From 250ce84b3bb33fd78d11c9fd2f6a355430b8b753 Mon Sep 17 00:00:00 2001
From: MachulinaDV <machulinadv@mpei.ru>
Date: Sun, 21 Sep 2025 20:27:33 +0000
Subject: [PATCH] =?UTF-8?q?=D0=A3=D0=B4=D0=B0=D0=BB=D0=B8=D1=82=D1=8C=20'l?=
 =?UTF-8?q?abworks/LW1/IS=5FLR1.ipynb'?=
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-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","mount_file_id":"150ibV5FVGjhfi9jyOYyFoMLj6A0FBqNq","authorship_tag":"ABX9TyPCBflnuhhJkmymsWEmQNEN"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":2,"metadata":{"id":"_h4DGjN7sHZa","executionInfo":{"status":"ok","timestamp":1758318025676,"user_tz":-180,"elapsed":261,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"outputs":[],"source":["import os\n","os.chdir('/content/drive/MyDrive/Colab Notebooks')"]},{"cell_type":"code","source":["from tensorflow import keras\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import sklearn"],"metadata":{"id":"dyr70xmcsXQU","executionInfo":{"status":"ok","timestamp":1758318035269,"user_tz":-180,"elapsed":9559,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["from keras.datasets import mnist\n","(X_train, y_train), (X_test, y_test) = mnist.load_data()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1-4Nf4M-spPi","executionInfo":{"status":"ok","timestamp":1758318035894,"user_tz":-180,"elapsed":642,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"545423b0-5758-4e06-f352-7ba88265c85b"},"execution_count":4,"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"]}]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split"],"metadata":{"id":"Y0-OHY-GstmN","executionInfo":{"status":"ok","timestamp":1758318036298,"user_tz":-180,"elapsed":403,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["X = np.concatenate((X_train, X_test))\n","y = np.concatenate((y_train, y_test))"],"metadata":{"id":"tmaCmlbdsw01","executionInfo":{"status":"ok","timestamp":1758318036363,"user_tz":-180,"elapsed":60,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 10000, train_size = 60000, random_state = 7)"],"metadata":{"id":"7i0LOumLszkn","executionInfo":{"status":"ok","timestamp":1758318036411,"user_tz":-180,"elapsed":31,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["print('Shape of X train:', X_train.shape)\n","print('Shape of y train:', y_train.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pSHJE5y-tCaS","executionInfo":{"status":"ok","timestamp":1758318036458,"user_tz":-180,"elapsed":41,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"207f46bd-67bc-4e61-d9e1-c62f415c5cb7"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of X train: (60000, 28, 28)\n","Shape of y train: (60000,)\n"]}]},{"cell_type":"code","source":["fig, axes = plt.subplots(1, 4, figsize=(10, 3))\n","\n","for i in range(4):\n","    axes[i].imshow(X_train[i], cmap=plt.get_cmap('gray'))\n","    axes[i].set_title(f'Label: {y_train[i]}')\n","\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":251},"id":"x5Aki0dYu9k8","executionInfo":{"status":"ok","timestamp":1758318037087,"user_tz":-180,"elapsed":625,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"5269f11c-3dc2-4022-a813-f2fc5d7ad462"},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x300 with 4 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["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)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CGOWqtNUz_VP","executionInfo":{"status":"ok","timestamp":1758318037244,"user_tz":-180,"elapsed":150,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"01ec9707-871d-4481-a276-71930b77a975"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of transformed X train: (60000, 784)\n"]}]},{"cell_type":"code","source":["from keras.utils import to_categorical\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]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GnlaKGw11f0w","executionInfo":{"status":"ok","timestamp":1758318037264,"user_tz":-180,"elapsed":14,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"75e2f775-c2b4-45bf-844f-dacb77791968"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of transformed y train: (60000, 10)\n"]}]},{"cell_type":"code","source":["from keras.models import Sequential\n","from keras.layers import Dense"],"metadata":{"id":"JrUiXnVX4h7y","executionInfo":{"status":"ok","timestamp":1758318037271,"user_tz":-180,"elapsed":4,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["model_01 = Sequential()\n","model_01.add(Dense(units=num_classes,input_dim=num_pixels, activation='softmax'))\n","model_01.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01.summary()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":225},"id":"G8M-v6-G3378","executionInfo":{"status":"ok","timestamp":1758318040978,"user_tz":-180,"elapsed":3703,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"b07c873b-da89-4890-e64b-684556b0aa99"},"execution_count":13,"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":"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":{}}]},{"cell_type":"code","source":["H = model_01.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size = 512\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ut479Pn87OSB","executionInfo":{"status":"ok","timestamp":1758318148276,"user_tz":-180,"elapsed":52722,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"9c2bf00b-3f52-471b-f545-10d9f86b32c3"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 18ms/step - accuracy: 0.9018 - loss: 0.3509 - val_accuracy: 0.9048 - val_loss: 0.3490\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8996 - loss: 0.3609 - val_accuracy: 0.9052 - val_loss: 0.3482\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9010 - loss: 0.3542 - val_accuracy: 0.9052 - val_loss: 0.3476\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9029 - loss: 0.3519 - val_accuracy: 0.9057 - val_loss: 0.3469\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9004 - loss: 0.3579 - val_accuracy: 0.9057 - val_loss: 0.3462\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9005 - loss: 0.3560 - val_accuracy: 0.9057 - val_loss: 0.3456\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8996 - loss: 0.3522 - val_accuracy: 0.9055 - val_loss: 0.3449\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9044 - loss: 0.3488 - val_accuracy: 0.9058 - val_loss: 0.3443\n","Epoch 9/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8997 - loss: 0.3532 - val_accuracy: 0.9058 - val_loss: 0.3436\n","Epoch 10/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9014 - loss: 0.3508 - val_accuracy: 0.9060 - val_loss: 0.3431\n","Epoch 11/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9034 - loss: 0.3469 - val_accuracy: 0.9063 - val_loss: 0.3425\n","Epoch 12/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9036 - loss: 0.3480 - val_accuracy: 0.9072 - val_loss: 0.3419\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9028 - loss: 0.3458 - val_accuracy: 0.9070 - val_loss: 0.3413\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9022 - loss: 0.3492 - val_accuracy: 0.9072 - val_loss: 0.3407\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9052 - loss: 0.3455 - val_accuracy: 0.9073 - val_loss: 0.3401\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9027 - loss: 0.3505 - val_accuracy: 0.9075 - val_loss: 0.3396\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9046 - loss: 0.3444 - val_accuracy: 0.9078 - val_loss: 0.3390\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9036 - loss: 0.3421 - val_accuracy: 0.9078 - val_loss: 0.3386\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9040 - loss: 0.3429 - val_accuracy: 0.9080 - val_loss: 0.3380\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9030 - loss: 0.3434 - val_accuracy: 0.9082 - val_loss: 0.3375\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9051 - loss: 0.3403 - val_accuracy: 0.9082 - val_loss: 0.3370\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9074 - loss: 0.3363 - val_accuracy: 0.9080 - val_loss: 0.3364\n","Epoch 23/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9062 - loss: 0.3372 - val_accuracy: 0.9082 - val_loss: 0.3360\n","Epoch 24/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9061 - loss: 0.3376 - val_accuracy: 0.9082 - val_loss: 0.3355\n","Epoch 25/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9057 - loss: 0.3403 - val_accuracy: 0.9085 - val_loss: 0.3350\n","Epoch 26/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9037 - loss: 0.3419 - val_accuracy: 0.9090 - val_loss: 0.3345\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9024 - loss: 0.3474 - val_accuracy: 0.9088 - val_loss: 0.3340\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9038 - loss: 0.3406 - val_accuracy: 0.9092 - val_loss: 0.3336\n","Epoch 29/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9049 - loss: 0.3402 - val_accuracy: 0.9092 - val_loss: 0.3331\n","Epoch 30/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9059 - loss: 0.3347 - val_accuracy: 0.9090 - val_loss: 0.3327\n","Epoch 31/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9044 - loss: 0.3368 - val_accuracy: 0.9092 - val_loss: 0.3322\n","Epoch 32/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9061 - loss: 0.3373 - val_accuracy: 0.9092 - val_loss: 0.3318\n","Epoch 33/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9064 - loss: 0.3344 - val_accuracy: 0.9095 - val_loss: 0.3314\n","Epoch 34/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9059 - loss: 0.3350 - val_accuracy: 0.9093 - val_loss: 0.3309\n","Epoch 35/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9067 - loss: 0.3348 - val_accuracy: 0.9097 - val_loss: 0.3306\n","Epoch 36/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9060 - loss: 0.3310 - val_accuracy: 0.9095 - val_loss: 0.3301\n","Epoch 37/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9055 - loss: 0.3321 - val_accuracy: 0.9097 - val_loss: 0.3298\n","Epoch 38/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9076 - loss: 0.3328 - val_accuracy: 0.9100 - val_loss: 0.3293\n","Epoch 39/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9074 - loss: 0.3270 - val_accuracy: 0.9102 - val_loss: 0.3289\n","Epoch 40/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9060 - loss: 0.3318 - val_accuracy: 0.9105 - val_loss: 0.3286\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9079 - loss: 0.3318 - val_accuracy: 0.9103 - val_loss: 0.3282\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9066 - loss: 0.3345 - val_accuracy: 0.9103 - val_loss: 0.3278\n","Epoch 43/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9092 - loss: 0.3295 - val_accuracy: 0.9108 - val_loss: 0.3274\n","Epoch 44/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9078 - loss: 0.3284 - val_accuracy: 0.9105 - val_loss: 0.3270\n","Epoch 45/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9058 - loss: 0.3351 - val_accuracy: 0.9108 - val_loss: 0.3267\n","Epoch 46/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9066 - loss: 0.3321 - val_accuracy: 0.9110 - val_loss: 0.3263\n","Epoch 47/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9079 - loss: 0.3281 - val_accuracy: 0.9115 - val_loss: 0.3260\n","Epoch 48/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9069 - loss: 0.3299 - val_accuracy: 0.9115 - val_loss: 0.3256\n","Epoch 49/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9058 - loss: 0.3291 - val_accuracy: 0.9117 - val_loss: 0.3253\n","Epoch 50/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9078 - loss: 0.3307 - val_accuracy: 0.9113 - val_loss: 0.3249\n","Epoch 51/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9072 - loss: 0.3286 - val_accuracy: 0.9117 - val_loss: 0.3246\n","Epoch 52/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9082 - loss: 0.3255 - val_accuracy: 0.9118 - val_loss: 0.3243\n","Epoch 53/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9077 - loss: 0.3282 - val_accuracy: 0.9122 - val_loss: 0.3239\n","Epoch 54/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9062 - loss: 0.3329 - val_accuracy: 0.9122 - val_loss: 0.3236\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9107 - loss: 0.3224 - val_accuracy: 0.9123 - val_loss: 0.3233\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9077 - loss: 0.3252 - val_accuracy: 0.9120 - val_loss: 0.3230\n","Epoch 57/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9074 - loss: 0.3230 - val_accuracy: 0.9125 - val_loss: 0.3227\n","Epoch 58/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9092 - loss: 0.3254 - val_accuracy: 0.9122 - val_loss: 0.3224\n","Epoch 59/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9080 - loss: 0.3253 - val_accuracy: 0.9127 - val_loss: 0.3220\n","Epoch 60/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9077 - loss: 0.3280 - val_accuracy: 0.9123 - val_loss: 0.3217\n","Epoch 61/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9091 - loss: 0.3240 - val_accuracy: 0.9125 - val_loss: 0.3214\n","Epoch 62/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9076 - loss: 0.3254 - val_accuracy: 0.9125 - val_loss: 0.3212\n","Epoch 63/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9101 - loss: 0.3250 - val_accuracy: 0.9127 - val_loss: 0.3209\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9090 - loss: 0.3230 - val_accuracy: 0.9123 - val_loss: 0.3206\n","Epoch 65/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9091 - loss: 0.3229 - val_accuracy: 0.9125 - val_loss: 0.3203\n","Epoch 66/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9103 - loss: 0.3200 - val_accuracy: 0.9123 - val_loss: 0.3200\n","Epoch 67/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9084 - loss: 0.3214 - val_accuracy: 0.9123 - val_loss: 0.3197\n","Epoch 68/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9086 - loss: 0.3214 - val_accuracy: 0.9123 - val_loss: 0.3194\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9083 - loss: 0.3238 - val_accuracy: 0.9125 - val_loss: 0.3192\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9091 - loss: 0.3254 - val_accuracy: 0.9125 - val_loss: 0.3189\n","Epoch 71/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9106 - loss: 0.3172 - val_accuracy: 0.9127 - val_loss: 0.3186\n","Epoch 72/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9097 - loss: 0.3247 - val_accuracy: 0.9127 - val_loss: 0.3183\n","Epoch 73/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9111 - loss: 0.3165 - val_accuracy: 0.9125 - val_loss: 0.3181\n","Epoch 74/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9114 - loss: 0.3195 - val_accuracy: 0.9127 - val_loss: 0.3179\n","Epoch 75/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9092 - loss: 0.3213 - val_accuracy: 0.9130 - val_loss: 0.3176\n","Epoch 76/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9102 - loss: 0.3176 - val_accuracy: 0.9127 - val_loss: 0.3174\n","Epoch 77/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9124 - loss: 0.3149 - val_accuracy: 0.9128 - val_loss: 0.3171\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9094 - loss: 0.3207 - val_accuracy: 0.9130 - val_loss: 0.3169\n","Epoch 79/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9089 - loss: 0.3220 - val_accuracy: 0.9128 - val_loss: 0.3166\n","Epoch 80/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9097 - loss: 0.3195 - val_accuracy: 0.9128 - val_loss: 0.3163\n","Epoch 81/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9098 - loss: 0.3208 - val_accuracy: 0.9128 - val_loss: 0.3161\n","Epoch 82/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9083 - loss: 0.3227 - val_accuracy: 0.9133 - val_loss: 0.3159\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9118 - loss: 0.3162 - val_accuracy: 0.9133 - val_loss: 0.3157\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9103 - loss: 0.3182 - val_accuracy: 0.9132 - val_loss: 0.3154\n","Epoch 85/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9084 - loss: 0.3209 - val_accuracy: 0.9135 - val_loss: 0.3152\n","Epoch 86/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9083 - loss: 0.3243 - val_accuracy: 0.9135 - val_loss: 0.3150\n","Epoch 87/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9096 - loss: 0.3177 - val_accuracy: 0.9135 - val_loss: 0.3147\n","Epoch 88/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9091 - loss: 0.3241 - val_accuracy: 0.9138 - val_loss: 0.3145\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9113 - loss: 0.3155 - val_accuracy: 0.9138 - val_loss: 0.3143\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9095 - loss: 0.3165 - val_accuracy: 0.9140 - val_loss: 0.3141\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9104 - loss: 0.3179 - val_accuracy: 0.9140 - val_loss: 0.3139\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9117 - loss: 0.3165 - val_accuracy: 0.9142 - val_loss: 0.3136\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9105 - loss: 0.3160 - val_accuracy: 0.9138 - val_loss: 0.3134\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9113 - loss: 0.3147 - val_accuracy: 0.9140 - val_loss: 0.3132\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9103 - loss: 0.3149 - val_accuracy: 0.9140 - val_loss: 0.3130\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9126 - loss: 0.3117 - val_accuracy: 0.9147 - val_loss: 0.3129\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9118 - loss: 0.3116 - val_accuracy: 0.9143 - val_loss: 0.3126\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9114 - loss: 0.3170 - val_accuracy: 0.9145 - val_loss: 0.3124\n","Epoch 99/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9114 - loss: 0.3116 - val_accuracy: 0.9147 - val_loss: 0.3122\n","Epoch 100/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9122 - loss: 0.3146 - val_accuracy: 0.9145 - val_loss: 0.3120\n"]}]},{"cell_type":"code","source":["plt.figure(figsize=(12, 5))\n","\n","plt.subplot(1, 2, 1)\n","plt.plot(H.history['loss'], label='Обучающая ошибка')\n","plt.plot(H.history['val_loss'], label='Валидационная ошибка')\n","plt.title('Функция ошибки по эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"YfhWOnxq70K3","executionInfo":{"status":"ok","timestamp":1758318431981,"user_tz":-180,"elapsed":274,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"614a5fef-9a43-4b7b-8bc8-b7ba5d12fc27"},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["scores=model_01.evaluate(X_test,y_test)\n","print('Loss on test data:', scores[0])\n","print('Accuracy on test data:', scores[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3ZJ4HMej9UBE","executionInfo":{"status":"ok","timestamp":1758318698379,"user_tz":-180,"elapsed":1373,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"1a40c754-e1c6-465f-946b-ae3d76baa013"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9140 - loss: 0.3213\n","Loss on test data: 0.3256668746471405\n","Accuracy on test data: 0.9133999943733215\n"]}]},{"cell_type":"code","source":["model_01_100 = Sequential()\n","model_01_100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n","model_01_100.add(Dense(units=num_classes, activation='softmax'))\n","model_01_100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01_100.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":259},"id":"Xy4I-UYP91_a","executionInfo":{"status":"ok","timestamp":1758320213384,"user_tz":-180,"elapsed":88,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"5b8d03bf-ccd8-4360-a964-599df61547cc"},"execution_count":40,"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":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_4\"\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_4\"</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_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m78,500\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,010\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_7 (<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\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">78,500</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_8 (<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\">1,010</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 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\">79,510</span> (310.59 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 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\">79,510</span> (310.59 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":{}}]},{"cell_type":"code","source":["H_01_100 = model_01_100.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size = 512\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IfOOffSABn_u","executionInfo":{"status":"ok","timestamp":1758320279494,"user_tz":-180,"elapsed":54124,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"fa1d6215-d2f3-4b58-f382-49956659eff1"},"execution_count":41,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 20ms/step - accuracy: 0.1668 - loss: 2.3279 - val_accuracy: 0.3438 - val_loss: 2.1833\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.3911 - loss: 2.1561 - val_accuracy: 0.5177 - val_loss: 2.0637\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.5439 - loss: 2.0367 - val_accuracy: 0.6167 - val_loss: 1.9493\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6287 - loss: 1.9212 - val_accuracy: 0.6672 - val_loss: 1.8389\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6681 - loss: 1.8154 - val_accuracy: 0.6962 - val_loss: 1.7328\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6983 - loss: 1.7089 - val_accuracy: 0.7228 - val_loss: 1.6317\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7199 - loss: 1.6091 - val_accuracy: 0.7375 - val_loss: 1.5363\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7359 - loss: 1.5157 - val_accuracy: 0.7487 - val_loss: 1.4473\n","Epoch 9/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7500 - loss: 1.4304 - val_accuracy: 0.7630 - val_loss: 1.3651\n","Epoch 10/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7620 - loss: 1.3501 - val_accuracy: 0.7737 - val_loss: 1.2898\n","Epoch 11/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7762 - loss: 1.2745 - val_accuracy: 0.7803 - val_loss: 1.2212\n","Epoch 12/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7831 - loss: 1.2097 - val_accuracy: 0.7890 - val_loss: 1.1591\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7929 - loss: 1.1494 - val_accuracy: 0.7945 - val_loss: 1.1029\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8028 - loss: 1.0911 - val_accuracy: 0.8000 - val_loss: 1.0523\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8097 - loss: 1.0435 - val_accuracy: 0.8082 - val_loss: 1.0064\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8156 - loss: 0.9975 - val_accuracy: 0.8157 - val_loss: 0.9648\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8189 - loss: 0.9592 - val_accuracy: 0.8198 - val_loss: 0.9272\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8243 - loss: 0.9238 - val_accuracy: 0.8238 - val_loss: 0.8930\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8269 - loss: 0.8898 - val_accuracy: 0.8300 - val_loss: 0.8618\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8324 - loss: 0.8590 - val_accuracy: 0.8338 - val_loss: 0.8333\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8372 - loss: 0.8278 - val_accuracy: 0.8385 - val_loss: 0.8071\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8412 - loss: 0.8051 - val_accuracy: 0.8413 - val_loss: 0.7831\n","Epoch 23/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8419 - loss: 0.7835 - val_accuracy: 0.8432 - val_loss: 0.7611\n","Epoch 24/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8456 - loss: 0.7601 - val_accuracy: 0.8467 - val_loss: 0.7407\n","Epoch 25/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8462 - loss: 0.7405 - val_accuracy: 0.8495 - val_loss: 0.7218\n","Epoch 26/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8515 - loss: 0.7211 - val_accuracy: 0.8523 - val_loss: 0.7042\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8507 - loss: 0.7080 - val_accuracy: 0.8550 - val_loss: 0.6880\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8523 - loss: 0.6941 - val_accuracy: 0.8567 - val_loss: 0.6728\n","Epoch 29/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8552 - loss: 0.6741 - val_accuracy: 0.8580 - val_loss: 0.6586\n","Epoch 30/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8608 - loss: 0.6555 - val_accuracy: 0.8593 - val_loss: 0.6453\n","Epoch 31/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8580 - loss: 0.6485 - val_accuracy: 0.8603 - val_loss: 0.6329\n","Epoch 32/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8596 - loss: 0.6361 - val_accuracy: 0.8618 - val_loss: 0.6212\n","Epoch 33/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8616 - loss: 0.6254 - val_accuracy: 0.8640 - val_loss: 0.6102\n","Epoch 34/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8623 - loss: 0.6135 - val_accuracy: 0.8653 - val_loss: 0.5998\n","Epoch 35/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8661 - loss: 0.6023 - val_accuracy: 0.8658 - val_loss: 0.5900\n","Epoch 36/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8652 - loss: 0.5940 - val_accuracy: 0.8668 - val_loss: 0.5807\n","Epoch 37/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8663 - loss: 0.5830 - val_accuracy: 0.8682 - val_loss: 0.5719\n","Epoch 38/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8663 - loss: 0.5780 - val_accuracy: 0.8693 - val_loss: 0.5636\n","Epoch 39/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8695 - loss: 0.5650 - val_accuracy: 0.8702 - val_loss: 0.5557\n","Epoch 40/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8689 - loss: 0.5618 - val_accuracy: 0.8708 - val_loss: 0.5482\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8710 - loss: 0.5557 - val_accuracy: 0.8727 - val_loss: 0.5410\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.5098\n","Epoch 47/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8751 - loss: 0.5169 - val_accuracy: 0.8790 - val_loss: 0.5044\n","Epoch 48/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8761 - loss: 0.5092 - val_accuracy: 0.8803 - val_loss: 0.4991\n","Epoch 49/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8763 - loss: 0.5065 - val_accuracy: 0.8803 - val_loss: 0.4941\n","Epoch 50/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8790 - loss: 0.4958 - val_accuracy: 0.8812 - val_loss: 0.4893\n","Epoch 51/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8783 - loss: 0.4957 - val_accuracy: 0.8818 - val_loss: 0.4847\n","Epoch 52/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8792 - loss: 0.4904 - val_accuracy: 0.8820 - val_loss: 0.4802\n","Epoch 53/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8808 - loss: 0.4848 - val_accuracy: 0.8823 - val_loss: 0.4759\n","Epoch 54/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8797 - loss: 0.4865 - val_accuracy: 0.8827 - val_loss: 0.4718\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8821 - loss: 0.4751 - val_accuracy: 0.8838 - val_loss: 0.4678\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.4499\n","Epoch 61/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8837 - loss: 0.4567 - val_accuracy: 0.8870 - val_loss: 0.4467\n","Epoch 62/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8843 - loss: 0.4531 - val_accuracy: 0.8877 - val_loss: 0.4435\n","Epoch 63/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8851 - loss: 0.4517 - val_accuracy: 0.8875 - val_loss: 0.4405\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8842 - loss: 0.4463 - val_accuracy: 0.8882 - val_loss: 0.4376\n","Epoch 65/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8857 - loss: 0.4446 - val_accuracy: 0.8885 - val_loss: 0.4347\n","Epoch 66/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8868 - loss: 0.4385 - val_accuracy: 0.8888 - val_loss: 0.4319\n","Epoch 67/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8869 - loss: 0.4418 - val_accuracy: 0.8898 - val_loss: 0.4292\n","Epoch 68/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8880 - loss: 0.4336 - val_accuracy: 0.8898 - val_loss: 0.4265\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8869 - loss: 0.4347 - val_accuracy: 0.8897 - val_loss: 0.4240\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8878 - loss: 0.4302 - val_accuracy: 0.8907 - val_loss: 0.4215\n","Epoch 71/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8893 - loss: 0.4250 - val_accuracy: 0.8912 - val_loss: 0.4191\n","Epoch 72/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8900 - loss: 0.4239 - val_accuracy: 0.8913 - val_loss: 0.4168\n","Epoch 73/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8894 - loss: 0.4221 - val_accuracy: 0.8922 - val_loss: 0.4145\n","Epoch 74/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8898 - loss: 0.4225 - val_accuracy: 0.8918 - val_loss: 0.4123\n","Epoch 75/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8878 - loss: 0.4220 - val_accuracy: 0.8920 - val_loss: 0.4103\n","Epoch 76/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8907 - loss: 0.4169 - val_accuracy: 0.8925 - val_loss: 0.4081\n","Epoch 77/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8931 - loss: 0.4083 - val_accuracy: 0.8930 - val_loss: 0.4060\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8910 - loss: 0.4135 - val_accuracy: 0.8935 - val_loss: 0.4040\n","Epoch 79/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8904 - loss: 0.4143 - val_accuracy: 0.8932 - val_loss: 0.4021\n","Epoch 80/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8931 - loss: 0.4067 - val_accuracy: 0.8937 - val_loss: 0.4002\n","Epoch 81/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8922 - loss: 0.4075 - val_accuracy: 0.8940 - val_loss: 0.3984\n","Epoch 82/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8930 - loss: 0.4032 - val_accuracy: 0.8940 - val_loss: 0.3966\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8939 - loss: 0.4034 - val_accuracy: 0.8955 - val_loss: 0.3948\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.3865\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8963 - loss: 0.3915 - val_accuracy: 0.8968 - val_loss: 0.3850\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8947 - loss: 0.3908 - val_accuracy: 0.8968 - val_loss: 0.3835\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8955 - loss: 0.3917 - val_accuracy: 0.8977 - val_loss: 0.3819\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8956 - loss: 0.3874 - val_accuracy: 0.8985 - val_loss: 0.3805\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8935 - loss: 0.3915 - val_accuracy: 0.8978 - val_loss: 0.3791\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8954 - loss: 0.3876 - val_accuracy: 0.8980 - val_loss: 0.3777\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8952 - loss: 0.3856 - val_accuracy: 0.8990 - val_loss: 0.3763\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8968 - loss: 0.3804 - val_accuracy: 0.8995 - val_loss: 0.3750\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8968 - loss: 0.3825 - val_accuracy: 0.8998 - val_loss: 0.3737\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m 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эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"tKjsQBv9CFvt","executionInfo":{"status":"ok","timestamp":1758320282972,"user_tz":-180,"elapsed":231,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"a0827b36-4815-413f-e0fb-70bbb603c2b5"},"execution_count":42,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["scores_01_100=model_01_100.evaluate(X_test,y_test)\n","print('Loss on test data:', scores_01_100[0])\n","print('Accuracy on test data:', scores_01_100[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EL53RhenDJwj","executionInfo":{"status":"ok","timestamp":1758320309879,"user_tz":-180,"elapsed":769,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"0b5e45fe-1969-42d7-a3e8-7c13055d1e4d"},"execution_count":44,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9002 - loss: 0.3799\n","Loss on test data: 0.38375645875930786\n","Accuracy on test data: 0.9007999897003174\n"]}]},{"cell_type":"code","source":["model_01_300 = Sequential()\n","model_01_300.add(Dense(units=300,input_dim=num_pixels, activation='sigmoid'))\n","model_01_300.add(Dense(units=num_classes, activation='softmax'))\n","model_01_300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01_300.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"sa85CPpiD58n","executionInfo":{"status":"ok","timestamp":1758320425263,"user_tz":-180,"elapsed":110,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"4008964b-1102-4004-a484-ea7271e73f02"},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_6\"\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_6\"</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_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m)            │       \u001b[38;5;34m235,500\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_12 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m3,010\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_11 (<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\">300</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">235,500</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_12 (<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\">3,010</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m238,510\u001b[0m (931.68 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\">238,510</span> (931.68 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m238,510\u001b[0m (931.68 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\">238,510</span> (931.68 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":{}}]},{"cell_type":"code","source":["H_01_300 = model_01_300.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size = 512\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vVMVL1BQEn6o","executionInfo":{"status":"ok","timestamp":1758320636463,"user_tz":-180,"elapsed":53993,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"0fe44945-aec2-4cae-def2-5cfdadf6ef71"},"execution_count":47,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 15ms/step - accuracy: 0.1899 - loss: 2.2673 - val_accuracy: 0.4953 - val_loss: 2.1263\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5437 - loss: 2.0884 - val_accuracy: 0.6342 - val_loss: 1.9746\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6569 - loss: 1.9385 - val_accuracy: 0.6860 - val_loss: 1.8332\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6975 - loss: 1.8003 - val_accuracy: 0.7345 - val_loss: 1.7007\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7335 - loss: 1.6705 - val_accuracy: 0.7470 - val_loss: 1.5782\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7489 - loss: 1.5517 - val_accuracy: 0.7518 - val_loss: 1.4659\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7610 - loss: 1.4424 - val_accuracy: 0.7713 - val_loss: 1.3637\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7807 - loss: 1.3388 - val_accuracy: 0.7807 - val_loss: 1.2723\n","Epoch 9/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7913 - loss: 1.2549 - val_accuracy: 0.7928 - val_loss: 1.1906\n","Epoch 10/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7992 - loss: 1.1730 - val_accuracy: 0.7985 - val_loss: 1.1183\n","Epoch 11/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8076 - loss: 1.1047 - val_accuracy: 0.8083 - val_loss: 1.0546\n","Epoch 12/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8157 - loss: 1.0392 - val_accuracy: 0.8155 - val_loss: 0.9982\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8186 - loss: 0.9896 - val_accuracy: 0.8215 - val_loss: 0.9482\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8251 - loss: 0.9405 - val_accuracy: 0.8242 - val_loss: 0.9041\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8264 - loss: 0.8987 - val_accuracy: 0.8327 - val_loss: 0.8646\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8312 - loss: 0.8622 - val_accuracy: 0.8358 - val_loss: 0.8296\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8344 - loss: 0.8293 - val_accuracy: 0.8398 - val_loss: 0.7983\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8425 - loss: 0.7921 - val_accuracy: 0.8425 - val_loss: 0.7699\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8405 - loss: 0.7706 - val_accuracy: 0.8445 - val_loss: 0.7443\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8432 - loss: 0.7454 - val_accuracy: 0.8478 - val_loss: 0.7214\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8479 - loss: 0.7208 - val_accuracy: 0.8502 - val_loss: 0.7002\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8487 - loss: 0.7054 - val_accuracy: 0.8510 - val_loss: 0.6807\n","Epoch 23/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8512 - loss: 0.6828 - val_accuracy: 0.8535 - val_loss: 0.6633\n","Epoch 24/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8540 - loss: 0.6657 - val_accuracy: 0.8533 - val_loss: 0.6468\n","Epoch 25/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8555 - loss: 0.6497 - val_accuracy: 0.8560 - val_loss: 0.6318\n","Epoch 26/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8565 - loss: 0.6396 - val_accuracy: 0.8565 - val_loss: 0.6183\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8585 - loss: 0.6230 - val_accuracy: 0.8607 - val_loss: 0.6051\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.5528\n","Epoch 33/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8680 - loss: 0.5548 - val_accuracy: 0.8692 - val_loss: 0.5442\n","Epoch 34/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8681 - loss: 0.5499 - val_accuracy: 0.8692 - val_loss: 0.5362\n","Epoch 35/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.5326 - val_accuracy: 0.8722 - val_loss: 0.5282\n","Epoch 36/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8695 - loss: 0.5347 - val_accuracy: 0.8733 - val_loss: 0.5211\n","Epoch 37/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.5274 - val_accuracy: 0.8745 - val_loss: 0.5142\n","Epoch 38/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8710 - loss: 0.5211 - val_accuracy: 0.8745 - val_loss: 0.5077\n","Epoch 39/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8736 - loss: 0.5116 - val_accuracy: 0.8772 - val_loss: 0.5015\n","Epoch 40/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8731 - loss: 0.5104 - val_accuracy: 0.8770 - val_loss: 0.4957\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8749 - loss: 0.5015 - val_accuracy: 0.8763 - val_loss: 0.4903\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.4658\n","Epoch 47/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8777 - loss: 0.4718 - val_accuracy: 0.8822 - val_loss: 0.4619\n","Epoch 48/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8785 - loss: 0.4703 - val_accuracy: 0.8825 - val_loss: 0.4575\n","Epoch 49/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8771 - loss: 0.4688 - val_accuracy: 0.8838 - val_loss: 0.4536\n","Epoch 50/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8809 - loss: 0.4605 - val_accuracy: 0.8843 - val_loss: 0.4499\n","Epoch 51/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8819 - loss: 0.4504 - val_accuracy: 0.8843 - val_loss: 0.4462\n","Epoch 52/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8821 - loss: 0.4527 - val_accuracy: 0.8860 - val_loss: 0.4428\n","Epoch 53/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8842 - loss: 0.4510 - val_accuracy: 0.8863 - val_loss: 0.4395\n","Epoch 54/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8829 - loss: 0.4446 - val_accuracy: 0.8865 - val_loss: 0.4361\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8827 - loss: 0.4466 - val_accuracy: 0.8862 - val_loss: 0.4333\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8836 - loss: 0.4422 - val_accuracy: 0.8870 - val_loss: 0.4301\n","Epoch 57/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8850 - loss: 0.4345 - val_accuracy: 0.8865 - val_loss: 0.4273\n","Epoch 58/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8847 - loss: 0.4347 - val_accuracy: 0.8867 - val_loss: 0.4245\n","Epoch 59/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8858 - loss: 0.4290 - val_accuracy: 0.8878 - val_loss: 0.4217\n","Epoch 60/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8859 - loss: 0.4276 - val_accuracy: 0.8890 - val_loss: 0.4190\n","Epoch 61/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8858 - loss: 0.4297 - val_accuracy: 0.8893 - val_loss: 0.4166\n","Epoch 62/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8884 - loss: 0.4232 - val_accuracy: 0.8898 - val_loss: 0.4141\n","Epoch 63/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8871 - loss: 0.4220 - val_accuracy: 0.8898 - val_loss: 0.4117\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8880 - loss: 0.4192 - val_accuracy: 0.8913 - val_loss: 0.4094\n","Epoch 65/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8868 - loss: 0.4166 - val_accuracy: 0.8922 - val_loss: 0.4072\n","Epoch 66/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8884 - loss: 0.4140 - val_accuracy: 0.8920 - val_loss: 0.4051\n","Epoch 67/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8883 - loss: 0.4116 - val_accuracy: 0.8928 - val_loss: 0.4030\n","Epoch 68/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8908 - loss: 0.4077 - val_accuracy: 0.8928 - val_loss: 0.4010\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8889 - loss: 0.4076 - val_accuracy: 0.8922 - val_loss: 0.3990\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.3898\n","Epoch 75/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8892 - loss: 0.3987 - val_accuracy: 0.8940 - val_loss: 0.3882\n","Epoch 76/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8897 - loss: 0.4011 - val_accuracy: 0.8937 - val_loss: 0.3864\n","Epoch 77/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8913 - loss: 0.3960 - val_accuracy: 0.8942 - val_loss: 0.3849\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8913 - loss: 0.3919 - val_accuracy: 0.8935 - val_loss: 0.3835\n","Epoch 79/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8943 - loss: 0.3889 - val_accuracy: 0.8943 - val_loss: 0.3820\n","Epoch 80/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8924 - loss: 0.3896 - val_accuracy: 0.8958 - val_loss: 0.3803\n","Epoch 81/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8956 - loss: 0.3819 - val_accuracy: 0.8953 - val_loss: 0.3789\n","Epoch 82/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8918 - loss: 0.3897 - val_accuracy: 0.8962 - val_loss: 0.3776\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8936 - loss: 0.3873 - val_accuracy: 0.8952 - val_loss: 0.3762\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8930 - loss: 0.3861 - val_accuracy: 0.8963 - val_loss: 0.3748\n","Epoch 85/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8927 - loss: 0.3870 - val_accuracy: 0.8973 - val_loss: 0.3735\n","Epoch 86/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8972 - loss: 0.3717 - val_accuracy: 0.8977 - val_loss: 0.3721\n","Epoch 87/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8945 - loss: 0.3760 - val_accuracy: 0.8988 - val_loss: 0.3708\n","Epoch 88/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8943 - loss: 0.3832 - val_accuracy: 0.8985 - val_loss: 0.3696\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8952 - loss: 0.3784 - val_accuracy: 0.8988 - val_loss: 0.3684\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8975 - loss: 0.3723 - val_accuracy: 0.8988 - val_loss: 0.3672\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8961 - loss: 0.3775 - val_accuracy: 0.9003 - val_loss: 0.3660\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8938 - loss: 0.3783 - val_accuracy: 0.8998 - val_loss: 0.3649\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8969 - loss: 0.3731 - val_accuracy: 0.9003 - val_loss: 0.3638\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8971 - loss: 0.3678 - val_accuracy: 0.9005 - val_loss: 0.3626\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8967 - loss: 0.3679 - val_accuracy: 0.9002 - val_loss: 0.3616\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8957 - loss: 0.3735 - val_accuracy: 0.9007 - val_loss: 0.3605\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8987 - loss: 0.3624 - val_accuracy: 0.9007 - val_loss: 0.3594\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8972 - loss: 0.3684 - val_accuracy: 0.9012 - val_loss: 0.3584\n","Epoch 99/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8957 - loss: 0.3729 - val_accuracy: 0.9017 - val_loss: 0.3575\n","Epoch 100/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8975 - loss: 0.3649 - val_accuracy: 0.9018 - val_loss: 0.3566\n"]}]},{"cell_type":"code","source":["plt.figure(figsize=(12, 5))\n","\n","plt.subplot(1, 2, 1)\n","plt.plot(H_01_300.history['loss'], label='Обучающая ошибка')\n","plt.plot(H_01_300.history['val_loss'], label='Валидационная ошибка')\n","plt.title('Функция ошибки по эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"6bOcTrCEFjct","executionInfo":{"status":"ok","timestamp":1758320829105,"user_tz":-180,"elapsed":257,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"62c48eec-3671-4804-eac8-4f1b2da77452"},"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["scores_01_300=model_01_300.evaluate(X_test,y_test)\n","print('Loss on test data:', scores_01_300[0])\n","print('Accuracy on test data:', scores_01_300[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nYzwB9pGGEQD","executionInfo":{"status":"ok","timestamp":1758320966658,"user_tz":-180,"elapsed":1551,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"cdaca51d-e09c-4ecf-b2cc-8b7a52b231ed"},"execution_count":49,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8996 - loss: 0.3659\n","Loss on test data: 0.36969417333602905\n","Accuracy on test data: 0.9010999798774719\n"]}]},{"cell_type":"code","source":["model_01_500 = Sequential()\n","model_01_500.add(Dense(units=500,input_dim=num_pixels, activation='sigmoid'))\n","model_01_500.add(Dense(units=num_classes, activation='softmax'))\n","model_01_500.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01_500.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"0kOcD8BSGslt","executionInfo":{"status":"ok","timestamp":1758321310985,"user_tz":-180,"elapsed":78,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"bbc45b89-a56d-42e1-b3d7-99e38c6e8088"},"execution_count":51,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_8\"\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_8\"</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_15 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m)            │       \u001b[38;5;34m392,500\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_16 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m5,010\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_15 (<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\">500</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">392,500</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_16 (<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\">5,010</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m397,510\u001b[0m (1.52 MB)\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\">397,510</span> (1.52 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m397,510\u001b[0m (1.52 MB)\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\">397,510</span> (1.52 MB)\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":{}}]},{"cell_type":"code","source":["H_01_500 = model_01_500.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size = 512\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LxLlRJdkH0zB","executionInfo":{"status":"ok","timestamp":1758321460790,"user_tz":-180,"elapsed":56509,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"713a49cb-2a06-410b-fa39-41813dac3857"},"execution_count":52,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 16ms/step - accuracy: 0.1676 - loss: 2.3292 - val_accuracy: 0.4338 - val_loss: 2.1321\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5053 - loss: 2.0898 - val_accuracy: 0.6400 - val_loss: 1.9678\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6542 - loss: 1.9307 - val_accuracy: 0.7107 - val_loss: 1.8165\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7091 - loss: 1.7828 - val_accuracy: 0.7320 - val_loss: 1.6769\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7381 - loss: 1.6449 - val_accuracy: 0.7582 - val_loss: 1.5480\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7592 - loss: 1.5192 - val_accuracy: 0.7722 - val_loss: 1.4319\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7756 - loss: 1.4020 - val_accuracy: 0.7847 - val_loss: 1.3268\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7844 - loss: 1.3060 - val_accuracy: 0.7955 - val_loss: 1.2342\n","Epoch 9/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7961 - loss: 1.2138 - val_accuracy: 0.8050 - val_loss: 1.1522\n","Epoch 10/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8033 - loss: 1.1402 - val_accuracy: 0.8062 - val_loss: 1.0814\n","Epoch 11/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8079 - loss: 1.0730 - val_accuracy: 0.8188 - val_loss: 1.0182\n","Epoch 12/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8197 - loss: 1.0065 - val_accuracy: 0.8235 - val_loss: 0.9629\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8223 - loss: 0.9522 - val_accuracy: 0.8317 - val_loss: 0.9141\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8279 - loss: 0.9114 - val_accuracy: 0.8332 - val_loss: 0.8715\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8319 - loss: 0.8657 - val_accuracy: 0.8390 - val_loss: 0.8337\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8337 - loss: 0.8307 - val_accuracy: 0.8427 - val_loss: 0.8004\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8365 - loss: 0.8014 - val_accuracy: 0.8448 - val_loss: 0.7700\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8368 - loss: 0.7737 - val_accuracy: 0.8473 - val_loss: 0.7434\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8455 - loss: 0.7399 - val_accuracy: 0.8517 - val_loss: 0.7188\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8453 - loss: 0.7221 - val_accuracy: 0.8528 - val_loss: 0.6973\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8480 - loss: 0.7000 - val_accuracy: 0.8553 - val_loss: 0.6769\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8489 - loss: 0.6854 - val_accuracy: 0.8573 - val_loss: 0.6587\n","Epoch 23/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8529 - loss: 0.6648 - val_accuracy: 0.8617 - val_loss: 0.6420\n","Epoch 24/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8550 - loss: 0.6474 - val_accuracy: 0.8620 - val_loss: 0.6267\n","Epoch 25/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8579 - loss: 0.6302 - val_accuracy: 0.8640 - val_loss: 0.6125\n","Epoch 26/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8575 - loss: 0.6219 - val_accuracy: 0.8655 - val_loss: 0.5995\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8597 - loss: 0.6040 - val_accuracy: 0.8660 - val_loss: 0.5873\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8611 - loss: 0.5950 - val_accuracy: 0.8667 - val_loss: 0.5762\n","Epoch 29/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8629 - loss: 0.5814 - val_accuracy: 0.8667 - val_loss: 0.5661\n","Epoch 30/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8660 - loss: 0.5693 - val_accuracy: 0.8693 - val_loss: 0.5557\n","Epoch 31/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8628 - loss: 0.5661 - val_accuracy: 0.8712 - val_loss: 0.5466\n","Epoch 32/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8632 - loss: 0.5542 - val_accuracy: 0.8715 - val_loss: 0.5379\n","Epoch 33/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8646 - loss: 0.5501 - val_accuracy: 0.8747 - val_loss: 0.5296\n","Epoch 34/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8690 - loss: 0.5331 - val_accuracy: 0.8745 - val_loss: 0.5222\n","Epoch 35/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8674 - loss: 0.5326 - val_accuracy: 0.8768 - val_loss: 0.5149\n","Epoch 36/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8716 - loss: 0.5216 - val_accuracy: 0.8767 - val_loss: 0.5084\n","Epoch 37/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.5147 - val_accuracy: 0.8775 - val_loss: 0.5016\n","Epoch 38/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8737 - loss: 0.5064 - val_accuracy: 0.8792 - val_loss: 0.4958\n","Epoch 39/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8745 - loss: 0.5022 - val_accuracy: 0.8777 - val_loss: 0.4902\n","Epoch 40/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8735 - loss: 0.4974 - val_accuracy: 0.8810 - val_loss: 0.4843\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8742 - loss: 0.4936 - val_accuracy: 0.8812 - val_loss: 0.4789\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.4564\n","Epoch 47/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8799 - loss: 0.4647 - val_accuracy: 0.8817 - val_loss: 0.4523\n","Epoch 48/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8798 - loss: 0.4624 - val_accuracy: 0.8830 - val_loss: 0.4483\n","Epoch 49/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8818 - loss: 0.4554 - val_accuracy: 0.8845 - val_loss: 0.4450\n","Epoch 50/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8817 - loss: 0.4548 - val_accuracy: 0.8843 - val_loss: 0.4412\n","Epoch 51/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8818 - loss: 0.4509 - val_accuracy: 0.8852 - val_loss: 0.4377\n","Epoch 52/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8829 - loss: 0.4468 - val_accuracy: 0.8857 - val_loss: 0.4346\n","Epoch 53/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8844 - loss: 0.4432 - val_accuracy: 0.8860 - val_loss: 0.4314\n","Epoch 54/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8848 - loss: 0.4384 - val_accuracy: 0.8860 - val_loss: 0.4286\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8822 - loss: 0.4396 - val_accuracy: 0.8875 - val_loss: 0.4255\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8860 - loss: 0.4317 - val_accuracy: 0.8868 - val_loss: 0.4228\n","Epoch 57/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8840 - loss: 0.4336 - val_accuracy: 0.8882 - val_loss: 0.4200\n","Epoch 58/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8844 - loss: 0.4275 - val_accuracy: 0.8890 - val_loss: 0.4175\n","Epoch 59/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8872 - loss: 0.4287 - val_accuracy: 0.8880 - val_loss: 0.4148\n","Epoch 60/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8871 - loss: 0.4261 - val_accuracy: 0.8887 - val_loss: 0.4123\n","Epoch 61/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8878 - loss: 0.4199 - val_accuracy: 0.8895 - val_loss: 0.4100\n","Epoch 62/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8868 - loss: 0.4188 - val_accuracy: 0.8897 - val_loss: 0.4077\n","Epoch 63/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8876 - loss: 0.4186 - val_accuracy: 0.8910 - val_loss: 0.4055\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8915 - loss: 0.4084 - val_accuracy: 0.8908 - val_loss: 0.4037\n","Epoch 65/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8877 - loss: 0.4157 - val_accuracy: 0.8915 - val_loss: 0.4013\n","Epoch 66/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8893 - loss: 0.4100 - val_accuracy: 0.8928 - val_loss: 0.3992\n","Epoch 67/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8902 - loss: 0.4055 - val_accuracy: 0.8935 - val_loss: 0.3974\n","Epoch 68/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8920 - loss: 0.4020 - val_accuracy: 0.8933 - val_loss: 0.3956\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8897 - loss: 0.4018 - val_accuracy: 0.8942 - val_loss: 0.3935\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.3850\n","Epoch 75/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8918 - loss: 0.3954 - val_accuracy: 0.8970 - val_loss: 0.3834\n","Epoch 76/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8943 - loss: 0.3844 - val_accuracy: 0.8970 - val_loss: 0.3819\n","Epoch 77/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8912 - loss: 0.3945 - val_accuracy: 0.8973 - val_loss: 0.3805\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8921 - loss: 0.3898 - val_accuracy: 0.8978 - val_loss: 0.3791\n","Epoch 79/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8926 - loss: 0.3872 - val_accuracy: 0.8980 - val_loss: 0.3776\n","Epoch 80/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8941 - loss: 0.3831 - val_accuracy: 0.8980 - val_loss: 0.3761\n","Epoch 81/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8926 - loss: 0.3828 - val_accuracy: 0.8982 - val_loss: 0.3748\n","Epoch 82/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8944 - loss: 0.3810 - val_accuracy: 0.8988 - val_loss: 0.3735\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8937 - loss: 0.3791 - val_accuracy: 0.8985 - val_loss: 0.3721\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8938 - loss: 0.3811 - val_accuracy: 0.8988 - val_loss: 0.3710\n","Epoch 85/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8931 - loss: 0.3778 - val_accuracy: 0.8985 - val_loss: 0.3696\n","Epoch 86/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8944 - loss: 0.3798 - val_accuracy: 0.8992 - val_loss: 0.3684\n","Epoch 87/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8954 - loss: 0.3733 - val_accuracy: 0.8997 - val_loss: 0.3674\n","Epoch 88/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8937 - loss: 0.3767 - val_accuracy: 0.8995 - val_loss: 0.3661\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8946 - loss: 0.3734 - val_accuracy: 0.8995 - val_loss: 0.3651\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8934 - loss: 0.3760 - val_accuracy: 0.9002 - val_loss: 0.3639\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8951 - loss: 0.3732 - val_accuracy: 0.9003 - val_loss: 0.3630\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8967 - loss: 0.3675 - val_accuracy: 0.9007 - val_loss: 0.3618\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8953 - loss: 0.3681 - val_accuracy: 0.9010 - val_loss: 0.3607\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8952 - loss: 0.3670 - val_accuracy: 0.9007 - val_loss: 0.3599\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8941 - loss: 0.3717 - val_accuracy: 0.9013 - val_loss: 0.3587\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8955 - loss: 0.3707 - val_accuracy: 0.9005 - val_loss: 0.3577\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8960 - loss: 0.3682 - val_accuracy: 0.9013 - val_loss: 0.3568\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8951 - loss: 0.3689 - val_accuracy: 0.9015 - val_loss: 0.3559\n","Epoch 99/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8974 - loss: 0.3655 - val_accuracy: 0.9017 - val_loss: 0.3548\n","Epoch 100/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9004 - loss: 0.3553 - val_accuracy: 0.9017 - val_loss: 0.3541\n"]}]},{"cell_type":"code","source":["plt.figure(figsize=(12, 5))\n","\n","plt.subplot(1, 2, 1)\n","plt.plot(H_01_500.history['loss'], label='Обучающая ошибка')\n","plt.plot(H_01_500.history['val_loss'], label='Валидационная ошибка')\n","plt.title('Функция ошибки по эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"s6lj_lfsIIFp","executionInfo":{"status":"ok","timestamp":1758321507102,"user_tz":-180,"elapsed":285,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"1130e04a-652a-4a30-ce71-14e8b2d1e07f"},"execution_count":53,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["scores_01_500=model_01_500.evaluate(X_test,y_test)\n","print('Loss on test data:',scores_01_500[0]) #значение функции ошибки\n","print('Accuracy on test data:',scores_01_500[1]) #значение метрики качества"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zMa8AsG1IftR","executionInfo":{"status":"ok","timestamp":1758321647380,"user_tz":-180,"elapsed":1360,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"4569722a-0794-4971-d1d6-21fef4aeb785"},"execution_count":55,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8990 - loss: 0.3647\n","Loss on test data: 0.3678894639015198\n","Accuracy on test data: 0.9003999829292297\n"]}]},{"cell_type":"code","source":["model_01_100_50 = Sequential()\n","model_01_100_50.add(Dense(units=100, input_dim=num_pixels, activation='sigmoid'))\n","model_01_100_50.add(Dense(units=50, activation='sigmoid'))\n","model_01_100_50.add(Dense(units=num_classes, activation='softmax'))\n","model_01_100_50.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01_100_50.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"KDE5Vru8J7kp","executionInfo":{"status":"ok","timestamp":1758322092120,"user_tz":-180,"elapsed":130,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"92f577cb-b20d-48ba-95c9-3d076052ca82"},"execution_count":57,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_10\"\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_10\"</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_17 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m78,500\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_18 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │         \u001b[38;5;34m5,050\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_19 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m510\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_17 (<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\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">78,500</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_18 (<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\">50</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">5,050</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_19 (<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\">510</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m84,060\u001b[0m (328.36 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\">84,060</span> (328.36 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m84,060\u001b[0m (328.36 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\">84,060</span> (328.36 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":{}}]},{"cell_type":"code","source":["H_01_100_50 = model_01_100_50.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size=512\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SC5DsfcyLMVo","executionInfo":{"status":"ok","timestamp":1758322532935,"user_tz":-180,"elapsed":59103,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"320b3194-04d7-44e9-bff3-2f3f49f2c050"},"execution_count":61,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8593 - loss: 0.5403 - val_accuracy: 0.8612 - val_loss: 0.5303\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8615 - loss: 0.5362 - val_accuracy: 0.8612 - val_loss: 0.5264\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8625 - loss: 0.5323 - val_accuracy: 0.8625 - val_loss: 0.5226\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8638 - loss: 0.5259 - val_accuracy: 0.8652 - val_loss: 0.5188\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8647 - loss: 0.5236 - val_accuracy: 0.8650 - val_loss: 0.5152\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8659 - loss: 0.5164 - val_accuracy: 0.8658 - val_loss: 0.5117\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8645 - loss: 0.5180 - val_accuracy: 0.8672 - val_loss: 0.5082\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8652 - loss: 0.5128 - val_accuracy: 0.8685 - val_loss: 0.5048\n","Epoch 9/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8664 - loss: 0.5122 - val_accuracy: 0.8695 - val_loss: 0.5014\n","Epoch 10/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8680 - loss: 0.5059 - val_accuracy: 0.8703 - val_loss: 0.4981\n","Epoch 11/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8690 - loss: 0.5010 - val_accuracy: 0.8707 - val_loss: 0.4949\n","Epoch 12/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8687 - loss: 0.5009 - val_accuracy: 0.8717 - val_loss: 0.4918\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8685 - loss: 0.4964 - val_accuracy: 0.8735 - val_loss: 0.4887\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8710 - loss: 0.4941 - val_accuracy: 0.8735 - val_loss: 0.4857\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8682 - loss: 0.4932 - val_accuracy: 0.8737 - val_loss: 0.4827\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8734 - loss: 0.4838 - val_accuracy: 0.8750 - val_loss: 0.4798\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.4879 - val_accuracy: 0.8758 - val_loss: 0.4770\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8723 - loss: 0.4803 - val_accuracy: 0.8763 - val_loss: 0.4742\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8736 - loss: 0.4773 - val_accuracy: 0.8773 - val_loss: 0.4715\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8744 - loss: 0.4774 - val_accuracy: 0.8780 - val_loss: 0.4689\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8759 - loss: 0.4758 - val_accuracy: 0.8778 - val_loss: 0.4662\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.4772 - val_accuracy: 0.8785 - val_loss: 0.4637\n","Epoch 23/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8747 - loss: 0.4745 - val_accuracy: 0.8785 - val_loss: 0.4611\n","Epoch 24/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8761 - loss: 0.4653 - val_accuracy: 0.8797 - val_loss: 0.4587\n","Epoch 25/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8779 - loss: 0.4646 - val_accuracy: 0.8800 - val_loss: 0.4562\n","Epoch 26/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8776 - loss: 0.4635 - val_accuracy: 0.8798 - val_loss: 0.4539\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8788 - loss: 0.4616 - val_accuracy: 0.8807 - val_loss: 0.4516\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8789 - loss: 0.4552 - val_accuracy: 0.8815 - val_loss: 0.4493\n","Epoch 29/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8783 - loss: 0.4544 - val_accuracy: 0.8815 - val_loss: 0.4470\n","Epoch 30/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8806 - loss: 0.4502 - val_accuracy: 0.8817 - val_loss: 0.4448\n","Epoch 31/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8806 - loss: 0.4512 - val_accuracy: 0.8823 - val_loss: 0.4427\n","Epoch 32/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8794 - loss: 0.4521 - val_accuracy: 0.8833 - val_loss: 0.4406\n","Epoch 33/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8800 - loss: 0.4493 - val_accuracy: 0.8835 - val_loss: 0.4385\n","Epoch 34/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8823 - loss: 0.4442 - val_accuracy: 0.8853 - val_loss: 0.4364\n","Epoch 35/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8825 - loss: 0.4432 - val_accuracy: 0.8848 - val_loss: 0.4345\n","Epoch 36/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8834 - loss: 0.4355 - val_accuracy: 0.8853 - val_loss: 0.4324\n","Epoch 37/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8864 - loss: 0.4326 - val_accuracy: 0.8858 - val_loss: 0.4305\n","Epoch 38/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8838 - loss: 0.4352 - val_accuracy: 0.8858 - val_loss: 0.4286\n","Epoch 39/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8819 - loss: 0.4332 - val_accuracy: 0.8863 - val_loss: 0.4266\n","Epoch 40/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8856 - loss: 0.4269 - val_accuracy: 0.8870 - val_loss: 0.4248\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8853 - loss: 0.4317 - val_accuracy: 0.8872 - val_loss: 0.4230\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.4144\n","Epoch 47/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8858 - loss: 0.4220 - val_accuracy: 0.8890 - val_loss: 0.4127\n","Epoch 48/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8870 - loss: 0.4189 - val_accuracy: 0.8888 - val_loss: 0.4111\n","Epoch 49/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8829 - loss: 0.4264 - val_accuracy: 0.8898 - val_loss: 0.4094\n","Epoch 50/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8911 - loss: 0.4096 - val_accuracy: 0.8900 - val_loss: 0.4079\n","Epoch 51/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8871 - loss: 0.4146 - val_accuracy: 0.8902 - val_loss: 0.4063\n","Epoch 52/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8902 - loss: 0.4092 - val_accuracy: 0.8903 - val_loss: 0.4048\n","Epoch 53/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8882 - loss: 0.4161 - val_accuracy: 0.8903 - val_loss: 0.4033\n","Epoch 54/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8870 - loss: 0.4159 - val_accuracy: 0.8907 - val_loss: 0.4018\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8886 - loss: 0.4100 - val_accuracy: 0.8912 - val_loss: 0.4004\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8908 - loss: 0.4042 - val_accuracy: 0.8907 - val_loss: 0.3990\n","Epoch 57/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8879 - loss: 0.4068 - val_accuracy: 0.8912 - val_loss: 0.3976\n","Epoch 58/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8901 - loss: 0.4051 - val_accuracy: 0.8912 - val_loss: 0.3962\n","Epoch 59/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8903 - loss: 0.4025 - val_accuracy: 0.8917 - val_loss: 0.3947\n","Epoch 60/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.8895 - loss: 0.4012 - val_accuracy: 0.8915 - val_loss: 0.3935\n","Epoch 61/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8898 - loss: 0.3990 - val_accuracy: 0.8913 - val_loss: 0.3921\n","Epoch 62/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8921 - loss: 0.3930 - val_accuracy: 0.8917 - val_loss: 0.3908\n","Epoch 63/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8919 - loss: 0.3946 - val_accuracy: 0.8920 - val_loss: 0.3895\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8918 - loss: 0.3947 - val_accuracy: 0.8925 - val_loss: 0.3882\n","Epoch 65/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8926 - loss: 0.3932 - val_accuracy: 0.8923 - val_loss: 0.3870\n","Epoch 66/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8930 - loss: 0.3871 - val_accuracy: 0.8927 - val_loss: 0.3857\n","Epoch 67/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8932 - loss: 0.3878 - val_accuracy: 0.8925 - val_loss: 0.3845\n","Epoch 68/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8929 - loss: 0.3891 - val_accuracy: 0.8930 - val_loss: 0.3833\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8940 - loss: 0.3887 - val_accuracy: 0.8930 - val_loss: 0.3821\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.3763\n","Epoch 75/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8946 - loss: 0.3807 - val_accuracy: 0.8953 - val_loss: 0.3752\n","Epoch 76/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8949 - loss: 0.3807 - val_accuracy: 0.8957 - val_loss: 0.3741\n","Epoch 77/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8952 - loss: 0.3799 - val_accuracy: 0.8957 - val_loss: 0.3730\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8968 - loss: 0.3702 - val_accuracy: 0.8958 - val_loss: 0.3720\n","Epoch 79/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8954 - loss: 0.3751 - val_accuracy: 0.8958 - val_loss: 0.3709\n","Epoch 80/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8949 - loss: 0.3740 - val_accuracy: 0.8960 - val_loss: 0.3699\n","Epoch 81/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8958 - loss: 0.3760 - val_accuracy: 0.8960 - val_loss: 0.3689\n","Epoch 82/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8968 - loss: 0.3730 - val_accuracy: 0.8958 - val_loss: 0.3679\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8960 - loss: 0.3720 - val_accuracy: 0.8965 - val_loss: 0.3669\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8948 - loss: 0.3735 - val_accuracy: 0.8963 - val_loss: 0.3658\n","Epoch 85/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8972 - loss: 0.3692 - val_accuracy: 0.8963 - val_loss: 0.3649\n","Epoch 86/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8973 - loss: 0.3672 - val_accuracy: 0.8972 - val_loss: 0.3639\n","Epoch 87/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8974 - loss: 0.3706 - val_accuracy: 0.8970 - val_loss: 0.3630\n","Epoch 88/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8985 - loss: 0.3633 - val_accuracy: 0.8975 - val_loss: 0.3621\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8977 - loss: 0.3696 - val_accuracy: 0.8977 - val_loss: 0.3611\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8962 - loss: 0.3706 - val_accuracy: 0.8980 - val_loss: 0.3602\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8981 - loss: 0.3599 - val_accuracy: 0.8982 - val_loss: 0.3593\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8996 - loss: 0.3631 - val_accuracy: 0.8983 - val_loss: 0.3584\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8992 - loss: 0.3586 - val_accuracy: 0.8987 - val_loss: 0.3575\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8983 - loss: 0.3646 - val_accuracy: 0.8985 - val_loss: 0.3566\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8989 - loss: 0.3583 - val_accuracy: 0.8987 - val_loss: 0.3558\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8982 - loss: 0.3672 - val_accuracy: 0.8995 - val_loss: 0.3549\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8983 - loss: 0.3577 - val_accuracy: 0.8997 - val_loss: 0.3540\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8980 - loss: 0.3606 - val_accuracy: 0.8998 - val_loss: 0.3532\n","Epoch 99/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9001 - loss: 0.3605 - val_accuracy: 0.9000 - val_loss: 0.3524\n","Epoch 100/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8982 - loss: 0.3602 - val_accuracy: 0.9007 - val_loss: 0.3516\n"]}]},{"cell_type":"code","source":["plt.figure(figsize=(12, 5))\n","\n","plt.subplot(1, 2, 1)\n","plt.plot(H_01_100_50.history['loss'], label='Обучающая ошибка')\n","plt.plot(H_01_100_50.history['val_loss'], label='Валидационная ошибка')\n","plt.title('Функция ошибки по эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"Wh6GsTrvLo0c","executionInfo":{"status":"ok","timestamp":1758322541696,"user_tz":-180,"elapsed":4389,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"2afde94e-81d9-42cb-d30c-2b02570fa743"},"execution_count":62,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["scores_01_100_50=model_01_100_50.evaluate(X_test,y_test)\n","print('Loss on test data:',scores_01_100_50[0])\n","print('Accuracy on test data:',scores_01_100_50[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pP5dd_4LMZqn","executionInfo":{"status":"ok","timestamp":1758322655570,"user_tz":-180,"elapsed":2193,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"517d5aad-f149-4711-e4d7-cf1bd0ae54c8"},"execution_count":64,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9030 - loss: 0.3591\n","Loss on test data: 0.36366331577301025\n","Accuracy on test data: 0.9025999903678894\n"]}]},{"cell_type":"code","source":["model_01_100_100 = Sequential()\n","model_01_100_100.add(Dense(units=100, input_dim=num_pixels, activation='sigmoid'))\n","model_01_100_100.add(Dense(units=100, activation='sigmoid'))\n","model_01_100_100.add(Dense(units=num_classes, activation='softmax'))\n","model_01_100_100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01_100_100.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":238},"id":"2KGjZmc1NVSI","executionInfo":{"status":"ok","timestamp":1758323400915,"user_tz":-180,"elapsed":101,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"7a2ec113-e6e5-4a72-971d-33a67c2a37cd"},"execution_count":70,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_13\"\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_13\"</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_26 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m78,500\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_27 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m10,100\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_28 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,010\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_26 (<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\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">78,500</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_27 (<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\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,100</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_28 (<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\">1,010</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m89,610\u001b[0m (350.04 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\">89,610</span> (350.04 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m89,610\u001b[0m (350.04 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\">89,610</span> (350.04 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":{}}]},{"cell_type":"code","source":["H_01_100_100 = model_01_100_100.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size=512\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_t8LqaEiOiqw","executionInfo":{"status":"ok","timestamp":1758323457738,"user_tz":-180,"elapsed":53187,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"381d4b96-878a-43c0-ed94-4ba047131eac"},"execution_count":71,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.1073 - loss: 2.4837 - val_accuracy: 0.1122 - val_loss: 2.2894\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1178 - loss: 2.2860 - val_accuracy: 0.1142 - val_loss: 2.2789\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.1207 - loss: 2.2762 - val_accuracy: 0.2067 - val_loss: 2.2699\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2022 - loss: 2.2679 - val_accuracy: 0.1690 - val_loss: 2.2606\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1958 - loss: 2.2579 - val_accuracy: 0.2172 - val_loss: 2.2510\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2523 - loss: 2.2480 - val_accuracy: 0.2332 - val_loss: 2.2407\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.2580 - loss: 2.2370 - val_accuracy: 0.3473 - val_loss: 2.2298\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3498 - loss: 2.2265 - val_accuracy: 0.3458 - val_loss: 2.2183\n","Epoch 9/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.3581 - loss: 2.2148 - val_accuracy: 0.4035 - val_loss: 2.2058\n","Epoch 10/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4066 - loss: 2.2015 - val_accuracy: 0.4662 - val_loss: 2.1924\n","Epoch 11/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4599 - loss: 2.1889 - val_accuracy: 0.4650 - val_loss: 2.1780\n","Epoch 12/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.4670 - loss: 2.1740 - val_accuracy: 0.4937 - val_loss: 2.1623\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5023 - loss: 2.1587 - val_accuracy: 0.4862 - val_loss: 2.1451\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4977 - loss: 2.1399 - val_accuracy: 0.5017 - val_loss: 2.1264\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.5257 - loss: 2.1211 - val_accuracy: 0.5215 - val_loss: 2.1058\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5361 - loss: 2.1001 - val_accuracy: 0.5403 - val_loss: 2.0835\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5495 - loss: 2.0767 - val_accuracy: 0.5763 - val_loss: 2.0590\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5812 - loss: 2.0535 - val_accuracy: 0.5633 - val_loss: 2.0326\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.5688 - loss: 2.0242 - val_accuracy: 0.5788 - val_loss: 2.0036\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.5849 - loss: 1.9953 - val_accuracy: 0.5948 - val_loss: 1.9724\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.5998 - loss: 1.9642 - val_accuracy: 0.5807 - val_loss: 1.9389\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.5941 - loss: 1.9289 - val_accuracy: 0.6053 - val_loss: 1.9029\n","Epoch 23/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.6060 - loss: 1.8916 - val_accuracy: 0.6297 - val_loss: 1.8649\n","Epoch 24/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6240 - loss: 1.8557 - val_accuracy: 0.6225 - val_loss: 1.8250\n","Epoch 25/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6296 - loss: 1.8128 - val_accuracy: 0.6247 - val_loss: 1.7835\n","Epoch 26/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6325 - loss: 1.7714 - val_accuracy: 0.6358 - val_loss: 1.7408\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6410 - loss: 1.7280 - val_accuracy: 0.6500 - val_loss: 1.6969\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6536 - loss: 1.6868 - val_accuracy: 0.6468 - val_loss: 1.6526\n","Epoch 29/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6529 - loss: 1.6403 - val_accuracy: 0.6717 - val_loss: 1.6081\n","Epoch 30/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6694 - loss: 1.5977 - val_accuracy: 0.6655 - val_loss: 1.5637\n","Epoch 31/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6739 - loss: 1.5550 - val_accuracy: 0.6722 - val_loss: 1.5200\n","Epoch 32/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6809 - loss: 1.5113 - val_accuracy: 0.6727 - val_loss: 1.4771\n","Epoch 33/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6818 - loss: 1.4667 - val_accuracy: 0.6865 - val_loss: 1.4351\n","Epoch 34/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6899 - loss: 1.4263 - val_accuracy: 0.7088 - val_loss: 1.3945\n","Epoch 35/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7076 - loss: 1.3852 - val_accuracy: 0.7043 - val_loss: 1.3553\n","Epoch 36/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7033 - loss: 1.3471 - val_accuracy: 0.7132 - val_loss: 1.3176\n","Epoch 37/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7116 - loss: 1.3125 - val_accuracy: 0.7253 - val_loss: 1.2812\n","Epoch 38/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7241 - loss: 1.2754 - val_accuracy: 0.7258 - val_loss: 1.2464\n","Epoch 39/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7308 - loss: 1.2390 - val_accuracy: 0.7290 - val_loss: 1.2132\n","Epoch 40/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7341 - loss: 1.2083 - val_accuracy: 0.7377 - val_loss: 1.1816\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7376 - loss: 1.1803 - val_accuracy: 0.7408 - val_loss: 1.1514\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 1.0207\n","Epoch 47/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7703 - loss: 1.0197 - val_accuracy: 0.7707 - val_loss: 0.9979\n","Epoch 48/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7717 - loss: 0.9985 - val_accuracy: 0.7752 - val_loss: 0.9765\n","Epoch 49/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7756 - loss: 0.9788 - val_accuracy: 0.7795 - val_loss: 0.9559\n","Epoch 50/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7798 - loss: 0.9563 - val_accuracy: 0.7828 - val_loss: 0.9363\n","Epoch 51/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7849 - loss: 0.9372 - val_accuracy: 0.7873 - val_loss: 0.9176\n","Epoch 52/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7920 - loss: 0.9185 - val_accuracy: 0.7872 - val_loss: 0.8997\n","Epoch 53/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7896 - loss: 0.8999 - val_accuracy: 0.7982 - val_loss: 0.8825\n","Epoch 54/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7964 - loss: 0.8852 - val_accuracy: 0.7963 - val_loss: 0.8661\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7962 - loss: 0.8723 - val_accuracy: 0.7973 - val_loss: 0.8504\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7982 - loss: 0.8516 - val_accuracy: 0.8043 - val_loss: 0.8351\n","Epoch 57/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8010 - loss: 0.8382 - val_accuracy: 0.8048 - val_loss: 0.8205\n","Epoch 58/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8057 - loss: 0.8245 - val_accuracy: 0.8082 - val_loss: 0.8066\n","Epoch 59/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8098 - loss: 0.8058 - val_accuracy: 0.8115 - val_loss: 0.7933\n","Epoch 60/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8123 - loss: 0.7966 - val_accuracy: 0.8128 - val_loss: 0.7805\n","Epoch 61/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8157 - loss: 0.7812 - val_accuracy: 0.8150 - val_loss: 0.7680\n","Epoch 62/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8180 - loss: 0.7709 - val_accuracy: 0.8182 - val_loss: 0.7560\n","Epoch 63/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8212 - loss: 0.7561 - val_accuracy: 0.8220 - val_loss: 0.7445\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8221 - loss: 0.7454 - val_accuracy: 0.8227 - val_loss: 0.7335\n","Epoch 65/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8249 - loss: 0.7361 - val_accuracy: 0.8263 - val_loss: 0.7227\n","Epoch 66/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8275 - loss: 0.7266 - val_accuracy: 0.8265 - val_loss: 0.7124\n","Epoch 67/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8272 - loss: 0.7197 - val_accuracy: 0.8297 - val_loss: 0.7026\n","Epoch 68/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8298 - loss: 0.7060 - val_accuracy: 0.8315 - val_loss: 0.6929\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8302 - loss: 0.6975 - val_accuracy: 0.8337 - val_loss: 0.6838\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.6422\n","Epoch 75/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8397 - loss: 0.6491 - val_accuracy: 0.8405 - val_loss: 0.6346\n","Epoch 76/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8388 - loss: 0.6457 - val_accuracy: 0.8428 - val_loss: 0.6273\n","Epoch 77/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8421 - loss: 0.6328 - val_accuracy: 0.8443 - val_loss: 0.6203\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8430 - loss: 0.6285 - val_accuracy: 0.8463 - val_loss: 0.6136\n","Epoch 79/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8439 - loss: 0.6200 - val_accuracy: 0.8468 - val_loss: 0.6070\n","Epoch 80/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8445 - loss: 0.6151 - val_accuracy: 0.8488 - val_loss: 0.6005\n","Epoch 81/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8467 - loss: 0.6089 - val_accuracy: 0.8500 - val_loss: 0.5944\n","Epoch 82/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8490 - loss: 0.6013 - val_accuracy: 0.8507 - val_loss: 0.5884\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8491 - loss: 0.5950 - val_accuracy: 0.8520 - val_loss: 0.5825\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8508 - loss: 0.5886 - val_accuracy: 0.8532 - val_loss: 0.5769\n","Epoch 85/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8526 - loss: 0.5837 - val_accuracy: 0.8550 - val_loss: 0.5714\n","Epoch 86/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8540 - loss: 0.5789 - val_accuracy: 0.8553 - val_loss: 0.5661\n","Epoch 87/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8538 - loss: 0.5709 - val_accuracy: 0.8560 - val_loss: 0.5610\n","Epoch 88/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8536 - loss: 0.5710 - val_accuracy: 0.8573 - val_loss: 0.5560\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8574 - loss: 0.5600 - val_accuracy: 0.8575 - val_loss: 0.5511\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8530 - loss: 0.5643 - val_accuracy: 0.8575 - val_loss: 0.5464\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8593 - loss: 0.5525 - val_accuracy: 0.8583 - val_loss: 0.5417\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8592 - loss: 0.5515 - val_accuracy: 0.8597 - val_loss: 0.5372\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8593 - loss: 0.5429 - val_accuracy: 0.8608 - val_loss: 0.5328\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8597 - loss: 0.5415 - val_accuracy: 0.8613 - val_loss: 0.5286\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8627 - loss: 0.5351 - val_accuracy: 0.8618 - val_loss: 0.5244\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8644 - loss: 0.5264 - val_accuracy: 0.8625 - val_loss: 0.5204\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8628 - loss: 0.5307 - val_accuracy: 0.8632 - val_loss: 0.5164\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8641 - loss: 0.5240 - val_accuracy: 0.8637 - val_loss: 0.5125\n","Epoch 99/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8644 - loss: 0.5198 - val_accuracy: 0.8643 - val_loss: 0.5087\n","Epoch 100/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8619 - loss: 0.5195 - val_accuracy: 0.8655 - val_loss: 0.5051\n"]}]},{"cell_type":"code","source":["plt.figure(figsize=(12, 5))\n","\n","plt.subplot(1, 2, 1)\n","plt.plot(H_01_100_100.history['loss'], label='Обучающая ошибка')\n","plt.plot(H_01_100_100.history['val_loss'], label='Валидационная ошибка')\n","plt.title('Функция ошибки по эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"uvTQqGxRO38O","executionInfo":{"status":"ok","timestamp":1758323495914,"user_tz":-180,"elapsed":257,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"80788916-ee09-496f-94c6-1e8e6aa5aedc"},"execution_count":73,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["scores_01_100_100=model_01_100_100.evaluate(X_test,y_test)\n","print('Loss on test data:',scores_01_100_100[0])\n","print('Accuracy on test data:',scores_01_100_100[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RGl_z5fmPG7t","executionInfo":{"status":"ok","timestamp":1758323499638,"user_tz":-180,"elapsed":1295,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"8b4fd32a-2fa2-4ebb-d6d3-4eefd4850345"},"execution_count":74,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8695 - loss: 0.5132\n","Loss on test data: 0.5176764726638794\n","Accuracy on test data: 0.8664000034332275\n"]}]},{"cell_type":"code","source":["model_01_100_50.save(filepath='best_model.keras')\n"],"metadata":{"id":"fVMBwoRJVXlZ","executionInfo":{"status":"ok","timestamp":1758325056928,"user_tz":-180,"elapsed":65,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":75,"outputs":[]},{"cell_type":"code","source":["from keras.models import load_model\n","model = load_model('best_model.keras')"],"metadata":{"id":"yFKJ50yqW0jD","executionInfo":{"status":"ok","timestamp":1758325403348,"user_tz":-180,"elapsed":66,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":80,"outputs":[]},{"cell_type":"code","source":["n = 123\n","result = 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":"WXonZCRTWLEr","executionInfo":{"status":"ok","timestamp":1758325497315,"user_tz":-180,"elapsed":236,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"6a264109-f7ef-424a-9e56-ccb0392dd286"},"execution_count":84,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n","NN output: [[2.6870447e-03 2.7966622e-04 1.0132463e-02 2.3693037e-04 3.8022294e-03\n","  1.6124520e-02 9.6385807e-01 2.6948280e-06 2.7878550e-03 8.8560744e-05]]\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:  6\n","NN answer:  6\n"]}]},{"cell_type":"code","source":["n = 765\n","result = 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":"cKmqXXcOX80K","executionInfo":{"status":"ok","timestamp":1758325635857,"user_tz":-180,"elapsed":1601,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"cfd5fde5-0e2f-427e-fbbc-83605c376866"},"execution_count":85,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n","NN output: [[2.0366670e-04 4.1921300e-04 1.3058564e-03 9.4342142e-01 1.3128246e-04\n","  4.1461430e-02 1.4031133e-05 8.9495274e-04 9.2723602e-03 2.8756857e-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:  3\n","NN answer:  3\n"]}]},{"cell_type":"code","source":["from PIL import Image\n","file_07_data = Image.open('7.png')\n","file_07_data = file_07_data.convert('L')\n","test_07_img = np.array(file_07_data)"],"metadata":{"id":"43z4eLyxiLs5","executionInfo":{"status":"ok","timestamp":1758328690188,"user_tz":-180,"elapsed":449,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":138,"outputs":[]},{"cell_type":"code","source":["plt.imshow(test_07_img, cmap=plt.get_cmap('gray'))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"oZR5iCUtiYAh","executionInfo":{"status":"ok","timestamp":1758328692789,"user_tz":-180,"elapsed":136,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"f33f13e0-51c1-4780-f44e-2b52c50c5587"},"execution_count":139,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAGHxJREFUeJzt3X9MVff9x/HXVeFWW7gUES63IkVtNamVZU4ZcXVNJIpbTP3xh+v6h12MjfbaTF27xSVquyxhs0mzdDHr/qpZVm1nMjT1DxNFwWxDm1qNMeuIMDYwcnE14VxEQQOf7x+s97urIIL38r738nwkn6Tcc7j37fHIsweOV59zzgkAgHE2yXoAAMDERIAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAICJKdYD3GtgYEDXrl1TTk6OfD6f9TgAgFFyzqm7u1uhUEiTJg1/nZNyAbp27ZpKSkqsxwAAPKL29nbNnDlz2O0p9y24nJwc6xEAAAkw0tfzpAVo//79evrpp/XYY4+poqJCn3322UN9Ht92A4DMMNLX86QE6JNPPtHOnTu1d+9effHFFyovL9fKlSt1/fr1ZLwcACAduSRYsmSJC4fDsY/7+/tdKBRyNTU1I36u53lOEovFYrHSfHme98Cv9wm/Arpz547Onz+vqqqq2GOTJk1SVVWVGhsb79u/r69P0Wg0bgEAMl/CA/TVV1+pv79fRUVFcY8XFRUpEonct39NTY0CgUBscQccAEwM5nfB7dq1S57nxVZ7e7v1SACAcZDwvwdUUFCgyZMnq7OzM+7xzs5OBYPB+/b3+/3y+/2JHgMAkOISfgWUnZ2tRYsWqa6uLvbYwMCA6urqVFlZmeiXAwCkqaS8E8LOnTu1ceNGfetb39KSJUv0m9/8Rj09PfrRj36UjJcDAKShpARow4YN+s9//qM9e/YoEonoG9/4ho4fP37fjQkAgInL55xz1kP8r2g0qkAgYD0GAOAReZ6n3NzcYbeb3wUHAJiYCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACAiYQH6O2335bP54tb8+fPT/TLAADS3JRkPOlzzz2nkydP/v+LTEnKywAA0lhSyjBlyhQFg8FkPDUAIEMk5WdAV65cUSgU0uzZs/XKK6+ora1t2H37+voUjUbjFgAg8yU8QBUVFTpw4ICOHz+u3/3ud2ptbdULL7yg7u7uIfevqalRIBCIrZKSkkSPBABIQT7nnEvmC3R1dam0tFTvvfeeNm3adN/2vr4+9fX1xT6ORqNECAAygOd5ys3NHXZ70u8OyMvL07PPPqvm5uYht/v9fvn9/mSPAQBIMUn/e0A3b95US0uLiouLk/1SAIA0kvAAvfnmm2poaNC//vUv/e1vf9PatWs1efJkvfzyy4l+KQBAGkv4t+CuXr2ql19+WTdu3NCMGTP0ne98R2fPntWMGTMS/VIAgDSW9JsQRisajSoQCFiPAaScFPujOiH4fD7rEdLaSDch8F5wAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAICJpP+DdMhcvDlm6svEN9PkvMscXAEBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABO+GjXGVie/OjNTHeZeauAICAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEzwZqQAzDjnrEeAIa6AAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATvBkpeENIpBWfz2c9AhKEKyAAgAkCBAAwMeoAnTlzRqtXr1YoFJLP59ORI0fitjvntGfPHhUXF2vq1KmqqqrSlStXEjUvACBDjDpAPT09Ki8v1/79+4fcvm/fPr3//vv64IMPdO7cOT3++ONauXKlent7H3lYAEAGcY9AkqutrY19PDAw4ILBoHv33Xdjj3V1dTm/3+8OHTr0UM/peZ6TxBrHNZ6sf62s1FqcQ5m9PM974O9lQn8G1NraqkgkoqqqqthjgUBAFRUVamxsHPJz+vr6FI1G4xYAIPMlNECRSESSVFRUFPd4UVFRbNu9ampqFAgEYqukpCSRIwEAUpT5XXC7du2S53mx1d7ebj0SAGAcJDRAwWBQktTZ2Rn3eGdnZ2zbvfx+v3Jzc+MWACDzJTRAZWVlCgaDqquriz0WjUZ17tw5VVZWJvKlAABpbtRvxXPz5k01NzfHPm5tbdXFixeVn5+vWbNmafv27frlL3+pZ555RmVlZdq9e7dCoZDWrFmTyLkBAOlutLdAnj59esjb7TZu3OicG7wVe/fu3a6oqMj5/X63fPly19TU9NDPz23Y47/Gk/WvlZVai3Mos9dIt2H7/vsbmjKi0agCgYD1GBPKWE8B3hQS/2u8vpRw3qUPz/Me+HN987vgAAATEwECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEyM+t8DApD5eGdrjAeugAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAE7wZKXhDSCQE5xFGiysgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEb0YKZDDnnPUIwLC4AgIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATPBmpADu4/P5rEfABMAVEADABAECAJgYdYDOnDmj1atXKxQKyefz6ciRI3HbX331Vfl8vrhVXV2dqHkBABli1AHq6elReXm59u/fP+w+1dXV6ujoiK1Dhw490pAAgMwz6psQVq1apVWrVj1wH7/fr2AwOOahAACZLyk/A6qvr1dhYaHmzZunrVu36saNG8Pu29fXp2g0GrcAAJkv4QGqrq7WH/7wB9XV1enXv/61GhoatGrVKvX39w+5f01NjQKBQGyVlJQkeiQAQAryOefcmD/Z51Ntba3WrFkz7D7//Oc/NWfOHJ08eVLLly+/b3tfX5/6+vpiH0ejUSIEJMhY/3jz94CQCJ7nKTc3d9jtSb8Ne/bs2SooKFBzc/OQ2/1+v3Jzc+MWACDzJT1AV69e1Y0bN1RcXJzslwIApJFR3wV38+bNuKuZ1tZWXbx4Ufn5+crPz9c777yj9evXKxgMqqWlRT/96U81d+5crVy5MqGDAwDSnBul06dPO0n3rY0bN7pbt265FStWuBkzZrisrCxXWlrqNm/e7CKRyEM/v+d5Qz4/i8Ua/Ror67lZmbE8z3vgefZINyEkQzQaVSAQsB4DyAhj/ePNTQhIBPObEAAAGAoBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgYor1AACSx+fzjenznHPj9lqYuLgCAgCYIEAAABOjClBNTY0WL16snJwcFRYWas2aNWpqaorbp7e3V+FwWNOnT9cTTzyh9evXq7OzM6FDAwDS36gC1NDQoHA4rLNnz+rEiRO6e/euVqxYoZ6entg+O3bs0KeffqrDhw+roaFB165d07p16xI+OAAgzblHcP36dSfJNTQ0OOec6+rqcllZWe7w4cOxfb788ksnyTU2Nj7Uc3qe5ySxWCzDNRbWM7NSb3me98Bz5pF+BuR5niQpPz9fknT+/HndvXtXVVVVsX3mz5+vWbNmqbGxccjn6OvrUzQajVsAgMw35gANDAxo+/btWrp0qRYsWCBJikQiys7OVl5eXty+RUVFikQiQz5PTU2NAoFAbJWUlIx1JABAGhlzgMLhsC5fvqyPP/74kQbYtWuXPM+Lrfb29kd6PgBAehjTX0Tdtm2bjh07pjNnzmjmzJmxx4PBoO7cuaOurq64q6DOzk4Fg8Ehn8vv98vv949lDABAGhvVFZBzTtu2bVNtba1OnTqlsrKyuO2LFi1SVlaW6urqYo81NTWpra1NlZWViZkYAJARRnUFFA6HdfDgQR09elQ5OTmxn+sEAgFNnTpVgUBAmzZt0s6dO5Wfn6/c3Fy98cYbqqys1Le//e2k/AIAAGkqEbdZfvjhh7F9bt++7V5//XX35JNPumnTprm1a9e6jo6Oh34NbsNmsezXWFjPzEq9NdJt2L7/njgpIxqNKhAIWI8BTGhj+bLAm5HiXp7nKTc3d9jtvBccAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYGKK9QAAUo/P57MeARMAV0AAABMECABgYlQBqqmp0eLFi5WTk6PCwkKtWbNGTU1Ncfu8+OKL8vl8cWvLli0JHRoAkP5GFaCGhgaFw2GdPXtWJ06c0N27d7VixQr19PTE7bd582Z1dHTE1r59+xI6NAAg/Y3qJoTjx4/HfXzgwAEVFhbq/PnzWrZsWezxadOmKRgMJmZCAEBGeqSfAXmeJ0nKz8+Pe/yjjz5SQUGBFixYoF27dunWrVvDPkdfX5+i0WjcAgBMAG6M+vv73fe//323dOnSuMd///vfu+PHj7tLly65P/7xj+6pp55ya9euHfZ59u7d6ySxWCwWK8OW53kP7MiYA7RlyxZXWlrq2tvbH7hfXV2dk+Sam5uH3N7b2+s8z4ut9vZ284PGYrFYrEdfIwVoTH8Rddu2bTp27JjOnDmjmTNnPnDfiooKSVJzc7PmzJlz33a/3y+/3z+WMQAAaWxUAXLO6Y033lBtba3q6+tVVlY24udcvHhRklRcXDymAQEAmWlUAQqHwzp48KCOHj2qnJwcRSIRSVIgENDUqVPV0tKigwcP6nvf+56mT5+uS5cuaceOHVq2bJkWLlyYlF8AACBNjebnPhrm+3wffvihc865trY2t2zZMpefn+/8fr+bO3eue+utt0b8PuD/8jzP/PuWLBaLxXr0NdLXft9/w5IyotGoAoGA9RgAgEfkeZ5yc3OH3c57wQEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATKRcgJxz1iMAABJgpK/nKReg7u5u6xEAAAkw0tdzn0uxS46BgQFdu3ZNOTk58vl8cdui0ahKSkrU3t6u3NxcowntcRwGcRwGcRwGcRwGpcJxcM6pu7tboVBIkyYNf50zZRxneiiTJk3SzJkzH7hPbm7uhD7BvsZxGMRxGMRxGMRxGGR9HAKBwIj7pNy34AAAEwMBAgCYSKsA+f1+7d27V36/33oUUxyHQRyHQRyHQRyHQel0HFLuJgQAwMSQVldAAIDMQYAAACYIEADABAECAJhImwDt379fTz/9tB577DFVVFTos88+sx5p3L399tvy+Xxxa/78+dZjJd2ZM2e0evVqhUIh+Xw+HTlyJG67c0579uxRcXGxpk6dqqqqKl25csVm2CQa6Ti8+uqr950f1dXVNsMmSU1NjRYvXqycnBwVFhZqzZo1ampqitunt7dX4XBY06dP1xNPPKH169ers7PTaOLkeJjj8OKLL953PmzZssVo4qGlRYA++eQT7dy5U3v37tUXX3yh8vJyrVy5UtevX7cebdw999xz6ujoiK2//OUv1iMlXU9Pj8rLy7V///4ht+/bt0/vv/++PvjgA507d06PP/64Vq5cqd7e3nGeNLlGOg6SVF1dHXd+HDp0aBwnTL6GhgaFw2GdPXtWJ06c0N27d7VixQr19PTE9tmxY4c+/fRTHT58WA0NDbp27ZrWrVtnOHXiPcxxkKTNmzfHnQ/79u0zmngYLg0sWbLEhcPh2Mf9/f0uFAq5mpoaw6nG3969e115ebn1GKYkudra2tjHAwMDLhgMunfffTf2WFdXl/P7/e7QoUMGE46Pe4+Dc85t3LjRvfTSSybzWLl+/bqT5BoaGpxzg7/3WVlZ7vDhw7F9vvzySyfJNTY2Wo2ZdPceB+ec++53v+t+/OMf2w31EFL+CujOnTs6f/68qqqqYo9NmjRJVVVVamxsNJzMxpUrVxQKhTR79my98soramtrsx7JVGtrqyKRSNz5EQgEVFFRMSHPj/r6ehUWFmrevHnaunWrbty4YT1SUnmeJ0nKz8+XJJ0/f153796NOx/mz5+vWbNmZfT5cO9x+NpHH32kgoICLViwQLt27dKtW7csxhtWyr0Z6b2++uor9ff3q6ioKO7xoqIi/eMf/zCaykZFRYUOHDigefPmqaOjQ++8845eeOEFXb58WTk5OdbjmYhEIpI05Pnx9baJorq6WuvWrVNZWZlaWlr085//XKtWrVJjY6MmT55sPV7CDQwMaPv27Vq6dKkWLFggafB8yM7OVl5eXty+mXw+DHUcJOmHP/yhSktLFQqFdOnSJf3sZz9TU1OT/vznPxtOGy/lA4T/t2rVqth/L1y4UBUVFSotLdWf/vQnbdq0yXAypIIf/OAHsf9+/vnntXDhQs2ZM0f19fVavny54WTJEQ6Hdfny5Qnxc9AHGe44vPbaa7H/fv7551VcXKzly5erpaVFc+bMGe8xh5Ty34IrKCjQ5MmT77uLpbOzU8Fg0Giq1JCXl6dnn31Wzc3N1qOY+foc4Py43+zZs1VQUJCR58e2bdt07NgxnT59Ou6fbwkGg7pz5466urri9s/U82G44zCUiooKSUqp8yHlA5Sdna1Fixaprq4u9tjAwIDq6upUWVlpOJm9mzdvqqWlRcXFxdajmCkrK1MwGIw7P6LRqM6dOzfhz4+rV6/qxo0bGXV+OOe0bds21dbW6tSpUyorK4vbvmjRImVlZcWdD01NTWpra8uo82Gk4zCUixcvSlJqnQ/Wd0E8jI8//tj5/X534MAB9/e//9299tprLi8vz0UiEevRxtVPfvITV19f71pbW91f//pXV1VV5QoKCtz169etR0uq7u5ud+HCBXfhwgUnyb333nvuwoUL7t///rdzzrlf/epXLi8vzx09etRdunTJvfTSS66srMzdvn3bePLEetBx6O7udm+++aZrbGx0ra2t7uTJk+6b3/yme+aZZ1xvb6/16AmzdetWFwgEXH19vevo6IitW7duxfbZsmWLmzVrljt16pT7/PPPXWVlpausrDScOvFGOg7Nzc3uF7/4hfv8889da2urO3r0qJs9e7ZbtmyZ8eTx0iJAzjn329/+1s2aNctlZ2e7JUuWuLNnz1qPNO42bNjgiouLXXZ2tnvqqafchg0bXHNzs/VYSXf69Gkn6b61ceNG59zgrdi7d+92RUVFzu/3u+XLl7umpibboZPgQcfh1q1bbsWKFW7GjBkuKyvLlZaWus2bN2fc/6QN9euX5D788MPYPrdv33avv/66e/LJJ920adPc2rVrXUdHh93QSTDScWhra3PLli1z+fn5zu/3u7lz57q33nrLeZ5nO/g9+OcYAAAmUv5nQACAzESAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmPg/fvtR1bWuPFIAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["test_07_img = test_07_img / 255\n","test_07_img = test_07_img.reshape(1, num_pixels)"],"metadata":{"id":"PdnQPo_ziduu","executionInfo":{"status":"ok","timestamp":1758328695229,"user_tz":-180,"elapsed":70,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":140,"outputs":[]},{"cell_type":"code","source":["result = model.predict(test_07_img)\n","print('I think it\\'s ', np.argmax(result))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PJydX_A6ih1c","executionInfo":{"status":"ok","timestamp":1758328696314,"user_tz":-180,"elapsed":105,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"a49ea408-ba72-4235-83cc-3a6520c2bfcc"},"execution_count":141,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n","I think it's  7\n"]}]},{"cell_type":"code","source":["from PIL import Image\n","file_05_data = Image.open('5.png')\n","file_05_data = file_05_data.convert('L') # перевод в градации серого\n","test_05_img = np.array(file_05_data)"],"metadata":{"id":"-vu4le7Ii1kD","executionInfo":{"status":"ok","timestamp":1758328499226,"user_tz":-180,"elapsed":451,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":131,"outputs":[]},{"cell_type":"code","source":["plt.imshow(test_05_img, cmap=plt.get_cmap('gray'))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"d9ddUAozi7Xe","executionInfo":{"status":"ok","timestamp":1758328512128,"user_tz":-180,"elapsed":93,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"04c15d7c-a791-47e9-8a9c-04120adfe51c"},"execution_count":132,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["test_05_img = test_05_img / 255\n","test_05_img = test_05_img.reshape(1, num_pixels)"],"metadata":{"id":"Cg9NgtUZi-pN","executionInfo":{"status":"ok","timestamp":1758328533199,"user_tz":-180,"elapsed":6,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":133,"outputs":[]},{"cell_type":"code","source":["result = model.predict(test_05_img)\n","print('I think it\\'s ', np.argmax(result))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZPBBAH1fjFEi","executionInfo":{"status":"ok","timestamp":1758328546687,"user_tz":-180,"elapsed":328,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"87d4558f-0b77-4c92-f8a6-995e7ba4144a"},"execution_count":134,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step\n","I think it's  5\n"]}]},{"cell_type":"code","source":["from PIL import Image\n","file_07_90_data = Image.open('7-90.png')\n","file_07_90_data = file_07_90_data.convert('L')\n","test_07_90_img = np.array(file_07_90_data)"],"metadata":{"id":"1Prj83mdlIqH","executionInfo":{"status":"ok","timestamp":1758329127070,"user_tz":-180,"elapsed":381,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":142,"outputs":[]},{"cell_type":"code","source":["plt.imshow(test_07_90_img, cmap=plt.get_cmap('gray'))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"J-h0NoPblWHP","executionInfo":{"status":"ok","timestamp":1758329141034,"user_tz":-180,"elapsed":105,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"667a6a30-14fb-41e8-b11e-dd60836ce08f"},"execution_count":143,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["test_07_90_img = test_07_90_img / 255\n","test_07_90_img = test_07_90_img.reshape(1, num_pixels)"],"metadata":{"id":"XDyQEozwlaan","executionInfo":{"status":"ok","timestamp":1758329166611,"user_tz":-180,"elapsed":21,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":144,"outputs":[]},{"cell_type":"code","source":["result = model.predict(test_07_90_img)\n","print('I think it\\'s ', np.argmax(result))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jTT1aO7HlgA0","executionInfo":{"status":"ok","timestamp":1758329182715,"user_tz":-180,"elapsed":101,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"abbd135a-f4ed-434c-c86b-4ec27a6d9837"},"execution_count":145,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n","I think it's  2\n"]}]},{"cell_type":"code","source":["from PIL import Image\n","file_05_90_data = Image.open('5-90.png')\n","file_05_90_data = file_05_90_data.convert('L')\n","test_05_90_img = np.array(file_05_90_data)"],"metadata":{"id":"r151rpruli75","executionInfo":{"status":"ok","timestamp":1758329219105,"user_tz":-180,"elapsed":426,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":146,"outputs":[]},{"cell_type":"code","source":["plt.imshow(test_05_90_img, cmap=plt.get_cmap('gray'))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"voOAg_CslqvJ","executionInfo":{"status":"ok","timestamp":1758329229651,"user_tz":-180,"elapsed":193,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"2bf23b2e-7083-45d7-c4c2-ad72ac11d1d3"},"execution_count":147,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["test_05_90_img = test_05_90_img / 255\n","test_05_90_img = test_05_90_img.reshape(1, num_pixels)"],"metadata":{"id":"goK2uhgwlvAF","executionInfo":{"status":"ok","timestamp":1758329245306,"user_tz":-180,"elapsed":69,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"execution_count":148,"outputs":[]},{"cell_type":"code","source":["result = model.predict(test_05_90_img)\n","print('I think it\\'s ', np.argmax(result))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uqtsomP7lxTu","executionInfo":{"status":"ok","timestamp":1758329258983,"user_tz":-180,"elapsed":106,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"b01368c4-cd82-4902-d0a2-a48659a37ee3"},"execution_count":149,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n","I think it's  4\n"]}]}]}
\ No newline at end of file