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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["9\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["7\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["5\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}],"source":["# вывод размерностей\n","print('Shape of X train:', X_train.shape)\n","print('Shape of y train:', y_train.shape)\n","\n","# вывод изображения\n","print(y_train[0])\n","plt.imshow(X_train[0], cmap=plt.get_cmap('gray'))\n","plt.show()\n","print(y_train[1])\n","plt.imshow(X_train[1], cmap=plt.get_cmap('gray'))\n","plt.show()\n","print(y_train[2])\n","plt.imshow(X_train[2], cmap=plt.get_cmap('gray'))\n","plt.show()\n","print(y_train[3])\n","plt.imshow(X_train[3], cmap=plt.get_cmap('gray'))\n","plt.show()"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":172,"status":"ok","timestamp":1760536452557,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"},"user_tz":-180},"id":"xeJhwRSJuRjt","outputId":"1aaebc4f-d010-4120-973e-1f2a378f13e5"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of transformed X train: (60000, 784)\n"]}],"source":["# развернем каждое изображение 28*28 в вектор 784\n","num_pixels = X_train.shape[1] * X_train.shape[2]\n","X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255\n","X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255\n","print('Shape of transformed X train:', X_train.shape)"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1760536453358,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"},"user_tz":-180},"id":"d0xJ4pqvvf10","outputId":"7c264b78-f193-40e4-c1fa-52c7db3480f8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of transformed y train: (60000, 10)\n"]}],"source":["# переведем метки в one-hot\n","y_train = to_categorical(y_train)\n","y_test = to_categorical(y_test)\n","print('Shape of transformed y train:', y_train.shape)\n","num_classes = y_train.shape[1]"]},{"cell_type":"code","source":["model_p = Sequential()\n","model_p.add(Dense(units=num_classes,input_dim=num_pixels, activation='softmax'))\n","model_p.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"],"metadata":{"id":"sl7aZUOCaGlU","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1760536456637,"user_tz":-180,"elapsed":2410,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"2d46689b-b9db-451b-a5c3-135ee5dfb88a"},"execution_count":9,"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","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"4HMAu6tW_wec","executionInfo":{"status":"ok","timestamp":1760536702724,"user_tz":-180,"elapsed":93709,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"bfc7554f-c6d9-4cea-9ea1-683236a3e0fa"},"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","Epoch 1/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 16ms/step - accuracy: 0.2607 - loss: 2.1410 - val_accuracy: 0.6642 - val_loss: 1.5642\n","Epoch 2/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6983 - loss: 1.4582 - val_accuracy: 0.7710 - val_loss: 1.1843\n","Epoch 3/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7812 - loss: 1.1383 - val_accuracy: 0.8133 - val_loss: 0.9831\n","Epoch 4/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8073 - loss: 0.9630 - val_accuracy: 0.8300 - val_loss: 0.8611\n","Epoch 5/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8275 - loss: 0.8456 - val_accuracy: 0.8370 - val_loss: 0.7797\n","Epoch 6/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8298 - loss: 0.7795 - val_accuracy: 0.8457 - val_loss: 0.7211\n","Epoch 7/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8405 - loss: 0.7204 - val_accuracy: 0.8505 - val_loss: 0.6767\n","Epoch 8/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8490 - loss: 0.6771 - val_accuracy: 0.8545 - val_loss: 0.6419\n","Epoch 9/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8509 - loss: 0.6467 - val_accuracy: 0.8580 - val_loss: 0.6138\n","Epoch 10/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8553 - loss: 0.6186 - val_accuracy: 0.8610 - val_loss: 0.5906\n","Epoch 11/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8588 - loss: 0.5957 - val_accuracy: 0.8628 - val_loss: 0.5709\n","Epoch 12/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8601 - loss: 0.5780 - val_accuracy: 0.8658 - val_loss: 0.5541\n","Epoch 13/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8618 - loss: 0.5620 - val_accuracy: 0.8695 - val_loss: 0.5394\n","Epoch 14/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8626 - loss: 0.5477 - val_accuracy: 0.8727 - val_loss: 0.5265\n","Epoch 15/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8651 - loss: 0.5357 - val_accuracy: 0.8753 - val_loss: 0.5150\n","Epoch 16/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8684 - loss: 0.5241 - val_accuracy: 0.8772 - val_loss: 0.5048\n","Epoch 17/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8692 - loss: 0.5132 - val_accuracy: 0.8793 - val_loss: 0.4955\n","Epoch 18/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8693 - loss: 0.5087 - val_accuracy: 0.8803 - val_loss: 0.4872\n","Epoch 19/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8745 - loss: 0.4936 - val_accuracy: 0.8810 - val_loss: 0.4798\n","Epoch 20/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8720 - loss: 0.4923 - val_accuracy: 0.8822 - val_loss: 0.4728\n","Epoch 21/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8740 - loss: 0.4866 - val_accuracy: 0.8840 - val_loss: 0.4662\n","Epoch 22/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8763 - loss: 0.4748 - val_accuracy: 0.8852 - val_loss: 0.4603\n","Epoch 23/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8775 - loss: 0.4686 - val_accuracy: 0.8863 - val_loss: 0.4547\n","Epoch 24/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8786 - loss: 0.4628 - val_accuracy: 0.8880 - val_loss: 0.4496\n","Epoch 25/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8774 - loss: 0.4656 - val_accuracy: 0.8882 - val_loss: 0.4448\n","Epoch 26/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8803 - loss: 0.4571 - val_accuracy: 0.8892 - val_loss: 0.4404\n","Epoch 27/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8795 - loss: 0.4567 - val_accuracy: 0.8897 - val_loss: 0.4362\n","Epoch 28/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8837 - loss: 0.4405 - val_accuracy: 0.8910 - val_loss: 0.4323\n","Epoch 29/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8826 - loss: 0.4391 - val_accuracy: 0.8913 - val_loss: 0.4285\n","Epoch 30/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8816 - loss: 0.4422 - val_accuracy: 0.8930 - val_loss: 0.4248\n","Epoch 31/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8849 - loss: 0.4314 - val_accuracy: 0.8935 - val_loss: 0.4216\n","Epoch 32/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8844 - loss: 0.4336 - val_accuracy: 0.8933 - val_loss: 0.4184\n","Epoch 33/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8858 - loss: 0.4305 - val_accuracy: 0.8947 - val_loss: 0.4155\n","Epoch 34/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8867 - loss: 0.4207 - val_accuracy: 0.8947 - val_loss: 0.4126\n","Epoch 35/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8888 - loss: 0.4146 - val_accuracy: 0.8965 - val_loss: 0.4098\n","Epoch 36/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8881 - loss: 0.4175 - val_accuracy: 0.8975 - val_loss: 0.4074\n","Epoch 37/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8897 - loss: 0.4126 - val_accuracy: 0.8982 - val_loss: 0.4048\n","Epoch 38/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8906 - loss: 0.4071 - val_accuracy: 0.8988 - val_loss: 0.4023\n","Epoch 39/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8899 - loss: 0.4130 - val_accuracy: 0.8992 - val_loss: 0.4000\n","Epoch 40/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8870 - loss: 0.4170 - val_accuracy: 0.9000 - val_loss: 0.3978\n","Epoch 41/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8895 - loss: 0.4055 - val_accuracy: 0.9007 - val_loss: 0.3956\n","Epoch 42/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8895 - loss: 0.4080 - val_accuracy: 0.9005 - val_loss: 0.3937\n","Epoch 43/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8923 - loss: 0.3990 - val_accuracy: 0.9005 - val_loss: 0.3918\n","Epoch 44/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8908 - loss: 0.4029 - val_accuracy: 0.9013 - val_loss: 0.3897\n","Epoch 45/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8911 - loss: 0.4020 - val_accuracy: 0.9010 - val_loss: 0.3879\n","Epoch 46/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8911 - loss: 0.4015 - val_accuracy: 0.9018 - val_loss: 0.3862\n","Epoch 47/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8929 - loss: 0.3973 - val_accuracy: 0.9015 - val_loss: 0.3845\n","Epoch 48/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8940 - loss: 0.3908 - val_accuracy: 0.9020 - val_loss: 0.3829\n","Epoch 49/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8937 - loss: 0.3913 - val_accuracy: 0.9032 - val_loss: 0.3813\n","Epoch 50/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8931 - loss: 0.3913 - val_accuracy: 0.9027 - val_loss: 0.3797\n","Epoch 51/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8922 - loss: 0.3939 - val_accuracy: 0.9038 - val_loss: 0.3782\n","Epoch 52/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8923 - loss: 0.3913 - val_accuracy: 0.9038 - val_loss: 0.3767\n","Epoch 53/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8946 - loss: 0.3887 - val_accuracy: 0.9038 - val_loss: 0.3753\n","Epoch 54/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8951 - loss: 0.3847 - val_accuracy: 0.9040 - val_loss: 0.3741\n","Epoch 55/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8941 - loss: 0.3901 - val_accuracy: 0.9043 - val_loss: 0.3727\n","Epoch 56/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8960 - loss: 0.3813 - val_accuracy: 0.9045 - val_loss: 0.3715\n","Epoch 57/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8948 - loss: 0.3804 - val_accuracy: 0.9048 - val_loss: 0.3701\n","Epoch 58/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8947 - loss: 0.3826 - val_accuracy: 0.9052 - val_loss: 0.3690\n","Epoch 59/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8959 - loss: 0.3794 - val_accuracy: 0.9060 - val_loss: 0.3677\n","Epoch 60/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8988 - loss: 0.3707 - val_accuracy: 0.9068 - val_loss: 0.3666\n","Epoch 61/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8973 - loss: 0.3772 - val_accuracy: 0.9070 - val_loss: 0.3655\n","Epoch 62/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8955 - loss: 0.3782 - val_accuracy: 0.9073 - val_loss: 0.3644\n","Epoch 63/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8974 - loss: 0.3715 - val_accuracy: 0.9068 - val_loss: 0.3633\n","Epoch 64/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8989 - loss: 0.3675 - val_accuracy: 0.9070 - val_loss: 0.3622\n","Epoch 65/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8965 - loss: 0.3767 - val_accuracy: 0.9072 - val_loss: 0.3612\n","Epoch 66/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8985 - loss: 0.3673 - val_accuracy: 0.9078 - val_loss: 0.3602\n","Epoch 67/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8956 - loss: 0.3755 - val_accuracy: 0.9078 - val_loss: 0.3593\n","Epoch 68/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8990 - loss: 0.3661 - val_accuracy: 0.9078 - val_loss: 0.3584\n","Epoch 69/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8986 - loss: 0.3683 - val_accuracy: 0.9078 - val_loss: 0.3575\n","Epoch 70/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9009 - loss: 0.3644 - val_accuracy: 0.9083 - val_loss: 0.3565\n","Epoch 71/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8972 - loss: 0.3669 - val_accuracy: 0.9083 - val_loss: 0.3558\n","Epoch 72/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9009 - loss: 0.3568 - val_accuracy: 0.9083 - val_loss: 0.3547\n","Epoch 73/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9000 - loss: 0.3639 - val_accuracy: 0.9085 - val_loss: 0.3540\n","Epoch 74/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9002 - loss: 0.3631 - val_accuracy: 0.9092 - val_loss: 0.3531\n","Epoch 75/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9009 - loss: 0.3619 - val_accuracy: 0.9093 - val_loss: 0.3523\n","Epoch 76/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9007 - loss: 0.3575 - val_accuracy: 0.9092 - val_loss: 0.3516\n","Epoch 77/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9004 - loss: 0.3583 - val_accuracy: 0.9098 - val_loss: 0.3508\n","Epoch 78/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9013 - loss: 0.3587 - val_accuracy: 0.9098 - val_loss: 0.3500\n","Epoch 79/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9018 - loss: 0.3568 - val_accuracy: 0.9098 - val_loss: 0.3494\n","Epoch 80/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9010 - loss: 0.3595 - val_accuracy: 0.9098 - val_loss: 0.3485\n","Epoch 81/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8988 - loss: 0.3638 - val_accuracy: 0.9100 - val_loss: 0.3478\n","Epoch 82/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9036 - loss: 0.3485 - val_accuracy: 0.9098 - val_loss: 0.3471\n","Epoch 83/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8987 - loss: 0.3625 - val_accuracy: 0.9103 - val_loss: 0.3464\n","Epoch 84/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9008 - loss: 0.3580 - val_accuracy: 0.9108 - val_loss: 0.3457\n","Epoch 85/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9016 - loss: 0.3482 - val_accuracy: 0.9107 - val_loss: 0.3451\n","Epoch 86/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9001 - loss: 0.3557 - val_accuracy: 0.9108 - val_loss: 0.3445\n","Epoch 87/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9009 - loss: 0.3531 - val_accuracy: 0.9112 - val_loss: 0.3438\n","Epoch 88/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9027 - loss: 0.3500 - val_accuracy: 0.9113 - val_loss: 0.3432\n","Epoch 89/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9026 - loss: 0.3548 - val_accuracy: 0.9115 - val_loss: 0.3426\n","Epoch 90/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9011 - loss: 0.3550 - val_accuracy: 0.9117 - val_loss: 0.3421\n","Epoch 91/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9030 - loss: 0.3480 - val_accuracy: 0.9118 - val_loss: 0.3414\n","Epoch 92/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9038 - loss: 0.3480 - val_accuracy: 0.9118 - val_loss: 0.3408\n","Epoch 93/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9054 - loss: 0.3420 - val_accuracy: 0.9122 - val_loss: 0.3402\n","Epoch 94/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9032 - loss: 0.3470 - val_accuracy: 0.9120 - val_loss: 0.3397\n","Epoch 95/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9037 - loss: 0.3433 - val_accuracy: 0.9125 - val_loss: 0.3392\n","Epoch 96/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9014 - loss: 0.3516 - val_accuracy: 0.9127 - val_loss: 0.3387\n","Epoch 97/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9050 - loss: 0.3374 - val_accuracy: 0.9123 - val_loss: 0.3381\n","Epoch 98/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9033 - loss: 0.3464 - val_accuracy: 0.9123 - val_loss: 0.3375\n","Epoch 99/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9050 - loss: 0.3400 - val_accuracy: 0.9123 - val_loss: 0.3369\n","Epoch 100/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9045 - loss: 0.3444 - val_accuracy: 0.9130 - val_loss: 0.3365\n","Epoch 101/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9049 - loss: 0.3404 - val_accuracy: 0.9130 - val_loss: 0.3360\n","Epoch 102/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9032 - loss: 0.3453 - val_accuracy: 0.9133 - val_loss: 0.3356\n","Epoch 103/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9040 - loss: 0.3418 - val_accuracy: 0.9133 - val_loss: 0.3349\n","Epoch 104/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9047 - loss: 0.3435 - val_accuracy: 0.9133 - val_loss: 0.3345\n","Epoch 105/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9033 - loss: 0.3428 - val_accuracy: 0.9133 - val_loss: 0.3340\n","Epoch 106/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9031 - loss: 0.3458 - val_accuracy: 0.9138 - val_loss: 0.3336\n","Epoch 107/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9028 - loss: 0.3407 - val_accuracy: 0.9137 - val_loss: 0.3332\n","Epoch 108/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9043 - loss: 0.3412 - val_accuracy: 0.9142 - val_loss: 0.3327\n","Epoch 109/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9049 - loss: 0.3357 - val_accuracy: 0.9138 - val_loss: 0.3322\n","Epoch 110/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9053 - loss: 0.3365 - val_accuracy: 0.9140 - val_loss: 0.3317\n","Epoch 111/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9061 - loss: 0.3368 - val_accuracy: 0.9142 - val_loss: 0.3313\n","Epoch 112/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9058 - loss: 0.3377 - val_accuracy: 0.9140 - val_loss: 0.3309\n","Epoch 113/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9067 - loss: 0.3334 - val_accuracy: 0.9143 - val_loss: 0.3305\n","Epoch 114/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9054 - loss: 0.3384 - val_accuracy: 0.9153 - val_loss: 0.3301\n","Epoch 115/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9053 - loss: 0.3350 - val_accuracy: 0.9148 - val_loss: 0.3297\n","Epoch 116/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9045 - loss: 0.3363 - val_accuracy: 0.9152 - val_loss: 0.3294\n","Epoch 117/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9057 - loss: 0.3350 - val_accuracy: 0.9152 - val_loss: 0.3289\n","Epoch 118/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9047 - loss: 0.3422 - val_accuracy: 0.9155 - val_loss: 0.3285\n","Epoch 119/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9048 - loss: 0.3390 - val_accuracy: 0.9153 - val_loss: 0.3282\n","Epoch 120/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9051 - loss: 0.3379 - val_accuracy: 0.9158 - val_loss: 0.3277\n","Epoch 121/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9083 - loss: 0.3304 - val_accuracy: 0.9150 - val_loss: 0.3274\n","Epoch 122/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9076 - loss: 0.3304 - val_accuracy: 0.9157 - val_loss: 0.3270\n","Epoch 123/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9083 - loss: 0.3270 - val_accuracy: 0.9162 - val_loss: 0.3267\n","Epoch 124/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9038 - loss: 0.3357 - val_accuracy: 0.9158 - val_loss: 0.3262\n","Epoch 125/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9075 - loss: 0.3314 - val_accuracy: 0.9163 - val_loss: 0.3258\n","Epoch 126/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9066 - loss: 0.3286 - val_accuracy: 0.9163 - val_loss: 0.3255\n","Epoch 127/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9060 - loss: 0.3348 - val_accuracy: 0.9163 - val_loss: 0.3252\n","Epoch 128/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9061 - loss: 0.3317 - val_accuracy: 0.9160 - val_loss: 0.3249\n","Epoch 129/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9079 - loss: 0.3297 - val_accuracy: 0.9167 - val_loss: 0.3246\n","Epoch 130/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9045 - loss: 0.3353 - val_accuracy: 0.9162 - val_loss: 0.3242\n","Epoch 131/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9060 - loss: 0.3310 - val_accuracy: 0.9167 - val_loss: 0.3238\n","Epoch 132/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9060 - loss: 0.3307 - val_accuracy: 0.9163 - val_loss: 0.3235\n","Epoch 133/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9103 - loss: 0.3242 - val_accuracy: 0.9163 - val_loss: 0.3233\n","Epoch 134/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9069 - loss: 0.3309 - val_accuracy: 0.9167 - val_loss: 0.3229\n","Epoch 135/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9071 - loss: 0.3278 - val_accuracy: 0.9165 - val_loss: 0.3226\n","Epoch 136/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9078 - loss: 0.3253 - val_accuracy: 0.9170 - val_loss: 0.3222\n","Epoch 137/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9073 - loss: 0.3282 - val_accuracy: 0.9168 - val_loss: 0.3221\n","Epoch 138/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9070 - loss: 0.3277 - val_accuracy: 0.9170 - val_loss: 0.3217\n","Epoch 139/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9063 - loss: 0.3306 - val_accuracy: 0.9167 - val_loss: 0.3214\n","Epoch 140/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9065 - loss: 0.3279 - val_accuracy: 0.9172 - val_loss: 0.3211\n","Epoch 141/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9068 - loss: 0.3320 - val_accuracy: 0.9175 - val_loss: 0.3209\n","Epoch 142/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9079 - loss: 0.3250 - val_accuracy: 0.9172 - val_loss: 0.3205\n","Epoch 143/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9073 - loss: 0.3304 - val_accuracy: 0.9170 - val_loss: 0.3202\n","Epoch 144/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9071 - loss: 0.3289 - val_accuracy: 0.9177 - val_loss: 0.3200\n","Epoch 145/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9069 - loss: 0.3281 - val_accuracy: 0.9172 - val_loss: 0.3197\n","Epoch 146/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9075 - loss: 0.3274 - val_accuracy: 0.9170 - val_loss: 0.3194\n","Epoch 147/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9078 - loss: 0.3272 - val_accuracy: 0.9173 - val_loss: 0.3191\n","Epoch 148/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.9093 - loss: 0.3241 - val_accuracy: 0.9170 - val_loss: 0.3189\n","Epoch 149/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.9073 - loss: 0.3233 - val_accuracy: 0.9175 - val_loss: 0.3185\n","Epoch 150/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9085 - loss: 0.3293 - val_accuracy: 0.9177 - val_loss: 0.3184\n","Epoch 151/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9066 - loss: 0.3235 - val_accuracy: 0.9177 - val_loss: 0.3180\n","Epoch 152/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9088 - loss: 0.3237 - val_accuracy: 0.9175 - val_loss: 0.3178\n","Epoch 153/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9096 - loss: 0.3202 - val_accuracy: 0.9173 - val_loss: 0.3175\n","Epoch 154/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9079 - loss: 0.3254 - val_accuracy: 0.9178 - val_loss: 0.3173\n","Epoch 155/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9089 - loss: 0.3214 - val_accuracy: 0.9178 - val_loss: 0.3170\n","Epoch 156/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9100 - loss: 0.3186 - val_accuracy: 0.9178 - val_loss: 0.3168\n","Epoch 157/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9109 - loss: 0.3219 - val_accuracy: 0.9178 - val_loss: 0.3165\n","Epoch 158/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9085 - loss: 0.3232 - val_accuracy: 0.9178 - val_loss: 0.3163\n","Epoch 159/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9093 - loss: 0.3167 - val_accuracy: 0.9180 - val_loss: 0.3161\n","Epoch 160/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9085 - loss: 0.3197 - val_accuracy: 0.9178 - val_loss: 0.3158\n","Epoch 161/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9097 - loss: 0.3220 - val_accuracy: 0.9177 - val_loss: 0.3156\n","Epoch 162/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9082 - loss: 0.3283 - val_accuracy: 0.9177 - val_loss: 0.3154\n","Epoch 163/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9102 - loss: 0.3180 - val_accuracy: 0.9180 - val_loss: 0.3151\n","Epoch 164/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9089 - loss: 0.3193 - val_accuracy: 0.9178 - val_loss: 0.3149\n","Epoch 165/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9111 - loss: 0.3160 - val_accuracy: 0.9178 - val_loss: 0.3147\n","Epoch 166/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9088 - loss: 0.3237 - val_accuracy: 0.9178 - val_loss: 0.3145\n","Epoch 167/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9086 - loss: 0.3234 - val_accuracy: 0.9175 - val_loss: 0.3142\n","Epoch 168/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9113 - loss: 0.3170 - val_accuracy: 0.9180 - val_loss: 0.3140\n","Epoch 169/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9098 - loss: 0.3207 - val_accuracy: 0.9187 - val_loss: 0.3138\n","Epoch 170/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9076 - loss: 0.3252 - val_accuracy: 0.9183 - val_loss: 0.3136\n","Epoch 171/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9103 - loss: 0.3156 - val_accuracy: 0.9182 - val_loss: 0.3134\n","Epoch 172/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9091 - loss: 0.3157 - val_accuracy: 0.9180 - val_loss: 0.3131\n","Epoch 173/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9121 - loss: 0.3140 - val_accuracy: 0.9178 - val_loss: 0.3129\n","Epoch 174/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9097 - loss: 0.3184 - val_accuracy: 0.9187 - val_loss: 0.3128\n","Epoch 175/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9105 - loss: 0.3139 - val_accuracy: 0.9183 - val_loss: 0.3126\n","Epoch 176/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.9103 - loss: 0.3184 - val_accuracy: 0.9180 - val_loss: 0.3123\n","Epoch 177/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9107 - loss: 0.3165 - val_accuracy: 0.9183 - val_loss: 0.3120\n","Epoch 178/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9097 - loss: 0.3166 - val_accuracy: 0.9183 - val_loss: 0.3119\n","Epoch 179/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9097 - loss: 0.3196 - val_accuracy: 0.9183 - val_loss: 0.3117\n","Epoch 180/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9111 - loss: 0.3158 - val_accuracy: 0.9185 - val_loss: 0.3116\n","Epoch 181/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9129 - loss: 0.3104 - val_accuracy: 0.9183 - val_loss: 0.3113\n","Epoch 182/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9109 - loss: 0.3165 - val_accuracy: 0.9185 - val_loss: 0.3112\n","Epoch 183/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9106 - loss: 0.3161 - val_accuracy: 0.9185 - val_loss: 0.3110\n","Epoch 184/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9108 - loss: 0.3167 - val_accuracy: 0.9185 - val_loss: 0.3108\n","Epoch 185/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9112 - loss: 0.3157 - val_accuracy: 0.9187 - val_loss: 0.3106\n","Epoch 186/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9108 - loss: 0.3159 - val_accuracy: 0.9187 - val_loss: 0.3104\n","Epoch 187/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9103 - loss: 0.3172 - val_accuracy: 0.9185 - val_loss: 0.3102\n","Epoch 188/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9085 - loss: 0.3197 - val_accuracy: 0.9183 - val_loss: 0.3100\n","Epoch 189/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9136 - loss: 0.3102 - val_accuracy: 0.9193 - val_loss: 0.3098\n","Epoch 190/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9104 - loss: 0.3181 - val_accuracy: 0.9192 - val_loss: 0.3097\n","Epoch 191/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9128 - loss: 0.3085 - val_accuracy: 0.9192 - val_loss: 0.3095\n","Epoch 192/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9130 - loss: 0.3091 - val_accuracy: 0.9192 - val_loss: 0.3093\n","Epoch 193/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9118 - loss: 0.3137 - val_accuracy: 0.9193 - val_loss: 0.3091\n","Epoch 194/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9109 - loss: 0.3142 - val_accuracy: 0.9192 - val_loss: 0.3090\n","Epoch 195/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9133 - loss: 0.3092 - val_accuracy: 0.9193 - val_loss: 0.3088\n","Epoch 196/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9121 - loss: 0.3075 - val_accuracy: 0.9193 - val_loss: 0.3086\n","Epoch 197/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9125 - loss: 0.3133 - val_accuracy: 0.9197 - val_loss: 0.3085\n","Epoch 198/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9101 - loss: 0.3145 - val_accuracy: 0.9197 - val_loss: 0.3083\n","Epoch 199/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9105 - loss: 0.3125 - val_accuracy: 0.9197 - val_loss: 0.3081\n","Epoch 200/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9132 - loss: 0.3065 - val_accuracy: 0.9197 - val_loss: 0.3079\n"]}],"source":["print(model_p.summary())\n","H_p = model_p.fit(X_train, y_train,batch_size = 512, validation_split=0.1, epochs=200)"]},{"cell_type":"code","source":["plt.plot(H_p.history['loss'])\n","plt.plot(H_p.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","scores=model_p.evaluate(X_test,y_test);\n","print('Loss on test data:',scores[0]);\n","print('Accuracy on test data:',scores[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":524},"id":"HXL-TRG0Y6Sn","executionInfo":{"status":"ok","timestamp":1760536915332,"user_tz":-180,"elapsed":1950,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"8b848b5e-9a5b-457a-f02a-1ae7cc2dba67"},"execution_count":19,"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 4ms/step - accuracy: 0.9166 - loss: 0.3058\n","Loss on test data: 0.31511011719703674\n","Accuracy on test data: 0.9151999950408936\n"]}]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":93731,"status":"ok","timestamp":1760536812600,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"},"user_tz":-180},"outputId":"1210f569-1e36-4b4f-cf64-04b58d764a3a","id":"I9LpWl31aIKo"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_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","
\n"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Epoch 1/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.1505 - loss: 2.3318 - val_accuracy: 0.4090 - val_loss: 2.1818\n","Epoch 2/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4761 - loss: 2.1471 - val_accuracy: 0.5975 - val_loss: 2.0553\n","Epoch 3/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6142 - loss: 2.0241 - val_accuracy: 0.6650 - val_loss: 1.9354\n","Epoch 4/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6729 - loss: 1.9040 - val_accuracy: 0.7038 - val_loss: 1.8204\n","Epoch 5/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7064 - loss: 1.7934 - val_accuracy: 0.7268 - val_loss: 1.7107\n","Epoch 6/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7272 - loss: 1.6876 - val_accuracy: 0.7463 - val_loss: 1.6071\n","Epoch 7/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7443 - loss: 1.5841 - val_accuracy: 0.7620 - val_loss: 1.5105\n","Epoch 8/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7597 - loss: 1.4893 - val_accuracy: 0.7747 - val_loss: 1.4213\n","Epoch 9/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7672 - loss: 1.4063 - val_accuracy: 0.7865 - val_loss: 1.3396\n","Epoch 10/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7777 - loss: 1.3252 - val_accuracy: 0.7937 - val_loss: 1.2654\n","Epoch 11/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7878 - loss: 1.2556 - val_accuracy: 0.7982 - val_loss: 1.1985\n","Epoch 12/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7930 - loss: 1.1913 - val_accuracy: 0.8068 - val_loss: 1.1377\n","Epoch 13/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7997 - loss: 1.1307 - val_accuracy: 0.8128 - val_loss: 1.0831\n","Epoch 14/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8055 - loss: 1.0794 - val_accuracy: 0.8175 - val_loss: 1.0341\n","Epoch 15/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8108 - loss: 1.0317 - val_accuracy: 0.8190 - val_loss: 0.9895\n","Epoch 16/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8165 - loss: 0.9865 - val_accuracy: 0.8240 - val_loss: 0.9496\n","Epoch 17/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8202 - loss: 0.9495 - val_accuracy: 0.8295 - val_loss: 0.9131\n","Epoch 18/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8245 - loss: 0.9130 - val_accuracy: 0.8335 - val_loss: 0.8802\n","Epoch 19/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8306 - loss: 0.8769 - val_accuracy: 0.8370 - val_loss: 0.8502\n","Epoch 20/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8321 - loss: 0.8515 - val_accuracy: 0.8405 - val_loss: 0.8226\n","Epoch 21/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8314 - loss: 0.8271 - val_accuracy: 0.8438 - val_loss: 0.7975\n","Epoch 22/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8383 - loss: 0.7967 - val_accuracy: 0.8452 - val_loss: 0.7744\n","Epoch 23/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8387 - loss: 0.7776 - val_accuracy: 0.8468 - val_loss: 0.7529\n","Epoch 24/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8418 - loss: 0.7579 - val_accuracy: 0.8478 - val_loss: 0.7334\n","Epoch 25/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8422 - loss: 0.7397 - val_accuracy: 0.8512 - val_loss: 0.7151\n","Epoch 26/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8485 - loss: 0.7174 - val_accuracy: 0.8530 - val_loss: 0.6981\n","Epoch 27/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8463 - loss: 0.7102 - val_accuracy: 0.8545 - val_loss: 0.6824\n","Epoch 28/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8497 - loss: 0.6902 - val_accuracy: 0.8548 - val_loss: 0.6679\n","Epoch 29/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8526 - loss: 0.6766 - val_accuracy: 0.8572 - val_loss: 0.6539\n","Epoch 30/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8530 - loss: 0.6643 - val_accuracy: 0.8595 - val_loss: 0.6411\n","Epoch 31/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8558 - loss: 0.6491 - val_accuracy: 0.8618 - val_loss: 0.6290\n","Epoch 32/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8571 - loss: 0.6349 - val_accuracy: 0.8625 - val_loss: 0.6176\n","Epoch 33/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8576 - loss: 0.6273 - val_accuracy: 0.8638 - val_loss: 0.6069\n","Epoch 34/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8595 - loss: 0.6168 - val_accuracy: 0.8657 - val_loss: 0.5969\n","Epoch 35/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8581 - loss: 0.6056 - val_accuracy: 0.8675 - val_loss: 0.5872\n","Epoch 36/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8630 - loss: 0.5946 - val_accuracy: 0.8687 - val_loss: 0.5781\n","Epoch 37/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8589 - loss: 0.5929 - val_accuracy: 0.8703 - val_loss: 0.5695\n","Epoch 38/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8641 - loss: 0.5789 - val_accuracy: 0.8707 - val_loss: 0.5613\n","Epoch 39/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8626 - loss: 0.5762 - val_accuracy: 0.8722 - val_loss: 0.5536\n","Epoch 40/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8643 - loss: 0.5660 - val_accuracy: 0.8732 - val_loss: 0.5463\n","Epoch 41/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8671 - loss: 0.5552 - val_accuracy: 0.8738 - val_loss: 0.5393\n","Epoch 42/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8686 - loss: 0.5478 - val_accuracy: 0.8760 - val_loss: 0.5326\n","Epoch 43/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8685 - loss: 0.5413 - val_accuracy: 0.8765 - val_loss: 0.5262\n","Epoch 44/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8705 - loss: 0.5346 - val_accuracy: 0.8777 - val_loss: 0.5200\n","Epoch 45/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8744 - loss: 0.5244 - val_accuracy: 0.8787 - val_loss: 0.5142\n","Epoch 46/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8722 - loss: 0.5249 - val_accuracy: 0.8802 - val_loss: 0.5086\n","Epoch 47/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8735 - loss: 0.5182 - val_accuracy: 0.8812 - val_loss: 0.5034\n","Epoch 48/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8750 - loss: 0.5123 - val_accuracy: 0.8822 - val_loss: 0.4979\n","Epoch 49/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8758 - loss: 0.5054 - val_accuracy: 0.8830 - val_loss: 0.4929\n","Epoch 50/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8742 - loss: 0.5039 - val_accuracy: 0.8847 - val_loss: 0.4882\n","Epoch 51/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8752 - loss: 0.4980 - val_accuracy: 0.8850 - val_loss: 0.4835\n","Epoch 52/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8765 - loss: 0.4941 - val_accuracy: 0.8855 - val_loss: 0.4791\n","Epoch 53/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8789 - loss: 0.4865 - val_accuracy: 0.8862 - val_loss: 0.4749\n","Epoch 54/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8801 - loss: 0.4820 - val_accuracy: 0.8877 - val_loss: 0.4707\n","Epoch 55/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8781 - loss: 0.4815 - val_accuracy: 0.8880 - val_loss: 0.4669\n","Epoch 56/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8811 - loss: 0.4737 - val_accuracy: 0.8887 - val_loss: 0.4631\n","Epoch 57/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8783 - loss: 0.4770 - val_accuracy: 0.8895 - val_loss: 0.4593\n","Epoch 58/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8823 - loss: 0.4652 - val_accuracy: 0.8915 - val_loss: 0.4557\n","Epoch 59/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8814 - loss: 0.4652 - val_accuracy: 0.8920 - val_loss: 0.4523\n","Epoch 60/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8796 - loss: 0.4694 - val_accuracy: 0.8918 - val_loss: 0.4491\n","Epoch 61/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8817 - loss: 0.4564 - val_accuracy: 0.8927 - val_loss: 0.4458\n","Epoch 62/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8812 - loss: 0.4577 - val_accuracy: 0.8933 - val_loss: 0.4426\n","Epoch 63/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8843 - loss: 0.4529 - val_accuracy: 0.8950 - val_loss: 0.4396\n","Epoch 64/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8853 - loss: 0.4466 - val_accuracy: 0.8962 - val_loss: 0.4365\n","Epoch 65/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8841 - loss: 0.4467 - val_accuracy: 0.8958 - val_loss: 0.4338\n","Epoch 66/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8851 - loss: 0.4427 - val_accuracy: 0.8965 - val_loss: 0.4310\n","Epoch 67/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8852 - loss: 0.4412 - val_accuracy: 0.8965 - val_loss: 0.4283\n","Epoch 68/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8838 - loss: 0.4410 - val_accuracy: 0.8975 - val_loss: 0.4257\n","Epoch 69/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8857 - loss: 0.4325 - val_accuracy: 0.8982 - val_loss: 0.4230\n","Epoch 70/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8852 - loss: 0.4351 - val_accuracy: 0.8982 - val_loss: 0.4206\n","Epoch 71/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8863 - loss: 0.4293 - val_accuracy: 0.8990 - val_loss: 0.4182\n","Epoch 72/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8863 - loss: 0.4259 - val_accuracy: 0.8997 - val_loss: 0.4158\n","Epoch 73/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8865 - loss: 0.4276 - val_accuracy: 0.8995 - val_loss: 0.4136\n","Epoch 74/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8881 - loss: 0.4235 - val_accuracy: 0.9003 - val_loss: 0.4114\n","Epoch 75/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8883 - loss: 0.4191 - val_accuracy: 0.9007 - val_loss: 0.4092\n","Epoch 76/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8871 - loss: 0.4231 - val_accuracy: 0.9008 - val_loss: 0.4072\n","Epoch 77/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8886 - loss: 0.4205 - val_accuracy: 0.9012 - val_loss: 0.4051\n","Epoch 78/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8890 - loss: 0.4139 - val_accuracy: 0.9007 - val_loss: 0.4032\n","Epoch 79/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8889 - loss: 0.4113 - val_accuracy: 0.9015 - val_loss: 0.4012\n","Epoch 80/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8914 - loss: 0.4130 - val_accuracy: 0.9015 - val_loss: 0.3993\n","Epoch 81/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8917 - loss: 0.4061 - val_accuracy: 0.9023 - val_loss: 0.3975\n","Epoch 82/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8891 - loss: 0.4073 - val_accuracy: 0.9025 - val_loss: 0.3956\n","Epoch 83/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8893 - loss: 0.4055 - val_accuracy: 0.9028 - val_loss: 0.3938\n","Epoch 84/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8913 - loss: 0.4045 - val_accuracy: 0.9028 - val_loss: 0.3921\n","Epoch 85/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8908 - loss: 0.4000 - val_accuracy: 0.9028 - val_loss: 0.3903\n","Epoch 86/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8924 - loss: 0.3993 - val_accuracy: 0.9033 - val_loss: 0.3888\n","Epoch 87/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8946 - loss: 0.3935 - val_accuracy: 0.9032 - val_loss: 0.3871\n","Epoch 88/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8917 - loss: 0.3967 - val_accuracy: 0.9033 - val_loss: 0.3856\n","Epoch 89/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8922 - loss: 0.3957 - val_accuracy: 0.9030 - val_loss: 0.3841\n","Epoch 90/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8919 - loss: 0.3979 - val_accuracy: 0.9040 - val_loss: 0.3826\n","Epoch 91/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8919 - loss: 0.3944 - val_accuracy: 0.9040 - val_loss: 0.3811\n","Epoch 92/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8941 - loss: 0.3888 - val_accuracy: 0.9040 - val_loss: 0.3796\n","Epoch 93/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8924 - loss: 0.3858 - val_accuracy: 0.9045 - val_loss: 0.3782\n","Epoch 94/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8931 - loss: 0.3881 - val_accuracy: 0.9038 - val_loss: 0.3767\n","Epoch 95/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8954 - loss: 0.3809 - val_accuracy: 0.9048 - val_loss: 0.3753\n","Epoch 96/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8944 - loss: 0.3877 - val_accuracy: 0.9043 - val_loss: 0.3740\n","Epoch 97/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8954 - loss: 0.3804 - val_accuracy: 0.9048 - val_loss: 0.3727\n","Epoch 98/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8953 - loss: 0.3848 - val_accuracy: 0.9048 - val_loss: 0.3715\n","Epoch 99/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8953 - loss: 0.3786 - val_accuracy: 0.9053 - val_loss: 0.3702\n","Epoch 100/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8938 - loss: 0.3813 - val_accuracy: 0.9058 - val_loss: 0.3688\n","Epoch 101/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8964 - loss: 0.3772 - val_accuracy: 0.9057 - val_loss: 0.3678\n","Epoch 102/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8971 - loss: 0.3729 - val_accuracy: 0.9063 - val_loss: 0.3665\n","Epoch 103/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8960 - loss: 0.3783 - val_accuracy: 0.9070 - val_loss: 0.3653\n","Epoch 104/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8973 - loss: 0.3732 - val_accuracy: 0.9068 - val_loss: 0.3642\n","Epoch 105/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8977 - loss: 0.3718 - val_accuracy: 0.9070 - val_loss: 0.3631\n","Epoch 106/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8986 - loss: 0.3684 - val_accuracy: 0.9067 - val_loss: 0.3620\n","Epoch 107/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8971 - loss: 0.3720 - val_accuracy: 0.9067 - val_loss: 0.3609\n","Epoch 108/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8970 - loss: 0.3730 - val_accuracy: 0.9068 - val_loss: 0.3597\n","Epoch 109/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8960 - loss: 0.3732 - val_accuracy: 0.9072 - val_loss: 0.3587\n","Epoch 110/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8971 - loss: 0.3667 - val_accuracy: 0.9080 - val_loss: 0.3576\n","Epoch 111/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8972 - loss: 0.3704 - val_accuracy: 0.9085 - val_loss: 0.3566\n","Epoch 112/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8996 - loss: 0.3614 - val_accuracy: 0.9092 - val_loss: 0.3556\n","Epoch 113/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8988 - loss: 0.3648 - val_accuracy: 0.9092 - val_loss: 0.3547\n","Epoch 114/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8998 - loss: 0.3596 - val_accuracy: 0.9092 - val_loss: 0.3536\n","Epoch 115/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9008 - loss: 0.3591 - val_accuracy: 0.9098 - val_loss: 0.3526\n","Epoch 116/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8998 - loss: 0.3594 - val_accuracy: 0.9098 - val_loss: 0.3517\n","Epoch 117/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8977 - loss: 0.3644 - val_accuracy: 0.9097 - val_loss: 0.3508\n","Epoch 118/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9009 - loss: 0.3554 - val_accuracy: 0.9100 - val_loss: 0.3499\n","Epoch 119/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8995 - loss: 0.3597 - val_accuracy: 0.9102 - val_loss: 0.3490\n","Epoch 120/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9015 - loss: 0.3577 - val_accuracy: 0.9103 - val_loss: 0.3480\n","Epoch 121/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8993 - loss: 0.3600 - val_accuracy: 0.9107 - val_loss: 0.3472\n","Epoch 122/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9028 - loss: 0.3512 - val_accuracy: 0.9103 - val_loss: 0.3464\n","Epoch 123/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9015 - loss: 0.3564 - val_accuracy: 0.9105 - val_loss: 0.3455\n","Epoch 124/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9029 - loss: 0.3472 - val_accuracy: 0.9108 - val_loss: 0.3446\n","Epoch 125/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9012 - loss: 0.3531 - val_accuracy: 0.9103 - val_loss: 0.3440\n","Epoch 126/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9011 - loss: 0.3531 - val_accuracy: 0.9110 - val_loss: 0.3431\n","Epoch 127/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9013 - loss: 0.3541 - val_accuracy: 0.9108 - val_loss: 0.3423\n","Epoch 128/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9008 - loss: 0.3512 - val_accuracy: 0.9112 - val_loss: 0.3414\n","Epoch 129/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9026 - loss: 0.3459 - val_accuracy: 0.9117 - val_loss: 0.3406\n","Epoch 130/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9014 - loss: 0.3531 - val_accuracy: 0.9110 - val_loss: 0.3399\n","Epoch 131/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9020 - loss: 0.3498 - val_accuracy: 0.9110 - val_loss: 0.3392\n","Epoch 132/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9025 - loss: 0.3465 - val_accuracy: 0.9117 - val_loss: 0.3384\n","Epoch 133/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9027 - loss: 0.3439 - val_accuracy: 0.9122 - val_loss: 0.3376\n","Epoch 134/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9024 - loss: 0.3465 - val_accuracy: 0.9117 - val_loss: 0.3370\n","Epoch 135/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9043 - loss: 0.3420 - val_accuracy: 0.9125 - val_loss: 0.3362\n","Epoch 136/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9026 - loss: 0.3431 - val_accuracy: 0.9120 - val_loss: 0.3354\n","Epoch 137/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9067 - loss: 0.3375 - val_accuracy: 0.9118 - val_loss: 0.3346\n","Epoch 138/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9049 - loss: 0.3392 - val_accuracy: 0.9123 - val_loss: 0.3341\n","Epoch 139/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9047 - loss: 0.3418 - val_accuracy: 0.9127 - val_loss: 0.3335\n","Epoch 140/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9033 - loss: 0.3448 - val_accuracy: 0.9123 - val_loss: 0.3327\n","Epoch 141/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9040 - loss: 0.3404 - val_accuracy: 0.9125 - val_loss: 0.3320\n","Epoch 142/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9041 - loss: 0.3421 - val_accuracy: 0.9130 - val_loss: 0.3314\n","Epoch 143/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9061 - loss: 0.3345 - val_accuracy: 0.9128 - val_loss: 0.3307\n","Epoch 144/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9075 - loss: 0.3336 - val_accuracy: 0.9128 - val_loss: 0.3301\n","Epoch 145/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9059 - loss: 0.3358 - val_accuracy: 0.9132 - val_loss: 0.3295\n","Epoch 146/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9061 - loss: 0.3380 - val_accuracy: 0.9133 - val_loss: 0.3287\n","Epoch 147/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9067 - loss: 0.3318 - val_accuracy: 0.9135 - val_loss: 0.3281\n","Epoch 148/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9073 - loss: 0.3311 - val_accuracy: 0.9138 - val_loss: 0.3274\n","Epoch 149/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9069 - loss: 0.3329 - val_accuracy: 0.9143 - val_loss: 0.3270\n","Epoch 150/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9038 - loss: 0.3382 - val_accuracy: 0.9147 - val_loss: 0.3263\n","Epoch 151/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9038 - loss: 0.3352 - val_accuracy: 0.9147 - val_loss: 0.3256\n","Epoch 152/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9068 - loss: 0.3308 - val_accuracy: 0.9143 - val_loss: 0.3251\n","Epoch 153/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9064 - loss: 0.3305 - val_accuracy: 0.9152 - val_loss: 0.3245\n","Epoch 154/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9070 - loss: 0.3270 - val_accuracy: 0.9153 - val_loss: 0.3238\n","Epoch 155/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9050 - loss: 0.3377 - val_accuracy: 0.9157 - val_loss: 0.3232\n","Epoch 156/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9071 - loss: 0.3292 - val_accuracy: 0.9155 - val_loss: 0.3228\n","Epoch 157/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9050 - loss: 0.3347 - val_accuracy: 0.9155 - val_loss: 0.3223\n","Epoch 158/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9074 - loss: 0.3280 - val_accuracy: 0.9158 - val_loss: 0.3217\n","Epoch 159/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9090 - loss: 0.3284 - val_accuracy: 0.9160 - val_loss: 0.3211\n","Epoch 160/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9058 - loss: 0.3308 - val_accuracy: 0.9162 - val_loss: 0.3205\n","Epoch 161/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9097 - loss: 0.3210 - val_accuracy: 0.9165 - val_loss: 0.3200\n","Epoch 162/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9068 - loss: 0.3305 - val_accuracy: 0.9163 - val_loss: 0.3194\n","Epoch 163/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9081 - loss: 0.3294 - val_accuracy: 0.9167 - val_loss: 0.3188\n","Epoch 164/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9090 - loss: 0.3228 - val_accuracy: 0.9168 - val_loss: 0.3183\n","Epoch 165/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9089 - loss: 0.3261 - val_accuracy: 0.9163 - val_loss: 0.3178\n","Epoch 166/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9084 - loss: 0.3239 - val_accuracy: 0.9167 - val_loss: 0.3173\n","Epoch 167/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9069 - loss: 0.3304 - val_accuracy: 0.9168 - val_loss: 0.3170\n","Epoch 168/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9097 - loss: 0.3196 - val_accuracy: 0.9170 - val_loss: 0.3164\n","Epoch 169/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9095 - loss: 0.3187 - val_accuracy: 0.9168 - val_loss: 0.3158\n","Epoch 170/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9082 - loss: 0.3226 - val_accuracy: 0.9168 - val_loss: 0.3153\n","Epoch 171/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9111 - loss: 0.3191 - val_accuracy: 0.9170 - val_loss: 0.3148\n","Epoch 172/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9116 - loss: 0.3160 - val_accuracy: 0.9175 - val_loss: 0.3143\n","Epoch 173/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9102 - loss: 0.3206 - val_accuracy: 0.9175 - val_loss: 0.3138\n","Epoch 174/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9097 - loss: 0.3201 - val_accuracy: 0.9175 - val_loss: 0.3134\n","Epoch 175/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9093 - loss: 0.3207 - val_accuracy: 0.9175 - val_loss: 0.3129\n","Epoch 176/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9083 - loss: 0.3236 - val_accuracy: 0.9178 - val_loss: 0.3125\n","Epoch 177/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9090 - loss: 0.3209 - val_accuracy: 0.9177 - val_loss: 0.3119\n","Epoch 178/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9117 - loss: 0.3162 - val_accuracy: 0.9183 - val_loss: 0.3114\n","Epoch 179/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9093 - loss: 0.3229 - val_accuracy: 0.9185 - val_loss: 0.3110\n","Epoch 180/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9087 - loss: 0.3224 - val_accuracy: 0.9182 - val_loss: 0.3106\n","Epoch 181/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9093 - loss: 0.3178 - val_accuracy: 0.9183 - val_loss: 0.3102\n","Epoch 182/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9099 - loss: 0.3174 - val_accuracy: 0.9187 - val_loss: 0.3096\n","Epoch 183/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9098 - loss: 0.3159 - val_accuracy: 0.9187 - val_loss: 0.3092\n","Epoch 184/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9089 - loss: 0.3225 - val_accuracy: 0.9188 - val_loss: 0.3087\n","Epoch 185/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9135 - loss: 0.3091 - val_accuracy: 0.9185 - val_loss: 0.3083\n","Epoch 186/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9105 - loss: 0.3172 - val_accuracy: 0.9190 - val_loss: 0.3079\n","Epoch 187/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9112 - loss: 0.3129 - val_accuracy: 0.9192 - val_loss: 0.3075\n","Epoch 188/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9116 - loss: 0.3125 - val_accuracy: 0.9190 - val_loss: 0.3070\n","Epoch 189/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9107 - loss: 0.3117 - val_accuracy: 0.9192 - val_loss: 0.3067\n","Epoch 190/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9107 - loss: 0.3140 - val_accuracy: 0.9193 - val_loss: 0.3062\n","Epoch 191/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9102 - loss: 0.3137 - val_accuracy: 0.9193 - val_loss: 0.3057\n","Epoch 192/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9139 - loss: 0.3047 - val_accuracy: 0.9197 - val_loss: 0.3053\n","Epoch 193/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9114 - loss: 0.3108 - val_accuracy: 0.9200 - val_loss: 0.3049\n","Epoch 194/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9123 - loss: 0.3110 - val_accuracy: 0.9197 - val_loss: 0.3045\n","Epoch 195/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9131 - loss: 0.3042 - val_accuracy: 0.9202 - val_loss: 0.3042\n","Epoch 196/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9126 - loss: 0.3071 - val_accuracy: 0.9198 - val_loss: 0.3037\n","Epoch 197/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9128 - loss: 0.3069 - val_accuracy: 0.9198 - val_loss: 0.3034\n","Epoch 198/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9127 - loss: 0.3055 - val_accuracy: 0.9207 - val_loss: 0.3029\n","Epoch 199/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9134 - loss: 0.3062 - val_accuracy: 0.9208 - val_loss: 0.3025\n","Epoch 200/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9120 - loss: 0.3099 - val_accuracy: 0.9203 - val_loss: 0.3021\n"]}],"source":["model_2l_100 = Sequential()\n","model_2l_100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n","model_2l_100.add(Dense(units=num_classes, activation='softmax'))\n","model_2l_100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_2l_100.summary()\n","\n","H_2l_100=model_2l_100.fit(X_train,y_train,batch_size =512, validation_split=0.1,epochs=200)"]},{"cell_type":"code","source":["plt.plot(H_2l_100.history['loss'])\n","plt.plot(H_2l_100.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","scores=model_2l_100.evaluate(X_test,y_test);\n","print('Loss on test data:',scores[0]);\n","print('Accuracy on test data:',scores[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":524},"id":"PQYE0zdOcJVe","executionInfo":{"status":"ok","timestamp":1760536814263,"user_tz":-180,"elapsed":1658,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"529c1c51-dd67-4b15-f417-bd7462003a9b"},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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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[1m1s\u001b[0m 3ms/step - accuracy: 0.9166 - loss: 0.3003\n","Loss on test data: 0.30692651867866516\n","Accuracy on test data: 0.9150000214576721\n"]}]},{"cell_type":"code","source":["model_2l_300 = Sequential()\n","model_2l_300.add(Dense(units=300,input_dim=num_pixels, activation='sigmoid'))\n","model_2l_300.add(Dense(units=num_classes, activation='softmax'))\n","model_2l_300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_2l_300.summary()\n","\n","H_2l_300=model_2l_300.fit(X_train,y_train,batch_size = 512,validation_split=0.1,epochs=200)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"C7dXxGwPeYSH","executionInfo":{"status":"ok","timestamp":1760458258611,"user_tz":-180,"elapsed":94713,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"094248b4-8be2-422e-b2ca-0ff5ff866668"},"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_9 (\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_10 (\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_9 (Dense)                 │ (None, 300)            │       235,500 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_10 (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":["Epoch 1/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 16ms/step - accuracy: 0.1803 - loss: 2.3055 - val_accuracy: 0.4517 - val_loss: 2.1474\n","Epoch 2/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4919 - loss: 2.1119 - val_accuracy: 0.5880 - val_loss: 1.9973\n","Epoch 3/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6154 - loss: 1.9641 - val_accuracy: 0.6795 - val_loss: 1.8558\n","Epoch 4/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6862 - loss: 1.8234 - val_accuracy: 0.7207 - val_loss: 1.7234\n","Epoch 5/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7182 - loss: 1.6952 - val_accuracy: 0.7388 - val_loss: 1.5997\n","Epoch 6/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7396 - loss: 1.5765 - val_accuracy: 0.7540 - val_loss: 1.4858\n","Epoch 7/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7591 - loss: 1.4642 - val_accuracy: 0.7807 - val_loss: 1.3821\n","Epoch 8/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7750 - loss: 1.3636 - val_accuracy: 0.7885 - val_loss: 1.2897\n","Epoch 9/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7870 - loss: 1.2736 - val_accuracy: 0.7945 - val_loss: 1.2069\n","Epoch 10/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7941 - loss: 1.1977 - val_accuracy: 0.7988 - val_loss: 1.1339\n","Epoch 11/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7978 - loss: 1.1242 - val_accuracy: 0.8092 - val_loss: 1.0692\n","Epoch 12/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8079 - loss: 1.0619 - val_accuracy: 0.8188 - val_loss: 1.0112\n","Epoch 13/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8145 - loss: 1.0092 - val_accuracy: 0.8250 - val_loss: 0.9605\n","Epoch 14/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8194 - loss: 0.9611 - val_accuracy: 0.8288 - val_loss: 0.9155\n","Epoch 15/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8229 - loss: 0.9145 - val_accuracy: 0.8348 - val_loss: 0.8750\n","Epoch 16/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8293 - loss: 0.8717 - val_accuracy: 0.8370 - val_loss: 0.8393\n","Epoch 17/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8351 - loss: 0.8407 - val_accuracy: 0.8438 - val_loss: 0.8068\n","Epoch 18/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8351 - loss: 0.8081 - val_accuracy: 0.8442 - val_loss: 0.7778\n","Epoch 19/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8404 - loss: 0.7771 - val_accuracy: 0.8468 - val_loss: 0.7516\n","Epoch 20/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8438 - loss: 0.7550 - val_accuracy: 0.8495 - val_loss: 0.7277\n","Epoch 21/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8421 - loss: 0.7371 - val_accuracy: 0.8520 - val_loss: 0.7061\n","Epoch 22/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8453 - loss: 0.7137 - val_accuracy: 0.8537 - val_loss: 0.6865\n","Epoch 23/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8480 - loss: 0.6938 - val_accuracy: 0.8557 - val_loss: 0.6685\n","Epoch 24/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8477 - loss: 0.6807 - val_accuracy: 0.8593 - val_loss: 0.6515\n","Epoch 25/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8536 - loss: 0.6579 - val_accuracy: 0.8612 - val_loss: 0.6362\n","Epoch 26/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8546 - loss: 0.6445 - val_accuracy: 0.8617 - val_loss: 0.6221\n","Epoch 27/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8561 - loss: 0.6336 - val_accuracy: 0.8650 - val_loss: 0.6084\n","Epoch 28/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8572 - loss: 0.6224 - val_accuracy: 0.8660 - val_loss: 0.5963\n","Epoch 29/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8588 - loss: 0.6085 - val_accuracy: 0.8677 - val_loss: 0.5846\n","Epoch 30/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8598 - loss: 0.5999 - val_accuracy: 0.8702 - val_loss: 0.5739\n","Epoch 31/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8642 - loss: 0.5812 - val_accuracy: 0.8713 - val_loss: 0.5638\n","Epoch 32/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8639 - loss: 0.5755 - val_accuracy: 0.8715 - val_loss: 0.5544\n","Epoch 33/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8659 - loss: 0.5651 - val_accuracy: 0.8740 - val_loss: 0.5454\n","Epoch 34/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8658 - loss: 0.5572 - val_accuracy: 0.8748 - val_loss: 0.5373\n","Epoch 35/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8688 - loss: 0.5495 - val_accuracy: 0.8763 - val_loss: 0.5291\n","Epoch 36/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8700 - loss: 0.5405 - val_accuracy: 0.8772 - val_loss: 0.5218\n","Epoch 37/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8673 - loss: 0.5370 - val_accuracy: 0.8780 - val_loss: 0.5148\n","Epoch 38/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8721 - loss: 0.5230 - val_accuracy: 0.8790 - val_loss: 0.5080\n","Epoch 39/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8744 - loss: 0.5118 - val_accuracy: 0.8812 - val_loss: 0.5014\n","Epoch 40/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8729 - loss: 0.5143 - val_accuracy: 0.8808 - val_loss: 0.4956\n","Epoch 41/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8727 - loss: 0.5106 - val_accuracy: 0.8830 - val_loss: 0.4898\n","Epoch 42/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8767 - loss: 0.4980 - val_accuracy: 0.8830 - val_loss: 0.4845\n","Epoch 43/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8763 - loss: 0.4944 - val_accuracy: 0.8842 - val_loss: 0.4792\n","Epoch 44/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8740 - loss: 0.4967 - val_accuracy: 0.8845 - val_loss: 0.4741\n","Epoch 45/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8771 - loss: 0.4842 - val_accuracy: 0.8870 - val_loss: 0.4696\n","Epoch 46/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8785 - loss: 0.4827 - val_accuracy: 0.8888 - val_loss: 0.4648\n","Epoch 47/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8784 - loss: 0.4781 - val_accuracy: 0.8895 - val_loss: 0.4604\n","Epoch 48/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8769 - loss: 0.4760 - val_accuracy: 0.8897 - val_loss: 0.4561\n","Epoch 49/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8754 - loss: 0.4776 - val_accuracy: 0.8907 - val_loss: 0.4523\n","Epoch 50/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8813 - loss: 0.4643 - val_accuracy: 0.8902 - val_loss: 0.4485\n","Epoch 51/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8821 - loss: 0.4575 - val_accuracy: 0.8908 - val_loss: 0.4450\n","Epoch 52/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8840 - loss: 0.4528 - val_accuracy: 0.8912 - val_loss: 0.4413\n","Epoch 53/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8827 - loss: 0.4502 - val_accuracy: 0.8925 - val_loss: 0.4379\n","Epoch 54/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8809 - loss: 0.4517 - val_accuracy: 0.8930 - val_loss: 0.4346\n","Epoch 55/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8803 - loss: 0.4510 - val_accuracy: 0.8933 - val_loss: 0.4311\n","Epoch 56/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8830 - loss: 0.4440 - val_accuracy: 0.8943 - val_loss: 0.4283\n","Epoch 57/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8839 - loss: 0.4395 - val_accuracy: 0.8945 - val_loss: 0.4252\n","Epoch 58/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8848 - loss: 0.4405 - val_accuracy: 0.8940 - val_loss: 0.4227\n","Epoch 59/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8853 - loss: 0.4369 - val_accuracy: 0.8950 - val_loss: 0.4200\n","Epoch 60/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8856 - loss: 0.4319 - val_accuracy: 0.8965 - val_loss: 0.4170\n","Epoch 61/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8839 - loss: 0.4322 - val_accuracy: 0.8967 - val_loss: 0.4145\n","Epoch 62/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8849 - loss: 0.4278 - val_accuracy: 0.8968 - val_loss: 0.4120\n","Epoch 63/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8863 - loss: 0.4276 - val_accuracy: 0.8965 - val_loss: 0.4098\n","Epoch 64/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8890 - loss: 0.4170 - val_accuracy: 0.8967 - val_loss: 0.4074\n","Epoch 65/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8876 - loss: 0.4194 - val_accuracy: 0.8985 - val_loss: 0.4049\n","Epoch 66/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8852 - loss: 0.4229 - val_accuracy: 0.8980 - val_loss: 0.4029\n","Epoch 67/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8871 - loss: 0.4185 - val_accuracy: 0.8990 - val_loss: 0.4007\n","Epoch 68/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8903 - loss: 0.4060 - val_accuracy: 0.8995 - val_loss: 0.3987\n","Epoch 69/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8885 - loss: 0.4134 - val_accuracy: 0.8997 - val_loss: 0.3967\n","Epoch 70/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8890 - loss: 0.4110 - val_accuracy: 0.9002 - val_loss: 0.3947\n","Epoch 71/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8890 - loss: 0.4080 - val_accuracy: 0.9002 - val_loss: 0.3925\n","Epoch 72/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8916 - loss: 0.4038 - val_accuracy: 0.9007 - val_loss: 0.3909\n","Epoch 73/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8896 - loss: 0.4073 - val_accuracy: 0.9007 - val_loss: 0.3891\n","Epoch 74/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8910 - loss: 0.3992 - val_accuracy: 0.9018 - val_loss: 0.3873\n","Epoch 75/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8906 - loss: 0.3998 - val_accuracy: 0.9010 - val_loss: 0.3860\n","Epoch 76/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8921 - loss: 0.3969 - val_accuracy: 0.9022 - val_loss: 0.3842\n","Epoch 77/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8914 - loss: 0.3962 - val_accuracy: 0.9025 - val_loss: 0.3828\n","Epoch 78/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8898 - loss: 0.3981 - val_accuracy: 0.9022 - val_loss: 0.3811\n","Epoch 79/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8927 - loss: 0.3909 - val_accuracy: 0.9027 - val_loss: 0.3794\n","Epoch 80/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8936 - loss: 0.3874 - val_accuracy: 0.9027 - val_loss: 0.3779\n","Epoch 81/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8903 - loss: 0.3955 - val_accuracy: 0.9027 - val_loss: 0.3768\n","Epoch 82/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8929 - loss: 0.3883 - val_accuracy: 0.9033 - val_loss: 0.3754\n","Epoch 83/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8938 - loss: 0.3867 - val_accuracy: 0.9035 - val_loss: 0.3736\n","Epoch 84/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8924 - loss: 0.3900 - val_accuracy: 0.9032 - val_loss: 0.3722\n","Epoch 85/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8931 - loss: 0.3863 - val_accuracy: 0.9045 - val_loss: 0.3710\n","Epoch 86/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8928 - loss: 0.3844 - val_accuracy: 0.9042 - val_loss: 0.3698\n","Epoch 87/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8958 - loss: 0.3785 - val_accuracy: 0.9042 - val_loss: 0.3683\n","Epoch 88/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8976 - loss: 0.3705 - val_accuracy: 0.9042 - val_loss: 0.3673\n","Epoch 89/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8949 - loss: 0.3789 - val_accuracy: 0.9045 - val_loss: 0.3660\n","Epoch 90/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8942 - loss: 0.3816 - val_accuracy: 0.9043 - val_loss: 0.3650\n","Epoch 91/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8954 - loss: 0.3788 - val_accuracy: 0.9048 - val_loss: 0.3634\n","Epoch 92/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8947 - loss: 0.3767 - val_accuracy: 0.9048 - val_loss: 0.3626\n","Epoch 93/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8962 - loss: 0.3746 - val_accuracy: 0.9047 - val_loss: 0.3614\n","Epoch 94/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8949 - loss: 0.3748 - val_accuracy: 0.9053 - val_loss: 0.3602\n","Epoch 95/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8959 - loss: 0.3716 - val_accuracy: 0.9050 - val_loss: 0.3591\n","Epoch 96/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8935 - loss: 0.3745 - val_accuracy: 0.9053 - val_loss: 0.3582\n","Epoch 97/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8962 - loss: 0.3705 - val_accuracy: 0.9055 - val_loss: 0.3572\n","Epoch 98/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8953 - loss: 0.3724 - val_accuracy: 0.9055 - val_loss: 0.3563\n","Epoch 99/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8953 - loss: 0.3708 - val_accuracy: 0.9053 - val_loss: 0.3552\n","Epoch 100/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8981 - loss: 0.3628 - val_accuracy: 0.9050 - val_loss: 0.3545\n","Epoch 101/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8962 - loss: 0.3674 - val_accuracy: 0.9063 - val_loss: 0.3533\n","Epoch 102/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8956 - loss: 0.3707 - val_accuracy: 0.9055 - val_loss: 0.3523\n","Epoch 103/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8976 - loss: 0.3612 - val_accuracy: 0.9060 - val_loss: 0.3515\n","Epoch 104/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8956 - loss: 0.3702 - val_accuracy: 0.9065 - val_loss: 0.3505\n","Epoch 105/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8957 - loss: 0.3673 - val_accuracy: 0.9067 - val_loss: 0.3498\n","Epoch 106/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8980 - loss: 0.3608 - val_accuracy: 0.9065 - val_loss: 0.3490\n","Epoch 107/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9003 - loss: 0.3533 - val_accuracy: 0.9067 - val_loss: 0.3482\n","Epoch 108/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8979 - loss: 0.3616 - val_accuracy: 0.9070 - val_loss: 0.3471\n","Epoch 109/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8995 - loss: 0.3575 - val_accuracy: 0.9072 - val_loss: 0.3462\n","Epoch 110/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8973 - loss: 0.3600 - val_accuracy: 0.9075 - val_loss: 0.3456\n","Epoch 111/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8986 - loss: 0.3578 - val_accuracy: 0.9077 - val_loss: 0.3448\n","Epoch 112/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9004 - loss: 0.3538 - val_accuracy: 0.9078 - val_loss: 0.3440\n","Epoch 113/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8990 - loss: 0.3575 - val_accuracy: 0.9075 - val_loss: 0.3433\n","Epoch 114/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8998 - loss: 0.3549 - val_accuracy: 0.9088 - val_loss: 0.3423\n","Epoch 115/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9010 - loss: 0.3539 - val_accuracy: 0.9075 - val_loss: 0.3418\n","Epoch 116/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8978 - loss: 0.3590 - val_accuracy: 0.9082 - val_loss: 0.3409\n","Epoch 117/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8995 - loss: 0.3541 - val_accuracy: 0.9082 - val_loss: 0.3402\n","Epoch 118/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8993 - loss: 0.3534 - val_accuracy: 0.9083 - val_loss: 0.3395\n","Epoch 119/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9003 - loss: 0.3530 - val_accuracy: 0.9088 - val_loss: 0.3390\n","Epoch 120/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9003 - loss: 0.3507 - val_accuracy: 0.9082 - val_loss: 0.3385\n","Epoch 121/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8980 - loss: 0.3534 - val_accuracy: 0.9087 - val_loss: 0.3375\n","Epoch 122/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9021 - loss: 0.3481 - val_accuracy: 0.9095 - val_loss: 0.3369\n","Epoch 123/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9021 - loss: 0.3458 - val_accuracy: 0.9083 - val_loss: 0.3365\n","Epoch 124/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9008 - loss: 0.3513 - val_accuracy: 0.9088 - val_loss: 0.3356\n","Epoch 125/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9017 - loss: 0.3465 - val_accuracy: 0.9088 - val_loss: 0.3350\n","Epoch 126/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9032 - loss: 0.3426 - val_accuracy: 0.9092 - val_loss: 0.3343\n","Epoch 127/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9005 - loss: 0.3492 - val_accuracy: 0.9098 - val_loss: 0.3335\n","Epoch 128/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9032 - loss: 0.3406 - val_accuracy: 0.9097 - val_loss: 0.3330\n","Epoch 129/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9002 - loss: 0.3467 - val_accuracy: 0.9098 - val_loss: 0.3327\n","Epoch 130/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9029 - loss: 0.3398 - val_accuracy: 0.9105 - val_loss: 0.3318\n","Epoch 131/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9017 - loss: 0.3480 - val_accuracy: 0.9100 - val_loss: 0.3315\n","Epoch 132/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9009 - loss: 0.3480 - val_accuracy: 0.9103 - val_loss: 0.3307\n","Epoch 133/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9018 - loss: 0.3435 - val_accuracy: 0.9103 - val_loss: 0.3301\n","Epoch 134/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9017 - loss: 0.3435 - val_accuracy: 0.9105 - val_loss: 0.3296\n","Epoch 135/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9023 - loss: 0.3418 - val_accuracy: 0.9110 - val_loss: 0.3292\n","Epoch 136/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9047 - loss: 0.3378 - val_accuracy: 0.9113 - val_loss: 0.3286\n","Epoch 137/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9038 - loss: 0.3363 - val_accuracy: 0.9108 - val_loss: 0.3283\n","Epoch 138/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9016 - loss: 0.3411 - val_accuracy: 0.9122 - val_loss: 0.3275\n","Epoch 139/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9019 - loss: 0.3402 - val_accuracy: 0.9118 - val_loss: 0.3270\n","Epoch 140/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9051 - loss: 0.3345 - val_accuracy: 0.9115 - val_loss: 0.3265\n","Epoch 141/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9038 - loss: 0.3379 - val_accuracy: 0.9118 - val_loss: 0.3261\n","Epoch 142/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9040 - loss: 0.3381 - val_accuracy: 0.9125 - val_loss: 0.3255\n","Epoch 143/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9039 - loss: 0.3371 - val_accuracy: 0.9125 - val_loss: 0.3249\n","Epoch 144/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9037 - loss: 0.3371 - val_accuracy: 0.9117 - val_loss: 0.3247\n","Epoch 145/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9026 - loss: 0.3359 - val_accuracy: 0.9123 - val_loss: 0.3241\n","Epoch 146/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9036 - loss: 0.3362 - val_accuracy: 0.9120 - val_loss: 0.3236\n","Epoch 147/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9014 - loss: 0.3395 - val_accuracy: 0.9128 - val_loss: 0.3232\n","Epoch 148/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9069 - loss: 0.3290 - val_accuracy: 0.9128 - val_loss: 0.3226\n","Epoch 149/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9046 - loss: 0.3309 - val_accuracy: 0.9132 - val_loss: 0.3221\n","Epoch 150/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9026 - loss: 0.3382 - val_accuracy: 0.9142 - val_loss: 0.3216\n","Epoch 151/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9074 - loss: 0.3250 - val_accuracy: 0.9138 - val_loss: 0.3212\n","Epoch 152/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9057 - loss: 0.3311 - val_accuracy: 0.9140 - val_loss: 0.3209\n","Epoch 153/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9053 - loss: 0.3324 - val_accuracy: 0.9140 - val_loss: 0.3204\n","Epoch 154/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9050 - loss: 0.3321 - val_accuracy: 0.9142 - val_loss: 0.3202\n","Epoch 155/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9034 - loss: 0.3356 - val_accuracy: 0.9147 - val_loss: 0.3196\n","Epoch 156/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9065 - loss: 0.3267 - val_accuracy: 0.9148 - val_loss: 0.3191\n","Epoch 157/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9059 - loss: 0.3286 - val_accuracy: 0.9150 - val_loss: 0.3186\n","Epoch 158/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9051 - loss: 0.3312 - val_accuracy: 0.9147 - val_loss: 0.3183\n","Epoch 159/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9048 - loss: 0.3317 - val_accuracy: 0.9152 - val_loss: 0.3179\n","Epoch 160/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9068 - loss: 0.3235 - val_accuracy: 0.9155 - val_loss: 0.3175\n","Epoch 161/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9079 - loss: 0.3243 - val_accuracy: 0.9157 - val_loss: 0.3171\n","Epoch 162/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9031 - loss: 0.3346 - val_accuracy: 0.9153 - val_loss: 0.3167\n","Epoch 163/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9053 - loss: 0.3345 - val_accuracy: 0.9160 - val_loss: 0.3163\n","Epoch 164/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9064 - loss: 0.3289 - val_accuracy: 0.9162 - val_loss: 0.3159\n","Epoch 165/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9060 - loss: 0.3248 - val_accuracy: 0.9150 - val_loss: 0.3155\n","Epoch 166/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9058 - loss: 0.3302 - val_accuracy: 0.9165 - val_loss: 0.3152\n","Epoch 167/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9069 - loss: 0.3215 - val_accuracy: 0.9165 - val_loss: 0.3148\n","Epoch 168/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9083 - loss: 0.3195 - val_accuracy: 0.9162 - val_loss: 0.3142\n","Epoch 169/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9047 - loss: 0.3307 - val_accuracy: 0.9167 - val_loss: 0.3140\n","Epoch 170/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9086 - loss: 0.3223 - val_accuracy: 0.9160 - val_loss: 0.3137\n","Epoch 171/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9063 - loss: 0.3237 - val_accuracy: 0.9165 - val_loss: 0.3133\n","Epoch 172/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9076 - loss: 0.3239 - val_accuracy: 0.9170 - val_loss: 0.3130\n","Epoch 173/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9074 - loss: 0.3209 - val_accuracy: 0.9175 - val_loss: 0.3126\n","Epoch 174/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9070 - loss: 0.3178 - val_accuracy: 0.9172 - val_loss: 0.3124\n","Epoch 175/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9070 - loss: 0.3229 - val_accuracy: 0.9177 - val_loss: 0.3119\n","Epoch 176/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9074 - loss: 0.3210 - val_accuracy: 0.9177 - val_loss: 0.3115\n","Epoch 177/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9074 - loss: 0.3232 - val_accuracy: 0.9175 - val_loss: 0.3113\n","Epoch 178/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9080 - loss: 0.3188 - val_accuracy: 0.9180 - val_loss: 0.3109\n","Epoch 179/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9061 - loss: 0.3250 - val_accuracy: 0.9178 - val_loss: 0.3107\n","Epoch 180/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9089 - loss: 0.3189 - val_accuracy: 0.9175 - val_loss: 0.3101\n","Epoch 181/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9086 - loss: 0.3227 - val_accuracy: 0.9178 - val_loss: 0.3099\n","Epoch 182/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9088 - loss: 0.3183 - val_accuracy: 0.9185 - val_loss: 0.3095\n","Epoch 183/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9088 - loss: 0.3180 - val_accuracy: 0.9182 - val_loss: 0.3096\n","Epoch 184/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9096 - loss: 0.3157 - val_accuracy: 0.9180 - val_loss: 0.3090\n","Epoch 185/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9074 - loss: 0.3212 - val_accuracy: 0.9180 - val_loss: 0.3088\n","Epoch 186/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9082 - loss: 0.3194 - val_accuracy: 0.9185 - val_loss: 0.3084\n","Epoch 187/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9110 - loss: 0.3127 - val_accuracy: 0.9180 - val_loss: 0.3079\n","Epoch 188/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9097 - loss: 0.3154 - val_accuracy: 0.9188 - val_loss: 0.3077\n","Epoch 189/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9101 - loss: 0.3158 - val_accuracy: 0.9182 - val_loss: 0.3076\n","Epoch 190/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9105 - loss: 0.3157 - val_accuracy: 0.9187 - val_loss: 0.3070\n","Epoch 191/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9092 - loss: 0.3166 - val_accuracy: 0.9183 - val_loss: 0.3067\n","Epoch 192/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9100 - loss: 0.3109 - val_accuracy: 0.9187 - val_loss: 0.3065\n","Epoch 193/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9095 - loss: 0.3135 - val_accuracy: 0.9185 - val_loss: 0.3062\n","Epoch 194/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9096 - loss: 0.3150 - val_accuracy: 0.9183 - val_loss: 0.3058\n","Epoch 195/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9105 - loss: 0.3138 - val_accuracy: 0.9185 - val_loss: 0.3057\n","Epoch 196/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9118 - loss: 0.3109 - val_accuracy: 0.9183 - val_loss: 0.3055\n","Epoch 197/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9116 - loss: 0.3111 - val_accuracy: 0.9187 - val_loss: 0.3049\n","Epoch 198/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9092 - loss: 0.3178 - val_accuracy: 0.9187 - val_loss: 0.3050\n","Epoch 199/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9116 - loss: 0.3066 - val_accuracy: 0.9187 - val_loss: 0.3045\n","Epoch 200/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9116 - loss: 0.3090 - val_accuracy: 0.9182 - val_loss: 0.3045\n"]}]},{"cell_type":"code","source":["plt.plot(H_2l_300.history['loss'])\n","plt.plot(H_2l_300.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","scores=model_2l_300.evaluate(X_test,y_test);\n","print('Loss on test data:',scores[0]);\n","print('Accuracy on test data:',scores[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":524},"id":"iW3EchbGe4Bf","executionInfo":{"status":"ok","timestamp":1760458260217,"user_tz":-180,"elapsed":1579,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"de08b153-3bcc-4fa8-ac06-1befa1f0affc"},"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[1m1s\u001b[0m 3ms/step - accuracy: 0.9155 - loss: 0.3049\n","Loss on test data: 0.3119920790195465\n","Accuracy on test data: 0.9139000177383423\n"]}]},{"cell_type":"code","source":["model_2l_500 = Sequential()\n","model_2l_500.add(Dense(units=500,input_dim=num_pixels, activation='sigmoid'))\n","model_2l_500.add(Dense(units=num_classes, activation='softmax'))\n","model_2l_500.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_2l_500.summary()\n","\n","H_2l_500=model_2l_500.fit(X_train,y_train,batch_size = 512,validation_split=0.1,epochs=200)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"O6l-e8cHfeIY","executionInfo":{"status":"ok","timestamp":1760458638502,"user_tz":-180,"elapsed":93908,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"4c9da120-cd6b-4b78-9b7e-a2fc3c032adc"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_6\"\u001b[0m\n"],"text/html":["
Model: \"sequential_6\"\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_11 (\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_12 (\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_11 (Dense)                │ (None, 500)            │       392,500 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_12 (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":["Epoch 1/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 16ms/step - accuracy: 0.2202 - loss: 2.2868 - val_accuracy: 0.5052 - val_loss: 2.1290\n","Epoch 2/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5460 - loss: 2.0884 - val_accuracy: 0.6242 - val_loss: 1.9659\n","Epoch 3/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6475 - loss: 1.9314 - val_accuracy: 0.6770 - val_loss: 1.8161\n","Epoch 4/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6943 - loss: 1.7853 - val_accuracy: 0.7212 - val_loss: 1.6758\n","Epoch 5/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7223 - loss: 1.6492 - val_accuracy: 0.7638 - val_loss: 1.5472\n","Epoch 6/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7498 - loss: 1.5208 - val_accuracy: 0.7755 - val_loss: 1.4307\n","Epoch 7/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7656 - loss: 1.4106 - val_accuracy: 0.7878 - val_loss: 1.3266\n","Epoch 8/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7815 - loss: 1.3081 - val_accuracy: 0.7948 - val_loss: 1.2337\n","Epoch 9/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7896 - loss: 1.2217 - val_accuracy: 0.8092 - val_loss: 1.1517\n","Epoch 10/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7974 - loss: 1.1436 - val_accuracy: 0.8193 - val_loss: 1.0798\n","Epoch 11/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8086 - loss: 1.0697 - val_accuracy: 0.8237 - val_loss: 1.0173\n","Epoch 12/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8166 - loss: 1.0124 - val_accuracy: 0.8312 - val_loss: 0.9610\n","Epoch 13/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8215 - loss: 0.9571 - val_accuracy: 0.8337 - val_loss: 0.9123\n","Epoch 14/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8253 - loss: 0.9115 - val_accuracy: 0.8395 - val_loss: 0.8695\n","Epoch 15/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8328 - loss: 0.8691 - val_accuracy: 0.8403 - val_loss: 0.8315\n","Epoch 16/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8358 - loss: 0.8336 - val_accuracy: 0.8447 - val_loss: 0.7976\n","Epoch 17/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8374 - loss: 0.8032 - val_accuracy: 0.8488 - val_loss: 0.7670\n","Epoch 18/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8395 - loss: 0.7735 - val_accuracy: 0.8532 - val_loss: 0.7398\n","Epoch 19/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8455 - loss: 0.7463 - val_accuracy: 0.8543 - val_loss: 0.7154\n","Epoch 20/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8451 - loss: 0.7265 - val_accuracy: 0.8555 - val_loss: 0.6936\n","Epoch 21/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8467 - loss: 0.7051 - val_accuracy: 0.8600 - val_loss: 0.6730\n","Epoch 22/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8520 - loss: 0.6828 - val_accuracy: 0.8600 - val_loss: 0.6549\n","Epoch 23/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8539 - loss: 0.6610 - val_accuracy: 0.8615 - val_loss: 0.6383\n","Epoch 24/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8566 - loss: 0.6466 - val_accuracy: 0.8642 - val_loss: 0.6226\n","Epoch 25/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8558 - loss: 0.6351 - val_accuracy: 0.8672 - val_loss: 0.6087\n","Epoch 26/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8563 - loss: 0.6223 - val_accuracy: 0.8678 - val_loss: 0.5951\n","Epoch 27/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8574 - loss: 0.6089 - val_accuracy: 0.8702 - val_loss: 0.5830\n","Epoch 28/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8620 - loss: 0.5948 - val_accuracy: 0.8700 - val_loss: 0.5719\n","Epoch 29/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8615 - loss: 0.5852 - val_accuracy: 0.8718 - val_loss: 0.5615\n","Epoch 30/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8650 - loss: 0.5732 - val_accuracy: 0.8735 - val_loss: 0.5516\n","Epoch 31/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8634 - loss: 0.5624 - val_accuracy: 0.8753 - val_loss: 0.5421\n","Epoch 32/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8668 - loss: 0.5485 - val_accuracy: 0.8763 - val_loss: 0.5331\n","Epoch 33/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8689 - loss: 0.5415 - val_accuracy: 0.8783 - val_loss: 0.5251\n","Epoch 34/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8673 - loss: 0.5390 - val_accuracy: 0.8787 - val_loss: 0.5175\n","Epoch 35/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8707 - loss: 0.5292 - val_accuracy: 0.8783 - val_loss: 0.5105\n","Epoch 36/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8708 - loss: 0.5214 - val_accuracy: 0.8803 - val_loss: 0.5035\n","Epoch 37/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8711 - loss: 0.5163 - val_accuracy: 0.8812 - val_loss: 0.4974\n","Epoch 38/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8722 - loss: 0.5067 - val_accuracy: 0.8837 - val_loss: 0.4908\n","Epoch 39/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8735 - loss: 0.5060 - val_accuracy: 0.8850 - val_loss: 0.4849\n","Epoch 40/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8749 - loss: 0.4964 - val_accuracy: 0.8848 - val_loss: 0.4798\n","Epoch 41/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8749 - loss: 0.4934 - val_accuracy: 0.8867 - val_loss: 0.4744\n","Epoch 42/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8769 - loss: 0.4841 - val_accuracy: 0.8858 - val_loss: 0.4697\n","Epoch 43/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8771 - loss: 0.4820 - val_accuracy: 0.8875 - val_loss: 0.4644\n","Epoch 44/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8800 - loss: 0.4697 - val_accuracy: 0.8877 - val_loss: 0.4605\n","Epoch 45/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8761 - loss: 0.4801 - val_accuracy: 0.8895 - val_loss: 0.4557\n","Epoch 46/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8796 - loss: 0.4704 - val_accuracy: 0.8895 - val_loss: 0.4515\n","Epoch 47/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8791 - loss: 0.4628 - val_accuracy: 0.8910 - val_loss: 0.4475\n","Epoch 48/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8782 - loss: 0.4620 - val_accuracy: 0.8923 - val_loss: 0.4437\n","Epoch 49/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8806 - loss: 0.4569 - val_accuracy: 0.8913 - val_loss: 0.4402\n","Epoch 50/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8816 - loss: 0.4512 - val_accuracy: 0.8923 - val_loss: 0.4368\n","Epoch 51/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8817 - loss: 0.4450 - val_accuracy: 0.8937 - val_loss: 0.4331\n","Epoch 52/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8829 - loss: 0.4482 - val_accuracy: 0.8922 - val_loss: 0.4306\n","Epoch 53/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8842 - loss: 0.4422 - val_accuracy: 0.8948 - val_loss: 0.4268\n","Epoch 54/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8831 - loss: 0.4375 - val_accuracy: 0.8950 - val_loss: 0.4240\n","Epoch 55/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8823 - loss: 0.4395 - val_accuracy: 0.8962 - val_loss: 0.4210\n","Epoch 56/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8834 - loss: 0.4355 - val_accuracy: 0.8958 - val_loss: 0.4181\n","Epoch 57/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8830 - loss: 0.4342 - val_accuracy: 0.8970 - val_loss: 0.4154\n","Epoch 58/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8857 - loss: 0.4282 - val_accuracy: 0.8970 - val_loss: 0.4129\n","Epoch 59/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8818 - loss: 0.4318 - val_accuracy: 0.8977 - val_loss: 0.4104\n","Epoch 60/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8877 - loss: 0.4173 - val_accuracy: 0.8977 - val_loss: 0.4083\n","Epoch 61/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8853 - loss: 0.4214 - val_accuracy: 0.8982 - val_loss: 0.4057\n","Epoch 62/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8894 - loss: 0.4122 - val_accuracy: 0.8982 - val_loss: 0.4037\n","Epoch 63/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8861 - loss: 0.4173 - val_accuracy: 0.8990 - val_loss: 0.4013\n","Epoch 64/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8881 - loss: 0.4119 - val_accuracy: 0.8995 - val_loss: 0.3992\n","Epoch 65/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8890 - loss: 0.4100 - val_accuracy: 0.8993 - val_loss: 0.3974\n","Epoch 66/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8907 - loss: 0.4033 - val_accuracy: 0.8987 - val_loss: 0.3953\n","Epoch 67/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8888 - loss: 0.4048 - val_accuracy: 0.9000 - val_loss: 0.3932\n","Epoch 68/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8878 - loss: 0.4087 - val_accuracy: 0.9007 - val_loss: 0.3910\n","Epoch 69/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8864 - loss: 0.4079 - val_accuracy: 0.9012 - val_loss: 0.3891\n","Epoch 70/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8877 - loss: 0.4061 - val_accuracy: 0.9015 - val_loss: 0.3881\n","Epoch 71/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8879 - loss: 0.4025 - val_accuracy: 0.9012 - val_loss: 0.3865\n","Epoch 72/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8882 - loss: 0.4019 - val_accuracy: 0.9013 - val_loss: 0.3844\n","Epoch 73/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8911 - loss: 0.3960 - val_accuracy: 0.9013 - val_loss: 0.3825\n","Epoch 74/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8924 - loss: 0.3870 - val_accuracy: 0.9015 - val_loss: 0.3808\n","Epoch 75/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8925 - loss: 0.3881 - val_accuracy: 0.9022 - val_loss: 0.3797\n","Epoch 76/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8903 - loss: 0.3936 - val_accuracy: 0.9023 - val_loss: 0.3781\n","Epoch 77/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8901 - loss: 0.3948 - val_accuracy: 0.9028 - val_loss: 0.3764\n","Epoch 78/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8920 - loss: 0.3876 - val_accuracy: 0.9027 - val_loss: 0.3750\n","Epoch 79/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8933 - loss: 0.3863 - val_accuracy: 0.9028 - val_loss: 0.3737\n","Epoch 80/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8924 - loss: 0.3819 - val_accuracy: 0.9027 - val_loss: 0.3722\n","Epoch 81/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8915 - loss: 0.3853 - val_accuracy: 0.9035 - val_loss: 0.3708\n","Epoch 82/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8943 - loss: 0.3812 - val_accuracy: 0.9038 - val_loss: 0.3701\n","Epoch 83/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8938 - loss: 0.3792 - val_accuracy: 0.9047 - val_loss: 0.3685\n","Epoch 84/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8939 - loss: 0.3810 - val_accuracy: 0.9032 - val_loss: 0.3671\n","Epoch 85/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8929 - loss: 0.3813 - val_accuracy: 0.9053 - val_loss: 0.3665\n","Epoch 86/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8958 - loss: 0.3762 - val_accuracy: 0.9043 - val_loss: 0.3648\n","Epoch 87/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8935 - loss: 0.3790 - val_accuracy: 0.9053 - val_loss: 0.3637\n","Epoch 88/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8960 - loss: 0.3742 - val_accuracy: 0.9055 - val_loss: 0.3626\n","Epoch 89/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8942 - loss: 0.3782 - val_accuracy: 0.9050 - val_loss: 0.3613\n","Epoch 90/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8951 - loss: 0.3696 - val_accuracy: 0.9055 - val_loss: 0.3602\n","Epoch 91/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8953 - loss: 0.3741 - val_accuracy: 0.9065 - val_loss: 0.3593\n","Epoch 92/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8941 - loss: 0.3747 - val_accuracy: 0.9057 - val_loss: 0.3585\n","Epoch 93/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8955 - loss: 0.3707 - val_accuracy: 0.9062 - val_loss: 0.3575\n","Epoch 94/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8963 - loss: 0.3698 - val_accuracy: 0.9073 - val_loss: 0.3565\n","Epoch 95/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8970 - loss: 0.3652 - val_accuracy: 0.9067 - val_loss: 0.3552\n","Epoch 96/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8965 - loss: 0.3695 - val_accuracy: 0.9063 - val_loss: 0.3543\n","Epoch 97/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8952 - loss: 0.3677 - val_accuracy: 0.9073 - val_loss: 0.3532\n","Epoch 98/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8966 - loss: 0.3639 - val_accuracy: 0.9080 - val_loss: 0.3527\n","Epoch 99/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8955 - loss: 0.3688 - val_accuracy: 0.9070 - val_loss: 0.3514\n","Epoch 100/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8976 - loss: 0.3631 - val_accuracy: 0.9067 - val_loss: 0.3505\n","Epoch 101/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8965 - loss: 0.3635 - val_accuracy: 0.9078 - val_loss: 0.3498\n","Epoch 102/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8988 - loss: 0.3615 - val_accuracy: 0.9067 - val_loss: 0.3489\n","Epoch 103/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8981 - loss: 0.3563 - val_accuracy: 0.9080 - val_loss: 0.3481\n","Epoch 104/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8973 - loss: 0.3584 - val_accuracy: 0.9067 - val_loss: 0.3472\n","Epoch 105/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8949 - loss: 0.3640 - val_accuracy: 0.9073 - val_loss: 0.3465\n","Epoch 106/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8978 - loss: 0.3587 - val_accuracy: 0.9075 - val_loss: 0.3462\n","Epoch 107/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8972 - loss: 0.3600 - val_accuracy: 0.9078 - val_loss: 0.3448\n","Epoch 108/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8967 - loss: 0.3601 - val_accuracy: 0.9078 - val_loss: 0.3439\n","Epoch 109/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8994 - loss: 0.3568 - val_accuracy: 0.9077 - val_loss: 0.3438\n","Epoch 110/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8989 - loss: 0.3597 - val_accuracy: 0.9078 - val_loss: 0.3426\n","Epoch 111/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9000 - loss: 0.3490 - val_accuracy: 0.9077 - val_loss: 0.3422\n","Epoch 112/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8999 - loss: 0.3506 - val_accuracy: 0.9075 - val_loss: 0.3414\n","Epoch 113/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8985 - loss: 0.3560 - val_accuracy: 0.9073 - val_loss: 0.3409\n","Epoch 114/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9008 - loss: 0.3519 - val_accuracy: 0.9083 - val_loss: 0.3397\n","Epoch 115/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9023 - loss: 0.3540 - val_accuracy: 0.9082 - val_loss: 0.3392\n","Epoch 116/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9022 - loss: 0.3422 - val_accuracy: 0.9093 - val_loss: 0.3382\n","Epoch 117/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8955 - loss: 0.3614 - val_accuracy: 0.9080 - val_loss: 0.3379\n","Epoch 118/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9004 - loss: 0.3484 - val_accuracy: 0.9080 - val_loss: 0.3373\n","Epoch 119/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9005 - loss: 0.3503 - val_accuracy: 0.9088 - val_loss: 0.3370\n","Epoch 120/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9014 - loss: 0.3476 - val_accuracy: 0.9087 - val_loss: 0.3360\n","Epoch 121/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9029 - loss: 0.3431 - val_accuracy: 0.9095 - val_loss: 0.3352\n","Epoch 122/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9015 - loss: 0.3447 - val_accuracy: 0.9093 - val_loss: 0.3346\n","Epoch 123/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9019 - loss: 0.3446 - val_accuracy: 0.9085 - val_loss: 0.3343\n","Epoch 124/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9004 - loss: 0.3467 - val_accuracy: 0.9102 - val_loss: 0.3333\n","Epoch 125/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9013 - loss: 0.3438 - val_accuracy: 0.9095 - val_loss: 0.3331\n","Epoch 126/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9011 - loss: 0.3462 - val_accuracy: 0.9097 - val_loss: 0.3325\n","Epoch 127/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8998 - loss: 0.3451 - val_accuracy: 0.9100 - val_loss: 0.3319\n","Epoch 128/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9012 - loss: 0.3457 - val_accuracy: 0.9102 - val_loss: 0.3316\n","Epoch 129/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9033 - loss: 0.3352 - val_accuracy: 0.9100 - val_loss: 0.3311\n","Epoch 130/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9039 - loss: 0.3387 - val_accuracy: 0.9110 - val_loss: 0.3303\n","Epoch 131/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9029 - loss: 0.3418 - val_accuracy: 0.9118 - val_loss: 0.3295\n","Epoch 132/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9023 - loss: 0.3407 - val_accuracy: 0.9108 - val_loss: 0.3295\n","Epoch 133/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9028 - loss: 0.3401 - val_accuracy: 0.9108 - val_loss: 0.3291\n","Epoch 134/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9007 - loss: 0.3389 - val_accuracy: 0.9110 - val_loss: 0.3285\n","Epoch 135/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9028 - loss: 0.3365 - val_accuracy: 0.9110 - val_loss: 0.3277\n","Epoch 136/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9017 - loss: 0.3405 - val_accuracy: 0.9112 - val_loss: 0.3276\n","Epoch 137/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9025 - loss: 0.3364 - val_accuracy: 0.9105 - val_loss: 0.3273\n","Epoch 138/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9022 - loss: 0.3386 - val_accuracy: 0.9128 - val_loss: 0.3261\n","Epoch 139/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9028 - loss: 0.3390 - val_accuracy: 0.9130 - val_loss: 0.3259\n","Epoch 140/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9046 - loss: 0.3341 - val_accuracy: 0.9133 - val_loss: 0.3253\n","Epoch 141/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9021 - loss: 0.3397 - val_accuracy: 0.9132 - val_loss: 0.3246\n","Epoch 142/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9028 - loss: 0.3348 - val_accuracy: 0.9125 - val_loss: 0.3247\n","Epoch 143/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9029 - loss: 0.3356 - val_accuracy: 0.9137 - val_loss: 0.3239\n","Epoch 144/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9051 - loss: 0.3301 - val_accuracy: 0.9133 - val_loss: 0.3234\n","Epoch 145/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9041 - loss: 0.3346 - val_accuracy: 0.9133 - val_loss: 0.3233\n","Epoch 146/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9014 - loss: 0.3406 - val_accuracy: 0.9140 - val_loss: 0.3225\n","Epoch 147/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9052 - loss: 0.3286 - val_accuracy: 0.9128 - val_loss: 0.3227\n","Epoch 148/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9035 - loss: 0.3338 - val_accuracy: 0.9138 - val_loss: 0.3217\n","Epoch 149/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9045 - loss: 0.3349 - val_accuracy: 0.9147 - val_loss: 0.3213\n","Epoch 150/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9048 - loss: 0.3331 - val_accuracy: 0.9143 - val_loss: 0.3208\n","Epoch 151/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9039 - loss: 0.3288 - val_accuracy: 0.9147 - val_loss: 0.3206\n","Epoch 152/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9025 - loss: 0.3350 - val_accuracy: 0.9147 - val_loss: 0.3203\n","Epoch 153/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9033 - loss: 0.3292 - val_accuracy: 0.9147 - val_loss: 0.3198\n","Epoch 154/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9045 - loss: 0.3328 - val_accuracy: 0.9140 - val_loss: 0.3197\n","Epoch 155/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9042 - loss: 0.3274 - val_accuracy: 0.9143 - val_loss: 0.3190\n","Epoch 156/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9054 - loss: 0.3280 - val_accuracy: 0.9148 - val_loss: 0.3182\n","Epoch 157/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9026 - loss: 0.3332 - val_accuracy: 0.9152 - val_loss: 0.3181\n","Epoch 158/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9054 - loss: 0.3242 - val_accuracy: 0.9152 - val_loss: 0.3180\n","Epoch 159/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9039 - loss: 0.3288 - val_accuracy: 0.9162 - val_loss: 0.3176\n","Epoch 160/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9049 - loss: 0.3286 - val_accuracy: 0.9155 - val_loss: 0.3173\n","Epoch 161/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9057 - loss: 0.3258 - val_accuracy: 0.9167 - val_loss: 0.3170\n","Epoch 162/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9078 - loss: 0.3232 - val_accuracy: 0.9162 - val_loss: 0.3163\n","Epoch 163/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9058 - loss: 0.3299 - val_accuracy: 0.9160 - val_loss: 0.3161\n","Epoch 164/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9045 - loss: 0.3287 - val_accuracy: 0.9157 - val_loss: 0.3159\n","Epoch 165/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9054 - loss: 0.3233 - val_accuracy: 0.9157 - val_loss: 0.3150\n","Epoch 166/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9067 - loss: 0.3240 - val_accuracy: 0.9165 - val_loss: 0.3148\n","Epoch 167/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9065 - loss: 0.3232 - val_accuracy: 0.9165 - val_loss: 0.3149\n","Epoch 168/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9060 - loss: 0.3210 - val_accuracy: 0.9167 - val_loss: 0.3148\n","Epoch 169/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9049 - loss: 0.3290 - val_accuracy: 0.9158 - val_loss: 0.3139\n","Epoch 170/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9074 - loss: 0.3251 - val_accuracy: 0.9158 - val_loss: 0.3135\n","Epoch 171/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9082 - loss: 0.3189 - val_accuracy: 0.9172 - val_loss: 0.3131\n","Epoch 172/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9076 - loss: 0.3223 - val_accuracy: 0.9170 - val_loss: 0.3133\n","Epoch 173/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9056 - loss: 0.3221 - val_accuracy: 0.9158 - val_loss: 0.3126\n","Epoch 174/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9064 - loss: 0.3231 - val_accuracy: 0.9163 - val_loss: 0.3124\n","Epoch 175/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9062 - loss: 0.3229 - val_accuracy: 0.9163 - val_loss: 0.3121\n","Epoch 176/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9083 - loss: 0.3166 - val_accuracy: 0.9167 - val_loss: 0.3116\n","Epoch 177/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9071 - loss: 0.3219 - val_accuracy: 0.9165 - val_loss: 0.3116\n","Epoch 178/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9061 - loss: 0.3220 - val_accuracy: 0.9168 - val_loss: 0.3114\n","Epoch 179/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9075 - loss: 0.3155 - val_accuracy: 0.9167 - val_loss: 0.3110\n","Epoch 180/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9073 - loss: 0.3213 - val_accuracy: 0.9168 - val_loss: 0.3107\n","Epoch 181/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9073 - loss: 0.3193 - val_accuracy: 0.9178 - val_loss: 0.3101\n","Epoch 182/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9101 - loss: 0.3125 - val_accuracy: 0.9168 - val_loss: 0.3104\n","Epoch 183/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9070 - loss: 0.3246 - val_accuracy: 0.9173 - val_loss: 0.3099\n","Epoch 184/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9063 - loss: 0.3255 - val_accuracy: 0.9172 - val_loss: 0.3092\n","Epoch 185/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9097 - loss: 0.3115 - val_accuracy: 0.9167 - val_loss: 0.3092\n","Epoch 186/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9098 - loss: 0.3090 - val_accuracy: 0.9172 - val_loss: 0.3091\n","Epoch 187/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9081 - loss: 0.3161 - val_accuracy: 0.9168 - val_loss: 0.3087\n","Epoch 188/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9096 - loss: 0.3164 - val_accuracy: 0.9175 - val_loss: 0.3084\n","Epoch 189/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9085 - loss: 0.3182 - val_accuracy: 0.9173 - val_loss: 0.3080\n","Epoch 190/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9074 - loss: 0.3176 - val_accuracy: 0.9172 - val_loss: 0.3076\n","Epoch 191/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9092 - loss: 0.3154 - val_accuracy: 0.9173 - val_loss: 0.3078\n","Epoch 192/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9086 - loss: 0.3183 - val_accuracy: 0.9173 - val_loss: 0.3070\n","Epoch 193/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9069 - loss: 0.3164 - val_accuracy: 0.9180 - val_loss: 0.3071\n","Epoch 194/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9108 - loss: 0.3122 - val_accuracy: 0.9178 - val_loss: 0.3065\n","Epoch 195/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9095 - loss: 0.3133 - val_accuracy: 0.9173 - val_loss: 0.3067\n","Epoch 196/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9086 - loss: 0.3206 - val_accuracy: 0.9178 - val_loss: 0.3066\n","Epoch 197/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9075 - loss: 0.3199 - val_accuracy: 0.9175 - val_loss: 0.3061\n","Epoch 198/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9094 - loss: 0.3152 - val_accuracy: 0.9180 - val_loss: 0.3058\n","Epoch 199/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9080 - loss: 0.3178 - val_accuracy: 0.9177 - val_loss: 0.3056\n","Epoch 200/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9107 - loss: 0.3106 - val_accuracy: 0.9180 - val_loss: 0.3051\n"]}]},{"cell_type":"code","source":["plt.plot(H_2l_500.history['loss'])\n","plt.plot(H_2l_500.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","scores=model_2l_500.evaluate(X_test,y_test);\n","print('Loss on test data:',scores[0]);\n","print('Accuracy on test data:',scores[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":524},"id":"nbSODB7VfnBl","executionInfo":{"status":"ok","timestamp":1760458640220,"user_tz":-180,"elapsed":1711,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"5a1d51c4-6eec-4a29-f652-c4681c517c25"},"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[1m1s\u001b[0m 3ms/step - accuracy: 0.9138 - loss: 0.3062\n","Loss on test data: 0.3137015998363495\n","Accuracy on test data: 0.9122999906539917\n"]}]},{"cell_type":"markdown","source":["Лучший результат при 100 нейронах 0.9154999852180481"],"metadata":{"id":"jSd6gmTnhDcC"}},{"cell_type":"code","source":["model_3l_100_50 = Sequential()\n","model_3l_100_50.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n","model_3l_100_50.add(Dense(units=50, activation='sigmoid'))\n","model_3l_100_50.add(Dense(units=num_classes, activation='softmax'))\n","model_3l_100_50.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_3l_100_50.summary()\n","\n","H_3l_100_50=model_3l_100_50.fit(X_train,y_train,batch_size = 512,validation_split=0.1,epochs=200)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"s16pQqb3g5n2","executionInfo":{"status":"ok","timestamp":1760459222536,"user_tz":-180,"elapsed":90899,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"674474b4-b1d1-4410-f515-abc8736ce4f0"},"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"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_7\"\u001b[0m\n"],"text/html":["
Model: \"sequential_7\"\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_13 (\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_14 (\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_15 (\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_13 (Dense)                │ (None, 100)            │        78,500 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_14 (Dense)                │ (None, 50)             │         5,050 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_15 (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":["Epoch 1/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 27ms/step - accuracy: 0.1225 - loss: 2.3840 - val_accuracy: 0.1647 - val_loss: 2.2899\n","Epoch 2/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2040 - loss: 2.2820 - val_accuracy: 0.1755 - val_loss: 2.2745\n","Epoch 3/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1817 - loss: 2.2701 - val_accuracy: 0.2053 - val_loss: 2.2633\n","Epoch 4/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.2338 - loss: 2.2596 - val_accuracy: 0.2228 - val_loss: 2.2521\n","Epoch 5/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.2481 - loss: 2.2478 - val_accuracy: 0.2772 - val_loss: 2.2404\n","Epoch 6/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3295 - loss: 2.2371 - val_accuracy: 0.2857 - val_loss: 2.2282\n","Epoch 7/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3099 - loss: 2.2242 - val_accuracy: 0.3733 - val_loss: 2.2151\n","Epoch 8/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3743 - loss: 2.2098 - val_accuracy: 0.4220 - val_loss: 2.2012\n","Epoch 9/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4274 - loss: 2.1971 - val_accuracy: 0.4130 - val_loss: 2.1863\n","Epoch 10/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4302 - loss: 2.1822 - val_accuracy: 0.4330 - val_loss: 2.1702\n","Epoch 11/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4430 - loss: 2.1650 - val_accuracy: 0.4730 - val_loss: 2.1528\n","Epoch 12/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4754 - loss: 2.1474 - val_accuracy: 0.4983 - val_loss: 2.1337\n","Epoch 13/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4887 - loss: 2.1280 - val_accuracy: 0.5120 - val_loss: 2.1132\n","Epoch 14/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5124 - loss: 2.1067 - val_accuracy: 0.5085 - val_loss: 2.0908\n","Epoch 15/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5091 - loss: 2.0847 - val_accuracy: 0.5313 - val_loss: 2.0666\n","Epoch 16/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5303 - loss: 2.0582 - val_accuracy: 0.5437 - val_loss: 2.0403\n","Epoch 17/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5445 - loss: 2.0343 - val_accuracy: 0.5515 - val_loss: 2.0119\n","Epoch 18/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5457 - loss: 2.0034 - val_accuracy: 0.5652 - val_loss: 1.9816\n","Epoch 19/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5643 - loss: 1.9727 - val_accuracy: 0.5613 - val_loss: 1.9491\n","Epoch 20/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5612 - loss: 1.9400 - val_accuracy: 0.5833 - val_loss: 1.9147\n","Epoch 21/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5766 - loss: 1.9060 - val_accuracy: 0.5898 - val_loss: 1.8787\n","Epoch 22/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5867 - loss: 1.8687 - val_accuracy: 0.5948 - val_loss: 1.8410\n","Epoch 23/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5951 - loss: 1.8305 - val_accuracy: 0.6007 - val_loss: 1.8023\n","Epoch 24/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5966 - loss: 1.7935 - val_accuracy: 0.6075 - val_loss: 1.7627\n","Epoch 25/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6043 - loss: 1.7539 - val_accuracy: 0.6210 - val_loss: 1.7226\n","Epoch 26/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6163 - loss: 1.7129 - val_accuracy: 0.6282 - val_loss: 1.6824\n","Epoch 27/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6250 - loss: 1.6724 - val_accuracy: 0.6387 - val_loss: 1.6420\n","Epoch 28/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6357 - loss: 1.6352 - val_accuracy: 0.6447 - val_loss: 1.6023\n","Epoch 29/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6412 - loss: 1.5965 - val_accuracy: 0.6525 - val_loss: 1.5631\n","Epoch 30/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6494 - loss: 1.5569 - val_accuracy: 0.6627 - val_loss: 1.5248\n","Epoch 31/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6554 - loss: 1.5205 - val_accuracy: 0.6690 - val_loss: 1.4872\n","Epoch 32/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6696 - loss: 1.4800 - val_accuracy: 0.6805 - val_loss: 1.4507\n","Epoch 33/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6782 - loss: 1.4446 - val_accuracy: 0.6875 - val_loss: 1.4152\n","Epoch 34/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6877 - loss: 1.4117 - val_accuracy: 0.6978 - val_loss: 1.3809\n","Epoch 35/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6959 - loss: 1.3778 - val_accuracy: 0.7062 - val_loss: 1.3475\n","Epoch 36/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7018 - loss: 1.3448 - val_accuracy: 0.7128 - val_loss: 1.3153\n","Epoch 37/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7101 - loss: 1.3115 - val_accuracy: 0.7215 - val_loss: 1.2839\n","Epoch 38/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7166 - loss: 1.2837 - val_accuracy: 0.7285 - val_loss: 1.2537\n","Epoch 39/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7269 - loss: 1.2527 - val_accuracy: 0.7370 - val_loss: 1.2244\n","Epoch 40/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7328 - loss: 1.2209 - val_accuracy: 0.7437 - val_loss: 1.1961\n","Epoch 41/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7366 - loss: 1.1961 - val_accuracy: 0.7507 - val_loss: 1.1688\n","Epoch 42/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7464 - loss: 1.1697 - val_accuracy: 0.7545 - val_loss: 1.1425\n","Epoch 43/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7514 - loss: 1.1425 - val_accuracy: 0.7605 - val_loss: 1.1173\n","Epoch 44/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7618 - loss: 1.1128 - val_accuracy: 0.7648 - val_loss: 1.0931\n","Epoch 45/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7598 - loss: 1.0952 - val_accuracy: 0.7692 - val_loss: 1.0696\n","Epoch 46/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7689 - loss: 1.0653 - val_accuracy: 0.7732 - val_loss: 1.0470\n","Epoch 47/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7705 - loss: 1.0463 - val_accuracy: 0.7745 - val_loss: 1.0258\n","Epoch 48/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7740 - loss: 1.0271 - val_accuracy: 0.7788 - val_loss: 1.0049\n","Epoch 49/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7806 - loss: 1.0052 - val_accuracy: 0.7813 - val_loss: 0.9852\n","Epoch 50/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7835 - loss: 0.9843 - val_accuracy: 0.7863 - val_loss: 0.9663\n","Epoch 51/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7826 - loss: 0.9693 - val_accuracy: 0.7875 - val_loss: 0.9481\n","Epoch 52/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7829 - loss: 0.9530 - val_accuracy: 0.7903 - val_loss: 0.9309\n","Epoch 53/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.7888 - loss: 0.9340 - val_accuracy: 0.7940 - val_loss: 0.9142\n","Epoch 54/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7940 - loss: 0.9123 - val_accuracy: 0.7945 - val_loss: 0.8985\n","Epoch 55/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7920 - loss: 0.9047 - val_accuracy: 0.7977 - val_loss: 0.8832\n","Epoch 56/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7983 - loss: 0.8883 - val_accuracy: 0.7990 - val_loss: 0.8688\n","Epoch 57/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7988 - loss: 0.8730 - val_accuracy: 0.8003 - val_loss: 0.8547\n","Epoch 58/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8025 - loss: 0.8583 - val_accuracy: 0.8023 - val_loss: 0.8415\n","Epoch 59/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8018 - loss: 0.8458 - val_accuracy: 0.8037 - val_loss: 0.8287\n","Epoch 60/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8041 - loss: 0.8347 - val_accuracy: 0.8053 - val_loss: 0.8163\n","Epoch 61/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8040 - loss: 0.8228 - val_accuracy: 0.8070 - val_loss: 0.8045\n","Epoch 62/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8102 - loss: 0.8035 - val_accuracy: 0.8092 - val_loss: 0.7934\n","Epoch 63/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8122 - loss: 0.7944 - val_accuracy: 0.8122 - val_loss: 0.7824\n","Epoch 64/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8109 - loss: 0.7906 - val_accuracy: 0.8133 - val_loss: 0.7719\n","Epoch 65/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8107 - loss: 0.7786 - val_accuracy: 0.8162 - val_loss: 0.7616\n","Epoch 66/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8143 - loss: 0.7691 - val_accuracy: 0.8172 - val_loss: 0.7519\n","Epoch 67/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8174 - loss: 0.7579 - val_accuracy: 0.8185 - val_loss: 0.7426\n","Epoch 68/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8195 - loss: 0.7475 - val_accuracy: 0.8208 - val_loss: 0.7334\n","Epoch 69/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8216 - loss: 0.7357 - val_accuracy: 0.8222 - val_loss: 0.7246\n","Epoch 70/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8220 - loss: 0.7294 - val_accuracy: 0.8232 - val_loss: 0.7162\n","Epoch 71/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8247 - loss: 0.7208 - val_accuracy: 0.8243 - val_loss: 0.7080\n","Epoch 72/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8258 - loss: 0.7107 - val_accuracy: 0.8255 - val_loss: 0.7000\n","Epoch 73/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8271 - loss: 0.7056 - val_accuracy: 0.8282 - val_loss: 0.6923\n","Epoch 74/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8285 - loss: 0.6943 - val_accuracy: 0.8282 - val_loss: 0.6848\n","Epoch 75/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8284 - loss: 0.6888 - val_accuracy: 0.8303 - val_loss: 0.6776\n","Epoch 76/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8320 - loss: 0.6803 - val_accuracy: 0.8323 - val_loss: 0.6703\n","Epoch 77/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8305 - loss: 0.6777 - val_accuracy: 0.8333 - val_loss: 0.6635\n","Epoch 78/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8333 - loss: 0.6713 - val_accuracy: 0.8343 - val_loss: 0.6569\n","Epoch 79/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8341 - loss: 0.6656 - val_accuracy: 0.8355 - val_loss: 0.6504\n","Epoch 80/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8376 - loss: 0.6525 - val_accuracy: 0.8370 - val_loss: 0.6441\n","Epoch 81/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8346 - loss: 0.6525 - val_accuracy: 0.8385 - val_loss: 0.6378\n","Epoch 82/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8400 - loss: 0.6436 - val_accuracy: 0.8398 - val_loss: 0.6318\n","Epoch 83/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8396 - loss: 0.6358 - val_accuracy: 0.8405 - val_loss: 0.6260\n","Epoch 84/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8388 - loss: 0.6380 - val_accuracy: 0.8417 - val_loss: 0.6202\n","Epoch 85/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8434 - loss: 0.6217 - val_accuracy: 0.8435 - val_loss: 0.6145\n","Epoch 86/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8443 - loss: 0.6170 - val_accuracy: 0.8450 - val_loss: 0.6091\n","Epoch 87/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8441 - loss: 0.6155 - val_accuracy: 0.8455 - val_loss: 0.6039\n","Epoch 88/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8447 - loss: 0.6130 - val_accuracy: 0.8477 - val_loss: 0.5986\n","Epoch 89/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8463 - loss: 0.6046 - val_accuracy: 0.8478 - val_loss: 0.5935\n","Epoch 90/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8479 - loss: 0.6017 - val_accuracy: 0.8495 - val_loss: 0.5887\n","Epoch 91/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8482 - loss: 0.5957 - val_accuracy: 0.8498 - val_loss: 0.5837\n","Epoch 92/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8498 - loss: 0.5914 - val_accuracy: 0.8518 - val_loss: 0.5790\n","Epoch 93/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8505 - loss: 0.5860 - val_accuracy: 0.8527 - val_loss: 0.5742\n","Epoch 94/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8499 - loss: 0.5863 - val_accuracy: 0.8540 - val_loss: 0.5697\n","Epoch 95/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8556 - loss: 0.5724 - val_accuracy: 0.8545 - val_loss: 0.5652\n","Epoch 96/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8526 - loss: 0.5746 - val_accuracy: 0.8557 - val_loss: 0.5609\n","Epoch 97/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8547 - loss: 0.5674 - val_accuracy: 0.8570 - val_loss: 0.5565\n","Epoch 98/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8558 - loss: 0.5660 - val_accuracy: 0.8580 - val_loss: 0.5522\n","Epoch 99/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8567 - loss: 0.5615 - val_accuracy: 0.8582 - val_loss: 0.5482\n","Epoch 100/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8598 - loss: 0.5525 - val_accuracy: 0.8593 - val_loss: 0.5441\n","Epoch 101/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8591 - loss: 0.5489 - val_accuracy: 0.8600 - val_loss: 0.5400\n","Epoch 102/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8599 - loss: 0.5456 - val_accuracy: 0.8610 - val_loss: 0.5362\n","Epoch 103/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8594 - loss: 0.5422 - val_accuracy: 0.8622 - val_loss: 0.5324\n","Epoch 104/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8622 - loss: 0.5384 - val_accuracy: 0.8635 - val_loss: 0.5287\n","Epoch 105/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8620 - loss: 0.5355 - val_accuracy: 0.8650 - val_loss: 0.5250\n","Epoch 106/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8652 - loss: 0.5289 - val_accuracy: 0.8658 - val_loss: 0.5213\n","Epoch 107/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8650 - loss: 0.5248 - val_accuracy: 0.8672 - val_loss: 0.5176\n","Epoch 108/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8665 - loss: 0.5198 - val_accuracy: 0.8680 - val_loss: 0.5141\n","Epoch 109/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8652 - loss: 0.5225 - val_accuracy: 0.8690 - val_loss: 0.5108\n","Epoch 110/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8679 - loss: 0.5097 - val_accuracy: 0.8705 - val_loss: 0.5073\n","Epoch 111/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8660 - loss: 0.5141 - val_accuracy: 0.8705 - val_loss: 0.5042\n","Epoch 112/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8701 - loss: 0.5083 - val_accuracy: 0.8712 - val_loss: 0.5009\n","Epoch 113/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8686 - loss: 0.5068 - val_accuracy: 0.8717 - val_loss: 0.4977\n","Epoch 114/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8719 - loss: 0.4982 - val_accuracy: 0.8720 - val_loss: 0.4947\n","Epoch 115/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8671 - loss: 0.5019 - val_accuracy: 0.8725 - val_loss: 0.4915\n","Epoch 116/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8697 - loss: 0.5016 - val_accuracy: 0.8735 - val_loss: 0.4885\n","Epoch 117/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8708 - loss: 0.4956 - val_accuracy: 0.8737 - val_loss: 0.4856\n","Epoch 118/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8717 - loss: 0.4918 - val_accuracy: 0.8748 - val_loss: 0.4826\n","Epoch 119/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8738 - loss: 0.4869 - val_accuracy: 0.8753 - val_loss: 0.4797\n","Epoch 120/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8741 - loss: 0.4799 - val_accuracy: 0.8760 - val_loss: 0.4769\n","Epoch 121/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8746 - loss: 0.4857 - val_accuracy: 0.8765 - val_loss: 0.4742\n","Epoch 122/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8761 - loss: 0.4739 - val_accuracy: 0.8772 - val_loss: 0.4715\n","Epoch 123/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8747 - loss: 0.4788 - val_accuracy: 0.8778 - val_loss: 0.4689\n","Epoch 124/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8747 - loss: 0.4789 - val_accuracy: 0.8795 - val_loss: 0.4663\n","Epoch 125/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8738 - loss: 0.4773 - val_accuracy: 0.8803 - val_loss: 0.4637\n","Epoch 126/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8757 - loss: 0.4716 - val_accuracy: 0.8812 - val_loss: 0.4611\n","Epoch 127/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8784 - loss: 0.4647 - val_accuracy: 0.8810 - val_loss: 0.4588\n","Epoch 128/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8776 - loss: 0.4659 - val_accuracy: 0.8813 - val_loss: 0.4563\n","Epoch 129/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8785 - loss: 0.4632 - val_accuracy: 0.8827 - val_loss: 0.4539\n","Epoch 130/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8798 - loss: 0.4573 - val_accuracy: 0.8833 - val_loss: 0.4515\n","Epoch 131/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8800 - loss: 0.4579 - val_accuracy: 0.8843 - val_loss: 0.4491\n","Epoch 132/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8827 - loss: 0.4530 - val_accuracy: 0.8850 - val_loss: 0.4470\n","Epoch 133/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8814 - loss: 0.4539 - val_accuracy: 0.8855 - val_loss: 0.4448\n","Epoch 134/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8822 - loss: 0.4484 - val_accuracy: 0.8862 - val_loss: 0.4425\n","Epoch 135/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8833 - loss: 0.4442 - val_accuracy: 0.8868 - val_loss: 0.4404\n","Epoch 136/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8818 - loss: 0.4457 - val_accuracy: 0.8882 - val_loss: 0.4382\n","Epoch 137/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8820 - loss: 0.4438 - val_accuracy: 0.8883 - val_loss: 0.4363\n","Epoch 138/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8842 - loss: 0.4408 - val_accuracy: 0.8895 - val_loss: 0.4342\n","Epoch 139/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8820 - loss: 0.4448 - val_accuracy: 0.8898 - val_loss: 0.4321\n","Epoch 140/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8829 - loss: 0.4425 - val_accuracy: 0.8903 - val_loss: 0.4301\n","Epoch 141/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8850 - loss: 0.4349 - val_accuracy: 0.8898 - val_loss: 0.4283\n","Epoch 142/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8858 - loss: 0.4319 - val_accuracy: 0.8907 - val_loss: 0.4264\n","Epoch 143/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8868 - loss: 0.4268 - val_accuracy: 0.8902 - val_loss: 0.4246\n","Epoch 144/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8876 - loss: 0.4247 - val_accuracy: 0.8912 - val_loss: 0.4227\n","Epoch 145/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8878 - loss: 0.4230 - val_accuracy: 0.8922 - val_loss: 0.4208\n","Epoch 146/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8890 - loss: 0.4225 - val_accuracy: 0.8927 - val_loss: 0.4191\n","Epoch 147/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8887 - loss: 0.4230 - val_accuracy: 0.8923 - val_loss: 0.4174\n","Epoch 148/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8895 - loss: 0.4193 - val_accuracy: 0.8927 - val_loss: 0.4156\n","Epoch 149/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8899 - loss: 0.4193 - val_accuracy: 0.8935 - val_loss: 0.4139\n","Epoch 150/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8888 - loss: 0.4168 - val_accuracy: 0.8935 - val_loss: 0.4124\n","Epoch 151/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8871 - loss: 0.4199 - val_accuracy: 0.8942 - val_loss: 0.4106\n","Epoch 152/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8899 - loss: 0.4146 - val_accuracy: 0.8943 - val_loss: 0.4091\n","Epoch 153/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8904 - loss: 0.4126 - val_accuracy: 0.8950 - val_loss: 0.4076\n","Epoch 154/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8898 - loss: 0.4126 - val_accuracy: 0.8957 - val_loss: 0.4060\n","Epoch 155/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8899 - loss: 0.4141 - val_accuracy: 0.8960 - val_loss: 0.4045\n","Epoch 156/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8902 - loss: 0.4094 - val_accuracy: 0.8962 - val_loss: 0.4030\n","Epoch 157/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8928 - loss: 0.4042 - val_accuracy: 0.8963 - val_loss: 0.4015\n","Epoch 158/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8889 - loss: 0.4087 - val_accuracy: 0.8965 - val_loss: 0.4000\n","Epoch 159/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8906 - loss: 0.4071 - val_accuracy: 0.8967 - val_loss: 0.3986\n","Epoch 160/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8915 - loss: 0.4033 - val_accuracy: 0.8973 - val_loss: 0.3972\n","Epoch 161/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8937 - loss: 0.3998 - val_accuracy: 0.8973 - val_loss: 0.3959\n","Epoch 162/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8942 - loss: 0.3943 - val_accuracy: 0.8980 - val_loss: 0.3946\n","Epoch 163/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8915 - loss: 0.3981 - val_accuracy: 0.8982 - val_loss: 0.3932\n","Epoch 164/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8917 - loss: 0.4014 - val_accuracy: 0.8982 - val_loss: 0.3919\n","Epoch 165/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8930 - loss: 0.3959 - val_accuracy: 0.8983 - val_loss: 0.3907\n","Epoch 166/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8921 - loss: 0.3997 - val_accuracy: 0.8985 - val_loss: 0.3894\n","Epoch 167/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8929 - loss: 0.3965 - val_accuracy: 0.8993 - val_loss: 0.3880\n","Epoch 168/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8937 - loss: 0.3914 - val_accuracy: 0.8998 - val_loss: 0.3868\n","Epoch 169/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8932 - loss: 0.3932 - val_accuracy: 0.9000 - val_loss: 0.3857\n","Epoch 170/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8931 - loss: 0.3923 - val_accuracy: 0.9000 - val_loss: 0.3845\n","Epoch 171/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8923 - loss: 0.3908 - val_accuracy: 0.9003 - val_loss: 0.3833\n","Epoch 172/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8954 - loss: 0.3847 - val_accuracy: 0.9012 - val_loss: 0.3821\n","Epoch 173/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8940 - loss: 0.3866 - val_accuracy: 0.9003 - val_loss: 0.3809\n","Epoch 174/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8954 - loss: 0.3784 - val_accuracy: 0.9008 - val_loss: 0.3799\n","Epoch 175/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8939 - loss: 0.3855 - val_accuracy: 0.9012 - val_loss: 0.3788\n","Epoch 176/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8956 - loss: 0.3836 - val_accuracy: 0.9013 - val_loss: 0.3777\n","Epoch 177/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8965 - loss: 0.3825 - val_accuracy: 0.9018 - val_loss: 0.3765\n","Epoch 178/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8963 - loss: 0.3791 - val_accuracy: 0.9017 - val_loss: 0.3755\n","Epoch 179/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8949 - loss: 0.3837 - val_accuracy: 0.9025 - val_loss: 0.3745\n","Epoch 180/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8978 - loss: 0.3768 - val_accuracy: 0.9027 - val_loss: 0.3735\n","Epoch 181/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8967 - loss: 0.3796 - val_accuracy: 0.9027 - val_loss: 0.3724\n","Epoch 182/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8952 - loss: 0.3792 - val_accuracy: 0.9025 - val_loss: 0.3713\n","Epoch 183/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8963 - loss: 0.3750 - val_accuracy: 0.9033 - val_loss: 0.3704\n","Epoch 184/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8981 - loss: 0.3714 - val_accuracy: 0.9032 - val_loss: 0.3696\n","Epoch 185/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8989 - loss: 0.3698 - val_accuracy: 0.9033 - val_loss: 0.3685\n","Epoch 186/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8972 - loss: 0.3728 - val_accuracy: 0.9033 - val_loss: 0.3676\n","Epoch 187/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8975 - loss: 0.3699 - val_accuracy: 0.9037 - val_loss: 0.3666\n","Epoch 188/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8984 - loss: 0.3706 - val_accuracy: 0.9038 - val_loss: 0.3656\n","Epoch 189/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8979 - loss: 0.3679 - val_accuracy: 0.9042 - val_loss: 0.3646\n","Epoch 190/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8981 - loss: 0.3677 - val_accuracy: 0.9040 - val_loss: 0.3638\n","Epoch 191/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8983 - loss: 0.3671 - val_accuracy: 0.9037 - val_loss: 0.3630\n","Epoch 192/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9010 - loss: 0.3617 - val_accuracy: 0.9043 - val_loss: 0.3620\n","Epoch 193/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8991 - loss: 0.3649 - val_accuracy: 0.9048 - val_loss: 0.3613\n","Epoch 194/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8995 - loss: 0.3633 - val_accuracy: 0.9048 - val_loss: 0.3605\n","Epoch 195/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9007 - loss: 0.3576 - val_accuracy: 0.9047 - val_loss: 0.3596\n","Epoch 196/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8997 - loss: 0.3622 - val_accuracy: 0.9050 - val_loss: 0.3588\n","Epoch 197/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9002 - loss: 0.3603 - val_accuracy: 0.9052 - val_loss: 0.3579\n","Epoch 198/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9007 - loss: 0.3570 - val_accuracy: 0.9052 - val_loss: 0.3570\n","Epoch 199/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9006 - loss: 0.3556 - val_accuracy: 0.9053 - val_loss: 0.3563\n","Epoch 200/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9020 - loss: 0.3573 - val_accuracy: 0.9055 - val_loss: 0.3554\n"]}]},{"cell_type":"code","source":["plt.plot(H_3l_100_50.history['loss'])\n","plt.plot(H_3l_100_50.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","scores=model_3l_100_50.evaluate(X_test,y_test);\n","print('Loss on test data:',scores[0]);\n","print('Accuracy on test data:',scores[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":524},"id":"E6PO-aYziOWk","executionInfo":{"status":"ok","timestamp":1760459224520,"user_tz":-180,"elapsed":1647,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"dbf1541f-6817-4154-9dee-8a572985fd0d"},"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[1m1s\u001b[0m 3ms/step - accuracy: 0.9067 - loss: 0.3484\n","Loss on test data: 0.3573007583618164\n","Accuracy on test data: 0.9021999835968018\n"]}]},{"cell_type":"code","source":["model_3l_100_100 = Sequential()\n","model_3l_100_100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n","model_3l_100_100.add(Dense(units=100, activation='sigmoid'))\n","model_3l_100_100.add(Dense(units=num_classes, activation='softmax'))\n","model_3l_100_100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_3l_100_100.summary()\n","\n","H_3l_100_100=model_3l_100_100.fit(X_train,y_train,batch_size = 512,validation_split=0.1,epochs=200)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"PsdALlCkkCAE","executionInfo":{"status":"ok","timestamp":1760538395520,"user_tz":-180,"elapsed":91053,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"20637a50-6d27-43c8-a4e8-34909096a6f8"},"execution_count":20,"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;34m100\u001b[0m) │ \u001b[38;5;34m78,500\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_4 (\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_5 (\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_3 (Dense)                 │ (None, 100)            │        78,500 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_4 (Dense)                 │ (None, 100)            │        10,100 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_5 (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":["Epoch 1/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 16ms/step - accuracy: 0.0956 - loss: 2.4088 - val_accuracy: 0.1248 - val_loss: 2.2986\n","Epoch 2/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1356 - loss: 2.2938 - val_accuracy: 0.1328 - val_loss: 2.2876\n","Epoch 3/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1345 - loss: 2.2842 - val_accuracy: 0.1958 - val_loss: 2.2788\n","Epoch 4/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1998 - loss: 2.2756 - val_accuracy: 0.1888 - val_loss: 2.2696\n","Epoch 5/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.2143 - loss: 2.2666 - val_accuracy: 0.2223 - val_loss: 2.2602\n","Epoch 6/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.2532 - loss: 2.2573 - val_accuracy: 0.2412 - val_loss: 2.2508\n","Epoch 7/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.2566 - loss: 2.2467 - val_accuracy: 0.3153 - val_loss: 2.2401\n","Epoch 8/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.3149 - loss: 2.2370 - val_accuracy: 0.3047 - val_loss: 2.2296\n","Epoch 9/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.3226 - loss: 2.2261 - val_accuracy: 0.3795 - val_loss: 2.2180\n","Epoch 10/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3815 - loss: 2.2137 - val_accuracy: 0.4060 - val_loss: 2.2053\n","Epoch 11/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4106 - loss: 2.2015 - val_accuracy: 0.4135 - val_loss: 2.1923\n","Epoch 12/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4130 - loss: 2.1879 - val_accuracy: 0.4558 - val_loss: 2.1777\n","Epoch 13/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.4583 - loss: 2.1741 - val_accuracy: 0.4448 - val_loss: 2.1620\n","Epoch 14/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4634 - loss: 2.1575 - val_accuracy: 0.4672 - val_loss: 2.1449\n","Epoch 15/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4745 - loss: 2.1393 - val_accuracy: 0.4698 - val_loss: 2.1260\n","Epoch 16/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4846 - loss: 2.1209 - val_accuracy: 0.5052 - val_loss: 2.1054\n","Epoch 17/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5100 - loss: 2.0989 - val_accuracy: 0.5200 - val_loss: 2.0831\n","Epoch 18/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5247 - loss: 2.0773 - val_accuracy: 0.5228 - val_loss: 2.0589\n","Epoch 19/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5389 - loss: 2.0539 - val_accuracy: 0.5345 - val_loss: 2.0324\n","Epoch 20/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5446 - loss: 2.0271 - val_accuracy: 0.5332 - val_loss: 2.0035\n","Epoch 21/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5470 - loss: 1.9937 - val_accuracy: 0.5487 - val_loss: 1.9725\n","Epoch 22/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5619 - loss: 1.9646 - val_accuracy: 0.5780 - val_loss: 1.9396\n","Epoch 23/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5808 - loss: 1.9312 - val_accuracy: 0.5700 - val_loss: 1.9043\n","Epoch 24/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5773 - loss: 1.8949 - val_accuracy: 0.5888 - val_loss: 1.8672\n","Epoch 25/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5928 - loss: 1.8599 - val_accuracy: 0.5952 - val_loss: 1.8283\n","Epoch 26/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5992 - loss: 1.8198 - val_accuracy: 0.5955 - val_loss: 1.7883\n","Epoch 27/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6093 - loss: 1.7795 - val_accuracy: 0.6092 - val_loss: 1.7468\n","Epoch 28/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6150 - loss: 1.7372 - val_accuracy: 0.6313 - val_loss: 1.7045\n","Epoch 29/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6347 - loss: 1.6975 - val_accuracy: 0.6277 - val_loss: 1.6621\n","Epoch 30/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6324 - loss: 1.6488 - val_accuracy: 0.6447 - val_loss: 1.6193\n","Epoch 31/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6459 - loss: 1.6086 - val_accuracy: 0.6617 - val_loss: 1.5764\n","Epoch 32/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6590 - loss: 1.5698 - val_accuracy: 0.6603 - val_loss: 1.5341\n","Epoch 33/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6596 - loss: 1.5230 - val_accuracy: 0.6767 - val_loss: 1.4926\n","Epoch 34/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6722 - loss: 1.4868 - val_accuracy: 0.6792 - val_loss: 1.4517\n","Epoch 35/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6761 - loss: 1.4449 - val_accuracy: 0.6887 - val_loss: 1.4122\n","Epoch 36/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6853 - loss: 1.4070 - val_accuracy: 0.7003 - val_loss: 1.3734\n","Epoch 37/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6949 - loss: 1.3670 - val_accuracy: 0.7037 - val_loss: 1.3363\n","Epoch 38/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7021 - loss: 1.3312 - val_accuracy: 0.7097 - val_loss: 1.3003\n","Epoch 39/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7062 - loss: 1.2970 - val_accuracy: 0.7180 - val_loss: 1.2658\n","Epoch 40/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7130 - loss: 1.2644 - val_accuracy: 0.7248 - val_loss: 1.2323\n","Epoch 41/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7208 - loss: 1.2290 - val_accuracy: 0.7338 - val_loss: 1.2002\n","Epoch 42/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7276 - loss: 1.1976 - val_accuracy: 0.7353 - val_loss: 1.1697\n","Epoch 43/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7324 - loss: 1.1669 - val_accuracy: 0.7400 - val_loss: 1.1404\n","Epoch 44/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7374 - loss: 1.1391 - val_accuracy: 0.7457 - val_loss: 1.1123\n","Epoch 45/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7401 - loss: 1.1130 - val_accuracy: 0.7498 - val_loss: 1.0856\n","Epoch 46/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7476 - loss: 1.0865 - val_accuracy: 0.7565 - val_loss: 1.0599\n","Epoch 47/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7517 - loss: 1.0605 - val_accuracy: 0.7595 - val_loss: 1.0357\n","Epoch 48/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7587 - loss: 1.0354 - val_accuracy: 0.7635 - val_loss: 1.0127\n","Epoch 49/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7616 - loss: 1.0163 - val_accuracy: 0.7722 - val_loss: 0.9899\n","Epoch 50/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7666 - loss: 0.9910 - val_accuracy: 0.7743 - val_loss: 0.9688\n","Epoch 51/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7686 - loss: 0.9709 - val_accuracy: 0.7802 - val_loss: 0.9484\n","Epoch 52/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.7719 - loss: 0.9559 - val_accuracy: 0.7815 - val_loss: 0.9294\n","Epoch 53/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7772 - loss: 0.9298 - val_accuracy: 0.7840 - val_loss: 0.9113\n","Epoch 54/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7815 - loss: 0.9163 - val_accuracy: 0.7882 - val_loss: 0.8934\n","Epoch 55/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7820 - loss: 0.8962 - val_accuracy: 0.7917 - val_loss: 0.8766\n","Epoch 56/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7885 - loss: 0.8800 - val_accuracy: 0.7908 - val_loss: 0.8609\n","Epoch 57/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7863 - loss: 0.8668 - val_accuracy: 0.7953 - val_loss: 0.8454\n","Epoch 58/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7946 - loss: 0.8518 - val_accuracy: 0.7987 - val_loss: 0.8307\n","Epoch 59/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7962 - loss: 0.8348 - val_accuracy: 0.8012 - val_loss: 0.8168\n","Epoch 60/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7966 - loss: 0.8200 - val_accuracy: 0.8032 - val_loss: 0.8035\n","Epoch 61/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7984 - loss: 0.8130 - val_accuracy: 0.8082 - val_loss: 0.7905\n","Epoch 62/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8032 - loss: 0.7945 - val_accuracy: 0.8122 - val_loss: 0.7782\n","Epoch 63/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8048 - loss: 0.7857 - val_accuracy: 0.8138 - val_loss: 0.7663\n","Epoch 64/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8084 - loss: 0.7740 - val_accuracy: 0.8152 - val_loss: 0.7550\n","Epoch 65/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8081 - loss: 0.7649 - val_accuracy: 0.8160 - val_loss: 0.7442\n","Epoch 66/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8135 - loss: 0.7472 - val_accuracy: 0.8195 - val_loss: 0.7335\n","Epoch 67/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8141 - loss: 0.7418 - val_accuracy: 0.8203 - val_loss: 0.7232\n","Epoch 68/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8169 - loss: 0.7308 - val_accuracy: 0.8238 - val_loss: 0.7134\n","Epoch 69/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8181 - loss: 0.7193 - val_accuracy: 0.8268 - val_loss: 0.7040\n","Epoch 70/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8253 - loss: 0.7041 - val_accuracy: 0.8278 - val_loss: 0.6949\n","Epoch 71/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8230 - loss: 0.7057 - val_accuracy: 0.8317 - val_loss: 0.6861\n","Epoch 72/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8245 - loss: 0.6956 - val_accuracy: 0.8342 - val_loss: 0.6777\n","Epoch 73/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8223 - loss: 0.6889 - val_accuracy: 0.8343 - val_loss: 0.6696\n","Epoch 74/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8270 - loss: 0.6785 - val_accuracy: 0.8370 - val_loss: 0.6616\n","Epoch 75/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8287 - loss: 0.6728 - val_accuracy: 0.8375 - val_loss: 0.6538\n","Epoch 76/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8317 - loss: 0.6667 - val_accuracy: 0.8392 - val_loss: 0.6467\n","Epoch 77/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8314 - loss: 0.6543 - val_accuracy: 0.8418 - val_loss: 0.6393\n","Epoch 78/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8329 - loss: 0.6478 - val_accuracy: 0.8428 - val_loss: 0.6322\n","Epoch 79/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8329 - loss: 0.6435 - val_accuracy: 0.8437 - val_loss: 0.6258\n","Epoch 80/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8359 - loss: 0.6325 - val_accuracy: 0.8445 - val_loss: 0.6190\n","Epoch 81/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8403 - loss: 0.6259 - val_accuracy: 0.8453 - val_loss: 0.6126\n","Epoch 82/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8422 - loss: 0.6203 - val_accuracy: 0.8473 - val_loss: 0.6065\n","Epoch 83/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8411 - loss: 0.6140 - val_accuracy: 0.8480 - val_loss: 0.6004\n","Epoch 84/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8444 - loss: 0.6070 - val_accuracy: 0.8492 - val_loss: 0.5946\n","Epoch 85/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8426 - loss: 0.6070 - val_accuracy: 0.8512 - val_loss: 0.5888\n","Epoch 86/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8435 - loss: 0.5976 - val_accuracy: 0.8510 - val_loss: 0.5834\n","Epoch 87/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8458 - loss: 0.5923 - val_accuracy: 0.8528 - val_loss: 0.5779\n","Epoch 88/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8484 - loss: 0.5901 - val_accuracy: 0.8532 - val_loss: 0.5728\n","Epoch 89/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8492 - loss: 0.5813 - val_accuracy: 0.8547 - val_loss: 0.5676\n","Epoch 90/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8492 - loss: 0.5762 - val_accuracy: 0.8553 - val_loss: 0.5628\n","Epoch 91/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8491 - loss: 0.5718 - val_accuracy: 0.8558 - val_loss: 0.5577\n","Epoch 92/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8513 - loss: 0.5659 - val_accuracy: 0.8560 - val_loss: 0.5533\n","Epoch 93/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8497 - loss: 0.5647 - val_accuracy: 0.8578 - val_loss: 0.5486\n","Epoch 94/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8524 - loss: 0.5583 - val_accuracy: 0.8577 - val_loss: 0.5441\n","Epoch 95/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8538 - loss: 0.5539 - val_accuracy: 0.8587 - val_loss: 0.5396\n","Epoch 96/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8537 - loss: 0.5501 - val_accuracy: 0.8593 - val_loss: 0.5354\n","Epoch 97/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8582 - loss: 0.5447 - val_accuracy: 0.8607 - val_loss: 0.5313\n","Epoch 98/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8571 - loss: 0.5371 - val_accuracy: 0.8612 - val_loss: 0.5272\n","Epoch 99/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8589 - loss: 0.5373 - val_accuracy: 0.8615 - val_loss: 0.5234\n","Epoch 100/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8588 - loss: 0.5325 - val_accuracy: 0.8620 - val_loss: 0.5193\n","Epoch 101/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8623 - loss: 0.5238 - val_accuracy: 0.8628 - val_loss: 0.5155\n","Epoch 102/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8599 - loss: 0.5266 - val_accuracy: 0.8645 - val_loss: 0.5120\n","Epoch 103/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8621 - loss: 0.5196 - val_accuracy: 0.8655 - val_loss: 0.5084\n","Epoch 104/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8648 - loss: 0.5097 - val_accuracy: 0.8670 - val_loss: 0.5047\n","Epoch 105/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8654 - loss: 0.5058 - val_accuracy: 0.8685 - val_loss: 0.5012\n","Epoch 106/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8621 - loss: 0.5145 - val_accuracy: 0.8690 - val_loss: 0.4979\n","Epoch 107/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8656 - loss: 0.5053 - val_accuracy: 0.8697 - val_loss: 0.4945\n","Epoch 108/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8638 - loss: 0.5063 - val_accuracy: 0.8703 - val_loss: 0.4912\n","Epoch 109/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8673 - loss: 0.4996 - val_accuracy: 0.8715 - val_loss: 0.4885\n","Epoch 110/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8672 - loss: 0.4985 - val_accuracy: 0.8717 - val_loss: 0.4851\n","Epoch 111/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8682 - loss: 0.4922 - val_accuracy: 0.8718 - val_loss: 0.4821\n","Epoch 112/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8672 - loss: 0.4935 - val_accuracy: 0.8730 - val_loss: 0.4792\n","Epoch 113/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8691 - loss: 0.4902 - val_accuracy: 0.8740 - val_loss: 0.4762\n","Epoch 114/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8693 - loss: 0.4863 - val_accuracy: 0.8750 - val_loss: 0.4734\n","Epoch 115/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8721 - loss: 0.4826 - val_accuracy: 0.8758 - val_loss: 0.4706\n","Epoch 116/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.4814 - val_accuracy: 0.8757 - val_loss: 0.4678\n","Epoch 117/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8734 - loss: 0.4717 - val_accuracy: 0.8762 - val_loss: 0.4652\n","Epoch 118/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8718 - loss: 0.4759 - val_accuracy: 0.8785 - val_loss: 0.4625\n","Epoch 119/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8744 - loss: 0.4688 - val_accuracy: 0.8780 - val_loss: 0.4601\n","Epoch 120/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8729 - loss: 0.4704 - val_accuracy: 0.8792 - val_loss: 0.4574\n","Epoch 121/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8723 - loss: 0.4770 - val_accuracy: 0.8800 - val_loss: 0.4550\n","Epoch 122/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8759 - loss: 0.4632 - val_accuracy: 0.8802 - val_loss: 0.4527\n","Epoch 123/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8779 - loss: 0.4600 - val_accuracy: 0.8803 - val_loss: 0.4502\n","Epoch 124/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8791 - loss: 0.4557 - val_accuracy: 0.8810 - val_loss: 0.4479\n","Epoch 125/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8766 - loss: 0.4571 - val_accuracy: 0.8825 - val_loss: 0.4456\n","Epoch 126/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8789 - loss: 0.4536 - val_accuracy: 0.8827 - val_loss: 0.4434\n","Epoch 127/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8795 - loss: 0.4524 - val_accuracy: 0.8832 - val_loss: 0.4414\n","Epoch 128/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8801 - loss: 0.4486 - val_accuracy: 0.8840 - val_loss: 0.4390\n","Epoch 129/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8773 - loss: 0.4510 - val_accuracy: 0.8842 - val_loss: 0.4370\n","Epoch 130/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8793 - loss: 0.4457 - val_accuracy: 0.8850 - val_loss: 0.4349\n","Epoch 131/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8796 - loss: 0.4424 - val_accuracy: 0.8855 - val_loss: 0.4329\n","Epoch 132/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8817 - loss: 0.4409 - val_accuracy: 0.8858 - val_loss: 0.4308\n","Epoch 133/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8817 - loss: 0.4379 - val_accuracy: 0.8872 - val_loss: 0.4289\n","Epoch 134/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8834 - loss: 0.4354 - val_accuracy: 0.8873 - val_loss: 0.4270\n","Epoch 135/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8805 - loss: 0.4358 - val_accuracy: 0.8890 - val_loss: 0.4250\n","Epoch 136/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8829 - loss: 0.4305 - val_accuracy: 0.8883 - val_loss: 0.4233\n","Epoch 137/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8826 - loss: 0.4344 - val_accuracy: 0.8895 - val_loss: 0.4213\n","Epoch 138/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8851 - loss: 0.4295 - val_accuracy: 0.8912 - val_loss: 0.4195\n","Epoch 139/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8858 - loss: 0.4229 - val_accuracy: 0.8907 - val_loss: 0.4179\n","Epoch 140/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8838 - loss: 0.4286 - val_accuracy: 0.8907 - val_loss: 0.4162\n","Epoch 141/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8843 - loss: 0.4294 - val_accuracy: 0.8910 - val_loss: 0.4144\n","Epoch 142/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8852 - loss: 0.4240 - val_accuracy: 0.8917 - val_loss: 0.4127\n","Epoch 143/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8844 - loss: 0.4251 - val_accuracy: 0.8912 - val_loss: 0.4113\n","Epoch 144/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8854 - loss: 0.4169 - val_accuracy: 0.8922 - val_loss: 0.4095\n","Epoch 145/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8856 - loss: 0.4186 - val_accuracy: 0.8923 - val_loss: 0.4078\n","Epoch 146/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8856 - loss: 0.4151 - val_accuracy: 0.8932 - val_loss: 0.4062\n","Epoch 147/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8872 - loss: 0.4143 - val_accuracy: 0.8942 - val_loss: 0.4047\n","Epoch 148/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8879 - loss: 0.4103 - val_accuracy: 0.8947 - val_loss: 0.4032\n","Epoch 149/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8878 - loss: 0.4097 - val_accuracy: 0.8950 - val_loss: 0.4019\n","Epoch 150/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8906 - loss: 0.4051 - val_accuracy: 0.8950 - val_loss: 0.4003\n","Epoch 151/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8875 - loss: 0.4079 - val_accuracy: 0.8957 - val_loss: 0.3989\n","Epoch 152/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8888 - loss: 0.4053 - val_accuracy: 0.8955 - val_loss: 0.3976\n","Epoch 153/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8888 - loss: 0.4104 - val_accuracy: 0.8957 - val_loss: 0.3961\n","Epoch 154/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8906 - loss: 0.4023 - val_accuracy: 0.8960 - val_loss: 0.3949\n","Epoch 155/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8914 - loss: 0.4037 - val_accuracy: 0.8967 - val_loss: 0.3932\n","Epoch 156/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8900 - loss: 0.4014 - val_accuracy: 0.8963 - val_loss: 0.3921\n","Epoch 157/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8885 - loss: 0.4030 - val_accuracy: 0.8968 - val_loss: 0.3908\n","Epoch 158/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8898 - loss: 0.4021 - val_accuracy: 0.8975 - val_loss: 0.3894\n","Epoch 159/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8890 - loss: 0.3990 - val_accuracy: 0.8975 - val_loss: 0.3883\n","Epoch 160/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8896 - loss: 0.3998 - val_accuracy: 0.8980 - val_loss: 0.3869\n","Epoch 161/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8917 - loss: 0.3928 - val_accuracy: 0.8988 - val_loss: 0.3856\n","Epoch 162/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8924 - loss: 0.3900 - val_accuracy: 0.8993 - val_loss: 0.3844\n","Epoch 163/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8917 - loss: 0.3922 - val_accuracy: 0.8998 - val_loss: 0.3832\n","Epoch 164/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8930 - loss: 0.3885 - val_accuracy: 0.9002 - val_loss: 0.3821\n","Epoch 165/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8925 - loss: 0.3892 - val_accuracy: 0.9008 - val_loss: 0.3809\n","Epoch 166/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8927 - loss: 0.3867 - val_accuracy: 0.9008 - val_loss: 0.3799\n","Epoch 167/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8905 - loss: 0.3910 - val_accuracy: 0.9010 - val_loss: 0.3788\n","Epoch 168/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8955 - loss: 0.3823 - val_accuracy: 0.9020 - val_loss: 0.3777\n","Epoch 169/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8920 - loss: 0.3873 - val_accuracy: 0.9017 - val_loss: 0.3764\n","Epoch 170/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8929 - loss: 0.3819 - val_accuracy: 0.9022 - val_loss: 0.3755\n","Epoch 171/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8933 - loss: 0.3852 - val_accuracy: 0.9027 - val_loss: 0.3743\n","Epoch 172/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8952 - loss: 0.3836 - val_accuracy: 0.9020 - val_loss: 0.3734\n","Epoch 173/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8943 - loss: 0.3779 - val_accuracy: 0.9030 - val_loss: 0.3724\n","Epoch 174/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8933 - loss: 0.3829 - val_accuracy: 0.9027 - val_loss: 0.3712\n","Epoch 175/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8950 - loss: 0.3776 - val_accuracy: 0.9030 - val_loss: 0.3702\n","Epoch 176/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8940 - loss: 0.3790 - val_accuracy: 0.9032 - val_loss: 0.3692\n","Epoch 177/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8981 - loss: 0.3679 - val_accuracy: 0.9033 - val_loss: 0.3683\n","Epoch 178/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8969 - loss: 0.3735 - val_accuracy: 0.9042 - val_loss: 0.3670\n","Epoch 179/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8958 - loss: 0.3714 - val_accuracy: 0.9047 - val_loss: 0.3663\n","Epoch 180/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8979 - loss: 0.3701 - val_accuracy: 0.9040 - val_loss: 0.3656\n","Epoch 181/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8949 - loss: 0.3726 - val_accuracy: 0.9048 - val_loss: 0.3644\n","Epoch 182/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8964 - loss: 0.3661 - val_accuracy: 0.9053 - val_loss: 0.3634\n","Epoch 183/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8961 - loss: 0.3682 - val_accuracy: 0.9053 - val_loss: 0.3626\n","Epoch 184/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8982 - loss: 0.3666 - val_accuracy: 0.9050 - val_loss: 0.3617\n","Epoch 185/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8977 - loss: 0.3638 - val_accuracy: 0.9053 - val_loss: 0.3609\n","Epoch 186/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8976 - loss: 0.3617 - val_accuracy: 0.9055 - val_loss: 0.3601\n","Epoch 187/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8963 - loss: 0.3677 - val_accuracy: 0.9055 - val_loss: 0.3592\n","Epoch 188/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8969 - loss: 0.3661 - val_accuracy: 0.9062 - val_loss: 0.3581\n","Epoch 189/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8967 - loss: 0.3674 - val_accuracy: 0.9058 - val_loss: 0.3573\n","Epoch 190/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8966 - loss: 0.3649 - val_accuracy: 0.9063 - val_loss: 0.3564\n","Epoch 191/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8990 - loss: 0.3626 - val_accuracy: 0.9063 - val_loss: 0.3557\n","Epoch 192/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9018 - loss: 0.3560 - val_accuracy: 0.9068 - val_loss: 0.3547\n","Epoch 193/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8989 - loss: 0.3581 - val_accuracy: 0.9063 - val_loss: 0.3540\n","Epoch 194/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8963 - loss: 0.3623 - val_accuracy: 0.9068 - val_loss: 0.3533\n","Epoch 195/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8994 - loss: 0.3601 - val_accuracy: 0.9072 - val_loss: 0.3525\n","Epoch 196/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8990 - loss: 0.3574 - val_accuracy: 0.9075 - val_loss: 0.3516\n","Epoch 197/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8992 - loss: 0.3582 - val_accuracy: 0.9075 - val_loss: 0.3508\n","Epoch 198/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8989 - loss: 0.3578 - val_accuracy: 0.9075 - val_loss: 0.3500\n","Epoch 199/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8989 - loss: 0.3584 - val_accuracy: 0.9077 - val_loss: 0.3494\n","Epoch 200/200\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9004 - loss: 0.3551 - val_accuracy: 0.9075 - val_loss: 0.3486\n"]}]},{"cell_type":"code","source":["plt.plot(H_3l_100_100.history['loss'])\n","plt.plot(H_3l_100_100.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","scores=model_3l_100_100.evaluate(X_test,y_test);\n","print('Loss on test data:',scores[0]);\n","print('Accuracy on test data:',scores[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":524},"id":"I5SwJ-jRkKMa","executionInfo":{"status":"ok","timestamp":1760538397082,"user_tz":-180,"elapsed":1540,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"6c6905cc-c2d7-44dc-9dae-83c82c0b7120"},"execution_count":21,"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[1m1s\u001b[0m 3ms/step - accuracy: 0.9062 - loss: 0.3420\n","Loss on test data: 0.35140201449394226\n","Accuracy on test data: 0.9049000144004822\n"]}]},{"cell_type":"markdown","source":["Лучший результат по итогу показала сеть с одним скрытым слоеv со 100 нейронами"],"metadata":{"id":"7iI0uJLUmhiY"}},{"cell_type":"code","source":["model_2l_100.save(filepath='best_model_2l_100_LR1.keras')"],"metadata":{"id":"ro7-VdKSmhNG","executionInfo":{"status":"ok","timestamp":1760541541921,"user_tz":-180,"elapsed":27,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["n = 70\n","result = model_2l_100.predict(X_test[n:n+1])\n","print('NN output:', result)\n","\n","plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n","plt.show()\n","print('Real mark: ', str(np.argmax(y_test[n])))\n","print('NN answer: ', str(np.argmax(result)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":537},"id":"9BfuAjEdnG9-","executionInfo":{"status":"ok","timestamp":1760465005070,"user_tz":-180,"elapsed":178,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"4fcccd9c-0d2d-4787-a071-54f88ce6ea80"},"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 33ms/step\n","NN output: [[2.1906348e-05 3.4767098e-05 9.9508625e-01 2.6498403e-04 6.9696616e-05\n"," 1.0428299e-05 4.2126467e-03 3.0855140e-06 2.8133177e-04 1.4690979e-05]]\n"]},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark: 2\n","NN answer: 2\n"]}]},{"cell_type":"code","source":["n = 888\n","result = model_2l_100.predict(X_test[n:n+1])\n","print('NN output:', result)\n","\n","plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n","plt.show()\n","print('Real mark: ', str(np.argmax(y_test[n])))\n","print('NN answer: ', str(np.argmax(result)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":537},"id":"ffvJLRAEneTQ","executionInfo":{"status":"ok","timestamp":1760463820385,"user_tz":-180,"elapsed":162,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"c16b9d85-88d2-4936-c007-9506cadc8031"},"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 29ms/step\n","NN output: [[4.7663169e-04 4.5776782e-05 2.2629092e-03 2.0417338e-04 2.9407460e-03\n"," 1.9718589e-02 9.7267509e-01 4.5765455e-06 1.4325225e-03 2.3906169e-04]]\n"]},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark: 6\n","NN answer: 6\n"]}]},{"cell_type":"code","source":["from PIL import Image\n","file_1_data = Image.open('6.png')\n","file_1_data = file_1_data.convert('L') #перевод в градации серого\n","test_1_img = np.array(file_1_data)"],"metadata":{"id":"De7O50auuv4D"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.imshow(test_1_img, cmap=plt.get_cmap('gray'))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"yI1QHJaOxV_n","executionInfo":{"status":"ok","timestamp":1760465409619,"user_tz":-180,"elapsed":85,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"b7764516-00b7-499a-a90f-057d2711a76f"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["test_1_img = test_1_img / 255\n","test_1_img = test_1_img.reshape(1, num_pixels)"],"metadata":{"id":"EhNSlmtOxeje"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["result_1 = model_2l_100.predict(test_1_img)\n","print('I think it\\'s', np.argmax(result_1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pWXwCka7xj86","executionInfo":{"status":"ok","timestamp":1760465411815,"user_tz":-180,"elapsed":83,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"fa4189c2-2dd2-4288-e8af-23cfbe8619d2"},"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 39ms/step\n","I think it's 6\n"]}]},{"cell_type":"code","source":["file_2_data = Image.open('2.png')\n","file_2_data = file_2_data.convert('L') #перевод в градации серого\n","test_2_img = np.array(file_2_data)\n","plt.imshow(test_2_img, cmap=plt.get_cmap('gray'))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"pPv_ECsZ6hHZ","executionInfo":{"status":"ok","timestamp":1760465455745,"user_tz":-180,"elapsed":102,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"1a37307e-7a78-41d4-ad1f-a72d91c06df9"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["test_2_img = test_2_img / 255\n","test_2_img = test_2_img.reshape(1, num_pixels)"],"metadata":{"id":"CmPqh-AN60w6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["result_2 = model_2l_100.predict(test_2_img)\n","print('I think it\\'s', np.argmax(result_2))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NcsSdmcG6_fu","executionInfo":{"status":"ok","timestamp":1760465535789,"user_tz":-180,"elapsed":141,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"0b2889ce-d247-431c-898a-516997c25b3f"},"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 51ms/step\n","I think it's 2\n"]}]},{"cell_type":"code","source":["file_3_data = Image.open('6_90.png')\n","file_3_data = file_3_data.convert('L') #перевод в градации серого\n","test_3_img = np.array(file_3_data)\n","plt.imshow(test_3_img, cmap=plt.get_cmap('gray'))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"d0fC7xPM7mmm","executionInfo":{"status":"ok","timestamp":1760465779394,"user_tz":-180,"elapsed":106,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"96a83bc5-5e02-4bd8-82ab-25d75aba0717"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["test_3_img = test_3_img / 255\n","test_3_img = test_3_img.reshape(1, num_pixels)\n","result_3 = model_2l_100.predict(test_3_img)\n","print('I think it\\'s', np.argmax(result_3))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OOWSXHcR7t-3","executionInfo":{"status":"ok","timestamp":1760465781505,"user_tz":-180,"elapsed":81,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"73ae1567-21a0-45e4-c200-0feb3fc166c8"},"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 35ms/step\n","I think it's 9\n"]}]},{"cell_type":"code","source":["file_4_data = Image.open('2_90.png')\n","file_4_data = file_4_data.convert('L') #перевод в градации серого\n","test_4_img = np.array(file_4_data)\n","plt.imshow(test_4_img, cmap=plt.get_cmap('gray'))\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"0pesAuMV8CaV","executionInfo":{"status":"ok","timestamp":1760465842804,"user_tz":-180,"elapsed":102,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"b588c804-62b1-467f-ebb9-e29da5b0c69a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["test_4_img = test_4_img / 255\n","test_4_img = test_4_img.reshape(1, num_pixels)\n","result_4 = model_2l_100.predict(test_4_img)\n","print('I think it\\'s', np.argmax(result_4))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L9EDZyHt8Dsp","executionInfo":{"status":"ok","timestamp":1760465845509,"user_tz":-180,"elapsed":98,"user":{"displayName":"Юнус Юсуфов","userId":"02479076271460851883"}},"outputId":"fafc2d0c-d481-4fca-9b8e-5d63a026db6d"},"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 41ms/step\n","I think it's 5\n"]}]}],"metadata":{"colab":{"provenance":[],"gpuType":"T4","mount_file_id":"1K6EFayR7IhhQ-ZP-iOuyUinHEQLjm-FU","authorship_tag":"ABX9TyMIwqVgPsiFzQqZRx7bfnpP"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/labworks/LW1/best_model_2l_100_LR1.keras b/labworks/LW1/best_model_2l_100_LR1.keras new file mode 100644 index 0000000..d820b28 Binary files /dev/null and b/labworks/LW1/best_model_2l_100_LR1.keras differ diff --git a/labworks/LW1/p2.png b/labworks/LW1/p2.png new file mode 100644 index 0000000..dba486c Binary files /dev/null and b/labworks/LW1/p2.png differ diff --git a/labworks/LW1/p6.png b/labworks/LW1/p6.png new file mode 100644 index 0000000..78b5d3b Binary files /dev/null and b/labworks/LW1/p6.png differ diff --git a/labworks/LW1/report.md b/labworks/LW1/report.md new file mode 100644 index 0000000..5c5c90d --- /dev/null +++ b/labworks/LW1/report.md @@ -0,0 +1,563 @@ +# Отчет по лабораторной работе №1 +Юсуфов Юнус,Романов Мирон , А-01-22 + +## 1. В среде GoogleColab создали блокнот. Импортировали все нужные модули для работы +``` +from tensorflow import keras +import matplotlib.pyplot as plt +import numpy as np +import sklearn +from tensorflow.keras.utils import to_categorical +from keras.models import Sequential +from keras.layers import Dense +``` + +## 2. Загрузили датасет MNIST, содержащий рукописные цифры +``` +from keras.datasets import mnist +(X_train, y_train), (X_test, y_test) = mnist.load_data() +``` + +## 3. Разбили набор данных на обучающие и тестовые выборки +``` +from sklearn.model_selection import train_test_split +``` +* объединили в один набор +``` +X = np.concatenate((X_train, X_test)) +y = np.concatenate((y_train, y_test)) +``` +* разбили по вариантам +``` +X_train, X_test, y_train, y_test = train_test_split(X, y,test_size = 10000,train_size = 60000, random_state = 35) +``` + +* Вывели размерности +``` +print('Shape of X train:', X_train.shape) +print('Shape of y train:', y_train.shape) +``` + +> Shape of X train: (60000, 28, 28) +> Shape of y train: (60000,) + +## 4. Вывод элементов обучающих данных +* Вывел 4-е элемента выборки +``` +print(y_train[0]) +plt.imshow(X_train[0], cmap=plt.get_cmap('gray')) +plt.show() + +print(y_train[1]) +plt.imshow(X_train[1], cmap=plt.get_cmap('gray')) +plt.show() + +print(y_train[2]) +plt.imshow(X_train[2], cmap=plt.get_cmap('gray')) +plt.show() + +print(y_train[3]) +plt.imshow(X_train[3], cmap=plt.get_cmap('gray')) +plt.show() +``` +>0 +![Первый элемент](0n.png) +>9 +![Второй элемент](9n.png) +>7 +![Третий элемен](7n.png) +> 5 +![Четвертый элемент](5n.png) +## 5. Предобработка данных +* развернули каждое изображение 28*28 в вектор 784 +``` +num_pixels = X_train.shape[1] * X_train.shape[2] +X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255 +X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255 + +print('Shape of transformed X train:', X_train.shape) +``` + +> Shape of transformed X train: (60000, 784) + +* перевели метки в one-hot +``` +from keras.utils import to_categorical +y_train = to_categorical(y_train) +y_test = to_categorical(y_test) + +print('Shape of transformed y train:', y_train.shape) + +num_classes = y_train.shape[1] +``` + +> Shape of transformed y train: (60000, 10) + +## 6. Реализация и обучение однослойной нейронной сети +* 6.1.Создали модель, объявиил ее объектом класса Sequential и скомпилировали. +``` +model_p = Sequential() +model_p.add(Dense(units=num_classes,input_dim=num_pixels, activation='softmax')) +model_p.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) +``` +* 6.2. Вывели архитектуру модели +``` +model_p.summary() +``` + +> Model: "sequential" +> ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +> ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +> ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +> │ dense (Dense) │ (None, 10) │ 7,850 │ +> └─────────────────────────────────┴────────────────────────┴───────────────┘ +> Total params: 7,852 (30.68 KB) +> Trainable params: 7,850 (30.66 KB) +> Non-trainable params: 0 (0.00 B) +> Optimizer params: 2 (12.00 B) + +* Обучил модель +``` +H_p = model_p.fit(X_train, y_train,batch_size = 512, validation_split=0.1, epochs=200) +``` + +* Вывели график функции ошибок +``` +plt.plot(H_p.history['loss']) +plt.plot(H_p.history['val_loss']) +plt.grid() +plt.xlabel('Epochs') +plt.ylabel('loss') +plt.legend(['train_loss','val_loss']) +plt.title('Loss by epochs') +plt.show() +``` + +![график функции ошибки](H_p.png) + +## 7. Примененили модели к тестовым данным +``` +scores=model_p.evaluate(X_test,y_test); +print('Loss on test data:',scores[0]); +print('Accuracy on test data:',scores[1]) +``` + +> accuracy: 0.9178 - loss: 0.2926 +>Loss on test data: 0.3017258942127228 +>Accuracy on test data: 0.9168999791145325 + +## 8. Добавили один скрытый слой и повторил п. 6-7 +* при 100 нейронах в скрытом слое +``` +model_2l_100 = Sequential() +model_2l_100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid')) +model_2l_100.add(Dense(units=num_classes, activation='softmax')) +model_2l_100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) +model_2l_100.summary() +``` + + +>Model: "sequential" +>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +>┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +>│ dense_34 (Dense) │ (None, 100) │ 78,500 │ +>├─────────────────────────────────┼────────────────────────┼───────────────┤ +>│ dense_35 (Dense) │ (None, 10) │ 1,010 │ +>└─────────────────────────────────┴────────────────────────┴───────────────┘ +> Total params: 79,510 (310.59 KB) +> Trainable params: 79,510 (310.59 KB) +> Non-trainable params: 0 (0.00 B) + +* Обучили модель +``` +H_2l_100=model_2l_100.fit(X_train,y_train,batch_size =512, validation_split=0.1,epochs=200) +``` + +* Вывели график функции ошибки +``` +plt.plot(H_2l_100.history['loss']) +plt.plot(H_2l_100.history['val_loss']) +plt.grid() +plt.xlabel('Epochs') +plt.ylabel('loss') +plt.legend(['train_loss','val_loss']) +plt.title('Loss by epochs') +plt.show() +``` + +![график функции ошибки](H_2l_100.png) + +``` +scores=model_2l_100.evaluate(X_test,y_test); + +print('Loss on test data:',scores[0]); +print('Accuracy on test data:',scores[1]) +``` + +> accuracy: 0.9166 - loss: 0.3003 +>Loss on test data: 0.30692651867866516 +>Accuracy on test data: 0.9154999852180481 + +* при 300 нейронах в скрытом слое +``` +model_2l_300 = Sequential() +model_2l_300.add(Dense(units=300,input_dim=num_pixels, activation='sigmoid')) +model_2l_300.add(Dense(units=num_classes, activation='softmax')) +model_2l_300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) +model_2l_300.summary() +``` + + +>Model: "sequential_5" +>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +>┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +>│ dense_9 (Dense) │ (None, 300) │ 235,500 │ +>├─────────────────────────────────┼────────────────────────┼───────────────┤ +>│ dense_10 (Dense) │ (None, 10) │ 3,010 │ +>└─────────────────────────────────┴────────────────────────┴───────────────┘ +> Total params: 238,510 (931.68 KB) +> Trainable params: 238,510 (931.68 KB) +> Non-trainable params: 0 (0.00 B) + +* Обучили модель +``` +H_2l_300=model_2l_300.fit(X_train,y_train,batch_size = 512,validation_split=0.1,epochs=200) +``` + +* Вывели график функции ошибки +``` +plt.plot(H_2l_300.history['loss']) +plt.plot(H_2l_300.history['val_loss']) +plt.grid() +plt.xlabel('Epochs') +plt.ylabel('loss') +plt.legend(['train_loss','val_loss']) +plt.title('Loss by epochs') +plt.show() +``` + +![график функции ошибки](H_2l_300.png) + +``` +scores=model_2l_300.evaluate(X_test,y_test); + +print('Loss on test data:',scores[0]); +print('Accuracy on test data:',scores[1]) +``` + +> - accuracy: 0.9155 - loss: 0.3049 +>Loss on test data: 0.3119920790195465 +>Accuracy on test data: 0.9139000177383423 + +* при 500 нейронах в скрытом слое +``` +model_2l_500 = Sequential() +model_2l_500.add(Dense(units=500,input_dim=num_pixels, activation='sigmoid')) +model_2l_500.add(Dense(units=num_classes, activation='softmax')) +model_2l_500.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) +model_2l_500.summary() +``` + +> Архитектура нейронной сети: +>Model: "sequential_6" +>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +>┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +>│ dense_24 (Dense) │ (None, 500) │ 392,500 │ +>├─────────────────────────────────┼────────────────────────┼───────────────┤ +>│ dense_25 (Dense) │ (None, 10) │ 5,010 │ +>└─────────────────────────────────┴────────────────────────┴───────────────┘ +> Total params: 397,510 (1.52 MB) +> Trainable params: 397,510 (1.52 MB) +> Non-trainable params: 0 (0.00 B) + +* Обучаем модель +``` +H_2l_500=model_2l_500.fit(X_train,y_train,batch_size = 512,validation_split=0.1,epochs=200) +``` + +* Выводим график функции ошибки +``` +plt.plot(H_2l_500.history['loss']) +plt.plot(H_2l_500.history['val_loss']) +plt.grid() +plt.xlabel('Epochs') +plt.ylabel('loss') +plt.legend(['train_loss','val_loss']) +plt.title('Loss by epochs') +plt.show() +``` + +![график функции ошибки](H_2l_500.png) + +``` +scores=model_2l_500.evaluate(X_test,y_test); + +print('Loss on test data:',scores[0]); +print('Accuracy on test data:',scores[1]) +``` + +> accuracy: 0.9138 - loss: 0.3062 +>Loss on test data: 0.3137015998363495 +>Accuracy on test data: 0.9122999906539917 + +Наилучший результат получился у ИНС с 100 нейронами в скрытом слое (0.9154999852180481). В следующих пунктах будем строить 3-х слойную сеть на основе этой конфигурации. + +## 9. Добавление второго скрытого слоя +* при 50 нейронах во втором скрытом слое +``` +model_3l_100_50 = Sequential() +model_3l_100_50.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid')) +model_3l_100_50.add(Dense(units=50, activation='sigmoid')) +model_3l_100_50.add(Dense(units=num_classes, activation='softmax')) +model_3l_100_50.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) +model_3l_100_50.summary() +``` + +>Model: "sequential_7" +>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +>┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +>│ dense_13 (Dense) │ (None, 100) │ 78,500 │ +>├─────────────────────────────────┼────────────────────────┼───────────────┤ +>│ dense_14 (Dense) │ (None, 50) │ 5,050 │ +>├─────────────────────────────────┼────────────────────────┼───────────────┤ +>│ dense_15 (Dense) │ (None, 10) │ 510 │ +>└─────────────────────────────────┴────────────────────────┴───────────────┘ +> Total params: 84,060 (328.36 KB) +> Trainable params: 84,060 (328.36 KB) +> Non-trainable params: 0 (0.00 B) + +* Обучили модель +``` +H_3l_100_50=model_3l_100_50.fit(X_train,y_train,batch_size = 512,validation_split=0.1,epochs=200) +``` + +* Вывели график функции ошибки +``` +plt.plot(H_3l_100_50.history['loss']) +plt.plot(H_3l_100_50.history['val_loss']) +plt.grid() +plt.xlabel('Epochs') +plt.ylabel('loss') +plt.legend(['train_loss','val_loss']) +plt.title('Loss by epochs') +plt.show() +``` + +![график функции ошибки](H_3l_100_50.png) + +``` +scores=model_3l_100_50.evaluate(X_test,y_test); +print('Loss on test data:',scores[0]); +print('Accuracy on test data:',scores[1]) +``` + +> - accuracy: 0.9067 - loss: 0.3484 +>Loss on test data: 0.3573007583618164 +>Accuracy on test data: 0.9021999835968018 + +* при 100 нейронах во втором скрытом слое +``` +model_3l_100_100 = Sequential() +model_3l_100_100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid')) +model_3l_100_100.add(Dense(units=100, activation='sigmoid')) +model_3l_100_100.add(Dense(units=num_classes, activation='softmax')) +model_3l_100_100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) +model_3l_100_100.summary() +``` + + +>Model: "sequential_2" +>┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ +>┃ Layer (type) ┃ Output Shape ┃ Param # ┃ +>┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ +>│ dense_3 (Dense) │ (None, 100) │ 78,500 │ +>├─────────────────────────────────┼────────────────────────┼───────────────┤ +>│ dense_4 (Dense) │ (None, 100) │ 10,100 │ +>├─────────────────────────────────┼────────────────────────┼───────────────┤ +>│ dense_5 (Dense) │ (None, 10) │ 1,010 │ +>└─────────────────────────────────┴────────────────────────┴───────────────┘ +> Total params: 89,610 (350.04 KB) +> Trainable params: 89,610 (350.04 KB) +> Non-trainable params: 0 (0.00 B) ' + +* Обучили модель +``` +H_3l_100_100=model_3l_100_100.fit(X_train,y_train,batch_size = 512,validation_split=0.1,epochs=200) +``` + +* Вывели график функции ошибки +``` +plt.plot(H_3l_100_100.history['loss']) +plt.plot(H_3l_100_100.history['val_loss']) +plt.grid() +plt.xlabel('Epochs') +plt.ylabel('loss') +plt.legend(['train_loss','val_loss']) +plt.title('Loss by epochs') +plt.show() +``` + +![график функции ошибки](H_3l_100_100.png) + +``` +scores=model_3l_100_100.evaluate(X_test,y_test); +print('Loss on test data:',scores[0]); +print('Accuracy on test data:',scores[1]) +``` + +> accuracy: 0.9062 - loss: 0.3420 +>Loss on test data: 0.35140201449394226 +>Accuracy on test data: 0.9049000144004822 + +Количество Количество нейронов в Количество нейронов во Значение метрики +скрытых слоев первом скрытом слое втором скрытом слое качества классификации +0 - - 0.9151999950408936 +1 100 - 0.9154999852180481 +1 300 - 0.9139000177383423 +1 500 - 0.9122999906539917 +2 100 50 0.9021999835968018 +2 100 100 0.9049000144004822 + +По значениям метрики качества классификации можно увидеть, что лучше всего справилась двухслойная сеть с 100 нейронами в скрытом слое. Наращивание кол-во слоев и кол-во нейронов в них не привели к желаемому росту значения метрики качества, а наоборот ухудшили ее. Вероятно связано это с тем, что для более мощных архитектур нужно увеличить обучающую выборку, чем есть сейчас у нас, иначе это приводит к переобучению сети. + +## 11. Сохранение наилучшей модели на диск +``` +model_2l_100.save(filepath='best_model_2l_100_LR1.keras') +``` + +## 12. Вывод тестовых изображений и результатов распознаваний +``` +n = 70 +result = model_2l_100.predict(X_test[n:n+1]) +print('NN output:', result) + +plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray')) +plt.show() +print('Real mark: ', str(np.argmax(y_test[n]))) +print('NN answer: ', str(np.argmax(result))) +``` + +> NN output: [[2.1906348e-05 3.4767098e-05 9.9508625e-01 2.6498403e-04 6.9696616e-05 +> 1.0428299e-05 4.2126467e-03 3.0855140e-06 2.8133177e-04 1.4690979e-05]] +![alt text](p2.png) +>Real mark: 2 +>NN answer: 2 + +``` +n = 888 +result = model_2l_100.predict(X_test[n:n+1]) +print('NN output:', result) + +plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray')) +plt.show() +print('Real mark: ', str(np.argmax(y_test[n]))) +print('NN answer: ', str(np.argmax(result))) +``` + +> NN output: [[4.7663169e-04 4.5776782e-05 2.2629092e-03 2.0417338e-04 2.9407460e-03 +> 1.9718589e-02 9.7267509e-01 4.5765455e-06 1.4325225e-03 2.3906169e-04]] +![alt text](p6.png) +>Real mark: 6 +>NN answer: 6 + +## 12. Тестирование на собственных изображениях +* загрузили 1-ое собственное изображения +``` +from PIL import Image +file_1_data = Image.open('6.png') +file_1_data = file_1_data.convert('L') #перевод в градации серого +test_1_img = np.array(file_1_data) +``` + +* вывели собственное изображения +``` +plt.imshow(test_1_img, cmap=plt.get_cmap('gray')) +plt.show()) +``` + +![1-ое изображение](6.png) + +* предобработка +``` +test_1_img = test_1_img / 255 +test_1_img = test_1_img.reshape(1, num_pixels) +``` + +* распознавание +``` +result_1 = model_2l_100.predict(test_1_img) +print('I think it\'s', np.argmax(result_1)) +``` +> I think it's 6 + +* тест на 2-ом изображении +``` +file_2_data = Image.open('2.png') +file_2_data = file_2_data.convert('L') #перевод в градации серого +test_2_img = np.array(file_2_data) +plt.imshow(test_2_img, cmap=plt.get_cmap('gray')) +plt.show() +``` + +![2-ое изображение](2.png) + +``` +test_2_img = test_2_img / 255 +test_2_img = test_2_img.reshape(1, num_pixels) + +result_2 = model.predict(test_2_img) +print('I think it\'s', np.argmax(result_2)) +``` + +> I think it's 2 + +Сеть корректно распознала обе цифры + +## 14. Тестирование на собственных повернутых изображениях +``` +file_3_data = Image.open('6_90.png') +file_3_data = file_3_data.convert('L') #перевод в градации серого +test_3_img = np.array(file_3_data) + +plt.imshow(test_3_img, cmap=plt.get_cmap('gray')) +plt.show() +``` + +![1-ое перевернутое изображение](6_90.png) + +``` +test_3_img = test_3_img / 255 +test_3_img = test_3_img.reshape(1, num_pixels) +result_3 = model_2l_100.predict(test_3_img) + +print('I think it\'s', np.argmax(result_3)) +``` + +> I think it's 9 + +``` +file_4_data = Image.open('2_90.png') +file_4_data = file_4_data.convert('L') #перевод в градации серого +test_4_img = np.array(file_4_data) + +plt.imshow(test_4_img, cmap=plt.get_cmap('gray')) +plt.show() +``` + +![2-ое перевернутое изображение](2_90.png) + +``` +test_4_img = test_4_img / 255 +test_4_img = test_4_img.reshape(1, num_pixels) +result_4 = model_2l_100.predict(test_4_img) + +print('I think it\'s', np.argmax(result_4)) +``` + +> I think it's 5 + +Сеть не смогла распознать ни одну из перевернутых изображений.Связано это с тем, что мы не использовали при обучении перевернутые изображения. \ No newline at end of file