoch 112/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.5496\n","Epoch 113/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.5336\n","Epoch 114/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.5177\n","Epoch 115/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.5020\n","Epoch 116/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.4865\n","Epoch 117/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.4710\n","Epoch 118/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.4557\n","Epoch 119/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.4406\n","Epoch 120/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.4256\n","Epoch 121/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.4107\n","Epoch 122/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.3960\n","Epoch 123/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.3813\n","Epoch 124/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.3669\n","Epoch 125/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 1.3525\n","Epoch 126/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.3383\n","Epoch 127/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.3242\n","Epoch 128/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.3103\n","Epoch 129/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.2965\n","Epoch 130/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.2828\n","Epoch 131/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.2692\n","Epoch 132/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.2558\n","Epoch 133/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.2424\n","Epoch 134/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.2292\n","Epoch 135/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.2162\n","Epoch 136/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.2032\n","Epoch 137/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.1904\n","Epoch 138/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.1777\n","Epoch 139/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.1651\n","Epoch 140/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.1526\n","Epoch 141/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.1402\n","Epoch 142/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.1280\n","Epoch 143/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.1158\n","Epoch 144/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.1038\n","Epoch 145/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.0919\n","Epoch 146/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.0801\n","Epoch 147/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.0684\n","Epoch 148/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.0569\n","Epoch 149/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.0454\n","Epoch 150/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.0340\n","Epoch 151/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 1.0228\n","Epoch 152/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.0116\n","Epoch 153/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.0006\n","Epoch 154/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9897\n","Epoch 155/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.9788\n","Epoch 156/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9681\n","Epoch 157/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.9575\n","Epoch 158/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.9469\n","Epoch 159/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.9365\n","Epoch 160/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.9262\n","Epoch 161/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.9160\n","Epoch 162/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.9058\n","Epoch 163/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.8958\n","Epoch 164/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.8859\n","Epoch 165/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.8761\n","Epoch 166/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.8663\n","Epoch 167/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.8567\n","Epoch 168/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.8471\n","Epoch 169/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.8377\n","Epoch 170/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.8283\n","Epoch 171/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.8190\n","Epoch 172/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.8098\n","Epoch 173/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.8007\n","Epoch 174/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.7917\n","Epoch 175/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.7828\n","Epoch 176/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.7740\n","Epoch 177/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.7652\n","Epoch 178/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.7566\n","Epoch 179/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.7480\n","Epoch 180/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.7395\n","Epoch 181/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.7311\n","Epoch 182/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.7228\n","Epoch 183/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.7146\n","Epoch 184/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.7064\n","Epoch 185/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.6984\n","Epoch 186/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.6904\n","Epoch 187/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6825\n","Epoch 188/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.6747\n","Epoch 189/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6669\n","Epoch 190/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.6592\n","Epoch 191/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6517\n","Epoch 192/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.6441\n","Epoch 193/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6367\n","Epoch 194/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6293\n","Epoch 195/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6221\n","Epoch 196/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.6148\n","Epoch 197/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.6077\n","Epoch 198/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6006\n","Epoch 199/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5936\n","Epoch 200/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5867\n","Epoch 201/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5799\n","Epoch 202/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5731\n","Epoch 203/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.5664\n","Epoch 204/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5598\n","Epoch 205/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5532\n","Epoch 206/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5467\n","Epoch 207/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5403\n","Epoch 208/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5339\n","Epoch 209/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5276\n","Epoch 210/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.5214\n","Epoch 211/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5152\n","Epoch 212/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5091\n","Epoch 213/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.5031\n","Epoch 214/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.4971\n","Epoch 215/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4912\n","Epoch 216/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.4853\n","Epoch 217/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4796\n","Epoch 218/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.4738\n","Epoch 219/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4682\n","Epoch 220/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.4626\n","Epoch 221/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4570\n","Epoch 222/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.4516\n","Epoch 223/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4461\n","Epoch 224/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.4408\n","Epoch 225/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4355\n","Epoch 226/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.4302\n","Epoch 227/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4250\n","Epoch 228/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.4199\n","Epoch 229/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.4148\n","Epoch 230/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4098\n","Epoch 231/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4048\n","Epoch 232/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.3999\n","Epoch 233/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.3951\n","Epoch 234/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.3903\n","Epoch 235/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.3855\n","Epoch 236/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.3808\n","Epoch 237/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.3762\n","Epoch 238/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.3716\n","Epoch 239/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.3670\n","Epoch 240/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.3625\n","Epoch 241/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.3581\n","Epoch 242/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.3537\n","Epoch 243/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.3493\n","Epoch 244/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.3450\n","Epoch 245/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.3408\n","Epoch 246/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.3366\n","Epoch 247/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.3324\n","Epoch 248/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.3283\n","Epoch 249/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.3243\n","Epoch 250/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.3202\n","Epoch 251/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.3163\n","Epoch 252/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.3123\n","Epoch 253/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3085\n","Epoch 254/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.3046\n","Epoch 255/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.3008\n","Epoch 256/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.2971\n","Epoch 257/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.2934\n","Epoch 258/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.2897\n","Epoch 259/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.2861\n","Epoch 260/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.2825\n","Epoch 261/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.2790\n","Epoch 262/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.2755\n","Epoch 263/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.2720\n","Epoch 264/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.2686\n","Epoch 265/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.2652\n","Epoch 266/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.2619\n","Epoch 267/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.2586\n","Epoch 268/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.2553\n","Epoch 269/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.2521\n","Epoch 270/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.2489\n","Epoch 271/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.2458\n","Epoch 272/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.2427\n","Epoch 273/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2396\n","Epoch 274/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2366\n","Epoch 275/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2336\n","Epoch 276/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.2306\n","Epoch 277/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.2277\n","Epoch 278/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.2248\n","Epoch 279/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.2219\n","Epoch 280/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.2191\n","Epoch 281/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.2163\n","Epoch 282/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2135\n","Epoch 283/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.2108\n","Epoch 284/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2081\n","Epoch 285/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.2055\n","Epoch 286/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.2028\n","Epoch 287/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.2002\n","Epoch 288/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1977\n","Epoch 289/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.1951\n","Epoch 290/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.1926\n","Epoch 291/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.1901\n","Epoch 292/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1877\n","Epoch 293/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.1853\n","Epoch 294/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1829\n","Epoch 295/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1805\n","Epoch 296/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1782\n","Epoch 297/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1759\n","Epoch 298/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1737\n","Epoch 299/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1714\n","Epoch 300/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1692\n","Epoch 301/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1670\n","Epoch 302/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.1648\n","Epoch 303/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1627\n","Epoch 304/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1606\n","Epoch 305/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1585\n","Epoch 306/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.1565\n","Epoch 307/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1544\n","Epoch 308/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1524\n","Epoch 309/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1505\n","Epoch 310/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1485\n","Epoch 311/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.1466\n","Epoch 312/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1447\n","Epoch 313/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1428\n","Epoch 314/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1409\n","Epoch 315/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.1391\n","Epoch 316/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1373\n","Epoch 317/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1355\n","Epoch 318/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.1337\n","Epoch 319/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1320\n","Epoch 320/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1303\n","Epoch 321/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.1286\n","Epoch 322/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.1269\n","Epoch 323/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.1253\n","Epoch 324/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.1236\n","Epoch 325/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.1220\n","Epoch 326/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.1204\n","Epoch 327/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.1188\n","Epoch 328/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.1173\n","Epoch 329/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.1158\n","Epoch 330/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.1142\n","Epoch 331/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.1128\n","Epoch 332/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.1113\n","Epoch 333/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.1098\n","Epoch 334/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.1084\n","Epoch 335/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.1070\n","Epoch 336/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.1056\n","Epoch 337/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.1042\n","Epoch 338/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.1028\n","Epoch 339/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.1015\n","Epoch 340/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.1002\n","Epoch 341/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0989\n","Epoch 342/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0976\n","Epoch 343/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0963\n","Epoch 344/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0951\n","Epoch 345/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0938\n","Epoch 346/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0926\n","Epoch 347/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0914\n","Epoch 348/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0902\n","Epoch 349/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0890\n","Epoch 350/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0879\n","Epoch 351/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0867\n","Epoch 352/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0856\n","Epoch 353/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0845\n","Epoch 354/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0834\n","Epoch 355/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0823\n","Epoch 356/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0812\n","Epoch 357/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0802\n","Epoch 358/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0791\n","Epoch 359/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0781\n","Epoch 360/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0771\n","Epoch 361/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0761\n","Epoch 362/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0751\n","Epoch 363/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0741\n","Epoch 364/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0732\n","Epoch 365/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0722\n","Epoch 366/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0713\n","Epoch 367/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0704\n","Epoch 368/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0695\n","Epoch 369/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0686\n","Epoch 370/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0677\n","Epoch 371/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0668\n","Epoch 372/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0660\n","Epoch 373/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0651\n","Epoch 374/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0643\n","Epoch 375/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0635\n","Epoch 376/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0626\n","Epoch 377/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0618\n","Epoch 378/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0611\n","Epoch 379/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0603\n","Epoch 380/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0595\n","Epoch 381/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0588\n","Epoch 382/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0580\n","Epoch 383/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0573\n","Epoch 384/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0565\n","Epoch 385/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0558\n","Epoch 386/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0551\n","Epoch 387/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0544\n","Epoch 388/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0537\n","Epoch 389/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.0531\n","Epoch 390/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0524\n","Epoch 391/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0517\n","Epoch 392/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0511\n","Epoch 393/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0505\n","Epoch 394/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0498\n","Epoch 395/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0492\n","Epoch 396/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0486\n","Epoch 397/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0480\n","Epoch 398/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0474\n","Epoch 399/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0468\n","Epoch 400/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0462\n","Epoch 401/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0457\n","Epoch 402/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0451\n","Epoch 403/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0446\n","Epoch 404/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0440\n","Epoch 405/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0435\n","Epoch 406/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0430\n","Epoch 407/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0424\n","Epoch 408/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0419\n","Epoch 409/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0414\n","Epoch 410/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0409\n","Epoch 411/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0404\n","Epoch 412/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0399\n","Epoch 413/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0395\n","Epoch 414/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0390\n","Epoch 415/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0385\n","Epoch 416/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0381\n","Epoch 417/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0376\n","Epoch 418/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0372\n","Epoch 419/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0367\n","Epoch 420/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0363\n","Epoch 421/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0359\n","Epoch 422/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0355\n","Epoch 423/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0351\n","Epoch 424/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0346\n","Epoch 425/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0342\n","Epoch 426/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0339\n","Epoch 427/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0335\n","Epoch 428/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0331\n","Epoch 429/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0327\n","Epoch 430/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0323\n","Epoch 431/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0320\n","Epoch 432/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0316\n","Epoch 433/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0313\n","Epoch 434/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0309\n","Epoch 435/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0306\n","Epoch 436/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0302\n","Epoch 437/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0299\n","Epoch 438/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0296\n","Epoch 439/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0292\n","Epoch 440/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0289\n","Epoch 441/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0286\n","Epoch 442/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0283\n","Epoch 443/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0280\n","Epoch 444/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0277\n","Epoch 445/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0274\n","Epoch 446/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0271\n","Epoch 447/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0268\n","Epoch 448/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.0265\n","Epoch 449/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.0263\n","Epoch 450/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0260\n","Epoch 451/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0257\n","Epoch 452/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0255\n","Epoch 453/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0252\n","Epoch 454/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0249\n","Epoch 455/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0247\n","Epoch 456/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0244\n","Epoch 457/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0242\n","Epoch 458/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0239\n","Epoch 459/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0237\n","Epoch 460/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0235\n","Epoch 461/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0232\n","Epoch 462/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0230\n","Epoch 463/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0228\n","Epoch 464/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0226\n","Epoch 465/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0224\n","Epoch 466/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0221\n","Epoch 467/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0219\n","Epoch 468/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0217\n","Epoch 469/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0215\n","Epoch 470/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0213\n","Epoch 471/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0211\n","Epoch 472/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0209\n","Epoch 473/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0207\n","Epoch 474/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0206\n","Epoch 475/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0204\n","Epoch 476/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0202\n","Epoch 477/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0200\n","Epoch 478/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0198\n","Epoch 479/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0197\n","Epoch 480/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0195\n","Epoch 481/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0193\n","Epoch 482/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0192\n","Epoch 483/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0190\n","Epoch 484/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0188\n","Epoch 485/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0187\n","Epoch 486/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0185\n","Epoch 487/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0184\n","Epoch 488/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0182\n","Epoch 489/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0181\n","Epoch 490/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0179\n","Epoch 491/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0178\n","Epoch 492/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0176\n","Epoch 493/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0175\n","Epoch 494/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0174\n","Epoch 495/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0172\n","Epoch 496/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0171\n","Epoch 497/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.0170\n","Epoch 498/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0168\n","Epoch 499/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0167\n","Epoch 500/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0166\n","Epoch 501/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0165\n","Epoch 502/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0163\n","Epoch 503/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0162\n","Epoch 504/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0161\n","Epoch 505/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0160\n","Epoch 506/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0159\n","Epoch 507/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0158\n","Epoch 508/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0157\n","Epoch 509/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0156\n","Epoch 510/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0155\n","Epoch 511/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0153\n","Epoch 512/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0152\n","Epoch 513/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0151\n","Epoch 514/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0150\n","Epoch 515/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0150\n","Epoch 516/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0149\n","Epoch 517/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0148\n","Epoch 518/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0147\n","Epoch 519/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0146\n","Epoch 520/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0145\n","Epoch 521/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0144\n","Epoch 522/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0143\n","Epoch 523/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0142\n","Epoch 524/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0141\n","Epoch 525/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0141\n","Epoch 526/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0140\n","Epoch 527/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0139\n","Epoch 528/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0138\n","Epoch 529/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0137\n","Epoch 530/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0137\n","Epoch 531/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0136\n","Epoch 532/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0135\n","Epoch 533/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0135\n","Epoch 534/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0134\n","Epoch 535/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0133\n","Epoch 536/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0132\n","Epoch 537/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0132\n","Epoch 538/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0131\n","Epoch 539/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0130\n","Epoch 540/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0130\n","Epoch 541/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0129\n","Epoch 542/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0129\n","Epoch 543/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0128\n","Epoch 544/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0127\n","Epoch 545/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0127\n","Epoch 546/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0126\n","Epoch 547/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0126\n","Epoch 548/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0125\n","Epoch 549/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0125\n","Epoch 550/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0124\n","Epoch 551/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0123\n","Epoch 552/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0123\n","Epoch 553/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0122\n","Epoch 554/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0122\n","Epoch 555/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0121\n","Epoch 556/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0121\n","Epoch 557/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0120\n","Epoch 558/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0120\n","Epoch 559/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0120\n","Epoch 560/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0119\n","Epoch 561/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0119\n","Epoch 562/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0118\n","Epoch 563/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0118\n","Epoch 564/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0117\n","Epoch 565/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0117\n","Epoch 566/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0117\n","Epoch 567/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0116\n","Epoch 568/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0116\n","Epoch 569/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0115\n","Epoch 570/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0115\n","Epoch 571/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0115\n","Epoch 572/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0114\n","Epoch 573/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0114\n","Epoch 574/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0113\n","Epoch 575/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0113\n","Epoch 576/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0113\n","Epoch 577/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0112\n","Epoch 578/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0112\n","Epoch 579/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0112\n","Epoch 580/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0111\n","Epoch 581/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0111\n","Epoch 582/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0111\n","Epoch 583/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0111\n","Epoch 584/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0110\n","Epoch 585/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0110\n","Epoch 586/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0110\n","Epoch 587/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0109\n","Epoch 588/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0109\n","Epoch 589/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0109\n","Epoch 590/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0109\n","Epoch 591/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0108\n","Epoch 592/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0108\n","Epoch 593/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0108\n","Epoch 594/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0108\n","Epoch 595/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0107\n","Epoch 596/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0107\n","Epoch 597/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0107\n","Epoch 598/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0107\n","Epoch 599/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0106\n","Epoch 600/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0106\n","Epoch 601/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0106\n","Epoch 602/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0106\n","Epoch 603/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0105\n","Epoch 604/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0105\n","Epoch 605/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0105\n","Epoch 606/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0105\n","Epoch 607/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0105\n","Epoch 608/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0104\n","Epoch 609/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0104\n","Epoch 610/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0104\n","Epoch 611/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0104\n","Epoch 612/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0104\n","Epoch 613/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0103\n","Epoch 614/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0103\n","Epoch 615/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0103\n","Epoch 616/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0103\n","Epoch 617/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0103\n","Epoch 618/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0103\n","Epoch 619/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0102\n","Epoch 620/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0102\n","Epoch 621/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0102\n","Epoch 622/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0102\n","Epoch 623/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0102\n","Epoch 624/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0102\n","Epoch 625/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0102\n","Epoch 626/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0101\n","Epoch 627/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0101\n","Epoch 628/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0101\n","Epoch 629/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0101\n","Epoch 630/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0101\n","Epoch 631/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0101\n","Epoch 632/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0101\n","Epoch 633/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 634/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0100\n","Epoch 635/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0100\n","Epoch 636/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0100\n","Epoch 637/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0100\n","Epoch 638/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0100\n","Epoch 639/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0100\n","Epoch 640/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0100\n","Epoch 641/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0100\n","Epoch 642/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0099\n","Epoch 643/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0099\n","Epoch 644/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0099\n","Epoch 645/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0099\n","Epoch 646/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0099\n","Epoch 647/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0099\n","Epoch 648/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0099\n","Epoch 649/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0099\n","Epoch 650/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0099\n","Epoch 651/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0099\n","Epoch 652/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0098\n","Epoch 653/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0098\n","Epoch 654/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0098\n","Epoch 655/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0098\n","Epoch 656/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0098\n","Epoch 657/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0098\n","Epoch 658/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0098\n","Epoch 659/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0098\n","Epoch 660/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0098\n","Epoch 661/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0098\n","Epoch 662/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0098\n","Epoch 663/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0098\n","Epoch 664/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0098\n","Epoch 665/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0097\n","Epoch 666/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0097\n","Epoch 667/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0097\n","Epoch 668/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0097\n","Epoch 669/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0097\n","Epoch 670/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0097\n","Epoch 671/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0097\n","Epoch 672/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0097\n","Epoch 673/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0097\n","Epoch 674/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0097\n","Epoch 675/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0097\n","Epoch 676/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0097\n","Epoch 677/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0097\n","Epoch 678/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0097\n","Epoch 679/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0097\n","Epoch 680/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0097\n","Epoch 681/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0097\n","Epoch 682/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0096\n","Epoch 683/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0096\n","Epoch 684/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0096\n","Epoch 685/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0096\n","Epoch 686/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0096\n","Epoch 687/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0096\n","Epoch 688/3000\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 689/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0096\n","Epoch 690/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.0096\n","Epoch 691/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0096\n","Epoch 692/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0096\n","Epoch 693/3000\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 694/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0096\n","Epoch 695/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0096\n","Epoch 696/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0096\n","Epoch 697/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0096\n","Epoch 698/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0096\n","Epoch 699/3000\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 700/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0096\n","Epoch 701/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0096\n","Epoch 702/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.0096\n","Epoch 703/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0096\n","Epoch 704/3000\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 705/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0096\n","Epoch 706/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0096\n","Epoch 707/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0095\n","Epoch 708/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0095\n","Epoch 709/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0095\n","Epoch 710/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0095\n","Epoch 711/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0095\n","Epoch 712/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0095\n","Epoch 713/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0095\n","Epoch 714/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0095\n","Epoch 715/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.0095\n","Epoch 716/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0095\n","Epoch 717/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0095\n","Epoch 718/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0095\n","Epoch 719/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0095\n","Epoch 720/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0095\n","Epoch 721/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0095\n","Epoch 722/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0095\n","Epoch 723/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0095\n","Epoch 724/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0095\n","Epoch 725/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0095\n","Epoch 726/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0095\n","Epoch 727/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0095\n","Epoch 728/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0095\n","Epoch 729/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0095\n","Epoch 730/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0095\n","Epoch 731/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0095\n","Epoch 732/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0095\n","Epoch 733/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0095\n","Epoch 734/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0095\n","Epoch 735/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0095\n","Epoch 736/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0095\n","Epoch 737/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0095\n","Epoch 738/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0095\n","Epoch 739/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0095\n","Epoch 740/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0095\n","Epoch 741/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0095\n","Epoch 742/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0095\n","Epoch 743/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.0095\n","Epoch 744/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0095\n","Epoch 745/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0095\n","Epoch 746/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0095\n","Epoch 747/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0095\n","Epoch 748/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0095\n","Epoch 749/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0095\n","Epoch 750/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0095\n","Epoch 751/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0095\n","Epoch 752/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0095\n","Epoch 753/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0095\n","Epoch 754/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0095\n","Epoch 755/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0094\n","Epoch 756/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0094\n","Epoch 757/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0094\n","Epoch 758/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0094\n","Epoch 759/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0094\n","Epoch 760/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0094\n","Epoch 761/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0094\n","Epoch 762/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0094\n","Epoch 763/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0094\n","Epoch 764/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0094\n","Epoch 765/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0094\n","Epoch 766/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0094\n","Epoch 767/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0094\n","Epoch 768/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0094\n","Epoch 769/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0094\n","Epoch 770/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0094\n","Epoch 771/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0094\n","Epoch 772/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0094\n","Epoch 773/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0094\n","Epoch 774/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0094\n","Epoch 775/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0094\n","Epoch 776/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0094\n","Epoch 777/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0094\n","Epoch 778/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0094\n","Epoch 779/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0094\n","Epoch 780/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0094\n","Epoch 781/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0094\n","Epoch 782/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0094\n","Epoch 783/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0094\n","Epoch 784/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0094\n","Epoch 785/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0094\n","Epoch 786/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0094\n","Epoch 787/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0094\n","Epoch 788/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0094\n","Epoch 789/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0094\n","Epoch 790/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0094\n","Epoch 791/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0094\n","Epoch 792/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0094\n","Epoch 793/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0094\n","Epoch 794/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0094\n","Epoch 795/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0094\n","Epoch 796/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0094\n","Epoch 797/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0094\n","Epoch 798/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0094\n","Epoch 799/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0094\n","Epoch 800/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0094\n","Epoch 801/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0094\n","Epoch 802/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0094\n","Epoch 803/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0094\n","Epoch 804/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0094\n","Epoch 805/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0094\n","Epoch 806/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0094\n","Epoch 807/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0094\n","Epoch 808/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0094\n","Epoch 809/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0094\n","Epoch 810/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0094\n","Epoch 811/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0094\n","Epoch 812/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0094\n","Epoch 813/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0094\n","Epoch 814/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0094\n","Epoch 815/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0094\n","Epoch 816/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0094\n","Epoch 817/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0094\n","Epoch 818/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0094\n","Epoch 819/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0094\n","Epoch 820/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0094\n","Epoch 821/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0094\n","Epoch 822/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0094\n","Epoch 823/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0094\n","Epoch 824/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0094\n","Epoch 825/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0094\n","Epoch 826/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0094\n","Epoch 827/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0094\n","Epoch 828/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0094\n","Epoch 829/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0094\n","Epoch 830/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0094\n","Epoch 831/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0094\n","Epoch 832/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0094\n","Epoch 833/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0094\n","Epoch 834/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0094\n","Epoch 835/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0094\n","Epoch 836/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0094\n","Epoch 836: early stopping\n","Restoring model weights from the end of the best epoch: 536.\n","\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step\n"]},{"output_type":"stream","name":"stderr","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"]},{"output_type":"stream","name":"stdout","text":["\n","\n"]}]},{"cell_type":"code","source":["lib.ire_plot('training', IRE2, IREth2, 'AE2')"],"metadata":{"id":"qKul4ubYliBs","executionInfo":{"status":"ok","timestamp":1760898018603,"user_tz":-180,"elapsed":698,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/","height":744},"collapsed":true,"outputId":"89be1149-fba6-4772-eca4-7a410d409434"},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 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Xrrbb0E5zc3MhwN2pUyf95S9/MQ1YGv3hD38oDGhz//33pyJo+fjjjxe212abbaajjz663TRnnHFGIWh5//336+qrr24XqLYyf/583Xrrra7LM2TIEN9BywULFuiHP/xh4e/hw4ebrg8AAGlC0BIAgCpkfOaclZ122knXX3+944AjafLqq6+qsbFRktSnTx8df/zxttPX1tbqBz/4gX7xi19Ikl588UXHZXjJSo3KkUceqU033dTz9xYvXqzXX39dM2bM0IoVK7R27drCKMtS7vmJedOnTw8kaNm5c2fH/bLXXnsVXudH/A6C8XmGZmpqajR48ODC4E0zZ85st87GAWuOPfZY9erVy3aeBx98sL72ta9p9uzZvspcauutt9aXX36pFStW6OGHH9app57qeR5vv/12YbTsww8/XFtssYXjd/r376+dd95ZM2bM0AcffKBVq1bZ/iCQBPfcc0/h9WmnnWb6zNlTTjlFP/nJT7Ru3TotWrRIzz77bOLqxKamJp1yyimFZ49uttlmmjBhQryFAgAgAAQtAQBAO3379tW9995rOuCLk0suuUSXXnpp8IUKwLRp0wqvhw4d6ipj6sADDyz6fjabbTdCr9E+++xTXiFD4LVMH330kX7961/rP//5j+vRh708EsDOTjvt5DhgkTHzN58pFwQ3QVenZU+fPr3w2m3W3NChQwMLWn73u98tZHeOHDmyELg89NBDXQUfJem1114rvJ47d67rLs4rV66UJGWzWc2dOzfRQcv58+frueeeK/xd2jU8b5NNNtEJJ5ygBx54QFIu0Ok2aHnIIYeYjroepGw2q1GjRunVV1+VlHvcw4MPPqj+/fuHulwAAKJA0BIAgCp0wgknFI3Su2zZMn3xxReFbt2LFi3SQQcdpH/961+FZ/hVgiVLlhReb7PNNq6+M3DgwMLrpqYmrVmzRj169LCcfvPNN/ddvrB4KdOzzz6rb3/724WMVLfyo0KXy02gyxjUzI+SHceym5ub231uPMa23nprV8vdaqutXE3nxsUXX6wpU6bo9ddfVzab1WOPPabHHntMkrTjjjvqoIMO0uGHH67jjz/edPRuKRfQy3v//ff1/vvvey7HihUr/K1ARO67775CQH7HHXe0DTCfccYZhaDlk08+qRUrVvjKXA7Deeedp4ceekhSLjP8nnvu0fDhw2MuFQAAwbB/OA0AAKhIF1xwgW655ZbCvwcffFBvvvmm3nvvPQ0ePFhSLkA3cuRIffHFFzGXNjj5AXik3PMG3Sidzik416VLF+8FC5nbMi1ZskSnnnpqIWC5zTbb6Morr9Qrr7yi+fPna926dcpkMspms8pms0UDzmQymUDKapfFGrYglm08xrp27erqO36fPWmmW7dueumll3TttdcWBdyl3MAwd911l04//XT169dPv/rVr7R+/fp281i1alXZ5QgymBwGY9fw73//+7bTDh8+XP369ZOUGyjK+FzcOI0bN0633XZb4e9bb71VI0eOjLFEAAAEi6AlAAAo2GOPPTRp0qRCFmZDQ4POOuusmEsVHGNwaO3ata6+UzqdVXZaJbjjjjsKAavBgwfr/fff10UXXaQDDzxQW265pbp06VIU2Asqu7KSGI+xdevWufqO22PRrY4dO+rCCy/Ul19+qenTp+tPf/qTTjvttKLs6nXr1unaa6/VoYce2i5waQzU//SnPy0Eqb38GzZsWKDrFKS3335bH330UeHvSy65RDU1NZb/6uvrC88xlYoDnnH5/e9/XzTI09VXX61zzjknxhIBABA8gpYAAKDIFltsoZtvvrnw95QpU/T000/HWKLgGLtJu32GoHGgl44dO1Z00PL5558vvL744ottu8FL0qxZs8IuUur06dOn8Hru3LmuvuN2Oq/yAwedf/75evDBBzV37ly9++67GjNmTGGaN954o90I13379i28NgbrKkW5QcfXX39dn376aUCl8e7GG2/UxRdfXPj74osv1q9+9avYygMAQFh4piUAAGjnxBNP1IEHHqj//ve/knI3xUkbMdcP46jTb775plpbW1VXV2f7nfwAF/nvx9F9OaplGp9l6DQoTWtra+H4QJs999yzEPx94403XH3nzTffDLNIRfbaay/dddddqqur05133ikp95zGCy+8sDCN8fmOr776quPgU27F2fU/r6mpSQ8++GDh75122slxhPe8zz77TMuXL5eUC3z+/ve/D6OItm6//Xb9/Oc/L/x9wQUX6PLLL4+8HAAARIFMSwAAYMo4Avj06dP15JNPxleYgBxwwAHq1KmTpNzzG50ySDOZjO6+++7C34cddlio5bPSuXPnwmuzwV+CUlvb1jR06tr8+OOPV2QWXrmM3aKffvppx+dDvvLKK7FkrH7rW98qvF60aFHRZwceeGAhkDd37lw99dRTgSwzquPYztNPP61ly5ZJkurr6/Xyyy/r9ddfd/Xv//2//1eYz9///vfAnuPq1n333VfUBfz//u//9Mc//jHSMgAAECWClgAAwNQRRxyhAw44oPD3FVdcEWNpgtGrVy+deuqphb9/+ctf2j6X8ZZbbtH//vc/SbmA3g9/+MPQy2hms802K7yeN29eaMvZbrvtCq/tgtRLliwpyvZCm6OOOkr9+/eXlHsmrDHQVaqpqakow7FcjY2NRQMB2ZkzZ07h9RZbbFH0WadOnfSzn/2s8Pe5557r6bgrDYLmRXUc2zF2DR8+fHi7dbczcuTIQmB/zpw5evHFFwMvn5VHH31Uo0ePVjabLZTl9ttvT0T2KgAAYSFoCQAALI0fP77w+q233tIzzzwTY2mCMX78+MJgKZ9++qlGjBihL7/8smiaTCajm266SWPHji2895Of/KTdaMxR2W233QqvJ02aFMjozmaOP/74wusrr7xS9913X7tp3n33XR1yyCGaM2eO6xHYq0l9fX1RlvJtt92mX//612pqaiqabsmSJTr55JP1xhtvFLJ/y7VgwQJtvfXWuvDCC/X2229bTjd58uSikd+PPvrodtP84he/0KBBgyTlAoxDhgzRP//5T8vswqVLl+r222/X3nvvrWuvvdZ0GuNx/M9//tPVOk2YMKFoUBzjM2a9Wrp0qSZOnFj4+/TTT/f0/f79++vQQw8t/B3VgDzPPPOMRo4cqdbWVknSt7/9bd17771FmdEAAFQinmkJAAAsjRgxQvvuu2/h2XyXX365jjrqKNvvTJw4UUuXLnW9jK5du+qaa64pq5xebL/99rrzzjt1+umnq7W1Va+99pp22mknHXTQQdp+++3V0NCgqVOnFmWC7bfffpGWsdTQoUO19dZba86cOVqwYIF23nlnHXnkkerTp08h0+ob3/hGURapH6NGjdL111+vTz/9VI2NjTrjjDP0hz/8QYMHD1bnzp31wQcfFIJhgwcP1ogRI2LdLkl11lln6emnn9YTTzwhSbrmmmv0t7/9TcOGDVPv3r01d+5cvfjii9qwYYO22247ffvb3y508y03ELVy5Updf/31uv7669W7d2/ttddeGjBggDp37qzFixfr/fffLwrSf/3rX9cFF1zQbj6bbLKJnnzySR1xxBH66quvtHDhQn33u99Vnz59tN9++6lfv37KZrNavny5PvroI3322WeFgKbVYxROPvlk/fWvf5WUC+a+88472nvvvdW1a9fCND/+8Y+1/fbbl7UNrDzwwAOFbundunXTCSec4Hkep59+euGZpY8++qhuu+22ohHjjT777DOdd955nuY/bty4olHely5dqpNOOqkQ9K6rq9Pmm29elAlr54wzzih6RikAAGlC0BIAANgaP358YRCeV199Vc8//7wOP/xwy+nfeustvfXWW67n37Nnz8gDX6eeeqq6deums846S4sWLVJLS4tefPFF0+6eI0eO1J133ln0PL6o1dbW6rbbbtPJJ5+spqYmLVy4UPfee2/RNKNGjSo7aNmpUyc99dRTOvroowuBrRkzZmjGjBlF0x144IF6+OGHdccdd5S1vEpVU1Ojhx9+WKNHj9ZDDz0kSVq2bJn+9a9/FU23yy676LHHHival04jttvp0KGDOnXqpMbGRknS8uXLi0aELzVs2DA9+OCDlhmz2223nd5++22dc845euSRR5TNZrV06VL9+9//tpxnr169LAdxGj58uEaOHFkYCOeNN95oN1jRcccdF1rQ0pgZecIJJ/jKFD755JN17rnnasOGDVq7dq0eeeQRjR492nTa+fPntxuZ3clZZ51VFLRsaGjQ+vXrC3+3trYWBlByY8iQIQQtAQCpRZ8CAABg65hjjtGQIUMKf//ud7+LsTTBOe644/T555/rpptu0vDhw9W/f3917NhRPXv21C677KJzzz1Xr7/+uh544IGiTLA4y/v222/rRz/6kQYNGqTu3buH8jy7r3/965o2bZr+8Ic/aMiQIerevbs6deqkbbbZRscdd5weeOABvfTSS0WBFbTXqVMnPfjgg3r22Wf1ne98R1tttZU6duyovn376sADD9TNN9+st956SzvttFNhRGpJrkeyNjNgwAAtW7ZMTz75pH75y1/qsMMO09e+9jV16dJFdXV16tWrlwYPHqyzzjpLkydP1osvvqh+/frZzrN37976xz/+offff1//7//9Px1wwAHacsst1bFjR3Xu3Fn9+vXTN7/5Tf30pz/Vk08+qQULFmjUqFGW87v//vt1//3367jjjtNWW20V2Y8BH3zwgd59993C3167huf16NGj6DEKEyZMKLdoAADAQk02/zRnAAAAAJE78MAD9eqrr0qSXn/9dTLjAAAARNASAAAAiM2sWbO0/fbbq7W1VR07dtSqVatifRQBAABAUtA9HAAAAIhBNpvVBRdcUBgV+sQTTyRgCQAAsBFBSwAAACBg48eP10033aSlS5eafj5z5kydeOKJhRHG6+rqdOGFF0ZZRAAAgERj9HAAAAAgYLNnz9bll1+uCy+8ULvvvrt23nln9ezZUw0NDfr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\n"},"metadata":{}}]},{"cell_type":"code","source":["numb_square = 20\n","xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)"],"metadata":{"id":"eWxkpw0xlmHD","executionInfo":{"status":"ok","timestamp":1760898453695,"user_tz":-180,"elapsed":3759,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"collapsed":true,"outputId":"276b489e-9c17-4ea2-a04f-4b892ef18b8b"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["amount:  19\n","amount_ae:  104\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x700 with 2 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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качества AE1\n","IDEAL = 0. Excess:  4.473684210526316\n","IDEAL = 0. Deficit:  0.0\n","IDEAL = 1. Coating:  1.0\n","summa:  1.0\n","IDEAL = 1. Extrapolation precision (Approx):  0.18269230769230768\n","\n","\n"]}]},{"cell_type":"code","source":["numb_square = 20\n","xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)"],"metadata":{"id":"8z0sLMRVn05v","executionInfo":{"status":"ok","timestamp":1760898890931,"user_tz":-180,"elapsed":4552,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"collapsed":true,"outputId":"06eea577-65bd-4b46-803b-948d2a4c6d2d"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 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rysrKRo/Zq1evgPvS0tK0Y8eORveN5HNVXFysPn36BGwX6eoId999t6ZMmaL8/HwddthhOvHEEzV58mQdcMABktw/C5I7eIVSWVmptLS0iJ4vHHPIw65du5p8DJfLpQceeECPPvqo1q9f73Ohr0ePHp7vr7vuOi1ZskSHH364+vbtq+OPP17nnnuujjrqKM82jZ2bxvj//ujTp4/sdrtnnL75c+z/XmVlZalbt26en/em8P/cme+P+bkzj+3fxvT09ID3MtJzagr2c9StWzdNmDBBzz//vG677TZJ7gu0ubm5+sMf/hDtywOAqBDSASAK77//vjZv3qwXX3xRL774YsDjCxcu9IT0UGN/W7raLrn/KLXZbHr77beDztjtHxZDzeptBJmMri2ZFc7//d//DRmy/Jcei7SKbhiGXnjhBe3Zs0cDBw4MeLy8vFy7d+9ucrCuqanRaaedplWrVundd9/VwQcf3KTjRCPYaz/rrLO0fPlyzZw5U0OHDlXXrl3lcrl0wgknRFRBbs5noy0+V2eddZaOPvpoLVq0SO+9957uuece3XXXXXrttdc0btw4z2u85557NHTo0KDHaKmLJykpKcrJydHq1aubfIz/+7//080336zzzz9ft912m7p37y673a4rrrjC5/066KCDtGbNGr355pt655139Oqrr+rRRx/VLbfcojlz5khq/NxEK9TvsMbmNQgn1O+/lvzsRHpOTaF+h0yePFkvv/yyli9frkMOOUT//ve/dckllzDzO4BWR0gHgCgsXLhQGRkZntnYvb322mtatGiRHn/8cXXq1MlT3dm5c6fPdsGqTaH+6C0oKNCSJUu0a9cun2r1Tz/95HlckmcSt969e6t///5Nem3++vTpo88//1y1tbUBlfho21dQUCCXy+WpzpvWrFnjczxz5nen0xn10mWN+fDDD7Vx40bdeuutPuuNS+5q3UUXXaTXX3+9SV1ZXS6XJk+erKVLl+qf//ynRo4c2VLNjsqOHTu0dOlSzZkzR7fccovnfrO6bAUFBQX64YcfZBiGz+fe7NkQiezsbF1yySW65JJLVF5ermHDhumOO+7QuHHjPL01UlJSGv0MNSdsmsaPH68nnnhCn376qYYPHx71/q+88oqOO+44PfXUUz7379y5M2DiwS5duujss8/W2Wef7bkodMcdd+iGG27wLIkX7tw05ueff/apKq9bt04ul0uFhYWSGn6Of/75Z5+fobKyMu3cudPz8y65K+H+v/tqamq0efPmiM6LP/PYP//8s0/PgIqKioBeHtGc03BOOOEEpaena+HChTriiCP022+/6Y9//GOT2g8A0eBSIABEaO/evXrttdc0fvx4nXHGGQFfl156qXbt2qV///vfktx/VMbFxemjjz7yOc6jjz4acGxzfWv/P2pPPPFEOZ1OPfzwwz73/+1vf5PNZvP84X3aaacpLi5Oc+bMCag8GYYRsPxWJE4//XRt3bo14LnNY0bTPvPfBx980Ge7+++/3+d2XFycTj/9dL366qtBq5P+SzJFw+zqPnPmzID37sILL1S/fv2CLqUXicsuu0wvvfSSHn30UZ122mkht9u6dat++ukn/fbbb019GWGZ1Uj/z4D/eY6lsWPHatOmTZ6fE8k9dv7JJ59sdF+n0xnQZT8jI0M5OTmeJcAOO+ww9enTR/fee692794dcAzvz1ConzvJvVSfebEpnGuvvVZdunTRtGnTVFZWFvD4L7/8ogceeCDk/nFxcQHv18svv6xNmzb53Of/M5yYmKiBAwfKMAzV1tZGdG4a43/x8aGHHpLU8PN74oknSgr8PP31r3+VJJ/hIn369An43ffEE080uSfR6NGjlZCQoIceesjnfAX7bEd6ThsTHx+vSZMm6Z///KcWLFigQw45JKAnDwC0BirpABChf//739q1a5fPhFfejjzySE/V5eyzz1ZqaqrOPPNMPfTQQ7LZbOrTp4/efPPNgHHCkjtYSO6J1caOHau4uDidc845mjBhgo477jjdeOONKioq0pAhQ/Tee+/pX//6l6644gpP1bBPnz66/fbbdcMNN6ioqEgTJ05UcnKy1q9fr0WLFumiiy7SNddcE9XrnTx5sv7+97/rqquu0hdffKGjjz5ae/bs0ZIlS3TJJZfolFNOibh9Q4cO1aRJk/Too4+qsrJSI0aM0NKlS4NWT++880598MEHOuKII3ThhRdq4MCB2r59u1asWKElS5Zo+/btUb0Oyb1G+6uvvqoxY8Z4Ko7+Tj75ZD3wwAMqLy9XRkaGJPea0M8991zQ7U899VR16dJF999/vx599FENHz5cnTt3Dtje3E6SHn74Yc2ZM0cffPCBjj322KhfR2NSUlJ0zDHH6O6771Ztba1yc3P13nvv+cyfEGvTp0/Xww8/rEmTJunyyy9Xdna2Fi5c6HlfwlW3d+3apby8PJ1xxhkaMmSIunbtqiVLlujLL7/UfffdJ0my2+2aP3++xo0bp0GDBmnq1KnKzc3Vpk2b9MEHHyglJUVvvPGGpIafuxtvvFHnnHOOEhISNGHCBHXp0kWTJ0/Whx9+2Gh36z59+uj555/X2WefrYMOOkiTJ0/WwQcfrJqaGi1fvlwvv/yyz7rg/saPH69bb71VU6dO1YgRI/Tdd99p4cKFAePIjz/+eGVlZemoo45SZmamfvzxRz388MM66aSTlJycrJ07dzZ6bhqzfv16nXzyyTrhhBP06aefepZMHDJkiCRpyJAhmjJlip544gnt3LlTI0eO1BdffKFnn31WEydO9EymKbmXarv44ot1+umna8yYMfr222/17rvvNnlZwvT0dF1zzTWaO3euxo8frxNPPFHffPON3n777YBjRnpOIzF58mQ9+OCD+uCDD3TXXXc1qe0AELU2nUseAPZjEyZMMJKSkow9e/aE3Oa8884zEhISPMuHVVRUGKeffrrRuXNnIy0tzZg+fbqxevXqgCXY6urqjMsuu8xIT083bDabz3Jsu3btMq688kojJyfHSEhIMPr162fcc889PssQmV599VXj97//vdGlSxejS5cuxoEHHmjMmDHDWLNmjWebkSNHBl3KacqUKUZBQYHPfb/99ptx4403Gr179zYSEhKMrKws44wzzjB++eWXqNu3d+9e489//rPRo0cPo0uXLsaECROMkpKSoEs1lZWVGTNmzDDy8/M9zztq1CjjiSee8GxjLp/08ssvh3w/vM+LJOOpp54Kuc2yZcsMScYDDzzgOR8KsQSbvJbtinQ7w2hY6qqxZca8hVuCraKiImD7jRs3GqeeeqrRrVs3IzU11TjzzDON0tLSgPMcagm2k046KeCYI0eONEaOHOm5HWoJtkg/V7/++qtx0kknGZ06dTLS09ONq6++2vMeffbZZyHPRXV1tTFz5kxjyJAhRnJystGlSxdjyJAhxqOPPhqw7TfffGOcdtppRo8ePQyHw2EUFBQYZ511lrF06VKf7W677TYjNzfXsNvtPufDXFIuUmvXrjUuvPBCo7Cw0EhMTDSSk5ONo446ynjooYeMffv2ebYLtgTb1VdfbWRnZxudOnUyjjrqKOPTTz8NOOfz5s0zjjnmGM/r6dOnjzFz5kyjsrIy6nPjz/w8/fDDD8YZZ5xhJCcnG2lpacall15q7N2712fb2tpaY86cOZ7fCfn5+cYNN9zg8xoNwzCcTqdx3XXXGT179jQ6d+5sjB071li3bl3IJdi+/PJLn/2DfcacTqcxZ84cz7k69thjjdWrVzf5nEb6O2TQoEGG3W43Nm7c2Oi5BICWYDOMGM8SBAAAOrz7779fV155pTZu3Kjc3NxYNwfwOPTQQ9W9e3ctXbo01k0B0EEwJh0AALSpvXv3+tzet2+f5s2bp379+hHQYSlfffWVVq5cqcmTJ8e6KQA6EMakAwCANnXaaaepV69eGjp0qCorK/Xcc8/pp59+avLEfUBLW716tb7++mvdd999ys7O1tlnnx3rJgHoQAjpAACgTY0dO1bz58/XwoUL5XQ6NXDgQL344osEIVjGK6+8oltvvVUDBgzQCy+8EHLCSQBoDYxJBwAAAADAIhiTDgAAAACARRDSAQAAAACwiA43Jt3lcqm0tFTJycmy2Wyxbg4AAAAAoJ0zDEO7du1STk6O7PbwtfIOF9JLS0uVn58f62YAAAAAADqYkpIS5eXlhd2mw4X05ORkSdJf/vu1krp2jXFrAAAAAADt3b7du/V/Rx3myaPhdLiQbnZxT+raVUkRnCAAAAAAAFpCJEOumTgOAAAAAACLIKQDAAAAAGARhHQAAAAAACyiw41JBwAAAAC0nDjDpUSXSzYZsW5KzBiyqcZul9PW/Do4IR0AAAAA0CQpdTU6aG+VEiOYEK29qzEM/dgpRVXxic06DiEdAAAAABC1OMOlg/ZWKT05WV27d5c6ck43pN3bt0u7qvRV1+7NqqgT0gEAAAAAUUt0uZRos6lr9+5KSEqKdXNirmv37qrcvVuJLpf2xjU9pDNxHAAAAAAgap4x6B25gu7NZv7TvLH5hHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAACgwynfskU3XnmFjjhwgApSknVYnz6afNqp+vj99yVJ/5g/X6eNGaN+6T2VneRQ5c6dbdIuQjoAAAAAoEMpKSrS2BHD9cmyZbp57p16/+uv9fwbb2jEyJG64YrLJUl79/6m444/Xn++9ro2bRtLsAEAAAAAOpTrL/+zbDab3v7kv+rcpYvn/gEDB2rSlPMkSRdd9mdJ0vIPP2zTtlFJBwAAAADEnNPZNs+zY/t2ffDeezpv+sU+Ad2U2q1b2zQkBEI6AAAAACBm1q2VRg6NV16XRI0cGq91a1v3+Yp++UWGYajvgAGt+0RNREgHAAAAAMTMBWfF65efbZKkX3626YKzWndUtmEYrXr85mJMOgAAAAAgJpxOae1Pdq/bNq39ySanU4qLa53n7N23r2w2m9atWdM6T9BMVNIBAAAAADERFyf1P9CluDij/rZRf7v1njOte3cdO2aMFsx7XL/t2RPweFsttRYKIR0AAAAAEDNP/bNOffq5Q3qffoae+mddqz/n3PsfkNPp1LjfH6U3Fy3Sr+t+1tqfftT8Rx7W+JHHSHKvo77622+1/pdfJEk/rl6t1d9+qx3bt7dq2+juDgAAAACImb79pQ9X1rVqF3d/BQccoPc+/UwP3HWn5lx3ncq3bFaP9HQNPvRQ3fXgQ5Kkvz/5pO6743bPPqeOHiVJuv+JJ3X25Mmt1jabYfVR8y2sqqpKqampuvXbNUpKTo51cwAAAABgv9TZWavD9lYpt6CXEhxJsW5OzNVW79Om4g36ulOKfotL8Hls365dumXIAFVWViolJSXscejuDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAgKgZspnfQPKcB895aSJCOgAAAAAganU2mwzDkLO2NtZNsQRnba1chqE6W/NCOuukAwAAAACiVmuza5s9Xp23bZM9Pl42e/PC6f7McBmq3LpV2+3xqrU1rxZOSAcAAAAARM9m0y+dktV1z07tLSmJdWtibp8h/dKlm0QlHQAAAAAQCzX2OH3VtbuSXE7ZOvDgdEM27bPHyWhmQJcI6QAAAACAZjBsNu2NI1q2FCaOAwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAImIa0h977DENHjxYKSkpSklJ0fDhw/X222+H3efll1/WgQceqKSkJB1yyCF666232qi1AAAAAAC0rpiG9Ly8PN155536+uuv9dVXX+kPf/iDTjnlFH3//fdBt1++fLkmTZqkCy64QN98840mTpyoiRMnavXq1W3ccgAAAAAAWp7NMAwj1o3w1r17d91zzz264IILAh47++yztWfPHr355pue+4488kgNHTpUjz/+eETHr6qqUmpqqm79do2SkpNbrN0AAAAAAASzb9cu3TJkgCorK5WSkhJ2W8uMSXc6nXrxxRe1Z88eDR8+POg2n376qUaPHu1z39ixY/Xpp5+GPG51dbWqqqp8vgAAAAAAsKKYh/TvvvtOXbt2lcPh0MUXX6xFixZp4MCBQbfdsmWLMjMzfe7LzMzUli1bQh5/7ty5Sk1N9Xzl5+e3aPsBAAAAAGgpMQ/pAwYM0MqVK/X555/rT3/6k6ZMmaIffvihxY5/ww03qLKy0vNVUlLSYscGAAAAAKAlxce6AYmJierbt68k6bDDDtOXX36pBx54QPPmzQvYNisrS2VlZT73lZWVKSsrK+TxHQ6HHA5HyzYaAAAAAIBWEPNKuj+Xy6Xq6uqgjw0fPlxLly71uW/x4sUhx7ADAAAAALA/iWkl/YYbbtC4cePUq1cv7dq1S88//7yWLVumd999V5I0efJk5ebmau7cuZKkyy+/XCNHjtR9992nk046SS+++KK++uorPfHEE7F8GQAAAAAAtIiYhvTy8nJNnjxZmzdvVmpqqgYPHqx3331XY8aMkSRt2LBBdntDsX/EiBF6/vnnddNNN+kvf/mL+vXrp9dff10HH3xwrF4CAAAAAAAtxnLrpLc21kkHAAAAALSl/XKddAAAAAAAOjpCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARMQ3pc+fO1e9+9zslJycrIyNDEydO1Jo1a8Lus2DBAtlsNp+vpKSkNmoxAAAAAACtJ6Yh/cMPP9SMGTP02WefafHixaqtrdXxxx+vPXv2hN0vJSVFmzdv9nwVFxe3UYsBAAAAAGg98bF88nfeecfn9oIFC5SRkaGvv/5axxxzTMj9bDabsrKyWrt5AAAAAAC0KUuNSa+srJQkde/ePex2u3fvVkFBgfLz83XKKafo+++/D7ltdXW1qqqqfL4AAAAAALAiy4R0l8ulK664QkcddZQOPvjgkNsNGDBATz/9tP71r3/pueeek8vl0ogRI7Rx48ag28+dO1epqamer/z8/NZ6CQAAAAAANIvNMAwj1o2QpD/96U96++239cknnygvLy/i/Wpra3XQQQdp0qRJuu222wIer66uVnV1ted2VVWV8vPzdeu3a5SUnNwibQcAAAAAIJR9u3bpliEDVFlZqZSUlLDbxnRMuunSSy/Vm2++qY8++iiqgC5JCQkJOvTQQ7Vu3bqgjzscDjkcjpZoJgAAAAAArSqm3d0Nw9Cll16qRYsW6f3331fv3r2jPobT6dR3332n7OzsVmghAAAAAABtJ6aV9BkzZuj555/Xv/71LyUnJ2vLli2SpNTUVHXq1EmSNHnyZOXm5mru3LmSpFtvvVVHHnmk+vbtq507d+qee+5RcXGxpk2bFrPXAQAAAABAS4hpSH/sscckSccee6zP/c8884zOO+88SdKGDRtktzcU/Hfs2KELL7xQW7ZsUVpamg477DAtX75cAwcObKtmAwAAAADQKiwzcVxbqaqqUmpqKhPHAQAAAADaRDQTx1lmCTYAAAAAADo6QjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALAIQjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgEYR0AAAAAAAsgpAOAAAAAIBFENIBAAAAALCI+Fg3AAAAAG5FRUWxboKlFBYWxroJANDmCOkAAAAWUFRUpLod1Tp1x85YN8Uy3qj/l7AOoCMhpAMAAMSYGdBHrV2j/E65sW6OZUz4tUyL0rqpSEUEdQAdBiEdAAAgRszu7RN+LVNVRZn6bZdKXYtj26gW4nTZFGc3mnWM/PwxGlWxRkv7D1CRiiRRVQfQ/hHSAQAAYsC7e3v+7njVbJdWVS1Reo/OsW5as2zclqXbX/2zSrbmKr/nJt10+oPK67GlSccqLVmsfvaB0to1SknPpKoOoEMgpAMAALQh/+p55nap1PWDSneXqtouvTCsb2wb2ExvXHGxqrb3kCRt3J6ta/9zjSbc/3iTjjVpxTqt2rZEgzVaiZ3iqaoD6BAI6QAAAG0kXPW8+jfp41GDNSzl7Fg3s8lcTum5jeme24bLrsqN6Rra5WzZ46I/3gvDXtLRS1dpc8JyJZR0o6oOoEMgpAMAADRDNMumeU8OV1qyOKB6vj8HdEmyx0mZvapVsTFRLpdNdruh9LyaJgV0yX0+Ph7l/r6xqnokCPQA9geEdAAAgCaKdtk078nhah0720X13N/UWaV6Zk6OyjY4lJ5Xo6mzSpt1PPPcmFX1VVVLlOPK8amqR4LKO4D9BSEdAAAgSv7jyvM75apm06ZG94tz/aBVu0uV3qNzu6me+8vIr9V184vlcqrJFfRgzKr60UtXqdax01NV1/ayRvdNzM1lPDuA/QYhHQAAIAr+a5rXbJe+qno24lnZq39zd29vb+HcX0sGdJN393dzrHokzPHsg0p2q2THTqrqACyNkA4AABCBUGuaN2VW9vYe0FuTee7MsB6Jo5euUqmrVDmuHNZeB2B5hHQAAIBGtPdZ2fdH0Zxv727yrL0OwOoI6QAAACGY1fP2Pit7exfpLPEEdQBWQEgHAAAIwrt63hFmZW/vIpklftGOasWnOQjrAGKKkA4AAOAlaPV8q3tW9mq79PHwwZKonu+vQs0ST1UdgFUQ0gEAAOpRPe8YhqWcrT2nnq0XqkJX1d+o35awDqCt2WPdAAAAACvwWVptd7z61U8OV+vYqReG9SWgR8HljHULIuOuqg9WtV3uqnrVEvXbLuXvjteEX8tUt6Pa07MCANoKlXQAANChRbK0GuE8MuUlCXpmTo7KNjiU2ataU2eVKiO/NtbNCst/7XWzqs5SbQBihUo6AADosMzq+cjPVwVUz6vtdG+PRnlJgu6ZXqCyDQ737Y2JemZOToxbFZlhKWcHVNW/+vFZ9dsujVq7hqo6gDZFJR0AAHQ4kVbPh8W2mfuVp2fnyFnXUP8xXDaVbXDI5ZTscTFsWBRCVdVzOuVSVQfQZgjpAACgQ/GeHC5/d7xq6qvn6T06MzlcE7mcUnmJI+D+zF7VLRrQ2yLwm++99wzwpSWLfZdqS+vGDPAAWg0hHQAAdAhBl1Yr8a2eSyyt1hT2OHcgLy9JlGHYJElx8S5NnVXaIsePxVh376r6pBXrWKoNQJthTDoAAGj3zOr5hF/LNGrtGnf39pLFAWPP21NAj3SG9Zaaid0dnGskSRn51Zo5rzjqIB2qLc/MyVHFxkRJUkUEY91b6jWZn4kXhvVVtd3d46K0ZLHPWPV136xhrDqAFkUlHQAAtFtBq+dbf9Cq+ur5x8MHS2pf1fNIq84tXZ3OyK/VzHnFkqLvkh6uLS6nPJPRSZIrzFj31qq4m1V1s/s7VXUArYlKOgAAaJc6YvVcirzqHG11OpzykgTdNa1A14zrr3umF6i8JKHF2mx2pbfbDfdtuxFyrLv3ccpLWnZ2+WEpZ2vPqXeEraoXFRVRVQfQbIR0AADQ7pgBfdTaNQFLq70wrG+7nRzOrDq7XO5x4d5V56ZuF4knb8pV2QZ3OC7bkKgnb8ptdpvrahq2mTqrVOl57jvS82qCjnX3P45huI+zpTi6CwaN8V+qbVXVEvXbLuXvjmepNgAtgu7uAACg3Yh0abX2yqw6V2xMlMtlk91uKD2vJqDq7D/Rm/92kXQbN7ubu5zSts2JXo/YtG1zYkB39FAzs/u32WY3ZLcbunZ8f89zR/Pa3RcLbPX3Gnr2thxdN784omNEKtRSbfn5Y1iqDUCzUUkHAADtglk9H/n5qoDquXf39vYukqpzeUmC6mptnpnY0zJrfbZ7Zk6OykuCdz/37tp+x5RCzT2/MGRb3EuzNWx/17TgXeG922y3G3I5bT7PHWnX/Ck3l6ohoEtS61TTpYZJ5byr6v7d36mqA2gKm2EYRqwb0ZaqqqqUmpqqW79do6Tk5Fg3BwAANFOw6vlW1w+e6rnVwnlbrPXd2PPcNa0goNpuVpu3FCfo7gt7B+xz79trZY/z3Vcy/4w0v3f/2y29Vo5Ohso2OBQX75LLZZPh91xmV3rvNtbVSNeO7x/R6zPbE+y1+VfTM3vVtHg13duKqpckuavqDpeU0zVHOflj9P3eTVraf4Di09wT31FVBzqufbt26ZYhA1RZWamUlJSw29LdHQAA7LfM6vmpO3Yqf3e8auqr5+k9Oqv6N2sF9LZe6ztUQG9stvSnbslVQ+CWzJBrdm333te/ai1JGfk1ctbZPJVvZ11Dx03zuW6fXKjtW9yP98iu0YW3b1JGfq3iE4N315fU0B3eZigjP7ALv2nKzaV+FxlCzwbfUszPmPcM8KUli9XPPlBau0Yp6ZlalNaNGeABRITu7gAAYL9jzqLtPTlcaclirapa4hl7bqWALrXsbOrNEW629Ibx5b7h292N3M17X3eY9z3ONY8Vu8eku7yPIc82cfEubd/S0P182+YEn3Mx5ebA7vpTZ5UqLdN9QcMwbKqrtYXswp5VUKvMXtWy2bzbqCbNOh8ts/v7C8P6BkwqN2rtGrq/A4gIIR0AAOxX9sel1SKdTb2tRDJu3Z85rryu1uYJzD2ya9Uj2/19Wmat6mptunZ8f8XFu9TQFV6e79Pzauor68HHjd81rcBTBb/2yfW6bn6xeubUuqvsCYbn4sC2ze4u+eHGuGfk1/jc11YXRszP3gvD+iq9R2fPUm2DOuXq1B07PUu1AUAodHcHAAD7BTPYeKrnnXJVuvUHrTLHng8fLEmWCuemSGddbysZ+bWeceHebbDHubufb9ucIHN8efesWj17W0MvgB1lCUrPq/EZE+5ySndfVKCtm8wu7jYF6wo/c16x7pkeOGa8R7b7Oco3msu4OXTfnwrULb1O2zYnKiO/WuUlgd3sy0sS9fTsHF3/VLjx5oEXRtrqvBedeYQKX/5cqnbfHt7rUH264Zu2eXIA+y0q6QAAwPL2x+q5v6ZUr1tbsLB64e2blNnL3c64eEPbtyQG7QVgKi9JqO9K7vDq4u7+1xakS/2Um0tlj/Odt9jlcgdzw6uLvLPOXn+xQNq6KVFx8S6vbvZuhmFTeYkjoKLuPbTAuzt+Rn51zC6MAECkqKQDAABL8xl73inXZ3K4F4b1lWTN6rm/UNVrq+mZ426nOYu7W8Ms7v69AAIDccNM73a7IafLpvS8Gp08vaJ+5nXvirj7mDvKEmWPc8nl9K8fNVwYkMtWvw66I+B5zK7sM+e5K+rBJrez2Q1PoJ86q1Q9c2ot/T4A6LgI6QAAwJKCLa1W6lrsWVrthWF994tw7q8tg2E0FwS8Z58P1b1c8u0FEH62d3c13B7n0tRZpUGr297bm2ujB2OzG0rPdS+jtqU4QQtuzalvn291/5px/ZXZq1o9smu0oyzBM7TAZjc8VfrykkTdM71Azjp7m8yyDwDRors7AACwHLN6PvLzVcrfHa9+9dVz/+7tCK68JMEz0Zt3V/BwE9U9PbshRJvdy80Z0s3u6ve+vVbXzS9WRn6tyksSNPf8whBH8w7fdj01K8eny7z7cf9QHiyku5/fcLm7td8xpVB2u3T9U8UhZ5k3X4M5tKBnrnuyOvO5DcPmWRbOezK5WE3iBwD+qKQDAADLiKR6Lu0f3dtjyX+5tydvylV8ghF0jXazgu5dOTe7l5vSMms1dVapT1X+mTk5njHjvrzXWFd9GxyKi3d5TSgXuE1wvtts2+yugs+cV+ypzrsr+V4XBVw2bducqLvfdE9sZ4+Tp+u+/7JwZgX+zgvcY+qprAOwAirpAADAUs7QPknSoE652ur6QUML+im9R+f9YnK4pghXwW1KdTfYcm/bNieGXKM9eDd0w+f7bZvd+5hrkzd0cw8WtP3vcx/Hd8b3SAJ6cM46u56enaOM/FrNnFesu99c61NVt9WvxX7t+P6etdG9J+3zf22S4ZmVvqWXafv0l23auOM3le4uVWnJYm384TtVVZR51ktnKTYAwVBJBwAAiAHvMeChqtvBHmuM/3JvNpshw7CFXKM9+JjywOXTyjYk6u4Leysjv1rnzy6tn8TNdym18KG96cHcn9n1fdtmd7jull6jtMxabducKLvd8Ixv955Q7rr5xaqrka4d3z+gbS6X6v9t+jJt/vuYF5M+HiUdvXSVah07VVqyWP3sA6W1a5SSnqlFad1UpCIVFhY25TQAaKeopAMAAMSAf5f0UNVt87FoqupTbm6oHGfk16hHdo2n0uy9HJoZ6H3Hdss9Ht1vubOGtckduvuiAlXvDTauvDn8ny/cdoZPV/udFQmKTzB095tr5ayzyzACJ5S7a1qBtpcl+Lxee33V3f/cRCPU+H/TsJSz9fGowXphWF/VOnZqVdUS9dsu5e+O16i1a6iqAwhASAcAAGhjwbqkmxXcUI+FCoHm8SR3YLxjSqHuvrC3yjY41D2rRlNuLtWFt28Kuka7y+levz0t06zSu5/TWWfzWbM88Pns2lnh3Q6XQk38Zo9zRXxe3MfxDuvBgrtNgRPP1Y8tn1boc0HCf0K5Z+bkBKxXf/6chttpmbWqrbGFPdf+wl1sMZnDNF4Y1lfVdvckiKUli9VvuzRq7RpN+LWhCzwA0N0dAACgjfl3Sfdfe9z7Me/lyswQeN1893rg/t3i62ptPhXm7VsSdPeFvT1d5s21wbcUJ3jWLM/sVa3aGvnxDsCNdWOX3HWfwO3scUb92ueRTBQXesZ3m91Vf9HA+3z4t8H9entk1yo9rybohHJlGxw+y8yZwwgO+p17abf7/lTgmfm9vMT3XHszu7b7L0HXWHd5d1Xd/f2kFeu0atsSDdZoJXaK16iKNVrafwDd3wFQSQcAAIgF/4quWd32f8w7vPqPJ/eu4paXJNaP0Q4cT26G+62lCbrzgoL6Srt7v7INiaralqjQIu3SHrhdw9rn0XSL96+eBwn/9lDHdE+SN+XmUt37tntCOXMZOfNY5V7LzHlXvZ+6JdcT0CX3Um3e51pquLhhVtq3lgZ2nzeHEoQSSVWd7u9Ax0ZIBwAAiIGM/FpdN7/YZ+3xYI+FCoH+3eLNcdjBuoib4d4927lZ+W3uhG6RjCEPPtN7+GMGVtMbquju2/5LqfkfY8GtObLHqX7ZON/Ku+E3jGBLcYLqauSZhM5bRr77XJvjzr0vboTqPu99sSUUl7NhrHq1Xap17NRW1w8a1ClX+bvjNeHXMkkiqAMdFCEdAAAghsJVXc2gGSwE2uOkHtk18l5OzGb3H//tO0Gad6U4cqGCdWPh3n+ps0j2Cf24b0U8/DHKSxy6a1qBXC418poN3fenAr8Z3xucd4v7XLvXkTdDvG/I75kT+mKL/2R//pPM5VX+rz4eNdjzeGVOped7cylCAB0PY9IBAAAszKyqR7IsmOHyD6TuQNkzt8argh4J74p2tJV2975x8Ya6pNbVd6Vv7izwhlJ71tVPVufdNiPIuHf3vxUbE/XsbTlBxvf7Dgdwr99utlueY/TIrlVWQW3AuHOT/zwC3u9NqCX0gk0yN+avzTw1ANodKukAAAD7Af+A7nLKbwy6b9g0u8bf/eZaXf9UsXt8tt13fLbvv95sSk2vDfN445x1tvqAHm7/YOPPzaq572NV280T4BuyXU67umfVqHuW7wz1ZqV7ys2l6plbE2Rffw1d6jN71ejC2zdJCr1MXbiu7aGW0As+o39LLmMHoD2gkg4AAGBB4SrnZqXWzV0dttkN2e2Gp3t3So9a1dXadO34/srsVa1jTtuh1x7OkNOsKPtPlO5hKC7eUGVFsD8TI52lPdi/4bb1vd0zt0YVGx0+97uctpBd9neUJSgjv6b+8YZZ4OPiDT17W059LwLfSrn/c9tshjLyazRzXnHAeZ86q9SrMu5e1i6roFbBhJrxXQo1o3/TLoIAaL8I6QAAABYSrKu0uXSaybtSa7LbDU9V1m43tGtHvGeStIqNiXrt4Qyvtc9tXgE9MCg3dAFveLxrt1rt3tn4uuGR8e2a7nkmu6HumbV+z98g1PhycyZ2X+7X4T+WPPB7N3ucUT/RXODxoxly4L+8nhn+zfkFzPfWrMRvDH84AB0QIR0AAMBC/JdVu2e6e+1u78DuG0jdgdM7wLpcNslrBnT/202xe2e8/MeDRz7WPHAsuO+/9Vu5gs+ybh4jLt6Qy2XzutjgG/bj4l1yOm2S0XBcw4isjc46u7pn1oYN4o0FdNPUWaV68qZcbducKMOwqa7WpvKShKBhf2NVZMcE0HEwJh0AAMAigi2rZoZvc2yz/xhpu91Qj2x3V2+Tzeaezd1/HLUv78dCfe/Nexk0KfKA7tZ4exo7pvtcpGXU+tzn/a+zzuZ36HCvx3e7uHiXrh3fX9eM6687phSqvKTpvQYy8msVn2B4Xu+OsgSfNdkjDfsAOiZCOgAAgEWYATzYcmMNE425K7VpmbWe+3dWxPtMQGaPM3T+nMYmTAt1X/CJ6HxFMxmc+1jOOrtcrobbTWPI5fReji2SCwkNk8H5L1lnsscZPl3st232DdWN8V9qLdQkcXdeUNCs8A+gYyCkAwAAWER5SYLqam2eLtpx8S7PjOzmbO32uIZKrRlWnXV2n27dzjq7/j0vPcJl1/yDrSnc2ubRTAbnfzz/76OzsyLR67WGanfDOUvPq9a9b6/1mondvU+P7Fpd++R63f3m2vol3HzDvXlBJBz/dc/NAB5qRvitmxKjCv8AOiZCOgAAgEU8MydHO8rqg57dULf0OmXkuavh3kt+mZXaYOOt7XZ31+2GieUClzMLzXvbMEHc5gr9WOidFFilb8oxvIV6bWYFW6rY6NB1E/rq8evzfM5tfIKhrIJaxSe6A7X/RYQe2TWNdksPttSaaeqsUqXn+fZk8O4NAQChENIBAAAsIFgX6W2bE1W2waGMfPekcRn5DeOxvSu15hh0yb18mbtruXcgjjQUR7Ztas9wKbMtlxQL1t7AbvrOOpt2ViQEdD+vq8/QU2eVKi4+eLtdzsDu7Ob9wdc9dz9uThKXke87f4DZGwIAQiGkAwAAtLJIKqfBu0j7dpP27l5dV2vzjEvPyK/R1Y8V69on18vmyawNwTAjv7pFX09lRYLSMoOP7256lbylBVbuzaEDNnvDRHF3TStQXa3/8m7uCyR3TCnUNeOCTyYXbAI/d0Xe1/mzGyrq3r0hACCUiEN6aSm/UAAAQOur/rVSVRVlnttfbvkyhq1pnlBjlkMJ7CLtW6X17l69oyxB8QmGrn1yvSTp7gt7674/FXitC+6Wnlej82eX1lfam1rlDhxPvqMsUfa4CLrGR3TMcPdFe6zQxzCXbjNcDWvBl21I1L0X9/YZ/28eY9vmhvfLfzI5c/4As5Ke0qNWdbW2gPfarKjf+/ba+sp6Ld3dAYQVcUgfNGiQnn/++dZsCwAA6KCKioq07ps1mvBrmaoqytRvu1Raslilu0u1ccdvemFYXw1LOTvWzYxauDHLwXgHOv8qbUZ+ddDu1QtubXgO3wnk3P9e/WixXC4pOa1OjYfpcLOmB37vnnDNf3v/faMJ3s2rwtvjvMfKN7a0XODybQ29GIK1x7c7u/f8ATaboV074j23g73X9rjoL9oA6JgiDul33HGHpk+frjPPPFPbt29vzTYBAIAOpKioSHU7qjVq7Rrl745Xv+3SqqolqnXsVLVd+njU4P0yoDc2Zjkce5xvVd2shvsvIdYju0blJQ6v8efe3NtdO76/7r6wt3ZWJPjcH1xTJ3YLt356qGNF+xyNt9t3lnb/7u6NvTabV5f34OczLt4le1zo9ewbe6+fvClXZRvcF1TKNiTqyZtyg7bE2ZR5+QC0GxGH9EsuuUSrVq3Stm3bNHDgQL3xxhvNfvK5c+fqd7/7nZKTk5WRkaGJEydqzZo1je738ssv68ADD1RSUpIOOeQQvfXWW81uCwAAaFtFRUUqKirShF/LNGrtGk/1fFXVElXbpReG9dWeU+/YLwO65LXmuVd1Ni7epa2lkVVPM/JrNXNeQzdpSdpRHi//AOk9MZk51jq4cOufN6f63ZIiuYAQ6THqeyHE+Xfz96+oh6u2+z6/s86uOy8o0NbShICeDr7d5QPfa5dT2rY5Ud7vw7bNiT7r2xeXJuuUuSM1/qKJmjr1cO3YkBThawbQnkQ1cVzv3r31/vvv66abbtJpp52mwYMHa9iwYT5f0fjwww81Y8YMffbZZ1q8eLFqa2t1/PHHa8+ePSH3Wb58uSZNmqQLLrhA33zzjSZOnKiJEydq9erVUT03AACIHbN6PuHXsnZVPfc3dVapTxdql9MW0TrZ3t2i75nu7hb91Kwcr+7lkhnyvCvpNpvRSDXYW2PV75YK6uGCr7+WmHTO9xjB1kAP/lyB1Xbfcfy+k/gF9HSY07T32ttf7j1GRRVdJEklJZ30zqwBUe0PoH2Ij3aH4uJivfbaa0pLS9Mpp5yi+PioD+Hxzjvv+NxesGCBMjIy9PXXX+uYY44Jus8DDzygE044QTNnzpQk3XbbbVq8eLEefvhhPf74401uCwAAaH1FRUWS1NC9vVOuZ+y5WT2X1C4CuiT1zKn1mTXcMBq6QYdbhst7LHt5SaLuurBAhitYbcWQd2Xcd4x4cwNvuP2jOX5z10Zvqujal9mrWhUbE+Vyucem98x1DzF4Zk6OyjY4PNuZXdl75rjnDzDfS5dTYd9re5zUI7umfjI690WQHtm1nsn3nC6bijZ28+zvctm1o7izXM5KlmwDOpioEvaTTz6pq6++WqNHj9b333+v9PT0Fm1MZWWlJKl79+4ht/n000911VVX+dw3duxYvf7660G3r66uVnV1w3IYVVVVzW8oAACImlk9P3XHzobJ4VyL3dXz39pP9dyb2eXdO/yl59WEDV3meGeTYdgkI1TgDFUNjySg+gb86EJ0WwTuSNrUEhcj3KbcXKpnb3MHcnOpNHMivzsvKNDWTcHfQ+9/G3uvL7x9kyf0Z/ZyP8fG+sfi7IYK83aqpDRFTpdddrtLqfn7COhABxRxd/cTTjhB1113nR5++GG99tprLR7QXS6XrrjiCh111FE6+OCDQ263ZcsWZWZm+tyXmZmpLVu2BN1+7ty5Sk1N9Xzl5+e3aLsBAEDkTt2xU8N7HapBnXK11fWDah07VXTmEe0yoJv8u0U3tk52sLHswfhOIheZbuk1XreiDfWRaqlu8pF0uW9Ot3zfbuzP3ubuwu69VJop0rXOG3uvgy3H5u3/rvlIhenuYZ/5+Xt1wpzG52oC0P5EXEl3Op1atWqV8vLyWqUhM2bM0OrVq/XJJ5+06HFvuOEGn8p7VVUVQR0AALQZM5g11sXd29RZpbr7ogIZIWYZN6uwT96UWz8Zmfv+cGE7tWeNHJ3cgTQu3lW/TnhDJd0eZ9RPYtbU6ropFt3ao29r96xabd/SsKa8uWyaOUmfN3MSPyn8exjpex3qsYKcXfrXDR+qqHOdOvdN1Sti4jigI4o4pC9evLjVGnHppZfqzTff1EcffdToRYCsrCyVlZX53FdWVqasrKyg2zscDjkcjqCPAQAAtBXvYBYqxJn398yp9ZskrkGP7FpPV+wbny2SyymVb0zwdNW22V0yXN5h2233znhVba9fE9wZGGhTuju9lmlTwP4NWq6LeeOiHffultqzVpVbzZnwjaD/xif4rpfuvWya+d64nNLW0gSvLurVnnMfTnO7qMdFNbUzgPam6bO+tQDDMHTZZZdp0aJFWrZsmXr37t3oPsOHD9fSpUt1xRVXeO5bvHixhg8f3ootBQAAaL7ykuCBL9j9mb2qVV6S6B6TLnf1++rHipVV4BsQ7XFSVoE7uD89O0flJcGLE96TmgWOcbf5BfRw2rpSHo2GAN4ju9arl4E3d/vLSwJ7IHgvm2a+H3HxLs8yad7V9mh6RgBANGJ6nW7GjBl67rnn9Pzzzys5OVlbtmzRli1btHfvXs82kydP1g033OC5ffnll+udd97Rfffdp59++kmzZ8/WV199pUsvvTQWLwEAAOxnXM7YPbf3rO1m4At1/9RZpeqe1RDIu6XXyR7mL7dn5uRo6ybv4BmJcGulB9s2mu0jec6W4nvMHWUJAZXy4Euu+TKXTfN+P5x1ds+FErPabi6Pd9c09/J40YrlZxCA9cU0pD/22GOqrKzUscceq+zsbM/XSy+95Nlmw4YN2rx5s+f2iBEj9Pzzz+uJJ57QkCFD9Morr+j1118PO9kcAACA99rjTQ1XzWHO2m6uaW4Gvrqa4Pf3zKlVfILhmUBu2+ZEz5rpjR27ad3E/ffxD9OBXeibpuUr8WmZ5sUM33PoFsnkc/Vb1i+b5nsu/RkqD3KhJRKx/gwC2D/ENKQbhhH067zzzvNss2zZMi1YsMBnvzPPPFNr1qxRdXW1Vq9erRNPPLFtGw4AAPY7oarYbSXYrO1x8S5t3Zzgvt/mvt9uN5TZy718bNkGR/34cjdnnV1Pzw5st7kGt/+M5c2rWjcWpq3T7X1HWaICq/y+rz0u3hVib+9tDcXFu5TZq1r2+vfJ/X55H8vmeU+8x7FHItafQQD7B6alAAAA7V6oKnZbdzueOqvUE/4kyVln090X9tbW0gRPl+puGe7x5Wao91de0pLtjiTEt0Tgb03+3drN7317FaT0qAuxv++2zjq7qvfaPJ8V98WT4BckzAsqkYxND/0ZtM7FDgDWQEgHAADtnhl4zYAcTbhqST1zan0ncKsPf+7l0CTJ0PYt7gpreUmCps4q9akA22xG0ODucqp+kjTfYGrz5L9QATuSgBhtF/qmCNW+hiXjQgs3C31DJdxdbQ///Dabu5LuPYmee5b9hmEH5jbmsc0x6o11XQ/9GbTqxQ8AsUJIBwAAHcLUWaVKz6uRJKXnudcZb2sN1fFg470b/i0vcQd1c33ujHx3MO+eVau6WlvAmGZ7nBlkfavehuEfsMN3CW+q8CE6EqEvAKTnVftd2DD5tz34OQ22HF2ofQzDVv9cgZPMmeE6I7/G856Y3d4j7bpuhc8gAOuL6RJsAAAAbSUjv9YSS2dNublUd18YftlZcwIzl9Pd7uufcrf7nukFAWOazdcUrEJvsxn1Qd3ssh1qojjvLt3RroNu6Pw5pXryxtxG9+ueVaOdFfEh14APfG6bKjY61CO7Rts2J4Rpv3nb/3UE285/H/9tgu/nrLPr7jfXKj7R3XPBe6k7s+v6luKEgCXyvAX7DG6sCtM8AB0SlXQAANChxDKgl5ck6NnbzIprsOXMwnfHDzeu3r8rdY/sGmXku6u2cfHBq+Zx8S5d++R69ciuVWShNhibnrolRw0hObTtZeECevDnNnsRRNoW3++b0kU/2H7u13XfJe7eC8F7RBhe760v/zkEWF8dQDiEdAAAgDbiPbu3KbNXta5/qkjXPrneE6qDdYX2H9Nss7mD+D3T3Ut61dXaPEuRpefV6MLbN+m6+cW6819rQ3Thdo+Fn39zrt949ug1BO9GjmGEezz4RYTJN5VG0T7fWdqbul58Zq/q+vfD9wKBd7f2KTeXyv+igP9khE1dcs3Z3NEDAPZrhHQAANBmFqV106cbvtH3ezepp32gEqq7qfDlz3X00lVaUfVSrJvXqkKtZT5zXrEk6dnbclRe4lBmr2pNnVWqjPzAbtNTZ5V6grhh2LSzIl7lJe7Qv31LguITDN379lpdN999zLumFej6U/or3MRx27ckqlu6dyU9WIXf//tIhRp7H7wt/vu6XNK903srLt7ltXRduHY0HCOpi/8U+OH29w3bU24uVVZBra59otjnce/eC1kFtUGXzvOukke75FpxabJOmTtS4y+aqKlTD9eODUlhtwfQPhHSAQBAmygsLFR8mkNvHJCppf0H6OfuUk7+GCVUd5PDJU9Qb69hPdwM842FObM6m5Ffq/gEw3MMZ53dMzmcOY69fKO7WuueIT7cjOYNdlbEK2BmeLtLgd3Ho9WcGeFtMlzuP1WddTYZRjTd8W3aUxlqDHuwSfR8ZeTVesaNh1sVwH0xJXjvh6Ys+/eXe49RUUUXSVJJSSe9M2tABK8VQHvDxHEAAKDNFBYWSpKKVKSl/QcoZcdO5eePUc9Nm7SqaokmrVinjTt+08ejpGEpZ8e2sa1g6qxSPTMnR2UbHJ5QZ4Y5k3eY21qa4Nk+s1e1ptxc6rNtIPe46MDtGgu2gY/bbFL37BptL0vwzGLu/1yNHzfSSega285W31U+1HbRTnansNvfM73Ac87HX1ihN59M93nPTOEmI7THKWDCu7h4l7aWJgTtJeF02VS0sZvntstl147iznI5KxnDDnQwhHQAANDmCgsLVaQivZGWqQm/lqmquzRYo7W1+gc5XL/p6KWr9PEo97btKayHCnWZvapVsTFRLpd7qa/0vJqgFfZnb8vx2TaQO+C7JzALNmN75NVol9OubZsTFRfvktPlHZDda4abVe7wwjyPzVBcnOE3K31Tg380FwIMxcUb6plTq/KSRL/Z7w3Z4wzPOS/bkKinbs7zjFEPNXN7pCHaWWfzzMjvL85uqDBvp0pKU+R02WW3u5Sav4+ADnRAdHcHAAAxUVhYqMLCQk/397IBmcrJH6PBKaPlcEmTVqxrt2PV/YPXydMrPGOubXZDJ0+vCNldesrNDWttB06OZigjv7q+ih6qq3fjs7B7M1w22eNcfvc1509Iw/NPQ0APthRaME2dsd33OZx1dk25udRv9nv3Gugupz1g3gDzAkk0XE4FmfAufJf3/7vmIxWm75Ek5efv1Qlz1kT1nADaByrpAAAgpiKtqrenirq/f89L93QpN1w2/Xteuq6bXxy0wp5VUOuZbG5raYKevMmcnV3qkV2r82eXeirwLpdv9dxuN+rvC7bEWPDwG7h9Y93NI/k+3HOHq/Y39ljoAG+uGe99Hr17NZj/umdhT/SM9Zd8hyBEWtk2x7OXbfAO6oYye9X4PJ+3gpxd+tcNH6qoc506903VK2LiOKAjopIOAABizruqnpKe6ZlULqdrjvLSOmvSinXtsqIuhZ9gbOqshqp5el6NTp5e4VnS657pBdpWP6O75A6EF96+SRn5tT779ciurV8H3X2M7lk1QVoRfmm0uHiXZ/I0dxgOVomPtDrvXzUP1nU9eHtsdskeF2qW9vAVdu/z6D2u3D8oe08GZz6HzRZ83frGTJ1V6jn3kvu98H4P75pWoMpNPQL2i+MvdKBDo5IOAAAsxXFAqlIkabf79u+yfqcvt3wZyya1KrPiGmxMuv8Y9rumFfiMUX96Vo6nAm/OCn/d/OKgY9/N78tLEnT3RQWetc1tdvds8b5jwxvExRtKTqvTzgpzpvjgY+Ej/t5mNDIJnCnwccNl84rmkXZ7N9Qju1bXPxV8grfyEt/J+abOKtXUWaU+PRTMmfPvmFLouRASiYz8Wt34bJGne3uw9/Cje87UxZM/j/C1AOgIuE4HAAAQY/4Vc+9Kr9TQHdu/4u6ss4dd4ss7kJoB/Zk5OXI57fXj2aWMvBqdP6dUoSrhzjq7dlYkhG2/e8x6JOuYG0EeDr2Ge/DtohuTntmrRhfevklS8K7qwZa/e2ZOjnaUma+5oX3bNic0utZ5MPa40O9h5cb0+on5AMCNSjoAAECMhVvKy+RfcXfPsC75j/m+Z3pBfZfthmqveVzvQGq4bMrsVe153qDLsNmN+kp9qBBpqFt6naq2x4XYxr8a3vC93S51y6iRzSZPxdp3H/9/I6+ce8/iHmwmdVOo5e98BZ/4zft9inSserBeE8k5WxVnj3SoAICOgEo6AABACws1e3djGgt63hV3e4hgZ1aDJWlLcYLP+OdgY983/pKge6YX1O/te0zDZ+K5QHHxhnZWJHi6zrsFC+WBx3C5bNq+JdEzs3p6XrW6pNYFbNcwPtyfEeJ731ncw70XZmg2z6XNZnh6GIQad+89Nr28xPf8lpeE73EgBfaaOGbmy43uA6BjoZIOAADQQoKNb450/HIkzIp7XY107fj+fo/6hu87LyhQeYlDZtis2Fi/5nmd7zJsf/1Tb4UKpKEq2HHxLqX2rNPO8sZDqbvdNSovcQQ8vz3OUHmJ2dU8dAU7Oa1Wu3bEe/ZLTqtVYpJ/Bd5ss3tf77H94UydVep5z+xxRsDya956ZNf6DEUI1lU+XOVeCuw1saJqm1QWvo0AOhYq6QAAAC0kWGhrKv8KsPft+ETfCrD3jOt2u7sabLbDO7z7Tg4Xah31YI97M3T1Y8XaviXRK9D6Pu7//bbN7jDfENDN12QP8zxu5SWJ2lPlm7T3VMVr2+ZEpedVB2m/+3tzvfmG5wp+fDM03/3mWjnr7J6J+PxfU0Z+tW58tshz0SXcrPyRiHameAAdB5V0AACAFhBqfHOo8cqh7vevxp88vUL/npceUJ33rgD3yK6Vs87m7nrusklBgmbw8eXh1kv3HxPecN+zt+XIZneFGK8e+NzucO7/WKRjzG1yOW1Bb28rdfcOMFy2gDXhzfXme2SVRtS7wbzw4bu+vPt54uINnT87cDK/ULPyA0BzUEkHAABoAf7jm+324GtrNzaO2b8a//Ss4NV57wrwjc8WydHJJZst2Frm7n8z8mrUIzvU+O5wAoN1xcZEGa5QVXB31dl33+bPXh5sDL7/DPfelXTzIkmkvRv816X3brOzzq6eOYHBvrFZ+QGgKaikAwAAtBDv6nao0BZuHHOwarx3Vdy7Or+1tKHinpFfXT/+3NSwT2avGk25uVRZBbUqL3FPEtfQ7T3cDOo29cyt1tZN/mPFFaKbe8N+vm3x5l+V921r8DYZ6pZeK0cnQ2Ub3OPaXU6bDMPmdwzf/ex2Qz1zaxrt3RBsHoGnZ+do66bGK+SRzMoPANEipAMAALSQxkJbY13ig3WhNrup+wdG77C/dZNvt29zu5nzin3akZFfq5nzivX07Jz6idwMOetsyuxVo7pam3aUJfjsP3VWqV+od7PZDdntht8kdMHCd/iLAMH5Vt8z8mt0/uxSTyXb++JEuHH1PXNrdMrFFXp6Vo6n/Ta7oQy/wB3sosn5sxu/2OKNgA6gJRHSAQAAWlika50Hq9L6V+O9x6SbgTFUxT2zV7XPdqGqv9c/1XAhwfzXu6LcM7fGM3Z75rzigKButxs6f06pp12+AoOzObu7v8xe7m7x5RsTvSZsa6iEm6+jYey9u2u5OWlc4Izw8ux33fxi3TWtwGc8u91u+ATuUBdNeua0foWc6juAUAjpAAAAbaixLvH+1XiXU0EDo/9yZnHxRlTB0tzG/Nd/Mrpn5uRo6ix3Bdu/ku6ss2vAsN80c16xrhnXX6G6rPsH5vKSxPpu6u72m6/97osKAxaB8w7oZqXbnCVecvceCKZHTvALGWa7vceWN3bRpDVCtH/3+sOv7iFpXcs/EYD9FhPHAQAAS3lFSZ7ve9oHamXxz57bK6peikWTWpQZwu99e62um18cch31raW+E8xtLW0IqC6n6oNzQxdvZ5292dXZYF2/G5sQz6yG+3K3y/sihLsyX1N/Dqo1c577tffMqQ2YvV2SZs4rVs+cWp9lzry7zJth33+CvIqNDj15U662liZENJFfW0/+5n2Oyzcm6KvZEzyPpZamer73/jkA0LFQSQcAAJZRWFiooqIiLUrrplO1U1XdpX7bB2pr9Q+atGKdNu74TR+PkoalnB3rpjZbY2E63ARzrbH8V7jx8sG64N81rcDTBd0e56pf81yy2Qxl5AcfDx+s0h9qXXFzu8Bl0aTg49wbgv62zQmengCNjS1vy8nf/M+x4bJr84582femqWf8Qfp+7yalpGfqjQMyJbl/HgB0PIR0AABgKYWFhSpSkd5Iy1RdWjdp7RoN6jRGNZs2qcK1RJNWrNMLw9wV9fYQ1oOJZM31SAJotEIFf3NsuuR+/rumFXguIOwoS1BaZq3i4g2VlziUkR96PLy5v7f4RNWH/Iau+/Y4Q/dd0nARIKVHrXZWJCr4RHGGArvbRz+2vC3Gh9vjpNS8Cu3a1F0uI052m1OF3SuVXzBa3+/dpKX9Byg+zUE4Bzo4QjoAALAcM6QUqUhL+w9Qyg53VX2wRrfLqrq/SCrlLVUB9h4j3SO7RmmZtfUTs7nDtv8Y6ik3lwZcQNi2OVF3v7nWM0N9tC64tbR+Fnab4uINJafVqbyk4SKAOZt8QzXdfU7SMmsVn2DUt8d/hnmb7ple4JkAzwpWVL2kq36/VfPevUalO/JV2L1Sl015TyVdk/Th4MGKF9VzAJLNMAz/eTrataqqKqWmpurWb9coKTk51s0BAACNKCoqkiTV7ajWqLVrNKhTrkpLFqt0d6mq7dLHowZLan9hPdj63a0RNs2qeKil24I9LslzX8NybPZmt7Nmr7S9PEF3X9i70W0z8qt1/mz3c20pTtBTt+Rq22ZzMrnAietiyZxLYdKKdarY9psGp4xWXHaufqqmeg50FPt27dItQwaosrJSKSkpYbelkg4AACwtWFU9P3+Mem7apFVVS9ptVb0txkqH6lbf2OPXPrlez97mvoBgtxueid/8x85HyvuCRFy8S/5V8bj4wLXivZ8jq6BWNz5bpLoa6drxDbPNBxsm0NZWVL2ko5euUl5aZyVUd9PglBH6ubuUklKnpWkEdACBmN0dAADsFwoLCxWf5tAbB2SqpGudfu4uDU4ZrYTqbnK4pKOXrtKKqpfaxQzw3lozXDY2c7t7PHrg41kF7gsId7+5Vs46u2emde9QHCmX03eSPN9Z6yXJpvMinIE9PjH862lL5mfx6KWr5HBJCdXdlJM/Rj93l5b2H6A3DsgkoAMIiko6AADYb5iB5g1JEyQldopXl5yuyvn8C9U6dmp4nx769JdtsWzififUBHRmdbu8pL667bIFBGQzFDdllnnv6nko5vHScyLvPt8aE+pFy6d63jlHPe0D9XN3ydm1TkvzqZ4DCI+QDgAA0IGF6lbvXd02XDZl9qoO2o29qaHY+/jBl1aT0jJrPccPtRxdpK+nLZi9OPyr594ztzM5HIDGENIBAAAQsHZ5Y0vAmZoSiv2PH7iEmruKHp9gqGdObcRtCfV62oJ39bzC5R6KkZibqxKq5wCixJh0AAAA+GhsrHqofULxH6Me7PgZ+dX1j/qOb5esM848FP+x54NTRuvn7lJJ1zrGngOIGiEdAAAAAaZGOFlbOOUlCbprWoGuGddfd00rUHlJQsjjn3JxRf3M7m42W0MYb4m2tIYVVS+py6IbNWnFOjnqq+f+k8MVFhYS0AFEhe7uAAAACNASY7vDjSX3P/5d0wrkcjV0e7fHGZ4wHstx5qEEVs/rl1brWqcPBw9m7DmAJqOSDgAA9lvf792k1NJUz+3Clz+PYWvap6aGYnPcuRm8Qy3PZo9r2NbwCunOOrt6+s3q3hIBPZrl4UJZUfWSJq1Yp7y0zsrpmuOpnqekZ1I9B9BshHQAALBfeuOATC3tP0Df792knPwxSqjupoptv7Xb9dL3N9GMa2/KGPhohet631S/y/qd5/uU9Ew5DkgNszUARIaQDgAA9jtmpTI+zaGl/QeopGudcvLHaHDKaDlc0qQV6zxhHbETzVjylh537l8xD9b1HgCsiDHpAABgv1VYWKgiFemNtEzV7ajWqL3S/8udotKSxXK43FX1j0e5tx2WcnZsG9sBRTOWvKXGnZeXJHjWbc/sVa2ps0qbvIwbAMQClXQAALBfo6pufdEE4eaG5mAV87boTg8ALYWQDgAA2gUzqL9xQKZKutbp5+7uJbESqrvJ4RJBvQMIN1mdVZdxAwB/dHcHAADthjmj9huS6tK6SWvXaFCnMeq5aZNWVS3RpBXr9MIwd1Cn+3v7Y1bMKzYmyuWyyW43lJ5XI3ucNZdxA4BgqKQDAIB2x7/7u3dVffTiVVTV27HGKubNCejmqgFHL12lim2/aWXxz+ppH6jv926SJL2ipKYfHADq2QzDMGLdiLZUVVWl1NRU3frtGiUlJ8e6OQAAoBUVFRVJkntSubVrNKhTrmrqq+rpPTrrhWF9JVFVb4+iqZibwTsSeWmdVbHtNw1OGa3E3FyVdK3TorRuik9zsDY6gJD27dqlW4YMUGVlpVJSUsJuS3d3AADQbpmhqUhFWtp/gFJ27FRVd2mwRmtr9Q+atGKdNu74TR+PIqi3N5EEdLM3xaQV61ThknK6RrAsW7U0OGWEfu4upXSt0xsHZCpeIqADaDGEdAAA0O75LNXmNVadpdo6LrN6npfWWQnV3TQ4ZYQSc3Mj2vf7vZu0tP8AqucAWgUhHQAAdAjBqur5+b6TylFVb//M6vnRS1fJ4ZISqrspJ3+Mvt+7SSld6yI6xtJ8AjqA1kNIBwAAHYpPVX1HtUbtbej+TlW9ffOpnnfOUU/7QP3cXXJ2rfME70jQvR1AayKkAwCADidoVX13LlX1dipc9dzstk7wBmAVhHQAANBheVfVJ/xa5jOpnH9VPRIEeuvxrp5XuOQzKzvd1gFYESEdAAB0aGZAe0PymVTOu6oeCSrv1mMGdLN6zqzsAPYHhHQAAAA1VNWDLdUWCcazW4cZzif5Vc+ZlR3A/oCQDgAAUC/UUm2R6Llpk7a6flAe49ljKlz1nO7tAPYHhHQAAAAvwSaVi0RVd7H2egyZk8NNWrEuZPW8L+EcwH6AkA4AABCEd1U9EnVp3Vh7PUZ8llajeg5gP0dIBwAACCGaYBfJLPEE9ZbF0moA2iNCOgAAQAuIZJb4jTtu1MejBkd0vPYY6M1Q3VL8l1b7ubvkpHoOYD9HSAcAAGhBZjf5D48YrBS/qnrvzt3U+9OtYfcv3V2q9B6d9cIwd6BtL2Hdv0t6i+icI1VL/++gM6meA2g3COkAAAAtLFRVPRI9N23S1uof2s14dv8J3bJrRygxP7dFn6OE6jmAdoSQDgAA0Er8116PRHuaJT7chG4t6Y0DMqmeA2g3COkAAACtqCPOEh/JhG4tiXAOoD0hpAMAALSylp4lXrJuWPepnnfOUU/7QCZ0A4AoENIBAAAsxH88e8qOncrfnRu0qm5FLIcGAM1DSAcAALAg727ywarqk1asi3UTgzKXQ0vMzWVCNwBoAkI6AACARYVbe31r9Q+xbFpI3pPDMaEbAESPkA4AAGBx/rPEmzPAW5F393bCOQBEj5AOAACwH/Du/m6OVbciurcDQPMQ0gEAAPYTZvCNZkm3ttaXcA4AzUJIBwAA2M9QpQaA9sse6wYAAAAAAAA3QjoAAAAAABZBSAcAAAAAwCII6QAAAAAAWAQhHQAAAAAAiyCkAwAAAABgETEN6R999JEmTJignJwc2Ww2vf7662G3X7ZsmWw2W8DXli1b2qbBAAAAAAC0opiG9D179mjIkCF65JFHotpvzZo12rx5s+crIyOjlVoIAAAAAEDbiY/lk48bN07jxo2Ler+MjAx169at5RsEAAAAAEAM7Zdj0ocOHars7GyNGTNG//3vf8NuW11draqqKp8vAAAAAACsaL8K6dnZ2Xr88cf16quv6tVXX1V+fr6OPfZYrVixIuQ+c+fOVWpqqucrPz+/DVsMAAAAAEDkbIZhGLFuhCTZbDYtWrRIEydOjGq/kSNHqlevXvrHP/4R9PHq6mpVV1d7bldVVSk/P1+3frtGScnJzWkyAAAAAACN2rdrl24ZMkCVlZVKSUkJu21Mx6S3hMMPP1yffPJJyMcdDoccDkcbtggAAAAAgKbZr7q7B7Ny5UplZ2fHuhkAAAAAADRbTCvpu3fv1rp16zy3169fr5UrV6p79+7q1auXbrjhBm3atEl///vfJUn333+/evfurUGDBmnfvn2aP3++3n//fb333nuxegkAAAAAALSYmIb0r776Sscdd5zn9lVXXSVJmjJlihYsWKDNmzdrw4YNnsdramp09dVXa9OmTercubMGDx6sJUuW+BwDAAAAAID9lWUmjmsrVVVVSk1NZeI4AAAAAECbiGbiuP1+TDoAAAAAAO0FIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwiJiG9I8++kgTJkxQTk6ObDabXn/99Ub3WbZsmYYNGyaHw6G+fftqwYIFrd5OAAAAAADaQkxD+p49ezRkyBA98sgjEW2/fv16nXTSSTruuOO0cuVKXXHFFZo2bZrefffdVm4pAAAAAACtLz6WTz5u3DiNGzcu4u0ff/xx9e7dW/fdd58k6aCDDtInn3yiv/3tbxo7dmxrNRMAAAAAgDaxX41J//TTTzV69Gif+8aOHatPP/005D7V1dWqqqry+QIAAAAAwIr2q5C+ZcsWZWZm+tyXmZmpqqoq7d27N+g+c+fOVWpqqucrPz+/LZoKAAAAAEDU9quQ3hQ33HCDKisrPV8lJSWxbhIAAAAAAEHFdEx6tLKyslRWVuZzX1lZmVJSUtSpU6eg+zgcDjkcjrZoHgAAAAAAzbJfVdKHDx+upUuX+ty3ePFiDR8+PEYtAgAAAACg5cQ0pO/evVsrV67UypUrJbmXWFu5cqU2bNggyd1VffLkyZ7tL774Yv3666+69tpr9dNPP+nRRx/VP//5T1155ZWxaD4AAAAAAC0qpiH9q6++0qGHHqpDDz1UknTVVVfp0EMP1S233CJJ2rx5syewS1Lv3r31n//8R4sXL9aQIUN03333af78+Sy/BgAAAABoF2yGYRixbkRbqqqqUmpqqm79do2SkpNj3RwAAAAAQDu3b9cu3TJkgCorK5WSkhJ22/1qTDoAAAAAAO0ZIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdAAAAAAALIKQDgAAAACARRDSAQAAAACwCEI6AAAAAAAWQUgHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIuIj3UD2pphGJKkfbt3x7glAAAAAICOwMyfZh4Nx2ZEslU7snHjRuXn58e6GQAAAACADqakpER5eXlht+lwId3lcqm0tFTJycmy2Wyxbk6HUFVVpfz8fJWUlCglJSXWzUEb4/0Hn4GOjfcffAY6Nt5/8BlwMwxDu3btUk5Ojuz28KPOO1x3d7vd3uiVC7SOlJSUDv2D2dHx/oPPQMfG+w8+Ax0b7z/4DEipqakRbcfEcQAAAAAAWAQhHQAAAAAAiyCko9U5HA7NmjVLDocj1k1BDPD+g89Ax8b7Dz4DHRvvP/gMRK/DTRwHAAAAAIBVUUkHAAAAAMAiCOkAAAAAAFgEIR0AAAAAAIsgpAMAAAAAYBGEdLSIRx55RIWFhUpKStIRRxyhL774Iuz2L7/8sg488EAlJSXpkEMO0VtvvdVGLUVriOb9X7BggWw2m89XUlJSG7YWLemjjz7ShAkTlJOTI5vNptdff73RfZYtW6Zhw4bJ4XCob9++WrBgQau3E60n2s/AsmXLAn4H2Gw2bdmypW0ajBY1d+5c/e53v1NycrIyMjI0ceJErVmzptH9+DugfWjK+8/fAe3LY489psGDByslJUUpKSkaPny43n777bD78PPfOEI6mu2ll17SVVddpVmzZmnFihUaMmSIxo4dq/Ly8qDbL1++XJMmTdIFF1ygb775RhMnTtTEiRO1evXqNm45WkK0778kpaSkaPPmzZ6v4uLiNmwxWtKePXs0ZMgQPfLIIxFtv379ep100kk67rjjtHLlSl1xxRWaNm2a3n333VZuKVpLtJ8B05o1a3x+D2RkZLRSC9GaPvzwQ82YMUOfffaZFi9erNraWh1//PHas2dPyH34O6D9aMr7L/F3QHuSl5enO++8U19//bW++uor/eEPf9App5yi77//Puj2/PxHyACa6fDDDzdmzJjhue10Oo2cnBxj7ty5Qbc/66yzjJNOOsnnviOOOMKYPn16q7YTrSPa9/+ZZ54xUlNT26h1aEuSjEWLFoXd5tprrzUGDRrkc9/ZZ59tjB07thVbhrYSyWfggw8+MCQZO3bsaJM2oW2Vl5cbkowPP/ww5Db8HdB+RfL+83dA+5eWlmbMnz8/6GP8/EeGSjqapaamRl9//bVGjx7tuc9ut2v06NH69NNPg+7z6aef+mwvSWPHjg25PayrKe+/JO3evVsFBQXKz88Pe7UV7Q8//zANHTpU2dnZGjNmjP773//GujloIZWVlZKk7t27h9yG3wPtVyTvv8TfAe2V0+nUiy++qD179mj48OFBt+HnPzKEdDTL1q1b5XQ6lZmZ6XN/ZmZmyPGFW7ZsiWp7WFdT3v8BAwbo6aef1r/+9S8999xzcrlcGjFihDZu3NgWTUaMhfr5r6qq0t69e2PUKrSl7OxsPf7443r11Vf16quvKj8/X8cee6xWrFgR66ahmVwul6644godddRROvjgg0Nux98B7VOk7z9/B7Q/3333nbp27SqHw6GLL75YixYt0sCBA4Nuy89/ZOJj3QAAHcvw4cN9rq6OGDFCBx10kObNm6fbbrsthi0D0BYGDBigAQMGeG6PGDFCv/zyi/72t7/pH//4RwxbhuaaMWOGVq9erU8++STWTUEMRPr+83dA+zNgwACtXLlSlZWVeuWVVzRlyhR9+OGHIYM6GkclHc3Ss2dPxcXFqayszOf+srIyZWVlBd0nKysrqu1hXU15//0lJCTo0EMP1bp161qjibCYUD//KSkp6tSpU4xahVg7/PDD+R2wn7v00kv15ptv6oMPPlBeXl7Ybfk7oP2J5v33x98B+7/ExET17dtXhx12mObOnashQ4bogQceCLotP/+RIaSjWRITE3XYYYdp6dKlnvtcLpeWLl0acizK8OHDfbaXpMWLF4fcHtbVlPffn9Pp1Hfffafs7OzWaiYshJ9/BLNy5Up+B+ynDMPQpZdeqkWLFun9999X7969G92H3wPtR1Pef3/8HdD+uFwuVVdXB32Mn/8IxXrmOuz/XnzxRcPhcBgLFiwwfvjhB+Oiiy4yunXrZmzZssUwDMP44x//aFx//fWe7f/73/8a8fHxxr333mv8+OOPxqxZs4yEhATju+++i9VLQDNE+/7PmTPHePfdd41ffvnF+Prrr41zzjnHSEpKMr7//vtYvQQ0w65du4xvvvnG+OabbwxJxl//+lfjm2++MYqLiw3DMIzrr7/e+OMf/+jZ/tdffzU6d+5szJw50/jxxx+NRx55xIiLizPeeeedWL0ENFO0n4G//e1vxuuvv278/PPPxnfffWdcfvnlht1uN5YsWRKrl4Bm+NOf/mSkpqYay5YtMzZv3uz5+u233zzb8HdA+9WU95+/A9qX66+/3vjwww+N9evXG6tWrTKuv/56w2azGe+9955hGPz8NxUhHS3ioYceMnr16mUkJiYahx9+uPHZZ595Hhs5cqQxZcoUn+3/+c9/Gv379zcSExONQYMGGf/5z3/auMVoSdG8/1dccYVn28zMTOPEE080VqxYEYNWoyWYy2n5f5nv+ZQpU4yRI0cG7DN06FAjMTHROOCAA4xnnnmmzduNlhPtZ+Cuu+4y+vTpYyQlJRndu3c3jj32WOP999+PTePRbMHee0k+P9f8HdB+NeX95++A9uX88883CgoKjMTERCM9Pd0YNWqUJ6AbBj//TWUzDMNou7o9AAAAAAAIhTHpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAACE5HQ6NWLECJ122mk+91dWVio/P1833nhjjFoGAED7ZDMMw4h1IwAAgHWtXbtWQ4cO1ZNPPqn/+Z//kSRNnjxZ3377rb788kslJibGuIUAALQfhHQAANCoBx98ULNnz9b333+vL774Qmeeeaa+/PJLDRkyJNZNAwCgXSGkAwCARhmGoT/84Q+Ki4vTd999p8suu0w33XRTrJsFAEC7Q0gHAAAR+emnn3TQQQfpkEMO0YoVKxQfHx/rJgEA0O4wcRwAAIjI008/rc6dO2v9+vXauHFjrJsDAEC7RCUdAAA0avny5Ro5cqTee+893X777ZKkJUuWyGazxbhlAAC0L1TSAQBAWL/99pvOO+88/elPf9Jxxx2np556Sl988YUef/zxWDcNAIB2h0o6AAAI6/LLL9dbb72lb7/9Vp07d5YkzZs3T9dcc42+++47FRYWxraBAAC0I4R0AAAQ0ocffqhRo0Zp2bJl+v3vf+/z2NixY1VXV0e3dwAAWhAhHQAAAAAAi2BMOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYBCEdAAAAAACLIKQDAAAAAGARhHQAAAAAACyCkA4AAAAAgEUQ0gEAAAAAsAhCOgAAAAAAFkFIBwAAAADAIgjpAAAAAABYxP8HZ8Wvm1h0P3QAAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["amount:  19\n","amount_ae:  31\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x700 with 2 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качества AE2\n","IDEAL = 0. Excess:  0.631578947368421\n","IDEAL = 0. Deficit:  0.0\n","IDEAL = 1. Coating:  1.0\n","summa:  1.0\n","IDEAL = 1. Extrapolation precision (Approx):  0.6129032258064516\n","\n","\n"]}]},{"cell_type":"code","source":["lib.plot2in1(data, xx, yy, Z1, Z2)"],"metadata":{"id":"wLmFlULHntcV","executionInfo":{"status":"ok","timestamp":1760899005095,"user_tz":-180,"elapsed":437,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/","height":472},"collapsed":true,"outputId":"a5a7d7a8-b543-4db8-8bfd-f1fb57934819"},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"pVAFih0Ppk3k"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["with open('data_test.txt', 'w') as file:\n","    file.write(\"1.5327 1.5591\\n\")\n","    file.write(\"1.4373 1.4932\\n\")\n","    file.write(\"1.1231 1.3212\\n\")\n","    file.write(\"1.3211 1.1231\\n\")\n","data_test = np.loadtxt('data_test.txt', dtype=float)\n","print(data_test)\n"],"metadata":{"id":"ylR8TGaRoDos","executionInfo":{"status":"ok","timestamp":1760899192723,"user_tz":-180,"elapsed":8,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"outputId":"05c204fa-31d2-442a-b426-b2f53e7f4137"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["[[1.5327 1.5591]\n"," [1.4373 1.4932]\n"," [1.1231 1.3212]\n"," [1.3211 1.1231]]\n"]}]},{"cell_type":"code","source":["predicted_labels1, ire1 = lib.predict_ae(ae1_trained, data_test, IREth1)\n"],"metadata":{"id":"mdW3_luarkjQ","executionInfo":{"status":"ok","timestamp":1760899193699,"user_tz":-180,"elapsed":73,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"00ad3614-10e4-4f09-ca0c-5dc28dfb4463"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n"]}]},{"cell_type":"code","source":["lib.anomaly_detection_ae(predicted_labels1, ire1, IREth1)\n","lib.ire_plot('test', ire1, IREth1, 'AE1')"],"metadata":{"id":"Vw1-0mlXrn3O","executionInfo":{"status":"ok","timestamp":1760899194764,"user_tz":-180,"elapsed":311,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/","height":762},"collapsed":true,"outputId":"fc51ee83-c162-484a-d313-a925cb1be701"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Аномалий не обнаружено\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 Axes>"],"image/png":"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ire2 = lib.predict_ae(ae2_trained, data_test, IREth2)"],"metadata":{"id":"JGQ2kHeVruPc","executionInfo":{"status":"ok","timestamp":1760899196424,"user_tz":-180,"elapsed":65,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"cac3ef9b-cd35-47c0-d1ab-4c21229ba0b4"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n"]}]},{"cell_type":"code","source":["lib.anomaly_detection_ae(predicted_labels2, ire2, IREth2)\n","lib.ire_plot('test', ire2, IREth2, 'AE2')"],"metadata":{"id":"kC_nqlbarxkq","executionInfo":{"status":"ok","timestamp":1760899198398,"user_tz":-180,"elapsed":326,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/","height":870},"collapsed":true,"outputId":"0f0bfffb-0d65-4aed-e7db-3e5c918a3c17"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","i         Labels    IRE       IREth     \n","0         [1.]      [0.57]    0.38      \n","1         [1.]      [0.68]    0.38      \n","2         [1.]      [1.03]    0.38      \n","3         [1.]      [1.04]    0.38      \n","Обнаружено  4.0  аномалий\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)\n"],"metadata":{"id":"A82tl8M6tdBP","executionInfo":{"status":"ok","timestamp":1760899674272,"user_tz":-180,"elapsed":548,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"colab":{"base_uri":"https://localhost:8080/","height":472},"outputId":"54fd68c0-bc3b-4859-ee2f-752aeebb2172"},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"lCrGofrmwewx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train = np.loadtxt('WBC_train.txt', dtype=float)"],"metadata":{"id":"NSKUrtrE64hO","executionInfo":{"status":"ok","timestamp":1760905417375,"user_tz":-180,"elapsed":416,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["print('train:\\n', train)\n","print('train.shape:', np.shape(train))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2-ZZUK6x71cb","executionInfo":{"status":"ok","timestamp":1760901951083,"user_tz":-180,"elapsed":19,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"3c79952f-093d-4c50-e80c-28f3c46aee55"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["train:\n"," [[3.1042643e-01 1.5725397e-01 3.0177597e-01 ... 4.4261168e-01\n","  2.7833629e-01 1.1511216e-01]\n"," [2.8865540e-01 2.0290835e-01 2.8912998e-01 ... 2.5027491e-01\n","  3.1914055e-01 1.7571822e-01]\n"," [1.1940934e-01 9.2323301e-02 1.1436666e-01 ... 2.1398625e-01\n","  1.7445299e-01 1.4882592e-01]\n"," ...\n"," [3.3456387e-01 5.8978695e-01 3.2886463e-01 ... 3.6013746e-01\n","  1.3502858e-01 1.8476978e-01]\n"," [1.9967817e-01 6.6486304e-01 1.8575081e-01 ... 0.0000000e+00\n","  1.9712202e-04 2.6301981e-02]\n"," [3.6868759e-02 5.0152181e-01 2.8539838e-02 ... 0.0000000e+00\n","  2.5744136e-01 1.0068215e-01]]\n","train.shape: (357, 30)\n"]}]},{"cell_type":"code","source":["from time import time\n","\n","patience = 6500\n","start = time()\n","ae3_v1_trained, IRE3_v1, IREth3_v1 = lib.create_fit_save_ae(train,'out/AE3_V1.h5','out/AE3_v1_ire_th.txt',\n","60000, False, patience, early_stopping_delta = 0.001)\n","print(\"Время на обучение: \", time() - start)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ROmwISJJ77tV","executionInfo":{"status":"ok","timestamp":1760903325571,"user_tz":-180,"elapsed":269553,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"3931e866-8461-4f8a-d483-afe21a3f063d"},"execution_count":47,"outputs":[{"output_type":"stream","name":"stdout","text":["Задать архитектуру автокодировщиков или использовать архитектуру по умолчанию? (1/2): 1\n","Задайте количество скрытых слоёв (нечетное число) : 11\n","Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 53 47 43 35 27 13 27 35 43 47 53\n","\n","Epoch 1000/60000\n"," - loss: 0.0003\n","\n","Epoch 2000/60000\n"," - loss: 0.0002\n","\n","Epoch 3000/60000\n"," - loss: 0.0002\n","\n","Epoch 4000/60000\n"," - loss: 0.0003\n","\n","Epoch 5000/60000\n"," - loss: 0.0002\n","\n","Epoch 6000/60000\n"," - loss: 0.0001\n","\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step\n"]},{"output_type":"stream","name":"stderr","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"]},{"output_type":"stream","name":"stdout","text":["\n","\n","Время на обучение:  269.5005657672882\n"]}]},{"cell_type":"code","source":["test = np.loadtxt('WBC_test.txt', dtype=float)"],"metadata":{"id":"mCrYKX418-aU","executionInfo":{"status":"ok","timestamp":1760905420824,"user_tz":-180,"elapsed":339,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["print('\\n test:\\n', test)\n","print('test.shape:', np.shape(test))"],"metadata":{"collapsed":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"TC2IC37H9KuC","executionInfo":{"status":"ok","timestamp":1760903330592,"user_tz":-180,"elapsed":35,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"2dbfe429-b34d-456a-9b7f-8bb2cdcae1b5"},"execution_count":49,"outputs":[{"output_type":"stream","name":"stdout","text":["\n"," test:\n"," [[0.18784609 0.3936422  0.19425057 0.09654295 0.632572   0.31415251\n","  0.24461106 0.28175944 0.42171717 0.3946925  0.04530147 0.23598833\n","  0.05018141 0.01899148 0.21589557 0.11557064 0.0655303  0.19643872\n","  0.08003602 0.07411246 0.17467094 0.62153518 0.18332586 0.08081007\n","  0.79066235 0.23528442 0.32132588 0.48934708 0.2757737  0.26905418]\n"," [0.71129727 0.41224214 0.71460162 0.56776246 0.48451747 0.53990553\n","  0.57357076 0.74602386 0.38585859 0.24094356 0.3246424  0.07507514\n","  0.32059558 0.23047901 0.0769963  0.19495599 0.09030303 0.27865126\n","  0.10269038 0.10023078 0.70188545 0.36727079 0.72010558 0.50181872\n","  0.38453411 0.35044775 0.3798722  0.83573883 0.23181549 0.20136429]\n"," [0.38567845 0.67974298 0.36569691 0.24432662 0.27597725 0.0818048\n","  0.10979381 0.1361332  0.4        0.06276327 0.12913272 0.27996818\n","  0.10771333 0.07205481 0.17398103 0.09026046 0.06285354 0.17213487\n","  0.33247031 0.02954549 0.33191035 0.66337953 0.29727576 0.1833956\n","  0.28811992 0.06924353 0.1235623  0.22594502 0.32879953 0.04335563]\n"," [0.3956174  0.15387217 0.40570797 0.23792153 0.49354518 0.59542359\n","  0.4866448  0.48489066 0.73787879 0.42881213 0.11852254 0.07721888\n","  0.1237808  0.07117696 0.17255329 0.38324271 0.16277778 0.42659595\n","  0.40578038 0.12089051 0.36072572 0.18816631 0.37198068 0.19556134\n","  0.44792974 0.55118317 0.50359425 0.82233677 0.6114725  0.29135511]\n"," [0.49595343 1.         0.48103103 0.32962884 0.41067076 0.33869088\n","  0.33200562 0.43792247 0.37828283 0.20429655 0.15393808 0.109596\n","  0.10578146 0.07881613 0.10697896 0.19225223 0.06570707 0.26027657\n","  0.06160297 0.06440446 0.51867663 0.87553305 0.45216395 0.30053087\n","  0.43142046 0.33589467 0.25886581 0.70996564 0.25389316 0.1962482 ]\n"," [0.59676274 0.28542442 0.60058047 0.45408271 0.53597544 0.45156739\n","  0.58762887 0.63916501 0.48838384 0.22872789 0.22629006 0.15200672\n","  0.19012392 0.16859981 0.07968182 0.1540992  0.10174242 0.24682705\n","  0.12632971 0.08371682 0.66880114 0.38299574 0.620001   0.50304758\n","  0.47962755 0.34666395 0.54392971 0.77216495 0.40961955 0.24393283]\n"," [0.34118983 0.47683463 0.33916108 0.19817603 0.37916403 0.34114472\n","  0.26124649 0.32117296 0.59343434 0.30265375 0.11196813 0.3281471\n","  0.13084861 0.04519628 0.31162253 0.2617238  0.09313131 0.30820231\n","  0.5221478  0.13381148 0.31768054 0.60847548 0.32167937 0.15387829\n","  0.55953246 0.36732932 0.29904153 0.60893471 0.62270846 0.31195068]\n"," [0.76809125 0.5836997  0.75813696 0.64750795 0.38331678 0.45647506\n","  0.45688847 0.61481113 0.42878788 0.27653749 0.34274851 0.13333186\n","  0.30580031 0.2782939  0.16028147 0.19811037 0.11356061 0.32506156\n","  0.11282152 0.07456158 0.82106012 0.59941365 0.77488919 0.67803775\n","  0.50802351 0.37382969 0.46485623 0.89106529 0.30317366 0.20812016]\n"," [0.39703725 0.44132567 0.38981411 0.24801697 0.35542114 0.25832771\n","  0.2628866  0.37191849 0.33181818 0.23188711 0.07293138 0.10632514\n","  0.06210244 0.04228256 0.27769657 0.16664163 0.11441919 0.33396477\n","  0.2367873  0.04308832 0.30238349 0.36833689 0.28432691 0.15869544\n","  0.36010038 0.16727304 0.22731629 0.50721649 0.19534792 0.08684245]\n"," [0.50257939 0.46060196 0.51972911 0.35503712 0.36345581 0.55524201\n","  0.5004686  0.49801193 0.32121212 0.49978939 0.29599855 0.24416549\n","  0.23766668 0.18322444 0.17177142 0.51069487 0.16643939 0.43777231\n","  0.12450048 0.35947929 0.48523657 0.44909382 0.46411674 0.30765828\n","  0.32708182 0.4377662  0.41253994 0.68591065 0.14508181 0.44182081]\n"," [0.29007525 0.19783564 0.30004837 0.16402969 0.78694592 0.48193362\n","  0.48523899 0.47718688 0.43686869 0.56781803 0.10114068 0.12455799\n","  0.0778872  0.05203232 0.18523303 0.20179049 0.13820707 0.26292858\n","  0.10677098 0.21430842 0.29811455 0.27665245 0.27884855 0.15778608\n","  0.75962491 0.37121014 0.50926518 0.68247423 0.31184703 0.56054047]\n"," [0.25931185 0.48461278 0.27765877 0.14099682 0.59555836 0.67548003\n","  0.53256795 0.42460239 0.48989899 0.68386689 0.06739091 0.27378006\n","  0.06040616 0.03200983 0.18479111 0.52511491 0.1955303  0.2712635\n","  0.14082287 0.31733068 0.25471363 0.76385928 0.23527068 0.1293256\n","  0.75368157 1.         0.88258786 0.75945017 0.55213877 1.        ]\n"," [0.3449761  0.43422388 0.34538042 0.20627784 0.46194818 0.29452181\n","  0.34278351 0.30511928 0.43737374 0.20766639 0.03302553 0.32947313\n","  0.05362107 0.02192388 0.14957338 0.17708114 0.11729798 0.24171245\n","  0.09326279 0.09884886 0.26182853 0.59301706 0.26838986 0.13347916\n","  0.44132603 0.23868013 0.33817891 0.46804124 0.22333925 0.1867375 ]\n"," [0.39372427 0.526209   0.40501693 0.24979852 0.50167013 0.46107601\n","  0.3943299  0.43494036 0.43737374 0.32518955 0.11859497 0.14405057\n","  0.12915233 0.0685434  0.1196587  0.21268063 0.09030303 0.20515249\n","  0.13786796 0.07159045 0.43898968 0.65804904 0.49250461 0.26636846\n","  0.61368289 0.56631836 0.50599042 0.69553265 0.48531441 0.28676374]\n"," [0.41265559 0.35847142 0.39672448 0.26430541 0.39126117 0.21044721\n","  0.15447516 0.25790258 0.28181818 0.11647009 0.09357233 0.17454915\n","  0.07769872 0.06383662 0.09902437 0.15304774 0.04934343 0.1850161\n","  0.10677098 0.05303815 0.43329776 0.554371   0.39289805 0.26636846\n","  0.46377864 0.31765482 0.23178914 0.52955326 0.36901242 0.20510298]\n"," [0.55132756 0.52079811 0.55980927 0.40063627 0.48542024 0.51935464\n","  0.54334583 0.61829026 0.56717172 0.25294861 0.26043817 0.24438649\n","  0.22697074 0.18341122 0.15416256 0.23648872 0.13121212 0.21935973\n","  0.17149772 0.12662549 0.54144433 0.58608742 0.54828428 0.36492332\n","  0.51462722 0.38653938 0.48985623 0.63505155 0.37039227 0.28059819]\n"," [0.35964788 0.39972946 0.37053417 0.21264051 0.47639253 0.51352678\n","  0.33388004 0.43653082 0.6020202  0.40606571 0.05178345 0.13768564\n","  0.06375159 0.02661198 0.09310943 0.21253042 0.06770202 0.25610911\n","  0.09368492 0.0972942  0.34471718 0.56476546 0.35853379 0.17491644\n","  0.53707984 0.61803029 0.44241214 0.92817869 0.53203233 0.4752722 ]\n"," [0.18784609 0.3936422  0.19425057 0.09654295 0.632572   0.31415251\n","  0.24461106 0.28175944 0.42171717 0.3946925  0.04530147 0.23598833\n","  0.05018141 0.01899148 0.21589557 0.11557064 0.0655303  0.19643872\n","  0.08003602 0.07411246 0.17467094 0.62153518 0.18332586 0.08081007\n","  0.79066235 0.23528442 0.32132588 0.48934708 0.2757737  0.26905418]\n"," [0.72360263 0.33682787 0.7532997  0.57921527 0.72194638 0.78958346\n","  0.99906279 0.90606362 0.75555556 0.43028644 0.39960167 0.26184583\n","  0.437874   0.30481623 0.16323894 0.63409139 0.26262626 0.46978594\n","  0.32698261 0.1431049  0.7282106  0.42617271 0.77887345 0.53450649\n","  0.65330516 0.65237555 0.76741214 1.         0.49083383 0.28105733]\n"," [0.52103744 0.0226581  0.54598853 0.36373277 0.59375282 0.7920373\n","  0.70313964 0.73111332 0.68636364 0.60551811 0.35614702 0.12046941\n","  0.3690336  0.27381126 0.15929565 0.35139844 0.13568182 0.30062512\n","  0.31164518 0.18304244 0.62077552 0.14152452 0.66831017 0.45069799\n","  0.60113584 0.61929156 0.56861022 0.91202749 0.59846245 0.41886396]\n"," [0.32367836 0.49983091 0.33542948 0.1918982  0.57389185 0.45616833\n","  0.31794752 0.33593439 0.61363636 0.47198821 0.13166757 0.25808876\n","  0.10446214 0.06023183 0.27082979 0.27268904 0.08777778 0.30611858\n","  0.23158102 0.21074997 0.28744219 0.5575693  0.27685642 0.14815179\n","  0.71471967 0.35830641 0.27004792 0.52268041 0.41119653 0.41492851]]\n","test.shape: (21, 30)\n"]}]},{"cell_type":"code","source":["predicted_labels3_v1, ire3_v1 = lib.predict_ae(ae3_v1_trained, test, IREth3_v1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DWVUevso9AfJ","executionInfo":{"status":"ok","timestamp":1760903332642,"user_tz":-180,"elapsed":489,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"690c24e5-2884-4cbe-8526-e1f052618ad3"},"execution_count":50,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 441ms/step\n"]}]},{"cell_type":"code","source":["lib.ire_plot('test', ire3_v1, IREth3_v1, 'AE3_v1')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":744},"id":"k8hAi6HM9RgD","executionInfo":{"status":"ok","timestamp":1760903333722,"user_tz":-180,"elapsed":404,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"0376820b-2c54-4b5e-fd1c-f47cf02bcdc4"},"execution_count":51,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["lib.anomaly_detection_ae(predicted_labels3_v1, IRE3_v1, IREth3_v1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WQ7POVcZ9Ueb","executionInfo":{"status":"ok","timestamp":1760903336223,"user_tz":-180,"elapsed":19,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"f3d18460-e49d-4784-db68-0a7565218369"},"execution_count":52,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","i         Labels    IRE       IREth     \n","0         [0.]      0.09      0.67      \n","1         [1.]      0.14      0.67      \n","2         [0.]      0.14      0.67      \n","3         [0.]      0.21      0.67      \n","4         [0.]      0.11      0.67      \n","5         [1.]      0.18      0.67      \n","6         [0.]      0.13      0.67      \n","7         [1.]      0.14      0.67      \n","8         [0.]      0.11      0.67      \n","9         [0.]      0.1       0.67      \n","10        [0.]      0.14      0.67      \n","11        [1.]      0.15      0.67      \n","12        [0.]      0.19      0.67      \n","13        [0.]      0.15      0.67      \n","14        [0.]      0.19      0.67      \n","15        [0.]      0.21      0.67      \n","16        [0.]      0.09      0.67      \n","17        [0.]      0.13      0.67      \n","18        [1.]      0.55      0.67      \n","19        [1.]      0.16      0.67      \n","20        [0.]      0.38      0.67      \n","Обнаружено  6.0  аномалий\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"BI0mVCrfBNWT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from time import time\n","\n","patience = 5000\n","start = time()\n","ae3_v2_trained, IRE3_v2, IREth3_v2 = lib.create_fit_save_ae(train,'out/AE3_V2.h5','out/AE3_v2_ire_th.txt',\n","50000, False, patience, early_stopping_delta = 0.001)\n","print(\"Время на обучение: \", time() - start)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"T10mbweuBNko","executionInfo":{"status":"ok","timestamp":1760903668100,"user_tz":-180,"elapsed":272676,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"c43b5f20-6c24-4f8a-8a4b-05807f3149a4"},"execution_count":53,"outputs":[{"output_type":"stream","name":"stdout","text":["Задать архитектуру автокодировщиков или использовать архитектуру по умолчанию? (1/2): 1\n","Задайте количество скрытых слоёв (нечетное число) : 9\n","Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 37 29 21 15 7 15 21 29 37\n","\n","Epoch 1000/50000\n"," - loss: 0.0010\n","\n","Epoch 2000/50000\n"," - loss: 0.0009\n","\n","Epoch 3000/50000\n"," - loss: 0.0009\n","\n","Epoch 4000/50000\n"," - loss: 0.0009\n","\n","Epoch 5000/50000\n"," - loss: 0.0007\n","\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 52ms/step\n"]},{"output_type":"stream","name":"stderr","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"]},{"output_type":"stream","name":"stdout","text":["\n","\n","Время на обучение:  272.63396859169006\n"]}]},{"cell_type":"code","source":["predicted_labels3_v2, ire3_v2 = lib.predict_ae(ae3_v2_trained, test, IREth3_v2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rpilTl-0Czwe","executionInfo":{"status":"ok","timestamp":1760903784846,"user_tz":-180,"elapsed":821,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"b1bf2241-7805-45eb-e7fa-fc9526fd48aa"},"execution_count":54,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 587ms/step\n"]}]},{"cell_type":"code","source":["lib.ire_plot('test', ire3_v2, IREth3_v2, 'AE3_v2')"],"metadata":{"collapsed":true,"colab":{"base_uri":"https://localhost:8080/","height":744},"id":"uGqPXE6-C3D3","executionInfo":{"status":"ok","timestamp":1760903799472,"user_tz":-180,"elapsed":422,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"077b74b4-3b35-49f0-e4ba-c0e4c327d093"},"execution_count":55,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["lib.anomaly_detection_ae(predicted_labels3_v2, IRE3_v2, IREth3_v2)"],"metadata":{"collapsed":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"FGL9zOOxC91u","executionInfo":{"status":"ok","timestamp":1760904897569,"user_tz":-180,"elapsed":36,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"431f5ec1-aa76-4b3b-b0b8-8b2c2b8f9d98"},"execution_count":58,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","i         Labels    IRE       IREth     \n","0         [0.]      0.2       0.8       \n","1         [1.]      0.22      0.8       \n","2         [0.]      0.16      0.8       \n","3         [0.]      0.3       0.8       \n","4         [0.]      0.13      0.8       \n","5         [0.]      0.2       0.8       \n","6         [0.]      0.17      0.8       \n","7         [1.]      0.15      0.8       \n","8         [0.]      0.14      0.8       \n","9         [0.]      0.15      0.8       \n","10        [0.]      0.17      0.8       \n","11        [1.]      0.14      0.8       \n","12        [0.]      0.22      0.8       \n","13        [0.]      0.25      0.8       \n","14        [0.]      0.24      0.8       \n","15        [0.]      0.29      0.8       \n","16        [0.]      0.09      0.8       \n","17        [0.]      0.21      0.8       \n","18        [1.]      0.69      0.8       \n","19        [1.]      0.16      0.8       \n","20        [0.]      0.56      0.8       \n","Обнаружено  5.0  аномалий\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"oM_0BEWBDCIm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from time import time\n","\n","patience = 5000\n","start = time()\n","ae3_v3_trained, IRE3_v3, IREth3_v3 = lib.create_fit_save_ae(train,'out/AE3_V3.h5','out/AE3_v3_ire_th.txt',\n","50000, False, patience, early_stopping_delta = 0.0001)\n","print(\"Время на обучение: \", time() - start)"],"metadata":{"collapsed":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"wpe7TkPUDEYv","executionInfo":{"status":"ok","timestamp":1760907221379,"user_tz":-180,"elapsed":816500,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"5d0fb536-bdb3-415b-e390-60a8da292a5b"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Задать архитектуру автокодировщиков или использовать архитектуру по умолчанию? (1/2): 1\n","Задайте количество скрытых слоёв (нечетное число) : 9\n","Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 30 25 20 15 7 15 20 25 30\n","\n","Epoch 1000/50000\n"," - loss: 0.0011\n","\n","Epoch 2000/50000\n"," - loss: 0.0010\n","\n","Epoch 3000/50000\n"," - loss: 0.0009\n","\n","Epoch 4000/50000\n"," - loss: 0.0008\n","\n","Epoch 5000/50000\n"," - loss: 0.0007\n","\n","Epoch 6000/50000\n"," - loss: 0.0007\n","\n","Epoch 7000/50000\n"," - loss: 0.0007\n","\n","Epoch 8000/50000\n"," - loss: 0.0006\n","\n","Epoch 9000/50000\n"," - loss: 0.0006\n","\n","Epoch 10000/50000\n"," - loss: 0.0006\n","\n","Epoch 11000/50000\n"," - loss: 0.0006\n","\n","Epoch 12000/50000\n"," - loss: 0.0006\n","\n","Epoch 13000/50000\n"," - loss: 0.0005\n","\n","Epoch 14000/50000\n"," - loss: 0.0005\n","\n","Epoch 15000/50000\n"," - loss: 0.0005\n","\n","Epoch 16000/50000\n"," - loss: 0.0005\n","\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n"]},{"output_type":"stream","name":"stderr","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"]},{"output_type":"stream","name":"stdout","text":["\n","\n","Время на обучение:  816.357284784317\n"]}]},{"cell_type":"code","source":["predicted_labels3_v3, ire3_v3 = lib.predict_ae(ae3_v3_trained, test, IREth3_v3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YKXad23YJZYE","executionInfo":{"status":"ok","timestamp":1760907224289,"user_tz":-180,"elapsed":117,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"f843a054-3bd9-4eea-d04d-7c899c1d9080"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step\n"]}]},{"cell_type":"code","source":["lib.ire_plot('test', ire3_v3, IREth3_v3, 'AE3_v3')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":744},"id":"eiIsZj6yJdCs","executionInfo":{"status":"ok","timestamp":1760907225972,"user_tz":-180,"elapsed":646,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"149bb59d-45ee-4986-cee0-e0b7d73f36a7"},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 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IRE3_v3, IREth3_v3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rGbXb5czJedS","executionInfo":{"status":"ok","timestamp":1760907229215,"user_tz":-180,"elapsed":45,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"db61090a-acad-491d-fd01-ecc7cd724ba7"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","i         Labels    IRE       IREth     \n","0         [0.]      0.08      0.27      \n","1         [1.]      0.11      0.27      \n","2         [0.]      0.13      0.27      \n","3         [1.]      0.17      0.27      \n","4         [1.]      0.13      0.27      \n","5         [1.]      0.18      0.27      \n","6         [1.]      0.11      0.27      \n","7         [1.]      0.12      0.27      \n","8         [1.]      0.11      0.27      \n","9         [1.]      0.14      0.27      \n","10        [1.]      0.17      0.27      \n","11        [1.]      0.12      0.27      \n","12        [0.]      0.11      0.27      \n","13        [1.]      0.09      0.27      \n","14        [0.]      0.23      0.27      \n","15        [1.]      0.12      0.27      \n","16        [1.]      0.1       0.27      \n","17        [0.]      0.14      0.27      \n","18        [1.]      0.05      0.27      \n","19        [1.]      0.12      0.27      \n","20        [0.]      0.1       0.27      \n","Обнаружено  15.0  аномалий\n"]}]}]}
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