From f3b819cc221452d6c652bda790874a58be496a26 Mon Sep 17 00:00:00 2001 From: VatkovAS Date: Thu, 25 Sep 2025 10:04:58 +0000 Subject: [PATCH] =?UTF-8?q?=D0=97=D0=B0=D0=B3=D1=80=D1=83=D0=B7=D0=B8?= =?UTF-8?q?=D0=BB(=D0=B0)=20=D1=84=D0=B0=D0=B9=D0=BB=D1=8B=20=D0=B2=20'lab?= =?UTF-8?q?works/LW1'?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- labworks/LW1/LR1 Vatkov Harisov.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 100644 labworks/LW1/LR1 Vatkov Harisov.ipynb diff --git a/labworks/LW1/LR1 Vatkov Harisov.ipynb b/labworks/LW1/LR1 Vatkov Harisov.ipynb new file mode 100644 index 0000000..8980a16 --- /dev/null +++ b/labworks/LW1/LR1 Vatkov Harisov.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","source":["import os\n","os.chdir('/content/drive/MyDrive/ColabNotebooks')"],"metadata":{"id":"GJqnw5F47SUt","colab":{"base_uri":"https://localhost:8080/","height":166},"executionInfo":{"status":"error","timestamp":1758794410471,"user_tz":-180,"elapsed":93,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"d7c58402-fe3a-4cfa-d6c8-234ebdbe7b6e"},"execution_count":30,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"[Errno 2] No such file or directory: '/content/drive/MyDrive/ColabNotebooks'","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipython-input-4162723034.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/ColabNotebooks'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/ColabNotebooks'"]}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"r5aOvzpOXzdn","executionInfo":{"status":"ok","timestamp":1758794444071,"user_tz":-180,"elapsed":29304,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"704b2ef5-347f-447c-c50a-92ea0767ea60"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["#импортмодулей\n","from tensorflow import keras\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import sklearn\n","from keras.models import Sequential\n","from keras.layers import Dense\n","\n","from keras.datasets import mnist\n","from keras.utils import to_categorical\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from PIL import Image"],"metadata":{"id":"EDn-2kSf8aKV"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["2. Загрузить набор данных MNIST, содержащий размеченные изображени ярукописных цифр\n"],"metadata":{"id":"fqy_Fk19EfMS"}},{"cell_type":"code","source":["# загрузка датасета\n","from keras.datasets import mnist\n","(X_train,y_train),(X_test,y_test)=mnist.load_data()"],"metadata":{"id":"m7tiKnC19AMr","executionInfo":{"status":"ok","timestamp":1758789780308,"user_tz":-180,"elapsed":2095,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"c473c122-0a7f-457d-d9b7-2d477f1817fd"},"execution_count":null,"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[1m2s\u001b[0m 0us/step\n"]}]},{"cell_type":"markdown","source":["3. Разбиение набора данных на обучающие и тестовые данные в соотношении 60000:10000 элементов. При разбиении параметр random_state выбрали равным (4k–1)=7, где k–номер бригады. Вывести размерности полученных обучающих и тестовых массивов данных"],"metadata":{"id":"sz9Rd_BDE_hx"}},{"cell_type":"code","source":["#создание своего разбиения датасета\n","from sklearn.model_selection import train_test_split\n","\n","#объединяем ввв один набор\n","X = np.concatenate((X_train, X_test))\n","y = np.concatenate((y_train, y_test))\n","\n","# разбиваем по вариантам\n","X_train, X_test, y_train, y_test = train_test_split(X, y,\n"," test_size = 10000,\n"," train_size = 60000,\n"," random_state = 7)"],"metadata":{"id":"sCiMQeFE9cm1"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["4. Вывести первые 4элементаобучающихданных (изображения и метки цифр)."],"metadata":{"id":"W4YTiJPQFYHh"}},{"cell_type":"code","source":["# вывод размерностей\n","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":"12QJyhmA-n1s","executionInfo":{"status":"ok","timestamp":1758530649254,"user_tz":-180,"elapsed":9,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"7bb97805-5605-4041-de8d-35cb255a21c7"},"execution_count":null,"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":["from matplotlib import pyplot as plt\n","#Создаем subplot для 4 изображений\n","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]}') # Добавляем метку как заголовок\n","\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":251},"id":"Wgm9c82fCQkB","executionInfo":{"status":"ok","timestamp":1758789787680,"user_tz":-180,"elapsed":424,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"59754d5c-b575-4d32-9e56-c481a87f5557"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["#вывод изображения\n","plt.imshow(X_train[7], cmap=plt.get_cmap('gray'))\n","plt.show()\n","\n","#вывод метки для этого изображения\n","print(y_train[7])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"sAh4llBy-xcL","executionInfo":{"status":"ok","timestamp":1758790544921,"user_tz":-180,"elapsed":94,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"40b7a624-3f1a-41eb-e3ef-68acde81beb2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["9\n"]}]},{"cell_type":"markdown","source":["5. Провести предобработку данных: привести обучающие и тестовые данные к формату, пригодному для обучения нейронной сети. Входныеданные должны принимать значения от 0 до 1, метки цифрдолжны быть закодированы по принципу «one-hotencoding».Вывести размерности предобработанных обучающих и тестовых массивов данных.\n"],"metadata":{"id":"pr8uaDcaFi1R"}},{"cell_type":"code","source":["#развернем каждое изображение 28*28 в вектор 784\n","num_pixels = X_train.shape[1] * X_train.shape[2]\n","X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255\n","X_test = X_test.reshape(X_test.shape[0],num_pixels) / 255\n","print('ShapeoftransformedXtrain:', X_train.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2f5kprtP_X0W","executionInfo":{"status":"ok","timestamp":1758790557314,"user_tz":-180,"elapsed":225,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"099cafe2-18d2-461e-a928-ce066e2af276"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["ShapeoftransformedXtrain: (60000, 784)\n"]}]},{"cell_type":"code","source":["#переведем метки в one-hot\n","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":"qumWyJfT_4Y8","executionInfo":{"status":"ok","timestamp":1758790560097,"user_tz":-180,"elapsed":42,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"1429f4da-e6bb-4af1-f9fc-45abebf118aa"},"execution_count":null,"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":"AONyEnGjBm3U"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["6.Реализовать модель однослойнойнейронной сети и обучить ее на обучающих данныхс выделением части обучающих данных в качестве валидационных. Вывести информацию обархитектуренейронной сети. Вывести график функции ошибкина обучающих и валидационных данныхпо эпохам. При реализациимодели нейронной сети задать следующую архитектуру и параметры обучения:количество скрытых слоев: 0функция активации выходного слоя: softmaxфункция ошибки: categorical_crossentropyалгоритм обучения: sgdметрика качества: accuracyколичество эпох:50долявалидационных данных от обучающих: 0.1"],"metadata":{"id":"xIQABlIEFqv5"}},{"cell_type":"code","source":["#1. создаем модель - объявляем ее объектом класса Sequential\n","model_1 = Sequential()\n","\n","#2. добавляем первый(последний) слой\n","model_1.add(Dense(units=num_classes, input_dim = num_pixels, activation='softmax'))\n","\n","#3. компилируем модель\n","model_1.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"],"metadata":{"id":"oNXKmY7QBxYv","executionInfo":{"status":"ok","timestamp":1758790621086,"user_tz":-180,"elapsed":3330,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"41a6ab18-f85e-4638-ba2b-d3f4ddbd8033"},"execution_count":null,"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"]}]},{"cell_type":"code","source":["# вывод информации об архитектуре модели\n","print(model_1.summary())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":185},"id":"4AoDXb0oDJkD","executionInfo":{"status":"ok","timestamp":1758790628634,"user_tz":-180,"elapsed":1012,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"f2584ec7-14bd-4af2-bfcb-8c4cec95cb90"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential\"\u001b[0m\n"],"text/html":["
Model: \"sequential\"\n","
\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":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense (Dense)                   │ (None, 10)             │         7,850 │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","
\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":["
 Total params: 7,850 (30.66 KB)\n","
\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":["
 Trainable params: 7,850 (30.66 KB)\n","
\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":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["None\n"]}]},{"cell_type":"code","source":["# Обучаем модель\n","H = model_1.fit(X_train, y_train, batch_size=512, validation_split=0.1, epochs=50)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"QBCUMdkWDYFL","outputId":"8600fda7-00d3-4c11-8ad0-5f24c1b69dbf","executionInfo":{"status":"ok","timestamp":1758790651666,"user_tz":-180,"elapsed":21990,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 18ms/step - accuracy: 0.2857 - loss: 2.1440 - val_accuracy: 0.6857 - val_loss: 1.5588\n","Epoch 2/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7140 - loss: 1.4477 - val_accuracy: 0.7763 - val_loss: 1.1793\n","Epoch 3/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7848 - loss: 1.1236 - val_accuracy: 0.8083 - val_loss: 0.9802\n","Epoch 4/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8106 - loss: 0.9510 - val_accuracy: 0.8262 - val_loss: 0.8598\n","Epoch 5/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8270 - loss: 0.8416 - val_accuracy: 0.8352 - val_loss: 0.7795\n","Epoch 6/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8343 - loss: 0.7715 - val_accuracy: 0.8438 - val_loss: 0.7219\n","Epoch 7/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8411 - loss: 0.7174 - val_accuracy: 0.8500 - val_loss: 0.6782\n","Epoch 8/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8470 - loss: 0.6742 - val_accuracy: 0.8537 - val_loss: 0.6438\n","Epoch 9/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8521 - loss: 0.6445 - val_accuracy: 0.8573 - val_loss: 0.6160\n","Epoch 10/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8558 - loss: 0.6158 - val_accuracy: 0.8588 - val_loss: 0.5931\n","Epoch 11/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8613 - loss: 0.5911 - val_accuracy: 0.8618 - val_loss: 0.5735\n","Epoch 12/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8634 - loss: 0.5708 - val_accuracy: 0.8648 - val_loss: 0.5569\n","Epoch 13/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8659 - loss: 0.5529 - val_accuracy: 0.8663 - val_loss: 0.5423\n","Epoch 14/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8650 - loss: 0.5450 - val_accuracy: 0.8692 - val_loss: 0.5295\n","Epoch 15/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8684 - loss: 0.5312 - val_accuracy: 0.8702 - val_loss: 0.5182\n","Epoch 16/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8692 - loss: 0.5246 - val_accuracy: 0.8713 - val_loss: 0.5080\n","Epoch 17/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8706 - loss: 0.5086 - val_accuracy: 0.8737 - val_loss: 0.4988\n","Epoch 18/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8725 - loss: 0.5003 - val_accuracy: 0.8747 - val_loss: 0.4905\n","Epoch 19/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8734 - loss: 0.4927 - val_accuracy: 0.8763 - val_loss: 0.4829\n","Epoch 20/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8732 - loss: 0.4915 - val_accuracy: 0.8768 - val_loss: 0.4760\n","Epoch 21/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8749 - loss: 0.4812 - val_accuracy: 0.8780 - val_loss: 0.4696\n","Epoch 22/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8780 - loss: 0.4737 - val_accuracy: 0.8792 - val_loss: 0.4637\n","Epoch 23/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8781 - loss: 0.4689 - val_accuracy: 0.8800 - val_loss: 0.4582\n","Epoch 24/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8782 - loss: 0.4649 - val_accuracy: 0.8808 - val_loss: 0.4530\n","Epoch 25/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8820 - loss: 0.4555 - val_accuracy: 0.8820 - val_loss: 0.4482\n","Epoch 26/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8814 - loss: 0.4529 - val_accuracy: 0.8825 - val_loss: 0.4438\n","Epoch 27/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8826 - loss: 0.4454 - val_accuracy: 0.8830 - val_loss: 0.4396\n","Epoch 28/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8814 - loss: 0.4480 - val_accuracy: 0.8842 - val_loss: 0.4356\n","Epoch 29/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8840 - loss: 0.4403 - val_accuracy: 0.8842 - val_loss: 0.4319\n","Epoch 30/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8836 - loss: 0.4363 - val_accuracy: 0.8857 - val_loss: 0.4283\n","Epoch 31/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8822 - loss: 0.4419 - val_accuracy: 0.8867 - val_loss: 0.4249\n","Epoch 32/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8837 - loss: 0.4308 - val_accuracy: 0.8868 - val_loss: 0.4217\n","Epoch 33/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8850 - loss: 0.4308 - val_accuracy: 0.8875 - val_loss: 0.4187\n","Epoch 34/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8880 - loss: 0.4200 - val_accuracy: 0.8882 - val_loss: 0.4158\n","Epoch 35/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8869 - loss: 0.4242 - val_accuracy: 0.8888 - val_loss: 0.4130\n","Epoch 36/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8874 - loss: 0.4175 - val_accuracy: 0.8893 - val_loss: 0.4104\n","Epoch 37/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8901 - loss: 0.4101 - val_accuracy: 0.8897 - val_loss: 0.4079\n","Epoch 38/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8889 - loss: 0.4142 - val_accuracy: 0.8900 - val_loss: 0.4055\n","Epoch 39/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8908 - loss: 0.4026 - val_accuracy: 0.8903 - val_loss: 0.4031\n","Epoch 40/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8890 - loss: 0.4085 - val_accuracy: 0.8910 - val_loss: 0.4009\n","Epoch 41/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8911 - loss: 0.4065 - val_accuracy: 0.8910 - val_loss: 0.3988\n","Epoch 42/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8910 - loss: 0.4044 - val_accuracy: 0.8905 - val_loss: 0.3967\n","Epoch 43/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8892 - loss: 0.4087 - val_accuracy: 0.8910 - val_loss: 0.3947\n","Epoch 44/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8931 - loss: 0.3984 - val_accuracy: 0.8917 - val_loss: 0.3928\n","Epoch 45/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8920 - loss: 0.3964 - val_accuracy: 0.8927 - val_loss: 0.3910\n","Epoch 46/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8927 - loss: 0.3958 - val_accuracy: 0.8927 - val_loss: 0.3892\n","Epoch 47/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8946 - loss: 0.3910 - val_accuracy: 0.8932 - val_loss: 0.3875\n","Epoch 48/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8929 - loss: 0.3927 - val_accuracy: 0.8933 - val_loss: 0.3858\n","Epoch 49/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8929 - loss: 0.3958 - val_accuracy: 0.8945 - val_loss: 0.3842\n","Epoch 50/50\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8955 - loss: 0.3871 - val_accuracy: 0.8947 - val_loss: 0.3827\n"]}]},{"cell_type":"markdown","source":["7.Применить обученную модель к тестовым данным. Вывести значение функции ошибки и значение метрики качества классификациина тестовых данных"],"metadata":{"id":"Ny0VvAjSKQ8J"}},{"cell_type":"code","source":["# вывод графика ошибки по эпохам\n","plt.plot(H.history['loss'])\n","plt.plot(H.history['val_loss'])\n","plt.grid()\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend(['train_loss', 'val_loss'])\n","plt.title('Loss by epochs')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"QXCEZm0WD4bg","executionInfo":{"status":"ok","timestamp":1758790654142,"user_tz":-180,"elapsed":675,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"f446c9f9-0300-4779-895d-e5bcce3fb4b0"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Оценка качества работы работы модели на тестовых данных\n","scores = model_1.evaluate(X_test, y_test)\n","print('Loss on test data:', scores[0])\n","print('Accuracy on test data:', scores[1])"],"metadata":{"id":"0JFR5o9zHsdB","executionInfo":{"status":"ok","timestamp":1758790668745,"user_tz":-180,"elapsed":1476,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"99549865-9ce9-4778-f656-1e717233230d"},"execution_count":null,"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: 0.8953 - loss: 0.3909\n","Loss on test data: 0.39573559165000916\n","Accuracy on test data: 0.8945000171661377\n"]}]},{"cell_type":"markdown","source":["8.Добавить в модель один скрытый и провести обучение и тестирование(повторить п.6–7)при 100, 300, 500нейронах в скрытом слое. По метрике качества классификации на тестовых данных выбрать наилучшее количество нейронов в скрытом слое.В качестве функции активации нейронов в скрытом слое использоватьфункцию sigmoid."],"metadata":{"id":"an41jer9KdDY"}},{"cell_type":"markdown","source":["a) Модель со 100 нейронами"],"metadata":{"id":"q6MdqOcsMmJx"}},{"cell_type":"code","source":["#1. создаем модель - объявляем ее объектом класса Sequential\n","model_1h100 = Sequential()\n","\n","#2. добавляем первый слой\n","model_1h100.add(Dense(units=100, input_dim = num_pixels, activation='sigmoid'))\n","\n","#2. добавляем выходной слой\n","model_1h100.add(Dense(units=num_classes, activation='sigmoid'))\n","\n","#4. компилируем модель\n","model_1h100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","\n","# вывод информации об архитектуре модели\n","print(model_1h100.summary())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":219},"id":"fCkui6AVKerj","executionInfo":{"status":"ok","timestamp":1758790799628,"user_tz":-180,"elapsed":56,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"6b983361-2df3-4ed5-d84d-86ddf4b5c81d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_1\"\u001b[0m\n"],"text/html":["
Model: \"sequential_1\"\n","
\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_1 (\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_2 (\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":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_1 (Dense)                 │ (None, 100)            │        78,500 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_2 (Dense)                 │ (None, 10)             │         1,010 │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","
\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":["
 Total params: 79,510 (310.59 KB)\n","
\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":["
 Trainable params: 79,510 (310.59 KB)\n","
\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":["
 Non-trainable params: 0 (0.00 B)\n","
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val_loss: 2.1675\n","Epoch 3/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4291 - loss: 2.1506 - val_accuracy: 0.4970 - val_loss: 2.1063\n","Epoch 4/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5161 - loss: 2.0908 - val_accuracy: 0.5512 - val_loss: 2.0473\n","Epoch 5/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5701 - loss: 2.0313 - val_accuracy: 0.5922 - val_loss: 1.9896\n","Epoch 6/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6106 - loss: 1.9737 - val_accuracy: 0.6193 - val_loss: 1.9333\n","Epoch 7/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6389 - loss: 1.9176 - val_accuracy: 0.6462 - val_loss: 1.8781\n","Epoch 8/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6571 - loss: 1.8644 - val_accuracy: 0.6635 - val_loss: 1.8243\n","Epoch 9/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6727 - loss: 1.8101 - val_accuracy: 0.6822 - val_loss: 1.7714\n","Epoch 10/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6930 - loss: 1.7582 - val_accuracy: 0.6897 - val_loss: 1.7199\n","Epoch 11/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6997 - loss: 1.7055 - val_accuracy: 0.7008 - val_loss: 1.6696\n","Epoch 12/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7082 - loss: 1.6591 - val_accuracy: 0.7082 - val_loss: 1.6208\n","Epoch 13/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7160 - loss: 1.6086 - val_accuracy: 0.7178 - val_loss: 1.5734\n","Epoch 14/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7278 - loss: 1.5605 - val_accuracy: 0.7287 - val_loss: 1.5274\n","Epoch 15/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7353 - loss: 1.5158 - val_accuracy: 0.7360 - val_loss: 1.4830\n","Epoch 16/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7448 - loss: 1.4689 - val_accuracy: 0.7468 - val_loss: 1.4402\n","Epoch 17/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7510 - loss: 1.4283 - val_accuracy: 0.7525 - val_loss: 1.3991\n","Epoch 18/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7550 - loss: 1.3901 - val_accuracy: 0.7600 - val_loss: 1.3596\n","Epoch 19/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.7629 - loss: 1.3506 - val_accuracy: 0.7673 - val_loss: 1.3217\n","Epoch 20/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.7664 - loss: 1.3161 - val_accuracy: 0.7725 - val_loss: 1.2855\n","Epoch 21/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.7771 - loss: 1.2782 - val_accuracy: 0.7755 - val_loss: 1.2508\n","Epoch 22/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7805 - loss: 1.2426 - val_accuracy: 0.7845 - val_loss: 1.2178\n","Epoch 23/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7864 - loss: 1.2121 - val_accuracy: 0.7882 - val_loss: 1.1863\n","Epoch 24/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7927 - loss: 1.1783 - val_accuracy: 0.7913 - val_loss: 1.1563\n","Epoch 25/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7942 - loss: 1.1513 - val_accuracy: 0.7977 - val_loss: 1.1277\n","Epoch 26/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8000 - loss: 1.1220 - val_accuracy: 0.8005 - val_loss: 1.1004\n","Epoch 27/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8051 - loss: 1.0937 - val_accuracy: 0.8047 - val_loss: 1.0745\n","Epoch 28/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8053 - loss: 1.0704 - val_accuracy: 0.8072 - val_loss: 1.0498\n","Epoch 29/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8058 - loss: 1.0473 - val_accuracy: 0.8108 - val_loss: 1.0263\n","Epoch 30/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8143 - loss: 1.0175 - val_accuracy: 0.8148 - val_loss: 1.0039\n","Epoch 31/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8145 - loss: 1.0011 - val_accuracy: 0.8170 - val_loss: 0.9827\n","Epoch 32/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8159 - loss: 0.9811 - val_accuracy: 0.8215 - val_loss: 0.9624\n","Epoch 33/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8191 - loss: 0.9599 - val_accuracy: 0.8242 - val_loss: 0.9431\n","Epoch 34/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8229 - loss: 0.9413 - val_accuracy: 0.8277 - val_loss: 0.9247\n","Epoch 35/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8244 - loss: 0.9224 - val_accuracy: 0.8307 - val_loss: 0.9072\n","Epoch 36/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8253 - loss: 0.9069 - val_accuracy: 0.8323 - val_loss: 0.8904\n","Epoch 37/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8294 - loss: 0.8889 - val_accuracy: 0.8330 - val_loss: 0.8745\n","Epoch 38/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8323 - loss: 0.8703 - val_accuracy: 0.8363 - val_loss: 0.8592\n","Epoch 39/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8320 - loss: 0.8604 - val_accuracy: 0.8382 - val_loss: 0.8446\n","Epoch 40/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8347 - loss: 0.8433 - val_accuracy: 0.8405 - val_loss: 0.8306\n","Epoch 41/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8377 - loss: 0.8267 - val_accuracy: 0.8418 - val_loss: 0.8173\n","Epoch 42/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8381 - loss: 0.8164 - val_accuracy: 0.8438 - val_loss: 0.8045\n","Epoch 43/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8413 - loss: 0.8005 - val_accuracy: 0.8453 - val_loss: 0.7922\n","Epoch 44/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8383 - loss: 0.7932 - val_accuracy: 0.8472 - val_loss: 0.7805\n","Epoch 45/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8411 - loss: 0.7815 - val_accuracy: 0.8488 - val_loss: 0.7692\n","Epoch 46/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8436 - loss: 0.7717 - val_accuracy: 0.8500 - val_loss: 0.7583\n","Epoch 47/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8473 - loss: 0.7574 - val_accuracy: 0.8503 - val_loss: 0.7479\n","Epoch 48/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8494 - loss: 0.7464 - val_accuracy: 0.8502 - val_loss: 0.7379\n","Epoch 49/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8452 - loss: 0.7419 - val_accuracy: 0.8517 - val_loss: 0.7283\n","Epoch 50/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8500 - loss: 0.7262 - val_accuracy: 0.8517 - val_loss: 0.7191\n","Epoch 51/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8505 - loss: 0.7182 - val_accuracy: 0.8530 - val_loss: 0.7101\n","Epoch 52/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8493 - loss: 0.7139 - val_accuracy: 0.8547 - val_loss: 0.7015\n","Epoch 53/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8526 - loss: 0.7021 - val_accuracy: 0.8550 - val_loss: 0.6932\n","Epoch 54/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8482 - loss: 0.7024 - val_accuracy: 0.8555 - val_loss: 0.6852\n","Epoch 55/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8531 - loss: 0.6876 - val_accuracy: 0.8562 - val_loss: 0.6775\n","Epoch 56/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8539 - loss: 0.6820 - val_accuracy: 0.8567 - val_loss: 0.6700\n","Epoch 57/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.8556 - loss: 0.6730 - val_accuracy: 0.8572 - val_loss: 0.6628\n","Epoch 58/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.8535 - loss: 0.6681 - val_accuracy: 0.8583 - val_loss: 0.6558\n","Epoch 59/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8568 - loss: 0.6626 - val_accuracy: 0.8600 - val_loss: 0.6491\n","Epoch 60/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.8559 - loss: 0.6548 - val_accuracy: 0.8615 - val_loss: 0.6426\n","Epoch 61/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.8579 - loss: 0.6465 - val_accuracy: 0.8628 - val_loss: 0.6363\n","Epoch 62/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.8587 - loss: 0.6404 - val_accuracy: 0.8637 - val_loss: 0.6301\n","Epoch 63/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8633 - loss: 0.6323 - val_accuracy: 0.8645 - val_loss: 0.6242\n","Epoch 64/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8600 - loss: 0.6306 - val_accuracy: 0.8652 - val_loss: 0.6184\n","Epoch 65/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.8620 - loss: 0.6197 - val_accuracy: 0.8668 - val_loss: 0.6128\n","Epoch 66/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.8628 - loss: 0.6167 - val_accuracy: 0.8677 - val_loss: 0.6074\n","Epoch 67/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.8658 - loss: 0.6063 - val_accuracy: 0.8678 - val_loss: 0.6021\n","Epoch 68/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8664 - loss: 0.6029 - val_accuracy: 0.8682 - val_loss: 0.5970\n","Epoch 69/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.8653 - loss: 0.6050 - val_accuracy: 0.8693 - val_loss: 0.5920\n","Epoch 70/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.8663 - loss: 0.5946 - val_accuracy: 0.8693 - val_loss: 0.5872\n","Epoch 71/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.8666 - loss: 0.5908 - val_accuracy: 0.8703 - val_loss: 0.5825\n","Epoch 72/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.8667 - loss: 0.5895 - val_accuracy: 0.8703 - val_loss: 0.5779\n","Epoch 73/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.8658 - loss: 0.5875 - val_accuracy: 0.8703 - val_loss: 0.5735\n","Epoch 74/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.8671 - loss: 0.5793 - val_accuracy: 0.8705 - val_loss: 0.5691\n","Epoch 75/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.8695 - loss: 0.5717 - val_accuracy: 0.8707 - val_loss: 0.5649\n","Epoch 76/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8688 - loss: 0.5704 - val_accuracy: 0.8712 - val_loss: 0.5608\n","Epoch 77/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8714 - loss: 0.5612 - val_accuracy: 0.8722 - val_loss: 0.5568\n","Epoch 78/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8704 - loss: 0.5626 - val_accuracy: 0.8727 - val_loss: 0.5528\n","Epoch 79/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8734 - loss: 0.5562 - val_accuracy: 0.8735 - val_loss: 0.5490\n","Epoch 80/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8739 - loss: 0.5480 - val_accuracy: 0.8747 - val_loss: 0.5453\n","Epoch 81/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8715 - loss: 0.5507 - val_accuracy: 0.8760 - val_loss: 0.5417\n","Epoch 82/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8714 - loss: 0.5483 - val_accuracy: 0.8762 - val_loss: 0.5382\n","Epoch 83/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8711 - loss: 0.5489 - val_accuracy: 0.8768 - val_loss: 0.5347\n","Epoch 84/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8743 - loss: 0.5452 - val_accuracy: 0.8770 - val_loss: 0.5313\n","Epoch 85/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8739 - loss: 0.5369 - val_accuracy: 0.8773 - val_loss: 0.5280\n","Epoch 86/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8751 - loss: 0.5326 - val_accuracy: 0.8777 - val_loss: 0.5248\n","Epoch 87/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8746 - loss: 0.5314 - val_accuracy: 0.8788 - val_loss: 0.5216\n","Epoch 88/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8726 - loss: 0.5336 - val_accuracy: 0.8787 - val_loss: 0.5186\n","Epoch 89/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8744 - loss: 0.5296 - val_accuracy: 0.8795 - val_loss: 0.5156\n","Epoch 90/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8762 - loss: 0.5223 - val_accuracy: 0.8798 - val_loss: 0.5126\n","Epoch 91/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8740 - loss: 0.5226 - val_accuracy: 0.8798 - val_loss: 0.5097\n","Epoch 92/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8745 - loss: 0.5182 - val_accuracy: 0.8808 - val_loss: 0.5069\n","Epoch 93/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8767 - loss: 0.5121 - val_accuracy: 0.8810 - val_loss: 0.5042\n","Epoch 94/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8766 - loss: 0.5102 - val_accuracy: 0.8810 - val_loss: 0.5014\n","Epoch 95/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8749 - loss: 0.5092 - val_accuracy: 0.8812 - val_loss: 0.4988\n","Epoch 96/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8805 - loss: 0.4969 - val_accuracy: 0.8818 - val_loss: 0.4962\n","Epoch 97/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8801 - loss: 0.4990 - val_accuracy: 0.8823 - val_loss: 0.4936\n","Epoch 98/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8781 - loss: 0.5006 - val_accuracy: 0.8825 - val_loss: 0.4912\n","Epoch 99/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8785 - loss: 0.4961 - val_accuracy: 0.8827 - val_loss: 0.4887\n","Epoch 100/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8778 - loss: 0.4949 - val_accuracy: 0.8823 - val_loss: 0.4863\n","Epoch 101/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8781 - loss: 0.4941 - val_accuracy: 0.8832 - val_loss: 0.4840\n","Epoch 102/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8787 - loss: 0.4944 - val_accuracy: 0.8840 - val_loss: 0.4817\n","Epoch 103/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8818 - loss: 0.4823 - val_accuracy: 0.8838 - val_loss: 0.4794\n","Epoch 104/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8835 - loss: 0.4826 - val_accuracy: 0.8842 - val_loss: 0.4772\n","Epoch 105/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8823 - loss: 0.4844 - val_accuracy: 0.8845 - val_loss: 0.4750\n","Epoch 106/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8824 - loss: 0.4776 - val_accuracy: 0.8850 - val_loss: 0.4729\n","Epoch 107/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8821 - loss: 0.4765 - val_accuracy: 0.8852 - val_loss: 0.4708\n","Epoch 108/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8820 - loss: 0.4784 - val_accuracy: 0.8855 - val_loss: 0.4688\n","Epoch 109/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8800 - loss: 0.4790 - val_accuracy: 0.8857 - val_loss: 0.4668\n","Epoch 110/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8830 - loss: 0.4706 - val_accuracy: 0.8860 - val_loss: 0.4648\n","Epoch 111/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8844 - loss: 0.4687 - val_accuracy: 0.8865 - val_loss: 0.4628\n","Epoch 112/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8826 - loss: 0.4694 - val_accuracy: 0.8867 - val_loss: 0.4609\n","Epoch 113/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8838 - loss: 0.4647 - val_accuracy: 0.8868 - val_loss: 0.4591\n","Epoch 114/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8827 - loss: 0.4674 - val_accuracy: 0.8868 - val_loss: 0.4572\n","Epoch 115/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8843 - loss: 0.4613 - val_accuracy: 0.8875 - val_loss: 0.4554\n","Epoch 116/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8833 - loss: 0.4646 - val_accuracy: 0.8875 - val_loss: 0.4536\n","Epoch 117/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8841 - loss: 0.4604 - val_accuracy: 0.8873 - val_loss: 0.4519\n","Epoch 118/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8840 - loss: 0.4588 - val_accuracy: 0.8877 - val_loss: 0.4501\n","Epoch 119/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8858 - loss: 0.4543 - val_accuracy: 0.8880 - val_loss: 0.4485\n","Epoch 120/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8843 - loss: 0.4520 - val_accuracy: 0.8882 - val_loss: 0.4468\n","Epoch 121/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8837 - loss: 0.4561 - val_accuracy: 0.8885 - val_loss: 0.4452\n","Epoch 122/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8826 - loss: 0.4554 - val_accuracy: 0.8892 - val_loss: 0.4435\n","Epoch 123/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8852 - loss: 0.4478 - val_accuracy: 0.8893 - val_loss: 0.4420\n","Epoch 124/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8871 - loss: 0.4488 - val_accuracy: 0.8892 - val_loss: 0.4404\n","Epoch 125/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8845 - loss: 0.4473 - val_accuracy: 0.8893 - val_loss: 0.4389\n","Epoch 126/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8860 - loss: 0.4449 - val_accuracy: 0.8897 - val_loss: 0.4374\n","Epoch 127/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8859 - loss: 0.4436 - val_accuracy: 0.8898 - val_loss: 0.4359\n","Epoch 128/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8865 - loss: 0.4434 - val_accuracy: 0.8905 - val_loss: 0.4344\n","Epoch 129/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8886 - loss: 0.4398 - val_accuracy: 0.8908 - val_loss: 0.4330\n","Epoch 130/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8860 - loss: 0.4431 - val_accuracy: 0.8912 - val_loss: 0.4316\n","Epoch 131/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8862 - loss: 0.4413 - val_accuracy: 0.8915 - val_loss: 0.4302\n","Epoch 132/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8857 - loss: 0.4386 - val_accuracy: 0.8917 - val_loss: 0.4288\n","Epoch 133/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8845 - loss: 0.4456 - val_accuracy: 0.8925 - val_loss: 0.4274\n","Epoch 134/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8866 - loss: 0.4356 - val_accuracy: 0.8927 - val_loss: 0.4261\n","Epoch 135/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8881 - loss: 0.4342 - val_accuracy: 0.8932 - val_loss: 0.4247\n","Epoch 136/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8886 - loss: 0.4308 - val_accuracy: 0.8928 - val_loss: 0.4234\n","Epoch 137/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8868 - loss: 0.4334 - val_accuracy: 0.8930 - val_loss: 0.4222\n","Epoch 138/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8862 - loss: 0.4344 - val_accuracy: 0.8930 - val_loss: 0.4209\n","Epoch 139/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8888 - loss: 0.4253 - val_accuracy: 0.8932 - val_loss: 0.4197\n","Epoch 140/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8874 - loss: 0.4284 - val_accuracy: 0.8933 - val_loss: 0.4184\n","Epoch 141/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8899 - loss: 0.4256 - val_accuracy: 0.8930 - val_loss: 0.4173\n","Epoch 142/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8881 - loss: 0.4200 - val_accuracy: 0.8937 - val_loss: 0.4160\n","Epoch 143/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8881 - loss: 0.4239 - val_accuracy: 0.8933 - val_loss: 0.4149\n","Epoch 144/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8887 - loss: 0.4254 - val_accuracy: 0.8937 - val_loss: 0.4137\n","Epoch 145/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8890 - loss: 0.4225 - val_accuracy: 0.8935 - val_loss: 0.4126\n","Epoch 146/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8889 - loss: 0.4227 - val_accuracy: 0.8940 - val_loss: 0.4115\n","Epoch 147/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8874 - loss: 0.4207 - val_accuracy: 0.8940 - val_loss: 0.4103\n","Epoch 148/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8911 - loss: 0.4173 - val_accuracy: 0.8938 - val_loss: 0.4092\n","Epoch 149/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8911 - loss: 0.4137 - val_accuracy: 0.8938 - val_loss: 0.4081\n","Epoch 150/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8890 - loss: 0.4138 - val_accuracy: 0.8938 - val_loss: 0.4070\n","Epoch 151/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8891 - loss: 0.4147 - val_accuracy: 0.8943 - val_loss: 0.4060\n","Epoch 152/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8887 - loss: 0.4129 - val_accuracy: 0.8947 - val_loss: 0.4050\n","Epoch 153/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8905 - loss: 0.4121 - val_accuracy: 0.8950 - val_loss: 0.4039\n","Epoch 154/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8896 - loss: 0.4137 - val_accuracy: 0.8948 - val_loss: 0.4029\n","Epoch 155/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8902 - loss: 0.4136 - val_accuracy: 0.8952 - val_loss: 0.4019\n","Epoch 156/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8892 - loss: 0.4105 - val_accuracy: 0.8957 - val_loss: 0.4009\n","Epoch 157/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8912 - loss: 0.4093 - val_accuracy: 0.8958 - val_loss: 0.3999\n","Epoch 158/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8890 - loss: 0.4141 - val_accuracy: 0.8958 - val_loss: 0.3990\n","Epoch 159/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8920 - loss: 0.4074 - val_accuracy: 0.8962 - val_loss: 0.3980\n","Epoch 160/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8929 - loss: 0.4026 - val_accuracy: 0.8963 - val_loss: 0.3971\n","Epoch 161/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8910 - loss: 0.4052 - val_accuracy: 0.8970 - val_loss: 0.3961\n","Epoch 162/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8922 - loss: 0.4038 - val_accuracy: 0.8968 - val_loss: 0.3952\n","Epoch 163/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8919 - loss: 0.4001 - val_accuracy: 0.8970 - val_loss: 0.3943\n","Epoch 164/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8911 - loss: 0.4050 - val_accuracy: 0.8968 - val_loss: 0.3934\n","Epoch 165/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8934 - loss: 0.3963 - val_accuracy: 0.8973 - val_loss: 0.3925\n","Epoch 166/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8909 - loss: 0.4071 - val_accuracy: 0.8978 - val_loss: 0.3916\n","Epoch 167/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8945 - loss: 0.3984 - val_accuracy: 0.8982 - val_loss: 0.3908\n","Epoch 168/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8930 - loss: 0.3975 - val_accuracy: 0.8982 - val_loss: 0.3899\n","Epoch 169/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8931 - loss: 0.3986 - val_accuracy: 0.8983 - val_loss: 0.3891\n","Epoch 170/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8927 - loss: 0.3951 - val_accuracy: 0.8983 - val_loss: 0.3882\n","Epoch 171/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8949 - loss: 0.3937 - val_accuracy: 0.8987 - val_loss: 0.3874\n","Epoch 172/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8948 - loss: 0.3942 - val_accuracy: 0.8990 - val_loss: 0.3866\n","Epoch 173/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8947 - loss: 0.3933 - val_accuracy: 0.8992 - val_loss: 0.3858\n","Epoch 174/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8960 - loss: 0.3905 - val_accuracy: 0.8992 - val_loss: 0.3850\n","Epoch 175/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8946 - loss: 0.3910 - val_accuracy: 0.8992 - val_loss: 0.3842\n","Epoch 176/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8926 - loss: 0.3953 - val_accuracy: 0.8993 - val_loss: 0.3834\n","Epoch 177/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8959 - loss: 0.3859 - val_accuracy: 0.8993 - val_loss: 0.3826\n","Epoch 178/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8969 - loss: 0.3889 - val_accuracy: 0.8993 - val_loss: 0.3819\n","Epoch 179/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8947 - loss: 0.3947 - val_accuracy: 0.8997 - val_loss: 0.3811\n","Epoch 180/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8967 - loss: 0.3871 - val_accuracy: 0.8995 - val_loss: 0.3803\n","Epoch 181/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8967 - loss: 0.3850 - val_accuracy: 0.8993 - val_loss: 0.3796\n","Epoch 182/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8956 - loss: 0.3872 - val_accuracy: 0.8998 - val_loss: 0.3789\n","Epoch 183/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8961 - loss: 0.3862 - val_accuracy: 0.9000 - val_loss: 0.3781\n","Epoch 184/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8984 - loss: 0.3789 - val_accuracy: 0.8998 - val_loss: 0.3774\n","Epoch 185/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8973 - loss: 0.3807 - val_accuracy: 0.8998 - val_loss: 0.3767\n","Epoch 186/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8945 - loss: 0.3884 - val_accuracy: 0.9003 - val_loss: 0.3760\n","Epoch 187/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8947 - loss: 0.3863 - val_accuracy: 0.9003 - val_loss: 0.3753\n","Epoch 188/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8964 - loss: 0.3840 - val_accuracy: 0.9010 - val_loss: 0.3746\n","Epoch 189/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8971 - loss: 0.3782 - val_accuracy: 0.9007 - val_loss: 0.3739\n","Epoch 190/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8970 - loss: 0.3817 - val_accuracy: 0.9012 - val_loss: 0.3732\n","Epoch 191/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8957 - loss: 0.3822 - val_accuracy: 0.9010 - val_loss: 0.3726\n","Epoch 192/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8974 - loss: 0.3824 - val_accuracy: 0.9012 - val_loss: 0.3719\n","Epoch 193/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8955 - loss: 0.3836 - val_accuracy: 0.9013 - val_loss: 0.3712\n","Epoch 194/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8983 - loss: 0.3781 - val_accuracy: 0.9013 - val_loss: 0.3706\n","Epoch 195/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8965 - loss: 0.3809 - val_accuracy: 0.9018 - val_loss: 0.3700\n","Epoch 196/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8957 - loss: 0.3781 - val_accuracy: 0.9020 - val_loss: 0.3693\n","Epoch 197/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8991 - loss: 0.3692 - val_accuracy: 0.9018 - val_loss: 0.3687\n","Epoch 198/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8959 - loss: 0.3748 - val_accuracy: 0.9017 - val_loss: 0.3680\n","Epoch 199/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8948 - loss: 0.3794 - val_accuracy: 0.9017 - val_loss: 0.3674\n","Epoch 200/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8967 - loss: 0.3758 - val_accuracy: 0.9013 - val_loss: 0.3668\n","Epoch 201/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8982 - loss: 0.3766 - val_accuracy: 0.9018 - val_loss: 0.3662\n","Epoch 202/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8974 - loss: 0.3738 - val_accuracy: 0.9015 - val_loss: 0.3656\n","Epoch 203/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8976 - loss: 0.3714 - val_accuracy: 0.9017 - val_loss: 0.3650\n","Epoch 204/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8951 - loss: 0.3794 - val_accuracy: 0.9015 - val_loss: 0.3644\n","Epoch 205/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8955 - loss: 0.3765 - val_accuracy: 0.9017 - val_loss: 0.3638\n","Epoch 206/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8990 - loss: 0.3711 - val_accuracy: 0.9020 - val_loss: 0.3632\n","Epoch 207/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8990 - loss: 0.3720 - val_accuracy: 0.9020 - val_loss: 0.3626\n","Epoch 208/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8973 - loss: 0.3702 - val_accuracy: 0.9025 - val_loss: 0.3621\n","Epoch 209/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9006 - loss: 0.3678 - val_accuracy: 0.9023 - val_loss: 0.3615\n","Epoch 210/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8975 - loss: 0.3715 - val_accuracy: 0.9025 - val_loss: 0.3609\n","Epoch 211/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8976 - loss: 0.3705 - val_accuracy: 0.9022 - val_loss: 0.3604\n","Epoch 212/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8985 - loss: 0.3649 - val_accuracy: 0.9025 - val_loss: 0.3598\n","Epoch 213/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9008 - loss: 0.3623 - val_accuracy: 0.9025 - val_loss: 0.3593\n","Epoch 214/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8996 - loss: 0.3665 - val_accuracy: 0.9027 - val_loss: 0.3587\n","Epoch 215/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8985 - loss: 0.3694 - val_accuracy: 0.9027 - val_loss: 0.3582\n","Epoch 216/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8984 - loss: 0.3671 - val_accuracy: 0.9032 - val_loss: 0.3577\n","Epoch 217/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9019 - loss: 0.3591 - val_accuracy: 0.9028 - val_loss: 0.3571\n","Epoch 218/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8998 - loss: 0.3661 - val_accuracy: 0.9028 - val_loss: 0.3566\n","Epoch 219/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8990 - loss: 0.3612 - val_accuracy: 0.9030 - val_loss: 0.3561\n","Epoch 220/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8993 - loss: 0.3631 - val_accuracy: 0.9030 - val_loss: 0.3556\n","Epoch 221/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9020 - loss: 0.3599 - val_accuracy: 0.9032 - val_loss: 0.3551\n","Epoch 222/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9001 - loss: 0.3618 - val_accuracy: 0.9032 - val_loss: 0.3546\n","Epoch 223/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8981 - loss: 0.3652 - val_accuracy: 0.9032 - val_loss: 0.3541\n","Epoch 224/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9014 - loss: 0.3568 - val_accuracy: 0.9033 - val_loss: 0.3536\n","Epoch 225/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9009 - loss: 0.3584 - val_accuracy: 0.9035 - val_loss: 0.3531\n","Epoch 226/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9026 - loss: 0.3552 - val_accuracy: 0.9040 - val_loss: 0.3526\n","Epoch 227/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9021 - loss: 0.3574 - val_accuracy: 0.9043 - val_loss: 0.3521\n","Epoch 228/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9009 - loss: 0.3574 - val_accuracy: 0.9043 - val_loss: 0.3516\n","Epoch 229/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9025 - loss: 0.3544 - val_accuracy: 0.9043 - val_loss: 0.3511\n","Epoch 230/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9018 - loss: 0.3581 - val_accuracy: 0.9045 - val_loss: 0.3507\n","Epoch 231/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9011 - loss: 0.3550 - val_accuracy: 0.9045 - val_loss: 0.3502\n","Epoch 232/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9021 - loss: 0.3541 - val_accuracy: 0.9045 - val_loss: 0.3497\n","Epoch 233/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9017 - loss: 0.3559 - val_accuracy: 0.9048 - val_loss: 0.3493\n","Epoch 234/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8988 - loss: 0.3609 - val_accuracy: 0.9050 - val_loss: 0.3488\n","Epoch 235/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8996 - loss: 0.3573 - val_accuracy: 0.9055 - val_loss: 0.3483\n","Epoch 236/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9023 - loss: 0.3536 - val_accuracy: 0.9055 - val_loss: 0.3479\n","Epoch 237/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9021 - loss: 0.3553 - val_accuracy: 0.9057 - val_loss: 0.3474\n","Epoch 238/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9043 - loss: 0.3486 - val_accuracy: 0.9057 - val_loss: 0.3470\n","Epoch 239/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9038 - loss: 0.3494 - val_accuracy: 0.9060 - val_loss: 0.3465\n","Epoch 240/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8998 - loss: 0.3572 - val_accuracy: 0.9060 - val_loss: 0.3461\n","Epoch 241/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9021 - loss: 0.3519 - val_accuracy: 0.9062 - val_loss: 0.3457\n","Epoch 242/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9011 - loss: 0.3533 - val_accuracy: 0.9063 - val_loss: 0.3452\n","Epoch 243/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9007 - loss: 0.3534 - val_accuracy: 0.9063 - val_loss: 0.3448\n","Epoch 244/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9032 - loss: 0.3486 - val_accuracy: 0.9062 - val_loss: 0.3444\n","Epoch 245/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9020 - loss: 0.3514 - val_accuracy: 0.9063 - val_loss: 0.3439\n","Epoch 246/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9030 - loss: 0.3504 - val_accuracy: 0.9065 - val_loss: 0.3435\n","Epoch 247/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9016 - loss: 0.3510 - val_accuracy: 0.9063 - val_loss: 0.3431\n","Epoch 248/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9015 - loss: 0.3551 - val_accuracy: 0.9063 - val_loss: 0.3427\n","Epoch 249/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9032 - loss: 0.3514 - val_accuracy: 0.9063 - val_loss: 0.3423\n","Epoch 250/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9011 - loss: 0.3512 - val_accuracy: 0.9065 - val_loss: 0.3418\n","Epoch 251/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9033 - loss: 0.3483 - val_accuracy: 0.9063 - val_loss: 0.3415\n","Epoch 252/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9006 - loss: 0.3524 - val_accuracy: 0.9065 - val_loss: 0.3410\n","Epoch 253/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9019 - loss: 0.3508 - val_accuracy: 0.9068 - val_loss: 0.3406\n","Epoch 254/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - accuracy: 0.9021 - loss: 0.3459 - val_accuracy: 0.9070 - val_loss: 0.3403\n","Epoch 255/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9027 - loss: 0.3516 - val_accuracy: 0.9068 - val_loss: 0.3399\n","Epoch 256/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9042 - loss: 0.3451 - val_accuracy: 0.9072 - val_loss: 0.3395\n","Epoch 257/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9040 - loss: 0.3446 - val_accuracy: 0.9072 - val_loss: 0.3391\n","Epoch 258/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9031 - loss: 0.3431 - val_accuracy: 0.9075 - val_loss: 0.3387\n","Epoch 259/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9050 - loss: 0.3436 - val_accuracy: 0.9075 - val_loss: 0.3383\n","Epoch 260/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9032 - loss: 0.3475 - val_accuracy: 0.9075 - val_loss: 0.3379\n","Epoch 261/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9021 - loss: 0.3509 - val_accuracy: 0.9073 - val_loss: 0.3375\n","Epoch 262/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9063 - loss: 0.3406 - val_accuracy: 0.9077 - val_loss: 0.3372\n","Epoch 263/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9039 - loss: 0.3441 - val_accuracy: 0.9077 - val_loss: 0.3368\n","Epoch 264/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9060 - loss: 0.3383 - val_accuracy: 0.9077 - val_loss: 0.3364\n","Epoch 265/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9044 - loss: 0.3456 - val_accuracy: 0.9077 - val_loss: 0.3360\n","Epoch 266/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9034 - loss: 0.3450 - val_accuracy: 0.9078 - val_loss: 0.3357\n","Epoch 267/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9023 - loss: 0.3468 - val_accuracy: 0.9080 - val_loss: 0.3353\n","Epoch 268/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9034 - loss: 0.3442 - val_accuracy: 0.9080 - val_loss: 0.3349\n","Epoch 269/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9053 - loss: 0.3424 - val_accuracy: 0.9082 - val_loss: 0.3346\n","Epoch 270/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9038 - loss: 0.3467 - val_accuracy: 0.9085 - val_loss: 0.3342\n","Epoch 271/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9061 - loss: 0.3331 - val_accuracy: 0.9085 - val_loss: 0.3339\n","Epoch 272/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9039 - loss: 0.3398 - val_accuracy: 0.9087 - val_loss: 0.3335\n","Epoch 273/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9058 - loss: 0.3388 - val_accuracy: 0.9088 - val_loss: 0.3332\n","Epoch 274/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9039 - loss: 0.3418 - val_accuracy: 0.9088 - val_loss: 0.3328\n","Epoch 275/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9069 - loss: 0.3358 - val_accuracy: 0.9090 - val_loss: 0.3325\n","Epoch 276/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9063 - loss: 0.3369 - val_accuracy: 0.9090 - val_loss: 0.3321\n","Epoch 277/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9053 - loss: 0.3413 - val_accuracy: 0.9090 - val_loss: 0.3318\n","Epoch 278/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9038 - loss: 0.3392 - val_accuracy: 0.9095 - val_loss: 0.3314\n","Epoch 279/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9064 - loss: 0.3359 - val_accuracy: 0.9095 - val_loss: 0.3311\n","Epoch 280/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9059 - loss: 0.3362 - val_accuracy: 0.9095 - val_loss: 0.3308\n","Epoch 281/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9040 - loss: 0.3416 - val_accuracy: 0.9095 - val_loss: 0.3304\n","Epoch 282/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9041 - loss: 0.3422 - val_accuracy: 0.9102 - val_loss: 0.3301\n","Epoch 283/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9051 - loss: 0.3404 - val_accuracy: 0.9100 - val_loss: 0.3298\n","Epoch 284/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9078 - loss: 0.3306 - val_accuracy: 0.9105 - val_loss: 0.3294\n","Epoch 285/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9052 - loss: 0.3358 - val_accuracy: 0.9105 - val_loss: 0.3291\n","Epoch 286/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9050 - loss: 0.3314 - val_accuracy: 0.9103 - val_loss: 0.3288\n","Epoch 287/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9072 - loss: 0.3360 - val_accuracy: 0.9107 - val_loss: 0.3285\n","Epoch 288/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9051 - loss: 0.3356 - val_accuracy: 0.9107 - val_loss: 0.3281\n","Epoch 289/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9060 - loss: 0.3353 - val_accuracy: 0.9107 - val_loss: 0.3278\n","Epoch 290/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9043 - loss: 0.3368 - val_accuracy: 0.9112 - val_loss: 0.3275\n","Epoch 291/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9064 - loss: 0.3312 - val_accuracy: 0.9110 - val_loss: 0.3272\n","Epoch 292/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9066 - loss: 0.3307 - val_accuracy: 0.9112 - val_loss: 0.3269\n","Epoch 293/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9055 - loss: 0.3320 - val_accuracy: 0.9112 - val_loss: 0.3266\n","Epoch 294/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9065 - loss: 0.3326 - val_accuracy: 0.9112 - val_loss: 0.3262\n","Epoch 295/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9069 - loss: 0.3313 - val_accuracy: 0.9112 - val_loss: 0.3259\n","Epoch 296/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9060 - loss: 0.3352 - val_accuracy: 0.9113 - val_loss: 0.3256\n","Epoch 297/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9046 - loss: 0.3353 - val_accuracy: 0.9113 - val_loss: 0.3253\n","Epoch 298/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9056 - loss: 0.3319 - val_accuracy: 0.9115 - val_loss: 0.3250\n","Epoch 299/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9062 - loss: 0.3330 - val_accuracy: 0.9115 - val_loss: 0.3247\n","Epoch 300/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9058 - loss: 0.3358 - val_accuracy: 0.9117 - val_loss: 0.3244\n","Epoch 301/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9063 - loss: 0.3340 - val_accuracy: 0.9117 - val_loss: 0.3241\n","Epoch 302/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9081 - loss: 0.3269 - val_accuracy: 0.9122 - val_loss: 0.3238\n","Epoch 303/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9088 - loss: 0.3276 - val_accuracy: 0.9122 - val_loss: 0.3235\n","Epoch 304/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9072 - loss: 0.3254 - val_accuracy: 0.9122 - val_loss: 0.3233\n","Epoch 305/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9063 - loss: 0.3312 - val_accuracy: 0.9125 - val_loss: 0.3230\n","Epoch 306/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9089 - loss: 0.3258 - val_accuracy: 0.9128 - val_loss: 0.3227\n","Epoch 307/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9076 - loss: 0.3282 - val_accuracy: 0.9128 - val_loss: 0.3224\n","Epoch 308/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9090 - loss: 0.3240 - val_accuracy: 0.9128 - val_loss: 0.3221\n","Epoch 309/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9068 - loss: 0.3282 - val_accuracy: 0.9128 - val_loss: 0.3218\n","Epoch 310/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9079 - loss: 0.3241 - val_accuracy: 0.9128 - val_loss: 0.3215\n","Epoch 311/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9065 - loss: 0.3301 - val_accuracy: 0.9128 - val_loss: 0.3212\n","Epoch 312/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9082 - loss: 0.3283 - val_accuracy: 0.9132 - val_loss: 0.3210\n","Epoch 313/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9064 - loss: 0.3313 - val_accuracy: 0.9132 - val_loss: 0.3207\n","Epoch 314/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9057 - loss: 0.3301 - val_accuracy: 0.9132 - val_loss: 0.3204\n","Epoch 315/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9062 - loss: 0.3285 - val_accuracy: 0.9133 - val_loss: 0.3201\n","Epoch 316/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9098 - loss: 0.3242 - val_accuracy: 0.9132 - val_loss: 0.3199\n","Epoch 317/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9082 - loss: 0.3267 - val_accuracy: 0.9132 - val_loss: 0.3196\n","Epoch 318/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9087 - loss: 0.3221 - val_accuracy: 0.9133 - val_loss: 0.3193\n","Epoch 319/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9098 - loss: 0.3213 - val_accuracy: 0.9133 - val_loss: 0.3190\n","Epoch 320/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9083 - loss: 0.3214 - val_accuracy: 0.9133 - val_loss: 0.3188\n","Epoch 321/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9096 - loss: 0.3219 - val_accuracy: 0.9135 - val_loss: 0.3185\n","Epoch 322/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9061 - loss: 0.3311 - val_accuracy: 0.9137 - val_loss: 0.3182\n","Epoch 323/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9089 - loss: 0.3235 - val_accuracy: 0.9135 - val_loss: 0.3179\n","Epoch 324/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9078 - loss: 0.3254 - val_accuracy: 0.9137 - val_loss: 0.3177\n","Epoch 325/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9071 - loss: 0.3254 - val_accuracy: 0.9137 - val_loss: 0.3174\n","Epoch 326/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9098 - loss: 0.3226 - val_accuracy: 0.9135 - val_loss: 0.3172\n","Epoch 327/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9086 - loss: 0.3218 - val_accuracy: 0.9135 - val_loss: 0.3169\n","Epoch 328/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9083 - loss: 0.3212 - val_accuracy: 0.9138 - val_loss: 0.3166\n","Epoch 329/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9081 - loss: 0.3250 - val_accuracy: 0.9137 - val_loss: 0.3164\n","Epoch 330/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9077 - loss: 0.3239 - val_accuracy: 0.9135 - val_loss: 0.3161\n","Epoch 331/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9090 - loss: 0.3215 - val_accuracy: 0.9140 - val_loss: 0.3159\n","Epoch 332/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9081 - loss: 0.3217 - val_accuracy: 0.9142 - val_loss: 0.3156\n","Epoch 333/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9109 - loss: 0.3172 - val_accuracy: 0.9142 - val_loss: 0.3154\n","Epoch 334/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9105 - loss: 0.3170 - val_accuracy: 0.9143 - val_loss: 0.3151\n","Epoch 335/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9104 - loss: 0.3177 - val_accuracy: 0.9145 - val_loss: 0.3149\n","Epoch 336/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9078 - loss: 0.3220 - val_accuracy: 0.9145 - val_loss: 0.3146\n","Epoch 337/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9075 - loss: 0.3272 - val_accuracy: 0.9145 - val_loss: 0.3144\n","Epoch 338/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9119 - loss: 0.3123 - val_accuracy: 0.9143 - val_loss: 0.3141\n","Epoch 339/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9100 - loss: 0.3190 - val_accuracy: 0.9145 - val_loss: 0.3139\n","Epoch 340/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9085 - loss: 0.3200 - val_accuracy: 0.9145 - val_loss: 0.3136\n","Epoch 341/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9075 - loss: 0.3231 - val_accuracy: 0.9147 - val_loss: 0.3134\n","Epoch 342/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9088 - loss: 0.3208 - val_accuracy: 0.9147 - val_loss: 0.3131\n","Epoch 343/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9084 - loss: 0.3236 - val_accuracy: 0.9147 - val_loss: 0.3129\n","Epoch 344/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9101 - loss: 0.3154 - val_accuracy: 0.9148 - val_loss: 0.3127\n","Epoch 345/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9082 - loss: 0.3213 - val_accuracy: 0.9150 - val_loss: 0.3124\n","Epoch 346/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9085 - loss: 0.3163 - val_accuracy: 0.9152 - val_loss: 0.3122\n","Epoch 347/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9082 - loss: 0.3205 - val_accuracy: 0.9157 - val_loss: 0.3119\n","Epoch 348/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9080 - loss: 0.3185 - val_accuracy: 0.9157 - val_loss: 0.3117\n","Epoch 349/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9094 - loss: 0.3199 - val_accuracy: 0.9153 - val_loss: 0.3115\n","Epoch 350/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9100 - loss: 0.3159 - val_accuracy: 0.9153 - val_loss: 0.3112\n","Epoch 351/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9097 - loss: 0.3151 - val_accuracy: 0.9153 - val_loss: 0.3110\n","Epoch 352/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9084 - loss: 0.3224 - val_accuracy: 0.9153 - val_loss: 0.3108\n","Epoch 353/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9095 - loss: 0.3221 - val_accuracy: 0.9155 - val_loss: 0.3105\n","Epoch 354/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9102 - loss: 0.3171 - val_accuracy: 0.9157 - val_loss: 0.3103\n","Epoch 355/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9090 - loss: 0.3203 - val_accuracy: 0.9157 - val_loss: 0.3101\n","Epoch 356/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9103 - loss: 0.3139 - val_accuracy: 0.9157 - val_loss: 0.3098\n","Epoch 357/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9099 - loss: 0.3158 - val_accuracy: 0.9157 - val_loss: 0.3096\n","Epoch 358/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9113 - loss: 0.3112 - val_accuracy: 0.9158 - val_loss: 0.3094\n","Epoch 359/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9081 - loss: 0.3162 - val_accuracy: 0.9160 - val_loss: 0.3092\n","Epoch 360/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9104 - loss: 0.3133 - val_accuracy: 0.9160 - val_loss: 0.3089\n","Epoch 361/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9107 - loss: 0.3131 - val_accuracy: 0.9160 - val_loss: 0.3087\n","Epoch 362/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9093 - loss: 0.3167 - val_accuracy: 0.9160 - val_loss: 0.3085\n","Epoch 363/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9084 - loss: 0.3176 - val_accuracy: 0.9160 - val_loss: 0.3083\n","Epoch 364/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9086 - loss: 0.3160 - val_accuracy: 0.9162 - val_loss: 0.3081\n","Epoch 365/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9107 - loss: 0.3123 - val_accuracy: 0.9160 - val_loss: 0.3078\n","Epoch 366/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9129 - loss: 0.3085 - val_accuracy: 0.9162 - val_loss: 0.3076\n","Epoch 367/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9115 - loss: 0.3102 - val_accuracy: 0.9160 - val_loss: 0.3074\n","Epoch 368/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9105 - loss: 0.3119 - val_accuracy: 0.9160 - val_loss: 0.3072\n","Epoch 369/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9123 - loss: 0.3103 - val_accuracy: 0.9160 - val_loss: 0.3070\n","Epoch 370/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9125 - loss: 0.3082 - val_accuracy: 0.9160 - val_loss: 0.3068\n","Epoch 371/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9111 - loss: 0.3132 - val_accuracy: 0.9162 - val_loss: 0.3065\n","Epoch 372/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9104 - loss: 0.3113 - val_accuracy: 0.9163 - val_loss: 0.3063\n","Epoch 373/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9122 - loss: 0.3098 - val_accuracy: 0.9163 - val_loss: 0.3061\n","Epoch 374/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9104 - loss: 0.3153 - val_accuracy: 0.9163 - val_loss: 0.3059\n","Epoch 375/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9097 - loss: 0.3146 - val_accuracy: 0.9163 - val_loss: 0.3057\n","Epoch 376/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9123 - loss: 0.3056 - val_accuracy: 0.9163 - val_loss: 0.3055\n","Epoch 377/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9125 - loss: 0.3070 - val_accuracy: 0.9165 - val_loss: 0.3053\n","Epoch 378/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9118 - loss: 0.3116 - val_accuracy: 0.9165 - val_loss: 0.3051\n","Epoch 379/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9119 - loss: 0.3143 - val_accuracy: 0.9165 - val_loss: 0.3049\n","Epoch 380/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9112 - loss: 0.3089 - val_accuracy: 0.9165 - val_loss: 0.3047\n","Epoch 381/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9109 - loss: 0.3105 - val_accuracy: 0.9165 - val_loss: 0.3045\n","Epoch 382/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9124 - loss: 0.3093 - val_accuracy: 0.9167 - val_loss: 0.3042\n","Epoch 383/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9116 - loss: 0.3071 - val_accuracy: 0.9167 - val_loss: 0.3040\n","Epoch 384/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9126 - loss: 0.3068 - val_accuracy: 0.9167 - val_loss: 0.3038\n","Epoch 385/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9115 - loss: 0.3115 - val_accuracy: 0.9167 - val_loss: 0.3036\n","Epoch 386/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9118 - loss: 0.3073 - val_accuracy: 0.9170 - val_loss: 0.3034\n","Epoch 387/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9130 - loss: 0.3084 - val_accuracy: 0.9170 - val_loss: 0.3032\n","Epoch 388/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9135 - loss: 0.3055 - val_accuracy: 0.9172 - val_loss: 0.3030\n","Epoch 389/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9104 - loss: 0.3120 - val_accuracy: 0.9172 - val_loss: 0.3028\n","Epoch 390/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9121 - loss: 0.3057 - val_accuracy: 0.9172 - val_loss: 0.3026\n","Epoch 391/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9088 - loss: 0.3140 - val_accuracy: 0.9172 - val_loss: 0.3024\n","Epoch 392/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9108 - loss: 0.3106 - val_accuracy: 0.9172 - val_loss: 0.3022\n","Epoch 393/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9120 - loss: 0.3071 - val_accuracy: 0.9172 - val_loss: 0.3020\n","Epoch 394/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9129 - loss: 0.3056 - val_accuracy: 0.9173 - val_loss: 0.3018\n","Epoch 395/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9123 - loss: 0.3089 - val_accuracy: 0.9173 - val_loss: 0.3016\n","Epoch 396/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9124 - loss: 0.3056 - val_accuracy: 0.9173 - val_loss: 0.3015\n","Epoch 397/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9121 - loss: 0.3076 - val_accuracy: 0.9175 - val_loss: 0.3013\n","Epoch 398/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9106 - loss: 0.3105 - val_accuracy: 0.9175 - val_loss: 0.3011\n","Epoch 399/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9104 - loss: 0.3107 - val_accuracy: 0.9175 - val_loss: 0.3009\n","Epoch 400/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9105 - loss: 0.3103 - val_accuracy: 0.9173 - val_loss: 0.3007\n","Epoch 401/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9118 - loss: 0.3063 - val_accuracy: 0.9173 - val_loss: 0.3005\n","Epoch 402/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9137 - loss: 0.3038 - val_accuracy: 0.9173 - val_loss: 0.3003\n","Epoch 403/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9127 - loss: 0.3004 - val_accuracy: 0.9175 - val_loss: 0.3001\n","Epoch 404/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9141 - loss: 0.3021 - val_accuracy: 0.9175 - val_loss: 0.2999\n","Epoch 405/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9139 - loss: 0.3031 - val_accuracy: 0.9177 - val_loss: 0.2997\n","Epoch 406/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9136 - loss: 0.3036 - val_accuracy: 0.9177 - val_loss: 0.2995\n","Epoch 407/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9116 - loss: 0.3022 - val_accuracy: 0.9177 - val_loss: 0.2994\n","Epoch 408/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9144 - loss: 0.3014 - val_accuracy: 0.9175 - val_loss: 0.2992\n","Epoch 409/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9132 - loss: 0.3047 - val_accuracy: 0.9175 - val_loss: 0.2990\n","Epoch 410/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9137 - loss: 0.3024 - val_accuracy: 0.9177 - val_loss: 0.2988\n","Epoch 411/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9122 - loss: 0.3061 - val_accuracy: 0.9177 - val_loss: 0.2986\n","Epoch 412/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9132 - loss: 0.3016 - val_accuracy: 0.9177 - val_loss: 0.2984\n","Epoch 413/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9150 - loss: 0.2974 - val_accuracy: 0.9177 - val_loss: 0.2982\n","Epoch 414/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9120 - loss: 0.3108 - val_accuracy: 0.9177 - val_loss: 0.2981\n","Epoch 415/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9118 - loss: 0.3069 - val_accuracy: 0.9177 - val_loss: 0.2979\n","Epoch 416/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9141 - loss: 0.3018 - val_accuracy: 0.9177 - val_loss: 0.2977\n","Epoch 417/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9129 - loss: 0.3039 - val_accuracy: 0.9177 - val_loss: 0.2975\n","Epoch 418/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9127 - loss: 0.3033 - val_accuracy: 0.9177 - val_loss: 0.2973\n","Epoch 419/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9144 - loss: 0.3005 - val_accuracy: 0.9177 - val_loss: 0.2971\n","Epoch 420/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9129 - loss: 0.3004 - val_accuracy: 0.9177 - val_loss: 0.2970\n","Epoch 421/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9121 - loss: 0.3032 - val_accuracy: 0.9177 - val_loss: 0.2968\n","Epoch 422/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9110 - loss: 0.3054 - val_accuracy: 0.9177 - val_loss: 0.2966\n","Epoch 423/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9152 - loss: 0.2927 - val_accuracy: 0.9178 - val_loss: 0.2964\n","Epoch 424/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9137 - loss: 0.2997 - val_accuracy: 0.9178 - val_loss: 0.2963\n","Epoch 425/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9131 - loss: 0.3022 - val_accuracy: 0.9182 - val_loss: 0.2961\n","Epoch 426/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9147 - loss: 0.2988 - val_accuracy: 0.9178 - val_loss: 0.2959\n","Epoch 427/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9153 - loss: 0.2987 - val_accuracy: 0.9180 - val_loss: 0.2957\n","Epoch 428/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9147 - loss: 0.2969 - val_accuracy: 0.9182 - val_loss: 0.2956\n","Epoch 429/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9140 - loss: 0.3003 - val_accuracy: 0.9183 - val_loss: 0.2954\n","Epoch 430/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9120 - loss: 0.3049 - val_accuracy: 0.9182 - val_loss: 0.2952\n","Epoch 431/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9145 - loss: 0.2989 - val_accuracy: 0.9182 - val_loss: 0.2950\n","Epoch 432/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9124 - loss: 0.3050 - val_accuracy: 0.9182 - val_loss: 0.2949\n","Epoch 433/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9136 - loss: 0.3014 - val_accuracy: 0.9183 - val_loss: 0.2947\n","Epoch 434/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9123 - loss: 0.3022 - val_accuracy: 0.9183 - val_loss: 0.2945\n","Epoch 435/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9149 - loss: 0.2952 - val_accuracy: 0.9183 - val_loss: 0.2944\n","Epoch 436/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9140 - loss: 0.2985 - val_accuracy: 0.9183 - val_loss: 0.2942\n","Epoch 437/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9177 - loss: 0.2904 - val_accuracy: 0.9183 - val_loss: 0.2940\n","Epoch 438/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9150 - loss: 0.2994 - val_accuracy: 0.9185 - val_loss: 0.2938\n","Epoch 439/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9145 - loss: 0.2986 - val_accuracy: 0.9185 - val_loss: 0.2937\n","Epoch 440/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9137 - loss: 0.2972 - val_accuracy: 0.9183 - val_loss: 0.2935\n","Epoch 441/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9138 - loss: 0.2988 - val_accuracy: 0.9185 - val_loss: 0.2933\n","Epoch 442/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9152 - loss: 0.2999 - val_accuracy: 0.9185 - val_loss: 0.2932\n","Epoch 443/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9156 - loss: 0.2940 - val_accuracy: 0.9187 - val_loss: 0.2930\n","Epoch 444/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9137 - loss: 0.2997 - val_accuracy: 0.9187 - val_loss: 0.2928\n","Epoch 445/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9151 - loss: 0.2966 - val_accuracy: 0.9188 - val_loss: 0.2927\n","Epoch 446/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9162 - loss: 0.2944 - val_accuracy: 0.9192 - val_loss: 0.2925\n","Epoch 447/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9149 - loss: 0.3001 - val_accuracy: 0.9192 - val_loss: 0.2923\n","Epoch 448/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9139 - loss: 0.2962 - val_accuracy: 0.9193 - val_loss: 0.2922\n","Epoch 449/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9153 - loss: 0.2951 - val_accuracy: 0.9195 - val_loss: 0.2920\n","Epoch 450/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9151 - loss: 0.2940 - val_accuracy: 0.9193 - val_loss: 0.2918\n","Epoch 451/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9137 - loss: 0.2999 - val_accuracy: 0.9197 - val_loss: 0.2917\n","Epoch 452/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9147 - loss: 0.2949 - val_accuracy: 0.9195 - val_loss: 0.2915\n","Epoch 453/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9163 - loss: 0.2947 - val_accuracy: 0.9195 - val_loss: 0.2914\n","Epoch 454/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9145 - loss: 0.2969 - val_accuracy: 0.9195 - val_loss: 0.2912\n","Epoch 455/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9146 - loss: 0.2957 - val_accuracy: 0.9195 - val_loss: 0.2910\n","Epoch 456/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9153 - loss: 0.2943 - val_accuracy: 0.9198 - val_loss: 0.2909\n","Epoch 457/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9159 - loss: 0.2931 - val_accuracy: 0.9195 - val_loss: 0.2907\n","Epoch 458/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9135 - loss: 0.3022 - val_accuracy: 0.9197 - val_loss: 0.2906\n","Epoch 459/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9162 - loss: 0.2935 - val_accuracy: 0.9195 - val_loss: 0.2904\n","Epoch 460/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9155 - loss: 0.3011 - val_accuracy: 0.9198 - val_loss: 0.2902\n","Epoch 461/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9183 - loss: 0.2893 - val_accuracy: 0.9198 - val_loss: 0.2901\n","Epoch 462/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9139 - loss: 0.2987 - val_accuracy: 0.9197 - val_loss: 0.2899\n","Epoch 463/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9168 - loss: 0.2898 - val_accuracy: 0.9198 - val_loss: 0.2898\n","Epoch 464/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9154 - loss: 0.2962 - val_accuracy: 0.9200 - val_loss: 0.2896\n","Epoch 465/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9174 - loss: 0.2925 - val_accuracy: 0.9198 - val_loss: 0.2895\n","Epoch 466/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9147 - loss: 0.2968 - val_accuracy: 0.9200 - val_loss: 0.2893\n","Epoch 467/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9180 - loss: 0.2884 - val_accuracy: 0.9200 - val_loss: 0.2891\n","Epoch 468/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9151 - loss: 0.2973 - val_accuracy: 0.9198 - val_loss: 0.2890\n","Epoch 469/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9147 - loss: 0.2990 - val_accuracy: 0.9200 - val_loss: 0.2888\n","Epoch 470/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9179 - loss: 0.2888 - val_accuracy: 0.9202 - val_loss: 0.2887\n","Epoch 471/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9168 - loss: 0.2929 - val_accuracy: 0.9202 - val_loss: 0.2885\n","Epoch 472/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9160 - loss: 0.2938 - val_accuracy: 0.9202 - val_loss: 0.2884\n","Epoch 473/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9160 - loss: 0.2971 - val_accuracy: 0.9202 - val_loss: 0.2882\n","Epoch 474/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9153 - loss: 0.2958 - val_accuracy: 0.9200 - val_loss: 0.2881\n","Epoch 475/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9134 - loss: 0.2999 - val_accuracy: 0.9202 - val_loss: 0.2879\n","Epoch 476/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9162 - loss: 0.2940 - val_accuracy: 0.9203 - val_loss: 0.2877\n","Epoch 477/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9149 - loss: 0.2942 - val_accuracy: 0.9202 - val_loss: 0.2876\n","Epoch 478/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9172 - loss: 0.2891 - val_accuracy: 0.9203 - val_loss: 0.2875\n","Epoch 479/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9156 - loss: 0.2924 - val_accuracy: 0.9203 - val_loss: 0.2873\n","Epoch 480/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9163 - loss: 0.2888 - val_accuracy: 0.9203 - val_loss: 0.2872\n","Epoch 481/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9164 - loss: 0.2919 - val_accuracy: 0.9202 - val_loss: 0.2870\n","Epoch 482/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9154 - loss: 0.2899 - val_accuracy: 0.9203 - val_loss: 0.2869\n","Epoch 483/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9144 - loss: 0.2945 - val_accuracy: 0.9203 - val_loss: 0.2867\n","Epoch 484/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9186 - loss: 0.2846 - val_accuracy: 0.9202 - val_loss: 0.2866\n","Epoch 485/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9161 - loss: 0.2906 - val_accuracy: 0.9200 - val_loss: 0.2864\n","Epoch 486/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9178 - loss: 0.2875 - val_accuracy: 0.9200 - val_loss: 0.2862\n","Epoch 487/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9161 - loss: 0.2920 - val_accuracy: 0.9202 - val_loss: 0.2861\n","Epoch 488/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9182 - loss: 0.2892 - val_accuracy: 0.9200 - val_loss: 0.2859\n","Epoch 489/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9189 - loss: 0.2872 - val_accuracy: 0.9202 - val_loss: 0.2858\n","Epoch 490/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9167 - loss: 0.2888 - val_accuracy: 0.9202 - val_loss: 0.2857\n","Epoch 491/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9150 - loss: 0.2949 - val_accuracy: 0.9200 - val_loss: 0.2855\n","Epoch 492/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9141 - loss: 0.2942 - val_accuracy: 0.9200 - val_loss: 0.2854\n","Epoch 493/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9162 - loss: 0.2911 - val_accuracy: 0.9202 - val_loss: 0.2852\n","Epoch 494/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9181 - loss: 0.2873 - val_accuracy: 0.9200 - val_loss: 0.2851\n","Epoch 495/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9183 - loss: 0.2850 - val_accuracy: 0.9200 - val_loss: 0.2849\n","Epoch 496/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9177 - loss: 0.2855 - val_accuracy: 0.9203 - val_loss: 0.2848\n","Epoch 497/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9162 - loss: 0.2909 - val_accuracy: 0.9203 - val_loss: 0.2846\n","Epoch 498/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9167 - loss: 0.2884 - val_accuracy: 0.9203 - val_loss: 0.2845\n","Epoch 499/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9161 - loss: 0.2906 - val_accuracy: 0.9203 - val_loss: 0.2844\n","Epoch 500/500\n","\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9143 - loss: 0.2930 - val_accuracy: 0.9203 - val_loss: 0.2842\n"]}]},{"cell_type":"code","source":["# вывод графика ошибки по эпохам\n","plt.plot(H.history['loss'])\n","plt.plot(H.history['val_loss'])\n","plt.grid()\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend(['train_loss', 'val_loss'])\n","plt.title('Loss by epochs')\n","plt.show()\n","\n","# Оценка качества работы работы модели на тестовых данных\n","scores = model_1h100.evaluate(X_test, y_test)\n","print('Loss on test data:', scores[0])\n","print('Accuracy on test data:', scores[1])"],"metadata":{"id":"KzlBRSl9N10S"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["б) модель с 300 нейронами"],"metadata":{"id":"VRERUR6BMqZA"}},{"cell_type":"code","source":["#1. создаем модель - объявляем ее объектом класса Sequential\n","model_1h300 = Sequential()\n","\n","#2. добавляем первый слой\n","model_1h300.add(Dense(units=300, input_dim = num_pixels, activation='sigmoid'))\n","\n","#2. добавляем выходной слой\n","model_1h300.add(Dense(units=num_classes, activation='sigmoid'))\n","\n","#4. компилируем модель\n","model_1h300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","\n","# вывод информации об архитектуре модели\n","print(model_1h300.summary())"],"metadata":{"id":"i45EgLf8MtVQ","executionInfo":{"status":"ok","timestamp":1758791450709,"user_tz":-180,"elapsed":179,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"colab":{"base_uri":"https://localhost:8080/","height":219},"outputId":"deabd3dd-2ace-443c-c2f5-3346ba8b5f32"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_2\"\u001b[0m\n"],"text/html":["
Model: \"sequential_2\"\n","
\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_3 (\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_4 (\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":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_3 (Dense)                 │ (None, 300)            │       235,500 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_4 (Dense)                 │ (None, 10)             │         3,010 │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","
\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":["
 Total params: 238,510 (931.68 KB)\n","
\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":["
 Trainable params: 238,510 (931.68 KB)\n","
\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":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["None\n"]}]},{"cell_type":"code","source":["# Обучаем модель\n","H = model_1h300.fit(X_train, y_train, batch_size=1024, validation_split=0.1, epochs=500)"],"metadata":{"collapsed":true,"id":"cVkGlXN9M5xR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# вывод графика ошибки по эпохам\n","plt.plot(H.history['loss'])\n","plt.plot(H.history['val_loss'])\n","plt.grid()\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend(['train_loss', 'val_loss'])\n","plt.title('Loss by epochs')\n","plt.show()\n","\n","# Оценка качества работы работы модели на тестовых данных\n","scores = model_1h300.evaluate(X_test, y_test)\n","print('Loss on test data:', scores[0])\n","print('Accuracy on test data:', scores[1])"],"metadata":{"id":"FzW4bgZaQ3Fn"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["в) модель с 500 нейронами"],"metadata":{"id":"bF4sFcx3Q98f"}},{"cell_type":"code","source":["#1. создаем модель - объявляем ее объектом класса Sequential\n","model_1h500 = Sequential()\n","\n","#2. добавляем первый слой\n","model_1h500.add(Dense(units=500, input_dim = num_pixels, activation='sigmoid'))\n","\n","#2. добавляем выходной слой\n","model_1h500.add(Dense(units=num_classes, activation='sigmoid'))\n","\n","#4. компилируем модель\n","model_1h500.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","\n","# вывод информации об архитектуре модели\n","print(model_1h500.summary())"],"metadata":{"id":"LSjEezu1RDvL","executionInfo":{"status":"ok","timestamp":1758791755229,"user_tz":-180,"elapsed":59,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"colab":{"base_uri":"https://localhost:8080/","height":219},"outputId":"dee3b49a-cea8-4cf9-f0e9-a882888d76f1"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_3\"\u001b[0m\n"],"text/html":["
Model: \"sequential_3\"\n","
\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_5 (\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_6 (\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":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_5 (Dense)                 │ (None, 500)            │       392,500 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_6 (Dense)                 │ (None, 10)             │         5,010 │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","
\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":["
 Total params: 397,510 (1.52 MB)\n","
\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":["
 Trainable params: 397,510 (1.52 MB)\n","
\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":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["None\n"]}]},{"cell_type":"code","source":["# Обучаем модель\n","H = model_1h500.fit(X_train, y_train, batch_size=1024, validation_split=0.1, epochs=500)"],"metadata":{"collapsed":true,"id":"kHMPljNQRSBQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# вывод графика ошибки по эпохам\n","plt.plot(H.history['loss'])\n","plt.plot(H.history['val_loss'])\n","plt.grid()\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend(['train_loss', 'val_loss'])\n","plt.title('Loss by epochs')\n","plt.show()\n","\n","# Оценка качества работы работы модели на тестовых данных\n","scores = model_1h500.evaluate(X_test, y_test)\n","print('Loss on test data:', scores[0])\n","print('Accuracy on test data:', scores[1])"],"metadata":{"id":"LQ0z2AL4RVYY"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Добавить внаилучшую архитектуру, определенную в п. 8 (300),второй скрытый слой и провести обучение и тестирование (повторить п. 6–7)при 50 и 100 нейронах во втором скрытом слое. В качестве функции активации нейронов в скрытом слое использовать функцию sigmoid."],"metadata":{"id":"K40vxpuMQ_gN"}},{"cell_type":"code","source":["#1. создаем модель - объявляем ее объектом класса Sequential\n","model_1h100_2h100 = Sequential()\n","\n","#2. добавляем первый слой\n","model_1h100_2h100.add(Dense(units=100, input_dim = num_pixels, activation='sigmoid'))\n","\n","#3. добавляем второй слой\n","model_1h100_2h100.add(Dense(units=100,activation='sigmoid'))\n","\n","#4. добавляем выходной слой\n","model_1h100_2h100.add(Dense(units=num_classes, activation='sigmoid'))\n","\n","#5. компилируем модель\n","model_1h100_2h100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","\n","# вывод информации об архитектуре модели\n","print(model_1h100_2h100.summary())"],"metadata":{"id":"EQ9DUHJ5Q-67","executionInfo":{"status":"ok","timestamp":1758792156925,"user_tz":-180,"elapsed":94,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"colab":{"base_uri":"https://localhost:8080/","height":252},"outputId":"7f2a588c-7a4b-4f3f-f041-87b34014355c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_4\"\u001b[0m\n"],"text/html":["
Model: \"sequential_4\"\n","
\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;34m100\u001b[0m) │ \u001b[38;5;34m10,100\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_9 (\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":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_7 (Dense)                 │ (None, 100)            │        78,500 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_8 (Dense)                 │ (None, 100)            │        10,100 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_9 (Dense)                 │ (None, 10)             │         1,010 │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","
\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":["
 Total params: 89,610 (350.04 KB)\n","
\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":["
 Trainable params: 89,610 (350.04 KB)\n","
\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":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["None\n"]}]},{"cell_type":"code","source":["# Обучаем модель\n","H = model_1h100_2h100.fit(X_train, y_train, batch_size=1024, validation_split=0.1, epochs=500)"],"metadata":{"collapsed":true,"id":"jNiX3PyxUO16"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# вывод графика ошибки по эпохам\n","plt.plot(H.history['loss'])\n","plt.plot(H.history['val_loss'])\n","plt.grid()\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend(['train_loss', 'val_loss'])\n","plt.title('Loss by epochs')\n","plt.show()\n","\n","# Оценка качества работы работы модели на тестовых данных\n","scores = model_1h100_2h100.evaluate(X_test, y_test)\n","print('Loss on test data:', scores[0])\n","print('Accuracy on test data:', scores[1])"],"metadata":{"id":"KTeABaKoUUAw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#1. создаем модель - объявляем ее объектом класса Sequential\n","model_1h100_2h50 = Sequential()\n","\n","#2. добавляем первый слой\n","model_1h100_2h50.add(Dense(units=100, input_dim = num_pixels, activation='sigmoid'))\n","\n","#3. добавляем второй слой\n","model_1h100_2h50.add(Dense(units=50,activation='sigmoid'))\n","\n","#4. добавляем выходной слой\n","model_1h100_2h50.add(Dense(units=num_classes, activation='sigmoid'))\n","\n","#5. компилируем модель\n","model_1h100_2h50.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","\n","# вывод информации об архитектуре модели\n","print(model_1h100_2h50.summary())"],"metadata":{"id":"j1rY02NpX-1D","executionInfo":{"status":"ok","timestamp":1758792185300,"user_tz":-180,"elapsed":88,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"colab":{"base_uri":"https://localhost:8080/","height":252},"outputId":"b53e7f5d-71ff-4791-de6b-733b4cb475a0"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_5\"\u001b[0m\n"],"text/html":["
Model: \"sequential_5\"\n","
\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_10 (\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_11 (\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_12 (\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":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_10 (Dense)                │ (None, 100)            │        78,500 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_11 (Dense)                │ (None, 50)             │         5,050 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_12 (Dense)                │ (None, 10)             │           510 │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","
\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":["
 Total params: 84,060 (328.36 KB)\n","
\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":["
 Trainable params: 84,060 (328.36 KB)\n","
\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":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["None\n"]}]},{"cell_type":"code","source":["# Обучаем модель\n","H = model_1h100_2h50.fit(X_train, y_train, batch_size=1024, validation_split=0.1, epochs=500)"],"metadata":{"collapsed":true,"id":"FZ4ALcHEX-uM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# вывод графика ошибки по эпохам\n","plt.plot(H.history['loss'])\n","plt.plot(H.history['val_loss'])\n","plt.grid()\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend(['train_loss', 'val_loss'])\n","plt.title('Loss by epochs')\n","plt.show()\n","\n","# Оценка качества работы работы модели на тестовых данных\n","scores = model_1h100_2h50.evaluate(X_test, y_test)\n","print('Loss on test data:', scores[0])\n","print('Accuracy on test data:', scores[1])"],"metadata":{"id":"REMayA17YSQ3","executionInfo":{"status":"ok","timestamp":1758792204159,"user_tz":-180,"elapsed":2030,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"colab":{"base_uri":"https://localhost:8080/","height":527},"outputId":"a8f2e73e-0c05-4e4e-e647-e001f06d8fd2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.1036 - loss: 2.4104\n","Loss on test data: 2.411836624145508\n","Accuracy on test data: 0.1039000004529953\n"]}]},{"cell_type":"markdown","source":["Сохранить наилучшую нейронную сеть на диск. Данную нейронную сеть потребуется загрузить с диска в одной из следующих лабораторных работ"],"metadata":{"id":"UNHy9imUkjKW"}},{"cell_type":"code","source":["#сохранение модели на диск (формат keras)\n","model_1h100.save('/content/drive/MyDrive/ColabNotebooks/best_model.keras')"],"metadata":{"id":"olC9r1SuaILh"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Для нейронной сети наилучшей архитектурывывести два тестовых изображения, истинные метки и результат распознавания изображений."],"metadata":{"id":"nLIvzpadkkVO"}},{"cell_type":"code","source":["#вывод тестового изображения и результата распознавания\n","n=456\n","result = model_1h100.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('Realmark:', str(np.argmax(y_test[n])))\n","print('NN answer:', str(np.argmax(result)))\n"],"metadata":{"id":"tKcDhihXkmAb","executionInfo":{"status":"ok","timestamp":1758793035424,"user_tz":-180,"elapsed":345,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"colab":{"base_uri":"https://localhost:8080/","height":522},"outputId":"778d1988-70ea-4d7a-ff0b-7ed37c206b39"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n","NN output: [[0.08515459 0.00697097 0.8648257 0.989508 0.1854792 0.2720432\n"," 0.00732216 0.9988655 0.12480782 0.97128534]]\n"]},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Realmark: 7\n","NN answer: 7\n"]}]},{"cell_type":"code","source":["# загрузка собственного изображения\n","from PIL import Image\n","file_data = Image.open('2.png')\n","file_data = file_data.convert('L') # перевод в градации серого\n","test_img_2 = np.array(file_data)"],"metadata":{"id":"UuC0wtIxXLwz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# вывод собственного изображения\n","plt.imshow(test_img_2, cmap = plt.get_cmap('gray'))\n","plt.show()\n","\n","# предобработка\n","test_img_2 = test_img_2 /255\n","test_img_2 = test_img_2.reshape(1, num_pixels)\n","\n","# распознование\n","result = model_1h100.predict(test_img_2)\n","print('I think it is', np.argmax(result))"],"metadata":{"id":"MUl6MotPYChD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# загрузка собственного изображения\n","file_data = Image.open('9.png')\n","file_data = file_data.convert('L') # перевод в градации серого\n","test_img_9 = np.array(file_data)\n","\n","# вывод собственного изображения\n","plt.imshow(test_img_9, cmap = plt.get_cmap('gray'))\n","plt.show()\n","\n","# предобработка\n","test_img_9 = test_img_9 /255\n","test_img_9 = test_img_9.reshape(1, num_pixels)\n","\n","# распознование\n","result = model_1h100.predict(test_img_9)\n","print('I think it is', np.argmax(result))"],"metadata":{"id":"8tmosIBfaD2j"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# загрузка собственного изображения\n","file_data = Image.open('9_turn.png')\n","file_data = file_data.convert('L') # перевод в градации серого\n","test_img_9_turn = np.array(file_data)\n","\n","# вывод собственного изображения\n","plt.imshow(test_img_9_turn, cmap = plt.get_cmap('gray'))\n","plt.show()\n","\n","# предобработка\n","test_img_9_turn = test_img_9_turn /255\n","test_img_9_turn = test_img_9_turn.reshape(1, num_pixels)\n","\n","# распознование\n","result = model_1h100.predict(test_img_9_turn)\n","print('I think it is', np.argmax(result))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"id":"l8CNZlvwkaxc","executionInfo":{"status":"ok","timestamp":1758530683167,"user_tz":-180,"elapsed":911,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"fc4799c8-0579-40e2-ed4e-3305df88778f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 430ms/step\n","I think it is 1\n"]}]},{"cell_type":"code","source":["# загрузка собственного изображения\n","file_data = Image.open('2_turn.png')\n","file_data = file_data.convert('L') # перевод в градации серого\n","test_img_2_turn = np.array(file_data)\n","\n","# вывод собственного изображения\n","plt.imshow(test_img_2_turn, cmap = plt.get_cmap('gray'))\n","plt.show()\n","\n","# предобработка\n","test_img_2_turn = test_img_2_turn /255\n","test_img_2_turn = test_img_2_turn.reshape(1, num_pixels)\n","\n","# распознование\n","result = model_1h100.predict(test_img_2_turn)\n","print('I think it is', np.argmax(result))"],"metadata":{"id":"UP7vt7jHmROU","executionInfo":{"status":"ok","timestamp":1758530724889,"user_tz":-180,"elapsed":175,"user":{"displayName":"Rex Nikeov","userId":"07925807856735122925"}},"outputId":"373d2495-9a09-4e4c-8ca1-a2f1c2d17cfd","colab":{"base_uri":"https://localhost:8080/","height":467}},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n","I think it is 1\n"]}]}]} \ No newline at end of file