From 57734ae3f3f4a66a3d9982f1ba8764cb6ef73c7e Mon Sep 17 00:00:00 2001
From: Troyanov Daniil <troyanov-2018@mail.ru>
Date: Sat, 11 Oct 2025 19:48:52 +0300
Subject: [PATCH] =?UTF-8?q?=D1=81=D0=BE=D0=B7=D0=B4=D0=B0=D0=BD=D0=B8?=
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---
 .DS_Store                                     |  Bin 0 -> 6148 bytes
 labworks/.DS_Store                            |  Bin 0 -> 6148 bytes
 labworks/LW1/.DS_Store                        |  Bin 0 -> 6148 bytes
 .../Lab1_Troyanov&Chernov-checkpoint.ipynb    |    1 +
 .../my_document-checkpoint.md                 |  533 +++++
 labworks/LW1/2.png                            |  Bin 0 -> 315 bytes
 labworks/LW1/2_90.png                         |  Bin 0 -> 948 bytes
 labworks/LW1/7.png                            |  Bin 0 -> 273 bytes
 labworks/LW1/7_90.png                         |  Bin 0 -> 920 bytes
 labworks/LW1/Lab1_Troyanov&Chernov.ipynb      | 1956 +++++++++++++++++
 labworks/LW1/architecture_of_1_layer_NN.png   |  Bin 0 -> 25092 bytes
 labworks/LW1/best_mnist_model.keras           |  Bin 0 -> 46150 bytes
 labworks/LW1/my_document.md                   |  533 +++++
 labworks/LW1/paragraph_10.png                 |  Bin 0 -> 43059 bytes
 labworks/LW1/paragraph_12_1.png               |  Bin 0 -> 81927 bytes
 labworks/LW1/paragraph_12_3.png               |  Bin 0 -> 87119 bytes
 labworks/LW1/paragraph_4.png                  |  Bin 0 -> 8366 bytes
 labworks/LW1/paragraph_6.png                  |  Bin 0 -> 38918 bytes
 labworks/LW1/paragraph_8.png                  |  Bin 0 -> 59680 bytes
 labworks/LW2/LW1                              |    1 +
 labworks/LW3/LW1                              |    1 +
 21 files changed, 3025 insertions(+)
 create mode 100644 .DS_Store
 create mode 100644 labworks/.DS_Store
 create mode 100644 labworks/LW1/.DS_Store
 create mode 100644 labworks/LW1/.ipynb_checkpoints/Lab1_Troyanov&Chernov-checkpoint.ipynb
 create mode 100644 labworks/LW1/.ipynb_checkpoints/my_document-checkpoint.md
 create mode 100644 labworks/LW1/2.png
 create mode 100644 labworks/LW1/2_90.png
 create mode 100644 labworks/LW1/7.png
 create mode 100644 labworks/LW1/7_90.png
 create mode 100644 labworks/LW1/Lab1_Troyanov&Chernov.ipynb
 create mode 100644 labworks/LW1/architecture_of_1_layer_NN.png
 create mode 100644 labworks/LW1/best_mnist_model.keras
 create mode 100644 labworks/LW1/my_document.md
 create mode 100644 labworks/LW1/paragraph_10.png
 create mode 100644 labworks/LW1/paragraph_12_1.png
 create mode 100644 labworks/LW1/paragraph_12_3.png
 create mode 100644 labworks/LW1/paragraph_4.png
 create mode 100644 labworks/LW1/paragraph_6.png
 create mode 100644 labworks/LW1/paragraph_8.png
 create mode 160000 labworks/LW2/LW1
 create mode 160000 labworks/LW3/LW1

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diff --git a/labworks/LW1/.ipynb_checkpoints/Lab1_Troyanov&Chernov-checkpoint.ipynb b/labworks/LW1/.ipynb_checkpoints/Lab1_Troyanov&Chernov-checkpoint.ipynb
new file mode 100644
index 0000000..db52219
--- /dev/null
+++ b/labworks/LW1/.ipynb_checkpoints/Lab1_Troyanov&Chernov-checkpoint.ipynb
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":5220,"status":"ok","timestamp":1759129041522,"user":{"displayName":"Legal People","userId":"00818738730090246603"},"user_tz":-180},"id":"lon2W0bNVyH8","colab":{"base_uri":"https://localhost:8080/"},"outputId":"7fe39a23-47a7-49cd-ca17-3703e109583a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'is_dnn'...\n","remote: Enumerating objects: 188, done.\u001b[K\n","remote: Counting objects: 100% (188/188), done.\u001b[K\n","remote: Compressing objects: 100% (186/186), done.\u001b[K\n","remote: Total 188 (delta 47), reused 0 (delta 0), pack-reused 0\u001b[K\n","Receiving objects: 100% (188/188), 8.53 MiB | 4.57 MiB/s, done.\n","Resolving deltas: 100% (47/47), done.\n"]}],"source":["!git clone http://uit.mpei.ru/git/TroyanovDS/is_dnn.git"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d5jFanC8NPTN"},"outputs":[],"source":["import os\n","os.chdir('/content/drive/MyDrive/Colab Notebooks/is_dnn/labworks/LW1')"]},{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":26468,"status":"ok","timestamp":1759209430625,"user":{"displayName":"Legal People","userId":"00818738730090246603"},"user_tz":-180},"id":"qlvyHRzuJPfI","colab":{"base_uri":"https://localhost:8080/"},"outputId":"597ba917-88e2-4f26-cc45-80cdb1dc5e55"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H39A4nsqNuxn"},"outputs":[],"source":["# импорт модулей\n","import tensorflow as tf\n","from tensorflow import keras\n","from keras.datasets import mnist\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from keras.utils import to_categorical\n","from sklearn.model_selection import train_test_split\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from PIL import Image\n","import os"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":462,"status":"ok","timestamp":1759127274975,"user":{"displayName":"Legal People","userId":"00818738730090246603"},"user_tz":-180},"id":"iNenKkcoRXrs","outputId":"041dc403-e177-4f0d-edbb-40902c8954fd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"]}],"source":["# Загрузка датасета\n","(X_train_orig, y_train_orig), (X_test_orig, y_test_orig) = mnist.load_data()\n","\n","# разбиваем выборку на обучающую и тестовую выборку\n","X = np.concatenate((X_train_orig, X_test_orig))\n","y = np.concatenate((y_train_orig, y_test_orig))\n","\n","\n","X_train, X_test, y_train, y_test = train_test_split(\n","    X, y,\n","    test_size=10000,\n","    train_size=60000,\n","    random_state=3,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":224},"executionInfo":{"elapsed":243,"status":"ok","timestamp":1759127328545,"user":{"displayName":"Legal People","userId":"00818738730090246603"},"user_tz":-180},"id":"bt-EmlYARsCL","outputId":"611d9110-39a2-46dd-94ce-b0fea7005b87"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x300 with 4 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Вывод первых 4 изображений\n","fig, axes = plt.subplots(1, 4, figsize=(12, 3))\n","for i in range(4):\n","    axes[i].imshow(X_train[i], cmap='gray')\n","    axes[i].set_title(f'Метка: {y_train[i]}')\n","    axes[i].axis('off')\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":134,"status":"ok","timestamp":1759127329383,"user":{"displayName":"Legal People","userId":"00818738730090246603"},"user_tz":-180},"id":"5WUu3_97TjSa","outputId":"7a75ca25-58fe-447d-8bef-547159e6479d"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of transformed X train: (60000, 784)\n"]}],"source":["# развернем каждое изображение 28*28 в вектор 784\n","num_pixels = X_train.shape[1] * X_train.shape[2]\n","X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255\n","X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255\n","print('Shape of transformed X train:', X_train.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1759127330016,"user":{"displayName":"Legal People","userId":"00818738730090246603"},"user_tz":-180},"id":"sUJfDepgUauM","outputId":"2a9bc3d0-8bdb-4971-f3d4-ca21cb14fca6"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of transformed y train: (60000, 10)\n"]}],"source":["# переведем метки в one-hot\n","y_train = to_categorical(y_train)\n","y_test = to_categorical(y_test)\n","print('Shape of transformed y train:', y_train.shape)\n","num_classes = y_train.shape[1]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":207500,"status":"ok","timestamp":1759127540337,"user":{"displayName":"Legal People","userId":"00818738730090246603"},"user_tz":-180},"id":"f3UzOyf_V2HQ","outputId":"9ffc63df-23bf-4947-f3c0-2a3e507034f1"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n","  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"stream","name":"stdout","text":["Архитектура однослойной сети:\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m7,850\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.7145 - loss: 1.1468 - val_accuracy: 0.8708 - val_loss: 0.5242\n","Epoch 2/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8769 - loss: 0.4791 - val_accuracy: 0.8838 - val_loss: 0.4376\n","Epoch 3/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8871 - loss: 0.4188 - val_accuracy: 0.8917 - val_loss: 0.4007\n","Epoch 4/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8939 - loss: 0.3855 - val_accuracy: 0.8957 - val_loss: 0.3796\n","Epoch 5/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8987 - loss: 0.3692 - val_accuracy: 0.8993 - val_loss: 0.3665\n","Epoch 6/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9035 - loss: 0.3523 - val_accuracy: 0.9008 - val_loss: 0.3555\n","Epoch 7/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9059 - loss: 0.3402 - val_accuracy: 0.9040 - val_loss: 0.3469\n","Epoch 8/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9053 - loss: 0.3389 - val_accuracy: 0.9060 - val_loss: 0.3405\n","Epoch 9/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9072 - loss: 0.3306 - val_accuracy: 0.9053 - val_loss: 0.3358\n","Epoch 10/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9115 - loss: 0.3209 - val_accuracy: 0.9067 - val_loss: 0.3310\n","Epoch 11/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9125 - loss: 0.3194 - val_accuracy: 0.9088 - val_loss: 0.3267\n","Epoch 12/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9110 - loss: 0.3165 - val_accuracy: 0.9093 - val_loss: 0.3236\n","Epoch 13/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9133 - loss: 0.3094 - val_accuracy: 0.9110 - val_loss: 0.3212\n","Epoch 14/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9155 - loss: 0.3031 - val_accuracy: 0.9123 - val_loss: 0.3176\n","Epoch 15/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9134 - loss: 0.3048 - val_accuracy: 0.9115 - val_loss: 0.3160\n","Epoch 16/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9129 - loss: 0.3096 - val_accuracy: 0.9120 - val_loss: 0.3141\n","Epoch 17/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9126 - loss: 0.3066 - val_accuracy: 0.9123 - val_loss: 0.3121\n","Epoch 18/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9157 - loss: 0.2999 - val_accuracy: 0.9117 - val_loss: 0.3099\n","Epoch 19/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9162 - loss: 0.2979 - val_accuracy: 0.9117 - val_loss: 0.3098\n","Epoch 20/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9166 - loss: 0.2941 - val_accuracy: 0.9133 - val_loss: 0.3072\n","Epoch 21/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9152 - loss: 0.2975 - val_accuracy: 0.9125 - val_loss: 0.3065\n","Epoch 22/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9173 - loss: 0.2926 - val_accuracy: 0.9137 - val_loss: 0.3049\n","Epoch 23/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9211 - loss: 0.2842 - val_accuracy: 0.9137 - val_loss: 0.3030\n","Epoch 24/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9185 - loss: 0.2929 - val_accuracy: 0.9138 - val_loss: 0.3024\n","Epoch 25/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9189 - loss: 0.2880 - val_accuracy: 0.9147 - val_loss: 0.3025\n","Epoch 26/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9179 - loss: 0.2926 - val_accuracy: 0.9150 - val_loss: 0.3007\n","Epoch 27/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9182 - loss: 0.2913 - val_accuracy: 0.9138 - val_loss: 0.3000\n","Epoch 28/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9197 - loss: 0.2881 - val_accuracy: 0.9133 - val_loss: 0.2993\n","Epoch 29/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9190 - loss: 0.2829 - val_accuracy: 0.9145 - val_loss: 0.2978\n","Epoch 30/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9190 - loss: 0.2878 - val_accuracy: 0.9157 - val_loss: 0.2977\n","Epoch 31/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9183 - loss: 0.2859 - val_accuracy: 0.9155 - val_loss: 0.2969\n","Epoch 32/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9207 - loss: 0.2849 - val_accuracy: 0.9158 - val_loss: 0.2962\n","Epoch 33/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9196 - loss: 0.2861 - val_accuracy: 0.9158 - val_loss: 0.2956\n","Epoch 34/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9205 - loss: 0.2784 - val_accuracy: 0.9155 - val_loss: 0.2952\n","Epoch 35/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9239 - loss: 0.2740 - val_accuracy: 0.9167 - val_loss: 0.2952\n","Epoch 36/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9227 - loss: 0.2727 - val_accuracy: 0.9165 - val_loss: 0.2945\n","Epoch 37/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9225 - loss: 0.2757 - val_accuracy: 0.9168 - val_loss: 0.2937\n","Epoch 38/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9227 - loss: 0.2748 - val_accuracy: 0.9165 - val_loss: 0.2935\n","Epoch 39/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9201 - loss: 0.2814 - val_accuracy: 0.9177 - val_loss: 0.2922\n","Epoch 40/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9239 - loss: 0.2749 - val_accuracy: 0.9170 - val_loss: 0.2915\n","Epoch 41/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9219 - loss: 0.2745 - val_accuracy: 0.9170 - val_loss: 0.2917\n","Epoch 42/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9214 - loss: 0.2766 - val_accuracy: 0.9178 - val_loss: 0.2917\n","Epoch 43/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9244 - loss: 0.2762 - val_accuracy: 0.9168 - val_loss: 0.2920\n","Epoch 44/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9264 - loss: 0.2676 - val_accuracy: 0.9183 - val_loss: 0.2905\n","Epoch 45/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9237 - loss: 0.2727 - val_accuracy: 0.9177 - val_loss: 0.2904\n","Epoch 46/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9243 - loss: 0.2697 - val_accuracy: 0.9167 - val_loss: 0.2903\n","Epoch 47/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9225 - loss: 0.2780 - val_accuracy: 0.9180 - val_loss: 0.2894\n","Epoch 48/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9230 - loss: 0.2719 - val_accuracy: 0.9172 - val_loss: 0.2893\n","Epoch 49/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9234 - loss: 0.2662 - val_accuracy: 0.9175 - val_loss: 0.2887\n","Epoch 50/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9249 - loss: 0.2689 - val_accuracy: 0.9185 - val_loss: 0.2882\n"]}],"source":["model_0 = Sequential()\n","model_0.add(Dense(units=num_classes, input_dim=num_pixels, activation='softmax'))\n","\n","# Компиляция модели\n","model_0.compile(loss='categorical_crossentropy',\n","                optimizer='sgd',\n","                metrics=['accuracy'])\n","\n","# Вывод информации об архитектуре\n","print(\"Архитектура однослойной сети:\")\n","model_0.summary()\n","\n","# Обучение модели\n","history_0 = model_0.fit(X_train, y_train,\n","                        validation_split=0.1,\n","                        epochs=50)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":533},"executionInfo":{"elapsed":260,"status":"ok","timestamp":1759127542723,"user":{"displayName":"Legal People","userId":"00818738730090246603"},"user_tz":-180},"id":"5yjxVnmFmpbV","outputId":"b182a139-da24-491e-9ebd-7ba1e39eec84"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# График функции ошибки по эпохам\n","plt.figure(figsize=(10, 6))\n","plt.plot(history_0.history['loss'], label='Обучающая выборка')\n","plt.plot(history_0.history['val_loss'], label='Валидационная выборка')\n","plt.title('Функция ошибки по эпохам (Однослойная сеть)')\n","plt.xlabel('Эпохи')\n","plt.ylabel('Ошибка')\n","plt.legend()\n","plt.grid(True)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1058,"status":"ok","timestamp":1759127544719,"user":{"displayName":"Legal People","userId":"00818738730090246603"},"user_tz":-180},"id":"NF_SsO8wiEUT","outputId":"ffe554c2-1c94-42b7-cc63-8f407418c4ce"},"outputs":[{"output_type":"stream","name":"stdout","text":["Результаты однослойной сети:\n","Ошибка на тестовых данных: 0.28625616431236267\n","Точность на тестовых данных: 0.92330002784729\n"]}],"source":["# Оценка на тестовых данных\n","scores_0 = model_0.evaluate(X_test, y_test, verbose=0)\n","print(\"Результаты однослойной сети:\")\n","print(f\"Ошибка на тестовых данных: {scores_0[0]}\")\n","print(f\"Точность на тестовых данных: {scores_0[1]}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fFcoQGdPnDFx"},"outputs":[],"source":["# Функция для создания и обучения модели\n","def create_and_train_model(hidden_units, model_name):\n","    model = Sequential()\n","    model.add(Dense(units=hidden_units, input_dim=num_pixels, activation='sigmoid'))\n","    model.add(Dense(units=num_classes, activation='softmax'))\n","\n","    model.compile(loss='categorical_crossentropy',\n","                  optimizer='sgd',\n","                  metrics=['accuracy'])\n","\n","    history = model.fit(X_train, y_train,\n","                        validation_split=0.1,\n","                        epochs=50)\n","\n","    scores = model.evaluate(X_test, y_test, verbose=0)\n","\n","    return model, history, scores"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XPlFkV40joAT"},"outputs":[],"source":["# Эксперименты с разным количеством нейронов\n","hidden_units_list = [100, 300, 500]\n","models_1 = {}\n","histories_1 = {}\n","scores_1 = {}"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"je0i_8HxjvpB","executionInfo":{"status":"ok","timestamp":1759128186868,"user_tz":-180,"elapsed":638747,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"5cb19edc-9162-4c23-92d8-1671e62b2bb3"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Обучение модели с 100 нейронами...\n","Epoch 1/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.5548 - loss: 1.8518 - val_accuracy: 0.8210 - val_loss: 0.9619\n","Epoch 2/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.3899\n","Epoch 7/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8974 - loss: 0.3775 - val_accuracy: 0.8967 - val_loss: 0.3729\n","Epoch 8/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9000 - loss: 0.3589 - val_accuracy: 0.8993 - val_loss: 0.3565\n","Epoch 9/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9052 - loss: 0.3424 - val_accuracy: 0.9033 - val_loss: 0.3450\n","Epoch 10/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9056 - loss: 0.3339 - val_accuracy: 0.9042 - val_loss: 0.3352\n","Epoch 11/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9065 - loss: 0.3293 - val_accuracy: 0.9073 - val_loss: 0.3271\n","Epoch 12/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9094 - loss: 0.3213 - val_accuracy: 0.9093 - val_loss: 0.3197\n","Epoch 13/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9097 - loss: 0.3159 - val_accuracy: 0.9088 - val_loss: 0.3139\n","Epoch 14/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9162 - loss: 0.2962 - val_accuracy: 0.9100 - val_loss: 0.3073\n","Epoch 15/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9162 - loss: 0.3001 - val_accuracy: 0.9127 - val_loss: 0.3019\n","Epoch 16/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.2795\n","Epoch 21/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9240 - loss: 0.2678 - val_accuracy: 0.9195 - val_loss: 0.2759\n","Epoch 22/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9236 - loss: 0.2689 - val_accuracy: 0.9202 - val_loss: 0.2722\n","Epoch 23/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9241 - loss: 0.2631 - val_accuracy: 0.9217 - val_loss: 0.2686\n","Epoch 24/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9265 - loss: 0.2556 - val_accuracy: 0.9218 - val_loss: 0.2649\n","Epoch 25/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9243 - loss: 0.2639 - val_accuracy: 0.9230 - val_loss: 0.2618\n","Epoch 26/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9259 - loss: 0.2545 - val_accuracy: 0.9247 - val_loss: 0.2586\n","Epoch 27/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9291 - loss: 0.2475 - val_accuracy: 0.9255 - val_loss: 0.2557\n","Epoch 28/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9289 - loss: 0.2465 - val_accuracy: 0.9275 - val_loss: 0.2531\n","Epoch 29/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9310 - loss: 0.2416 - val_accuracy: 0.9280 - val_loss: 0.2496\n","Epoch 30/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.2362\n","Epoch 35/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9355 - loss: 0.2295 - val_accuracy: 0.9325 - val_loss: 0.2334\n","Epoch 36/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9384 - loss: 0.2155 - val_accuracy: 0.9330 - val_loss: 0.2312\n","Epoch 37/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9366 - loss: 0.2197 - val_accuracy: 0.9340 - val_loss: 0.2288\n","Epoch 38/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9385 - loss: 0.2142 - val_accuracy: 0.9342 - val_loss: 0.2265\n","Epoch 39/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9396 - loss: 0.2104 - val_accuracy: 0.9348 - val_loss: 0.2244\n","Epoch 40/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9399 - loss: 0.2110 - val_accuracy: 0.9362 - val_loss: 0.2231\n","Epoch 41/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9391 - loss: 0.2092 - val_accuracy: 0.9367 - val_loss: 0.2201\n","Epoch 42/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9401 - loss: 0.2075 - val_accuracy: 0.9370 - val_loss: 0.2178\n","Epoch 43/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9404 - loss: 0.2018 - val_accuracy: 0.9387 - val_loss: 0.2164\n","Epoch 44/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.2056\n","Epoch 49/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9447 - loss: 0.1919 - val_accuracy: 0.9407 - val_loss: 0.2033\n","Epoch 50/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9439 - loss: 0.1936 - val_accuracy: 0.9425 - val_loss: 0.2020\n","Точность: 0.9422000050544739\n","\n","Обучение модели с 300 нейронами...\n","Epoch 1/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.5667 - loss: 1.8010 - val_accuracy: 0.8303 - val_loss: 0.8696\n","Epoch 2/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8399 - loss: 0.7595 - val_accuracy: 0.8657 - val_loss: 0.5806\n","Epoch 3/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.3662\n","Epoch 8/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9026 - loss: 0.3493 - val_accuracy: 0.8990 - val_loss: 0.3557\n","Epoch 9/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9035 - loss: 0.3417 - val_accuracy: 0.9008 - val_loss: 0.3452\n","Epoch 10/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9043 - loss: 0.3351 - val_accuracy: 0.9023 - val_loss: 0.3373\n","Epoch 11/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9060 - loss: 0.3240 - val_accuracy: 0.9030 - val_loss: 0.3303\n","Epoch 12/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9070 - loss: 0.3170 - val_accuracy: 0.9052 - val_loss: 0.3255\n","Epoch 13/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9087 - loss: 0.3202 - val_accuracy: 0.9062 - val_loss: 0.3209\n","Epoch 14/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9129 - loss: 0.3063 - val_accuracy: 0.9067 - val_loss: 0.3161\n","Epoch 15/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9135 - loss: 0.3046 - val_accuracy: 0.9092 - val_loss: 0.3119\n","Epoch 16/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9130 - loss: 0.2993 - val_accuracy: 0.9108 - val_loss: 0.3064\n","Epoch 17/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.2926\n","Epoch 22/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9187 - loss: 0.2851 - val_accuracy: 0.9145 - val_loss: 0.2894\n","Epoch 23/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9181 - loss: 0.2789 - val_accuracy: 0.9155 - val_loss: 0.2871\n","Epoch 24/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9215 - loss: 0.2705 - val_accuracy: 0.9158 - val_loss: 0.2848\n","Epoch 25/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.9217 - loss: 0.2715 - val_accuracy: 0.9172 - val_loss: 0.2829\n","Epoch 26/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9225 - loss: 0.2666 - val_accuracy: 0.9177 - val_loss: 0.2804\n","Epoch 27/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9248 - loss: 0.2643 - val_accuracy: 0.9187 - val_loss: 0.2783\n","Epoch 28/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9235 - loss: 0.2603 - val_accuracy: 0.9197 - val_loss: 0.2768\n","Epoch 29/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9240 - loss: 0.2628 - val_accuracy: 0.9210 - val_loss: 0.2741\n","Epoch 30/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9246 - loss: 0.2618 - val_accuracy: 0.9207 - val_loss: 0.2720\n","Epoch 31/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.2611\n","Epoch 36/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9299 - loss: 0.2398 - val_accuracy: 0.9248 - val_loss: 0.2588\n","Epoch 37/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.9288 - loss: 0.2457 - val_accuracy: 0.9262 - val_loss: 0.2562\n","Epoch 38/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9315 - loss: 0.2363 - val_accuracy: 0.9262 - val_loss: 0.2534\n","Epoch 39/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9296 - loss: 0.2424 - val_accuracy: 0.9283 - val_loss: 0.2512\n","Epoch 40/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9305 - loss: 0.2383 - val_accuracy: 0.9280 - val_loss: 0.2501\n","Epoch 41/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9331 - loss: 0.2313 - val_accuracy: 0.9285 - val_loss: 0.2469\n","Epoch 42/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9334 - loss: 0.2310 - val_accuracy: 0.9285 - val_loss: 0.2450\n","Epoch 43/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9350 - loss: 0.2267 - val_accuracy: 0.9290 - val_loss: 0.2436\n","Epoch 44/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9354 - loss: 0.2228 - val_accuracy: 0.9300 - val_loss: 0.2410\n","Epoch 45/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.2312\n","Epoch 50/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9369 - loss: 0.2181 - val_accuracy: 0.9345 - val_loss: 0.2288\n","Точность: 0.9376999735832214\n","\n","Обучение модели с 500 нейронами...\n","Epoch 1/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.5450 - loss: 1.7741 - val_accuracy: 0.8278 - val_loss: 0.8334\n","Epoch 2/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8439 - loss: 0.7246 - val_accuracy: 0.8673 - val_loss: 0.5635\n","Epoch 3/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8689 - loss: 0.5286 - val_accuracy: 0.8787 - val_loss: 0.4724\n","Epoch 4/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.3532\n","Epoch 9/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9045 - loss: 0.3383 - val_accuracy: 0.9023 - val_loss: 0.3457\n","Epoch 10/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9078 - loss: 0.3278 - val_accuracy: 0.9032 - val_loss: 0.3371\n","Epoch 11/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9070 - loss: 0.3236 - val_accuracy: 0.9047 - val_loss: 0.3317\n","Epoch 12/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9074 - loss: 0.3238 - val_accuracy: 0.9050 - val_loss: 0.3270\n","Epoch 13/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9109 - loss: 0.3110 - val_accuracy: 0.9065 - val_loss: 0.3210\n","Epoch 14/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9109 - loss: 0.3092 - val_accuracy: 0.9068 - val_loss: 0.3176\n","Epoch 15/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9116 - loss: 0.3076 - val_accuracy: 0.9082 - val_loss: 0.3153\n","Epoch 16/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9118 - loss: 0.3079 - val_accuracy: 0.9095 - val_loss: 0.3138\n","Epoch 17/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9165 - loss: 0.2949 - val_accuracy: 0.9107 - val_loss: 0.3078\n","Epoch 18/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.2968\n","Epoch 23/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9193 - loss: 0.2818 - val_accuracy: 0.9140 - val_loss: 0.2949\n","Epoch 24/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9178 - loss: 0.2903 - val_accuracy: 0.9147 - val_loss: 0.2939\n","Epoch 25/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9178 - loss: 0.2875 - val_accuracy: 0.9137 - val_loss: 0.2914\n","Epoch 26/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9208 - loss: 0.2832 - val_accuracy: 0.9150 - val_loss: 0.2889\n","Epoch 27/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9221 - loss: 0.2765 - val_accuracy: 0.9142 - val_loss: 0.2883\n","Epoch 28/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9220 - loss: 0.2752 - val_accuracy: 0.9167 - val_loss: 0.2868\n","Epoch 29/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9220 - loss: 0.2754 - val_accuracy: 0.9183 - val_loss: 0.2854\n","Epoch 30/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9239 - loss: 0.2700 - val_accuracy: 0.9173 - val_loss: 0.2820\n","Epoch 31/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9234 - loss: 0.2669 - val_accuracy: 0.9190 - val_loss: 0.2804\n","Epoch 32/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9228 - loss: 0.2678 - val_accuracy: 0.9193 - val_loss: 0.2797\n","Epoch 33/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9239 - loss: 0.2677 - val_accuracy: 0.9192 - val_loss: 0.2794\n","Epoch 34/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9240 - loss: 0.2631 - val_accuracy: 0.9208 - val_loss: 0.2765\n","Epoch 35/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9253 - loss: 0.2605 - val_accuracy: 0.9202 - val_loss: 0.2750\n","Epoch 36/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9254 - loss: 0.2570 - val_accuracy: 0.9205 - val_loss: 0.2734\n","Epoch 37/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9267 - loss: 0.2601 - val_accuracy: 0.9215 - val_loss: 0.2711\n","Epoch 38/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9279 - loss: 0.2569 - val_accuracy: 0.9212 - val_loss: 0.2715\n","Epoch 39/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9260 - loss: 0.2589 - val_accuracy: 0.9223 - val_loss: 0.2680\n","Epoch 40/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9284 - loss: 0.2550 - val_accuracy: 0.9218 - val_loss: 0.2671\n","Epoch 41/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9286 - loss: 0.2491 - val_accuracy: 0.9227 - val_loss: 0.2660\n","Epoch 42/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9316 - loss: 0.2432 - val_accuracy: 0.9237 - val_loss: 0.2625\n","Epoch 43/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9274 - loss: 0.2508 - val_accuracy: 0.9252 - val_loss: 0.2635\n","Epoch 44/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9290 - loss: 0.2472 - val_accuracy: 0.9255 - val_loss: 0.2595\n","Epoch 45/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9302 - loss: 0.2449 - val_accuracy: 0.9252 - val_loss: 0.2601\n","Epoch 46/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9322 - loss: 0.2399 - val_accuracy: 0.9270 - val_loss: 0.2568\n","Epoch 47/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9323 - loss: 0.2422 - val_accuracy: 0.9278 - val_loss: 0.2550\n","Epoch 48/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9321 - loss: 0.2399 - val_accuracy: 0.9282 - val_loss: 0.2527\n","Epoch 49/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9330 - loss: 0.2372 - val_accuracy: 0.9282 - val_loss: 0.2514\n","Epoch 50/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9347 - loss: 0.2327 - val_accuracy: 0.9277 - val_loss: 0.2495\n","Точность: 0.9312000274658203\n"]}],"source":["# Обучение сетей с одним скрытым слоем\n","for units in hidden_units_list:\n","    print(f\"\\nОбучение модели с {units} нейронами...\")\n","    model, history, scores = create_and_train_model(units, f\"model_{units}\")\n","\n","    models_1[units] = model\n","    histories_1[units] = history\n","    scores_1[units] = scores\n","\n","    print(f\"Точность: {scores[1]}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TbbBBxeMmy6c","executionInfo":{"status":"ok","timestamp":1759128269112,"user_tz":-180,"elapsed":20,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"08b5a164-1e62-456c-80b9-bc7247be3fd3"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Наилучшее количество нейронов: 100\n","Точность: 0.9422000050544739\n"]}],"source":["# Выбор наилучшей модели\n","best_units_1 = max(scores_1.items(), key=lambda x: x[1][1])[0]\n","print(f\"\\nНаилучшее количество нейронов: {best_units_1}\")\n","print(f\"Точность: {scores_1[best_units_1][1]}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"EFEBxB2qm1fF","executionInfo":{"status":"ok","timestamp":1759128272502,"user_tz":-180,"elapsed":620,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"d436ac7a-e33b-4cc2-e324-5decfc29de94"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x500 with 3 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Графики ошибок для всех моделей\n","plt.figure(figsize=(15, 5))\n","for i, units in enumerate(hidden_units_list, 1):\n","    plt.subplot(1, 3, i)\n","    plt.plot(histories_1[units].history['loss'], label='Обучающая')\n","    plt.plot(histories_1[units].history['val_loss'], label='Валидационная')\n","    plt.title(f'{units} нейронов')\n","    plt.xlabel('Эпохи')\n","    plt.ylabel('Ошибка')\n","    plt.legend()\n","    plt.grid(True)\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EJBwT5vhnnSG"},"outputs":[],"source":["# Добавление второго скрытого слоя\n","second_layer_units = [50, 100]\n","models_2 = {}\n","histories_2 = {}\n","scores_2 = {}"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K5vweMySno3t","executionInfo":{"status":"ok","timestamp":1759128698216,"user_tz":-180,"elapsed":420194,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"b67b155c-89ea-46e0-a590-a588433e0a38"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Обучение модели со вторым слоем 50 нейронов\n","Epoch 1/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.2096 - loss: 2.2675 - val_accuracy: 0.5588 - val_loss: 2.0950\n","Epoch 2/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.5971 - loss: 1.9743 - val_accuracy: 0.6620 - val_loss: 1.5239\n","Epoch 3/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.6823 - loss: 1.3658 - val_accuracy: 0.7380 - val_loss: 1.0431\n","Epoch 4/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7627 - loss: 0.9560 - val_accuracy: 0.7980 - val_loss: 0.8069\n","Epoch 5/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8084 - loss: 0.7568 - val_accuracy: 0.8352 - val_loss: 0.6673\n","Epoch 6/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8369 - loss: 0.6330 - val_accuracy: 0.8543 - val_loss: 0.5793\n","Epoch 7/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8554 - loss: 0.5512 - val_accuracy: 0.8660 - val_loss: 0.5197\n","Epoch 8/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8667 - loss: 0.5051 - val_accuracy: 0.8757 - val_loss: 0.4769\n","Epoch 9/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8770 - loss: 0.4593 - val_accuracy: 0.8798 - val_loss: 0.4444\n","Epoch 10/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8850 - loss: 0.4256 - val_accuracy: 0.8877 - val_loss: 0.4190\n","Epoch 11/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8889 - loss: 0.4076 - val_accuracy: 0.8910 - val_loss: 0.3995\n","Epoch 12/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8948 - loss: 0.3829 - val_accuracy: 0.8947 - val_loss: 0.3835\n","Epoch 13/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8982 - loss: 0.3699 - val_accuracy: 0.8997 - val_loss: 0.3689\n","Epoch 14/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9008 - loss: 0.3560 - val_accuracy: 0.9017 - val_loss: 0.3582\n","Epoch 15/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9046 - loss: 0.3446 - val_accuracy: 0.9028 - val_loss: 0.3471\n","Epoch 16/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9051 - loss: 0.3367 - val_accuracy: 0.9055 - val_loss: 0.3375\n","Epoch 17/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9087 - loss: 0.3266 - val_accuracy: 0.9072 - val_loss: 0.3295\n","Epoch 18/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9089 - loss: 0.3192 - val_accuracy: 0.9093 - val_loss: 0.3214\n","Epoch 19/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9116 - loss: 0.3087 - val_accuracy: 0.9127 - val_loss: 0.3142\n","Epoch 20/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9110 - loss: 0.3098 - val_accuracy: 0.9148 - val_loss: 0.3084\n","Epoch 21/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9144 - loss: 0.2970 - val_accuracy: 0.9158 - val_loss: 0.3017\n","Epoch 22/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9155 - loss: 0.2888 - val_accuracy: 0.9172 - val_loss: 0.2970\n","Epoch 23/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.2717\n","Epoch 28/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9224 - loss: 0.2637 - val_accuracy: 0.9250 - val_loss: 0.2669\n","Epoch 29/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9261 - loss: 0.2522 - val_accuracy: 0.9262 - val_loss: 0.2628\n","Epoch 30/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9250 - loss: 0.2523 - val_accuracy: 0.9258 - val_loss: 0.2587\n","Epoch 31/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9282 - loss: 0.2438 - val_accuracy: 0.9272 - val_loss: 0.2544\n","Epoch 32/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9298 - loss: 0.2417 - val_accuracy: 0.9288 - val_loss: 0.2506\n","Epoch 33/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9288 - loss: 0.2397 - val_accuracy: 0.9292 - val_loss: 0.2471\n","Epoch 34/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9311 - loss: 0.2364 - val_accuracy: 0.9297 - val_loss: 0.2433\n","Epoch 35/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9313 - loss: 0.2328 - val_accuracy: 0.9310 - val_loss: 0.2394\n","Epoch 36/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9318 - loss: 0.2303 - val_accuracy: 0.9320 - val_loss: 0.2362\n","Epoch 37/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.2209\n","Epoch 42/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9380 - loss: 0.2116 - val_accuracy: 0.9378 - val_loss: 0.2170\n","Epoch 43/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9406 - loss: 0.2028 - val_accuracy: 0.9388 - val_loss: 0.2144\n","Epoch 44/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9391 - loss: 0.2074 - val_accuracy: 0.9402 - val_loss: 0.2115\n","Epoch 45/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9421 - loss: 0.1970 - val_accuracy: 0.9400 - val_loss: 0.2085\n","Epoch 46/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9435 - loss: 0.1979 - val_accuracy: 0.9410 - val_loss: 0.2063\n","Epoch 47/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9450 - loss: 0.1922 - val_accuracy: 0.9415 - val_loss: 0.2036\n","Epoch 48/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9427 - loss: 0.1911 - val_accuracy: 0.9405 - val_loss: 0.2024\n","Epoch 49/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9467 - loss: 0.1825 - val_accuracy: 0.9418 - val_loss: 0.1990\n","Epoch 50/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9470 - loss: 0.1861 - val_accuracy: 0.9438 - val_loss: 0.1962\n","\u001b[1m313/313\u001b[0m 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- loss: 0.9139 - val_accuracy: 0.8165 - val_loss: 0.7597\n","Epoch 5/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8193 - loss: 0.7167 - val_accuracy: 0.8407 - val_loss: 0.6288\n","Epoch 6/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8450 - loss: 0.5949 - val_accuracy: 0.8585 - val_loss: 0.5491\n","Epoch 7/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8610 - loss: 0.5259 - val_accuracy: 0.8677 - val_loss: 0.4961\n","Epoch 8/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8754 - loss: 0.4709 - val_accuracy: 0.8793 - val_loss: 0.4570\n","Epoch 9/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.3570\n","Epoch 14/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9045 - loss: 0.3409 - val_accuracy: 0.9043 - val_loss: 0.3464\n","Epoch 15/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9065 - loss: 0.3318 - val_accuracy: 0.9063 - val_loss: 0.3364\n","Epoch 16/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9074 - loss: 0.3262 - val_accuracy: 0.9093 - val_loss: 0.3285\n","Epoch 17/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9096 - loss: 0.3151 - val_accuracy: 0.9103 - val_loss: 0.3205\n","Epoch 18/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9126 - loss: 0.3063 - val_accuracy: 0.9125 - val_loss: 0.3138\n","Epoch 19/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9145 - loss: 0.2975 - val_accuracy: 0.9118 - val_loss: 0.3085\n","Epoch 20/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9173 - loss: 0.2899 - val_accuracy: 0.9138 - val_loss: 0.3025\n","Epoch 21/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9158 - loss: 0.2888 - val_accuracy: 0.9172 - val_loss: 0.2962\n","Epoch 22/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9170 - loss: 0.2860 - val_accuracy: 0.9178 - val_loss: 0.2914\n","Epoch 23/50\n","\u001b[1m1688/1688\u001b[0m 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val_loss: 0.2679\n","Epoch 28/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9235 - loss: 0.2625 - val_accuracy: 0.9238 - val_loss: 0.2643\n","Epoch 29/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9279 - loss: 0.2493 - val_accuracy: 0.9250 - val_loss: 0.2606\n","Epoch 30/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9279 - loss: 0.2476 - val_accuracy: 0.9248 - val_loss: 0.2560\n","Epoch 31/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9286 - loss: 0.2439 - val_accuracy: 0.9277 - val_loss: 0.2529\n","Epoch 32/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9285 - loss: 0.2440 - val_accuracy: 0.9263 - val_loss: 0.2487\n","Epoch 33/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9295 - loss: 0.2395 - val_accuracy: 0.9288 - val_loss: 0.2456\n","Epoch 34/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9328 - loss: 0.2292 - val_accuracy: 0.9300 - val_loss: 0.2422\n","Epoch 35/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9316 - loss: 0.2318 - val_accuracy: 0.9322 - val_loss: 0.2389\n","Epoch 36/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9345 - loss: 0.2233 - val_accuracy: 0.9325 - val_loss: 0.2347\n","Epoch 37/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9354 - loss: 0.2206 - val_accuracy: 0.9330 - val_loss: 0.2321\n","Epoch 38/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9374 - loss: 0.2146 - val_accuracy: 0.9335 - val_loss: 0.2294\n","Epoch 39/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9370 - loss: 0.2149 - val_accuracy: 0.9330 - val_loss: 0.2264\n","Epoch 40/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9374 - loss: 0.2136 - val_accuracy: 0.9372 - val_loss: 0.2238\n","Epoch 41/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9387 - loss: 0.2118 - val_accuracy: 0.9360 - val_loss: 0.2209\n","Epoch 42/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9384 - loss: 0.2104 - val_accuracy: 0.9370 - val_loss: 0.2177\n","Epoch 43/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9410 - loss: 0.2020 - val_accuracy: 0.9382 - val_loss: 0.2147\n","Epoch 44/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9431 - loss: 0.1985 - val_accuracy: 0.9387 - val_loss: 0.2121\n","Epoch 45/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9440 - loss: 0.1946 - val_accuracy: 0.9398 - val_loss: 0.2096\n","Epoch 46/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9431 - loss: 0.1964 - val_accuracy: 0.9408 - val_loss: 0.2077\n","Epoch 47/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9458 - loss: 0.1880 - val_accuracy: 0.9402 - val_loss: 0.2057\n","Epoch 48/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9442 - loss: 0.1929 - val_accuracy: 0.9403 - val_loss: 0.2023\n","Epoch 49/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9451 - loss: 0.1885 - val_accuracy: 0.9415 - val_loss: 0.2002\n","Epoch 50/50\n","\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9456 - loss: 0.1835 - val_accuracy: 0.9432 - val_loss: 0.1982\n","\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9368 - loss: 0.2101\n","Точность: 0.942300021648407\n"]}],"source":["for units_2 in second_layer_units:\n","    print(f\"\\nОбучение модели со вторым слоем {units_2} нейронов\")\n","\n","    model = Sequential()\n","    model.add(Dense(units=best_units_1, input_dim=num_pixels, activation='sigmoid'))\n","    model.add(Dense(units=units_2, activation='sigmoid'))\n","    model.add(Dense(units=num_classes, activation='softmax'))\n","\n","    model.compile(loss='categorical_crossentropy',\n","                  optimizer='sgd',\n","                  metrics=['accuracy'])\n","\n","    history = model.fit(X_train, y_train,\n","                        validation_split=0.1,\n","                        epochs=50)\n","\n","    scores = model.evaluate(X_test, y_test)\n","\n","    models_2[units_2] = model\n","    histories_2[units_2] = history\n","    scores_2[units_2] = scores\n","\n","    print(f\"Точность: {scores[1]}\")"]},{"cell_type":"code","source":["# Выбор наилучшей двухслойной модели\n","best_units_2 = max(scores_2.items(), key=lambda x: x[1][1])[0]\n","print(f\"\\nНаилучшее количество нейронов во втором слое: {best_units_2}\")\n","print(f\"Точность: {scores_2[best_units_2][1]:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9lJtmn_oSVkB","executionInfo":{"status":"ok","timestamp":1759129484222,"user_tz":-180,"elapsed":14,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"b49d6a95-574a-4ce5-e23f-eea49ba83603"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Наилучшее количество нейронов во втором слое: 100\n","Точность: 0.9423\n"]}]},{"cell_type":"code","source":["# Сбор результатов\n","results = {\n","    '0 слоев': {'нейроны_1': '-', 'нейроны_2': '-', 'точность': scores_0[1]},\n","    '1 слой_100': {'нейроны_1': 100, 'нейроны_2': '-', 'точность': scores_1[100][1]},\n","    '1 слой_300': {'нейроны_1': 300, 'нейроны_2': '-', 'точность': scores_1[300][1]},\n","    '1 слой_500': {'нейроны_1': 500, 'нейроны_2': '-', 'точность': scores_1[500][1]},\n","    '2 слоя_50': {'нейроны_1': best_units_1, 'нейроны_2': 50, 'точность': scores_2[50][1]},\n","    '2 слоя_100': {'нейроны_1': best_units_1, 'нейроны_2': 100, 'точность': scores_2[100][1]}\n","}"],"metadata":{"id":"p0aeriYzShJk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Создаем DataFrame из результатов\n","df_results = pd.DataFrame([\n","    {'Кол-во скрытых слоев': 0, 'Нейроны_1_слоя': '-', 'Нейроны_2_слоя': '-', 'Точность': results['0 слоев']['точность']},\n","    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 100, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_100']['точность']},\n","    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 300, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_300']['точность']},\n","    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 500, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_500']['точность']},\n","    {'Кол-во скрытых слоев': 2, 'Нейроны_1_слоя': best_units_1, 'Нейроны_2_слоя': 50, 'Точность': results['2 слоя_50']['точность']},\n","    {'Кол-во скрытых слоев': 2, 'Нейроны_1_слоя': best_units_1, 'Нейроны_2_слоя': 100, 'Точность': results['2 слоя_100']['точность']}\n","])\n","\n","print(\" \" * 20 + \"ТАБЛИЦА РЕЗУЛЬТАТОВ\")\n","print(\"=\" * 70)\n","# print(df_results.to_string(index=False, formatters={\n","#     'Точность': '{:.4f}'.format\n","# }))\n","print(df_results.reset_index(drop=True))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SHr6z7jbSmOG","executionInfo":{"status":"ok","timestamp":1759130442386,"user_tz":-180,"elapsed":33,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"2d40f526-7756-4554-f110-2242e34e58a0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["                    ТАБЛИЦА РЕЗУЛЬТАТОВ\n","======================================================================\n","   Кол-во скрытых слоев Нейроны_1_слоя Нейроны_2_слоя  Точность\n","0                     0              -              -    0.9233\n","1                     1            100              -    0.9422\n","2                     1            300              -    0.9377\n","3                     1            500              -    0.9312\n","4                     2            100             50    0.9418\n","5                     2            100            100    0.9423\n"]}]},{"cell_type":"code","source":["# Выбор наилучшей модели\n","best_model_type = max(results.items(), key=lambda x: x[1]['точность'])[0]\n","best_accuracy = results[best_model_type]['точность']\n","print(f\"\\nНаилучшая архитектура: {best_model_type}\")\n","print(f\"Точность: {best_accuracy:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PTC5CUJeWQ_V","executionInfo":{"status":"ok","timestamp":1759130490602,"user_tz":-180,"elapsed":41,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"e8009546-5876-4427-9815-9aac53db8593"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Наилучшая архитектура: 2 слоя_100\n","Точность: 0.9423\n"]}]},{"cell_type":"code","source":["# Определение наилучшей модели\n","if '0' in best_model_type:\n","    best_model = model_0\n","elif '1' in best_model_type:\n","    best_neurons = int(best_model_type.split('_')[1])\n","    best_model = models_1[best_neurons]\n","else:\n","    best_neurons_2 = int(best_model_type.split('_')[1])\n","    best_model = models_2[best_neurons_2]\n"],"metadata":{"id":"JRPRpppHWV8-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Сохранение модели\n","best_model.save('best_mnist_model.keras')"],"metadata":{"id":"wIlYoP_HSFph"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":517},"id":"Kh6-u-8OoHny","executionInfo":{"status":"ok","timestamp":1759132236751,"user_tz":-180,"elapsed":178,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"7c89ce31-3967-41bb-ff79-6e77e7f5d19b"},"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","NN output: [[5.8587279e-06 9.7018647e-01 6.0002012e-03 5.5828933e-03 7.1756593e-05\n","  7.2469590e-03 3.2864737e-03 3.9730189e-04 6.0582636e-03 1.1638567e-03]]\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark:  1\n","NN answer:  1\n"]}],"source":["# вывод тестового изображения и результата распознавания (1)\n","n = 123\n","result = best_model.predict(X_test[n:n+1])\n","print('NN output:', result)\n","plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n","plt.show()\n","print('Real mark: ', str(np.argmax(y_test[n])))\n","print('NN answer: ', str(np.argmax(result)))"]},{"cell_type":"code","source":["# вывод тестового изображения и результата распознавания (1)\n","n = 353\n","result = best_model.predict(X_test[n:n+1])\n","print('NN output:', result)\n","plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n","plt.show()\n","print('Real mark: ', str(np.argmax(y_test[n])))\n","print('NN answer: ', str(np.argmax(result)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":517},"id":"WVGJRtVZc8V-","executionInfo":{"status":"ok","timestamp":1759132262259,"user_tz":-180,"elapsed":284,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"5d4ed790-d7ce-4569-d667-49733f75e3cb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n","NN output: [[5.6045882e-02 2.3120556e-06 3.2519495e-01 6.1816531e-01 2.2406326e-08\n","  2.7827255e-04 7.9103382e-05 1.1205349e-06 2.1714537e-04 1.5997215e-05]]\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAHJNJREFUeJzt3XtwVPX5x/HPcsmCkGwMMdlELgZQcATSFiFm0BRLhiTtMKJ0RqzTwY6XgQYtUm84FbR1Ji2dsY4O1VYdqFbwMi1Q+YOORhNGTXCIUEptUxKjhMIGxWY3BJMg+f7+4OeWlQCeZTdPEt6vme8Me8559jwcD/l49pz9xueccwIAoJcNsm4AAHB+IoAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgYoh1A1/V3d2tAwcOKDU1VT6fz7odAIBHzjm1tbUpNzdXgwad/jqnzwXQgQMHNGbMGOs2AADnqLm5WaNHjz7t+j73EVxqaqp1CwCABDjbz/OkBdCaNWt0ySWXaNiwYSooKNB77733ter42A0ABoaz/TxPSgC9/PLLWr58uVatWqX3339f+fn5Kikp0aFDh5KxOwBAf+SSYObMma68vDz6+vjx4y43N9dVVFSctTYcDjtJDAaDwejnIxwOn/HnfcKvgLq6ulRXV6fi4uLoskGDBqm4uFg1NTWnbN/Z2alIJBIzAAADX8ID6NNPP9Xx48eVnZ0dszw7O1uhUOiU7SsqKhQIBKKDJ+AA4Pxg/hTcihUrFA6Ho6O5udm6JQBAL0j494AyMzM1ePBgtbS0xCxvaWlRMBg8ZXu/3y+/35/oNgAAfVzCr4BSUlI0ffp0VVZWRpd1d3ersrJShYWFid4dAKCfSspMCMuXL9eiRYt05ZVXaubMmXr88cfV3t6uH/3oR8nYHQCgH0pKAN1444365JNPtHLlSoVCIX3jG9/Q1q1bT3kwAQBw/vI555x1EyeLRCIKBALWbQAAzlE4HFZaWtpp15s/BQcAOD8RQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAE0OsGwCQPBdffHFcdbfddpvnmgceeMBzzUcffeS5ZuHChZ5r/va3v3muQfJxBQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMCEzznnrJs4WSQSUSAQsG4D+NoGDfL+/3GFhYWeaxYsWOC55oc//KHnGkkaNWpUXHW94cMPP/RcU1paGte+Ghoa4qrDCeFwWGlpaaddzxUQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAE0xGCpwkIyPDc82zzz7ruWb+/PmeaxC/pqamuOomTJiQ4E7OL0xGCgDokwggAICJhAfQww8/LJ/PFzMmT56c6N0AAPq5Icl40yuuuEJvvPHG/3YyJCm7AQD0Y0lJhiFDhigYDCbjrQEAA0RS7gHt3btXubm5Gj9+vG6++Wbt27fvtNt2dnYqEonEDADAwJfwACooKNC6deu0detWPfXUU2pqatI111yjtra2HrevqKhQIBCIjjFjxiS6JQBAH5T07wG1trZq3Lhxeuyxx3Trrbeesr6zs1OdnZ3R15FIhBCCGb4HNDDxPSAbZ/seUNKfDkhPT9dll12mhoaGHtf7/X75/f5ktwEA6GOS/j2gI0eOqLGxUTk5OcneFQCgH0l4AN1zzz2qrq7WRx99pHfffVfXX3+9Bg8erJtuuinRuwIA9GMJ/whu//79uummm3T48GFddNFFuvrqq1VbW6uLLroo0bsCAPRjCQ+gl156KdFvCfSaGTNmeK7hgYIT/v73v3uuOXDggOeakpISzzUjR470XCMpru8zhkKhuPZ1PmIuOACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACaS/gvpgP7k6quv9lzzxRdfeK4ZMqR3/um1tbXFVffggw96rtmwYYPnmi1btniuicfnn38eV91nn32W4E5wMq6AAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmmA0bOMlDDz3kuWbo0KGeay655BLPNf/973891zz55JOeayTpgw8+8FwTz0zil19+ueeaeKSkpMRVN3LkSM81zKD99XEFBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwASTkQLn6IEHHrBuIeEmTpzouea2227zXBMIBDzXxOOVV16Jq46JRZOLKyAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmmIwUMHDFFVd4rnn++ec916SkpHiukaQxY8Z4rklLS4trX17V1dV5rrn//vuT0AnOFVdAAAATBBAAwITnANq2bZvmzZun3Nxc+Xw+bdq0KWa9c04rV65UTk6Ohg8fruLiYu3duzdR/QIABgjPAdTe3q78/HytWbOmx/WrV6/WE088oaefflrbt2/XiBEjVFJSoo6OjnNuFgAwcHh+CKGsrExlZWU9rnPO6fHHH9fPfvYzXXfddZJO3DjNzs7Wpk2btHDhwnPrFgAwYCT0HlBTU5NCoZCKi4ujywKBgAoKClRTU9NjTWdnpyKRSMwAAAx8CQ2gUCgkScrOzo5Znp2dHV33VRUVFQoEAtERz+OfAID+x/wpuBUrVigcDkdHc3OzdUsAgF6Q0AAKBoOSpJaWlpjlLS0t0XVf5ff7lZaWFjMAAANfQgMoLy9PwWBQlZWV0WWRSETbt29XYWFhIncFAOjnPD8Fd+TIETU0NERfNzU1adeuXcrIyNDYsWO1bNkyPfroo7r00kuVl5enhx56SLm5uZo/f34i+wYA9HOeA2jHjh269tpro6+XL18uSVq0aJHWrVun++67T+3t7brjjjvU2tqqq6++Wlu3btWwYcMS1zUAoN/zOeecdRMni0QiCgQC1m3gPJWenu655s477/Rcs2TJEs81p7uPirMrKiqKq+7tt99OcCfnl3A4fMb7+uZPwQEAzk8EEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABPMhg2cZNy4cZ5r3n//fc81F154oecaxG/btm1x1S1cuNBzTSgUimtfAxGzYQMA+iQCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmhlg3APQl8UyE25cnz+3q6oqr7rnnnvNc88wzz/RKzfTp0z3XxDPJrCR9+umncdXh6+EKCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkmI+0lw4cP91yzePFizzUffvih55rKykrPNZJ05MiRuOr6sv/85z+ea9avX++5ZtKkSZ5rGhsbPdesXr3ac40k7dq1K646rxoaGjzXxDMZ6bBhwzzXSFJ6errnGiYw/fq4AgIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGDC55xz1k2cLBKJKBAIWLdxRiNGjPBcM3XqVM81f/rTnzzX5OTkeK4JhUKeayTp/vvv91zzwgsvxLWvgSYlJcVzzRdffOG5pru723NNvK688krPNe+++67nmiFDvM+h/Mknn3iukaT8/HzPNfH+exqIwuGw0tLSTrueKyAAgAkCCABgwnMAbdu2TfPmzVNubq58Pp82bdoUs/6WW26Rz+eLGaWlpYnqFwAwQHgOoPb2duXn52vNmjWn3aa0tFQHDx6Mjg0bNpxTkwCAgcfz3byysjKVlZWdcRu/369gMBh3UwCAgS8p94CqqqqUlZWlSZMmacmSJTp8+PBpt+3s7FQkEokZAICBL+EBVFpaqueff16VlZX61a9+perqapWVlen48eM9bl9RUaFAIBAdY8aMSXRLAIA+yPsD9WexcOHC6J+nTp2qadOmacKECaqqqtKcOXNO2X7FihVavnx59HUkEiGEAOA8kPTHsMePH6/MzEw1NDT0uN7v9ystLS1mAAAGvqQH0P79+3X48OG4vqEPABi4PH8Ed+TIkZirmaamJu3atUsZGRnKyMjQI488ogULFigYDKqxsVH33XefJk6cqJKSkoQ2DgDo3zwH0I4dO3TttddGX395/2bRokV66qmntHv3bv3hD39Qa2urcnNzNXfuXP3iF7+Q3+9PXNcAgH7PcwDNnj1bZ5q/9K9//es5NdQffPOb3/RcU11d7bnG5/N5rolHvN/Zevzxxz3XxDOh5ssvv+y5pjcn4YxHV1eXdQtn1NMDQ2fz+9//3nNNPBOLxuO9996Lq46JRZOLueAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACZ6ZyraAWbUqFGea44ePeq5ZsSIEZ5retOFF17ouebFF1/0XLN7927PNf/4xz881wxE8f4alJUrV3quycvLi2tfXh0+fNhzzSOPPJKETnCuuAICAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABggslI47Bz507PNV1dXZ5r4pmMtKOjw3PNrl27PNdI0lVXXRVXnVfLli3zXPPcc8/Fta/a2lrPNYMHD/ZcM3HiRM81jz32mOeatLQ0zzWSNGvWLM81Pp/Pc01ra6vnmvLycs81O3bs8FyD5OMKCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAmfc85ZN3GySCSiQCBg3UbC/fvf//ZcE8+ElUeOHPFc8/3vf99zjSQ9++yznmtGjx4d1768+uyzz+Kqi+e/UzyTkc6YMcNzTV8XDoc919x1112ea1544QXPNbARDofPOCEuV0AAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMMBlpLxkzZoznmo8//jgJnSTOhx9+6Llm/PjxSegEibZnzx7PNY8++qjnmldeecVzDfoPJiMFAPRJBBAAwISnAKqoqNCMGTOUmpqqrKwszZ8/X/X19THbdHR0qLy8XKNGjdLIkSO1YMECtbS0JLRpAED/5ymAqqurVV5ertraWr3++us6duyY5s6dq/b29ug2d999t1577TW9+uqrqq6u1oEDB3TDDTckvHEAQP82xMvGW7dujXm9bt06ZWVlqa6uTkVFRQqHw3ruuee0fv16fec735EkrV27Vpdffrlqa2t11VVXJa5zAEC/dk73gL78FbwZGRmSpLq6Oh07dkzFxcXRbSZPnqyxY8eqpqamx/fo7OxUJBKJGQCAgS/uAOru7tayZcs0a9YsTZkyRZIUCoWUkpKi9PT0mG2zs7MVCoV6fJ+KigoFAoHoiOdxZQBA/xN3AJWXl2vPnj166aWXzqmBFStWKBwOR0dzc/M5vR8AoH/wdA/oS0uXLtWWLVu0bds2jR49Oro8GAyqq6tLra2tMVdBLS0tCgaDPb6X3++X3++Ppw0AQD/m6QrIOaelS5dq48aNevPNN5WXlxezfvr06Ro6dKgqKyujy+rr67Vv3z4VFhYmpmMAwIDg6QqovLxc69ev1+bNm5Wamhq9rxMIBDR8+HAFAgHdeuutWr58uTIyMpSWlqY777xThYWFPAEHAIjhKYCeeuopSdLs2bNjlq9du1a33HKLJOk3v/mNBg0apAULFqizs1MlJSX67W9/m5BmAQADB5OR9pJ4/k533XWX55qHH37Yc43P5/Ncg9732Wefea5555134trXTTfd5Lnm6NGjce0LAxeTkQIA+iQCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAlmwx5gNm/e7Llm3rx5Seikf4pnxum//OUvnmvi+dXzzzzzjOea/fv3e64BEoXZsAEAfRIBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATTEYKAEgKJiMFAPRJBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEx4CqCKigrNmDFDqampysrK0vz581VfXx+zzezZs+Xz+WLG4sWLE9o0AKD/8xRA1dXVKi8vV21trV5//XUdO3ZMc+fOVXt7e8x2t99+uw4ePBgdq1evTmjTAID+b4iXjbdu3Rrzet26dcrKylJdXZ2Kioqiyy+44AIFg8HEdAgAGJDO6R5QOByWJGVkZMQsf/HFF5WZmakpU6ZoxYoVOnr06Gnfo7OzU5FIJGYAAM4DLk7Hjx933/ve99ysWbNilv/ud79zW7dudbt373Z//OMf3cUXX+yuv/76077PqlWrnCQGg8FgDLARDofPmCNxB9DixYvduHHjXHNz8xm3q6ysdJJcQ0NDj+s7OjpcOByOjubmZvODxmAwGIxzH2cLIE/3gL60dOlSbdmyRdu2bdPo0aPPuG1BQYEkqaGhQRMmTDhlvd/vl9/vj6cNAEA/5imAnHO68847tXHjRlVVVSkvL++sNbt27ZIk5eTkxNUgAGBg8hRA5eXlWr9+vTZv3qzU1FSFQiFJUiAQ0PDhw9XY2Kj169fru9/9rkaNGqXdu3fr7rvvVlFRkaZNm5aUvwAAoJ/yct9Hp/mcb+3atc455/bt2+eKiopcRkaG8/v9buLEie7ee+896+eAJwuHw+afWzIYDAbj3MfZfvb7/j9Y+oxIJKJAIGDdBgDgHIXDYaWlpZ12PXPBAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBM9LkAcs5ZtwAASICz/TzvcwHU1tZm3QIAIAHO9vPc5/rYJUd3d7cOHDig1NRU+Xy+mHWRSERjxoxRc3Oz0tLSjDq0x3E4geNwAsfhBI7DCX3hODjn1NbWptzcXA0adPrrnCG92NPXMmjQII0ePfqM26SlpZ3XJ9iXOA4ncBxO4DicwHE4wfo4BAKBs27T5z6CAwCcHwggAICJfhVAfr9fq1atkt/vt27FFMfhBI7DCRyHEzgOJ/Sn49DnHkIAAJwf+tUVEABg4CCAAAAmCCAAgAkCCABgot8E0Jo1a3TJJZdo2LBhKigo0HvvvWfdUq97+OGH5fP5YsbkyZOt20q6bdu2ad68ecrNzZXP59OmTZti1jvntHLlSuXk5Gj48OEqLi7W3r17bZpNorMdh1tuueWU86O0tNSm2SSpqKjQjBkzlJqaqqysLM2fP1/19fUx23R0dKi8vFyjRo3SyJEjtWDBArW0tBh1nBxf5zjMnj37lPNh8eLFRh33rF8E0Msvv6zly5dr1apVev/995Wfn6+SkhIdOnTIurVed8UVV+jgwYPR8fbbb1u3lHTt7e3Kz8/XmjVrely/evVqPfHEE3r66ae1fft2jRgxQiUlJero6OjlTpPrbMdBkkpLS2POjw0bNvRih8lXXV2t8vJy1dbW6vXXX9exY8c0d+5ctbe3R7e5++679dprr+nVV19VdXW1Dhw4oBtuuMGw68T7OsdBkm6//faY82H16tVGHZ+G6wdmzpzpysvLo6+PHz/ucnNzXUVFhWFXvW/VqlUuPz/fug1TktzGjRujr7u7u10wGHS//vWvo8taW1ud3+93GzZsMOiwd3z1ODjn3KJFi9x1111n0o+VQ4cOOUmuurraOXfiv/3QoUPdq6++Gt3mn//8p5PkampqrNpMuq8eB+ec+/a3v+1+8pOf2DX1NfT5K6Curi7V1dWpuLg4umzQoEEqLi5WTU2NYWc29u7dq9zcXI0fP14333yz9u3bZ92SqaamJoVCoZjzIxAIqKCg4Lw8P6qqqpSVlaVJkyZpyZIlOnz4sHVLSRUOhyVJGRkZkqS6ujodO3Ys5nyYPHmyxo4dO6DPh68ehy+9+OKLyszM1JQpU7RixQodPXrUor3T6nOTkX7Vp59+quPHjys7OztmeXZ2tv71r38ZdWWjoKBA69at06RJk3Tw4EE98sgjuuaaa7Rnzx6lpqZat2ciFApJUo/nx5frzhelpaW64YYblJeXp8bGRj344IMqKytTTU2NBg8ebN1ewnV3d2vZsmWaNWuWpkyZIunE+ZCSkqL09PSYbQfy+dDTcZCkH/zgBxo3bpxyc3O1e/du3X///aqvr9ef//xnw25j9fkAwv+UlZVF/zxt2jQVFBRo3LhxeuWVV3Trrbcadoa+YOHChdE/T506VdOmTdOECRNUVVWlOXPmGHaWHOXl5dqzZ895cR/0TE53HO64447on6dOnaqcnBzNmTNHjY2NmjBhQm+32aM+/xFcZmamBg8efMpTLC0tLQoGg0Zd9Q3p6em67LLL1NDQYN2KmS/PAc6PU40fP16ZmZkD8vxYunSptmzZorfeeivm17cEg0F1dXWptbU1ZvuBej6c7jj0pKCgQJL61PnQ5wMoJSVF06dPV2VlZXRZd3e3KisrVVhYaNiZvSNHjqixsVE5OTnWrZjJy8tTMBiMOT8ikYi2b99+3p8f+/fv1+HDhwfU+eGc09KlS7Vx40a9+eabysvLi1k/ffp0DR06NOZ8qK+v1759+wbU+XC249CTXbt2SVLfOh+sn4L4Ol566SXn9/vdunXr3AcffODuuOMOl56e7kKhkHVrveqnP/2pq6qqck1NTe6dd95xxcXFLjMz0x06dMi6taRqa2tzO3fudDt37nSS3GOPPeZ27tzpPv74Y+ecc7/85S9denq627x5s9u9e7e77rrrXF5envv888+NO0+sMx2HtrY2d88997iamhrX1NTk3njjDfetb33LXXrppa6jo8O69YRZsmSJCwQCrqqqyh08eDA6jh49Gt1m8eLFbuzYse7NN990O3bscIWFha6wsNCw68Q723FoaGhwP//5z92OHTtcU1OT27x5sxs/frwrKioy7jxWvwgg55x78skn3dixY11KSoqbOXOmq62ttW6p1914440uJyfHpaSkuIsvvtjdeOONrqGhwbqtpHvrrbecpFPGokWLnHMnHsV+6KGHXHZ2tvP7/W7OnDmuvr7etukkONNxOHr0qJs7d6676KKL3NChQ924cePc7bffPuD+J62nv78kt3bt2ug2n3/+ufvxj3/sLrzwQnfBBRe466+/3h08eNCu6SQ423HYt2+fKyoqchkZGc7v97uJEye6e++914XDYdvGv4JfxwAAMNHn7wEBAAYmAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJv4PRZUo/Q3ITS8AAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark:  3\n","NN answer:  3\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"z5-YYw4uosUB"},"outputs":[],"source":["# загрузка собственного изображения (Цифры 2 и 7)\n","from PIL import Image\n","file_data_2 = Image.open('2.png')\n","file_data_7 = Image.open('7.png')\n","file_data_2 = file_data_2.convert('L') # перевод в градации серого\n","file_data_7 = file_data_7.convert('L') # перевод в градации серого\n","test_img_2 = np.array(file_data_2)\n","test_img_7 = np.array(file_data_7)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":465},"id":"dv17bJVVuslg","executionInfo":{"status":"ok","timestamp":1759130765327,"user_tz":-180,"elapsed":156,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"d9b5b55c-75d9-4180-f93c-22befad0633c"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAGmJJREFUeJzt3X9MVff9x/HX9QdX2nIvQ4TLnahoW12qsswpI21dO4nAkkarWbTtH7o0NThsZlnXlqUtdVtyO5esTReH/yyyJlXbJlVTs5i0WDDdwEZbY8w2IgQnjYCrifcqChr5fP8wvV9vBe3Fe3lfLs9HchK55xzuu2c3PHfg8sHjnHMCAGCUTbAeAAAwPhEgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgYpL1AN80ODioM2fOKCsrSx6Px3ocAECcnHO6cOGCgsGgJkwY/j4n5QJ05swZFRYWWo8BALhDXV1dmj59+rD7U+5bcFlZWdYjAAAS4HZfz5MWoG3btmnWrFmaMmWKSkpK9Nlnn32r8/i2GwCkh9t9PU9KgN59913V1NSorq5On3/+uYqLi1VeXq6zZ88m4+kAAGORS4IlS5a46urq6MfXrl1zwWDQhUKh254bDoedJDY2Nja2Mb6Fw+Fbfr1P+B3QlStXdPToUZWVlUUfmzBhgsrKytTS0nLT8QMDA4pEIjEbACD9JTxAX331la5du6b8/PyYx/Pz89XT03PT8aFQSH6/P7rxDjgAGB/M3wVXW1urcDgc3bq6uqxHAgCMgoT/HlBubq4mTpyo3t7emMd7e3sVCARuOt7r9crr9SZ6DABAikv4HVBGRoYWLVqkxsbG6GODg4NqbGxUaWlpop8OADBGJWUlhJqaGq1bt04//OEPtWTJEr355pvq6+vTz3/+82Q8HQBgDEpKgNasWaP//e9/evXVV9XT06Pvf//7OnDgwE1vTAAAjF8e55yzHuJGkUhEfr/fegwAwB0Kh8Py+XzD7jd/FxwAYHwiQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJiZZD4Cxq6KiIu5z6uvr4z5n1qxZcZ+DO3Pq1Km4z9myZUvc5zQ0NMR9DtIHd0AAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkWI8WIFhWVpF27dsV9TnZ29oieC6NrJAvAjmSh2ZFgAdP0wR0QAMAEAQIAmEh4gF577TV5PJ6Ybd68eYl+GgDAGJeUnwE98MAD+vjjj///SSbxoyYAQKyklGHSpEkKBALJ+NQAgDSRlJ8BnTx5UsFgULNnz9ZTTz2l06dPD3vswMCAIpFIzAYASH8JD1BJSYkaGhp04MAB1dfXq7OzUw8//LAuXLgw5PGhUEh+vz+6FRYWJnokAEAKSniAKisr9bOf/UwLFy5UeXm5/v73v+v8+fN67733hjy+trZW4XA4unV1dSV6JABACkr6uwOys7N1//33q729fcj9Xq9XXq832WMAAFJM0n8P6OLFi+ro6FBBQUGynwoAMIYkPEDPP/+8mpubderUKf3zn//U448/rokTJ+qJJ55I9FMBAMawhH8L7ssvv9QTTzyhc+fOadq0aXrooYfU2tqqadOmJfqpAABjmMc556yHuFEkEpHf77ceY1zp7u4e0Xn8rhfuVH9/f9znZGZmJmESJEM4HJbP5xt2P2vBAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmkv4H6TC6Kioq4j4n1RcV3b59e9znbNy4MQmTjD0vvfTSiM4LhUIJnmRoU6ZMGZXnQWriDggAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmPM45Zz3EjSKRiPx+v/UYY1Z3d3fc56T6atgej8d6hHHn8uXLcZ8zWitb19bWxn3O66+/noRJcDvhcFg+n2/Y/dwBAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmWIw0zaTyIpKStH379rjP2bhxYxImwa288cYbcZ+zefPmxA8yhP7+/rjPyczMTMIkuB0WIwUApCQCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwASLkaaZl156Ke5zQqHQiJ6LhUXT10gWqB3JQrijxePxWI8wLrEYKQAgJREgAICJuAN06NAhPfbYYwoGg/J4PNq7d2/MfuecXn31VRUUFCgzM1NlZWU6efJkouYFAKSJuAPU19en4uJibdu2bcj9W7du1VtvvaXt27fr8OHDuvvuu1VeXj6iPyIFAEhfk+I9obKyUpWVlUPuc87pzTff1Msvv6wVK1ZIkt5++23l5+dr7969Wrt27Z1NCwBIGwn9GVBnZ6d6enpUVlYWfczv96ukpEQtLS1DnjMwMKBIJBKzAQDSX0ID1NPTI0nKz8+PeTw/Pz+675tCoZD8fn90KywsTORIAIAUZf4uuNraWoXD4ejW1dVlPRIAYBQkNECBQECS1NvbG/N4b29vdN83eb1e+Xy+mA0AkP4SGqCioiIFAgE1NjZGH4tEIjp8+LBKS0sT+VQAgDEu7nfBXbx4Ue3t7dGPOzs7dezYMeXk5GjGjBnavHmzfv/73+u+++5TUVGRXnnlFQWDQa1cuTKRcwMAxri4A3TkyBE9+uij0Y9ramokSevWrVNDQ4NeeOEF9fX1acOGDTp//rweeughHThwYERrSwEA0heLkQK4SVVVVdzn1NfXJ2GSxGAxUhssRgoASEkECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwEfefYwCQ/urq6qxHGNbu3butR0CCcAcEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJjwOOec9RA3ikQi8vv91mMA41qKfVmIkZmZGfc5/f39SZgEtxMOh+Xz+Ybdzx0QAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGBikvUAAJKnqqrKeoSEY2HR9MEdEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABggsVIgTRWV1dnPcIt7d6923oEGOIOCABgggABAEzEHaBDhw7pscceUzAYlMfj0d69e2P2r1+/Xh6PJ2arqKhI1LwAgDQRd4D6+vpUXFysbdu2DXtMRUWFuru7o9uuXbvuaEgAQPqJ+00IlZWVqqysvOUxXq9XgUBgxEMBANJfUn4G1NTUpLy8PM2dO1cbN27UuXPnhj12YGBAkUgkZgMApL+EB6iiokJvv/22Ghsb9Yc//EHNzc2qrKzUtWvXhjw+FArJ7/dHt8LCwkSPBABIQQn/PaC1a9dG/71gwQItXLhQc+bMUVNTk5YtW3bT8bW1taqpqYl+HIlEiBAAjANJfxv27NmzlZubq/b29iH3e71e+Xy+mA0AkP6SHqAvv/xS586dU0FBQbKfCgAwhsT9LbiLFy/G3M10dnbq2LFjysnJUU5OjrZs2aLVq1crEAioo6NDL7zwgu69916Vl5cndHAAwNgWd4COHDmiRx99NPrx1z+/Wbdunerr63X8+HH97W9/0/nz5xUMBrV8+XL97ne/k9frTdzUAIAxz+Occ9ZD3CgSicjv91uPAaScqqqquM+pr69PwiSJk5mZGfc5/f39SZgEyRAOh2/5c33WggMAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAICJhP9JbgC3V1FREfc5oVAoCZMkzu7du+M+h5WtxzfugAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEx7nnLMe4kaRSER+v996DOBbG8nCort27Yr7nOzs7LjPGU2ZmZlxn8NipOktHA7L5/MNu587IACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADAxCTrAYBUwsKi123fvj3uc1hYFPHiDggAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMMFipEhLI1lUVEq/hUVHsqioJG3cuDHBkwA34w4IAGCCAAEATMQVoFAopMWLFysrK0t5eXlauXKl2traYo7p7+9XdXW1pk6dqnvuuUerV69Wb29vQocGAIx9cQWoublZ1dXVam1t1UcffaSrV69q+fLl6uvrix7z3HPP6cMPP9T777+v5uZmnTlzRqtWrUr44ACAsS2uNyEcOHAg5uOGhgbl5eXp6NGjWrp0qcLhsP76179q586d+slPfiJJ2rFjh773ve+ptbVVP/rRjxI3OQBgTLujnwGFw2FJUk5OjiTp6NGjunr1qsrKyqLHzJs3TzNmzFBLS8uQn2NgYECRSCRmAwCkvxEHaHBwUJs3b9aDDz6o+fPnS5J6enqUkZFx09tS8/Pz1dPTM+TnCYVC8vv90a2wsHCkIwEAxpARB6i6ulonTpzQ7t2772iA2tpahcPh6NbV1XVHnw8AMDaM6BdRN23apP379+vQoUOaPn169PFAIKArV67o/PnzMXdBvb29CgQCQ34ur9crr9c7kjEAAGNYXHdAzjlt2rRJe/bs0cGDB1VUVBSzf9GiRZo8ebIaGxujj7W1ten06dMqLS1NzMQAgLQQ1x1QdXW1du7cqX379ikrKyv6cx2/36/MzEz5/X49/fTTqqmpUU5Ojnw+n5599lmVlpbyDjgAQIy4AlRfXy9JeuSRR2Ie37Fjh9avXy9JeuONNzRhwgStXr1aAwMDKi8v11/+8peEDAsASB8e55yzHuJGkUhEfr/fegykkJEsLDqSRUWl9FtYlEVFYSkcDsvn8w27n7XgAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYGJEfxEVGKnRWtk6lVe1lljZGpC4AwIAGCFAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATHicc856iBtFIhH5/X7rMZAk3d3dcZ8TCASSMAmQHKdOnYr7nC1btsR9TkNDQ9znjLZwOCyfzzfsfu6AAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATLEaKUXX58uW4z5kyZUoSJgFSR39/f9znZGZmJmGSxGIxUgBASiJAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATEyyHgDjy5YtW+I+JxQKJWESIHU0NDRYj2CCOyAAgAkCBAAwEVeAQqGQFi9erKysLOXl5WnlypVqa2uLOeaRRx6Rx+OJ2aqqqhI6NABg7IsrQM3NzaqurlZra6s++ugjXb16VcuXL1dfX1/Mcc8884y6u7uj29atWxM6NABg7IvrTQgHDhyI+bihoUF5eXk6evSoli5dGn38rrvuUiAQSMyEAIC0dEc/AwqHw5KknJycmMffeecd5ebmav78+aqtrdWlS5eG/RwDAwOKRCIxGwAg/Y34bdiDg4PavHmzHnzwQc2fPz/6+JNPPqmZM2cqGAzq+PHjevHFF9XW1qYPPvhgyM8TCoVG9NZcAMDYNuIAVVdX68SJE/r0009jHt+wYUP03wsWLFBBQYGWLVumjo4OzZkz56bPU1tbq5qamujHkUhEhYWFIx0LADBGjChAmzZt0v79+3Xo0CFNnz79lseWlJRIktrb24cMkNfrldfrHckYAIAxLK4AOef07LPPas+ePWpqalJRUdFtzzl27JgkqaCgYEQDAgDSU1wBqq6u1s6dO7Vv3z5lZWWpp6dHkuT3+5WZmamOjg7t3LlTP/3pTzV16lQdP35czz33nJYuXaqFCxcm5T8AADA2xRWg+vp6Sdd/2fRGO3bs0Pr165WRkaGPP/5Yb775pvr6+lRYWKjVq1fr5ZdfTtjAAID0EPe34G6lsLBQzc3NdzQQAGB88LjbVWWURSIR+f1+6zEAAHcoHA7L5/MNu5/FSAEAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADCRcgFyzlmPAABIgNt9PU+5AF24cMF6BABAAtzu67nHpdgtx+DgoM6cOaOsrCx5PJ6YfZFIRIWFherq6pLP5zOa0B7X4Tquw3Vch+u4DtelwnVwzunChQsKBoOaMGH4+5xJozjTtzJhwgRNnz79lsf4fL5x/QL7GtfhOq7DdVyH67gO11lfB7/ff9tjUu5bcACA8YEAAQBMjKkAeb1e1dXVyev1Wo9iiutwHdfhOq7DdVyH68bSdUi5NyEAAMaHMXUHBABIHwQIAGCCAAEATBAgAICJMROgbdu2adasWZoyZYpKSkr02WefWY806l577TV5PJ6Ybd68edZjJd2hQ4f02GOPKRgMyuPxaO/evTH7nXN69dVXVVBQoMzMTJWVlenkyZM2wybR7a7D+vXrb3p9VFRU2AybJKFQSIsXL1ZWVpby8vK0cuVKtbW1xRzT39+v6upqTZ06Vffcc49Wr16t3t5eo4mT49tch0ceeeSm10NVVZXRxEMbEwF69913VVNTo7q6On3++ecqLi5WeXm5zp49az3aqHvggQfU3d0d3T799FPrkZKur69PxcXF2rZt25D7t27dqrfeekvbt2/X4cOHdffdd6u8vFz9/f2jPGly3e46SFJFRUXM62PXrl2jOGHyNTc3q7q6Wq2trfroo4909epVLV++XH19fdFjnnvuOX344Yd6//331dzcrDNnzmjVqlWGUyfet7kOkvTMM8/EvB62bt1qNPEw3BiwZMkSV11dHf342rVrLhgMulAoZDjV6Kurq3PFxcXWY5iS5Pbs2RP9eHBw0AUCAffHP/4x+tj58+ed1+t1u3btMphwdHzzOjjn3Lp169yKFStM5rFy9uxZJ8k1Nzc7567/bz958mT3/vvvR4/597//7SS5lpYWqzGT7pvXwTnnfvzjH7tf/vKXdkN9Cyl/B3TlyhUdPXpUZWVl0ccmTJigsrIytbS0GE5m4+TJkwoGg5o9e7aeeuopnT592nokU52dnerp6Yl5ffj9fpWUlIzL10dTU5Py8vI0d+5cbdy4UefOnbMeKanC4bAkKScnR5J09OhRXb16Neb1MG/ePM2YMSOtXw/fvA5fe+edd5Sbm6v58+ertrZWly5dshhvWCm3GOk3ffXVV7p27Zry8/NjHs/Pz9d//vMfo6lslJSUqKGhQXPnzlV3d7e2bNmihx9+WCdOnFBWVpb1eCZ6enokacjXx9f7xouKigqtWrVKRUVF6ujo0G9+8xtVVlaqpaVFEydOtB4v4QYHB7V582Y9+OCDmj9/vqTrr4eMjAxlZ2fHHJvOr4ehroMkPfnkk5o5c6aCwaCOHz+uF198UW1tbfrggw8Mp42V8gHC/6usrIz+e+HChSopKdHMmTP13nvv6emnnzacDKlg7dq10X8vWLBACxcu1Jw5c9TU1KRly5YZTpYc1dXVOnHixLj4OeitDHcdNmzYEP33ggULVFBQoGXLlqmjo0Nz5swZ7TGHlPLfgsvNzdXEiRNvehdLb2+vAoGA0VSpITs7W/fff7/a29utRzHz9WuA18fNZs+erdzc3LR8fWzatEn79+/XJ598EvPnWwKBgK5cuaLz58/HHJ+ur4fhrsNQSkpKJCmlXg8pH6CMjAwtWrRIjY2N0ccGBwfV2Nio0tJSw8nsXbx4UR0dHSooKLAexUxRUZECgUDM6yMSiejw4cPj/vXx5Zdf6ty5c2n1+nDOadOmTdqzZ48OHjyooqKimP2LFi3S5MmTY14PbW1tOn36dFq9Hm53HYZy7NgxSUqt14P1uyC+jd27dzuv1+saGhrcv/71L7dhwwaXnZ3tenp6rEcbVb/61a9cU1OT6+zsdP/4xz9cWVmZy83NdWfPnrUeLakuXLjgvvjiC/fFF184Se5Pf/qT++KLL9x///tf55xzr7/+usvOznb79u1zx48fdytWrHBFRUXu8uXLxpMn1q2uw4ULF9zzzz/vWlpaXGdnp/v444/dD37wA3ffffe5/v5+69ETZuPGjc7v97umpibX3d0d3S5duhQ9pqqqys2YMcMdPHjQHTlyxJWWlrrS0lLDqRPvdtehvb3d/fa3v3VHjhxxnZ2dbt++fW727Nlu6dKlxpPHGhMBcs65P//5z27GjBkuIyPDLVmyxLW2tlqPNOrWrFnjCgoKXEZGhvvud7/r1qxZ49rb263HSrpPPvnESbppW7dunXPu+luxX3nlFZefn++8Xq9btmyZa2trsx06CW51HS5duuSWL1/upk2b5iZPnuxmzpzpnnnmmbT7P2lD/fdLcjt27Igec/nyZfeLX/zCfec733F33XWXe/zxx113d7fd0Elwu+tw+vRpt3TpUpeTk+O8Xq+799573a9//WsXDodtB/8G/hwDAMBEyv8MCACQnggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAE/8Hyf2noiyd3fIAAAAASUVORK5CYII=\n"},"metadata":{}},{"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","I think it's  3\n"]}],"source":["# вывод собственного изображения (цифра 2)\n","plt.imshow(test_img_2, cmap=plt.get_cmap('gray'))\n","plt.show()\n","# предобработка\n","test_img_2 = test_img_2 / 255\n","test_img_2 = test_img_2.reshape(1, num_pixels)\n","# распознавание\n","result = best_model.predict(test_img_2)\n","print('I think it\\'s ', np.argmax(result))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":465},"id":"rhNzATtGuxbD","executionInfo":{"status":"ok","timestamp":1759130799101,"user_tz":-180,"elapsed":651,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"11cd1569-d370-4070-c8de-02035699d8bb"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n","I think it's  7\n"]}],"source":["# вывод собственного изображения (цифра 7)\n","plt.imshow(test_img_7, cmap=plt.get_cmap('gray'))\n","plt.show()\n","# предобработка\n","test_img_7 = test_img_7 / 255\n","test_img_7 = test_img_7.reshape(1, num_pixels)\n","# распознавание\n","result = best_model.predict(test_img_7)\n","print('I think it\\'s ', np.argmax(result))"]},{"cell_type":"code","source":["# Тестирование на собственных повернутых изображениях\n","from PIL import Image\n","file_data_2_90 = Image.open('2_90.png')\n","file_data_7_90 = Image.open('7_90.png')\n","file_data_2_90 = file_data_2_90.convert('L') # перевод в градации серого\n","file_data_7_90 = file_data_7_90.convert('L') # перевод в градации серого\n","test_img_2_90 = np.array(file_data_2_90)\n","test_img_7_90= np.array(file_data_7_90)"],"metadata":{"id":"4rIUmwfYXcgh"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# вывод собственного изображения (цифра 2)\n","plt.imshow(test_img_2_90, cmap=plt.get_cmap('gray'))\n","plt.show()\n","# предобработка\n","test_img_2_90 = test_img_2_90 / 255\n","test_img_2_90 = test_img_2_90.reshape(1, num_pixels)\n","# распознавание\n","result = best_model.predict(test_img_2_90)\n","print('I think it\\'s ', np.argmax(result))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":465},"id":"xeYNkU0OZqg2","executionInfo":{"status":"ok","timestamp":1759131775554,"user_tz":-180,"elapsed":445,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"b17a7b99-ce57-45fa-c069-74ea8374c3d3"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step\n","I think it's  7\n"]}]},{"cell_type":"code","source":["# вывод собственного изображения (цифра 7)\n","plt.imshow(test_img_7_90, cmap=plt.get_cmap('gray'))\n","plt.show()\n","# предобработка\n","test_img_7_90 = test_img_7_90 / 255\n","test_img_7_90 = test_img_7_90.reshape(1, num_pixels)\n","# распознавание\n","result = best_model.predict(test_img_7_90)\n","print('I think it\\'s ', np.argmax(result))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":465},"id":"8JZajicXbNSA","executionInfo":{"status":"ok","timestamp":1759131800104,"user_tz":-180,"elapsed":238,"user":{"displayName":"Legal People","userId":"00818738730090246603"}},"outputId":"016e8c12-472d-4a15-c420-cd955edef901"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n","I think it's  7\n"]}]},{"cell_type":"markdown","source":[],"metadata":{"id":"DQJNpFTOZ7Z6"}}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[{"file_id":"1HorM0jtoJfNfcuh_uXJu8ODaEJ6xOUu-","timestamp":1759209370437}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/labworks/LW1/.ipynb_checkpoints/my_document-checkpoint.md b/labworks/LW1/.ipynb_checkpoints/my_document-checkpoint.md
new file mode 100644
index 0000000..a8ee40a
--- /dev/null
+++ b/labworks/LW1/.ipynb_checkpoints/my_document-checkpoint.md
@@ -0,0 +1,533 @@
+
+## Отчёт по лабораторной работе №1
+
+**Троянов Д.С., Чернов Д.Е. — А-01-22**
+
+---
+
+## 1) В среде Google Colab создали блокнот. Импортировали необходимые библиотеки и модули.
+
+```python
+# импорт модулей
+import tensorflow as tf
+from tensorflow import keras
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+import os
+
+# Укажем текущую директорию
+os.chdir('/content/drive/MyDrive/Colab Notebooks/is_dnn/labworks/LW1')
+```
+
+---
+
+## 2) Загрузили набор данных MNIST, содержащий размеченные изображения рукописных цифр.
+
+```python
+# Загрузка датасета
+(X_train_orig, y_train_orig), (X_test_orig, y_test_orig) = mnist.load_data()
+
+```
+
+---
+
+## 3) Разбили набор данных на обучающие и тестовые данные в соотношении 60 000:10 000 элементов.
+При разбиении параметр `random_state` выбрали равным (4k – 1), где k - номер бригады, k = 6 ⇒ `random_state = 23`.
+
+```python
+# разбиваем выборку на обучающую и тестовую выборку
+X = np.concatenate((X_train_orig, X_test_orig))
+y = np.concatenate((y_train_orig, y_test_orig))
+
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y,
+    test_size=10000,
+    train_size=60000,
+    random_state=3,
+)
+
+# вывод размерности массивов данных
+print('Shape of X train:', X_train.shape)
+print('Shape of y train:', y_train.shape)
+print('Shape of X test:', X_test.shape)
+print('Shape of y test:', y_test.shape)
+```
+
+```
+Shape of X train: (60000, 28, 28)
+Shape of y train: (60000,)
+Shape of X test: (10000, 28, 28)
+Shape of y test: (10000,)
+```
+
+---
+
+## 4) Вывели первые 4 элемента обучающих данных (изображения и метки цифр).
+
+```python
+# Вывод первых 4 изображений
+fig, axes = plt.subplots(1, 4, figsize=(12, 3))
+for i in range(4):
+    axes[i].imshow(X_train[i], cmap='gray')
+    axes[i].set_title(f'Метка: {y_train[i]}')
+    axes[i].axis('off')
+plt.tight_layout()
+plt.show()
+```
+
+![4 цифры](paragraph_4.png)
+
+
+# Были выведены цифры 7, 8, 2, 2
+---
+
+## 5) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения нейронной сети. Входные данные должны принимать значения от 0 до 1, метки цифр должны быть закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных.
+
+```python
+# развернем каждое изображение 28*28 в вектор 784
+num_pixels = X_train.shape[1] * X_train.shape[2]
+X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255
+X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255
+print('Shape of transformed X train:', X_train.shape)
+```
+
+```
+Shape of transformed X train: (60000, 784)
+```
+
+```python
+# переведем метки в one-hot
+from keras.utils import to_categorical
+y_train = to_categorical(y_train)
+y_test = to_categorical(y_test)
+print('Shape of transformed y train:', y_train.shape)
+num_classes = y_train.shape[1]
+```
+
+```
+Shape of transformed y train: (60000, 10)
+```
+
+---
+
+## 6) Реализовали модель однослойной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети. Вывели график функции ошибки на обучающих и валидационных данных по эпохам.
+
+
+```python
+model_0 = Sequential()
+model_0.add(Dense(units=num_classes, input_dim=num_pixels, activation='softmax'))
+
+# Компиляция модели
+model_0.compile(loss='categorical_crossentropy',
+                optimizer='sgd',
+                metrics=['accuracy'])
+
+# Вывод информации об архитектуре
+print("Архитектура однослойной сети:")
+model_0.summary()
+```
+
+![архитектура модели](architecture_of_1_layer_NN.png)
+
+```
+# Обучение модели
+history_0 = model_0.fit(X_train, y_train,
+                        validation_split=0.1,
+                        epochs=50)
+```
+
+```python
+# График функции ошибки по эпохам
+plt.figure(figsize=(10, 6))
+plt.plot(history_0.history['loss'], label='Обучающая выборка')
+plt.plot(history_0.history['val_loss'], label='Валидационная выборка')
+plt.title('Функция ошибки по эпохам (Однослойная сеть)')
+plt.xlabel('Эпохи')
+plt.ylabel('Ошибка')
+plt.legend()
+plt.grid(True)
+plt.show()
+```
+
+![график обучения](paragraph_6.png)
+
+---
+
+## 7) Применили обученную модель к тестовым данным. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных.
+
+```python
+# Оценка на тестовых данных
+scores_0 = model_0.evaluate(X_test, y_test, verbose=0)
+print("Результаты однослойной сети:")
+print(f"Ошибка на тестовых данных: {scores_0[0]}")
+print(f"Точность на тестовых данных: {scores_0[1]}")
+```
+
+```
+Результаты однослойной сети:
+		Ошибка на тестовых данных: 0.28625616431236267
+		Точность на тестовых данных: 0.92330002784729
+```
+
+---
+
+## 8) Добавили в модель один скрытый и провели обучение и тестирование (повторить п. 6–7) при 100, 300, 500 нейронах в скрытом слое. По метрике качества классификации на тестовых данных выбрали наилучшее количество нейронов в скрытом слое. В качестве функции активации нейронов в скрытом слое использовали функцию `sigmoid`.
+
+```python
+# Функция для создания и обучения модели
+def create_and_train_model(hidden_units, model_name):
+    model = Sequential()
+    model.add(Dense(units=hidden_units, input_dim=num_pixels, activation='sigmoid'))
+    model.add(Dense(units=num_classes, activation='softmax'))
+
+    model.compile(loss='categorical_crossentropy',
+                  optimizer='sgd',
+                  metrics=['accuracy'])
+
+    history = model.fit(X_train, y_train,
+                        validation_split=0.1,
+                        epochs=50)
+
+    scores = model.evaluate(X_test, y_test, verbose=0)
+
+    return model, history, scores
+
+# Эксперименты с разным количеством нейронов
+hidden_units_list = [100, 300, 500]
+models_1 = {}
+histories_1 = {}
+scores_1 = {}
+
+# Обучение сетей с одним скрытым слоем
+for units in hidden_units_list:
+    print(f"
+Обучение модели с {units} нейронами...")
+    model, history, scores = create_and_train_model(units, f"model_{units}")
+
+    models_1[units] = model
+    histories_1[units] = history
+    scores_1[units] = scores
+
+    print(f"Точность: {scores[1]}")
+```
+
+# Определим лучшую модель по итогвой точности
+```python
+# Выбор наилучшей модели
+best_units_1 = max(scores_1.items(), key=lambda x: x[1][1])[0]
+print(f"
+Наилучшее количество нейронов: {best_units_1}")
+print(f"Точность: {scores_1[best_units_1][1]}")
+```
+
+```
+		Наилучшее количество нейронов: 100
+		Точность: 0.9422000050544739
+```
+
+# Отобразим графики ошибок для каждой из архитектур нейросети
+
+```python
+# Графики ошибок для всех моделей
+plt.figure(figsize=(15, 5))
+for i, units in enumerate(hidden_units_list, 1):
+    plt.subplot(1, 3, i)
+    plt.plot(histories_1[units].history['loss'], label='Обучающая')
+    plt.plot(histories_1[units].history['val_loss'], label='Валидационная')
+    plt.title(f'{units} нейронов')
+    plt.xlabel('Эпохи')
+    plt.ylabel('Ошибка')
+    plt.legend()
+    plt.grid(True)
+plt.tight_layout()
+plt.show()
+```
+
+![график обучения для каждой эпохи](paragraph_8.png)
+
+
+**По результатам проведнного эксперимента наилучший показатель точности продемонстрировала нейронная сеть со 100 нейронами в скрытом слое - примерно 0.9422.**
+
+---
+
+## 9) Добавили в архитектуру с лучшим показателем из п. 8, второй скрытый слой и провели обучение и тестирование при 50 и 100 нейронах во втором скрытом слое. В качестве функции активации нейронов в скрытом слое использовали функцию `sigmoid`.
+
+```python
+# Добавление второго скрытого слоя
+second_layer_units = [50, 100]
+models_2 = {}
+histories_2 = {}
+scores_2 = {}
+
+
+for units_2 in second_layer_units:
+    print(f"
+Обучение модели со вторым слоем {units_2} нейронов")
+
+    model = Sequential()
+    model.add(Dense(units=best_units_1, input_dim=num_pixels, activation='sigmoid'))
+    model.add(Dense(units=units_2, activation='sigmoid'))
+    model.add(Dense(units=num_classes, activation='softmax'))
+
+    model.compile(loss='categorical_crossentropy',
+                  optimizer='sgd',
+                  metrics=['accuracy'])
+
+    history = model.fit(X_train, y_train,
+                        validation_split=0.1,
+                        epochs=50)
+
+    scores = model.evaluate(X_test, y_test)
+
+    models_2[units_2] = model
+    histories_2[units_2] = history
+    scores_2[units_2] = scores
+
+    print(f"Точность: {scores[1]}")
+
+```
+
+# Результаты обучения моделей:
+
+```
+Обучение модели со вторым слоем 50 нейронов:
+Точность: 0.9417999982833862
+
+Обучение модели со вторым слоем 100 нейронов
+Точность: 0.942300021648407
+```
+
+# Выбор наилучшей двухслойной модели
+```python
+best_units_2 = max(scores_2.items(), key=lambda x: x[1][1])[0]
+print(f"
+Наилучшее количество нейронов во втором слое: {best_units_2}")
+print(f"Точность: {scores_2[best_units_2][1]}")
+```
+
+```
+	Наилучшее количество нейронов во втором слое: 100
+	Точность: 0.9423
+```
+
+
+## 10) Результаты исследования архитектуры нейронной сети занесли в таблицу:
+
+```python
+# Сбор результатов
+results = {
+    '0 слоев': {'нейроны_1': '-', 'нейроны_2': '-', 'точность': scores_0[1]},
+    '1 слой_100': {'нейроны_1': 100, 'нейроны_2': '-', 'точность': scores_1[100][1]},
+    '1 слой_300': {'нейроны_1': 300, 'нейроны_2': '-', 'точность': scores_1[300][1]},
+    '1 слой_500': {'нейроны_1': 500, 'нейроны_2': '-', 'точность': scores_1[500][1]},
+    '2 слоя_50': {'нейроны_1': best_units_1, 'нейроны_2': 50, 'точность': scores_2[50][1]},
+    '2 слоя_100': {'нейроны_1': best_units_1, 'нейроны_2': 100, 'точность': scores_2[100][1]}
+}
+
+# Создаем DataFrame из результатов
+df_results = pd.DataFrame([
+    {'Кол-во скрытых слоев': 0, 'Нейроны_1_слоя': '-', 'Нейроны_2_слоя': '-', 'Точность': results['0 слоев']['точность']},
+    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 100, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_100']['точность']},
+    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 300, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_300']['точность']},
+    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 500, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_500']['точность']},
+    {'Кол-во скрытых слоев': 2, 'Нейроны_1_слоя': best_units_1, 'Нейроны_2_слоя': 50, 'Точность': results['2 слоя_50']['точность']},
+    {'Кол-во скрытых слоев': 2, 'Нейроны_1_слоя': best_units_1, 'Нейроны_2_слоя': 100, 'Точность': results['2 слоя_100']['точность']}
+])
+
+print(" " * 20 + "ТАБЛИЦА РЕЗУЛЬТАТОВ")
+print("=" * 70)
+# print(df_results.to_string(index=False, formatters={
+#     'Точность': '{:.4f}'.format
+# }))
+print(df_results.reset_index(drop=True))
+
+
+
+```
+
+```
+		ТАБЛИЦА РЕЗУЛЬТАТОВ
+		======================================================================
+		   Кол-во скрытых слоев Нейроны_1_слоя Нейроны_2_слоя  Точность
+		0                     0              -              -    0.9233
+		1                     1            100              -    0.9422
+		2                     1            300              -    0.9377
+		3                     1            500              -    0.9312
+		4                     2            100             50    0.9418
+		5                     2            100            100    0.9423
+
+```
+
+```python
+# Выбор наилучшей модели
+best_model_type = max(results.items(), key=lambda x: x[1]['точность'])[0]
+best_accuracy = results[best_model_type]['точность']
+print(f"
+Наилучшая архитектура: {best_model_type}")
+print(f"Точность: {best_accuracy}")
+```
+
+```
+		Наилучшая архитектура: 2 слоя_100
+		Точность: 0.9423
+```
+### По результатам исследования сделали выводы и выбрали наилучшую архитектуру нейронной сети с точки зрения качества классификации.
+
+**Из таблицы следует, что лучшей архитектурой является НС с двумя скрытыми слоями по 100 и 50 нейронов, второе место занимает НС с одним скрытым слоем и 100 нейронами, на основе которой мы и строили НС с двумя скрытыми слоями. При увеличении количества нейронов в архитектуре НС с 1-м скрытым слоем в результате тестирования было вявлено, что метрики качества падают. Это связано с переобучение нашей НС с 1-м скрытым слоем (когда построенная модель хорошо объясняет примеры из обучающей выборки, но относительно плохо работает на примерах, не участвовавших в обучении) Такая тенденция вероятно возникает из-за простоты датасета MNIST, при усложнении архитектуры НС начинает переобучаться, а оценка качества на тестовых данных падает. Но также стоит отметить, что при усложнении структуры НС точнсть модели также и растет.**
+
+---
+
+## 11) Сохранили наилучшую нейронную сеть на диск.
+
+```python
+# Сохранение модели
+best_model.save('best_mnist_model.keras')
+```
+
+---
+
+## 12) Для нейронной сети наилучшей архитектуры вывели два тестовых изображения, истинные метки и результат распознавания изображений.
+
+```python
+# вывод тестового изображения и результата распознавания (1)
+n = 123
+result = best_model.predict(X_test[n:n+1])
+print('NN output:', result)
+plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))
+plt.show()
+print('Real mark: ', str(np.argmax(y_test[n])))
+print('NN answer: ', str(np.argmax(result)))
+```
+
+![результат 1](paragraph_12_1.png)
+
+```python
+# вывод тестового изображения и результата распознавания (3)
+n = 353
+result = best_model.predict(X_test[n:n+1])
+print('NN output:', result)
+plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))
+plt.show()
+print('Real mark: ', str(np.argmax(y_test[n])))
+print('NN answer: ', str(np.argmax(result)))
+```
+![результат 2](paragraph_12_3.png)
+
+---
+
+## 13) Создали собственные изображения рукописных цифр, подобное представленным в наборе MNIST. Цифру выбрали как остаток от деления на 10 числа своего дня рождения (12 марта → 2, 17 сентября → 7 ). Сохранили изображения. Загрузили, предобработали и подали на вход обученной нейронной сети собственные изображения. Вывели изображения и результаты распознавания.
+
+```python
+# загрузка собственного изображения
+file_data_2 = Image.open('2.png')
+file_data_7 = Image.open('7.png')
+file_data_2 = file_data_2.convert('L') # перевод в градации серого
+file_data_7 = file_data_7.convert('L') # перевод в градации серого
+test_img_2 = np.array(file_data_2)
+test_img_7 = np.array(file_data_7)
+
+
+# вывод собственного изображения (цифра 2)
+plt.imshow(test_img_2, cmap=plt.get_cmap('gray'))
+plt.show()
+# предобработка
+test_img_2 = test_img_2 / 255
+test_img_2 = test_img_2.reshape(1, num_pixels)
+# распознавание
+result = best_model.predict(test_img_2)
+print('I think it\'s ', np.argmax(result))
+```
+
+![собственные изображения](2.png)
+
+```
+1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
+```
+
+```python
+# вывод собственного изображения (цифра 7)
+plt.imshow(test_img_7, cmap=plt.get_cmap('gray'))
+plt.show()
+# предобработка
+test_img_7 = test_img_7 / 255
+test_img_7 = test_img_7.reshape(1, num_pixels)
+# распознавание
+result = best_model.predict(test_img_7)
+print('I think it\'s ', np.argmax(result))
+```
+
+```
+![собственные изображения](7.png)
+```
+
+```
+1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step
+I think it's  7
+```
+**Как видим в результате эксперимента наша НС недостаточно точно определила изображение цифры 2. Возможно это связано с малом размером используемой выборки. Для улучшения качества решения задачи классификации можно либо увеличить размерность выборки для обучения НС, либо изменить структуру НС для более точной ее работы**
+
+---
+
+## 14) Создать копию собственного изображения, отличающуюся от оригинала поворотом на 90 градусов в любую сторону. Сохранили изображения. Загрузили, предобработали и подали на вход обученной нейронной сети измененные изображения. Вывели изображения и результаты распознавания. Сделали выводы по результатам эксперимента.
+
+```python
+# загрузка собственного изображения
+file_data_2_90 = Image.open('2_90.png')
+file_data_7_90 = Image.open('7_90.png')
+file_data_2_90 = file_data_2_90.convert('L') # перевод в градации серого
+file_data_7_90 = file_data_7_90.convert('L') # перевод в градации серого
+test_img_2_90 = np.array(file_data_2_90)
+test_img_7_90= np.array(file_data_7_90)
+
+# вывод собственного изображения (цифра 2)
+plt.imshow(test_img_2_90, cmap=plt.get_cmap('gray'))
+plt.show()
+# предобработка
+test_img_2_90 = test_img_2_90 / 255
+test_img_2_90 = test_img_2_90.reshape(1, num_pixels)
+# распознавание
+result = best_model.predict(test_img_2_90)
+print('I think it\'s ', np.argmax(result))
+```
+
+![собственные изображения повернутые на 90 градусов](2_90.png)
+
+```
+1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 91ms/step
+I think it's  7
+```
+
+```python
+# вывод собственного изображения (цифра 7)
+plt.imshow(test_img_7_90, cmap=plt.get_cmap('gray'))
+plt.show()
+# предобработка
+test_img_7_90 = test_img_7_90 / 255
+test_img_7_90 = test_img_7_90.reshape(1, num_pixels)
+# распознавание
+result = best_model.predict(test_img_7_90)
+print('I think it\'s ', np.argmax(result))
+```
+
+![собственные изображения повернутые на 90 градусов](2_90.png)
+
+```
+1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step
+I think it's  7
+```
+
+
+**При повороте рисунков цифр НС не смогла распознать одну из цифр. Так получилось, во-первых, по той же причине, почему НС не распознала одну из цифр в пункте 13, во-вторых - наша НС не обучалась на перевернутых цифрах**
+
+---
+
+
+
+        
\ No newline at end of file
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diff --git a/labworks/LW1/Lab1_Troyanov&Chernov.ipynb b/labworks/LW1/Lab1_Troyanov&Chernov.ipynb
new file mode 100644
index 0000000..2598c41
--- /dev/null
+++ b/labworks/LW1/Lab1_Troyanov&Chernov.ipynb
@@ -0,0 +1,1956 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 5220,
+     "status": "ok",
+     "timestamp": 1759129041522,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "lon2W0bNVyH8",
+    "outputId": "7fe39a23-47a7-49cd-ca17-3703e109583a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cloning into 'is_dnn'...\n",
+      "remote: Enumerating objects: 188, done.\u001b[K\n",
+      "remote: Counting objects: 100% (188/188), done.\u001b[K\n",
+      "remote: Compressing objects: 100% (186/186), done.\u001b[K\n",
+      "remote: Total 188 (delta 47), reused 0 (delta 0), pack-reused 0\u001b[K\n",
+      "Receiving objects: 100% (188/188), 8.53 MiB | 4.57 MiB/s, done.\n",
+      "Resolving deltas: 100% (47/47), done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!git clone http://uit.mpei.ru/git/TroyanovDS/is_dnn.git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "d5jFanC8NPTN"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir('/content/drive/MyDrive/Colab Notebooks/is_dnn/labworks/LW1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 26468,
+     "status": "ok",
+     "timestamp": 1759209430625,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "qlvyHRzuJPfI",
+    "outputId": "597ba917-88e2-4f26-cc45-80cdb1dc5e55"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "H39A4nsqNuxn"
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'tensorflow'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# импорт модулей\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtensorflow\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtf\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtensorflow\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m keras\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mkeras\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdatasets\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m mnist\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'tensorflow'"
+     ]
+    }
+   ],
+   "source": [
+    "# импорт модулей\n",
+    "import tensorflow as tf\n",
+    "from tensorflow import keras\n",
+    "from keras.datasets import mnist\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.utils import to_categorical\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 462,
+     "status": "ok",
+     "timestamp": 1759127274975,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "iNenKkcoRXrs",
+    "outputId": "041dc403-e177-4f0d-edbb-40902c8954fd"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
+      "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Загрузка датасета\n",
+    "(X_train_orig, y_train_orig), (X_test_orig, y_test_orig) = mnist.load_data()\n",
+    "\n",
+    "# разбиваем выборку на обучающую и тестовую выборку\n",
+    "X = np.concatenate((X_train_orig, X_test_orig))\n",
+    "y = np.concatenate((y_train_orig, y_test_orig))\n",
+    "\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y,\n",
+    "    test_size=10000,\n",
+    "    train_size=60000,\n",
+    "    random_state=3,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 224
+    },
+    "executionInfo": {
+     "elapsed": 243,
+     "status": "ok",
+     "timestamp": 1759127328545,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "bt-EmlYARsCL",
+    "outputId": "611d9110-39a2-46dd-94ce-b0fea7005b87"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1200x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Вывод первых 4 изображений\n",
+    "fig, axes = plt.subplots(1, 4, figsize=(12, 3))\n",
+    "for i in range(4):\n",
+    "    axes[i].imshow(X_train[i], cmap='gray')\n",
+    "    axes[i].set_title(f'Метка: {y_train[i]}')\n",
+    "    axes[i].axis('off')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 134,
+     "status": "ok",
+     "timestamp": 1759127329383,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "5WUu3_97TjSa",
+    "outputId": "7a75ca25-58fe-447d-8bef-547159e6479d"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of transformed X train: (60000, 784)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# развернем каждое изображение 28*28 в вектор 784\n",
+    "num_pixels = X_train.shape[1] * X_train.shape[2]\n",
+    "X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255\n",
+    "X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255\n",
+    "print('Shape of transformed X train:', X_train.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 7,
+     "status": "ok",
+     "timestamp": 1759127330016,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "sUJfDepgUauM",
+    "outputId": "2a9bc3d0-8bdb-4971-f3d4-ca21cb14fca6"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of transformed y train: (60000, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# переведем метки в one-hot\n",
+    "y_train = to_categorical(y_train)\n",
+    "y_test = to_categorical(y_test)\n",
+    "print('Shape of transformed y train:', y_train.shape)\n",
+    "num_classes = y_train.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 207500,
+     "status": "ok",
+     "timestamp": 1759127540337,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "f3UzOyf_V2HQ",
+    "outputId": "9ffc63df-23bf-4947-f3c0-2a3e507034f1"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Архитектура однослойной сети:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m7,850\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.7145 - loss: 1.1468 - val_accuracy: 0.8708 - val_loss: 0.5242\n",
+      "Epoch 2/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8769 - loss: 0.4791 - val_accuracy: 0.8838 - val_loss: 0.4376\n",
+      "Epoch 3/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8871 - loss: 0.4188 - val_accuracy: 0.8917 - val_loss: 0.4007\n",
+      "Epoch 4/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8939 - loss: 0.3855 - val_accuracy: 0.8957 - val_loss: 0.3796\n",
+      "Epoch 5/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8987 - loss: 0.3692 - val_accuracy: 0.8993 - val_loss: 0.3665\n",
+      "Epoch 6/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9035 - loss: 0.3523 - val_accuracy: 0.9008 - val_loss: 0.3555\n",
+      "Epoch 7/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9059 - loss: 0.3402 - val_accuracy: 0.9040 - val_loss: 0.3469\n",
+      "Epoch 8/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9053 - loss: 0.3389 - val_accuracy: 0.9060 - val_loss: 0.3405\n",
+      "Epoch 9/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9072 - loss: 0.3306 - val_accuracy: 0.9053 - val_loss: 0.3358\n",
+      "Epoch 10/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9115 - loss: 0.3209 - val_accuracy: 0.9067 - val_loss: 0.3310\n",
+      "Epoch 11/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9125 - loss: 0.3194 - val_accuracy: 0.9088 - val_loss: 0.3267\n",
+      "Epoch 12/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9110 - loss: 0.3165 - val_accuracy: 0.9093 - val_loss: 0.3236\n",
+      "Epoch 13/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9133 - loss: 0.3094 - val_accuracy: 0.9110 - val_loss: 0.3212\n",
+      "Epoch 14/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9155 - loss: 0.3031 - val_accuracy: 0.9123 - val_loss: 0.3176\n",
+      "Epoch 15/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9134 - loss: 0.3048 - val_accuracy: 0.9115 - val_loss: 0.3160\n",
+      "Epoch 16/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9129 - loss: 0.3096 - val_accuracy: 0.9120 - val_loss: 0.3141\n",
+      "Epoch 17/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9126 - loss: 0.3066 - val_accuracy: 0.9123 - val_loss: 0.3121\n",
+      "Epoch 18/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9157 - loss: 0.2999 - val_accuracy: 0.9117 - val_loss: 0.3099\n",
+      "Epoch 19/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9162 - loss: 0.2979 - val_accuracy: 0.9117 - val_loss: 0.3098\n",
+      "Epoch 20/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9166 - loss: 0.2941 - val_accuracy: 0.9133 - val_loss: 0.3072\n",
+      "Epoch 21/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9152 - loss: 0.2975 - val_accuracy: 0.9125 - val_loss: 0.3065\n",
+      "Epoch 22/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9173 - loss: 0.2926 - val_accuracy: 0.9137 - val_loss: 0.3049\n",
+      "Epoch 23/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9211 - loss: 0.2842 - val_accuracy: 0.9137 - val_loss: 0.3030\n",
+      "Epoch 24/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9185 - loss: 0.2929 - val_accuracy: 0.9138 - val_loss: 0.3024\n",
+      "Epoch 25/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9189 - loss: 0.2880 - val_accuracy: 0.9147 - val_loss: 0.3025\n",
+      "Epoch 26/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9179 - loss: 0.2926 - val_accuracy: 0.9150 - val_loss: 0.3007\n",
+      "Epoch 27/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9182 - loss: 0.2913 - val_accuracy: 0.9138 - val_loss: 0.3000\n",
+      "Epoch 28/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9197 - loss: 0.2881 - val_accuracy: 0.9133 - val_loss: 0.2993\n",
+      "Epoch 29/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9190 - loss: 0.2829 - val_accuracy: 0.9145 - val_loss: 0.2978\n",
+      "Epoch 30/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9190 - loss: 0.2878 - val_accuracy: 0.9157 - val_loss: 0.2977\n",
+      "Epoch 31/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9183 - loss: 0.2859 - val_accuracy: 0.9155 - val_loss: 0.2969\n",
+      "Epoch 32/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9207 - loss: 0.2849 - val_accuracy: 0.9158 - val_loss: 0.2962\n",
+      "Epoch 33/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9196 - loss: 0.2861 - val_accuracy: 0.9158 - val_loss: 0.2956\n",
+      "Epoch 34/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9205 - loss: 0.2784 - val_accuracy: 0.9155 - val_loss: 0.2952\n",
+      "Epoch 35/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9239 - loss: 0.2740 - val_accuracy: 0.9167 - val_loss: 0.2952\n",
+      "Epoch 36/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9227 - loss: 0.2727 - val_accuracy: 0.9165 - val_loss: 0.2945\n",
+      "Epoch 37/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9225 - loss: 0.2757 - val_accuracy: 0.9168 - val_loss: 0.2937\n",
+      "Epoch 38/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9227 - loss: 0.2748 - val_accuracy: 0.9165 - val_loss: 0.2935\n",
+      "Epoch 39/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9201 - loss: 0.2814 - val_accuracy: 0.9177 - val_loss: 0.2922\n",
+      "Epoch 40/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9239 - loss: 0.2749 - val_accuracy: 0.9170 - val_loss: 0.2915\n",
+      "Epoch 41/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9219 - loss: 0.2745 - val_accuracy: 0.9170 - val_loss: 0.2917\n",
+      "Epoch 42/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9214 - loss: 0.2766 - val_accuracy: 0.9178 - val_loss: 0.2917\n",
+      "Epoch 43/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9244 - loss: 0.2762 - val_accuracy: 0.9168 - val_loss: 0.2920\n",
+      "Epoch 44/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9264 - loss: 0.2676 - val_accuracy: 0.9183 - val_loss: 0.2905\n",
+      "Epoch 45/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9237 - loss: 0.2727 - val_accuracy: 0.9177 - val_loss: 0.2904\n",
+      "Epoch 46/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9243 - loss: 0.2697 - val_accuracy: 0.9167 - val_loss: 0.2903\n",
+      "Epoch 47/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9225 - loss: 0.2780 - val_accuracy: 0.9180 - val_loss: 0.2894\n",
+      "Epoch 48/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9230 - loss: 0.2719 - val_accuracy: 0.9172 - val_loss: 0.2893\n",
+      "Epoch 49/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9234 - loss: 0.2662 - val_accuracy: 0.9175 - val_loss: 0.2887\n",
+      "Epoch 50/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9249 - loss: 0.2689 - val_accuracy: 0.9185 - val_loss: 0.2882\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_0 = Sequential()\n",
+    "model_0.add(Dense(units=num_classes, input_dim=num_pixels, activation='softmax'))\n",
+    "\n",
+    "# Компиляция модели\n",
+    "model_0.compile(loss='categorical_crossentropy',\n",
+    "                optimizer='sgd',\n",
+    "                metrics=['accuracy'])\n",
+    "\n",
+    "# Вывод информации об архитектуре\n",
+    "print(\"Архитектура однослойной сети:\")\n",
+    "model_0.summary()\n",
+    "\n",
+    "# Обучение модели\n",
+    "history_0 = model_0.fit(X_train, y_train,\n",
+    "                        validation_split=0.1,\n",
+    "                        epochs=50)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 533
+    },
+    "executionInfo": {
+     "elapsed": 260,
+     "status": "ok",
+     "timestamp": 1759127542723,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "5yjxVnmFmpbV",
+    "outputId": "b182a139-da24-491e-9ebd-7ba1e39eec84"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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u/frrr0hOTi7xF7O4uDikp6fbtQUAGjRoAIvFgps3b9qsX7ZsGdLT03Hx4kWsWLHC5osxAGRkZGDatGkICQmBTqeDr68v/Pz8kJSUhOTkZLvX8fPzQ+/evbF8+XIAwPLlyzFkyBC7suIlZQ1F+UNMXoWFK+vn6OHhUeTrGAwGu5/H8OHDbfYp7GcOiOewdbv14s3FmXMIAMnJyfedZ1Pac6045/+yZcvs3veff/5pt9/mzZvx6KOPQq/Xw9vbG35+fliyZIndudCuXTv8+uuv+Omnn3D37l3cu3cP6enphb6+lfXv+/0+s0uXLkEQBEydOtWuzdOnTwcAm7l7xZX3Z+/k5IQaNWrYzauyEgSBhSHoocLgRFQJPfXUU0hISMCyZcsK3ScmJgYrV66En58fHn30UWn9Cy+8AI1GI/U6rV69Gq1bty7wy8+0adOwfft2m6Ww/60+duwYNm7cCD8/P2luQnk4e/YsVqxYgQ8++KDAeQuurq5Yv349DAYDOnbsCH9/f/j5+UlzRIqjov7hN5lMmDp1Kl555RXUq1fPbnv+L+tFuXTpEmrUqIHvvvsOO3bswMqVK0v92nILDAy0O9defPHFCnv93bt3SwVBCrrGz5tvvokPP/wQzz//PH744Qf8+eef2L59O3x8fArtERs+fDi+//57nD17Fnv27LEpqlIUHx8fZGVl2QWkBg0aABDnDBXGuq1hw4Y2669duwaNRoNq1aoV+fp6vd7u5zFt2rRit7+0EhISYDKZEBgYWK7HLe75369fP7v3nb93au/evejbty/0ej0WL16MrVu3Yvv27Rg4cKDdNbS+/vprNGvWTCpe4+fnV2TPcHFZz7u3337brs3WJf9/ChVH3p/9L7/8gmbNmmHcuHH44Ycf7PZNSkqCr69vmd8LUWXB4hBEldCUKVOwYcMGvPHGGzh37hwGDhyI7OxsAOL/3O/cuRPTpk1DYmIi1q5dazPsxdvbG71798aaNWswaNAg7Nu3DwsWLCjwdZo0aWI3ubywa8P897//Rd++faFSqfDUU09h2bJleOWVV8r8XidPnozmzZtjwIABhe7TvXt3zJkzB4MGDcLSpUtRq1YtjB8/XvpMChMaGgqLxYKLFy9KX0gBMXQmJSUhNDS0zO3Pa/HixYiNjbWr/mZlnbB//vx5myFm1jbmn4AdFBSErVu3IiAgABs3bsT48ePRq1cv+Pn5lfi1y8r6WeVvu8lkkqo+3o9er7fbZ+HChXB3dy/xFzM/Pz84Ozvj/PnzdtvOnTsHpVKJkJAQad3du3fx5ptvokePHtBqtXj77bcRERFh8/P/6aefMGTIEHz22WfSuszMzPtWDOzZsyf0ej1eeOEFdOzYEbVr18bevXuL9R6svY5Xr15F06ZNpfVPPfUUZs+ejVWrVuGxxx6ze152djbWrl0LLy8vdOjQwWbbP//8g5YtWxar10ulUtn9PPK/17w/8yeeeMJm2/nz56Xt1vPh1KlTRb7umTNnAMDm9zG/0pxrxT3/q1evbvf8BQsW2Fzo9eeff4Zer8cff/xh87f122+/tTteWFgYVq9ejSZNmmD48OHo378/Vq1aZVdiPr/atWsDED+zwn53rO9do9EU+ftVEvl/9r1794a3tze2bduG559/Xlp/+/ZtmEym+/6siKoa9jgRVUKBgYE4cOAAevbsic8++wwtW7bE6tWrYTAYEBoaiuHDh8PJyQmbNm0q8H/tX375ZZw5cwYTJkyASqXCCy+8UOY2derUCYD4j+wLL7yACRMm2JWTLqkDBw5g48aN+Pjjj+/bK3Tz5k2MGjUK//nPf/Dqq6+iW7du8PLyKvL4vXr1AgC74Dhv3jwAsKmOVVapqan48MMP8dZbbxX6v+ldu3aFTqfDwoULbXox1qxZg5iYGLty6PXq1ZPmsX3xxRewWCxSta2SvnZZdevWDVqtFgsXLrT5X/dly5YhOTn5vp9lQT02//77L37//Xf079+/xMPbVCoVevTogY0bN9oMb4yJicHatWvRsWNHuLu7S+tHjhwJi8WCZcuW4euvv4ZarcYrr7xi8z5UKpVdb8IXX3xx33CuVqsxePBgnDhxwm6YW1HatWsHAHZly9u3b49u3brh22+/tSl5b/Xee+/hwoULeOedd2x6MM+cOYMzZ84UeDmC0mrdujX8/f2xdOlSm/L9v//+O86ePSv9zP38/PDYY49h+fLluHHjhs0x8n+m69atg1arRceOHQt93ZKea+V9/qtUKigUCpuf/bVr17Bhwwa7fbOysjBo0CA0atQI8+fPR7du3WzCXmFatmyJmjVrYsGCBXaB1fqe/f390blzZ3z11Ve4e/eu3THyl8MvLevr5f9Ps6NHjwIoXUU+osqKPU5ElVRISAg2btyIu3fvYt++fZg7dy6ioqKwdOlSNG/eHM2bNy80bPTu3Rs+Pj748ccf0bNnz2IVmiiJzz//HA0aNMCbb75pN7zjwIEDNnNWrBPYL126hMOHD6NNmzbSNuuFYO/3v6kWiwUvv/wyqlevjo8//rhE7WzWrJl0keCkpCQ8/vjjOHz4MFauXIn+/fujS5cuNvtbC19YWb8E5l1X2DVNjh07Bl9fX7zzzjuFtsfb2xtTpkzB1KlTERERgX79+uHKlSv48ssv0axZM4wYMaLQ5wYGBmLu3LkYMWIEXnrpJSkUFve1y8rPzw+TJ0/GzJkz8eSTT6Jv3744f/48Fi9ejEceeeS+pe5v3LiB3r1747nnnkNwcDBOnTqFb775Br6+vqWej/XBBx9g+/bt6NixI0aNGgW1Wo2vvvoKRqMRc+bMkfb79ttvsWXLFqxYsQLVq1cHIAail156CUuWLMGoUaMAiD093333HTw8PNCwYUMcOHAAO3bsKHLi/6xZszBhwoRiBfm8atWqhcaNG2PHjh12oWvVqlXo2rUr+vXrh4EDB6JTp04wGo345ZdfsHv3bgwYMAATJkyQ9v/jjz/w9ttvAxCHg65evVradvv2bRgMBqxevbrElyPQaDT45JNPMGzYMDz++ON48cUXpXLkYWFhNqXYFy5ciI4dO6Jly5Z49dVXUbNmTVy7dg1btmxBVFQULl68iOnTp+P777/HpEmTbIJtfiU918r7/O/duzfmzZuHJ598EgMHDkRsbCwWLVqEOnXq2A2hnDlzJk6ePIl///33viXS81MqlViyZAn69OmD5s2bY9iwYQgKCsK5c+dw+vRp/PHHHwCARYsWoWPHjmjSpAlGjhyJWrVqISYmBgcOHMCtW7dw/PjxEr+/7Oxs6W9aamoqvv32WxgMBvTv399mv+3bt6NGjRosRU4PF1lq+RFRuStpGdlRo0YJAIS1a9fabSttOfK8Vq5cKQAQfvvtN5v97rfkLZELQFAoFMLRo0dtjpu/lO5HH30k6HQ64fjx43b7FVWOXBAEwWw2CzNnzhRq1qwpaDQaISQkRJg8ebJNeV9BEMuRF9X+vEv+cuQAhPnz59sc01qWOL9FixYJ9evXFzQajRAQECC89tprQnx8/H0/B6snnnhCqFGjhlTGuqSvnV9xy5FbffnllzZtf+ONN+5bOlsQBCE1NVUYOXKkEBoaKmi1WsHPz094+eWX7UpXl7Sk/bFjx4SIiAjB1dVVcHZ2Frp06SLs379f2n7z5k3Bw8ND6NOnj12bnn76acHFxUW4cuWKIAiCkJiYKAwbNkzw9fUVXF1dhYiICOHcuXNCaGhogb8PhZUbL2p7XvPmzRNcXV0LLIWempoqzJgxQ2jUqJHg5OQkuLm5CR06dBBWrFhhV+Lbeg4UtVgVtxy51fr164UWLVoIOp1O8Pb2FgYNGiTcunXL7vmnTp0Snn76acHT01PQ6/VCeHi4MHXqVEEQBOH7778XGjduLHz++ed27c9fjtyqOOdaSc5/lKAc+bJly4S6desKOp1OqF+/vvDtt9/aHXPv3r2CSqUSvvrqqwJf+37lyK3+/vtvoXv37oKbm5vg4uIiNG3a1OayBIIgCJcvXxYGDx4sBAYGChqNRggODhaeeuop4aeffirwmEWVI897Tri6ugotW7YUvvvuO5v9srOzhaCgIGHKlClFvgeiqkQhCPn6yYnoofDWW29h2bJliI6Otrt4pxxmzJiB3bt3Y/fu3XI3hcghJCcno1atWpgzZ06Z5gt27twZnTt3LnR+z7Vr11CzZk27YXNEhdmwYQMGDhyIy5cvIygoSO7mEFUYznEieghlZmZi9erVeOaZZxwiNBGRPQ8PD7zzzjuYO3duia9lRfQgffLJJxgzZgxDEz10OMeJ6CESGxuLHTt24KeffkJ8fHyBhQTkUqdOnWJd34ToYTJx4kRMnDixTMfo3r37fSufubq6YtCgQWV6DXq4HDhwQO4mEMmCQ/WIHiK7d+9Gly5d4O/vj6lTp2LMmDFyN4mIiIioUmBwIiIiIiIiKgLnOBERERERERWBwYmIiIiIiKgID11xCIvFgjt37sDNza3Qi4MSEREREVHVJwgCUlNTUa1aNSiV9+9TeuiC0507dxASEiJ3M4iIiIiIyEHcvHkT1atXv+8+D11wcnNzAyB+OO7u7jK3BjCbzfjzzz/Ro0cPaDQauZtDlQjPHSoLnj9UFjx/qCx4/lBpPYhzJyUlBSEhIVJGuJ+HLjhZh+e5u7s7THBydnaGu7s7/3hQifDcobLg+UNlwfOHyoLnD5XWgzx3ijOFh8UhiIiIiIiIisDgREREREREVAQGJyIiIiIioiI8dHOciIiIiB4EQRCQlZWF7OxsuZvi0MxmM9RqNTIzM/lZUYmU9tzRaDRQqVRlfn0GJyIiIqIyMplMuHv3LtLT0+VuisMTBAGBgYG4efMmr6lJJVLac0ehUKB69epwdXUt0+szOBERERGVgcViwdWrV6FSqVCtWjVotVoGgvuwWCxIS0uDq6trkRccJcqrNOeOIAiIi4vDrVu3ULdu3TL1PDE4EREREZWByWSCxWJBSEgInJ2d5W6Ow7NYLDCZTNDr9QxOVCKlPXf8/Pxw7do1mM3mMgUnnq1ERERE5YAhgMgxlVcPsOy/4YsWLUJYWBj0ej3atm2Lw4cP33f/BQsWIDw8HE5OTggJCcFbb72FzMzMCmotERERERE9jGQNTuvXr0dkZCSmT5+OY8eOoVmzZoiIiEBsbGyB+69duxaTJk3C9OnTcfbsWSxbtgzr16/Hu+++W8EtJyIiInq4mc1muZtAOfizqBiyBqd58+Zh5MiRGDZsGBo2bIilS5fC2dkZy5cvL3D//fv3o0OHDhg4cCDCwsLQo0cPvPjii0X2UhERERFR2URFRWHIkCGoV68evLy84O7ujuTkZLmb9VDavXs3nn32WdSuXRseHh4IDQ2FIAhyN6vKk604hMlkwtGjRzF58mRpnVKpRLdu3XDgwIECn9O+fXusXr0ahw8fRps2bXDlyhVs3boVL7/8cqGvYzQaYTQapccpKSkAxGTuCOnc2gZHaAtVLjx3qCx4/lBZ8PyxZTabIQgCLBYLLBaL3M0pkZs3b2LGjBn4448/cO/ePQQFBaFfv36YOnUqfHx8pP12796Nvn37YtSoUVi7di3c3d3h5OQENze3Er9n6xd862dGJbNmzRqMGjUK7777LqZMmQJnZ2e4ublBEIQqH55Ke+5YLBYIglBgcYiS/B1TCDJ9wnfu3EFwcDD279+Pdu3aSevfeecd/O9//8OhQ4cKfN7ChQvx9ttvSxeZe/3117FkyZJCX2fGjBmYOXOm3fq1a9ey8g0RERGVmVqtRmBgIEJCQqDVauVuTrFdu3YNPXr0QO3atTFlyhTUqFED586dw7Rp02A2m7F9+3Z4eXlBEAS0bt0aY8eOxeDBg+Vu9kMtLS0NjRs3xrfffosuXbrI3ZxKw2Qy4ebNm4iOjkZWVpbNtvT0dAwcOBDJyclwd3e/73EqVTny3bt346OPPsLixYvRtm1bXLp0CWPHjsWsWbMwderUAp8zefJkREZGSo9TUlIQEhKCHj16FPnhVATrH6bu3btDo9HI3RyqRHjuUFnw/KGy4PljKzMzEzdv3oSrqyv0ej0EQUCGOVuWtjhpVMWuIDZp0iTodDrs2LEDTk5OAIBGjRqhQ4cOqFu3Lj755BMsXrwYZ8+exc2bN3H79m00a9YMMTExaN68OebMmYOOHTtCEASEh4fjtddew/jx46XjR0VFoVWrVjh//jxu3bqFrl27Ij4+Hh4eHkhNTcXYsWORlJSEX3/9FQCwbds2fPTRRzh16hRUKhUeffRRLFiwALVr1wYgBr3atWvj6NGjaN68OW7fvo233noLe/bsQUZGBrp27YqFCxeievXqAICZM2di48aNOHbsGAAgKSkJPj4+2LlzJzp37gwAGDZsmE0bLl++jPHjx+PQoUMwGAxo0KABPvzwQ3Tr1k16X3fv3sWYMWOwZ88eJCQkSOvj4+Ph6elp9zlb223l5eWFxx9/HF9//bXUq6dSqfDzzz+jf//+ds9v2bIl+vXrh+nTp2Pfvn1wdnbGn3/+idGjRyMpKQnt27fH/Pnz0ahRI+k5P//8M2bMmIFLly4hKCgIY8aMsfk+XKtWLQwfPhxnzpzBpk2b4OnpicmTJ2PUqFHSPnnbJAgChg4diuPHj2PXrl3w8vIq1mdV3gRBQGpqKtzc3EpUKS8zMxNOTk547LHHoNfrbbZZR6MVh2zBydfXFyqVCjExMTbrY2JiEBgYWOBzpk6dipdffhkjRowAADRp0gQGgwGvvvoq3nvvvQLLgOp0Ouh0Orv1Go3Gof7YO1p7qPLguUNlwfOHyoLnjyg7OxsKhQJKpRJKpRLppiw0nrFdlraceT8Cztqir1OTkJCAP//8Ex9++CFcXFxstlWrVg2DBg3CDz/8gCVLliA+Ph5msxmrV6/GN998g5o1a+Lzzz9Hr169cPHiRQQFBWH48OFYsWIFJkyYIB1n5cqVeOyxx1CvXj3cuXMHgDgtI+8XXuvnBgAZGRmIjIxE06ZNkZaWhmnTpuGZZ55BVFSU9Nlaj5GdnY2nnnoKGo0GmzZtgkajwdixY/F///d/OHLkCBQKhfQ6eZ9nvbXet+5nfZyeno7evXvjo48+gk6nw6pVq9CvXz+cP38eNWrUAABMmDABFy9exLZt2xASEoL9+/fjmWeesTluXtZ1O3bsQKNGjXD16lU899xz+PTTT/HJJ5/Y7FdYSXtrG+Pj43H37l3873//w7p16+Dl5YVp06ahV69euHDhApycnHD06FG88MILmDFjBgYMGID9+/dj1KhR8PX1xdChQ6Vjfvrpp3j33Xfx/vvv448//sC4ceMQHh6O7t2727XpzTffxIEDB/D3339LYa84n1V5sw7Py/szKw7reVfQ36yS/A2TrTiEVqtFq1atsHPnTmmdxWLBzp07bYbu5ZWenm73IVnHKVb1MZ1ERERE5eXixYsQBAENGjQocHuDBg2QmJiIuLg46cvq3Llz0atXLzRo0ACLFy9GtWrVsGjRIgDA0KFDcf78ealgl9lsxtq1azF8+HAAkHq0MjIyCm3TM888g//7v/9DnTp10Lx5cyxfvhwnT57EmTNn7PbdsWMHTpw4gVWrVqFt27Zo2bIl1qxZg6ioKJvvliXVrFkzvPbaa2jcuDHq1q2LWbNmoXbt2vjtt9+kfaKiojBw4EA88sgjCAwMhLe3d7GO7ePjg8DAQNSsWRNOTk7w8PAocfusP4v//ve/eOyxx9CkSROsWrUKaWlpWLNmDQCx+FrXrl0xdepU1KtXD0OHDsWYMWMwd+5cm2N16NABkyZNQr169fDmm2/i2Wefxfz58+1ec8qUKfj111+xY8cOm86N4nxWVY2sQ/UiIyMxZMgQtG7dGm3atMGCBQtgMBgwbNgwAMDgwYMRHByM2bNnAwD69OmDefPmoUWLFtJQvalTp6JPnz5lugqwXC7GpOLc3WTcNsjdEiIiIiovThoVzrwfIdtrl0RJ/uO5Q4cO0n2lUon27dtLoaZatWro3bs3li9fjjZt2mDTpk0wGo147rnnAAB169aFVqvF999/j3HjxhV4/IsXL2LatGk4dOgQ7t27J4WEGzduoHHjxtJ+7du3R3Z2Njw9PdGwYUNpfY0aNRASEoIzZ86UerhYWloaZsyYgS1btuDu3bvIyspCRkYGbty4Ie1Ts2ZNbN26Fa+//nqxQ5O13UqlEgaDAV26dLEZ1ggAL774IlQqFdzc3NCyZUvMnTvX5v1ZqdVqtG3bVnrs5uaGZs2aST+Ls2fPol+/fjbP6dChAxYsWIDs7GzpO3P+jop27dphwYIFNuu+/PJL7Ny5E0888QTCwsJsthXns6pqZC1HPmDAAHz66aeYNm0amjdvjqioKGzbtg0BAQEAxF+Uu3fvSvtPmTIF48ePx5QpU9CwYUO88soriIiIwFdffSXXWyiT9Udu4s11x3H0nuzXISYiIqJyolAo4KxVy7IUd95HnTp1oFAocPbs2QK3nz17Fl5eXvDz84OXl9d936vViBEjsG7dOmRkZODbb7/FgAEDpEJc3t7emDdvHiZNmgQXFxdUr14da9eutTlWnz59kJCQgG+++QaHDh2SCoWZTCab/davX49Zs2YVq00l9fbbb+PXX3/FRx99hL179yIqKgpNmjSxacP8+fNhNBrh6+sLV1dX9OzZs1jHXr9+PaKiorB3714kJyfj7bffttk+f/58REVFYdOmTTCbzXj++eftjlHcn0V5OXz4MLZu3YpTp07Zfd8uzmdV1cj+jX3MmDG4fv06jEYjDh06ZJOgd+/ejRUrVkiP1Wo1pk+fjkuXLkmJdtGiRQVOxKsMXPVih1+mPPNHiYiI6CHl4+OD7t27Y/HixXbD56Kjo7FmzRoMGDAACoUCtWvXhlqtxr59+6R9LBYL9u/fb9Mj0qtXL7i4uGDJkiXYtm2bNEzPavTo0UhOTsaJEyewZ88e9OnTR9oWHx+P8+fPY8qUKejatas0VLAgISEh6NixI5KSkmyG8d28eRM3b94ssJemuPbt24ehQ4fi6aefRpMmTRAYGIhr167Z7GMd/hYWFoZDhw7hv//9b7GOHRISgjp16qBjx44YNmyYVJDCKjAwEHXq1JEqGJ45c8auVHb9+vWRlZVlU306NTUVx48fl953gwYNbH5W1vdVr149mxFaBw8etNnn4MGDdkM3FyxYgJ49e2Lx4sWYMGGCTW9ScT6rqkb24PQwc9UxOBEREZE8vvzySxiNRkRERGDPnj24efMmtm3bhu7duyM4OBgffvghAMDV1RUjR47EhAkTsHXrVpw9exajRo3CnTt37KqwDR06FJMnT0bdunULnLPu5OSE2rVro1atWnBzc5PWe3l5wcfHB19//TUuXbqEv/76y6YKXH7t27dH27ZtMXjwYBw+fBjHjh3DoEGD0Lx5czzxxBPSfoIgIDMzE5mZmdJ1PU0mk7QuOzsbFotFCih169bFL7/8gqioKBw/fhwDBw60u17QwYMH8e677+Knn35Co0aNEBwcXKzPOz4+HtHR0Thx4gS+//571K9f32a72WxGZmYmoqOjsXr1atSrV8+ucEF4eDh69uyJESNGYO/evTh58iQGDx4MV1dXDBw4EAAwfvx47Ny5E7NmzcKFCxewcuVKfPnll3Y9XPv27cOcOXNw4cIFLFq0CD/++CPGjh1rs491KOIzzzyDXr16SQXaivtZVTUMTjJyy+lxMjI4ERERUQWrW7cu/vnnH9SqVQvPP/88ateujVdffRVdunTBgQMHbObvfPrpp+jfvz+GDBmC5s2b4/jx4/jjjz8QFBRkc8xXXnkFJpNJmq9eXEqlEuvWrcPRo0fRuHFjvPXWW3bFDPL7+eefERISgq5du+Lxxx+Hr68vNmzYYDNk7cSJE3BycoKTk5NU2CAiIkJat3r1amzatAkjR44EIBZW8PLyQvv27dGnTx9ERESgZcuW0vHi4uLw3HPPYd68eTbri6Nbt24ICgrC448/Dm9vb7uequeffx5OTk6oV68e7t69i/Xr1xd4nFWrVqFFixbo06cP2rZti7S0NPzxxx9SAY6WLVvihx9+wLp169C4cWNMmzYN77//vk1FPUAMWP/88w9atGiBDz74APPmzUNEROFz87788kscP34cX3/9dbE+q6pItgvgyiUlJQUeHh7FusjVg7blxF2MXnsMddwF/D4hgiVdqUTMZjO2bt2KXr168dyhEuP5Q2XB88dWZmYmrl69ipo1a9pdI+Zhs3fvXnTt2hU3b96U5qznZ7FYkJKSAnd39xKVlH5QNmzYgA0bNthMD6nqwsLCMG7cuEILdTiq0p479/sdLUk2qFQXwK1qOMeJiIiIqgKj0Yi4uDjMmDEDzz33XKGhyRGpVCr+BwAVi/wx/yHmqhMn6GVmydwQIiIiojL4/vvvERoaiqSkJMyZM0fu5pRInz598M0338jdDKoE2OMkI1ed+L8b7HEiIiKiymzo0KF2c2jIcVX16ncPCnucZMShekRERERElQODk4ys5cizBAWMWVW7fCMRERERUWXG4CQja3ACAIORE52IiIiIiBwVg5OMVEoFnLVigYg0BiciIiIiIofF4CQza68TgxMRERERkeNicJKZtSQ5gxMRERERVTZms1nuJlQYBieZWXucDEaW1iMiIiIix5WVlYV58+ahQ4cOCA4Ohl6vx9SpU+VuVoXhdZxkxqF6REREJIehQ4di5cqV0mNvb2888sgjmDNnDpo2bSpjy8gRCYKAPn364Pbt25g5cyYaNWoEpVKJ4OBguZtWYRicZObC4EREREQyefLJJ/Htt98CAKKjozFlyhQ89dRTuHHjhswtI0ezevVqXLt2DUeOHIGrq6vczZEFh+rJjHOciIiIqhhBAEwGeRZBKFFTdTodAgMDERgYiObNm2PSpEm4efMm4uLipH0mTpyIevXqwdnZGbVq1cLUqVPt5rVcu3YNCoXCbklKSgIAzJgxA82bN5f2N5lMqFevns0+VmFhYXbH2bBhg7R927Zt6NixIzw9PeHj44OnnnoKly9ftmtLVFSU3XEXLFggPe7cuTPGjRsnPT5//jw0Go1NOy0WC95//31Ur14dOp0OzZs3x7Zt20r8WvnfQ0Gv/91336F169Zwc3NDYGAgBg4ciNjYWJvnbN68Gc2aNYOTk5P02fTv3x+FmTFjhrSfWq1GWFgYPvvsM2n7ihUr4OnpWeBzo6KioFAocO3aNem1GzZsiN69e8PNzQ0BAQF46623YDKZpOcYjUb85z//gb+/P/R6PTp27IgjR45I23fv3g2FQoEtW7agadOm0Ov1ePTRR3Hq1KlC23T9+nWEhIRgypQpJfqsHgT2OMlMGqqXyTlOREREVYI5Hfiomjyv/e4dQOtSqqempaVh9erVqFOnDnx8fKT1bm5uWLFiBapVq4aTJ09i5MiRcHNzwzvvvCPtI+QEth07dqBRo0bYv38/nnnmmUJf65tvvkFMTEyh299//32MHDkSABAUFGSzzWAwIDIyEk2bNkVaWhqmTZuGp59+GlFRUVAqS98nMGHCBOj1ept1n3/+OT777DN89dVXaNGiBZYvX46+ffvi9OnTqFu3bqlfqyBmsxmzZs1CeHg4YmNjERkZiaFDh2Lr1q0AgKSkJAwYMAAjRozAhg0b4OTkhLFjx8JoNN73uI0aNcKOHTuQlZWFH3/8EZGRkejVqxcaNGhQovbFxcVh165deOONN7B06VJcuXIFI0aMgFKplMLYO++8g59//hkrV65EaGgo5syZg4iICFy6dAne3t7SsSZMmIDPP/8cgYGBePfdd9GnTx9cuHABGo3G5jWjo6PRrVs39OvXDx988AEsFkuxPqsHhcFJZpzjRERERHLZvHmzNOzKYDAgKCgImzdvtgkgef+nPywsDG+//TbWrVtnE5ysPVDW3qu8X5LzS0hIwKeffop33nkH06ZNs9tuNBrh7e2NwMDAAp+fP5AtX74cfn5+OHPmDBo3blyMd21v165d2L9/P0aMGIFdu3ZJ6z/99FNMnDgRL7zwAgDgk08+wa5du7BgwQIsWrSoVK9VmOHDh0v3a9WqhYULF+KRRx5BWloaXF1dceHCBaSnp2PixImoVk0M5k5OTkUGJ7VaLX2WNWrUgEqlgotLycO1xWJBeHg4Fi1aBIVCgQYNGmDu3Ll45ZVXMGvWLAiCgCVLlmDFihXo2bMnADEgb9++HcuWLcOECROkY02fPh3du3cHAKxcuRLVq1fHr7/+iueff17aJzExET169EDbtm3xxRdf2H1W1nO0oM/qQWFwkhnnOBEREVUxGmex50eu1y6BLl26YMmSJQDEL6qLFy9Gz549cfjwYYSGhgIA1q9fj4ULF+Ly5ctIS0tDVlYW3N3dbY6TkpICAMX6Qj5r1ix06tQJHTp0KHB7QkKC3fHzunjxIqZNm4ZDhw7h3r17Ui/EjRs3ShWcBEHA+PHjMX36dMTHx0vrU1JScOfOHbt2dujQAcePH7dZ1759e5uwmZ6ebvc6L774IlQqlfQ4IyPDZljg0aNHMWPGDBw/fhyJiYk276thw4YICQmBWq3G999/j7feeqvYvWsnT56Eq6srsrKykJ2djYULF6JGjRrS9uTkZLi6ukKpVCIgIAD9+vXD7NmzCzxWu3btoFAopMcdO3aEyWTCpUuXAIgBOu/npdFo0KZNG5w9e9buOFbe3t4IDw+32ScrKwu9evXCyZMn8fbbb9u8pvWzev/99wv9rB4UBieZueoZnIiIiKoUhaLUw+UqmouLC+rUqSM9/u9//wsPDw988803+OCDD3DgwAEMGjQIM2fOREREBDw8PLBu3TqbeTIAcOfOHSiVykJ7iawuXryIZcuWYc+ePXZzmwDg1q1bMJlMqFmzZqHH6NOnD0JDQ/HNN9+gWrVqsFgsaNy4sc1cm5JYtWoVDAYDXn/9dXz44YelOsb69etthr517tzZbp/58+ejW7du0uNBgwZJ9w0GAyIiIhAREYE1a9bAz88PN27cQEREhPS+goKCsGTJEkycOBGTJ0+GVquF0WhE796979u28PBw/Pbbb8jOzsbBgwcxevRotGzZEo8++igAcSjmsWPHIAgCzpw5gyFDhiAwMNCmrQDg5eVV6GsoFAppuGZ5MBgMcHJywldffYVx48ahR48e0rllMBjQs2fP+35WDwqLQ8iMQ/WIiIjIUSgUCiiVSmRkZAAA9u/fj9DQULz33nto3bo16tati+vXr9s978iRI6hfv77dHKH8Jk6ciFdeeQW1atUqcPv//vc/ODk5oXXr1gVuj4+Px/nz5zFlyhR07doVDRo0QGJiYgnfZa709HS89957+OSTT+zm17i7u6NatWrYt2+fzfp9+/bZ9WqEhISgTp060qJW2/dNBAYG2uzj5OQkbTt37hzi4+Px8ccfo1OnTqhfv36BxQ6GDBmC+vXr49VXX0VUVBT69u1b5HvUarWoU6cOwsPDpedv3rxZ2q5UKlGnTh3UrVsX/fr1Q/fu3e2KXQBA/fr1ceDAAZuA9Pfff0Or1aJ27dqoXbs2tFqtzedlNptx5MgRu8/r4MGD0v3ExERcuHDBJng6Ozvjt99+w6uvvooOHTrgtddek7ZdvHixWJ/Vg8AeJ5mxqh4RERHJxWg0Ijo6GoD4BfbLL79EWloa+vTpAwCoW7cubty4gXXr1uGRRx7Bli1b8Ouvv0rPN5lMWL9+PebNm4eZM2fe97UuXbqEGzdu4MKFCwVuv3z5Mj7++GP069fPrjcqKSkJJpMJXl5e8PHxwddff42goCDcuHEDkyZNKvB4JpMJmZmZ0mNBEKThatYhc2vXrkWrVq0KrUw3YcIETJ8+HbVr10bz5s3x7bffIioqCmvWrLnvey2pGjVqQKvV4osvvsDrr7+OU6dOYdasWXb7jR8/HgqFAvPnz4dGo4Gbm1uBPXd5ZWVlITo6GhaLBYcPH8bp06dtqvkBQGZmptTj9Pfff2Ps2LF2x3njjTcwf/58jB49Gm+++SauXr2KCRMmYMyYMXB2dpb2mTBhAry9vVGjRg3MmTMH6enpeOWVV2yO9f7778PHxwcBAQF477334Ovra/Mz0Gg00lylr7/+Go0aNcLq1asxcOBAVK9evVif1YPA4CQzVtUjIiIiuWzbtk2qWufm5ob69evjxx9/lIaa9e3bF2+99RbGjBkjDQubOnUqZsyYAUCcPzNjxgxMnToVkZGR930tg8GAmTNnwtvbW5oTlVfXrl1x/fp1nDp1CuvWrbPZNmzYMISFhaFz585Yt24d/vOf/6Bx48YIDw/HwoULCxwa17ZtW7t1EyZMgK+vL4YOHQpA7HHKP+wwr//85z9ITk7G+PHjERsbi4YNG+K3334r94p6fn5+WLFiBd59910sXLgQLVu2xKeffmrTo/T999/jhx9+wLFjx+x6x+7n9OnTCAoKki5WO2HCBJthgsnJyVJ584CAADz99NOIjIzEmTNnbI5To0YNbN68GZMmTUKzZs3g5eWFQYMG2cyH+vjjj2GxWPDyyy8jNTUVrVu3xh9//GE3zO/jjz/G2LFjcfHiRTRv3hybNm2CVqstsP1BQUH4/PPPMXbsWDzxxBPw9fXF8uXLMWXKlEI/qwdFIZTngMRKICUlBR4eHkhOTr7vxMOKEnU9Hv2XHESAuw6H3u1W9BOIcpjNZmzduhW9evUq0R9QIoDnD5UNzx9bmZmZuHr1KmrWrFnkUDUSq7OlpKTA3d3dpsBBWFgYdu/ejbCwMLvn9O/fH+PGjSswIJXEuHHj0Lx5cyk4UcXavXs3unTpgsTExEKvH3U/hZ07Rbnf72hJsgHnOMmMxSGIiIiIxF6XvFXn8vLy8iq0R6IkNBpNoa9BVBQO1ZOZdaiewZgNi0WAUqko4hlEREREVc+RI0cK3fbtt9+Wy2vMnTu3XI5DDycGJ5m5anP/18NgyoKbnkMeiIiIiKjq6dy5c7mWLa9oHKonM61aCZVCPIE4XI+IiIiIyDExOMlMoVBAn9PplJbJ4ERERFRZVeb/SSeqysrrd5PByQFYg1Mqe5yIiIgqHWtlwfT0dJlbQkQFMZlMAFDmwiCc4+QA2ONERERUealUKnh6eiI2NhYA4OzsDIWCxZ4KY7FYpIvTlqSkNFFpzh2LxYK4uDg4OztDrS5b9GFwcgBScGKPExERUaUUGBgIAFJ4osIJgoCMjAzpoqtExVXac0epVKJGjRplPt8YnByATiUAULDHiYiIqJJSKBQICgqCv78/zGaz3M1xaGazGXv27MFjjz3GCyhTiZT23NFqteXSu8ng5AA4x4mIiKhqUKlUvMBqEVQqFbKysqDX6xmcqETkPnc4sNQB6HPiq4HBiYiIiIjIITE4OQDOcSIiIiIicmwMTg5ArxJry6dyjhMRERERkUNicHIAOvY4ERERERE5NAYnB5B7HSdW4SEiIiIickQMTg6Ac5yIiIiIiBwbg5MDkMqRc44TEREREZFDYnByANbiEOxxIiIiIiJyTAxODoBD9YiIiIiIHBuDkwPILQ6RBUEQ5G0MERERERHZYXByAHq1eJtlEWDMssjbGCIiIiIissPg5AC0SkChEO9zuB4RERERkeNhcHIASgXgohW7ndJYWY+IiIiIyOEwODkIF5040Yk9TkREREREjofByUG46sQeJ17LiYiIiIjI8TA4OQhrcGKPExERERGR42FwchC5wcksc0uIiIiIiCg/BicH4Wqd48ShekREREREDofByUG45lzMKZVD9YiIiIiIHA6Dk4OQhuqxx4mIiIiIyOEwODkIFocgIiIiInJcDE4Ogj1ORERERESOi8HJQbDHiYiIiIjIcTE4OQipqh6DExERERGRw2FwchAu7HEiIiIiInJYDE4OgnOciIiIiIgcF4OTg7AGJ17HiYiIiIjI8TA4OQhXfc4cJ/Y4ERERERE5HAYnB2HtccowZyMr2yJza4iIiIiIKC8GJwfholVL9w3GbBlbQkRERERE+TE4OQitWgmdWvxxpBrNMreGiIiIiIjyYnByIG56liQnIiIiInJEDE4OhCXJiYiIiIgcE4OTA3FljxMRERERkUNicHIg1gIRDE5ERERERI6FwcmBSHOcOFSPiIiIiMihOERwWrRoEcLCwqDX69G2bVscPny40H07d+4MhUJht/Tu3bsCW/xgSHOc2ONERERERORQZA9O69evR2RkJKZPn45jx46hWbNmiIiIQGxsbIH7//LLL7h79660nDp1CiqVCs8991wFt7z8Wec4pbLHiYiIiIjIoaiL3uXBmjdvHkaOHIlhw4YBAJYuXYotW7Zg+fLlmDRpkt3+3t7eNo/XrVsHZ2fnQoOT0WiE0WiUHqekpAAAzGYzzGb5r5dkbYPZbIazRsyxKRkmh2gbOba85w5RSfH8obLg+UNlwfOHSutBnDslOZZCEASh3F65hEwmE5ydnfHTTz+hf//+0vohQ4YgKSkJGzduLPIYTZo0Qbt27fD1118XuH3GjBmYOXOm3fq1a9fC2dm51G1/EP68pcCWmyo86m/Bi7UtcjeHiIiIiKhKS09Px8CBA5GcnAx3d/f77itrj9O9e/eQnZ2NgIAAm/UBAQE4d+5ckc8/fPgwTp06hWXLlhW6z+TJkxEZGSk9TklJQUhICHr06FHkh1MRzGYztm/fju7duyP+6F1suXkOXn5B6NWrmdxNIweX99zRaDRyN4cqGZ4/VBY8f6gseP5QaT2Ic8c6Gq04ZB+qVxbLli1DkyZN0KZNm0L30el00Ol0dus1Go1D/bJqNBp4OIvtNJgtDtU2cmyOdi5T5cLzh8qC5w+VBc8fKq3yPHdKchxZi0P4+vpCpVIhJibGZn1MTAwCAwPv+1yDwYB169bhlVdeeZBNrFDSBXAzOeaXiIiIiMiRyBqctFotWrVqhZ07d0rrLBYLdu7ciXbt2t33uT/++COMRiNeeumlB93MCuPGcuRERERERA5J9qF6kZGRGDJkCFq3bo02bdpgwYIFMBgMUpW9wYMHIzg4GLNnz7Z53rJly9C/f3/4+PjI0ewHwtrjZDBmy9wSIiIiIiLKS/bgNGDAAMTFxWHatGmIjo5G8+bNsW3bNqlgxI0bN6BU2naMnT9/Hn///Tf+/PNPOZr8wLjorNdx4lA9IiIiIiJHIntwAoAxY8ZgzJgxBW7bvXu33brw8HDIWEX9gck7VE8QBCgUCplbREREREREgMxznMiWdaieRQAyzByuR0RERETkKBicHIiTRgVlTidTWiYLRBAREREROQoGJweiUCjgap3nxMp6REREREQOg8HJwbjpxYtwsceJiIiIiMhxMDg5GFdey4mIiIiIyOEwODkYa4GIVPY4ERERERE5DAYnB8MeJyIiIiIix8Pg5GCsPU5pvAguEREREZHDYHByMK5aMTgZTLyOExERERGRo2BwcjCc40RERERE5HgYnBxM7hwnDtUjIiIiInIUDE4Oxk2a48QeJyIiIiIiR8Hg5GBYVY+IiIiIyPEwODkYznEiIiIiInI8DE4Ohj1ORERERESOh8HJwUhznBiciIiIiIgcBoOTg3HVaQCwOAQRERERkSNhcHIw0hwn9jgRERERETkMBicHY53jZMqywJRlkbk1REREREQEMDg5HBetSrpvYK8TEREREZFDYHByMGqVEk4aMTyxQAQRERERkWNgcHJAvJYTEREREZFjYXByQG68lhMRERERkUNhcHJArtK1nMwyt4SIiIiIiAAGJ4dkrazHoXpERERERI6BwckBuXKoHhERERGRQ2FwckDSUD32OBEREREROQQGJwfE4hBERERERI6FwckBsRw5EREREZFjYXByQK46DQD2OBEREREROQoGJwfkqlMBAAwMTkREREREDoHByQHlXseJwYmIiIiIyBEwODkg61A9znEiIiIiInIMDE4OiNdxIiIiIiJyLAxODsiN13EiIiIiInIoDE4OiD1ORERERESOhcHJAeUtDmGxCDK3hoiIiIiIGJwckLXHCQAMJvY6ERERERHJjcHJAenUSmhUCgAcrkdERERE5AgYnByQQqHInefEAhFERERERLJjcHJQLjnBKZU9TkREREREsmNwclDWHicDgxMRERERkewYnBwUr+VEREREROQ4GJwclCuH6hEREREROQwGJwflqtcAYI8TEREREZEjYHByUFJVPfY4ERERERHJjsHJQUlznBiciIiIiIhkx+DkoKQ5ThyqR0REREQkOwYnB8WhekREREREjoPByUG5SuXIzTK3hIiIiIiIGJwclBt7nIiIiIiIHAaDk4Ny4RwnIiIiIiKHweDkoKxD9QwmBiciIiIiIrkxODkoaagee5yIiIiIiGTH4OSgXPNcx0kQBJlbQ0RERET0cGNwclDWcuTmbAHGLIvMrSEiIiIiergxODkoF61aus/KekRERERE8mJwclBKpSL3Iric50REREREJCsGJwfmyms5ERERERE5BAYnB2YtEMFrORERERERyYvByYGxx4mIiIiIyDEwODkwN6kkuVnmlhARERERPdwYnByYtbIei0MQEREREcmLwcmBSXOcOFSPiIiIiEhWDE4OzDrHycDgREREREQkKwYnBybNceJQPSIiIiIiWTE4OTBrjxOH6hERERERyYvByYG5sseJiIiIiMghMDg5MF7HiYiIiIjIMcgenBYtWoSwsDDo9Xq0bdsWhw8fvu/+SUlJGD16NIKCgqDT6VCvXj1s3bq1glpbsXKv48TgREREREQkJ7WcL75+/XpERkZi6dKlaNu2LRYsWICIiAicP38e/v7+dvubTCZ0794d/v7++OmnnxAcHIzr16/D09Oz4htfAVx1GgAcqkdEREREJDdZg9O8efMwcuRIDBs2DACwdOlSbNmyBcuXL8ekSZPs9l++fDkSEhKwf/9+aDRiqAgLC6vIJlcoFocgIiIiInIMsgUnk8mEo0ePYvLkydI6pVKJbt264cCBAwU+57fffkO7du0wevRobNy4EX5+fhg4cCAmTpwIlUpV4HOMRiOMRqP0OCUlBQBgNpthNpvL8R2VjrUNBbVFpxIAAGmZjtFWciz3O3eIisLzh8qC5w+VBc8fKq0Hce6U5FiyBad79+4hOzsbAQEBNusDAgJw7ty5Ap9z5coV/PXXXxg0aBC2bt2KS5cuYdSoUTCbzZg+fXqBz5k9ezZmzpxpt/7PP/+Es7Nz2d9IOdm+fbvdujQzAKiRYbZg05atUCkqvFlUCRR07hAVF88fKgueP1QWPH+otMrz3ElPTy/2vrIO1Sspi8UCf39/fP3111CpVGjVqhVu376NuXPnFhqcJk+ejMjISOlxSkoKQkJC0KNHD7i7u1dU0wtlNpuxfft2dO/eXRp+aGXMsuC9f3YAAB57ojs8nDQFHYIeUvc7d4iKwvOHyoLnD5UFzx8qrQdx7lhHoxWHbMHJ19cXKpUKMTExNutjYmIQGBhY4HOCgoKg0WhshuU1aNAA0dHRMJlM0Gq1ds/R6XTQ6XR26zUajUP9shbUHo0G0KqVMGVZYLQoHKq95Dgc7VymyoXnD5UFzx8qC54/VFrlee6U5DiylSPXarVo1aoVdu7cKa2zWCzYuXMn2rVrV+BzOnTogEuXLsFisUjrLly4gKCgoAJDU1XgpuNFcImIiIiI5CbrdZwiIyPxzTffYOXKlTh79izeeOMNGAwGqcre4MGDbYpHvPHGG0hISMDYsWNx4cIFbNmyBR999BFGjx4t11t44FylazlxAiURERERkVxkneM0YMAAxMXFYdq0aYiOjkbz5s2xbds2qWDEjRs3oFTmZruQkBD88ccfeOutt9C0aVMEBwdj7NixmDhxolxv4YGTSpKzx4mIiIiISDayF4cYM2YMxowZU+C23bt3261r164dDh48+IBb5TiswSmN13IiIiIiIpKNrEP1qGhues5xIiIiIiKSG4OTg2OPExERERGR/BicHJy1OATnOBERERERyYfBycG5sMeJiIiIiEh2DE4OjtdxIiIiIiKSH4OTg+McJyIiIiIi+TE4OThXvQYAgxMRERERkZwYnBwce5yIiIiIiOTH4OTgeB0nIiIiIiL5MTg5OPY4ERERERHJj8HJweVex8ksc0uIiIiIiB5eDE4Ozi1Pj5MgCDK3hoiIiIjo4cTg5OCsPU4WAcgwZ8vcGiIiIiKihxODk4Nz0qigVIj3WSCCiIiIiEgeDE4OTqFQwCVnuF4qC0QQEREREcmCwakSkOY5sceJiIiIiEgWDE6VgHWeE0uSExERERHJg8GpEuC1nIiIiIiI5MXgVAm46jUAOFSPiIiIiEguDE6VgBt7nIiIiIiIZMXgVAlwqB4RERERkbwYnCoBa3GIVA7VIyIiIiKShbosT05PT8eNGzdgMpls1jdt2rRMjSJbuT1OZplbQkRERET0cCpVcIqLi8OwYcPw+++/F7g9Ozu7TI0iW256XseJiIiIiEhOpRqqN27cOCQlJeHQoUNwcnLCtm3bsHLlStStWxe//fZbebfxoefCOU5ERERERLIqVY/TX3/9hY0bN6J169ZQKpUIDQ1F9+7d4e7ujtmzZ6N3797l3c6HmnWoHuc4ERERERHJo1Q9TgaDAf7+/gAALy8vxMXFAQCaNGmCY8eOlV/rCEBucQj2OBERERERyaNUwSk8PBznz58HADRr1gxfffUVbt++jaVLlyIoKKhcG0i8jhMRERERkdxKNVRv7NixuHv3LgBg+vTpePLJJ7FmzRpotVqsWLGiPNtHyO1xMjA4ERERERHJolTB6aWXXpLut2rVCtevX8e5c+dQo0YN+Pr6llvjSMQ5TkRERERE8iqXC+A6OzujZcuW8PX1RWxsbHkckvJw02kAAMYsC0xZFplbQ0RERET08ClVcJo2bVqB69esWYNGjRqVqUFkz0Wnku5zuB4RERERUcUrVXBasWIFxo4dKz2OjY1F//79MXbsWMyfP7/cGkcitUoJJ40YnlgggoiIiIio4pVqjtPevXvRvXt3JCUloXv37hg7diw6duyIU6dOITAwsLzbSBALRGSYsznPiYiIiIhIBqXqcQoNDcWePXvw77//YsiQIfjkk0+wceNGhqYHiCXJiYiIiIjkU+riEIGBgdizZw/atm2L9evXIyMjozzbRfm4SMHJLHNLiIiIiIgePqUaqufl5QWFQgEAMJvNMBgM8Pf3h0YjVn9LSEgovxYSAJYkJyIiIiKSU6mC04IFC8q5GVQU60VwOVSPiIiIiKjilSo4DRkypLzbQUWQ5jixx4mIiIiIqMKVKjgBgNFoxJo1a3DmzBkoFAo0atQIL774InQ6XXm2j3JYe5x4HSciIiIioopXrOIQWVlZqF69OuLi4gAAZ86cQd26dfH222/j6NGj+OeffxAZGYl69erh3LlzD7TBDytpjhODExERERFRhStWcFKr1UhLS0NqaioAYOzYsWjdujVu3LiBXbt2YdeuXbh+/Tpatmxpc2FcKj/SHCcO1SMiIiIiqnDFHqrn6+uL9PR0AMD+/ftx+PBhuLq6Stvd3Nzw/vvvo127duXfSuJ1nIiIiIiIZFTs6zi1aNECv//+OwDA09MTSUlJdvskJydDq9WWW+MoF6vqERERERHJp9jBafTo0fjggw/w119/oX///njttddw6NAhCIIAQRBw8OBBvP766+jdu/eDbO9Dy1UnXiOL13EiIiIiIqp4xR6q17lzZyxevBjPPvssnJyccPfuXbRv3x5qtXiIrKwsPPnkk/j8888fWGMfZq4cqkdEREREJJsSlSMfNGgQ+vfvj7179yIuLg4WiwUA4OXlhfr166NevXoPpJGUJzixx4mIiIiIqMKV+DpOLi4uePLJJx9EW+g+OMeJiIiIiEg+pboA7sKFC++7/T//+U+pGkOFyztUz2IRoFQqZG4REREREdHDo1TBady4cXB2doa/vz8EQbDZplAoGJweADd97o/KYMqCm14jY2uIiIiIiB4uxa6ql9d7770HpVKJbt264eDBg7h69aq0XLlypbzbSAB0aiXUOb1MBmO2zK0hIiIiInq4lCo4zZo1C2fPnoXJZEJ4eDg+/PBDGI3G8m4b5aFQKPLMczLL3BoiIiIioodLqYITAAQHB2PFihX466+/sHPnTtSpUwerVq0qz7ZVfTePQPn3Z/BJO1es3a3znHgtJyIiIiKiilWqOU4nTpzIPYBajQULFmDjxo0YM2YMPv/8cxw9erTcGlilnVgH1ZH/ItAvoli781pORERERETyKFVwat68ORQKhVQYIu/9qKiocmtclVetJQDAK/1qsXa3FojgtZyIiIiIiCpWqYLT1avF+6JPRQgWg5NHxjUIlmwA96+UJw3VY48TEREREVGFKlVwCg0NLe92PJx860HQuEBtNsB87wIQ3PS+u7vo2ONERERERCSHUgWn33777b7b+/btW6rGPHSUKghBTaG4cQCKu/8WGZykoXrscSIiIiIiqlClCk79+/cvdJtSqURWFr/YF5cQ1AK4cQCKO/8CGHLffVkcgoiIiIhIHqUKThaLpcD1mZmZcHZ2LlODHjZCtRYAIPY4FcFVJ86BYjlyIiIiIqKKVerrOBVEoVBAoVCU5yGrPCEoJzjFnAay7n8RYVcO1SMiIiIikkW5BicqBc9QmFQuUFjMQMyp++7qljNUz8DgRERERERUoUo1VK9mzZoF9ixZr+VEJaBQIMm5JvxTTwF3/gWCWxW6qyuv40REREREJItSBadx48YVuN5sNmPixIllac9DKdG5lhicbv8LPFL4fryOExERERGRPEoVnMaOHVvg+szMTAanUkhyrineuXPsvvvlznEyP+gmERERERFRHuVeHIJKLsmllngn7hxgMhS6nxsvgEtEREREJItS9TgtXLiwwPW8flPpZGq8ILgGQpEWDdw9DoS2L3C/vFX1BEFgUCUiIiIiqiClCk7z588vdFuNGjVK3ZiHmVCtBRQXfgduHys0OLnk9DiZswUYsyzQa1QV2UQiIiIioodWqYLT1atXAQBxcXFQKBTw9fUt10Y9jISgFsCF3+87z8lFm/vjSjNmMTgREREREVWQEs9xSkpKwujRo+Hr64vAwEAEBATA19cXY8aMQXJycqkasWjRIoSFhUGv16Nt27Y4fPhwofuuWLFCutCuddHr9aV6XUciBDUX79z5t9B9VEoFXLRiWOI8JyIiIiKiilOiHqeEhAS0a9cOt2/fxqBBg9CgQQMAwJkzZ7BixQrs3LkT+/fvh5eXV7GPuX79ekRGRmLp0qVo27YtFixYgIiICJw/fx7+/v4FPsfd3R3nz5+XHleFuT5ScEq4AmQkAk4Ff4auejUMpmyksSQ5EREREVGFKVFwev/996HVanH58mUEBATYbevRowfef//9+86Bym/evHkYOXIkhg0bBgBYunQptmzZguXLl2PSpEkFPkehUCAwMLAkTXd8zt6AVxiQeE3sdar9RIG7uerUiIERqexxIiIiIiKqMCUKThs2bMBXX31lF5oAIDAwEHPmzMHrr79e7OBkMplw9OhRTJ48WVqnVCrRrVs3HDhwoNDnpaWlITQ0FBaLBS1btsRHH32ERo0aFbiv0WiE0WiUHqekpAAQL9ZrNst/PSRrG8xmM1RBzaFMvIbsm//AUqNTgfu76MShesnpmQ7RfpJP3nOHqKR4/lBZ8PyhsuD5Q6X1IM6dkhyrRMHp7t27hQYUAGjcuDGio6OLfbx79+4hOzvbLogFBATg3LlzBT4nPDwcy5cvR9OmTZGcnIxPP/0U7du3x+nTp1G9enW7/WfPno2ZM2farf/zzz/h7Oxc7LY+aNu3b0ftJCc0BhD77zYcTg4vcL/MVCUAJfYdOgrjFaFC20iOafv27XI3gSoxnj9UFjx/qCx4/lBplee5k56eXux9SxScfH19ce3atQIDCiBW2/P29i7JIUusXbt2aNeunfS4ffv2aNCgAb766ivMmjXLbv/JkycjMjJSepySkoKQkBD06NED7u7uD7StxWE2m7F9+3Z0794d2ruewHffI9ByB7169Spw/y3JUbiQHIvaDRqjV5uQim0sOZS8545Go5G7OVTJ8PyhsuD5Q2XB84dK60GcO9bRaMVRouAUERGB9957D9u3b4dWq7XZZjQaMXXqVDz55JPFPp6vry9UKhViYmJs1sfExBR7DpNGo0GLFi1w6dKlArfrdDrodLoCn+dIv6wajQbq6q0AKKBIvQtNZjzgZv8ZuDuJn3uGWXCo9pN8HO1cpsqF5w+VBc8fKgueP1Ra5XnulOQ4JSpH/v777+P8+fOoW7cu5syZg99++w0bN27Exx9/jLp16+Ls2bMFDosrjFarRatWrbBz505pncViwc6dO216le4nOzsbJ0+eRFBQUEneimPSuQJ+OUP0bhd8PSfXnIvgphk5LpiIiIiIqKKUqMepevXqOHDgAEaNGoXJkydDEMQ5NgqFAt27d8eXX36JkJCSDR+LjIzEkCFD0Lp1a7Rp0wYLFiyAwWCQquwNHjwYwcHBmD17NgAxvD366KOoU6cOkpKSMHfuXFy/fh0jRowo0es6rGotgbhzYmW9+vbD9dz0OcGJVfWIiIiIiCpMiYITANSsWRO///47EhMTcfHiRQBAnTp1Sj23acCAAYiLi8O0adMQHR2N5s2bY9u2bVLBiBs3bkCpzO0YS0xMxMiRIxEdHQ0vLy+0atUK+/fvR8OGDUv1+g4nuCVwfC1wp+AeJ5ecHqdUXseJiIiIiKjClDg4WXl5eaFNmzbl0ogxY8ZgzJgxBW7bvXu3zeP58+eX6DpRlU61luLt7WOAIAD5Lu4rDdVjjxMRERERUYUp0RwnqgCBjQGlBshIAJKu222Whuqxx4mIiIiIqMIwODkatQ4IyLlWVgEFInKLQzA4ERERERFVFAYnRxScM1yvgHlOHKpHRERERFTxGJwckTTP6V+7Ta4cqkdEREREVOEYnBxRtRbi7d0owGKx2eSmEy/SxeBERERERFRxGJwckV99QO0EmNKA+Is2m6w9TummbGRbBDlaR0RERET00GFwckQqNRDUTLyfr0CEi04l3WevExERERFRxWBwclSFFIjQqVXQqsUfG4MTEREREVHFYHByVHkvhJsPK+sREREREVUsBidHZe1xij4JZJlsNuVey8lc0a0iIiIiInooMTg5Ku9agN4DyDYCsWdsNlmDUyp7nIiIiIiIKgSDk6NSKHLLkueb58RrORERERERVSwGJ0cmBSfbC+G6cY4TEREREVGFYnByZFKBCNvgxB4nIiIiIqKKxeDkyKwFImLPAKZ0aXVucQgGJyIiIiKiisDg5MjcgwEXf0DIFqvr5ZB6nDhUj4iIiIioQjA4OTKFosAL4bqxx4mIiIiIqEIxODm6Ai6EK5UjZ3AiIiIiIqoQDE6OroAeJ1e9BgCH6hERERERVRQGJ0dnLUkefwnITAYAuOpUADhUj4iIiIioojA4OToXX8Cjhnj/ThQAwFXHHiciIiIioorE4FQZBFsvhCsO1+N1nIiIiIiIKhaDU2WQr0CEj4sWABCXamR4IiIiIiKqAAxOlYFUIOJfAEB1LyeE+TjDlG3BrnOxMjaMiIiIiOjhwOBUGQQ1B6AAkm8CaXFQKBR4snEQAGDbqWhZm0ZERERE9DBgcKoM9O6Ab13xfs48p15NAgEAu87HItOcLVfLiIiIiIgeCgxOlUU12+F6TYI9EOzphHRTNv53IU7GhhERERERVX0MTpWF9XpOOQUixOF6Yq/T7yfvytUqIiIiIqKHAoNTZSEViDgGCAIAoGdOcNp5NhbGLA7XIyIiIiJ6UBicKovAJoBSDRjigORbAICWNbzg76ZDqjEL+y/Fy9xAIiIiIqKqi8GpstA4Af4NxPs5BSKUSgUiGuUM1zvF4XpERERERA8Kg1Nlku9CuEDucL3tZ2KQlW2Ro1VERERERFUeg1NlkneeU442Nb3h5axBYroZh64myNQwIiIiIqKqjcGpMpFKkkcBFrF3Sa1ScrgeEREREdEDxuBUmfg3ANR6wJgCJFyRVlvLkm87FYNsiyBX64iIiIiIqiwGp8pEpRGr6wE2w/Xa1/aFm16Ne2lGHL2eKFPjiIiIiIiqLganyqaAAhFatRLdGwQA4HA9IiIiIqIHgcGpsimgQASQO1zvj1PREAQO1yMiIiIiKk8MTpWNtcfp7gkgO0ta/Vg9PzhrVbiTnInjt5JlahwRERERUdXE4FTZ+NQBdO5AVgYQd1Zardeo0KW+PwAO1yMiIiIiKm8MTpWNUgkENRPv37YdrtdTqq7H4XpEREREROWJwakykuY5/Wuzuku4P3RqJa7Hp+PM3RQZGkZEREREVDUxOFVGUmW9ozarXXRqPF7PD4DY60REREREROWDwakyCmkDKJRA9AmxSEQePZuIw/V+Z3AiIiIiIio3DE6VkXs1oNHT4v29n9lseqJ+ADQqBS7FpuFSbKoMjSMiIiIiqnoYnCqrTuPF2zMbgbjz0moPJw061PEFAPx+kr1ORERERETlgcGpsgpoBNR/CoAA7J1ns8laXY/D9YiIiIiIygeDU2Vm7XU6+SOQcEVa3b1hIFRKBc7cTcGN+HSZGkdEREREVHUwOFVmwS2BOt0AIRv4e4G02ttFi0dreQPgxXCJiIiIiMoDg1Nl99gE8TZqLZB8S1r9ZOMgAMBWDtcjIiIiIiozBqfKrsajQFgnwGIG9i2UVkc0CoBCARy/mYQ7SRkyNpCIiIiIqPJjcKoKHntbvD22EkiNAQD4u+nROtQLAC+GS0RERERUVgxOVUHNx4HqjwBZmcCBL6XV1uF6DE5ERERERGXD4FQVKBS5c52OLAPSEwAAT+aUJT9yPQGxqZlytY6IiIiIqNJjcKoq6vYAApsAZgNwcAkAINjTCc2qe0AQgD9Px8jcQCIiIiKiyovBqarI2+t06CsgMxkAh+sREREREZUHBqeqpH4fwDccMCYDh78BAPTMGa534Eo8Eg0mOVtHRERERFRpMThVJUplboW9A4sAkwFhvi5oEOSObIuA7Wc4XI+IiIiIqDQYnKqaRv8HeNcCMhKAf74FkNvr9Pupu3K2jIiIiIio0mJwqmpUaqBjpHh//0LAnCkFp78v3UNKplnGxhERERERVU4MTlVR0wGARwiQFgP8+x3qBrihtp8LzNkC/jobK3friIiIiIgqHQanqkitBTqMFe/v+xzIMqFnTnU9DtcjIiIiIio5BqeqqsXLgGsAkHwTOLFeuhju7vNxMBizZG4cEREREVHlwuBUVWn0QPv/iPf/nodGgc4I8XaCMcuC3efj5G0bEREREVElw+BUlbUeBjh5AwlXoDi9Ab04XI+IiIiIqFQYnKoyrQvQbrR4f++neLKRPwBg17lYZJqzZWwYEREREVHlwuBU1bUZCeg8gLhzaG74G0EeehhM2fjp6C25W0ZEREREVGkwOFV1eg+g7WsAAMWeTzGiY00AwCe/n8Pd5Aw5W0ZEREREVGkwOD0MHn0D0LgA0Scw1P8imoV4ItWYhSm/noIgCHK3joiIiIjI4TE4PQycvYFHXgEAqPbOxZz/awKNSoGd52Kx6QQLRRARERERFcUhgtOiRYsQFhYGvV6Ptm3b4vDhw8V63rp166BQKNC/f/8H28CqoN0YQK0Hbh1BeMa/GN2lDgBgxm+nkWAwydw4IiIiIiLHJntwWr9+PSIjIzF9+nQcO3YMzZo1Q0REBGJjY+/7vGvXruHtt99Gp06dKqillZxbANByiHh/z1yM6lwH4QFuSDCY8P6m0/K2jYiIiIjIwanlbsC8efMwcuRIDBs2DACwdOlSbNmyBcuXL8ekSZMKfE52djYGDRqEmTNnYu/evUhKSir0+EajEUajUXqckpICADCbzTCbzeX3RkrJ2oYKaUvbUVD/sxyKa3uhPPsbPuzfEc9/fQgbou6gV+MAdAn3e/BtoHJToecOVTk8f6gseP5QWfD8odJ6EOdOSY6lEGSsDmAymeDs7IyffvrJZrjdkCFDkJSUhI0bNxb4vOnTp+PEiRP49ddfMXToUCQlJWHDhg0F7jtjxgzMnDnTbv3atWvh7OxcHm+jUml0+3vUif0dWUot/q47BStja2HXXSU8tQImN8uGXvYoTURERERUMdLT0zFw4EAkJyfD3d39vvvK+jX53r17yM7ORkBAgM36gIAAnDt3rsDn/P3331i2bBmioqKK9RqTJ09GZGSk9DglJQUhISHo0aNHkR9ORTCbzdi+fTu6d+8OjUbz4F/Q0gOW9S9CfWUXHr+zBK0G/Y5eK6/iZmIGjivCMLNXwwffBioXFX7uUJXC84fKgucPlQXPHyqtB3HuWEejFUel6l9ITU3Fyy+/jG+++Qa+vr7Feo5Op4NOp7Nbr9FoHOqXteLaowGeXwks6wFF3Dm4bRiMuf3W4IUVJ7H28C30a14dbWv5VEA7qLw42rlMlQvPHyoLnj9UFjx/qLTK89wpyXFkLQ7h6+sLlUqFmJgYm/UxMTEIDAy02//y5cu4du0a+vTpA7VaDbVajVWrVuG3336DWq3G5cuXK6rplZveAxi4HnD2BaJP4NGoyRj4SDAAYNIvJ5Fpzpa5gUREREREjkXW4KTVatGqVSvs3LlTWmexWLBz5060a9fObv/69evj5MmTiIqKkpa+ffuiS5cuiIqKQkhISEU2v3LzCgNeWAuodMC5zZju9CMC3HW4es+ABTsuyt06IiIiIiKHIns58sjISHzzzTdYuXIlzp49izfeeAMGg0Gqsjd48GBMnjwZAKDX69G4cWObxdPTE25ubmjcuDG0Wq2cb6XyqdEW6L8YAKA7/CW+bXoWAPDN3is4eStZzpYRERERETkU2YPTgAED8Omnn2LatGlo3rw5oqKisG3bNqlgxI0bN3D37l2ZW1mFNXkW6CwG04bHZiCyzh1kWwS88/MJmLMtMjeOiIiIiMgxOERxiDFjxmDMmDEFbtu9e/d9n7tixYryb9DD5vGJQPwl4OSPGBM3C385zUTUXeDrPVcwuksduVtHRERERCQ72XucyAEoFEDfL4GQtlAak7Ha+TN4IQWf77iIS7FpcreOiIiIiEh2DE4k0ujFYhGeoXA13MQ6jy+BbCMm/nwCFots10gmIiIiInIIDE6Uy8UXGPgDoHNHuPEU5ur+i6PXE/Ddwetyt4yIiIiISFYMTmTLv754gVyFCv0UezFatRGfbDuHW4npcreMiIiIiEg2DE5kr/YTQO9PAQATND+gS9Y+vPvrKQgCh+wRERER0cOJwYkK1no48OhoAMBnmiVIuXgAvxy7LXOjiIiIiIjkweBEhesxC6jXE3qFGd9oP8V/N+1GXKpR7lYREREREVU4BicqnFIFPPNfCAFN4KdIwXzLx3h79V4YjFlyt4yIiIiIqEIxONH96VyhGLgeZmd/1FfexDt3IzHp61+QkmmWu2VERERERBWGwYmK5hEMzUs/IEvvjUbK6/j43hh8++UHSDaY5G4ZEREREVGFYHCi4qnWAupR+5FWrT1cFEaMTVuAY58/i4SEeLlbRkRERET0wDE4UfG5B8F1xGbEPfIOsqBEF9P/kPFleyRePCR3y4iIiIiIHigGJyoZpQp+vd9D9NO/4C58EWyJhuuaXkj5az5gscjdOiIiIiKiB4LBiUqlerMuMI/Yg13KR6FBFtz3zEDmyv8D0uLkbhoRERERUbljcKJSq1E9GHVG/4K5mteRKWigv74L2YvbA5d3yd00IiIiIqJyxeBEZRLi44KXxszAG86f4bylOlTpsRC+exrYMQPIZslyIiIiIqoaGJyozII8nPDJGwMQ6TEfa7K6QgEB+Hs+sPxJIPGa3M0jIiIiIiozBicqF/7ueqx87XF85zsOb5jGIgUuwO1/gKWdgFM/y908IiIiIqIyYXCicuPrqsP3Ix/FzaDu6Jn5EaIQDhhTgJ+GAxtGAxmJcjeRiIiIiKhUGJyoXHm5aLFmxKPwC6mLZzKn4Gv8HwQogKjVwBetgePrAUGQu5lERERERCXC4ETlzsNJg+9eaYOWYb74KPNZDLZMR4ZHHSD9HvDrq8DKPkDcBbmbSURERERUbAxO9EC46TVYObwN2tXywV5TPbS+NwOHa78JQe0EXNsLLGkP/PUBYM6Qu6lEREREREVicKIHxlmrxrfDHkGPhgEwZCvx/Ol2GOu9FJlhXQGLGdgzF1j8KHBxh9xNJSIiIiK6LwYneqD0GhW+erkVZv9fEzhrVfjthgaPXH0Vhx5ZAMGtmliufM0zwA9DgJQ7cjeXiIiIiKhADE70wCkUCrzYpga2/qcTWtbwRKoxGwP2+mO8/zfIbP0GoFABZzYAX7YBDi4FsrPkbjIRERERkQ0GJ6owYb4u+OG1dni7Rz2olQr8cjoZjx3vhn8ifgGCWwOmVGDbROC/TwC3j8rdXCIiIiIiCYMTVSi1SokxT9TFr6M6oI6/K2JTjXh2gwHTfOfD9OQ8QO8B3D0OfNMV2DIeyEiSu8lERERERAxOJI8m1T2w+c2OGNYhDACw6tBNPPl3bZz6v7+Api8AEIAj/wW+fAQ4uAQwpcvaXiIiIiJ6uDE4kWz0GhWm92mE1a+0RaC7HlfuGdBvxQUscB+PrJd/A3zqAoZYYNskYEETYO88IDNF7mYTERER0UOIwYlk17GuL/4Y9xj6NquGbIuABTsu4pltalx57g/gqQWAZ6h48dydM4EFjYG/PgTSE+RuNhERERE9RBicyCF4OGuw8MUW+PyF5nDXq3H8ZhJ6LTqM78xdYBlzFHj6K8C3HpCZDOyZA8xvDPw5BUiNlrvpRERERPQQYHAih9KveTD+eOsxdKzji0yzBVM3nkavLw9gu6YLhFEHgedWAoFNALMB2P8FsKCpWEQi6YbcTSciIiKiKozBiRxOkIcTVg1vgxl9GsJNp8a56FSMXPUP+i8+gD2aDhBe3QMM/BEIaQtkG8UiEgtbABtGAfcuyd18IiIiIqqCGJzIISmVCgztUBN7J3bBqM614aRR4fitZAxefhgDvj6EQ+pWwPA/gCGbgZqPA5YsIGoN8GVr4MdhQPQpud8CEREREVUhDE7k0DydtXjnyfrYO7ELXulYE1q1EoevJWDA1wfx0rLDOKZqDAz5DRixE6jXE4AAnP4FWNoBWNkHOPadOC+KiIiIiKgMGJyoUvB11WHqUw2xZ0IXvPxoKDQqBf6+dA//t3g/XllxBKcUdYGB64DX/wYaPQ1AAVzdA/w2BphbF1j/EnBmI2DOlPutEBEREVElpJa7AUQlEeihx6z+jfHqY7XwxV8X8fOx29h5LhY7z8WiZ+NAvNW9Huo9twLodh049RNw4kcg7ixwdpO46NyBBn2BJs8CNR8DlCq53xIRERERVQLscaJKKcTbGXOebYbtbz2Gfs2rQaEAfj8VjYgFezBu3b+4mu0LdBoPjDoAvL4P6DAWcK8OGFOAqNXAd/2BeQ2AbZOB20cBQZD7LRERERGRA2OPE1Vqtfxc8fkLLTCqcx3M334B205HY0PUHWw6cRd9mgZhSPswtKjRGAhsDHSdAdw8CJz4ATizAUiLAQ4uFhfv2kCT58TFt47cb4uIiIiIHAyDE1UJ4YFuWPpyK5y6nYx52y/gr3Ox2BB1Bxui7qBZdQ8MbheG3k2DoA9tD4S2B3rOAS7vBE7+CJzbCiRcBv73sbgENgVqPwHU7gKEPApo9HK/PSIiIiKSGYMTVSmNgz2wfOgjOHErCSv2X8Pm43dx/FYyxv94HB9tPYsX2oRgUNtQVPN0AsJ7iosxDTi3RQxRl/8Cok+Iy74FgFoP1GgH1OosBqmAJoCSI1yJiIiIHjYMTlQlNa3uiXnPN8d7vRpg3ZGbWH3wOu4mZ2LRrstY+r8r6NEwAEPah6FtTW8odK5AswHiYrgHXNoBXNkNXN4FpEUDV3aJy47pgLOPeN0oa5DyrCH3WyUiIiKiCsDgRFWaj6sOo7vUwWuP1cL2MzFYeeAaDl5JwO+novH7qWiEB7hhcPtQPN0iGM5aNeDiCzR7QVwEAYg7nxOcdgPX/gbS48XrRJ3+RXwB71pArS5ikKrZCXDykvPtEhEREdEDwuBEDwW1SomeTYLQs0kQzkenYuWBa/j12G2cj0nFe7+ewse/n8PzrUMwuF0oQn1cxCcpFIB/fXF59A0gyyRW4LMGqVv/AAlXxOWfZYBCCVRrkRukQtoAap2cb5uIiIiIygmDEz10wgPd8NHTTTDxyfr48Z+b+O7gdVyPT8eyv69i+b6r6FTXD32bVUP3hgHwcNLkPlGtBULbiUuXd4HMZLEXyjqsL/6iGKxuHwX2fgponMVCFNYgFdBIDGNEREREVOkwONFDy8NJgxGdamF4h5r434U4rDxwDbvPx2HPBXHRqBToVNcPvZoE2YcoANB7APV7iwsAJN8SQ5R1McSJ86Uu7RC3u/gDtR7PDVIewRX3ZomIiIioTBic6KGnVCrQpb4/utT3x9V7BmyMuo2tJ+/iQkwa/joXi7/OxRYdogDAozrQ4iVxsViA2DM5IWoXcH0/YIgVK/ed/FHc37eeGKBqdQHCOohBjIiIiIgcEoMTUR41fV0wrls9jOtWDxdjUrHl5N3ShSilUrzobmBjoP0YIMsI3Dyc2xt15xhw74K4HP4agAIIaCwOA6zxKFCjPeAeVMHvnoiIiIgKw+BEVIi6AW4YF+BW9hAFiEUianYSl65TgYxEcX7U5ZxCEwmXgZiT4nL4a/E5nqHiHClrkPKtyzlSRERERDJhcCIqhuKGqDY1vdG5nj+61PdDbT9XKAoLOk5eQIM+4gIAqdHAjYPAjQPiEn0SSLouLse/F/dx9hEvxlvjUfHWt2HFvHkiIiIiYnAiKqn7hah9l+Kx71I8Ptx6FtW9nNA53A9dwv3RrraPeJ2owrgFAo36iwsAZKYAt47kBKmD4v30eODcZnEBoFY7oYM+FMo//wYCGwH+jcTS6Tq3B/4ZEBERET1sGJyIyiBviLocl4bd5+Ow+3wsDl1JwK3EDKw+eAOrD96AVq1E25re6BLuj87hfqjp61J4bxQA6N2BOl3FBRCvIXX3OHBjP3D9AHDzIBQZifBNOwccOWf7XM8aYogKaAj45yw+dcRy6kRERERUKgxOROWktp8ravu54pWONZFuysKBy/HYdT4Wu87F4XZSBvZevIe9F+/h/c1AqI8zOtfzQ+f6/mhXywd6jer+B1drgZBHxKXDWMBigTn6NE5tW4GmQRqo4s4BsWeBtGgg6Ya4XPg99/lKjThHyr+BGKQCGgHetcWQpdE/2A+GiIiIqApgcCJ6AJy1anRtEICuDQIgCILUG7XrfCwOX03A9fh0rDxwHSsPXIdOrUSbmt5oW9MbbWv5oGl1D+jURQQppRLwq48bPo+hcbdeUGlyilKkJ4hl0GPOiLexZ8RAZUzJfYyf8xxIAbhXA7zCCl5c/FiQgoiIiAgMTkQPnEKhQB1/N9Txd8OITrWQZszC/kv3sPtCHHafi8Wd5EypNwoAdGolWtTwRNuaPmhb0xstanjBSVtEkLJy9gbCOoqLlSCIF+e1BqeYnDCVeBUwpQEpt8Xl+j7742lc7MOUb12gWnOxwAURERHRQ4LBiaiCuerU6NEoED0aBUIQBFyMTcPBK/E4dCUBh67G416aCQevJODglQQAgEalQLPqnmhbyxttavqgVagXXHUl+NVVKADPEHGpF5G7XhDEHqrEq0DitTy318Xb5FuA2QDEnhaX/LxqAsEtgWotgGotgaBmgM61LB8NERERkcNicCKSkUKhQL0AN9QLcMPgdmEQBAFX7hmkEHXoSgKiUzLxz/VE/HM9EYt2XYZKqUDjau5oHeoJZYICbQ0mBHoWcv2o+7844OIjLtVb22/PMorhKeFqnlB1DYg5nfM4ZzllHfqnAPzCxRBVrYUYqgIacw4VERERVQkMTkQORKFQSEUmBratAUEQcDMhAwdzQtTha/G4mZCB47eScfxWMgAVvvl4N2p4O6NFDU80DxGXhtXci54nVRS1DvCpLS75pScAd6OA28eAO/+KS8ptIO6cuBxfK+6nVIvFKIJzeqTcqonzplx8xVutc9naSERERFRBGJyIHJhCoUANH2fU8HHG861DAAB3kjJw+GoCDlyOw+7TtxCTocCNhHTcSEjHxqg7AACtSomG1dylMNWyhheqezndvwR6STh7A7WfEBer1BjgTk6Qun1MvJ8eD0SfEJeCaFwAV7+cMJUnUNk89gdcA8TXZKEKIiIikgmDE1ElU83TCf1bBKN3Y39s1VxHxy7dcSbGgKgbSfj3ZhKibiYhwWBCVM59Kx8XrRSkWtTwQuNqHvBwLsUQv8K4BQDhPcUFyClKcTO3Vyr2DGCIAwz3gLRYINsozqFKNIhDAIui0gHuQWKvlXuQWA3Qet+tWs7jQEBVju+JiIiIKAeDE1El5+6kQae6fuhU1w8AIAgCbiSkI+pmEv7NCVNn7iQj3mDCjrOx2HE2VnpuNQ89GgS5o36QGxoEuaNBkDvCfFygUpZDz45CIV4nyrMG0Ki/7TZBAIypuUHKEJfnfqzt+rRYICNBDFrWeVaFv6jYU5U3THkEAx4hgHuweN+tGi8GTERERCXG4ERUxSgUCoT6uCDUxwX9mgcDADLN2ThzNwX/3kjK6YlKxM2EDNxJzsSd5EzsPJcbppw0KtQLdEPDPGGqfqAb3PTl2JOjUAB6d3EpaA5VfllGIPUukHIXSL0DpNzJcz/PrcWcE7xigbvHC3txceifR3BOmArJc7+6uLj4i9fKIiIiIsrB4ET0ENBrVGhZwwsta+Reeykl04xzd1Nx9m6KtJyPSUWGORvHbybheJ5hfgAQ4u2EBoHuqJ8TpOoFuCHMxxlqVQUEDLUu9zpShbFYxDlVecNUcs41qpJv5dzeFnuu0qLF5fbRgo+l1Ii9VZ41csJUSG6o8qwhhiwWtiAiInqoMDgRPaTc9Rq0qemNNjW9pXXZFgHX4g15wpQYrO4mZ+JmQgZuJmTgzzMx0v5atRJ1/FzFIBXohvBAN4QHuCHIQ19+hSiKS6kUC024+okV/AoiCOIQwJRbYphKvp1zP0+4Ss3puUq6Li6FcfbNCVIheYJVCKD3AJQqQKECFMqc+4p8j5V5HivFW7WTOMyQPV1EREQOicGJiCQqZW459KeaVpPWJxpMOBstBqnz0Sk4H52KCzFpyMgZAnjmborNcdz0aoQH5ASpnDAVHugGT2eZ5xYpFLnhqlqLgvfJzhLDU7I1XN3IvZ90Uyx4YUoD0u+Jy92o8muf2gnwrgX41AK8c0rBe9cW17kFsqogERGRjBiciKhIXi5atK/ti/a1faV1FouAW4kZOBedggsxqTgXnYoLMam4EmdAamaWdNHevHxctKjl54Javq6o7S/e1vJzQQ3vChryVxwqtdiL5BlS8HZBADKT7MNU8k3xvskACNmAYAEsObfWRXps3W7JfZyVCWRlALGnxSU/jUsBoSrnvrM3qwkSERE9YAxORFQqSmXuNaZ6NAqU1huzsnH1ngHno1Nzl5hU3ErMQLzBhHiDCUeu2QYqjUqBGt7OqOUnBimx10sMVl4uDlYBT6EAnLzEJbBJ+R032wwk3QDiLwMJl21vk2+KpdtjTopLQZRqQOMMaJxyFucCbnO3K1U61I65DcWJVMAjMPfaWc6+rDpIRERUAAYnIipXOrUK9QPdUT/Q3Wa9wZiFq/cMuByXhitxubdX7xmQYc7G5TgDLscZ7I7n5axBqI8LQrydUd3LCdW9nBDiJd4P9nKCTq2qqLf2YKk0Yi9SQVUGs0zifKv8oSrhitjLBQGwZAHGFHEpzssBaAwAd9bZb9R7FH1RYmvJd42+DG+aiIio8mBwIqIK4aJTo3GwBxoHe9ist1gERKdkSkHqSlwaLufc3knORGK6GYnpthfzzSvAXScFqdxw5YwQL2cEeeqhcZQhgGWh1gK+dcUlv2yzODzQnAGY03NuM8QeKrt16dJtttGAO5fPINhTC2V6vHjNrPR7YgDLTBaX+EtFt83ZR6xA6B6cc5v3fjDgFgToXMv/MyEiIqpgDhGcFi1ahLlz5yI6OhrNmjXDF198gTZt2hS47y+//IKPPvoIly5dgtlsRt26dTF+/Hi8/PLLFdxqIioPSqUC1TydUM3TSbqIr1W6KQtX4gy4lZiOW4kZuJmQc5uYjpsJGcgwZyMmxYiYFKPdfCoAUCqAap5OqOHtjFAfZ4R4OyPUW5xTVcPbGR7OVWBekEoDOHmKSwlYzGYc27oVgb16QanJ+RwsFnH+ls1FiQu4MHFajFjyPStDLAGfHg9EFzKEEAB0HrmhSu8BqPViiXmbW22+x/n2UVp/VkLucQXBdp2QZ5u0n0IMd26B4vBKFtggIqJSkj04rV+/HpGRkVi6dCnatm2LBQsWICIiAufPn4e/v7/d/t7e3njvvfdQv359aLVabN68GcOGDYO/vz8iIiJkeAdE9KA4awvupQIAQRCQYDBJQSp/sLqVmAFTlgW3EjNwKzED+y/H2x3Dw0kjhagaPuJtqLcYsALc9dCqq0BvVUkolWKhCWdvwK/e/fcVBCAjMedixHfEUu7W+9JFiu/kDB9MBuKSgbizFfM+CqPSAW4BgGugGKTcgsTHbkHiRZHdghiwiIioULIHp3nz5mHkyJEYNmwYAGDp0qXYsmULli9fjkmTJtnt37lzZ5vHY8eOxcqVK/H3338zOBE9RBQKBXxcdfBx1aFZiKfddotFQFyaETcS0nEjPh3XE9JxMyEdNxLScT0+HffSjEjOMOPk7WScvJ1cwPEBHxcdgjz0CPTQI9BdvA3Kcz/QQw9nrex/RuWhUOSGrMDGhe+XmSKWd7cGK2NaTgVBo+1tttF+XZYp93G2KU+YUeS2IbdBBa8TssVesowE8TWSbojL/VgDlpOXWCJeKq6hF+/brXPOtz5nm9ZZrIaoccq9z8IbRESVlqz/4ptMJhw9ehSTJ0+W1imVSnTr1g0HDhwo8vmCIOCvv/7C+fPn8cknnxS4j9FohNFolB6npIgTp81mM8xmcxnfQdlZ2+AIbaHKhedO0bydVPAOdkPzYDe7bemmLPGivoniciMhPedxOm4mZsCcLeBemhH30owFBisrd706J0jpEOCuR4CbeOvvrkOAmw7+bjp4u2ihUjpWD0aFnT8qJ8CzlrjIKSsTSIuFIi0GSIuGIjUaSIuBIi3nNjVaXJ+RWPyAVQqCUp2vyqELBClYOQN6LwguPoCzDwQn8RbOvtI6aFwcojeMf3+oLHj+UGk9iHOnJMdSCILNoPAKdefOHQQHB2P//v1o166dtP6dd97B//73Pxw6dKjA5yUnJyM4OBhGoxEqlQqLFy/G8OHDC9x3xowZmDlzpt36tWvXwtnZuXzeCBFVKRYBMGQBSUYg2aRAkin3VrpvBIyW4n2BVUKAuxbw0ALuGgEeWsBDa70F3LUCPDSAs9ohvhM/1JQWE3TmZOjNSdBkp0MlmKCyWBcjVBZzzq0p3zZTvnXiPmqLEapsI5TILpf2ZSs0MKndYFS75dy6w6R2g1nlBEABQaGAeMYpICjEWygU4i2UEKz3Fbn7ZCu0yFZqka3UIUupy7kvPs7OeWxR8OQkoqopPT0dAwcORHJyMtzd3e+7b6UcY+Lm5oaoqCikpaVh586diIyMRK1ateyG8QHA5MmTERkZKT1OSUlBSEgIevToUeSHUxHMZjO2b9+O7t27Q6OpAhPVqcLw3JFfamYWolMyEZNiRHRKJqKTMxGbakRsqliwIjZV7LGyCLmhSxpSVgCdWgl/Nx0C3HUIcBN7rfxzeq0C3MXF301XLsMDef5UnGwA2dkmsaKhKT2n4mE6FNZKh6Z0ICsdCpMByEgC0u9BkVN0Q5F+TyrAocjKhEoww8mcACdzQoW+B0GhsukpE9R6JKWb4eEbBIXOJXe9Nvc+tM4Q1HmGKVrXaZzzFADJKf6hcRKvRUYPBf79odJ6EOeOdTRaccj6V8rX1xcqlQoxMTE262NiYhAYGFjIs8ThfHXq1AEANG/eHGfPnsXs2bMLDE46nQ46nc5uvUajcahfVkdrD1UePHfk463RwNvNCQ2DC98nK9uCe2kmxKRk5lmM4m2qETHJmYhJzURSuhnGLIs0dPB+3PRqcViguzgs0M9VB19XHXxctfDNue/rqoW3ixbqIsqx8/ypIBoNoHcp2zFMBnG+Vvo9wBCfc5sTrExpgGDJtwgFrMuzWCxi+fmsjNyS9VJp+5yS9pYsAIBCyBZfw5QmPgbgDQA3LpftPeWlVNsGKo3e/rFKB6jUgEqbs2gKuJ9vnVIDKFW5nwmEPLeW3GqMebcJFvE+FDlz1FzEReOc5zYnEFpvlQ9ZMZlywL8/VFrlee6U5DiyBietVotWrVph586d6N+/PwDAYrFg586dGDNmTLGPY7FYbOYxERE5CrVKKRWSuJ9MczZiU4yISc0XrnKW2JxerXRTNlIzs5CamYZLsWlFvr6Xs0YKU9Zg5eemg6dehasJCgRcT4SvuzO8XbTwcNI43FwsysP65d0rtOJe0+46YeK1wLIyUnD0wB60atYQ6mxjTs+ZIbcHzWzIuS1kfd6iIFaWLJtwVumo9TmhyiV3zlqBISvfeq2r7T5qnRj0FEpAkXOrVIlDJW0e592uFIOn1pVDKokeINn7xSMjIzFkyBC0bt0abdq0wYIFC2AwGKQqe4MHD0ZwcDBmz54NAJg9ezZat26N2rVrw2g0YuvWrfjuu++wZMkSOd8GEVGZ6DUqsSS6T+FzLwVBQJoxyyZURadkIj7NhHtpRun2XpoRCQYTLAJyLiBsxsUCQ5YK/z1/RHqkUADueg28XbTwdNbAy1mbs2jg5WJ73ztn8XJ2vMIXVI4KuU6YYDYj+qwBQqNeYm9aaVksOZUT8yzm+zzONolhLtuU5765kPWm3EUQcgKFwvbWbp3Sdp1gydMTly/8WddZrxlmbSPsL31QYRSqnJ+XVwGLdwHrcvbVuYs9eUR0X7L/lgwYMABxcXGYNm0aoqOj0bx5c2zbtg0BAQEAgBs3bkCZp/vbYDBg1KhRuHXrFpycnFC/fn2sXr0aAwYMkOstEBFVCIVCATe9Bm56Der4u95332yLgMR0k02YuicFLCNiUzJx9U4cBK0zEtPNSM3MgiAAyRlmJGeUoMKQAvB0EsOWj4sOXi4aeLvo4JMTrHxcc0OWNWjpNaqyfhRUVSiVOb0ulbRYkyDk9sblDVemtJL3wlnXZxlzhhBm5wypzM4ZPpj3cZ7tNu3Jzr0odUmptDnz2Kw9Y055yulb565Z7+fMd1PrbNtmHQIqDQnNsz7PNmV2FprcvArlH3tKPsTRki32TuZdss0568151uXdJ+dvmpMX4OwrVqh0ybmV7ud5zBBJhXCIM2PMmDGFDs3bvXu3zeMPPvgAH3zwQQW0ioio8lIpFdIQvXDYl2M3m83YunUrevXqBI1Gg6xsC5IyzEg0mJCYbkaCwYSkdFNOj5UpZ33OY4MJ8QYTkjPMEPL0al2OMxSrbc5aldh75SL2ank6a+HtrIGnXe9W7j7OWhUUHIJEjkahyA1+Lr7ytMEaVCxmIDNZvDB1RiKQnpB7v8AlSby+mXVopLV3LrPwyy+UFxWAWgBw74G/VOnoPW3DlVon9kbm7ZWUHhfQY2mzb95FkWeYZUGL9Tmq3KG5WtfcW531vpt4q9ZxaGYFc4jgRERE8lKrlFLQKq6sbIsUsuIN4vDABIPYy5WYLoarhLScdTnBK9siIN2UjXRTBm4n3b8IRl5alRIezhp4OGng6STeejhr4Okkzs3ydBYX9zzbPZ21cNeriyyQQVSpKZUAlGIvicYJcCu8uFaBsky5PWR5hyVKPWKF3TeIz7WZg5Vn3pXNXCylzbpsi4BLly+jTp06UClL2AOtyHmvSnVO4Q+1+DoqTZ51KvG+Ks92ATmB8l5ukZX0eNtCKxmJAAQgM0lcEsqx+MmDoFDlhKk84UqlLXhYKlDIemv4y1s50ynfkv8i3/n2UetyirHoxIt8q3IeV8GCKQxORERUKmqVEn5uYrEJFNCrlZ/FIiDVmCX1XiXlhC7pfnpOL5chp5crp4fLlGWBKduCuFQj4lJLXgjITaeGuxSmckJXTvCy3rcGMOs+7k4auOnUUHL+FlV1ai2g9kZOncQKYTGbcS59K2o93gsqR6qqZ8kWw5NNsLonDgW0qcKY5761AqNQQLVGaVveIZbFqXiZnTOE0wAY08RbU2rOrXVuHcShkJnJFdJLWCpKTZ5Qpc0NVdZ1ah0wYLV8vbWlwOBEREQVQqlUSOEkDMUrzS0IYg9VYrpJmn+VnC7eJmWYkZRzPzlD3J6ULi4pGWakGsVS2qnGLKQas0rUwwWI/wnrqlXDTa+Gm14Ddyd1zhyz3HXStgLWuenVcNUyfBFVGkqV+CXe0b/IW7JzQ5S1EqUx51YKeflL71vL7hdUll/IuTRBZp7LEeT0QOa9XIE5XSzWYt1mXZdtEufmWfLNj7WYAVMRc2at7aokGJyIiMhhKRQKuOjUcNGpUd2rZM/Nyrbkhq38S7oYvPKHMTGQmZBptkAQckMXkjNL2X7b8GUNXa557rvnDWO6nN4uvVq6ZfgiIhtKFaB3FxdHYrHYVrPMMoqXHMgy5bk12a7Te8jd6hJhcCIioipJrVLCx1UHnxLM27LKNFuvl2XOuc1CSqZZepySZ1tKRs4+xtx9UzPNMGcL5Re+dLkByz1P75d7nkDmossJZTlB09W65KzTqZUssEFED45SCShzLlZdRTE4ERER5aPXqKDXqHLmb5WcIAgwZlmQkmlGmhSm8gQxY95QZrbbnpJpRkpmFkxZOT1fOdvLQq1UwFWvhktOD5hNuLKGLb0arjoVXHUauOhU4n7a3PBl3U9ZyYbXEBGVBwYnIiKicqZQKKTw5V903YxC5e35svZypWRYH9v2hhmMWUgzZiEtM+fWmAWDMRtpOXO9siz/396dx0ZR/38cf82ePSjlEGkrhxgOBVJUkFLRn/kKkS8qwSOGeOIR/cNiQCRRfwar0QTFxCBq1EQDieCdgGjwQMQSDaKANeAPK/VCA0o8gNLSPWY/vz9md7oLtQO0MFt5PpLJzHxmdvtZfWf1lc9nPmvcZ8A6Kxy0FLaCevz/1jthLOoErOJoMOvYCWFFkbbAVRQNqjgSUlEkmN5CKkwfh1n9EECeIzgBAJCnOjvyJTmrGbYk7JxAlRuuctub01ML22tvjtuSpIRtlJClln2tUhct6BUOWioMO8ErE6aKwm3HmX1xJLstpKJwUMXR9HEkqMJw+r7M+4SDLEkPoEsQnAAA+BcLBCx3ml1npVJGzfGk/j7QqvfWfKxxEyYqZksHYkm1xG03iDXHbTXHkmqJJ3Ug5hw77c4oWEvcuf9g3FYy5Uz7S9hGCdt5fqyrRUOBnOe9iqMhlURzpyBmrmdfK4o4z4aFgpbCwYAiwbZjZ3OOQwGL58eAkwDBCQAAHJFAwFJJQVgFQamsSKocUKpwJ3+HJ55M6WDcVnNWmGqJJ9WSsNWSDlkHE3b6h5NtHYw7wcy9zz220/clndclbNnpUBZLphRLOj/EfLwcGqoKIwF3WmJxNOSOlrVNV3SmNubsI0EVRUMqDDsjZwWRgHvMqBngP4ITAADwTSQUUCQUUGlR1/4QqjFGcTsTyjJTFZ3nwpxnv5zj7OmK2edNrc4IWdI2StgpxZMpJVPOccI+fHGMuJ2SM5PR7tLPkREOOs/NZaYiFoSd6YttISuoglBQBeFAeopnIH2e3XbIedb90cw+5IysMYIGHI7gBAAA/nUsy1I0FFQ0FFSvoq59b2OMErZRMpVSIukEtOzjhJ1yRr9imZG0timK7j5uqyU9rfHQ660JZ/TsYMJ2fx80M5Wxs6srHgnLkgpC6TCVDlfRzD4dvqKhTPgKqDAd5ApCgawAF1RhJDu8tYW0kJXS3pj0d0tcJYUBRUMBfqsM3QLBCQAA4ChYlqVIyFJEASly/P5OZll7N0ilpyO2Jmx3imLm/GDcVmvWvbGEc+xsKbUms44PaY+l95mQZozc4CZ1fhXG9oVUu+UT9ywSdAJUNJwJam2jZJnjtrZMkMsePQvkBLRoekTNCXVOgIuEnPd19kGFgzybhqNDcAIAAMhD2cva9zrOfysztbE1kVIs2Ra8MsEt096a1Z4JbbGEE9oOxtOBLH0cS7aNnmUHNicEJpUybaHFmeqYcn4s+gTKDlLRnGDV1h7poD0SDCgazuyDimadZ6ahZh87UyGDbdfS1wlx3QPBCQAA4CSXPbVR6trnzQ6VSCS0evVqXTLlv0pZwaxw1hbUDg1ssXZHzdra3BG25OGjapnAF0864Sybs3BISk06sYGtPZFQQNF0yAp3GLzavycT/KKZZ9Uy55lQGG47jhzS7ixu4gS4UIAg908ITgAAADjhQsGAwuGQio/9Z8qOWirljKzFkk6QiiXt9D51yP7QdtsNWdntcdsJbXE71bY/5LXx9OIihx5nluLPyLQrduL+eXQkGLAUCljuipGhYEDhgLMPBS2FA87+0KmU7e2jWfuCrP3/DO+ngnDQ7496xAhOAAAAOCkEApYKAsG8+J91O71KY7sBKxPKks4qjtlt8ezwln5NIuu1MXdzQl3mOOdaws65r72VIu2UkZ1ynrM7Xr7430l58e/iSBGcAAAAgBMsGLAUzJMQl0oZJVPplSJto2R62f2E7YyMZc6zryfTo3fxrOmVsXamW3a0L4j4/9mPBsEJAAAAOIkFApYigfRKkfhH/NMBAAAAAA8EJwAAAADwQHACAAAAAA8EJwAAAADwQHACAAAAAA8EJwAAAADwQHACAAAAAA8EJwAAAADwQHACAAAAAA8EJwAAAADwQHACAAAAAA8EJwAAAADwQHACAAAAAA8EJwAAAADwQHACAAAAAA8EJwAAAADwQHACAAAAAA8EJwAAAADwEPK7AyeaMUaStH//fp974kgkEmppadH+/fsVDof97g66EWoHnUH9oDOoH3QG9YNjdTxqJ5MJMhmhIyddcGpqapIkDRw40OeeAAAAAMgHTU1NKi0t7fAeyxxJvPoXSaVS2rVrl0pKSmRZlt/d0f79+zVw4ED98ssv6tmzp9/dQTdC7aAzqB90BvWDzqB+cKyOR+0YY9TU1KSKigoFAh0/xXTSjTgFAgENGDDA724cpmfPnnx54JhQO+gM6gedQf2gM6gfHKuurh2vkaYMFocAAAAAAA8EJwAAAADwQHDyWTQaVW1traLRqN9dQTdD7aAzqB90BvWDzqB+cKz8rp2TbnEIAAAAADhajDgBAAAAgAeCEwAAAAB4IDgBAAAAgAeCEwAAAAB4IDj56Nlnn9Xpp5+ugoICVVVV6YsvvvC7S8hD69ev17Rp01RRUSHLsrRy5cqc68YYPfjggyovL1dhYaEmT56sHTt2+NNZ5JUFCxbovPPOU0lJiU499VRdccUVamhoyLmntbVVNTU16tu3r3r06KGrr75av//+u089Rj557rnnVFlZ6f7QZHV1td577z33OrWDo/HYY4/JsizNmTPHbaOG8E8eeughWZaVs5155pnudb9qh+Dkk9dff11z585VbW2ttmzZojFjxmjKlCnas2eP311DnmlubtaYMWP07LPPtnt94cKFWrx4sZ5//nlt3LhRxcXFmjJlilpbW09wT5Fv6urqVFNTo88//1xr1qxRIpHQJZdcoubmZveeu+++W++8847efPNN1dXVadeuXbrqqqt87DXyxYABA/TYY49p8+bN2rRpky6++GJNnz5d33zzjSRqB0fuyy+/1AsvvKDKysqcdmoIHRk1apR2797tbp9++ql7zbfaMfDF+PHjTU1NjXtu27apqKgwCxYs8LFXyHeSzIoVK9zzVCplysrKzBNPPOG27d2710SjUfPqq6/60EPksz179hhJpq6uzhjj1Eo4HDZvvvmme8/27duNJLNhwwa/uok81rt3b/Piiy9SOzhiTU1NZtiwYWbNmjXmoosuMrNnzzbG8P2DjtXW1poxY8a0e83P2mHEyQfxeFybN2/W5MmT3bZAIKDJkydrw4YNPvYM3c2PP/6o3377LaeWSktLVVVVRS3hMPv27ZMk9enTR5K0efNmJRKJnPo588wzNWjQIOoHOWzb1muvvabm5mZVV1dTOzhiNTU1uuyyy3JqReL7B9527NihiooKnXHGGbr++uu1c+dOSf7WTui4vjva9ccff8i2bfXv3z+nvX///vr222996hW6o99++02S2q2lzDVAklKplObMmaOJEydq9OjRkpz6iUQi6tWrV8691A8ytm7dqurqarW2tqpHjx5asWKFRo4cqfr6emoHnl577TVt2bJFX3755WHX+P5BR6qqqrR06VKNGDFCu3fv1sMPP6wLL7xQ27Zt87V2CE4AcBKoqanRtm3bcuaIA15GjBih+vp67du3T2+99ZZmzpypuro6v7uFbuCXX37R7NmztWbNGhUUFPjdHXQzU6dOdY8rKytVVVWlwYMH64033lBhYaFv/WKqng9OOeUUBYPBw1b/+P3331VWVuZTr9AdZeqFWkJHZs2apXfffVfr1q3TgAED3PaysjLF43Ht3bs3537qBxmRSERDhw7V2LFjtWDBAo0ZM0ZPPfUUtQNPmzdv1p49e3TuuecqFAopFAqprq5OixcvVigUUv/+/akhHLFevXpp+PDhamxs9PX7h+Dkg0gkorFjx2rt2rVuWyqV0tq1a1VdXe1jz9DdDBkyRGVlZTm1tH//fm3cuJFagowxmjVrllasWKGPP/5YQ4YMybk+duxYhcPhnPppaGjQzp07qR+0K5VKKRaLUTvwNGnSJG3dulX19fXuNm7cOF1//fXuMTWEI3XgwAF9//33Ki8v9/X7h6l6Ppk7d65mzpypcePGafz48Vq0aJGam5t1yy23+N015JkDBw6osbHRPf/xxx9VX1+vPn36aNCgQZozZ44effRRDRs2TEOGDNH8+fNVUVGhK664wr9OIy/U1NTolVde0dtvv62SkhJ37ndpaakKCwtVWlqq2267TXPnzlWfPn3Us2dP3XXXXaqurtaECRN87j38dv/992vq1KkaNGiQmpqa9Morr+iTTz7RBx98QO3AU0lJifs8ZUZxcbH69u3rtlND+Cfz5s3TtGnTNHjwYO3atUu1tbUKBoO69tpr/f3+Oa5r9qFDTz/9tBk0aJCJRCJm/Pjx5vPPP/e7S8hD69atM5IO22bOnGmMcZYknz9/vunfv7+JRqNm0qRJpqGhwd9OIy+0VzeSzJIlS9x7Dh48aO68807Tu3dvU1RUZK688kqze/du/zqNvHHrrbeawYMHm0gkYvr162cmTZpkPvzwQ/c6tYOjlb0cuTHUEP7ZjBkzTHl5uYlEIua0004zM2bMMI2Nje51v2rHMsaY4xvNAAAAAKB74xknAAAAAPBAcAIAAAAADwQnAAAAAPBAcAIAAAAADwQnAAAAAPBAcAIAAAAADwQnAAAAAPBAcAIAAAAADwQnAAAAAPBAcAIA5L1EIqGlS5fqggsuUL9+/VRYWKjKyko9/vjjisfjfncPAHASsIwxxu9OAADQkfr6et1zzz268847dc4556i1tVVbt27VQw89pPLycn3wwQcKh8N+dxMA8C/GiBMAIO+NHj1aa9eu1dVXX60zzjhDI0eO1IwZM7R+/Xpt27ZNixYtkiRZltXuNmfOHPe9/v77b910003q3bu3ioqKNHXqVO3YscO9fuutt6qyslKxWEySFI/Hdc455+imm26SJP3000+yLEv19fXua+bPny/Lstx+AAD+fQhOAIC8FwqF2m3v16+frrrqKi1fvtxtW7JkiXbv3u1u1dXVOa+5+eabtWnTJq1atUobNmyQMUaXXnqpEomEJGnx4sVqbm7WfffdJ0l64IEHtHfvXj3zzDPt9uHXX3/VokWLVFhY2BUfFQCQp9r/LxEAAHlo1KhR+vnnn3PaEomEgsGge96rVy+VlZW555FIxD3esWOHVq1apc8++0znn3++JGn58uUaOHCgVq5cqWuuuUY9evTQsmXLdNFFF6mkpESLFi3SunXr1LNnz3b79MADD2jGjBn66KOPuvKjAgDyDMEJANBtrF692h0Zyli4cKGWLVt2RK/fvn27QqGQqqqq3La+fftqxIgR2r59u9tWXV2tefPm6ZFHHtG9996rCy64oN3327Jli1asWKGGhgaCEwD8yxGcAADdxuDBgw9r+/777zV8+PAu/TupVEqfffaZgsGgGhsb//G+e+65R/PmzVN5eXmX/n0AQP7hGScAQN7766+/1NTUdFj7pk2btG7dOl133XVH9D5nnXWWksmkNm7c6Lb9+eefamho0MiRI922J554Qt9++63q6ur0/vvva8mSJYe916pVq/Tdd99p3rx5x/CJAADdDcEJAJD3du7cqbPPPlsvvfSSGhsb9cMPP+jll1/W9OnTdeGFF+asmteRYcOGafr06br99tv16aef6uuvv9YNN9yg0047TdOnT5ckffXVV3rwwQf14osvauLEiXryySc1e/Zs/fDDDznvtXDhQj366KMqKirq6o8LAMhDBCcAQN4bPXq0amtrtXTpUk2YMEGjRo3SwoULNWvWLH344Yc5C0B4WbJkicaOHavLL79c1dXVMsZo9erVCofDam1t1Q033KCbb75Z06ZNkyTdcccd+s9//qMbb7xRtm277zN06FDNnDmzyz8rACA/8QO4AAAAAOCBEScAAAAA8EBwAgAAAAAPBCcAAAAA8EBwAgAAAAAPBCcAAAAA8EBwAgAAAAAPBCcAAAAA8EBwAgAAAAAPBCcAAAAA8EBwAgAAAAAPBCcAAAAA8PD/tokPZuIkV/oAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# График функции ошибки по эпохам\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(history_0.history['loss'], label='Обучающая выборка')\n",
+    "plt.plot(history_0.history['val_loss'], label='Валидационная выборка')\n",
+    "plt.title('Функция ошибки по эпохам (Однослойная сеть)')\n",
+    "plt.xlabel('Эпохи')\n",
+    "plt.ylabel('Ошибка')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 1058,
+     "status": "ok",
+     "timestamp": 1759127544719,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "NF_SsO8wiEUT",
+    "outputId": "ffe554c2-1c94-42b7-cc63-8f407418c4ce"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Результаты однослойной сети:\n",
+      "Ошибка на тестовых данных: 0.28625616431236267\n",
+      "Точность на тестовых данных: 0.92330002784729\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Оценка на тестовых данных\n",
+    "scores_0 = model_0.evaluate(X_test, y_test, verbose=0)\n",
+    "print(\"Результаты однослойной сети:\")\n",
+    "print(f\"Ошибка на тестовых данных: {scores_0[0]}\")\n",
+    "print(f\"Точность на тестовых данных: {scores_0[1]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "fFcoQGdPnDFx"
+   },
+   "outputs": [],
+   "source": [
+    "# Функция для создания и обучения модели\n",
+    "def create_and_train_model(hidden_units, model_name):\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(units=hidden_units, input_dim=num_pixels, activation='sigmoid'))\n",
+    "    model.add(Dense(units=num_classes, activation='softmax'))\n",
+    "\n",
+    "    model.compile(loss='categorical_crossentropy',\n",
+    "                  optimizer='sgd',\n",
+    "                  metrics=['accuracy'])\n",
+    "\n",
+    "    history = model.fit(X_train, y_train,\n",
+    "                        validation_split=0.1,\n",
+    "                        epochs=50)\n",
+    "\n",
+    "    scores = model.evaluate(X_test, y_test, verbose=0)\n",
+    "\n",
+    "    return model, history, scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "XPlFkV40joAT"
+   },
+   "outputs": [],
+   "source": [
+    "# Эксперименты с разным количеством нейронов\n",
+    "hidden_units_list = [100, 300, 500]\n",
+    "models_1 = {}\n",
+    "histories_1 = {}\n",
+    "scores_1 = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 638747,
+     "status": "ok",
+     "timestamp": 1759128186868,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "je0i_8HxjvpB",
+    "outputId": "5cb19edc-9162-4c23-92d8-1671e62b2bb3"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Обучение модели с 100 нейронами...\n",
+      "Epoch 1/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.5548 - loss: 1.8518 - val_accuracy: 0.8210 - val_loss: 0.9619\n",
+      "Epoch 2/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8339 - loss: 0.8359 - val_accuracy: 0.8597 - val_loss: 0.6320\n",
+      "Epoch 3/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8640 - loss: 0.5853 - val_accuracy: 0.8770 - val_loss: 0.5137\n",
+      "Epoch 4/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8797 - loss: 0.4859 - val_accuracy: 0.8847 - val_loss: 0.4522\n",
+      "Epoch 5/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8879 - loss: 0.4295 - val_accuracy: 0.8892 - val_loss: 0.4153\n",
+      "Epoch 6/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8947 - loss: 0.3969 - val_accuracy: 0.8947 - val_loss: 0.3899\n",
+      "Epoch 7/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8974 - loss: 0.3775 - val_accuracy: 0.8967 - val_loss: 0.3729\n",
+      "Epoch 8/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9000 - loss: 0.3589 - val_accuracy: 0.8993 - val_loss: 0.3565\n",
+      "Epoch 9/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9052 - loss: 0.3424 - val_accuracy: 0.9033 - val_loss: 0.3450\n",
+      "Epoch 10/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9056 - loss: 0.3339 - val_accuracy: 0.9042 - val_loss: 0.3352\n",
+      "Epoch 11/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9065 - loss: 0.3293 - val_accuracy: 0.9073 - val_loss: 0.3271\n",
+      "Epoch 12/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9094 - loss: 0.3213 - val_accuracy: 0.9093 - val_loss: 0.3197\n",
+      "Epoch 13/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9097 - loss: 0.3159 - val_accuracy: 0.9088 - val_loss: 0.3139\n",
+      "Epoch 14/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9162 - loss: 0.2962 - val_accuracy: 0.9100 - val_loss: 0.3073\n",
+      "Epoch 15/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9162 - loss: 0.3001 - val_accuracy: 0.9127 - val_loss: 0.3019\n",
+      "Epoch 16/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9178 - loss: 0.2928 - val_accuracy: 0.9137 - val_loss: 0.2972\n",
+      "Epoch 17/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9196 - loss: 0.2789 - val_accuracy: 0.9158 - val_loss: 0.2921\n",
+      "Epoch 18/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9194 - loss: 0.2849 - val_accuracy: 0.9163 - val_loss: 0.2875\n",
+      "Epoch 19/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9206 - loss: 0.2744 - val_accuracy: 0.9178 - val_loss: 0.2832\n",
+      "Epoch 20/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9219 - loss: 0.2756 - val_accuracy: 0.9187 - val_loss: 0.2795\n",
+      "Epoch 21/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9240 - loss: 0.2678 - val_accuracy: 0.9195 - val_loss: 0.2759\n",
+      "Epoch 22/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9236 - loss: 0.2689 - val_accuracy: 0.9202 - val_loss: 0.2722\n",
+      "Epoch 23/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9241 - loss: 0.2631 - val_accuracy: 0.9217 - val_loss: 0.2686\n",
+      "Epoch 24/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9265 - loss: 0.2556 - val_accuracy: 0.9218 - val_loss: 0.2649\n",
+      "Epoch 25/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9243 - loss: 0.2639 - val_accuracy: 0.9230 - val_loss: 0.2618\n",
+      "Epoch 26/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9259 - loss: 0.2545 - val_accuracy: 0.9247 - val_loss: 0.2586\n",
+      "Epoch 27/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9291 - loss: 0.2475 - val_accuracy: 0.9255 - val_loss: 0.2557\n",
+      "Epoch 28/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9289 - loss: 0.2465 - val_accuracy: 0.9275 - val_loss: 0.2531\n",
+      "Epoch 29/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9310 - loss: 0.2416 - val_accuracy: 0.9280 - val_loss: 0.2496\n",
+      "Epoch 30/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9314 - loss: 0.2364 - val_accuracy: 0.9292 - val_loss: 0.2473\n",
+      "Epoch 31/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9331 - loss: 0.2351 - val_accuracy: 0.9295 - val_loss: 0.2439\n",
+      "Epoch 32/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9328 - loss: 0.2307 - val_accuracy: 0.9303 - val_loss: 0.2415\n",
+      "Epoch 33/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9346 - loss: 0.2246 - val_accuracy: 0.9308 - val_loss: 0.2391\n",
+      "Epoch 34/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9341 - loss: 0.2286 - val_accuracy: 0.9317 - val_loss: 0.2362\n",
+      "Epoch 35/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9355 - loss: 0.2295 - val_accuracy: 0.9325 - val_loss: 0.2334\n",
+      "Epoch 36/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9384 - loss: 0.2155 - val_accuracy: 0.9330 - val_loss: 0.2312\n",
+      "Epoch 37/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9366 - loss: 0.2197 - val_accuracy: 0.9340 - val_loss: 0.2288\n",
+      "Epoch 38/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9385 - loss: 0.2142 - val_accuracy: 0.9342 - val_loss: 0.2265\n",
+      "Epoch 39/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9396 - loss: 0.2104 - val_accuracy: 0.9348 - val_loss: 0.2244\n",
+      "Epoch 40/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9399 - loss: 0.2110 - val_accuracy: 0.9362 - val_loss: 0.2231\n",
+      "Epoch 41/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9391 - loss: 0.2092 - val_accuracy: 0.9367 - val_loss: 0.2201\n",
+      "Epoch 42/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9401 - loss: 0.2075 - val_accuracy: 0.9370 - val_loss: 0.2178\n",
+      "Epoch 43/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9404 - loss: 0.2018 - val_accuracy: 0.9387 - val_loss: 0.2164\n",
+      "Epoch 44/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9411 - loss: 0.2044 - val_accuracy: 0.9383 - val_loss: 0.2141\n",
+      "Epoch 45/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9434 - loss: 0.1981 - val_accuracy: 0.9393 - val_loss: 0.2119\n",
+      "Epoch 46/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9445 - loss: 0.1931 - val_accuracy: 0.9392 - val_loss: 0.2094\n",
+      "Epoch 47/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9442 - loss: 0.1913 - val_accuracy: 0.9400 - val_loss: 0.2075\n",
+      "Epoch 48/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9428 - loss: 0.1961 - val_accuracy: 0.9412 - val_loss: 0.2056\n",
+      "Epoch 49/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9447 - loss: 0.1919 - val_accuracy: 0.9407 - val_loss: 0.2033\n",
+      "Epoch 50/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9439 - loss: 0.1936 - val_accuracy: 0.9425 - val_loss: 0.2020\n",
+      "Точность: 0.9422000050544739\n",
+      "\n",
+      "Обучение модели с 300 нейронами...\n",
+      "Epoch 1/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.5667 - loss: 1.8010 - val_accuracy: 0.8303 - val_loss: 0.8696\n",
+      "Epoch 2/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8399 - loss: 0.7595 - val_accuracy: 0.8657 - val_loss: 0.5806\n",
+      "Epoch 3/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8705 - loss: 0.5350 - val_accuracy: 0.8803 - val_loss: 0.4834\n",
+      "Epoch 4/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8785 - loss: 0.4604 - val_accuracy: 0.8867 - val_loss: 0.4317\n",
+      "Epoch 5/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8887 - loss: 0.4130 - val_accuracy: 0.8895 - val_loss: 0.4013\n",
+      "Epoch 6/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8937 - loss: 0.3841 - val_accuracy: 0.8950 - val_loss: 0.3820\n",
+      "Epoch 7/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8983 - loss: 0.3652 - val_accuracy: 0.8960 - val_loss: 0.3662\n",
+      "Epoch 8/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9026 - loss: 0.3493 - val_accuracy: 0.8990 - val_loss: 0.3557\n",
+      "Epoch 9/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9035 - loss: 0.3417 - val_accuracy: 0.9008 - val_loss: 0.3452\n",
+      "Epoch 10/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9043 - loss: 0.3351 - val_accuracy: 0.9023 - val_loss: 0.3373\n",
+      "Epoch 11/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9060 - loss: 0.3240 - val_accuracy: 0.9030 - val_loss: 0.3303\n",
+      "Epoch 12/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9070 - loss: 0.3170 - val_accuracy: 0.9052 - val_loss: 0.3255\n",
+      "Epoch 13/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9087 - loss: 0.3202 - val_accuracy: 0.9062 - val_loss: 0.3209\n",
+      "Epoch 14/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9129 - loss: 0.3063 - val_accuracy: 0.9067 - val_loss: 0.3161\n",
+      "Epoch 15/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9135 - loss: 0.3046 - val_accuracy: 0.9092 - val_loss: 0.3119\n",
+      "Epoch 16/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9130 - loss: 0.2993 - val_accuracy: 0.9108 - val_loss: 0.3064\n",
+      "Epoch 17/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9149 - loss: 0.2964 - val_accuracy: 0.9113 - val_loss: 0.3043\n",
+      "Epoch 18/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9152 - loss: 0.2935 - val_accuracy: 0.9128 - val_loss: 0.3011\n",
+      "Epoch 19/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9170 - loss: 0.2863 - val_accuracy: 0.9123 - val_loss: 0.2989\n",
+      "Epoch 20/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9190 - loss: 0.2821 - val_accuracy: 0.9138 - val_loss: 0.2945\n",
+      "Epoch 21/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9203 - loss: 0.2799 - val_accuracy: 0.9140 - val_loss: 0.2926\n",
+      "Epoch 22/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9187 - loss: 0.2851 - val_accuracy: 0.9145 - val_loss: 0.2894\n",
+      "Epoch 23/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9181 - loss: 0.2789 - val_accuracy: 0.9155 - val_loss: 0.2871\n",
+      "Epoch 24/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9215 - loss: 0.2705 - val_accuracy: 0.9158 - val_loss: 0.2848\n",
+      "Epoch 25/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.9217 - loss: 0.2715 - val_accuracy: 0.9172 - val_loss: 0.2829\n",
+      "Epoch 26/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9225 - loss: 0.2666 - val_accuracy: 0.9177 - val_loss: 0.2804\n",
+      "Epoch 27/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9248 - loss: 0.2643 - val_accuracy: 0.9187 - val_loss: 0.2783\n",
+      "Epoch 28/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9235 - loss: 0.2603 - val_accuracy: 0.9197 - val_loss: 0.2768\n",
+      "Epoch 29/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9240 - loss: 0.2628 - val_accuracy: 0.9210 - val_loss: 0.2741\n",
+      "Epoch 30/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9246 - loss: 0.2618 - val_accuracy: 0.9207 - val_loss: 0.2720\n",
+      "Epoch 31/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9270 - loss: 0.2541 - val_accuracy: 0.9202 - val_loss: 0.2686\n",
+      "Epoch 32/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9261 - loss: 0.2552 - val_accuracy: 0.9227 - val_loss: 0.2675\n",
+      "Epoch 33/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9270 - loss: 0.2577 - val_accuracy: 0.9243 - val_loss: 0.2639\n",
+      "Epoch 34/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9277 - loss: 0.2478 - val_accuracy: 0.9252 - val_loss: 0.2622\n",
+      "Epoch 35/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9288 - loss: 0.2471 - val_accuracy: 0.9263 - val_loss: 0.2611\n",
+      "Epoch 36/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9299 - loss: 0.2398 - val_accuracy: 0.9248 - val_loss: 0.2588\n",
+      "Epoch 37/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 2ms/step - accuracy: 0.9288 - loss: 0.2457 - val_accuracy: 0.9262 - val_loss: 0.2562\n",
+      "Epoch 38/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9315 - loss: 0.2363 - val_accuracy: 0.9262 - val_loss: 0.2534\n",
+      "Epoch 39/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9296 - loss: 0.2424 - val_accuracy: 0.9283 - val_loss: 0.2512\n",
+      "Epoch 40/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9305 - loss: 0.2383 - val_accuracy: 0.9280 - val_loss: 0.2501\n",
+      "Epoch 41/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9331 - loss: 0.2313 - val_accuracy: 0.9285 - val_loss: 0.2469\n",
+      "Epoch 42/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9334 - loss: 0.2310 - val_accuracy: 0.9285 - val_loss: 0.2450\n",
+      "Epoch 43/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9350 - loss: 0.2267 - val_accuracy: 0.9290 - val_loss: 0.2436\n",
+      "Epoch 44/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9354 - loss: 0.2228 - val_accuracy: 0.9300 - val_loss: 0.2410\n",
+      "Epoch 45/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9350 - loss: 0.2224 - val_accuracy: 0.9300 - val_loss: 0.2386\n",
+      "Epoch 46/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9363 - loss: 0.2207 - val_accuracy: 0.9303 - val_loss: 0.2374\n",
+      "Epoch 47/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9367 - loss: 0.2204 - val_accuracy: 0.9317 - val_loss: 0.2343\n",
+      "Epoch 48/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9352 - loss: 0.2196 - val_accuracy: 0.9322 - val_loss: 0.2318\n",
+      "Epoch 49/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9387 - loss: 0.2133 - val_accuracy: 0.9347 - val_loss: 0.2312\n",
+      "Epoch 50/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9369 - loss: 0.2181 - val_accuracy: 0.9345 - val_loss: 0.2288\n",
+      "Точность: 0.9376999735832214\n",
+      "\n",
+      "Обучение модели с 500 нейронами...\n",
+      "Epoch 1/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.5450 - loss: 1.7741 - val_accuracy: 0.8278 - val_loss: 0.8334\n",
+      "Epoch 2/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8439 - loss: 0.7246 - val_accuracy: 0.8673 - val_loss: 0.5635\n",
+      "Epoch 3/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8689 - loss: 0.5286 - val_accuracy: 0.8787 - val_loss: 0.4724\n",
+      "Epoch 4/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8843 - loss: 0.4409 - val_accuracy: 0.8863 - val_loss: 0.4282\n",
+      "Epoch 5/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8919 - loss: 0.4027 - val_accuracy: 0.8898 - val_loss: 0.3971\n",
+      "Epoch 6/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8951 - loss: 0.3794 - val_accuracy: 0.8938 - val_loss: 0.3770\n",
+      "Epoch 7/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8977 - loss: 0.3647 - val_accuracy: 0.8973 - val_loss: 0.3638\n",
+      "Epoch 8/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9008 - loss: 0.3517 - val_accuracy: 0.9008 - val_loss: 0.3532\n",
+      "Epoch 9/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9045 - loss: 0.3383 - val_accuracy: 0.9023 - val_loss: 0.3457\n",
+      "Epoch 10/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9078 - loss: 0.3278 - val_accuracy: 0.9032 - val_loss: 0.3371\n",
+      "Epoch 11/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9070 - loss: 0.3236 - val_accuracy: 0.9047 - val_loss: 0.3317\n",
+      "Epoch 12/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9074 - loss: 0.3238 - val_accuracy: 0.9050 - val_loss: 0.3270\n",
+      "Epoch 13/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9109 - loss: 0.3110 - val_accuracy: 0.9065 - val_loss: 0.3210\n",
+      "Epoch 14/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9109 - loss: 0.3092 - val_accuracy: 0.9068 - val_loss: 0.3176\n",
+      "Epoch 15/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9116 - loss: 0.3076 - val_accuracy: 0.9082 - val_loss: 0.3153\n",
+      "Epoch 16/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9118 - loss: 0.3079 - val_accuracy: 0.9095 - val_loss: 0.3138\n",
+      "Epoch 17/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9165 - loss: 0.2949 - val_accuracy: 0.9107 - val_loss: 0.3078\n",
+      "Epoch 18/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9141 - loss: 0.3022 - val_accuracy: 0.9103 - val_loss: 0.3072\n",
+      "Epoch 19/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9148 - loss: 0.2973 - val_accuracy: 0.9118 - val_loss: 0.3024\n",
+      "Epoch 20/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9168 - loss: 0.2933 - val_accuracy: 0.9123 - val_loss: 0.3004\n",
+      "Epoch 21/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9176 - loss: 0.2889 - val_accuracy: 0.9128 - val_loss: 0.2994\n",
+      "Epoch 22/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9177 - loss: 0.2870 - val_accuracy: 0.9122 - val_loss: 0.2968\n",
+      "Epoch 23/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9193 - loss: 0.2818 - val_accuracy: 0.9140 - val_loss: 0.2949\n",
+      "Epoch 24/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9178 - loss: 0.2903 - val_accuracy: 0.9147 - val_loss: 0.2939\n",
+      "Epoch 25/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9178 - loss: 0.2875 - val_accuracy: 0.9137 - val_loss: 0.2914\n",
+      "Epoch 26/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9208 - loss: 0.2832 - val_accuracy: 0.9150 - val_loss: 0.2889\n",
+      "Epoch 27/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9221 - loss: 0.2765 - val_accuracy: 0.9142 - val_loss: 0.2883\n",
+      "Epoch 28/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9220 - loss: 0.2752 - val_accuracy: 0.9167 - val_loss: 0.2868\n",
+      "Epoch 29/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9220 - loss: 0.2754 - val_accuracy: 0.9183 - val_loss: 0.2854\n",
+      "Epoch 30/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9239 - loss: 0.2700 - val_accuracy: 0.9173 - val_loss: 0.2820\n",
+      "Epoch 31/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9234 - loss: 0.2669 - val_accuracy: 0.9190 - val_loss: 0.2804\n",
+      "Epoch 32/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9228 - loss: 0.2678 - val_accuracy: 0.9193 - val_loss: 0.2797\n",
+      "Epoch 33/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9239 - loss: 0.2677 - val_accuracy: 0.9192 - val_loss: 0.2794\n",
+      "Epoch 34/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9240 - loss: 0.2631 - val_accuracy: 0.9208 - val_loss: 0.2765\n",
+      "Epoch 35/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9253 - loss: 0.2605 - val_accuracy: 0.9202 - val_loss: 0.2750\n",
+      "Epoch 36/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9254 - loss: 0.2570 - val_accuracy: 0.9205 - val_loss: 0.2734\n",
+      "Epoch 37/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9267 - loss: 0.2601 - val_accuracy: 0.9215 - val_loss: 0.2711\n",
+      "Epoch 38/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9279 - loss: 0.2569 - val_accuracy: 0.9212 - val_loss: 0.2715\n",
+      "Epoch 39/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9260 - loss: 0.2589 - val_accuracy: 0.9223 - val_loss: 0.2680\n",
+      "Epoch 40/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9284 - loss: 0.2550 - val_accuracy: 0.9218 - val_loss: 0.2671\n",
+      "Epoch 41/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9286 - loss: 0.2491 - val_accuracy: 0.9227 - val_loss: 0.2660\n",
+      "Epoch 42/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9316 - loss: 0.2432 - val_accuracy: 0.9237 - val_loss: 0.2625\n",
+      "Epoch 43/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9274 - loss: 0.2508 - val_accuracy: 0.9252 - val_loss: 0.2635\n",
+      "Epoch 44/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9290 - loss: 0.2472 - val_accuracy: 0.9255 - val_loss: 0.2595\n",
+      "Epoch 45/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9302 - loss: 0.2449 - val_accuracy: 0.9252 - val_loss: 0.2601\n",
+      "Epoch 46/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9322 - loss: 0.2399 - val_accuracy: 0.9270 - val_loss: 0.2568\n",
+      "Epoch 47/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9323 - loss: 0.2422 - val_accuracy: 0.9278 - val_loss: 0.2550\n",
+      "Epoch 48/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9321 - loss: 0.2399 - val_accuracy: 0.9282 - val_loss: 0.2527\n",
+      "Epoch 49/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9330 - loss: 0.2372 - val_accuracy: 0.9282 - val_loss: 0.2514\n",
+      "Epoch 50/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9347 - loss: 0.2327 - val_accuracy: 0.9277 - val_loss: 0.2495\n",
+      "Точность: 0.9312000274658203\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Обучение сетей с одним скрытым слоем\n",
+    "for units in hidden_units_list:\n",
+    "    print(f\"\\nОбучение модели с {units} нейронами...\")\n",
+    "    model, history, scores = create_and_train_model(units, f\"model_{units}\")\n",
+    "\n",
+    "    models_1[units] = model\n",
+    "    histories_1[units] = history\n",
+    "    scores_1[units] = scores\n",
+    "\n",
+    "    print(f\"Точность: {scores[1]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 20,
+     "status": "ok",
+     "timestamp": 1759128269112,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "TbbBBxeMmy6c",
+    "outputId": "08b5a164-1e62-456c-80b9-bc7247be3fd3"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Наилучшее количество нейронов: 100\n",
+      "Точность: 0.9422000050544739\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Выбор наилучшей модели\n",
+    "best_units_1 = max(scores_1.items(), key=lambda x: x[1][1])[0]\n",
+    "print(f\"\\nНаилучшее количество нейронов: {best_units_1}\")\n",
+    "print(f\"Точность: {scores_1[best_units_1][1]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 279
+    },
+    "executionInfo": {
+     "elapsed": 620,
+     "status": "ok",
+     "timestamp": 1759128272502,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "EFEBxB2qm1fF",
+    "outputId": "d436ac7a-e33b-4cc2-e324-5decfc29de94"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Графики ошибок для всех моделей\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "for i, units in enumerate(hidden_units_list, 1):\n",
+    "    plt.subplot(1, 3, i)\n",
+    "    plt.plot(histories_1[units].history['loss'], label='Обучающая')\n",
+    "    plt.plot(histories_1[units].history['val_loss'], label='Валидационная')\n",
+    "    plt.title(f'{units} нейронов')\n",
+    "    plt.xlabel('Эпохи')\n",
+    "    plt.ylabel('Ошибка')\n",
+    "    plt.legend()\n",
+    "    plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EJBwT5vhnnSG"
+   },
+   "outputs": [],
+   "source": [
+    "# Добавление второго скрытого слоя\n",
+    "second_layer_units = [50, 100]\n",
+    "models_2 = {}\n",
+    "histories_2 = {}\n",
+    "scores_2 = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 420194,
+     "status": "ok",
+     "timestamp": 1759128698216,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "K5vweMySno3t",
+    "outputId": "b67b155c-89ea-46e0-a590-a588433e0a38"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Обучение модели со вторым слоем 50 нейронов\n",
+      "Epoch 1/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.2096 - loss: 2.2675 - val_accuracy: 0.5588 - val_loss: 2.0950\n",
+      "Epoch 2/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.5971 - loss: 1.9743 - val_accuracy: 0.6620 - val_loss: 1.5239\n",
+      "Epoch 3/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.6823 - loss: 1.3658 - val_accuracy: 0.7380 - val_loss: 1.0431\n",
+      "Epoch 4/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7627 - loss: 0.9560 - val_accuracy: 0.7980 - val_loss: 0.8069\n",
+      "Epoch 5/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8084 - loss: 0.7568 - val_accuracy: 0.8352 - val_loss: 0.6673\n",
+      "Epoch 6/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8369 - loss: 0.6330 - val_accuracy: 0.8543 - val_loss: 0.5793\n",
+      "Epoch 7/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8554 - loss: 0.5512 - val_accuracy: 0.8660 - val_loss: 0.5197\n",
+      "Epoch 8/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8667 - loss: 0.5051 - val_accuracy: 0.8757 - val_loss: 0.4769\n",
+      "Epoch 9/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8770 - loss: 0.4593 - val_accuracy: 0.8798 - val_loss: 0.4444\n",
+      "Epoch 10/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8850 - loss: 0.4256 - val_accuracy: 0.8877 - val_loss: 0.4190\n",
+      "Epoch 11/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8889 - loss: 0.4076 - val_accuracy: 0.8910 - val_loss: 0.3995\n",
+      "Epoch 12/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8948 - loss: 0.3829 - val_accuracy: 0.8947 - val_loss: 0.3835\n",
+      "Epoch 13/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8982 - loss: 0.3699 - val_accuracy: 0.8997 - val_loss: 0.3689\n",
+      "Epoch 14/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9008 - loss: 0.3560 - val_accuracy: 0.9017 - val_loss: 0.3582\n",
+      "Epoch 15/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9046 - loss: 0.3446 - val_accuracy: 0.9028 - val_loss: 0.3471\n",
+      "Epoch 16/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9051 - loss: 0.3367 - val_accuracy: 0.9055 - val_loss: 0.3375\n",
+      "Epoch 17/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9087 - loss: 0.3266 - val_accuracy: 0.9072 - val_loss: 0.3295\n",
+      "Epoch 18/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9089 - loss: 0.3192 - val_accuracy: 0.9093 - val_loss: 0.3214\n",
+      "Epoch 19/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9116 - loss: 0.3087 - val_accuracy: 0.9127 - val_loss: 0.3142\n",
+      "Epoch 20/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9110 - loss: 0.3098 - val_accuracy: 0.9148 - val_loss: 0.3084\n",
+      "Epoch 21/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9144 - loss: 0.2970 - val_accuracy: 0.9158 - val_loss: 0.3017\n",
+      "Epoch 22/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9155 - loss: 0.2888 - val_accuracy: 0.9172 - val_loss: 0.2970\n",
+      "Epoch 23/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9168 - loss: 0.2848 - val_accuracy: 0.9192 - val_loss: 0.2909\n",
+      "Epoch 24/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9174 - loss: 0.2841 - val_accuracy: 0.9205 - val_loss: 0.2863\n",
+      "Epoch 25/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9202 - loss: 0.2728 - val_accuracy: 0.9213 - val_loss: 0.2814\n",
+      "Epoch 26/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9214 - loss: 0.2714 - val_accuracy: 0.9222 - val_loss: 0.2768\n",
+      "Epoch 27/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9221 - loss: 0.2645 - val_accuracy: 0.9240 - val_loss: 0.2717\n",
+      "Epoch 28/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9224 - loss: 0.2637 - val_accuracy: 0.9250 - val_loss: 0.2669\n",
+      "Epoch 29/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9261 - loss: 0.2522 - val_accuracy: 0.9262 - val_loss: 0.2628\n",
+      "Epoch 30/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9250 - loss: 0.2523 - val_accuracy: 0.9258 - val_loss: 0.2587\n",
+      "Epoch 31/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9282 - loss: 0.2438 - val_accuracy: 0.9272 - val_loss: 0.2544\n",
+      "Epoch 32/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9298 - loss: 0.2417 - val_accuracy: 0.9288 - val_loss: 0.2506\n",
+      "Epoch 33/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9288 - loss: 0.2397 - val_accuracy: 0.9292 - val_loss: 0.2471\n",
+      "Epoch 34/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9311 - loss: 0.2364 - val_accuracy: 0.9297 - val_loss: 0.2433\n",
+      "Epoch 35/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9313 - loss: 0.2328 - val_accuracy: 0.9310 - val_loss: 0.2394\n",
+      "Epoch 36/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9318 - loss: 0.2303 - val_accuracy: 0.9320 - val_loss: 0.2362\n",
+      "Epoch 37/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9335 - loss: 0.2261 - val_accuracy: 0.9333 - val_loss: 0.2325\n",
+      "Epoch 38/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9336 - loss: 0.2229 - val_accuracy: 0.9355 - val_loss: 0.2299\n",
+      "Epoch 39/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9370 - loss: 0.2145 - val_accuracy: 0.9347 - val_loss: 0.2263\n",
+      "Epoch 40/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9379 - loss: 0.2130 - val_accuracy: 0.9362 - val_loss: 0.2239\n",
+      "Epoch 41/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9386 - loss: 0.2097 - val_accuracy: 0.9375 - val_loss: 0.2209\n",
+      "Epoch 42/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9380 - loss: 0.2116 - val_accuracy: 0.9378 - val_loss: 0.2170\n",
+      "Epoch 43/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9406 - loss: 0.2028 - val_accuracy: 0.9388 - val_loss: 0.2144\n",
+      "Epoch 44/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9391 - loss: 0.2074 - val_accuracy: 0.9402 - val_loss: 0.2115\n",
+      "Epoch 45/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9421 - loss: 0.1970 - val_accuracy: 0.9400 - val_loss: 0.2085\n",
+      "Epoch 46/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9435 - loss: 0.1979 - val_accuracy: 0.9410 - val_loss: 0.2063\n",
+      "Epoch 47/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9450 - loss: 0.1922 - val_accuracy: 0.9415 - val_loss: 0.2036\n",
+      "Epoch 48/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9427 - loss: 0.1911 - val_accuracy: 0.9405 - val_loss: 0.2024\n",
+      "Epoch 49/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9467 - loss: 0.1825 - val_accuracy: 0.9418 - val_loss: 0.1990\n",
+      "Epoch 50/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9470 - loss: 0.1861 - val_accuracy: 0.9438 - val_loss: 0.1962\n",
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9385 - loss: 0.2108\n",
+      "Точность: 0.9417999982833862\n",
+      "\n",
+      "Обучение модели со вторым слоем 100 нейронов\n",
+      "Epoch 1/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.1980 - loss: 2.2876 - val_accuracy: 0.4552 - val_loss: 2.0895\n",
+      "Epoch 2/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.5365 - loss: 1.9630 - val_accuracy: 0.6475 - val_loss: 1.4925\n",
+      "Epoch 3/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.6977 - loss: 1.3304 - val_accuracy: 0.7748 - val_loss: 1.0001\n",
+      "Epoch 4/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.7854 - loss: 0.9139 - val_accuracy: 0.8165 - val_loss: 0.7597\n",
+      "Epoch 5/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8193 - loss: 0.7167 - val_accuracy: 0.8407 - val_loss: 0.6288\n",
+      "Epoch 6/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8450 - loss: 0.5949 - val_accuracy: 0.8585 - val_loss: 0.5491\n",
+      "Epoch 7/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8610 - loss: 0.5259 - val_accuracy: 0.8677 - val_loss: 0.4961\n",
+      "Epoch 8/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8754 - loss: 0.4709 - val_accuracy: 0.8793 - val_loss: 0.4570\n",
+      "Epoch 9/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8834 - loss: 0.4375 - val_accuracy: 0.8878 - val_loss: 0.4271\n",
+      "Epoch 10/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8868 - loss: 0.4188 - val_accuracy: 0.8923 - val_loss: 0.4044\n",
+      "Epoch 11/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8942 - loss: 0.3833 - val_accuracy: 0.8953 - val_loss: 0.3853\n",
+      "Epoch 12/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.8955 - loss: 0.3731 - val_accuracy: 0.8993 - val_loss: 0.3711\n",
+      "Epoch 13/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9010 - loss: 0.3543 - val_accuracy: 0.9022 - val_loss: 0.3570\n",
+      "Epoch 14/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9045 - loss: 0.3409 - val_accuracy: 0.9043 - val_loss: 0.3464\n",
+      "Epoch 15/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9065 - loss: 0.3318 - val_accuracy: 0.9063 - val_loss: 0.3364\n",
+      "Epoch 16/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9074 - loss: 0.3262 - val_accuracy: 0.9093 - val_loss: 0.3285\n",
+      "Epoch 17/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9096 - loss: 0.3151 - val_accuracy: 0.9103 - val_loss: 0.3205\n",
+      "Epoch 18/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9126 - loss: 0.3063 - val_accuracy: 0.9125 - val_loss: 0.3138\n",
+      "Epoch 19/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9145 - loss: 0.2975 - val_accuracy: 0.9118 - val_loss: 0.3085\n",
+      "Epoch 20/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9173 - loss: 0.2899 - val_accuracy: 0.9138 - val_loss: 0.3025\n",
+      "Epoch 21/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9158 - loss: 0.2888 - val_accuracy: 0.9172 - val_loss: 0.2962\n",
+      "Epoch 22/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9170 - loss: 0.2860 - val_accuracy: 0.9178 - val_loss: 0.2914\n",
+      "Epoch 23/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9205 - loss: 0.2788 - val_accuracy: 0.9188 - val_loss: 0.2854\n",
+      "Epoch 24/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9182 - loss: 0.2785 - val_accuracy: 0.9195 - val_loss: 0.2813\n",
+      "Epoch 25/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9230 - loss: 0.2696 - val_accuracy: 0.9207 - val_loss: 0.2772\n",
+      "Epoch 26/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9247 - loss: 0.2647 - val_accuracy: 0.9208 - val_loss: 0.2726\n",
+      "Epoch 27/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9218 - loss: 0.2645 - val_accuracy: 0.9218 - val_loss: 0.2679\n",
+      "Epoch 28/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9235 - loss: 0.2625 - val_accuracy: 0.9238 - val_loss: 0.2643\n",
+      "Epoch 29/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9279 - loss: 0.2493 - val_accuracy: 0.9250 - val_loss: 0.2606\n",
+      "Epoch 30/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9279 - loss: 0.2476 - val_accuracy: 0.9248 - val_loss: 0.2560\n",
+      "Epoch 31/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9286 - loss: 0.2439 - val_accuracy: 0.9277 - val_loss: 0.2529\n",
+      "Epoch 32/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9285 - loss: 0.2440 - val_accuracy: 0.9263 - val_loss: 0.2487\n",
+      "Epoch 33/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9295 - loss: 0.2395 - val_accuracy: 0.9288 - val_loss: 0.2456\n",
+      "Epoch 34/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9328 - loss: 0.2292 - val_accuracy: 0.9300 - val_loss: 0.2422\n",
+      "Epoch 35/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9316 - loss: 0.2318 - val_accuracy: 0.9322 - val_loss: 0.2389\n",
+      "Epoch 36/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9345 - loss: 0.2233 - val_accuracy: 0.9325 - val_loss: 0.2347\n",
+      "Epoch 37/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9354 - loss: 0.2206 - val_accuracy: 0.9330 - val_loss: 0.2321\n",
+      "Epoch 38/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9374 - loss: 0.2146 - val_accuracy: 0.9335 - val_loss: 0.2294\n",
+      "Epoch 39/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9370 - loss: 0.2149 - val_accuracy: 0.9330 - val_loss: 0.2264\n",
+      "Epoch 40/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9374 - loss: 0.2136 - val_accuracy: 0.9372 - val_loss: 0.2238\n",
+      "Epoch 41/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9387 - loss: 0.2118 - val_accuracy: 0.9360 - val_loss: 0.2209\n",
+      "Epoch 42/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9384 - loss: 0.2104 - val_accuracy: 0.9370 - val_loss: 0.2177\n",
+      "Epoch 43/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9410 - loss: 0.2020 - val_accuracy: 0.9382 - val_loss: 0.2147\n",
+      "Epoch 44/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9431 - loss: 0.1985 - val_accuracy: 0.9387 - val_loss: 0.2121\n",
+      "Epoch 45/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9440 - loss: 0.1946 - val_accuracy: 0.9398 - val_loss: 0.2096\n",
+      "Epoch 46/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9431 - loss: 0.1964 - val_accuracy: 0.9408 - val_loss: 0.2077\n",
+      "Epoch 47/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9458 - loss: 0.1880 - val_accuracy: 0.9402 - val_loss: 0.2057\n",
+      "Epoch 48/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9442 - loss: 0.1929 - val_accuracy: 0.9403 - val_loss: 0.2023\n",
+      "Epoch 49/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9451 - loss: 0.1885 - val_accuracy: 0.9415 - val_loss: 0.2002\n",
+      "Epoch 50/50\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9456 - loss: 0.1835 - val_accuracy: 0.9432 - val_loss: 0.1982\n",
+      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9368 - loss: 0.2101\n",
+      "Точность: 0.942300021648407\n"
+     ]
+    }
+   ],
+   "source": [
+    "for units_2 in second_layer_units:\n",
+    "    print(f\"\\nОбучение модели со вторым слоем {units_2} нейронов\")\n",
+    "\n",
+    "    model = Sequential()\n",
+    "    model.add(Dense(units=best_units_1, input_dim=num_pixels, activation='sigmoid'))\n",
+    "    model.add(Dense(units=units_2, activation='sigmoid'))\n",
+    "    model.add(Dense(units=num_classes, activation='softmax'))\n",
+    "\n",
+    "    model.compile(loss='categorical_crossentropy',\n",
+    "                  optimizer='sgd',\n",
+    "                  metrics=['accuracy'])\n",
+    "\n",
+    "    history = model.fit(X_train, y_train,\n",
+    "                        validation_split=0.1,\n",
+    "                        epochs=50)\n",
+    "\n",
+    "    scores = model.evaluate(X_test, y_test)\n",
+    "\n",
+    "    models_2[units_2] = model\n",
+    "    histories_2[units_2] = history\n",
+    "    scores_2[units_2] = scores\n",
+    "\n",
+    "    print(f\"Точность: {scores[1]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 14,
+     "status": "ok",
+     "timestamp": 1759129484222,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "9lJtmn_oSVkB",
+    "outputId": "b49d6a95-574a-4ce5-e23f-eea49ba83603"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Наилучшее количество нейронов во втором слое: 100\n",
+      "Точность: 0.9423\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Выбор наилучшей двухслойной модели\n",
+    "best_units_2 = max(scores_2.items(), key=lambda x: x[1][1])[0]\n",
+    "print(f\"\\nНаилучшее количество нейронов во втором слое: {best_units_2}\")\n",
+    "print(f\"Точность: {scores_2[best_units_2][1]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "p0aeriYzShJk"
+   },
+   "outputs": [],
+   "source": [
+    "# Сбор результатов\n",
+    "results = {\n",
+    "    '0 слоев': {'нейроны_1': '-', 'нейроны_2': '-', 'точность': scores_0[1]},\n",
+    "    '1 слой_100': {'нейроны_1': 100, 'нейроны_2': '-', 'точность': scores_1[100][1]},\n",
+    "    '1 слой_300': {'нейроны_1': 300, 'нейроны_2': '-', 'точность': scores_1[300][1]},\n",
+    "    '1 слой_500': {'нейроны_1': 500, 'нейроны_2': '-', 'точность': scores_1[500][1]},\n",
+    "    '2 слоя_50': {'нейроны_1': best_units_1, 'нейроны_2': 50, 'точность': scores_2[50][1]},\n",
+    "    '2 слоя_100': {'нейроны_1': best_units_1, 'нейроны_2': 100, 'точность': scores_2[100][1]}\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 33,
+     "status": "ok",
+     "timestamp": 1759130442386,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "SHr6z7jbSmOG",
+    "outputId": "2d40f526-7756-4554-f110-2242e34e58a0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                    ТАБЛИЦА РЕЗУЛЬТАТОВ\n",
+      "======================================================================\n",
+      "   Кол-во скрытых слоев Нейроны_1_слоя Нейроны_2_слоя  Точность\n",
+      "0                     0              -              -    0.9233\n",
+      "1                     1            100              -    0.9422\n",
+      "2                     1            300              -    0.9377\n",
+      "3                     1            500              -    0.9312\n",
+      "4                     2            100             50    0.9418\n",
+      "5                     2            100            100    0.9423\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Создаем DataFrame из результатов\n",
+    "df_results = pd.DataFrame([\n",
+    "    {'Кол-во скрытых слоев': 0, 'Нейроны_1_слоя': '-', 'Нейроны_2_слоя': '-', 'Точность': results['0 слоев']['точность']},\n",
+    "    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 100, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_100']['точность']},\n",
+    "    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 300, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_300']['точность']},\n",
+    "    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 500, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_500']['точность']},\n",
+    "    {'Кол-во скрытых слоев': 2, 'Нейроны_1_слоя': best_units_1, 'Нейроны_2_слоя': 50, 'Точность': results['2 слоя_50']['точность']},\n",
+    "    {'Кол-во скрытых слоев': 2, 'Нейроны_1_слоя': best_units_1, 'Нейроны_2_слоя': 100, 'Точность': results['2 слоя_100']['точность']}\n",
+    "])\n",
+    "\n",
+    "print(\" \" * 20 + \"ТАБЛИЦА РЕЗУЛЬТАТОВ\")\n",
+    "print(\"=\" * 70)\n",
+    "# print(df_results.to_string(index=False, formatters={\n",
+    "#     'Точность': '{:.4f}'.format\n",
+    "# }))\n",
+    "print(df_results.reset_index(drop=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 41,
+     "status": "ok",
+     "timestamp": 1759130490602,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "PTC5CUJeWQ_V",
+    "outputId": "e8009546-5876-4427-9815-9aac53db8593"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Наилучшая архитектура: 2 слоя_100\n",
+      "Точность: 0.9423\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Выбор наилучшей модели\n",
+    "best_model_type = max(results.items(), key=lambda x: x[1]['точность'])[0]\n",
+    "best_accuracy = results[best_model_type]['точность']\n",
+    "print(f\"\\nНаилучшая архитектура: {best_model_type}\")\n",
+    "print(f\"Точность: {best_accuracy:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "JRPRpppHWV8-"
+   },
+   "outputs": [],
+   "source": [
+    "# Определение наилучшей модели\n",
+    "if '0' in best_model_type:\n",
+    "    best_model = model_0\n",
+    "elif '1' in best_model_type:\n",
+    "    best_neurons = int(best_model_type.split('_')[1])\n",
+    "    best_model = models_1[best_neurons]\n",
+    "else:\n",
+    "    best_neurons_2 = int(best_model_type.split('_')[1])\n",
+    "    best_model = models_2[best_neurons_2]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "wIlYoP_HSFph"
+   },
+   "outputs": [],
+   "source": [
+    "# Сохранение модели\n",
+    "best_model.save('best_mnist_model.keras')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 517
+    },
+    "executionInfo": {
+     "elapsed": 178,
+     "status": "ok",
+     "timestamp": 1759132236751,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "Kh6-u-8OoHny",
+    "outputId": "7c89ce31-3967-41bb-ff79-6e77e7f5d19b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+      "NN output: [[5.8587279e-06 9.7018647e-01 6.0002012e-03 5.5828933e-03 7.1756593e-05\n",
+      "  7.2469590e-03 3.2864737e-03 3.9730189e-04 6.0582636e-03 1.1638567e-03]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Real mark:  1\n",
+      "NN answer:  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# вывод тестового изображения и результата распознавания (1)\n",
+    "n = 123\n",
+    "result = best_model.predict(X_test[n:n+1])\n",
+    "print('NN output:', result)\n",
+    "plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n",
+    "plt.show()\n",
+    "print('Real mark: ', str(np.argmax(y_test[n])))\n",
+    "print('NN answer: ', str(np.argmax(result)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 517
+    },
+    "executionInfo": {
+     "elapsed": 284,
+     "status": "ok",
+     "timestamp": 1759132262259,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "WVGJRtVZc8V-",
+    "outputId": "5d4ed790-d7ce-4569-d667-49733f75e3cb"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+      "NN output: [[5.6045882e-02 2.3120556e-06 3.2519495e-01 6.1816531e-01 2.2406326e-08\n",
+      "  2.7827255e-04 7.9103382e-05 1.1205349e-06 2.1714537e-04 1.5997215e-05]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Real mark:  3\n",
+      "NN answer:  3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# вывод тестового изображения и результата распознавания (3)\n",
+    "n = 353\n",
+    "result = best_model.predict(X_test[n:n+1])\n",
+    "print('NN output:', result)\n",
+    "plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n",
+    "plt.show()\n",
+    "print('Real mark: ', str(np.argmax(y_test[n])))\n",
+    "print('NN answer: ', str(np.argmax(result)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "z5-YYw4uosUB"
+   },
+   "outputs": [],
+   "source": [
+    "# загрузка собственного изображения (Цифры 2 и 7)\n",
+    "from PIL import Image\n",
+    "file_data_2 = Image.open('2.png')\n",
+    "file_data_7 = Image.open('7.png')\n",
+    "file_data_2 = file_data_2.convert('L') # перевод в градации серого\n",
+    "file_data_7 = file_data_7.convert('L') # перевод в градации серого\n",
+    "test_img_2 = np.array(file_data_2)\n",
+    "test_img_7 = np.array(file_data_7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 465
+    },
+    "executionInfo": {
+     "elapsed": 156,
+     "status": "ok",
+     "timestamp": 1759130765327,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "dv17bJVVuslg",
+    "outputId": "d9b5b55c-75d9-4180-f93c-22befad0633c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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h6NbV1XVHnw8AMDaM6BdRN23apP379+vQoUOaPn169PFAIKArV67o/PnzMXdBvb29CgQCQ34ur9crr9c7kjEAAGNYXHdAzjlt2rRJe/bs0cGDB1VUVBSzf9GiRZo8ebIaGxujj7W1ten06dMqLS1NzMQAgLQQ1x1QdXW1du7cqX379ikrKyv6cx2/36/MzEz5/X49/fTTqqmpUU5Ojnw+n5599lmVlpbyDjgAQIy4AlRfXy9JeuSRR2Ie37Fjh9avXy9JeuONNzRhwgStXr1aAwMDKi8v11/+8peEDAsASB8e55yzHuJGkUhEfr/fegykkJEsLDqSRUWl9FtYlEVFYSkcDsvn8w27n7XgAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYGJEfxEVGKnRWtk6lVe1lljZGpC4AwIAGCFAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATHicc856iBtFIhH5/X7rMZAk3d3dcZ8TCASSMAmQHKdOnYr7nC1btsR9TkNDQ9znjLZwOCyfzzfsfu6AAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATLEaKUXX58uW4z5kyZUoSJgFSR39/f9znZGZmJmGSxGIxUgBASiJAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATEyyHgDjy5YtW+I+JxQKJWESIHU0NDRYj2CCOyAAgAkCBAAwEVeAQqGQFi9erKysLOXl5WnlypVqa2uLOeaRRx6Rx+OJ2aqqqhI6NABg7IsrQM3NzaqurlZra6s++ugjXb16VcuXL1dfX1/Mcc8884y6u7uj29atWxM6NABg7IvrTQgHDhyI+bihoUF5eXk6evSoli5dGn38rrvuUiAQSMyEAIC0dEc/AwqHw5KknJycmMffeecd5ebmav78+aqtrdWlS5eG/RwDAwOKRCIxGwAg/Y34bdiDg4PavHmzHnzwQc2fPz/6+JNPPqmZM2cqGAzq+PHjevHFF9XW1qYPPvhgyM8TCoVG9NZcAMDYNuIAVVdX68SJE/r0009jHt+wYUP03wsWLFBBQYGWLVumjo4OzZkz56bPU1tbq5qamujHkUhEhYWFIx0LADBGjChAmzZt0v79+3Xo0CFNnz79lseWlJRIktrb24cMkNfrldfrHckYAIAxLK4AOef07LPPas+ePWpqalJRUdFtzzl27JgkqaCgYEQDAgDSU1wBqq6u1s6dO7Vv3z5lZWWpp6dHkuT3+5WZmamOjg7t3LlTP/3pTzV16lQdP35czz33nJYuXaqFCxcm5T8AADA2xRWg+vp6Sdd/2fRGO3bs0Pr165WRkaGPP/5Yb775pvr6+lRYWKjVq1fr5ZdfTtjAAID0EPe34G6lsLBQzc3NdzQQAGB88LjbVWWURSIR+f1+6zEAAHcoHA7L5/MNu5/FSAEAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADCRcgFyzlmPAABIgNt9PU+5AF24cMF6BABAAtzu67nHpdgtx+DgoM6cOaOsrCx5PJ6YfZFIRIWFherq6pLP5zOa0B7X4Tquw3Vch+u4DtelwnVwzunChQsKBoOaMGH4+5xJozjTtzJhwgRNnz79lsf4fL5x/QL7GtfhOq7DdVyH67gO11lfB7/ff9tjUu5bcACA8YEAAQBMjKkAeb1e1dXVyev1Wo9iiutwHdfhOq7DdVyH68bSdUi5NyEAAMaHMXUHBABIHwQIAGCCAAEATBAgAICJMROgbdu2adasWZoyZYpKSkr02WefWY806l577TV5PJ6Ybd68edZjJd2hQ4f02GOPKRgMyuPxaO/evTH7nXN69dVXVVBQoMzMTJWVlenkyZM2wybR7a7D+vXrb3p9VFRU2AybJKFQSIsXL1ZWVpby8vK0cuVKtbW1xRzT39+v6upqTZ06Vffcc49Wr16t3t5eo4mT49tch0ceeeSm10NVVZXRxEMbEwF69913VVNTo7q6On3++ecqLi5WeXm5zp49az3aqHvggQfU3d0d3T799FPrkZKur69PxcXF2rZt25D7t27dqrfeekvbt2/X4cOHdffdd6u8vFz9/f2jPGly3e46SFJFRUXM62PXrl2jOGHyNTc3q7q6Wq2trfroo4909epVLV++XH19fdFjnnvuOX344Yd6//331dzcrDNnzmjVqlWGUyfet7kOkvTMM8/EvB62bt1qNPEw3BiwZMkSV11dHf342rVrLhgMulAoZDjV6Kurq3PFxcXWY5iS5Pbs2RP9eHBw0AUCAffHP/4x+tj58+ed1+t1u3btMphwdHzzOjjn3Lp169yKFStM5rFy9uxZJ8k1Nzc7567/bz958mT3/vvvR4/597//7SS5lpYWqzGT7pvXwTnnfvzjH7tf/vKXdkN9Cyl/B3TlyhUdPXpUZWVl0ccmTJigsrIytbS0GE5m4+TJkwoGg5o9e7aeeuopnT592nokU52dnerp6Yl5ffj9fpWUlIzL10dTU5Py8vI0d+5cbdy4UefOnbMeKanC4bAkKScnR5J09OhRXb16Neb1MG/ePM2YMSOtXw/fvA5fe+edd5Sbm6v58+ertrZWly5dshhvWCm3GOk3ffXVV7p27Zry8/NjHs/Pz9d//vMfo6lslJSUqKGhQXPnzlV3d7e2bNmihx9+WCdOnFBWVpb1eCZ6enokacjXx9f7xouKigqtWrVKRUVF6ujo0G9+8xtVVlaqpaVFEydOtB4v4QYHB7V582Y9+OCDmj9/vqTrr4eMjAxlZ2fHHJvOr4ehroMkPfnkk5o5c6aCwaCOHz+uF198UW1tbfrggw8Mp42V8gHC/6usrIz+e+HChSopKdHMmTP13nvv6emnnzacDKlg7dq10X8vWLBACxcu1Jw5c9TU1KRly5YZTpYc1dXVOnHixLj4OeitDHcdNmzYEP33ggULVFBQoGXLlqmjo0Nz5swZ7TGHlPLfgsvNzdXEiRNvehdLb2+vAoGA0VSpITs7W/fff7/a29utRzHz9WuA18fNZs+erdzc3LR8fWzatEn79+/XJ598EvPnWwKBgK5cuaLz58/HHJ+ur4fhrsNQSkpKJCmlXg8pH6CMjAwtWrRIjY2N0ccGBwfV2Nio0tJSw8nsXbx4UR0dHSooKLAexUxRUZECgUDM6yMSiejw4cPj/vXx5Zdf6ty5c2n1+nDOadOmTdqzZ48OHjyooqKimP2LFi3S5MmTY14PbW1tOn36dFq9Hm53HYZy7NgxSUqt14P1uyC+jd27dzuv1+saGhrcv/71L7dhwwaXnZ3tenp6rEcbVb/61a9cU1OT6+zsdP/4xz9cWVmZy83NdWfPnrUeLakuXLjgvvjiC/fFF184Se5Pf/qT++KLL9x///tf55xzr7/+usvOznb79u1zx48fdytWrHBFRUXu8uXLxpMn1q2uw4ULF9zzzz/vWlpaXGdnp/v444/dD37wA3ffffe5/v5+69ETZuPGjc7v97umpibX3d0d3S5duhQ9pqqqys2YMcMdPHjQHTlyxJWWlrrS0lLDqRPvdtehvb3d/fa3v3VHjhxxnZ2dbt++fW727Nlu6dKlxpPHGhMBcs65P//5z27GjBkuIyPDLVmyxLW2tlqPNOrWrFnjCgoKXEZGhvvud7/r1qxZ49rb263HSrpPPvnESbppW7dunXPu+luxX3nlFZefn++8Xq9btmyZa2trsx06CW51HS5duuSWL1/upk2b5iZPnuxmzpzpnnnmmbT7P2lD/fdLcjt27Igec/nyZfeLX/zCfec733F33XWXe/zxx113d7fd0Elwu+tw+vRpt3TpUpeTk+O8Xq+799573a9//WsXDodtB/8G/hwDAMBEyv8MCACQnggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAE/8Hyf2noiyd3fIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+      "I think it's  3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# вывод собственного изображения (цифра 2)\n",
+    "plt.imshow(test_img_2, cmap=plt.get_cmap('gray'))\n",
+    "plt.show()\n",
+    "# предобработка\n",
+    "test_img_2 = test_img_2 / 255\n",
+    "test_img_2 = test_img_2.reshape(1, num_pixels)\n",
+    "# распознавание\n",
+    "result = best_model.predict(test_img_2)\n",
+    "print('I think it\\'s ', np.argmax(result))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 465
+    },
+    "executionInfo": {
+     "elapsed": 651,
+     "status": "ok",
+     "timestamp": 1759130799101,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "rhNzATtGuxbD",
+    "outputId": "11cd1569-d370-4070-c8de-02035699d8bb"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+      "I think it's  7\n"
+     ]
+    }
+   ],
+   "source": [
+    "# вывод собственного изображения (цифра 7)\n",
+    "plt.imshow(test_img_7, cmap=plt.get_cmap('gray'))\n",
+    "plt.show()\n",
+    "# предобработка\n",
+    "test_img_7 = test_img_7 / 255\n",
+    "test_img_7 = test_img_7.reshape(1, num_pixels)\n",
+    "# распознавание\n",
+    "result = best_model.predict(test_img_7)\n",
+    "print('I think it\\'s ', np.argmax(result))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "4rIUmwfYXcgh"
+   },
+   "outputs": [],
+   "source": [
+    "# Тестирование на собственных повернутых изображениях\n",
+    "from PIL import Image\n",
+    "file_data_2_90 = Image.open('2_90.png')\n",
+    "file_data_7_90 = Image.open('7_90.png')\n",
+    "file_data_2_90 = file_data_2_90.convert('L') # перевод в градации серого\n",
+    "file_data_7_90 = file_data_7_90.convert('L') # перевод в градации серого\n",
+    "test_img_2_90 = np.array(file_data_2_90)\n",
+    "test_img_7_90= np.array(file_data_7_90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 465
+    },
+    "executionInfo": {
+     "elapsed": 445,
+     "status": "ok",
+     "timestamp": 1759131775554,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "xeYNkU0OZqg2",
+    "outputId": "b17a7b99-ce57-45fa-c069-74ea8374c3d3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step\n",
+      "I think it's  7\n"
+     ]
+    }
+   ],
+   "source": [
+    "# вывод собственного изображения (цифра 2)\n",
+    "plt.imshow(test_img_2_90, cmap=plt.get_cmap('gray'))\n",
+    "plt.show()\n",
+    "# предобработка\n",
+    "test_img_2_90 = test_img_2_90 / 255\n",
+    "test_img_2_90 = test_img_2_90.reshape(1, num_pixels)\n",
+    "# распознавание\n",
+    "result = best_model.predict(test_img_2_90)\n",
+    "print('I think it\\'s ', np.argmax(result))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 465
+    },
+    "executionInfo": {
+     "elapsed": 238,
+     "status": "ok",
+     "timestamp": 1759131800104,
+     "user": {
+      "displayName": "Legal People",
+      "userId": "00818738730090246603"
+     },
+     "user_tz": -180
+    },
+    "id": "8JZajicXbNSA",
+    "outputId": "016e8c12-472d-4a15-c420-cd955edef901"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+      "I think it's  7\n"
+     ]
+    }
+   ],
+   "source": [
+    "# вывод собственного изображения (цифра 7)\n",
+    "plt.imshow(test_img_7_90, cmap=plt.get_cmap('gray'))\n",
+    "plt.show()\n",
+    "# предобработка\n",
+    "test_img_7_90 = test_img_7_90 / 255\n",
+    "test_img_7_90 = test_img_7_90.reshape(1, num_pixels)\n",
+    "# распознавание\n",
+    "result = best_model.predict(test_img_7_90)\n",
+    "print('I think it\\'s ', np.argmax(result))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DQJNpFTOZ7Z6"
+   },
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": [
+    {
+     "file_id": "1HorM0jtoJfNfcuh_uXJu8ODaEJ6xOUu-",
+     "timestamp": 1759209370437
+    }
+   ]
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/labworks/LW1/architecture_of_1_layer_NN.png b/labworks/LW1/architecture_of_1_layer_NN.png
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diff --git a/labworks/LW1/best_mnist_model.keras b/labworks/LW1/best_mnist_model.keras
new file mode 100644
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literal 0
HcmV?d00001

diff --git a/labworks/LW1/my_document.md b/labworks/LW1/my_document.md
new file mode 100644
index 0000000..dc49489
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+++ b/labworks/LW1/my_document.md
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+
+## Отчёт по лабораторной работе №1
+
+**Троянов Д.С., Чернов Д.Е. — А-01-22**
+
+---
+
+## 1) В среде Google Colab создали блокнот. Импортировали необходимые библиотеки и модули.
+
+```python
+# импорт модулей
+import tensorflow as tf
+from tensorflow import keras
+from keras.datasets import mnist
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.utils import to_categorical
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+import os
+
+# Укажем текущую директорию
+os.chdir('/content/drive/MyDrive/Colab Notebooks/is_dnn/labworks/LW1')
+```
+
+---
+
+## 2) Загрузили набор данных MNIST, содержащий размеченные изображения рукописных цифр.
+
+```python
+# Загрузка датасета
+(X_train_orig, y_train_orig), (X_test_orig, y_test_orig) = mnist.load_data()
+
+```
+
+---
+
+## 3) Разбили набор данных на обучающие и тестовые данные в соотношении 60 000:10 000 элементов.
+При разбиении параметр `random_state` выбрали равным (4k – 1), где k - номер бригады, k = 6 ⇒ `random_state = 23`.
+
+```python
+# разбиваем выборку на обучающую и тестовую выборку
+X = np.concatenate((X_train_orig, X_test_orig))
+y = np.concatenate((y_train_orig, y_test_orig))
+
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y,
+    test_size=10000,
+    train_size=60000,
+    random_state=3,
+)
+
+# вывод размерности массивов данных
+print('Shape of X train:', X_train.shape)
+print('Shape of y train:', y_train.shape)
+print('Shape of X test:', X_test.shape)
+print('Shape of y test:', y_test.shape)
+```
+
+```
+Shape of X train: (60000, 28, 28)
+Shape of y train: (60000,)
+Shape of X test: (10000, 28, 28)
+Shape of y test: (10000,)
+```
+
+---
+
+## 4) Вывели первые 4 элемента обучающих данных (изображения и метки цифр).
+
+```python
+# Вывод первых 4 изображений
+fig, axes = plt.subplots(1, 4, figsize=(12, 3))
+for i in range(4):
+    axes[i].imshow(X_train[i], cmap='gray')
+    axes[i].set_title(f'Метка: {y_train[i]}')
+    axes[i].axis('off')
+plt.tight_layout()
+plt.show()
+```
+
+![4 цифры](paragraph_4.png)
+
+
+# Были выведены цифры 7, 8, 2, 2
+---
+
+## 5) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения нейронной сети. Входные данные должны принимать значения от 0 до 1, метки цифр должны быть закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных.
+
+```python
+# развернем каждое изображение 28*28 в вектор 784
+num_pixels = X_train.shape[1] * X_train.shape[2]
+X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255
+X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255
+print('Shape of transformed X train:', X_train.shape)
+```
+
+```
+Shape of transformed X train: (60000, 784)
+```
+
+```python
+# переведем метки в one-hot
+from keras.utils import to_categorical
+y_train = to_categorical(y_train)
+y_test = to_categorical(y_test)
+print('Shape of transformed y train:', y_train.shape)
+num_classes = y_train.shape[1]
+```
+
+```
+Shape of transformed y train: (60000, 10)
+```
+
+---
+
+## 6) Реализовали модель однослойной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети. Вывели график функции ошибки на обучающих и валидационных данных по эпохам.
+
+
+```python
+model_0 = Sequential()
+model_0.add(Dense(units=num_classes, input_dim=num_pixels, activation='softmax'))
+
+# Компиляция модели
+model_0.compile(loss='categorical_crossentropy',
+                optimizer='sgd',
+                metrics=['accuracy'])
+
+# Вывод информации об архитектуре
+print("Архитектура однослойной сети:")
+model_0.summary()
+```
+
+![архитектура модели](architecture_of_1_layer_NN.png)
+
+```
+# Обучение модели
+history_0 = model_0.fit(X_train, y_train,
+                        validation_split=0.1,
+                        epochs=50)
+```
+
+```python
+# График функции ошибки по эпохам
+plt.figure(figsize=(10, 6))
+plt.plot(history_0.history['loss'], label='Обучающая выборка')
+plt.plot(history_0.history['val_loss'], label='Валидационная выборка')
+plt.title('Функция ошибки по эпохам (Однослойная сеть)')
+plt.xlabel('Эпохи')
+plt.ylabel('Ошибка')
+plt.legend()
+plt.grid(True)
+plt.show()
+```
+
+![график обучения](paragraph_6.png)
+
+---
+
+## 7) Применили обученную модель к тестовым данным. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных.
+
+```python
+# Оценка на тестовых данных
+scores_0 = model_0.evaluate(X_test, y_test, verbose=0)
+print("Результаты однослойной сети:")
+print(f"Ошибка на тестовых данных: {scores_0[0]}")
+print(f"Точность на тестовых данных: {scores_0[1]}")
+```
+
+```
+Результаты однослойной сети:
+		Ошибка на тестовых данных: 0.28625616431236267
+		Точность на тестовых данных: 0.92330002784729
+```
+
+---
+
+## 8) Добавили в модель один скрытый и провели обучение и тестирование (повторить п. 6–7) при 100, 300, 500 нейронах в скрытом слое. По метрике качества классификации на тестовых данных выбрали наилучшее количество нейронов в скрытом слое. В качестве функции активации нейронов в скрытом слое использовали функцию `sigmoid`.
+
+```python
+# Функция для создания и обучения модели
+def create_and_train_model(hidden_units, model_name):
+    model = Sequential()
+    model.add(Dense(units=hidden_units, input_dim=num_pixels, activation='sigmoid'))
+    model.add(Dense(units=num_classes, activation='softmax'))
+
+    model.compile(loss='categorical_crossentropy',
+                  optimizer='sgd',
+                  metrics=['accuracy'])
+
+    history = model.fit(X_train, y_train,
+                        validation_split=0.1,
+                        epochs=50)
+
+    scores = model.evaluate(X_test, y_test, verbose=0)
+
+    return model, history, scores
+
+# Эксперименты с разным количеством нейронов
+hidden_units_list = [100, 300, 500]
+models_1 = {}
+histories_1 = {}
+scores_1 = {}
+
+# Обучение сетей с одним скрытым слоем
+for units in hidden_units_list:
+    print(f"
+Обучение модели с {units} нейронами...")
+    model, history, scores = create_and_train_model(units, f"model_{units}")
+
+    models_1[units] = model
+    histories_1[units] = history
+    scores_1[units] = scores
+
+    print(f"Точность: {scores[1]}")
+```
+
+# Определим лучшую модель по итогвой точности
+```python
+# Выбор наилучшей модели
+best_units_1 = max(scores_1.items(), key=lambda x: x[1][1])[0]
+print(f"
+Наилучшее количество нейронов: {best_units_1}")
+print(f"Точность: {scores_1[best_units_1][1]}")
+```
+
+```
+		Наилучшее количество нейронов: 100
+		Точность: 0.9422000050544739
+```
+
+# Отобразим графики ошибок для каждой из архитектур нейросети
+
+```python
+# Графики ошибок для всех моделей
+plt.figure(figsize=(15, 5))
+for i, units in enumerate(hidden_units_list, 1):
+    plt.subplot(1, 3, i)
+    plt.plot(histories_1[units].history['loss'], label='Обучающая')
+    plt.plot(histories_1[units].history['val_loss'], label='Валидационная')
+    plt.title(f'{units} нейронов')
+    plt.xlabel('Эпохи')
+    plt.ylabel('Ошибка')
+    plt.legend()
+    plt.grid(True)
+plt.tight_layout()
+plt.show()
+```
+
+![график обучения для каждой эпохи](paragraph_8.png)
+
+
+**По результатам проведнного эксперимента наилучший показатель точности продемонстрировала нейронная сеть со 100 нейронами в скрытом слое - примерно 0.9422.**
+
+---
+
+## 9) Добавили в архитектуру с лучшим показателем из п. 8, второй скрытый слой и провели обучение и тестирование при 50 и 100 нейронах во втором скрытом слое. В качестве функции активации нейронов в скрытом слое использовали функцию `sigmoid`.
+
+```python
+# Добавление второго скрытого слоя
+second_layer_units = [50, 100]
+models_2 = {}
+histories_2 = {}
+scores_2 = {}
+
+
+for units_2 in second_layer_units:
+    print(f"
+Обучение модели со вторым слоем {units_2} нейронов")
+
+    model = Sequential()
+    model.add(Dense(units=best_units_1, input_dim=num_pixels, activation='sigmoid'))
+    model.add(Dense(units=units_2, activation='sigmoid'))
+    model.add(Dense(units=num_classes, activation='softmax'))
+
+    model.compile(loss='categorical_crossentropy',
+                  optimizer='sgd',
+                  metrics=['accuracy'])
+
+    history = model.fit(X_train, y_train,
+                        validation_split=0.1,
+                        epochs=50)
+
+    scores = model.evaluate(X_test, y_test)
+
+    models_2[units_2] = model
+    histories_2[units_2] = history
+    scores_2[units_2] = scores
+
+    print(f"Точность: {scores[1]}")
+
+```
+
+# Результаты обучения моделей:
+
+```
+Обучение модели со вторым слоем 50 нейронов:
+Точность: 0.9417999982833862
+
+Обучение модели со вторым слоем 100 нейронов
+Точность: 0.942300021648407
+```
+
+# Выбор наилучшей двухслойной модели
+```python
+best_units_2 = max(scores_2.items(), key=lambda x: x[1][1])[0]
+print(f"
+Наилучшее количество нейронов во втором слое: {best_units_2}")
+print(f"Точность: {scores_2[best_units_2][1]}")
+```
+
+```
+	Наилучшее количество нейронов во втором слое: 100
+	Точность: 0.9423
+```
+
+
+## 10) Результаты исследования архитектуры нейронной сети занесли в таблицу:
+
+```python
+# Сбор результатов
+results = {
+    '0 слоев': {'нейроны_1': '-', 'нейроны_2': '-', 'точность': scores_0[1]},
+    '1 слой_100': {'нейроны_1': 100, 'нейроны_2': '-', 'точность': scores_1[100][1]},
+    '1 слой_300': {'нейроны_1': 300, 'нейроны_2': '-', 'точность': scores_1[300][1]},
+    '1 слой_500': {'нейроны_1': 500, 'нейроны_2': '-', 'точность': scores_1[500][1]},
+    '2 слоя_50': {'нейроны_1': best_units_1, 'нейроны_2': 50, 'точность': scores_2[50][1]},
+    '2 слоя_100': {'нейроны_1': best_units_1, 'нейроны_2': 100, 'точность': scores_2[100][1]}
+}
+
+# Создаем DataFrame из результатов
+df_results = pd.DataFrame([
+    {'Кол-во скрытых слоев': 0, 'Нейроны_1_слоя': '-', 'Нейроны_2_слоя': '-', 'Точность': results['0 слоев']['точность']},
+    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 100, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_100']['точность']},
+    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 300, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_300']['точность']},
+    {'Кол-во скрытых слоев': 1, 'Нейроны_1_слоя': 500, 'Нейроны_2_слоя': '-', 'Точность': results['1 слой_500']['точность']},
+    {'Кол-во скрытых слоев': 2, 'Нейроны_1_слоя': best_units_1, 'Нейроны_2_слоя': 50, 'Точность': results['2 слоя_50']['точность']},
+    {'Кол-во скрытых слоев': 2, 'Нейроны_1_слоя': best_units_1, 'Нейроны_2_слоя': 100, 'Точность': results['2 слоя_100']['точность']}
+])
+
+print(" " * 20 + "ТАБЛИЦА РЕЗУЛЬТАТОВ")
+print("=" * 70)
+# print(df_results.to_string(index=False, formatters={
+#     'Точность': '{:.4f}'.format
+# }))
+print(df_results.reset_index(drop=True))
+
+
+
+```
+
+```
+		ТАБЛИЦА РЕЗУЛЬТАТОВ
+		======================================================================
+		   Кол-во скрытых слоев Нейроны_1_слоя Нейроны_2_слоя  Точность
+		0                     0              -              -    0.9233
+		1                     1            100              -    0.9422
+		2                     1            300              -    0.9377
+		3                     1            500              -    0.9312
+		4                     2            100             50    0.9418
+		5                     2            100            100    0.9423
+
+```
+
+```python
+# Выбор наилучшей модели
+best_model_type = max(results.items(), key=lambda x: x[1]['точность'])[0]
+best_accuracy = results[best_model_type]['точность']
+print(f"
+Наилучшая архитектура: {best_model_type}")
+print(f"Точность: {best_accuracy}")
+```
+
+```
+		Наилучшая архитектура: 2 слоя_100
+		Точность: 0.9423
+```
+### По результатам исследования сделали выводы и выбрали наилучшую архитектуру нейронной сети с точки зрения качества классификации.
+
+**Из таблицы следует, что лучшей архитектурой является НС с двумя скрытыми слоями по 100 и 50 нейронов, второе место занимает НС с одним скрытым слоем и 100 нейронами, на основе которой мы и строили НС с двумя скрытыми слоями. При увеличении количества нейронов в архитектуре НС с 1-м скрытым слоем в результате тестирования было вявлено, что метрики качества падают. Это связано с переобучение нашей НС с 1-м скрытым слоем (когда построенная модель хорошо объясняет примеры из обучающей выборки, но относительно плохо работает на примерах, не участвовавших в обучении) Такая тенденция вероятно возникает из-за простоты датасета MNIST, при усложнении архитектуры НС начинает переобучаться, а оценка качества на тестовых данных падает. Но также стоит отметить, что при усложнении структуры НС точнсть модели также и растет.**
+
+---
+
+## 11) Сохранили наилучшую нейронную сеть на диск.
+
+```python
+# Сохранение модели
+best_model.save('best_mnist_model.keras')
+```
+
+---
+
+## 12) Для нейронной сети наилучшей архитектуры вывели два тестовых изображения, истинные метки и результат распознавания изображений.
+
+```python
+# вывод тестового изображения и результата распознавания (1)
+n = 123
+result = best_model.predict(X_test[n:n+1])
+print('NN output:', result)
+plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))
+plt.show()
+print('Real mark: ', str(np.argmax(y_test[n])))
+print('NN answer: ', str(np.argmax(result)))
+```
+
+![результат 1](paragraph_12_1.png)
+
+```python
+# вывод тестового изображения и результата распознавания (3)
+n = 353
+result = best_model.predict(X_test[n:n+1])
+print('NN output:', result)
+plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))
+plt.show()
+print('Real mark: ', str(np.argmax(y_test[n])))
+print('NN answer: ', str(np.argmax(result)))
+```
+![результат 2](paragraph_12_3.png)
+
+---
+
+## 13) Создали собственные изображения рукописных цифр, подобное представленным в наборе MNIST. Цифру выбрали как остаток от деления на 10 числа своего дня рождения (12 марта → 2, 17 сентября → 7 ). Сохранили изображения. Загрузили, предобработали и подали на вход обученной нейронной сети собственные изображения. Вывели изображения и результаты распознавания.
+
+```python
+# загрузка собственного изображения
+file_data_2 = Image.open('2.png')
+file_data_7 = Image.open('7.png')
+file_data_2 = file_data_2.convert('L') # перевод в градации серого
+file_data_7 = file_data_7.convert('L') # перевод в градации серого
+test_img_2 = np.array(file_data_2)
+test_img_7 = np.array(file_data_7)
+
+
+# вывод собственного изображения (цифра 2)
+plt.imshow(test_img_2, cmap=plt.get_cmap('gray'))
+plt.show()
+# предобработка
+test_img_2 = test_img_2 / 255
+test_img_2 = test_img_2.reshape(1, num_pixels)
+# распознавание
+result = best_model.predict(test_img_2)
+print('I think it\'s ', np.argmax(result))
+```
+
+![собственные изображения](2.png)
+
+```
+1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step
+```
+
+```python
+# вывод собственного изображения (цифра 7)
+plt.imshow(test_img_7, cmap=plt.get_cmap('gray'))
+plt.show()
+# предобработка
+test_img_7 = test_img_7 / 255
+test_img_7 = test_img_7.reshape(1, num_pixels)
+# распознавание
+result = best_model.predict(test_img_7)
+print('I think it\'s ', np.argmax(result))
+```
+
+```
+![собственные изображения](7.png)
+```
+
+```
+1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step
+I think it's  7
+```
+**Как видим в результате эксперимента наша НС недостаточно точно определила изображение цифры 2. Возможно это связано с малом размером используемой выборки. Для улучшения качества решения задачи классификации можно либо увеличить размерность выборки для обучения НС, либо изменить структуру НС для более точной ее работы**
+
+---
+
+## 14) Создать копию собственного изображения, отличающуюся от оригинала поворотом на 90 градусов в любую сторону. Сохранили изображения. Загрузили, предобработали и подали на вход обученной нейронной сети измененные изображения. Вывели изображения и результаты распознавания. Сделали выводы по результатам эксперимента.
+
+```python
+# загрузка собственного изображения
+file_data_2_90 = Image.open('2_90.png')
+file_data_7_90 = Image.open('7_90.png')
+file_data_2_90 = file_data_2_90.convert('L') # перевод в градации серого
+file_data_7_90 = file_data_7_90.convert('L') # перевод в градации серого
+test_img_2_90 = np.array(file_data_2_90)
+test_img_7_90= np.array(file_data_7_90)
+
+# вывод собственного изображения (цифра 2)
+plt.imshow(test_img_2_90, cmap=plt.get_cmap('gray'))
+plt.show()
+# предобработка
+test_img_2_90 = test_img_2_90 / 255
+test_img_2_90 = test_img_2_90.reshape(1, num_pixels)
+# распознавание
+result = best_model.predict(test_img_2_90)
+print('I think it\'s ', np.argmax(result))
+```
+
+![собственные изображения повернутые на 90 градусов](2_90.png)
+
+```
+1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 91ms/step
+I think it's  7
+```
+
+```python
+# вывод собственного изображения (цифра 7)
+plt.imshow(test_img_7_90, cmap=plt.get_cmap('gray'))
+plt.show()
+# предобработка
+test_img_7_90 = test_img_7_90 / 255
+test_img_7_90 = test_img_7_90.reshape(1, num_pixels)
+# распознавание
+result = best_model.predict(test_img_7_90)
+print('I think it\'s ', np.argmax(result))
+```
+
+![собственные изображения повернутые на 90 градусов](7_90.png)
+
+```
+1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 48ms/step
+I think it's  7
+```
+
+
+**При повороте рисунков цифр НС не смогла распознать одну из цифр. Так получилось, во-первых, по той же причине, почему НС не распознала одну из цифр в пункте 13, во-вторых - наша НС не обучалась на перевернутых цифрах**
+
+---
+
+
+
+        
\ No newline at end of file
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diff --git a/labworks/LW2/LW1 b/labworks/LW2/LW1
new file mode 160000
index 0000000..3f51d49
--- /dev/null
+++ b/labworks/LW2/LW1
@@ -0,0 +1 @@
+Subproject commit 3f51d49924a6a14ad44f2a5e70c707fb576ac786
diff --git a/labworks/LW3/LW1 b/labworks/LW3/LW1
new file mode 160000
index 0000000..3f51d49
--- /dev/null
+++ b/labworks/LW3/LW1
@@ -0,0 +1 @@
+Subproject commit 3f51d49924a6a14ad44f2a5e70c707fb576ac786