diff --git a/labworks/LW1/IS_LR1.ipynb b/labworks/LW1/IS_LR1.ipynb
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+++ b/labworks/LW1/IS_LR1.ipynb
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+{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":28,"status":"ok","timestamp":1758484819406,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"_h4DGjN7sHZa"},"outputs":[],"source":["import os\n","os.chdir('/content/drive/MyDrive/Colab Notebooks')"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":8502,"status":"ok","timestamp":1758484829283,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"dyr70xmcsXQU"},"outputs":[],"source":["from tensorflow import keras\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import sklearn"]},{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":403,"status":"ok","timestamp":1758484830646,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"1-4Nf4M-spPi"},"outputs":[],"source":["from keras.datasets import mnist\n","(X_train, y_train), (X_test, y_test) = mnist.load_data()"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":82,"status":"ok","timestamp":1758484836270,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"Y0-OHY-GstmN"},"outputs":[],"source":["from sklearn.model_selection import train_test_split"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":64,"status":"ok","timestamp":1758484837665,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"tmaCmlbdsw01"},"outputs":[],"source":["X = np.concatenate((X_train, X_test))\n","y = np.concatenate((y_train, y_test))"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":60,"status":"ok","timestamp":1758484838516,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"7i0LOumLszkn"},"outputs":[],"source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 10000, train_size = 60000, random_state = 7)"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":31,"status":"ok","timestamp":1758484839301,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"pSHJE5y-tCaS","outputId":"3688b968-0b01-4abd-e310-682ea55d7db0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of X train: (60000, 28, 28)\n","Shape of y train: (60000,)\n"]}],"source":["print('Shape of X train:', X_train.shape)\n","print('Shape of y train:', y_train.shape)"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":251},"executionInfo":{"elapsed":479,"status":"ok","timestamp":1758484841128,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"x5Aki0dYu9k8","outputId":"c55ee86d-572a-4736-8c40-e4063613ffc6"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x300 with 4 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["fig, axes = plt.subplots(1, 4, figsize=(10, 3))\n","\n","for i in range(4):\n","    axes[i].imshow(X_train[i], cmap=plt.get_cmap('gray'))\n","    axes[i].set_title(f'Label: {y_train[i]}')\n","\n","plt.show()"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":245,"status":"ok","timestamp":1758484843822,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"CGOWqtNUz_VP","outputId":"7fabcc62-6138-43ad-f097-bd5104be8a67"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of transformed X train: (60000, 784)\n"]}],"source":["num_pixels = X_train.shape[1] * X_train.shape[2]\n","X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255\n","X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255\n","print('Shape of transformed X train:', X_train.shape)"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":63,"status":"ok","timestamp":1758484845939,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"GnlaKGw11f0w","outputId":"bc8532d6-7088-4140-80a9-0ed8ea65983e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of transformed y train: (60000, 10)\n"]}],"source":["from keras.utils import to_categorical\n","y_train = to_categorical(y_train)\n","y_test = to_categorical(y_test)\n","print('Shape of transformed y train:', y_train.shape)\n","num_classes = y_train.shape[1]"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1758484847258,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"JrUiXnVX4h7y"},"outputs":[],"source":["from keras.models import Sequential\n","from keras.layers import Dense"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":170},"executionInfo":{"elapsed":150,"status":"ok","timestamp":1758484854548,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"G8M-v6-G3378","outputId":"670fca24-77ad-4330-eae7-4f0fb9781b6d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_1\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m7,850\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}}],"source":["model_01 = Sequential()\n","model_01.add(Dense(units=num_classes,input_dim=num_pixels, activation='softmax'))\n","model_01.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01.summary()\n"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":60153,"status":"ok","timestamp":1758484917485,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"ut479Pn87OSB","outputId":"89a4d4ab-bdef-4c7f-c742-3caa5cbaff88"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 20ms/step - accuracy: 0.2095 - loss: 2.2063 - val_accuracy: 0.6653 - val_loss: 1.5891\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6920 - loss: 1.4764 - val_accuracy: 0.7613 - val_loss: 1.1972\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7728 - loss: 1.1430 - val_accuracy: 0.7987 - val_loss: 0.9912\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8014 - loss: 0.9647 - val_accuracy: 0.8177 - val_loss: 0.8671\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8211 - loss: 0.8510 - val_accuracy: 0.8283 - val_loss: 0.7843\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8318 - loss: 0.7777 - val_accuracy: 0.8390 - val_loss: 0.7248\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8396 - loss: 0.7273 - val_accuracy: 0.8442 - val_loss: 0.6802\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8461 - loss: 0.6806 - val_accuracy: 0.8497 - val_loss: 0.6450\n","Epoch 9/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8508 - loss: 0.6451 - val_accuracy: 0.8550 - val_loss: 0.6166\n","Epoch 10/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8543 - loss: 0.6222 - val_accuracy: 0.8587 - val_loss: 0.5931\n","Epoch 11/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8585 - loss: 0.5973 - val_accuracy: 0.8617 - val_loss: 0.5732\n","Epoch 12/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8626 - loss: 0.5734 - val_accuracy: 0.8660 - val_loss: 0.5562\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8643 - loss: 0.5583 - val_accuracy: 0.8682 - val_loss: 0.5415\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8670 - loss: 0.5490 - val_accuracy: 0.8715 - val_loss: 0.5286\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8682 - loss: 0.5379 - val_accuracy: 0.8733 - val_loss: 0.5171\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8707 - loss: 0.5242 - val_accuracy: 0.8753 - val_loss: 0.5068\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8712 - loss: 0.5152 - val_accuracy: 0.8767 - val_loss: 0.4976\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8739 - loss: 0.5033 - val_accuracy: 0.8768 - val_loss: 0.4892\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8754 - loss: 0.4947 - val_accuracy: 0.8783 - val_loss: 0.4816\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8790 - loss: 0.4828 - val_accuracy: 0.8792 - val_loss: 0.4745\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8787 - loss: 0.4765 - val_accuracy: 0.8812 - val_loss: 0.4681\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8805 - loss: 0.4713 - val_accuracy: 0.8823 - val_loss: 0.4622\n","Epoch 23/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8799 - loss: 0.4695 - val_accuracy: 0.8830 - val_loss: 0.4566\n","Epoch 24/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8828 - loss: 0.4591 - val_accuracy: 0.8832 - val_loss: 0.4515\n","Epoch 25/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8808 - loss: 0.4615 - val_accuracy: 0.8847 - val_loss: 0.4467\n","Epoch 26/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8831 - loss: 0.4495 - val_accuracy: 0.8862 - val_loss: 0.4422\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - accuracy: 0.8812 - loss: 0.4527 - val_accuracy: 0.8867 - val_loss: 0.4379\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8831 - loss: 0.4480 - val_accuracy: 0.8868 - val_loss: 0.4339\n","Epoch 29/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.8845 - loss: 0.4422 - val_accuracy: 0.8883 - val_loss: 0.4301\n","Epoch 30/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.8870 - loss: 0.4303 - val_accuracy: 0.8882 - val_loss: 0.4266\n","Epoch 31/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.4113\n","Epoch 36/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8893 - loss: 0.4161 - val_accuracy: 0.8927 - val_loss: 0.4086\n","Epoch 37/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8871 - loss: 0.4173 - val_accuracy: 0.8927 - val_loss: 0.4061\n","Epoch 38/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8894 - loss: 0.4145 - val_accuracy: 0.8932 - val_loss: 0.4037\n","Epoch 39/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8911 - loss: 0.4061 - val_accuracy: 0.8932 - val_loss: 0.4014\n","Epoch 40/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8889 - loss: 0.4107 - val_accuracy: 0.8938 - val_loss: 0.3992\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8902 - loss: 0.4036 - val_accuracy: 0.8937 - val_loss: 0.3970\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8920 - loss: 0.4016 - val_accuracy: 0.8948 - val_loss: 0.3949\n","Epoch 43/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8933 - loss: 0.3972 - val_accuracy: 0.8950 - val_loss: 0.3930\n","Epoch 44/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8935 - loss: 0.4007 - val_accuracy: 0.8952 - val_loss: 0.3910\n","Epoch 45/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.3824\n","Epoch 50/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8949 - loss: 0.3865 - val_accuracy: 0.8968 - val_loss: 0.3808\n","Epoch 51/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8951 - loss: 0.3862 - val_accuracy: 0.8970 - val_loss: 0.3793\n","Epoch 52/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8925 - loss: 0.3961 - val_accuracy: 0.8975 - val_loss: 0.3779\n","Epoch 53/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8960 - loss: 0.3798 - val_accuracy: 0.8978 - val_loss: 0.3765\n","Epoch 54/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8967 - loss: 0.3809 - val_accuracy: 0.8985 - val_loss: 0.3751\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8967 - loss: 0.3788 - val_accuracy: 0.8988 - val_loss: 0.3737\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8952 - loss: 0.3793 - val_accuracy: 0.8987 - val_loss: 0.3724\n","Epoch 57/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8937 - loss: 0.3808 - val_accuracy: 0.8987 - val_loss: 0.3712\n","Epoch 58/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8994 - loss: 0.3762 - val_accuracy: 0.8990 - val_loss: 0.3700\n","Epoch 59/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.3643\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9013 - loss: 0.3642 - val_accuracy: 0.9003 - val_loss: 0.3632\n","Epoch 65/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8990 - loss: 0.3690 - val_accuracy: 0.9008 - val_loss: 0.3622\n","Epoch 66/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8982 - loss: 0.3755 - val_accuracy: 0.9012 - val_loss: 0.3612\n","Epoch 67/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9025 - loss: 0.3612 - val_accuracy: 0.9015 - val_loss: 0.3602\n","Epoch 68/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8996 - loss: 0.3693 - val_accuracy: 0.9022 - val_loss: 0.3592\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9001 - loss: 0.3653 - val_accuracy: 0.9025 - val_loss: 0.3583\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8981 - loss: 0.3681 - val_accuracy: 0.9027 - val_loss: 0.3574\n","Epoch 71/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8998 - loss: 0.3668 - val_accuracy: 0.9027 - val_loss: 0.3565\n","Epoch 72/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8971 - loss: 0.3674 - val_accuracy: 0.9035 - val_loss: 0.3556\n","Epoch 73/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.3516\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8973 - loss: 0.3651 - val_accuracy: 0.9047 - val_loss: 0.3508\n","Epoch 79/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9019 - loss: 0.3582 - val_accuracy: 0.9053 - val_loss: 0.3500\n","Epoch 80/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9030 - loss: 0.3522 - val_accuracy: 0.9053 - val_loss: 0.3493\n","Epoch 81/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9038 - loss: 0.3513 - val_accuracy: 0.9053 - val_loss: 0.3485\n","Epoch 82/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9009 - loss: 0.3582 - val_accuracy: 0.9053 - val_loss: 0.3479\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9022 - loss: 0.3518 - val_accuracy: 0.9050 - val_loss: 0.3472\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9016 - loss: 0.3538 - val_accuracy: 0.9053 - val_loss: 0.3465\n","Epoch 85/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9033 - loss: 0.3485 - val_accuracy: 0.9052 - val_loss: 0.3458\n","Epoch 86/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9025 - loss: 0.3505 - val_accuracy: 0.9057 - val_loss: 0.3451\n","Epoch 87/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9023 - loss: 0.3536 - val_accuracy: 0.9058 - val_loss: 0.3445\n","Epoch 88/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9038 - loss: 0.3499 - val_accuracy: 0.9057 - val_loss: 0.3438\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.9030 - loss: 0.3479 - val_accuracy: 0.9057 - val_loss: 0.3433\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.9039 - loss: 0.3473 - val_accuracy: 0.9058 - val_loss: 0.3426\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9029 - loss: 0.3489 - val_accuracy: 0.9057 - val_loss: 0.3420\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.9023 - loss: 0.3500 - val_accuracy: 0.9057 - val_loss: 0.3414\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9031 - loss: 0.3477 - val_accuracy: 0.9060 - val_loss: 0.3408\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9052 - loss: 0.3436 - val_accuracy: 0.9065 - val_loss: 0.3403\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9057 - loss: 0.3427 - val_accuracy: 0.9068 - val_loss: 0.3397\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9030 - loss: 0.3457 - val_accuracy: 0.9068 - val_loss: 0.3392\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9044 - loss: 0.3381 - val_accuracy: 0.9068 - val_loss: 0.3386\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9027 - loss: 0.3466 - val_accuracy: 0.9072 - val_loss: 0.3381\n","Epoch 99/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9050 - loss: 0.3393 - val_accuracy: 0.9072 - val_loss: 0.3376\n","Epoch 100/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9071 - loss: 0.3384 - val_accuracy: 0.9073 - val_loss: 0.3371\n"]}],"source":["H = model_01.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size = 512\n",")"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"executionInfo":{"elapsed":351,"status":"ok","timestamp":1758484925312,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"YfhWOnxq70K3","outputId":"45e2df4d-6cfa-4986-b3b6-1ec22201aed1"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.figure(figsize=(12, 5))\n","\n","plt.subplot(1, 2, 1)\n","plt.plot(H.history['loss'], label='Обучающая ошибка')\n","plt.plot(H.history['val_loss'], label='Валидационная ошибка')\n","plt.title('Функция ошибки по эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2769,"status":"ok","timestamp":1758484931222,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"3ZJ4HMej9UBE","outputId":"bfd4d038-1ec6-4ab6-be81-7c4d1234a12f"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.9079 - loss: 0.3455\n","Loss on test data: 0.3511466085910797\n","Accuracy on test data: 0.9067999720573425\n"]}],"source":["scores=model_01.evaluate(X_test,y_test)\n","print('Loss on test data:', scores[0])\n","print('Accuracy on test data:', scores[1])"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"executionInfo":{"elapsed":169,"status":"ok","timestamp":1758484937403,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"Xy4I-UYP91_a","outputId":"814dcfdd-2ed4-4605-84a9-61e2ff4a0d26"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_2\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_2 (\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_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,010\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">78,500</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,010</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">79,510</span> (310.59 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m79,510\u001b[0m (310.59 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">79,510</span> (310.59 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}}],"source":["model_01_100 = Sequential()\n","model_01_100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))\n","model_01_100.add(Dense(units=num_classes, activation='softmax'))\n","model_01_100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01_100.summary()"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":58118,"status":"ok","timestamp":1758485009570,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"},"user_tz":-180},"id":"IfOOffSABn_u","outputId":"821867e4-5d5b-400b-e290-0910e3510b3a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 15ms/step - accuracy: 0.1144 - loss: 2.3654 - val_accuracy: 0.3688 - val_loss: 2.1933\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4250 - loss: 2.1612 - val_accuracy: 0.5125 - val_loss: 2.0693\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5401 - loss: 2.0408 - val_accuracy: 0.5837 - val_loss: 1.9510\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6047 - loss: 1.9234 - val_accuracy: 0.6332 - val_loss: 1.8370\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6477 - loss: 1.8073 - val_accuracy: 0.6737 - val_loss: 1.7282\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6824 - loss: 1.7042 - val_accuracy: 0.6938 - val_loss: 1.6254\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7016 - loss: 1.6027 - val_accuracy: 0.7125 - val_loss: 1.5291\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 1.1551\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7861 - loss: 1.1414 - val_accuracy: 0.7915 - val_loss: 1.0999\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7918 - loss: 1.0921 - val_accuracy: 0.7960 - val_loss: 1.0504\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7972 - loss: 1.0415 - val_accuracy: 0.8032 - val_loss: 1.0054\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8019 - loss: 1.0022 - val_accuracy: 0.8088 - val_loss: 0.9647\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8095 - loss: 0.9625 - val_accuracy: 0.8145 - val_loss: 0.9277\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8178 - loss: 0.9236 - val_accuracy: 0.8203 - val_loss: 0.8941\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8203 - loss: 0.8889 - val_accuracy: 0.8260 - val_loss: 0.8635\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8221 - loss: 0.8632 - val_accuracy: 0.8298 - val_loss: 0.8356\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8258 - loss: 0.8351 - val_accuracy: 0.8335 - val_loss: 0.8099\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.7085\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8455 - loss: 0.7081 - val_accuracy: 0.8498 - val_loss: 0.6924\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8491 - loss: 0.6921 - val_accuracy: 0.8515 - val_loss: 0.6773\n","Epoch 29/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8484 - loss: 0.6778 - val_accuracy: 0.8533 - val_loss: 0.6634\n","Epoch 30/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8515 - loss: 0.6648 - val_accuracy: 0.8563 - val_loss: 0.6501\n","Epoch 31/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8505 - loss: 0.6549 - val_accuracy: 0.8575 - val_loss: 0.6377\n","Epoch 32/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8557 - loss: 0.6389 - val_accuracy: 0.8587 - val_loss: 0.6261\n","Epoch 33/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8562 - loss: 0.6298 - val_accuracy: 0.8607 - val_loss: 0.6150\n","Epoch 34/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8590 - loss: 0.6148 - val_accuracy: 0.8612 - val_loss: 0.6047\n","Epoch 35/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.8597 - loss: 0.6065 - val_accuracy: 0.8630 - val_loss: 0.5949\n","Epoch 36/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.5528\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8694 - loss: 0.5554 - val_accuracy: 0.8742 - val_loss: 0.5456\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8707 - loss: 0.5489 - val_accuracy: 0.8738 - val_loss: 0.5387\n","Epoch 43/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8680 - loss: 0.5462 - val_accuracy: 0.8745 - val_loss: 0.5321\n","Epoch 44/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8710 - loss: 0.5413 - val_accuracy: 0.8758 - val_loss: 0.5257\n","Epoch 45/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8741 - loss: 0.5287 - val_accuracy: 0.8753 - val_loss: 0.5198\n","Epoch 46/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8735 - loss: 0.5253 - val_accuracy: 0.8768 - val_loss: 0.5139\n","Epoch 47/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8760 - loss: 0.5145 - val_accuracy: 0.8782 - val_loss: 0.5085\n","Epoch 48/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8737 - loss: 0.5136 - val_accuracy: 0.8783 - val_loss: 0.5031\n","Epoch 49/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8758 - loss: 0.5064 - val_accuracy: 0.8792 - val_loss: 0.4979\n","Epoch 50/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.4752\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8791 - loss: 0.4836 - val_accuracy: 0.8832 - val_loss: 0.4710\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8800 - loss: 0.4794 - val_accuracy: 0.8835 - val_loss: 0.4671\n","Epoch 57/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8793 - loss: 0.4749 - val_accuracy: 0.8840 - val_loss: 0.4633\n","Epoch 58/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8813 - loss: 0.4680 - val_accuracy: 0.8845 - val_loss: 0.4596\n","Epoch 59/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8820 - loss: 0.4681 - val_accuracy: 0.8855 - val_loss: 0.4561\n","Epoch 60/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8833 - loss: 0.4603 - val_accuracy: 0.8860 - val_loss: 0.4526\n","Epoch 61/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8844 - loss: 0.4572 - val_accuracy: 0.8870 - val_loss: 0.4493\n","Epoch 62/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8832 - loss: 0.4597 - val_accuracy: 0.8875 - val_loss: 0.4461\n","Epoch 63/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8853 - loss: 0.4462 - val_accuracy: 0.8877 - val_loss: 0.4429\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.4287\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8904 - loss: 0.4308 - val_accuracy: 0.8907 - val_loss: 0.4261\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8888 - loss: 0.4320 - val_accuracy: 0.8912 - val_loss: 0.4235\n","Epoch 71/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8885 - loss: 0.4294 - val_accuracy: 0.8918 - val_loss: 0.4210\n","Epoch 72/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8880 - loss: 0.4278 - val_accuracy: 0.8920 - val_loss: 0.4187\n","Epoch 73/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8869 - loss: 0.4253 - val_accuracy: 0.8925 - val_loss: 0.4163\n","Epoch 74/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8895 - loss: 0.4194 - val_accuracy: 0.8920 - val_loss: 0.4141\n","Epoch 75/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8882 - loss: 0.4211 - val_accuracy: 0.8930 - val_loss: 0.4118\n","Epoch 76/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8900 - loss: 0.4162 - val_accuracy: 0.8930 - val_loss: 0.4097\n","Epoch 77/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8889 - loss: 0.4184 - val_accuracy: 0.8937 - val_loss: 0.4075\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8916 - loss: 0.4116 - val_accuracy: 0.8937 - val_loss: 0.4054\n","Epoch 79/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8895 - loss: 0.4163 - val_accuracy: 0.8948 - val_loss: 0.4035\n","Epoch 80/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8917 - loss: 0.4078 - val_accuracy: 0.8950 - val_loss: 0.4015\n","Epoch 81/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8919 - loss: 0.4042 - val_accuracy: 0.8953 - val_loss: 0.3996\n","Epoch 82/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8933 - loss: 0.4036 - val_accuracy: 0.8960 - val_loss: 0.3977\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8926 - loss: 0.4025 - val_accuracy: 0.8960 - val_loss: 0.3959\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8931 - loss: 0.4006 - val_accuracy: 0.8955 - val_loss: 0.3941\n","Epoch 85/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8930 - loss: 0.3955 - val_accuracy: 0.8963 - val_loss: 0.3924\n","Epoch 86/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8921 - loss: 0.3990 - val_accuracy: 0.8967 - val_loss: 0.3907\n","Epoch 87/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8923 - loss: 0.4006 - val_accuracy: 0.8970 - val_loss: 0.3890\n","Epoch 88/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8934 - loss: 0.3962 - val_accuracy: 0.8970 - val_loss: 0.3874\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8929 - loss: 0.3946 - val_accuracy: 0.8978 - val_loss: 0.3858\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8930 - loss: 0.3918 - val_accuracy: 0.8982 - val_loss: 0.3843\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8957 - loss: 0.3865 - val_accuracy: 0.8987 - val_loss: 0.3827\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8957 - loss: 0.3871 - val_accuracy: 0.8987 - val_loss: 0.3812\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8948 - loss: 0.3862 - val_accuracy: 0.8983 - val_loss: 0.3797\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8953 - loss: 0.3856 - val_accuracy: 0.8992 - val_loss: 0.3784\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8948 - loss: 0.3884 - val_accuracy: 0.8997 - val_loss: 0.3769\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8962 - loss: 0.3833 - val_accuracy: 0.8997 - val_loss: 0.3755\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8961 - loss: 0.3814 - val_accuracy: 0.8995 - val_loss: 0.3742\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8946 - loss: 0.3817 - val_accuracy: 0.8997 - val_loss: 0.3728\n","Epoch 99/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8993 - loss: 0.3725 - val_accuracy: 0.8998 - val_loss: 0.3716\n","Epoch 100/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8956 - loss: 0.3770 - val_accuracy: 0.9008 - val_loss: 0.3703\n"]}],"source":["H_01_100 = model_01_100.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size = 512\n",")"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"tKjsQBv9CFvt","executionInfo":{"status":"ok","timestamp":1758485015648,"user_tz":-180,"elapsed":447,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"38ba88f1-c62f-420b-9d18-6de273e1d7cb"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 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\n"},"metadata":{}}],"source":["plt.figure(figsize=(12, 5))\n","\n","plt.subplot(1, 2, 1)\n","plt.plot(H_01_100.history['loss'], label='Обучающая ошибка')\n","plt.plot(H_01_100.history['val_loss'], label='Валидационная ошибка')\n","plt.title('Функция ошибки по эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EL53RhenDJwj","executionInfo":{"status":"ok","timestamp":1758485019932,"user_tz":-180,"elapsed":1457,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"56ad3805-e7af-4b2c-cb65-42bfc273817e"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8996 - loss: 0.3781\n","Loss on test data: 0.3824511766433716\n","Accuracy on test data: 0.9000999927520752\n"]}],"source":["scores_01_100=model_01_100.evaluate(X_test,y_test)\n","print('Loss on test data:', scores_01_100[0])\n","print('Accuracy on test data:', scores_01_100[1])"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"sa85CPpiD58n","executionInfo":{"status":"ok","timestamp":1758485023364,"user_tz":-180,"elapsed":202,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"c0ccea13-86da-41fe-a37b-935119b680d6"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_3\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_4 (\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_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m3,010\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">235,500</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">3,010</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m238,510\u001b[0m (931.68 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">238,510</span> (931.68 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m238,510\u001b[0m (931.68 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">238,510</span> (931.68 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}}],"source":["model_01_300 = Sequential()\n","model_01_300.add(Dense(units=300,input_dim=num_pixels, activation='sigmoid'))\n","model_01_300.add(Dense(units=num_classes, activation='softmax'))\n","model_01_300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01_300.summary()"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"vVMVL1BQEn6o","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485079791,"user_tz":-180,"elapsed":54178,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"3e249870-c81f-48e9-99ca-50aad4ad42f3"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 15ms/step - accuracy: 0.1505 - loss: 2.3045 - val_accuracy: 0.4097 - val_loss: 2.1516\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.4658 - loss: 2.1130 - val_accuracy: 0.6090 - val_loss: 2.0029\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6184 - loss: 1.9658 - val_accuracy: 0.6613 - val_loss: 1.8630\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6724 - loss: 1.8277 - val_accuracy: 0.6930 - val_loss: 1.7323\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7051 - loss: 1.6994 - val_accuracy: 0.7148 - val_loss: 1.6098\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7326 - loss: 1.5800 - val_accuracy: 0.7342 - val_loss: 1.4971\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7469 - loss: 1.4727 - val_accuracy: 0.7588 - val_loss: 1.3944\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7653 - loss: 1.3697 - val_accuracy: 0.7695 - val_loss: 1.3020\n","Epoch 9/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7743 - loss: 1.2805 - val_accuracy: 0.7807 - val_loss: 1.2195\n","Epoch 10/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7847 - loss: 1.2033 - val_accuracy: 0.7938 - val_loss: 1.1460\n","Epoch 11/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7962 - loss: 1.1317 - val_accuracy: 0.8002 - val_loss: 1.0810\n","Epoch 12/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8011 - loss: 1.0689 - val_accuracy: 0.8062 - val_loss: 1.0232\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8078 - loss: 1.0127 - val_accuracy: 0.8147 - val_loss: 0.9722\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8136 - loss: 0.9662 - val_accuracy: 0.8175 - val_loss: 0.9268\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8231 - loss: 0.9161 - val_accuracy: 0.8242 - val_loss: 0.8865\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8246 - loss: 0.8816 - val_accuracy: 0.8273 - val_loss: 0.8504\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8300 - loss: 0.8454 - val_accuracy: 0.8343 - val_loss: 0.8180\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8367 - loss: 0.8115 - val_accuracy: 0.8368 - val_loss: 0.7888\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8357 - loss: 0.7875 - val_accuracy: 0.8405 - val_loss: 0.7624\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8418 - loss: 0.7579 - val_accuracy: 0.8433 - val_loss: 0.7383\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8453 - loss: 0.7354 - val_accuracy: 0.8465 - val_loss: 0.7163\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8475 - loss: 0.7119 - val_accuracy: 0.8480 - val_loss: 0.6967\n","Epoch 23/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8481 - loss: 0.6960 - val_accuracy: 0.8522 - val_loss: 0.6779\n","Epoch 24/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8495 - loss: 0.6779 - val_accuracy: 0.8538 - val_loss: 0.6611\n","Epoch 25/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8523 - loss: 0.6607 - val_accuracy: 0.8553 - val_loss: 0.6455\n","Epoch 26/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8566 - loss: 0.6454 - val_accuracy: 0.8575 - val_loss: 0.6311\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8562 - loss: 0.6301 - val_accuracy: 0.8578 - val_loss: 0.6179\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8585 - loss: 0.6245 - val_accuracy: 0.8613 - val_loss: 0.6053\n","Epoch 29/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8580 - loss: 0.6083 - val_accuracy: 0.8628 - val_loss: 0.5934\n","Epoch 30/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8598 - loss: 0.6007 - val_accuracy: 0.8637 - val_loss: 0.5829\n","Epoch 31/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8611 - loss: 0.5887 - val_accuracy: 0.8658 - val_loss: 0.5725\n","Epoch 32/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8633 - loss: 0.5788 - val_accuracy: 0.8663 - val_loss: 0.5629\n","Epoch 33/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8652 - loss: 0.5647 - val_accuracy: 0.8685 - val_loss: 0.5539\n","Epoch 34/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8662 - loss: 0.5548 - val_accuracy: 0.8703 - val_loss: 0.5454\n","Epoch 35/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.5094\n","Epoch 40/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8704 - loss: 0.5181 - val_accuracy: 0.8760 - val_loss: 0.5032\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8729 - loss: 0.5101 - val_accuracy: 0.8780 - val_loss: 0.4974\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8767 - loss: 0.4976 - val_accuracy: 0.8792 - val_loss: 0.4918\n","Epoch 43/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8736 - loss: 0.5012 - val_accuracy: 0.8783 - val_loss: 0.4866\n","Epoch 44/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8749 - loss: 0.4963 - val_accuracy: 0.8803 - val_loss: 0.4815\n","Epoch 45/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8769 - loss: 0.4890 - val_accuracy: 0.8817 - val_loss: 0.4767\n","Epoch 46/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8757 - loss: 0.4865 - val_accuracy: 0.8827 - val_loss: 0.4719\n","Epoch 47/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8781 - loss: 0.4813 - val_accuracy: 0.8832 - val_loss: 0.4675\n","Epoch 48/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8791 - loss: 0.4729 - val_accuracy: 0.8835 - val_loss: 0.4633\n","Epoch 49/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.4446\n","Epoch 54/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8825 - loss: 0.4520 - val_accuracy: 0.8875 - val_loss: 0.4412\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8833 - loss: 0.4510 - val_accuracy: 0.8873 - val_loss: 0.4378\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8821 - loss: 0.4460 - val_accuracy: 0.8870 - val_loss: 0.4349\n","Epoch 57/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8861 - loss: 0.4369 - val_accuracy: 0.8875 - val_loss: 0.4318\n","Epoch 58/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8851 - loss: 0.4383 - val_accuracy: 0.8875 - val_loss: 0.4289\n","Epoch 59/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8832 - loss: 0.4400 - val_accuracy: 0.8875 - val_loss: 0.4261\n","Epoch 60/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8842 - loss: 0.4347 - val_accuracy: 0.8888 - val_loss: 0.4233\n","Epoch 61/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8846 - loss: 0.4308 - val_accuracy: 0.8890 - val_loss: 0.4206\n","Epoch 62/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8871 - loss: 0.4306 - val_accuracy: 0.8892 - val_loss: 0.4182\n","Epoch 63/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8843 - loss: 0.4278 - val_accuracy: 0.8898 - val_loss: 0.4158\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8876 - loss: 0.4205 - val_accuracy: 0.8905 - val_loss: 0.4132\n","Epoch 65/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8884 - loss: 0.4182 - val_accuracy: 0.8905 - val_loss: 0.4109\n","Epoch 66/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8884 - loss: 0.4183 - val_accuracy: 0.8908 - val_loss: 0.4088\n","Epoch 67/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8895 - loss: 0.4148 - val_accuracy: 0.8913 - val_loss: 0.4067\n","Epoch 68/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8890 - loss: 0.4125 - val_accuracy: 0.8922 - val_loss: 0.4044\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8903 - loss: 0.4092 - val_accuracy: 0.8920 - val_loss: 0.4025\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8879 - loss: 0.4104 - val_accuracy: 0.8923 - val_loss: 0.4004\n","Epoch 71/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8895 - loss: 0.4051 - val_accuracy: 0.8920 - val_loss: 0.3985\n","Epoch 72/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8900 - loss: 0.4037 - val_accuracy: 0.8915 - val_loss: 0.3967\n","Epoch 73/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8907 - loss: 0.4038 - val_accuracy: 0.8918 - val_loss: 0.3948\n","Epoch 74/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8895 - loss: 0.4043 - val_accuracy: 0.8938 - val_loss: 0.3929\n","Epoch 75/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8910 - loss: 0.4012 - val_accuracy: 0.8930 - val_loss: 0.3912\n","Epoch 76/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8904 - loss: 0.4014 - val_accuracy: 0.8933 - val_loss: 0.3895\n","Epoch 77/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8909 - loss: 0.3975 - val_accuracy: 0.8945 - val_loss: 0.3879\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8911 - loss: 0.3982 - val_accuracy: 0.8955 - val_loss: 0.3862\n","Epoch 79/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8925 - loss: 0.3950 - val_accuracy: 0.8947 - val_loss: 0.3847\n","Epoch 80/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8912 - loss: 0.3954 - val_accuracy: 0.8965 - val_loss: 0.3830\n","Epoch 81/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8921 - loss: 0.3918 - val_accuracy: 0.8968 - val_loss: 0.3816\n","Epoch 82/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8967 - loss: 0.3809 - val_accuracy: 0.8962 - val_loss: 0.3801\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8900 - loss: 0.3933 - val_accuracy: 0.8963 - val_loss: 0.3787\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8927 - loss: 0.3867 - val_accuracy: 0.8967 - val_loss: 0.3774\n","Epoch 85/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8946 - loss: 0.3859 - val_accuracy: 0.8963 - val_loss: 0.3760\n","Epoch 86/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8948 - loss: 0.3826 - val_accuracy: 0.8983 - val_loss: 0.3746\n","Epoch 87/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8944 - loss: 0.3795 - val_accuracy: 0.8988 - val_loss: 0.3734\n","Epoch 88/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8955 - loss: 0.3813 - val_accuracy: 0.8993 - val_loss: 0.3721\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8955 - loss: 0.3781 - val_accuracy: 0.8992 - val_loss: 0.3709\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8985 - loss: 0.3721 - val_accuracy: 0.8990 - val_loss: 0.3696\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8941 - loss: 0.3830 - val_accuracy: 0.8998 - val_loss: 0.3685\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8962 - loss: 0.3748 - val_accuracy: 0.9000 - val_loss: 0.3672\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8956 - loss: 0.3760 - val_accuracy: 0.8997 - val_loss: 0.3661\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8959 - loss: 0.3739 - val_accuracy: 0.9012 - val_loss: 0.3650\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8942 - loss: 0.3770 - val_accuracy: 0.9008 - val_loss: 0.3639\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8986 - loss: 0.3678 - val_accuracy: 0.9012 - val_loss: 0.3628\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8963 - loss: 0.3707 - val_accuracy: 0.9010 - val_loss: 0.3619\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8978 - loss: 0.3643 - val_accuracy: 0.9017 - val_loss: 0.3606\n","Epoch 99/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8947 - loss: 0.3761 - val_accuracy: 0.9018 - val_loss: 0.3596\n","Epoch 100/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8975 - loss: 0.3661 - val_accuracy: 0.9013 - val_loss: 0.3588\n"]}],"source":["H_01_300 = model_01_300.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size = 512\n",")"]},{"cell_type":"code","execution_count":23,"metadata":{"id":"6bOcTrCEFjct","colab":{"base_uri":"https://localhost:8080/","height":487},"executionInfo":{"status":"ok","timestamp":1758485094903,"user_tz":-180,"elapsed":264,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"e65cc5a7-c364-4fe7-b968-06a2b6451d92"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 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\n"},"metadata":{}}],"source":["plt.figure(figsize=(12, 5))\n","\n","plt.subplot(1, 2, 1)\n","plt.plot(H_01_300.history['loss'], label='Обучающая ошибка')\n","plt.plot(H_01_300.history['val_loss'], label='Валидационная ошибка')\n","plt.title('Функция ошибки по эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"]},{"cell_type":"code","execution_count":24,"metadata":{"id":"nYzwB9pGGEQD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485098629,"user_tz":-180,"elapsed":1478,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"ba8d8490-d9df-4985-9a50-191a11bad95f"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9016 - loss: 0.3667\n","Loss on test data: 0.37091827392578125\n","Accuracy on test data: 0.9013000130653381\n"]}],"source":["scores_01_300=model_01_300.evaluate(X_test,y_test)\n","print('Loss on test data:', scores_01_300[0])\n","print('Accuracy on test data:', scores_01_300[1])"]},{"cell_type":"code","execution_count":25,"metadata":{"id":"0kOcD8BSGslt","colab":{"base_uri":"https://localhost:8080/","height":204},"executionInfo":{"status":"ok","timestamp":1758485101393,"user_tz":-180,"elapsed":203,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"a1e2896b-aa12-449a-9655-e35be88cccfa"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_4\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_6 (\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_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m5,010\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">500</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">392,500</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">5,010</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m397,510\u001b[0m (1.52 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">397,510</span> (1.52 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m397,510\u001b[0m (1.52 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">397,510</span> (1.52 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}}],"source":["model_01_500 = Sequential()\n","model_01_500.add(Dense(units=500,input_dim=num_pixels, activation='sigmoid'))\n","model_01_500.add(Dense(units=num_classes, activation='softmax'))\n","model_01_500.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01_500.summary()"]},{"cell_type":"code","execution_count":26,"metadata":{"id":"LxLlRJdkH0zB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485160222,"user_tz":-180,"elapsed":55645,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"72cd15e1-300e-4c6f-bc80-05a30c1d243a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 22ms/step - accuracy: 0.2209 - loss: 2.2701 - val_accuracy: 0.4380 - val_loss: 2.1357\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5175 - loss: 2.0961 - val_accuracy: 0.5918 - val_loss: 1.9738\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6221 - loss: 1.9347 - val_accuracy: 0.6730 - val_loss: 1.8232\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6893 - loss: 1.7883 - val_accuracy: 0.7188 - val_loss: 1.6837\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7225 - loss: 1.6534 - val_accuracy: 0.7382 - val_loss: 1.5557\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7488 - loss: 1.5271 - val_accuracy: 0.7690 - val_loss: 1.4384\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7682 - loss: 1.4134 - val_accuracy: 0.7788 - val_loss: 1.3334\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7841 - loss: 1.3139 - val_accuracy: 0.7938 - val_loss: 1.2402\n","Epoch 9/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7940 - loss: 1.2225 - val_accuracy: 0.8033 - val_loss: 1.1581\n","Epoch 10/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8008 - loss: 1.1415 - val_accuracy: 0.8057 - val_loss: 1.0859\n","Epoch 11/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8111 - loss: 1.0746 - val_accuracy: 0.8173 - val_loss: 1.0224\n","Epoch 12/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8193 - loss: 1.0101 - val_accuracy: 0.8240 - val_loss: 0.9669\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8232 - loss: 0.9619 - val_accuracy: 0.8262 - val_loss: 0.9184\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8259 - loss: 0.9162 - val_accuracy: 0.8298 - val_loss: 0.8753\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8306 - loss: 0.8690 - val_accuracy: 0.8358 - val_loss: 0.8364\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8357 - loss: 0.8388 - val_accuracy: 0.8395 - val_loss: 0.8025\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8394 - loss: 0.7990 - val_accuracy: 0.8432 - val_loss: 0.7726\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8423 - loss: 0.7732 - val_accuracy: 0.8427 - val_loss: 0.7453\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8441 - loss: 0.7445 - val_accuracy: 0.8457 - val_loss: 0.7206\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8468 - loss: 0.7210 - val_accuracy: 0.8510 - val_loss: 0.6982\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8496 - loss: 0.7009 - val_accuracy: 0.8527 - val_loss: 0.6784\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8515 - loss: 0.6786 - val_accuracy: 0.8543 - val_loss: 0.6599\n","Epoch 23/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8555 - loss: 0.6617 - val_accuracy: 0.8558 - val_loss: 0.6436\n","Epoch 24/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8575 - loss: 0.6447 - val_accuracy: 0.8565 - val_loss: 0.6278\n","Epoch 25/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8580 - loss: 0.6338 - val_accuracy: 0.8582 - val_loss: 0.6137\n","Epoch 26/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8591 - loss: 0.6179 - val_accuracy: 0.8620 - val_loss: 0.6005\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8604 - loss: 0.6008 - val_accuracy: 0.8625 - val_loss: 0.5880\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8629 - loss: 0.5892 - val_accuracy: 0.8652 - val_loss: 0.5767\n","Epoch 29/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8631 - loss: 0.5845 - val_accuracy: 0.8677 - val_loss: 0.5660\n","Epoch 30/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8634 - loss: 0.5741 - val_accuracy: 0.8682 - val_loss: 0.5562\n","Epoch 31/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8646 - loss: 0.5652 - val_accuracy: 0.8698 - val_loss: 0.5474\n","Epoch 32/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8664 - loss: 0.5540 - val_accuracy: 0.8700 - val_loss: 0.5387\n","Epoch 33/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8687 - loss: 0.5449 - val_accuracy: 0.8733 - val_loss: 0.5306\n","Epoch 34/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8687 - loss: 0.5377 - val_accuracy: 0.8742 - val_loss: 0.5227\n","Epoch 35/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.4904\n","Epoch 40/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8738 - loss: 0.5006 - val_accuracy: 0.8798 - val_loss: 0.4847\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8751 - loss: 0.4933 - val_accuracy: 0.8802 - val_loss: 0.4794\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8800 - loss: 0.4798 - val_accuracy: 0.8810 - val_loss: 0.4745\n","Epoch 43/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8794 - loss: 0.4790 - val_accuracy: 0.8810 - val_loss: 0.4698\n","Epoch 44/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8782 - loss: 0.4756 - val_accuracy: 0.8812 - val_loss: 0.4654\n","Epoch 45/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8813 - loss: 0.4701 - val_accuracy: 0.8830 - val_loss: 0.4610\n","Epoch 46/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8797 - loss: 0.4657 - val_accuracy: 0.8832 - val_loss: 0.4566\n","Epoch 47/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8794 - loss: 0.4635 - val_accuracy: 0.8835 - val_loss: 0.4528\n","Epoch 48/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8837 - loss: 0.4544 - val_accuracy: 0.8838 - val_loss: 0.4489\n","Epoch 49/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.4322\n","Epoch 54/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8843 - loss: 0.4355 - val_accuracy: 0.8875 - val_loss: 0.4292\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8871 - loss: 0.4334 - val_accuracy: 0.8877 - val_loss: 0.4261\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8850 - loss: 0.4304 - val_accuracy: 0.8877 - val_loss: 0.4232\n","Epoch 57/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8854 - loss: 0.4304 - val_accuracy: 0.8873 - val_loss: 0.4207\n","Epoch 58/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8859 - loss: 0.4251 - val_accuracy: 0.8877 - val_loss: 0.4181\n","Epoch 59/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8887 - loss: 0.4200 - val_accuracy: 0.8882 - val_loss: 0.4154\n","Epoch 60/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8866 - loss: 0.4241 - val_accuracy: 0.8888 - val_loss: 0.4131\n","Epoch 61/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8880 - loss: 0.4179 - val_accuracy: 0.8895 - val_loss: 0.4107\n","Epoch 62/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8878 - loss: 0.4179 - val_accuracy: 0.8887 - val_loss: 0.4084\n","Epoch 63/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8863 - loss: 0.4177 - val_accuracy: 0.8905 - val_loss: 0.4061\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8871 - loss: 0.4175 - val_accuracy: 0.8910 - val_loss: 0.4041\n","Epoch 65/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8881 - loss: 0.4101 - val_accuracy: 0.8912 - val_loss: 0.4018\n","Epoch 66/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8898 - loss: 0.4067 - val_accuracy: 0.8920 - val_loss: 0.3998\n","Epoch 67/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8907 - loss: 0.4032 - val_accuracy: 0.8913 - val_loss: 0.3979\n","Epoch 68/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8908 - loss: 0.4072 - val_accuracy: 0.8918 - val_loss: 0.3960\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8889 - loss: 0.4056 - val_accuracy: 0.8925 - val_loss: 0.3941\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8921 - loss: 0.4025 - val_accuracy: 0.8925 - val_loss: 0.3924\n","Epoch 71/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8926 - loss: 0.3976 - val_accuracy: 0.8933 - val_loss: 0.3907\n","Epoch 72/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8918 - loss: 0.3939 - val_accuracy: 0.8937 - val_loss: 0.3889\n","Epoch 73/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8896 - loss: 0.3996 - val_accuracy: 0.8953 - val_loss: 0.3872\n","Epoch 74/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8920 - loss: 0.3886 - val_accuracy: 0.8938 - val_loss: 0.3857\n","Epoch 75/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8930 - loss: 0.3896 - val_accuracy: 0.8958 - val_loss: 0.3841\n","Epoch 76/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8906 - loss: 0.3949 - val_accuracy: 0.8962 - val_loss: 0.3827\n","Epoch 77/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8932 - loss: 0.3902 - val_accuracy: 0.8965 - val_loss: 0.3811\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8905 - loss: 0.3979 - val_accuracy: 0.8963 - val_loss: 0.3796\n","Epoch 79/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8953 - loss: 0.3802 - val_accuracy: 0.8973 - val_loss: 0.3781\n","Epoch 80/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8928 - loss: 0.3888 - val_accuracy: 0.8975 - val_loss: 0.3768\n","Epoch 81/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8940 - loss: 0.3820 - val_accuracy: 0.8977 - val_loss: 0.3754\n","Epoch 82/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8945 - loss: 0.3804 - val_accuracy: 0.8978 - val_loss: 0.3740\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8949 - loss: 0.3824 - val_accuracy: 0.8980 - val_loss: 0.3729\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8950 - loss: 0.3808 - val_accuracy: 0.8987 - val_loss: 0.3714\n","Epoch 85/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8945 - loss: 0.3799 - val_accuracy: 0.8990 - val_loss: 0.3702\n","Epoch 86/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8923 - loss: 0.3821 - val_accuracy: 0.8993 - val_loss: 0.3691\n","Epoch 87/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8935 - loss: 0.3821 - val_accuracy: 0.8982 - val_loss: 0.3679\n","Epoch 88/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8962 - loss: 0.3727 - val_accuracy: 0.8992 - val_loss: 0.3668\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8944 - loss: 0.3759 - val_accuracy: 0.8997 - val_loss: 0.3655\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8963 - loss: 0.3701 - val_accuracy: 0.8990 - val_loss: 0.3645\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8947 - loss: 0.3757 - val_accuracy: 0.8992 - val_loss: 0.3634\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8951 - loss: 0.3707 - val_accuracy: 0.9005 - val_loss: 0.3622\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8969 - loss: 0.3684 - val_accuracy: 0.8998 - val_loss: 0.3612\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8970 - loss: 0.3658 - val_accuracy: 0.9002 - val_loss: 0.3602\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8973 - loss: 0.3677 - val_accuracy: 0.9007 - val_loss: 0.3594\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8948 - loss: 0.3723 - val_accuracy: 0.9002 - val_loss: 0.3582\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8976 - loss: 0.3658 - val_accuracy: 0.9002 - val_loss: 0.3573\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8954 - loss: 0.3669 - val_accuracy: 0.9013 - val_loss: 0.3564\n","Epoch 99/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8960 - loss: 0.3657 - val_accuracy: 0.9018 - val_loss: 0.3555\n","Epoch 100/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8961 - loss: 0.3659 - val_accuracy: 0.9030 - val_loss: 0.3544\n"]}],"source":["H_01_500 = model_01_500.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size = 512\n",")"]},{"cell_type":"code","execution_count":27,"metadata":{"id":"s6lj_lfsIIFp","colab":{"base_uri":"https://localhost:8080/","height":487},"executionInfo":{"status":"ok","timestamp":1758485168758,"user_tz":-180,"elapsed":213,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"15e02626-365b-4f54-9ff5-15e9cf6deb71"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.figure(figsize=(12, 5))\n","\n","plt.subplot(1, 2, 1)\n","plt.plot(H_01_500.history['loss'], label='Обучающая ошибка')\n","plt.plot(H_01_500.history['val_loss'], label='Валидационная ошибка')\n","plt.title('Функция ошибки по эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"]},{"cell_type":"code","execution_count":28,"metadata":{"id":"zMa8AsG1IftR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485172643,"user_tz":-180,"elapsed":1450,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"a5af30f6-3f2e-4139-8e97-c184d89b5fd0"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.9007 - loss: 0.3635\n","Loss on test data: 0.36660370230674744\n","Accuracy on test data: 0.9010000228881836\n"]}],"source":["scores_01_500=model_01_500.evaluate(X_test,y_test)\n","print('Loss on test data:',scores_01_500[0]) #значение функции ошибки\n","print('Accuracy on test data:',scores_01_500[1]) #значение метрики качества"]},{"cell_type":"code","execution_count":29,"metadata":{"id":"KDE5Vru8J7kp","colab":{"base_uri":"https://localhost:8080/","height":238},"executionInfo":{"status":"ok","timestamp":1758485196170,"user_tz":-180,"elapsed":154,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"0632517b-6ce2-4f20-c624-1a9da573ec07"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_5\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_8 (\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_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m15,050\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;34m510\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">235,500</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">15,050</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">510</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m251,060\u001b[0m (980.70 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">251,060</span> (980.70 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m251,060\u001b[0m (980.70 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">251,060</span> (980.70 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}}],"source":["model_01_300_50 = Sequential()\n","model_01_300_50.add(Dense(units=300, input_dim=num_pixels, activation='sigmoid'))\n","model_01_300_50.add(Dense(units=50, activation='sigmoid'))\n","model_01_300_50.add(Dense(units=num_classes, activation='softmax'))\n","model_01_300_50.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01_300_50.summary()"]},{"cell_type":"code","execution_count":30,"metadata":{"id":"SC5DsfcyLMVo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485277730,"user_tz":-180,"elapsed":66773,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"ca48fe7c-bb60-4340-8655-bcda7db997dc"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 26ms/step - accuracy: 0.1039 - loss: 2.3373 - val_accuracy: 0.1325 - val_loss: 2.2951\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.1429 - loss: 2.2919 - val_accuracy: 0.1357 - val_loss: 2.2808\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.1592 - loss: 2.2776 - val_accuracy: 0.1543 - val_loss: 2.2670\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.1845 - loss: 2.2641 - val_accuracy: 0.2415 - val_loss: 2.2531\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.2785 - loss: 2.2490 - val_accuracy: 0.3228 - val_loss: 2.2384\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.3247 - loss: 2.2347 - val_accuracy: 0.3753 - val_loss: 2.2231\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.3946 - loss: 2.2195 - val_accuracy: 0.4215 - val_loss: 2.2068\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.4341 - loss: 2.2029 - val_accuracy: 0.4810 - val_loss: 2.1894\n","Epoch 9/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4842 - loss: 2.1844 - val_accuracy: 0.5177 - val_loss: 2.1708\n","Epoch 10/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5376 - loss: 2.1668 - val_accuracy: 0.5078 - val_loss: 2.1508\n","Epoch 11/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.5288 - loss: 2.1459 - val_accuracy: 0.5518 - val_loss: 2.1293\n","Epoch 12/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.5605 - loss: 2.1239 - val_accuracy: 0.5760 - val_loss: 2.1062\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.5711 - loss: 2.0998 - val_accuracy: 0.5848 - val_loss: 2.0809\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5877 - loss: 2.0751 - val_accuracy: 0.5900 - val_loss: 2.0539\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5919 - loss: 2.0465 - val_accuracy: 0.6038 - val_loss: 2.0247\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6025 - loss: 2.0177 - val_accuracy: 0.6132 - val_loss: 1.9934\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.6138 - loss: 1.9855 - val_accuracy: 0.6157 - val_loss: 1.9598\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.6178 - loss: 1.9511 - val_accuracy: 0.6273 - val_loss: 1.9242\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.6255 - loss: 1.9167 - val_accuracy: 0.6273 - val_loss: 1.8864\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.6341 - loss: 1.8757 - val_accuracy: 0.6400 - val_loss: 1.8466\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.6412 - loss: 1.8353 - val_accuracy: 0.6487 - val_loss: 1.8053\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.6493 - loss: 1.7953 - val_accuracy: 0.6492 - val_loss: 1.7625\n","Epoch 23/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6512 - loss: 1.7502 - val_accuracy: 0.6588 - val_loss: 1.7186\n","Epoch 24/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6550 - loss: 1.7108 - val_accuracy: 0.6600 - val_loss: 1.6738\n","Epoch 25/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.6633 - loss: 1.6628 - val_accuracy: 0.6707 - val_loss: 1.6288\n","Epoch 26/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.6710 - loss: 1.6181 - val_accuracy: 0.6745 - val_loss: 1.5836\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6737 - loss: 1.5751 - val_accuracy: 0.6778 - val_loss: 1.5387\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6801 - loss: 1.5288 - val_accuracy: 0.6858 - val_loss: 1.4943\n","Epoch 29/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6865 - loss: 1.4893 - val_accuracy: 0.6917 - val_loss: 1.4508\n","Epoch 30/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6966 - loss: 1.4409 - val_accuracy: 0.6985 - val_loss: 1.4084\n","Epoch 31/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6990 - loss: 1.4011 - val_accuracy: 0.7070 - val_loss: 1.3673\n","Epoch 32/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7073 - loss: 1.3601 - val_accuracy: 0.7098 - val_loss: 1.3275\n","Epoch 33/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7132 - loss: 1.3202 - val_accuracy: 0.7173 - val_loss: 1.2893\n","Epoch 34/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7199 - loss: 1.2800 - val_accuracy: 0.7238 - val_loss: 1.2527\n","Epoch 35/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7274 - loss: 1.2406 - val_accuracy: 0.7292 - val_loss: 1.2175\n","Epoch 36/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 1.0647\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7572 - loss: 1.0657 - val_accuracy: 0.7635 - val_loss: 1.0385\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7679 - loss: 1.0333 - val_accuracy: 0.7682 - val_loss: 1.0133\n","Epoch 43/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7692 - loss: 1.0102 - val_accuracy: 0.7732 - val_loss: 0.9894\n","Epoch 44/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7717 - loss: 0.9908 - val_accuracy: 0.7782 - val_loss: 0.9667\n","Epoch 45/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7800 - loss: 0.9665 - val_accuracy: 0.7797 - val_loss: 0.9451\n","Epoch 46/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7825 - loss: 0.9413 - val_accuracy: 0.7860 - val_loss: 0.9244\n","Epoch 47/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7860 - loss: 0.9243 - val_accuracy: 0.7898 - val_loss: 0.9047\n","Epoch 48/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7886 - loss: 0.9025 - val_accuracy: 0.7930 - val_loss: 0.8860\n","Epoch 49/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7955 - loss: 0.8839 - val_accuracy: 0.7958 - val_loss: 0.8681\n","Epoch 50/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.7899\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8099 - loss: 0.7926 - val_accuracy: 0.8105 - val_loss: 0.7761\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8130 - loss: 0.7780 - val_accuracy: 0.8133 - val_loss: 0.7628\n","Epoch 57/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8194 - loss: 0.7630 - val_accuracy: 0.8178 - val_loss: 0.7504\n","Epoch 58/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8202 - loss: 0.7558 - val_accuracy: 0.8190 - val_loss: 0.7382\n","Epoch 59/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8216 - loss: 0.7402 - val_accuracy: 0.8207 - val_loss: 0.7268\n","Epoch 60/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8222 - loss: 0.7317 - val_accuracy: 0.8235 - val_loss: 0.7155\n","Epoch 61/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8265 - loss: 0.7202 - val_accuracy: 0.8262 - val_loss: 0.7046\n","Epoch 62/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8288 - loss: 0.7088 - val_accuracy: 0.8293 - val_loss: 0.6943\n","Epoch 63/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8293 - loss: 0.6998 - val_accuracy: 0.8303 - val_loss: 0.6845\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8331 - loss: 0.6896 - val_accuracy: 0.8322 - val_loss: 0.6749\n","Epoch 65/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8351 - loss: 0.6782 - val_accuracy: 0.8365 - val_loss: 0.6655\n","Epoch 66/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8371 - loss: 0.6706 - val_accuracy: 0.8375 - val_loss: 0.6568\n","Epoch 67/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8417 - loss: 0.6563 - val_accuracy: 0.8400 - val_loss: 0.6481\n","Epoch 68/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8416 - loss: 0.6475 - val_accuracy: 0.8417 - val_loss: 0.6397\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8420 - loss: 0.6445 - val_accuracy: 0.8427 - val_loss: 0.6317\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8436 - loss: 0.6397 - val_accuracy: 0.8447 - val_loss: 0.6239\n","Epoch 71/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8446 - loss: 0.6303 - val_accuracy: 0.8445 - val_loss: 0.6166\n","Epoch 72/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8466 - loss: 0.6227 - val_accuracy: 0.8472 - val_loss: 0.6093\n","Epoch 73/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8463 - loss: 0.6187 - val_accuracy: 0.8490 - val_loss: 0.6023\n","Epoch 74/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8483 - loss: 0.6085 - val_accuracy: 0.8502 - val_loss: 0.5955\n","Epoch 75/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8531 - loss: 0.5998 - val_accuracy: 0.8512 - val_loss: 0.5892\n","Epoch 76/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8518 - loss: 0.5938 - val_accuracy: 0.8548 - val_loss: 0.5827\n","Epoch 77/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8518 - loss: 0.5921 - val_accuracy: 0.8552 - val_loss: 0.5765\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.5483\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8612 - loss: 0.5535 - val_accuracy: 0.8617 - val_loss: 0.5434\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8660 - loss: 0.5414 - val_accuracy: 0.8633 - val_loss: 0.5384\n","Epoch 85/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8651 - loss: 0.5382 - val_accuracy: 0.8633 - val_loss: 0.5337\n","Epoch 86/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8657 - loss: 0.5371 - val_accuracy: 0.8660 - val_loss: 0.5289\n","Epoch 87/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8664 - loss: 0.5331 - val_accuracy: 0.8662 - val_loss: 0.5244\n","Epoch 88/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8663 - loss: 0.5285 - val_accuracy: 0.8673 - val_loss: 0.5200\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8675 - loss: 0.5247 - val_accuracy: 0.8680 - val_loss: 0.5155\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8696 - loss: 0.5166 - val_accuracy: 0.8690 - val_loss: 0.5114\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8695 - loss: 0.5157 - val_accuracy: 0.8702 - val_loss: 0.5073\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8680 - loss: 0.5163 - val_accuracy: 0.8703 - val_loss: 0.5033\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8706 - loss: 0.5092 - val_accuracy: 0.8727 - val_loss: 0.4994\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8749 - loss: 0.5013 - val_accuracy: 0.8728 - val_loss: 0.4958\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8711 - loss: 0.5031 - val_accuracy: 0.8733 - val_loss: 0.4921\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8742 - loss: 0.4936 - val_accuracy: 0.8743 - val_loss: 0.4886\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8718 - loss: 0.4968 - val_accuracy: 0.8750 - val_loss: 0.4850\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8739 - loss: 0.4927 - val_accuracy: 0.8753 - val_loss: 0.4817\n","Epoch 99/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8746 - loss: 0.4847 - val_accuracy: 0.8758 - val_loss: 0.4783\n","Epoch 100/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8771 - loss: 0.4793 - val_accuracy: 0.8770 - val_loss: 0.4751\n"]}],"source":["H_01_300_50 = model_01_300_50.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size=512\n",")"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"Wh6GsTrvLo0c","colab":{"base_uri":"https://localhost:8080/","height":487},"executionInfo":{"status":"ok","timestamp":1758485311049,"user_tz":-180,"elapsed":394,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"619a23e6-9324-4d4f-d15b-1020b2c824c3"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAfYAAAHWCAYAAACFR6uKAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAhP1JREFUeJzs3XdcVfX/wPHX5QIXkL1BWYp771m598xy5iq1HKVpWpYzK81yZJqaOcuVlpp74xYnblHEgQo42evCPb8/+Hm/3UAFRC/j/Xw8zqPu53zOOe/z5uKbsz5HpSiKghBCCCEKBBNjByCEEEKI3COFXQghhChApLALIYQQBYgUdiGEEKIAkcIuhBBCFCBS2IUQQogCRAq7EEIIUYBIYRdCCCEKECnsQojXRqfT8fDhQ0JDQ40dihAFlhR2IcQrFRERwfDhw/Hx8cHc3BwXFxfKlStHTEyMsUMTokAyNXYAQmSmb9++rFu3jri4OGOHIl5CSEgIjRo1QqvV8sknn1CtWjVMTU2xtLSkSJEixg5PiAJJCrvIMx49esSKFSs4ePAgBw4cIDExkZYtW1K1alW6dOlC1apVjR2iyKYPP/wQc3Nzjh07RtGiRY0djhCFgkpeAiPygtWrVzNgwADi4uLw9fVFq9USERFB1apVOXv2LFqtlj59+vDrr79ibm5u7HBFFpw6dYoaNWqwc+dOmjVrZuxwhCg05Bq7MLrDhw/z3nvv4e7uzuHDh7lx4wZNmzbFwsKCEydOcO/ePbp3786yZcv49NNPAVAUBV9fXzp06JBhfUlJSdjZ2fHhhx8CEBAQgEqlYt26dRn6Wltb07dvX/3npUuXolKpuHnzpr7t4sWLODg40LZtW1JTUw36nTx50mB9Dx8+RKVSMXHiRIP2zNp++OEHVCoVDRs2NGgPDQ3l3XffxdPTExMTE1QqFSqVigoVKjwvjQCkpqYyefJkSpQogUajwdfXly+//JLk5GSDfr6+vrRt29agbejQoahUKoO23bt3o1Kp2Lx5s76tYcOGGWI+ceKEPs6njh07hoWFBdevX6d8+fJoNBrc3d358MMPefz4scHyma3z22+/xcTEhJUrV2Z728/SsGFDfd/Mpn//3AF++eUXfeyenp4MGTKEqKio524jNjaW/v374+Pjg0ajoVixYnz00UdERkYa9Hv6HXrW9N/vy5kzZ2jVqhW2trZYW1vTpEkTjh07pp+vKAqNGjXCxcWF+/fv69tTUlKoWLEiJUqUID4+HoBbt24xePBgSpcujaWlJU5OTrz77rsZ9v9pjObm5jx48MBg3tGjR/Wx/vf3QBiXnIoXRjd16lR0Oh2rV6+mevXqGeY7OzuzfPlyLl26xIIFC5gwYQKurq689957TJs2jcePH+Po6Kjvv2nTJmJiYnjvvfdeOrawsDBatmxJmTJl+PPPPzE1zZ1fmaioKKZMmZKhPS0tjfbt23Pr1i2GDx9OqVKlUKlUfPvtt1lab//+/Vm2bBnvvPMOI0eOJDAwkClTpnD58mXWr1+fK7Fn5vPPP8/Q9ujRI5KSkhg0aBCNGzfmo48+4vr168ydO5fAwEACAwPRaDSZrm/JkiWMHTuW6dOn06NHj2xv+3mKFSuWIfdbt25l1apVBm0TJ05k0qRJNG3alEGDBhEcHMy8efM4ceIEhw8fxszMLNP1P378mHPnztG/f3/c3d0JCQlh/vz5bN++nePHj+Pq6mrQ/+uvv8bPz0//OS4ujkGDBhn0uXjxIm+88Qa2traMHj0aMzMzFixYQMOGDdm/fz+1a9dGpVKxePFiKlWqxEcffcTff/8NwIQJE7h48SIBAQH6+xpOnDjBkSNH6NatG8WKFePmzZvMmzePhg0bcunSJaysrAy2r1ar+eOPP/R/WEP6z8jCwoKkpKSspF28TooQRubo6Kj4+PgYtPXp00cpUqSIQdu4ceMUQNm0aZOiKIoSHBysAMq8efMM+rVv317x9fVVdDqdoiiKsm/fPgVQ1q5dm2HbRYoUUfr06aP/vGTJEgVQbty4oTx+/FgpV66cUrp0aeXhw4cGyz3td+LECYP2Bw8eKIAyYcIEg/b/to0ePVpxdXVVqlevrrz11lv69qf7NGXKFIPl33rrLaV8+fIZ4v+3oKAgBVD69+9v0P7ZZ58pgLJ37159m4+Pj9KmTRuDfkOGDFH++0/Crl27DHL+NJZ/x7x161YFUFq2bGmw/IQJExRAadKkiZKamqpvf5q7n3/+OdN1btmyRTE1NVVGjhyZYR+zuu1neVYef/jhB/3PXVEU5f79+4q5ubnSvHlzJS0tTd9vzpw5CqAsXrz4hdv6twsXLigajUZ5//339W3Z+Q517NhRMTc3V65fv65vu3fvnmJjY6O8+eabBssvWLBAAZQ//vhDOXbsmKJWq5Xhw4cb9ElISMgQ49GjRxVAWb58eYYYu3fvrlSsWFHfHh8fr9ja2io9evTIdB+EccmpeGF0sbGxGY5iMuPm5gagf0yqVKlS1K5dmxUrVuj7PH78mG3bttGzZ88Mp2ZjY2N5+PChwfQsSUlJtG/fngcPHrB9+3acnJxysmuZunv3Lj///DPjxo3D2to6Q4xAjra3detWAEaMGGHQPnLkSAC2bNmSk3CfS1EUxowZQ+fOnaldu3amfUaMGIFardZ/7tWrF25ubpnGc/z4cbp06ULnzp354YcfXnrbObV7925SUlIYPnw4Jib/+2dywIAB2NravjCXT5/Xfzq5ubnRunVr/vrrL3Q6XbZiSUtLY+fOnXTs2JHixYvr2z08POjRoweHDh0yeHRw4MCBtGjRgo8//phevXpRokQJvvvuO4N1Wlpa6v9fq9Xy6NEj/P39sbe35/Tp0xli6NWrF1euXNGfcv/rr7+ws7OjSZMm2doX8XpIYRdG5+npyfXr11/YLyQkBMDg7urevXtz+PBhbt26BcDatWvRarX06tUrw/Lvv/8+Li4uBtPTa47/1a9fPw4dOkRsbKz+unpumTBhAp6envp7AP6tdOnSODg4MH36dA4fPsyDBw94+PAhWq32heu9desWJiYm+Pv7G7S7u7tjb2+vz1FuWrFiBRcvXsxQOAD9H1ZlypQxaFer1ZQsWTLD9dy7d+/Spk0b4uPjefTo0QuvmT9v2y/raa5Kly5t0G5ubk7x4sVfmMvbt29n+K6tX7+e6Ojo5/5BmZkHDx6QkJCQIRaAsmXLotPpCAsLM2hftGgRCQkJXLt2jaVLlxoUcoDExETGjx+Pl5cXGo0GZ2dnXFxciIqKIjo6OsN2XFxcaNOmDYsXLwZg8eLF9OnTx+CPHpF3yE9FGF3btm15/PgxixYtemafyMhIli1bhouLC3Xq1NG3d+vWDTMzM/1R+x9//EGNGjUy/Udw/Pjx7Nq1y2CysLDIdHunT59m48aNuLi4MHDgwJfcw/+5fPkyS5cu5Ztvvsn0Gq21tTVr1qwhPj6eBg0a4OrqiouLC0eOHMnyNrJyE1luSElJYdy4cXzwwQeUKlUqw/z/FpMXCQkJwdvbm99//53du3ezbNmyHG/b2Nzd3TN817p37/7ath8QEKC/YfL8+fMZ5n/88cd8++23dOnShT///JOdO3eya9cunJycnnlG4f3332fVqlVcvnyZAwcOGNx0KvIWuXlOGN3YsWPZsGEDgwYN4sqVK/To0YO0tDQg/chnz549jB8/nidPnrBy5UqDG64cHR1p06YNK1asoGfPnhw+fJhZs2Zlup2KFSvStGlTg7Z/nyL+t99++4327dujVqtp27YtixYt4oMPPnjpfR0zZgxVqlSha9euz+zTrFkzpk2bRs+ePZk/fz7Fixdn5MiR+pw8i4+PDzqdjmvXrlG2bFl9e2RkJFFRUfj4+Lx0/P/2yy+/cP/+/Qx3bz/19Iaw4OBgg1PIT2P877gEHh4ebN26FTc3NzZu3MjIkSNp3bo1Li4u2d72y3qaq//GnpKSon9q43ksLCwy9Jk9eza2trY4OztnKxYXFxesrKwIDg7OMO/KlSuYmJjg5eWlbwsPD+fjjz+mefPmmJub89lnn9GiRQuDn/+6devo06cP06dP17clJSU9947/Vq1aYWFhQbdu3WjQoAElSpTg4MGD2doX8XrIEbswOnd3d44ePUqrVq2YPn061apV448//iA+Ph4fHx/ef/99LC0t2bRpU6ZHPb169eLSpUuMGjUKtVpNt27dXjqmN954A4A2bdrQrVs3Ro0aleFxpew6evQoGzduZOrUqc89qg4LC2Pw4MF88sknDBw4kKZNm+Lg4PDC9bdu3Rogwx82M2bMANL3JbfExsby7bff8umnn+Lu7p5pnyZNmqDRaJg9e7bBUeCKFSuIjIzM8LhdqVKl9PdR/Pzzz+h0OoYNG5ajbb+spk2bYm5uzuzZs1H+NdTHokWLiI6Ofm4uMzviPXPmDNu2baNjx47ZPn2tVqtp3rw5GzduNLh8ERkZycqVK2nQoAG2trb69gEDBqDT6Vi0aBG//vorpqamfPDBBwb7oVarDT5Des6f98ejqakpvXv35ty5c7z//vvZ2gfxeskRu8gTvLy82LhxI+Hh4Rw+fJgffviBoKAg5s+fT5UqVahSpcozi2GbNm1wcnJi7dq1tGrVKks34mXHTz/9RNmyZfn444/5888/DeYdPXrU4Jrp05uYQkJCOH78OLVq1dLPezpQy/OO9nQ6Hb169aJYsWJMnTo1W3FWrlxZP4hPVFQUb731FsePH2fZsmV07NiRRo0aGfR/emPgU7dv3wYwaAsKCsp0W6dPn8bZ2ZnRo0c/Mx5HR0fGjh3LuHHjaNGiBR06dCA0NJQ5c+ZQuXJl+vfv/8xl3d3d+eGHH+jfvz/vvfee/o+WrG77Zbm4uDBmzBgmTZpEy5Ytad++PcHBwfzyyy/UrFnzuY9S3r59mzZt2vDuu+9StGhRLly4wMKFC3F2ds7x/QDffPMNu3btokGDBgwePBhTU1MWLFhAcnIy06ZN0/dbsmQJW7ZsYenSpRQrVgxIL9jvvfce8+bNY/DgwUD65a/ff/8dOzs7ypUrx9GjR9m9e/cLb9qcPHkyo0aNytIfmsKIjHpPvhDPkNnjbs8zePBgBVBWrlyZYV5OH3f7t2XLlimA8s8//xj0e97078eyAEWlUimnTp0yWO9/H9/67rvvFI1Go5w9ezZDvxc97qYoiqLVapVJkyYpfn5+ipmZmeLl5aWMGTNGSUpKMujn4+Pzwvj/Pf33cTdAmTlzpsE6nz7e9l9z585VypQpo5iZmSlubm7Khx9+qDx69Oi5eXiqcePGire3txIbG5ujbf9XVh93e2rOnDkGsQ8aNEh58uTJc7cRGxurDBgwQPHx8VHMzc0VFxcXpVevXsqtW7cM+mX3kcnTp08rLVq0UKytrRUrKyulUaNGypEjR/Tzw8LCFDs7O6Vdu3YZYurUqZNSpEgRJTQ0VFEURXny5InSr18/xdnZWbG2tlZatGihXLlyRfHx8cn09+FZj7O9aL4wDhlSVhQIn376KYsWLSIiIiLD4BrGMHHiRAICAggICDB2KEKIQkausYt8LykpiT/++IPOnTvniaIuhBDGJNfYRb51//59du/ezbp163j06FGmN1oZi7+/PwkJCcYOQwhRCMmpeJFvBQQE0KhRI1xdXRk3bhxDhw41dkhCCGF0UtiFEEKIAkSusQshhBAFiBR2IYQQogCRm+cyodPpuHfvHjY2Nq9t3G0hhBDieRRFITY2Fk9Pz+eOYCiFPRP37t0zGHtZCCGEyCvCwsL0IwtmRgp7JmxsbID05P17DOac0Gq17Ny5k+bNm2f6Ni+ROclbzkjesk9yljOSt+x72ZzFxMTg5eWlr1HPIoU9E09Pv9va2uZKYbeyssLW1la+/NkgecsZyVv2Sc5yRvKWfbmVsxddIpab54QQQogCRAq7EEIIUYBIYRdCCCEKELnGLoQA0h+lSU1NJS0tzdihvFZarRZTU1OSkpIK3b6/DMlb9r0oZ2q1GlNT05d+zFoKuxCClJQUwsPDC+WLaxRFwd3dnbCwMBm3Ihskb9mXlZxZWVnh4eGBubl5jrcjhV2IQk6n03Hjxg3UajWenp6Ym5sXqn+odTodcXFxWFtbP3fQD2FI8pZ9z8uZoiikpKTw4MEDbty4QcmSJXOcVynsQhRyKSkp6HQ6vLy8CuX77HU6HSkpKVhYWEiBygbJW/a9KGeWlpaYmZlx69Ytfb+ckJ+GEAJA/nEWIg/Ijd9D+U0WQgghChAp7EKIQkur1Ro7BJED8nN7PinsQohCIygoiD59+lCqVCkcHBywtbUlOjra2GGJFwgNDWXQoEGUK1cOJycnLC0tuXLlirHDyrOksAsh8rWwsDDef/99/R39Pj4+DBs2jEePHhn0CwgIoEGDBri7u7N69WpOnDhBSEgIdnZ2RopcZMXly5epXr06qampLF68mMDAQK5fv06ZMmWMHVqeJXfFvwaJqcaOQIiCKTQ0lLp161KqVClWrVqFn58fFy9eZNSoUWzbto1jx47h6OiIoigMGDCAWbNm0b9/f4N16HQ6I0UvsmLo0KEMGTKEb775xtih5BtyxP4KpaTq+GHnVb4JUvMgNtnY4QiRZYqikJCSapRJUZQsxzlkyBDMzc3ZuXMnb731Ft7e3rRq1Yrdu3dz9+5dvvrqKwCuXLnCrVu3CAkJwcfHBwsLC+rUqcOhQ4f0+1uqVCl+/PFHg/UHBQWhUqkICQkhICAAlUpFVFSUfn7fvn3p2LGj/vP27dtp0KAB9vb2ODk50bZtW65fv66ff/PmTVQqFUFBQQDcvXuXd999F1dXV2xsbOjUqRN37tzR9584cSJVqlTRf46KikKlUhEQEPDMGK5fv06HDh1wc3PD2tqamjVrsnv3boP9Cg8P5+2338bJyQmVSqWf/r1v/3X+/HkaN26MpaUlTk5OfPjhh8TFxT0zjqe5u3nzpr6tYcOGDB8+XP/Z19eXWbNm6T/v2bMHlUqlX098fDz79u0jJSWFkiVLYmFhQcWKFdm4ceMzc5qcnEzTpk1p2rQpycnp/+6eOHGCZs2a4ezsjJ2dHW+99RanT59+5r7md3LE/gopKBy49og4rYovN1xkSb9ahWrgD5F/JWrTKDd+h1G2fenrFliZv/ifpsePH7Njxw6+/fZbLC0tDea5u7vTs2dP1qxZwy+//MKDBw/QarX8/vvvLFy4ED8/P3766SdatmxJcHAwRYoUoV+/fixZsoTPPvtMv54lS5bw5ptv4u/vb1BwnyU+Pp4RI0ZQqVIl4uLiGD9+PJ06dSIoKCjDY0xarZbWrVtjZmbGpk2bMDMzY9iwYXTs2JETJ07k+N+KuLg4WrduzbfffotGo2H58uW0a9eO4OBgvL29ARg5ciRXr15l+/bteHl5ceTIETp37vzc/WrRogV169blxIkT3L9/n/79+xMfH88ff/yRozj/S6fTMXLkSKytrfVtjx49QlEUFixYwPz586levTorV67k7bff5tSpUwZ/9ACkpaXRrVs34uLi2L17NxqNBoDY2Fj69OnDzz//jKIoTJ8+ndatW3Pt2rUXvts8P5Ij9ldIY6pm+jsVUKsUAq4+ZPWJMGOHJESBce3aNRRFoWzZspnOL1u2LE+ePOHBgwf60+0//PADrVu3pmzZsvzyyy94enryyy+/ANCnTx+Cg4M5fvw4kF54V65cyfvvvw+g/+MhMTHxmTF17tyZt99+G39/f6pUqcLixYs5f/48ly5dytB39+7dnDt3juXLl1O7dm2qVavGihUrCAoKYs+ePTnOS+XKlfnwww+pUKECJUuWZPLkyZQoUYJ//vlH3ycoKIgePXpQs2ZN3N3dcXR0fO46V65cSVJSEsuXL6dChQo0btyY2bNns2bNGiIjI3Mc678tW7aM5ORkOnTooG97+nP7/PPP6d69O6VKlWLixIk0atQow9kVRVHo168fISEhbN261eAPhMaNG/Pee+9RpkwZypYty6+//kpCQgL79+/PldjzGjlif8VKudnQzlvHhltqJm++RN3iTvg6FzF2WEI8l6WZmktftzDatrMjO6fu69evr/9/ExMT6tWrpy+6np6etGnThsWLF1OrVi02bdpEcnIy7777LgAlS5bE3NycVatWMWLEiEzXf+3aNcaPH09gYCAPHz7UF6bbt29ToUIFfb969eqRlpaGvb095cqV07d7e3vj5eXFpUuXaNq0adaT8C9xcXFMnDiRLVu2EB4eTmpqKomJidy+fVvfx8/Pj61bt/LRRx+9sKhD+g1slStXpkiR//3bVb9+fXQ6HcHBwXh4eOQo1qcSEhIYO3Ys8+fP56+//sow/98/N4AGDRoY/KECMGrUKPbs2UO/fv0y7FNkZCRjx44lICCA+/fvk5aWRkJCgkFOChI5Yn/FVFe3MVT3B2/4FiEhJY0RfwaRmiY364i8TaVSYWVuapQpq6eg/f39UalUXL58OdP5ly9fxsHBARcXFxwcHJ67r0/179+f1atXk5iYyJIlS+jatat+mF1HR0dmzJjBF198gaWlJdbW1qxYscJgXe3atePx48csXLiQwMBAAgMDgfRhe/9tzZo1TJ48OUsxZddnn33G+vXr+e677zh48CBBQUFUrFjRIIaZM2eSnJyMs7Mz1tbWtGrVKsfbyw0//PADpUuXpl27dgbtWf25QfrPe9u2baxevZodOwwvI/Xp04egoCB++uknjhw5QlBQEE5OThl+LgWFFPZXKTkO9ZZP8X+wg0Upo6muucPp21HM33/9xcsKIZ7LycmJZs2a8csvv2Q4PR4REcGKFSvo2rUrKpWKEiVKYGpqyuHDh/V9dDodR44cMThibt26NUWKFGHevHls375dfxr+qSFDhhAdHc2FCxcICgqiffv2+nmPHj0iODiYsWPH0qRJE/2lgMx4eXnRoEEDoqKiDE7Th4WFERYWZhBTdh0+fJi+ffvSqVMnKlasiLu7u8ENbAClSpWib9+++Pr6EhgYyG+//fbcdZYtW5azZ88SHx9vsB0TExNKly6d41gh/Ua+6dOnM3369Azz7OzscHd3N/i5ARw6dChDjn7//XdatmzJ5MmTGTBgADExMQaxfvLJJ7Ru3Zry5cuj0Wh4+PDhS8Wdl0lhf5U01qS1nU2SqS3mj4P5U/0VA9SbmbnrCtN3BpOSKkfuQryMOXPmkJycTIsWLThw4ABhYWFs376dZs2aUbRoUb799lsArK2tGTBgAKNGjWLr1q1cvnyZwYMHc+/ePQYNGqRfn1qtpm/fvowZM4aSJUtSt27dDNu0tLSkRIkS+Pv7G9x45eDggJOTE7/++ishISHs3bv3mafsIf10fO3atenduzfHjx/n9OnT9OzZkypVqtC4cWN9P0VRSEpKIikpSX+Xd0pKir4tLS0NnU6nH42tZMmS/P333wQFBXH27Fl69OiR4ZG+Y8eO8eWXX7Ju3TrKly9P0aJFn5vnnj17YmFhQZ8+fbhw4QL79u1j2LBhdO3aFTc3N30/nU6nj+vp0XBycrK+LbNHC+fOnUunTp2oWrVqptv+9NNP+f7771m9ejVXr15l4sSJ7Nu3z+AmR0B/+v3TTz/Fy8vLIPclS5bk999/5/LlywQGBtKzZ88MN1wWKIrIIDo6WgGU6Ojol15XSkqKsnXtciXtjy6KMsFWUSbYKkfG1lHqf7FEaf3TAeVKeEwuRFzwpKSkKBs2bFBSUlKMHUq+kpO8JSYmKpcuXVISExNfYWSvzs2bN5U+ffoobm5uipmZmeLl5aV8/PHHysOHDw36xcfHK4MHD1acnZ0Vc3NzpU6dOsqhQ4eUtLQ05cmTJ0paWpqiKIpy/fp1BVCmTZv2wm336dNH6dChg/7zrl27lLJlyyoajUapVKmSEhAQoADK+vXrFUVRlBs3biiAcubMGUVRFOXOnTtKx44dFWtra8Xa2lrp1KmTEhYWpl/fhAkTFCBLU58+ffTbaNSokWJpaal4eXkpc+bMUd566y1l2LBhiqIoyv3795VixYopv/32m347+/btUwDlyZMnz9zXc+fOKY0aNVIsLCwUR0dHpX///kpYWJg+b3369MlSnE/jUBRF8fHxUSwtLQ32+b85TU1NVcaOHat4enoqZmZmSsWKFZUNGzbo5/83p4qiKMHBwYqlpaWyY8cORVEU5fTp00qNGjUUCwsLpWTJksratWsVHx8fZebMmc/c31fhv9+1zDzv9zGrtUmlKNm486SQiImJwc7OjujoaGxtbV9qXVqtlq1bt9K6VSvMzq+C7WNAG088Fnyj7clfNGVE89IMeKM4ahN5FO4pfd7+/3EgkTU5yVtSUhI3btzAz88vx6+JzM90Oh0xMTHY2tpiYmLCwYMHadKkCWFhYQZHo3nZhg0b2LBhA0uXLn1t2/xv3sSLZSVnz/t9zGptkp/G66JSQfU+8NFB8K5LEZKYYraI30y+Y9m2w3RZcJSbD+NfvB4hxCuRnJzMnTt3mDhxIu+++26+KeqQfglB/gAWTxm1sE+ZMoWaNWtiY2ODq6srHTt2JDg4+LnLLFy4kDfeeAMHBwccHBxo2rSp/rnTp/r27WswmpJKpaJly5avcleyzqkE9N0CLb5DMbXgTfV5dmpG4xu2kVY/HWD50ZvodHISRYjXbdWqVfj4+BAVFcW0adOMHU62tGvXjoULFxo7DJFHGLWw79+/nyFDhnDs2DF27dqFVqulefPmBnde/ldAQADdu3dn3759HD16FC8vL5o3b87du3cN+rVs2ZLw8HD9tGrVqle9O1lnooa6Q1B9dAiK1cRGlch08/nM4kdmbzxC78XHiYhOMnaUQhQqffv2JS0tjVOnTr3wZjIh8jKjDlCzfft2g89Lly7F1dWVU6dO8eabb2a6zH+fG/3tt9/466+/2LNnD71799a3azQa3N3dcz/o3ORcEvpthyM/oeybQgtOUtMkmDGhH9BiVjRT3q5I64ovN/CDEEKIwiVPjTz39L3IWRkJ6amEhAS0Wm2GZQICAnB1dcXBwYHGjRvzzTff4OTklOk6kpOT9Y+RAPrnH7Varf4Rkpx6uvxz11PnE/Brguk/g3G8f5EF5rNYpT3LyBW92F21OGNbl8HGIk/9qF65LOVNZJCTvGm1WhRFQafTFco3nT29f/hpDkTWSN6yLys50+l0KIqCVqtFrTYchTGrv9d55q54nU5H+/btiYqK0r9xKSsGDx7Mjh07uHjxov4OwtWrV2NlZYWfnx/Xr1/nyy+/xNramqNHj2ZIFKS/QWnSpEkZ2leuXKkfdep1UOlSKRPxNyUjt6BCIVTnwcfaoUSa+9KnVBre1i9ehxDZZWpqiru7O15eXpibmxs7HCEKtZSUFMLCwoiIiCA11fCd3wkJCfTo0eOFd8XnmcI+aNAgtm3bxqFDhyhWrFiWlpk6dSrTpk0jICCASpUqPbNfaGgoJUqUYPfu3TRp0iTD/MyO2L28vHj48GGuPO62a9cumjVrluW7VlU3D6L+ZzCq2HC0mPK9tivLaMPIZqXoV9cHk0LwWFxO8iZylrekpCTCwsLw9fUtlI+7KYpCbGwsNjY28vbFbJC8ZV9WcpaUlMTNmzfx8vLK9HE3Z2fnFxb2PHF+d+jQoWzevJkDBw5kuaj/+OOPTJ06ld27dz+3qAMUL14cZ2dnQkJCMi3sGo1G/3q/fzMzM8u1opKtdZVsDIOOwKZPMLu8ibFmK6iTdonPtn/E0dAnTO9SGWfrjPEWRLn5MyhMspO3tLQ0VCoVJiYmhfJ55KenRJ/mQGSN5C37spIzExMTVCpVpr/DWf2dNupPQ1EUhg4dyvr169m7dy9+fn5ZWm7atGlMnjyZ7du3U6NGjRf2v3PnDo8ePXrpNxC9VlaO0OV3aDMDRa2hqfoMWzVfEnvtMG1mH+TUrcfGjlAIIQqd/HDfj1EL+5AhQ/jjjz9YuXIlNjY2REREEBERYfBCh969ezNmzBj95++//55x48axePFifH199cvExcUB6a8sHDVqFMeOHePmzZvs2bOHDh064O/vT4sWxnkNZY6pVFDzA1T9d4NjCTxVj1ir+Zq28X/TdcFRlhy+ka1XVgohhMie9evX06ZNG3x9fbG2tuaNN94wdkgvZNTCPm/ePKKjo2nYsCEeHh76ac2aNfo+t2/fJjw83GCZlJQU3nnnHYNlfvzxRyB9BKZz587Rvn17SpUqxQcffED16tU5ePBgpqfb8wWPSjAwACp0Ro2OcWYrmK6ew/ebzvDxqjPEJ6e+cBVCFET/HYzKycmJli1bcu7cOWOHJgqAKVOmMGDAANq2bcuWLVsICgpi69atxg7rhYx6jT0rR5sBAQEGn//7+sH/srS0zPAu3gLBwhY6LwKvOig7xtCBI5RU3WXg+U/pfD+Ohb1r4OX4+u7gFyKvaNmyJUuWLAHSX9c6duxY2rZty+3bt40cmcjPQkND+e677zh27Bjly5c3djjZInc85CcqFdQeiKr3P1DEhXImt9isGYvD/WO0n3OIo9cfGTtCUVAoCqTEG2fK5uWlp4NRubu7U6VKFb744gvCwsJ48OCBvs/nn39OqVKlsLKyonjx4owbNy7DtdKbN29mGIpapVIRFRUFpD8WW6VKFX3/lJQU/P39Dfo85evrm2E9GzZs0M/fvn07DRo0wN7eHicnJ9q2bcv169czxBIUFJRhvbNmzdJ/btiwIcOHD9d/Dg4OxszMzCBOnU7H119/TbFixdBoNFSpUsVgcLCsbuu/+wDQtm1bPv30U/3n33//nRo1amBjY4O7uzs9evTg/v37Bsts3ryZypUrY2lpqc9Nx44deZ558+ZRokQJzM3NKV26NL///rvB/P/GNnz4cBo2bPjMfQwICMjwc+vVq5fBenbs2EGJEiX49ttvcXFxwcbGhrfffps7d+7ol/nvd+L06dPY29sbvN9+xowZVKxYkSJFiuDj48PIkSP1l45flTxxV7zIJt/66afm1/TC/t5pfjefypfJ79NrUSoT25fnvTo+xo5Q5HfaBPjO0zjb/vIemBfJ0aJxcXH88ccf+Pv7GwxIZWNjw9KlS/H09OT8+fMMGDAAGxsbRo8ere/z9Azi7t27KV++PEeOHKFz587P3NacOXOIjIx85vyvv/6aAQMGAGS4cTc+Pp4RI0ZQqVIl4uLiGD9+PJ06dSIoKOil7jAfNWpUhkekfvrpJ6ZPn86CBQuoWrUqixcvpn379ly8eJGSJUvmeFuZ0Wq1TJ48mdKlS3P//n1GjBhB37599aevo6Ki6Nq1K/3792fDhg1YWloybNgwg8eN/2v9+vUMGzaMWbNm0bRpUzZv3ky/fv0oVqwYjRo1ypW4T506xT///GPQ9uDBA86ePYuNjQ3btm0DYNiwYXTs2JETJ05keFztypUrtGjRgrFjx9K/f399u4mJCbNnz8bPz4+QkBAGDx7M559/zrx583Il9szIEXt+ZVcM+m2DCu9gShrTzBbymclKxm04x6RNF0mTF8mIQmLz5s1YW1tjbW2NjY0N//zzD2vWrDEokGPHjqVevXr4+vrSrl07PvvsM/7880+D9Tw9gn969P+8ETAfP37MN998w+eff57p/OTkZBwdHfXr+q/OnTvz9ttv4+/vT5UqVVi8eDHnz5/n0qVLOUkBAPv27ePIkSMGRQXSHw3+/PPP6datG6VLl+b777+nSpUqBkfjueX999+nVatWFC9enDp16jB79my2bdumP0K9evUqCQkJfP755/j5+eHu7o6lpeVz1/njjz/St29fBg8eTKlSpRgxYgRvv/22/r6q3DBixAhGjRpl0KbT6VCr1axcuZIaNWpQo0YNVq5cSVBQEHv27DHoe+vWLZo1a8bAgQP57LPPDOYNHz6cRo0a4evrS+PGjfnqq69Yu3ZtrsWeGTliz8/MLKDzb+DkD/un8pHpJnxUEXx6eDDhUUnM6lYFC7OMI+0J8UJmVulHzsbadjY0atRIf/Tz5MkTfvnlF1q1asXx48fx8Uk/e7VmzRpmz57N9evXiYuLIzU1NcMAH0+Hki5S5MVnC77++msaNWpEgwYNMp3/+PHj5w4gcu3aNcaPH09gYCAPHz7UP998+/ZtKlSo8OKd/g9FURg5ciQTJkzg0aP/XZKLiYnh3r171K9f36B//fr1OXv2rEFbvXr1DP4YSkhIyLCd7t27G4zemZiYSPXq1fWfT506xcSJEzl79ixPnjwx2K9y5crh5eWFqakpq1at4tNPP83S2YnLly8zcODADPH/9NNPL1w2KzZs2EBoaCgjR45k3LhxBvO8vLzw8vLSf/bx8aFYsWJcunSJpk2bAulnIZo2bcqdO3cyffJq9+7dTJkyhStXrhATE0NqaipJSUkkJCS8spFN5Yg9v1OpoNEYeHshqM1ppT7BCvMpHL0YQo+Fx3gcn2LsCEV+pFKlnw43xpTNUcyKFCmCv78//v7+1KxZk99++434+Hj9a0yPHj1Kz549ad26NZs3b+bMmTN89dVXpKQY/m7cu3cPExOTF7486tq1a/z22298//33mc6/c+cOKSkpzx2Xo127djx+/JiFCxcSGBhIYGAgQIaYsmr58uXEx8fz0Ucf5Wh5SP/jJygoSD95ema8FDNz5kz9/NOnT1O1alX9vPj4eFq0aIGtrS0rVqzgxIkTrF+/Hvjffnl4eDBv3jy+++47LCwssLa2zvBir9dJq9UyevRovv322wxnDhwcHJ653L9Pw9+6dYvatWszceJE3n//fYM/iG7evEnbtm2pVKkSf/31FydOnOCHH34Acv6zzgop7AVFpS7Q+x+wsKO6yVX+spjM3duhdJ53hLDHGf/yFqKgejqq19PxMI4cOYKPjw9fffUVNWrUoGTJkty6dSvDcidPnqRMmTIvHFb3888/p3///vj7+2c6f//+/VhaWj5z8KxHjx4RHBzM2LFjadKkCWXLluXJkyfZ3Mv/SUhI4KuvvuL777/PMDKZra0tnp6eHD582KD98OHDlCtXzqDNy8tL/weSv78/pqYZT+i6u7sb9Pl3rq5cucKjR4+YOnUqb7zxBmXKlMlw4xxAnz59KFOmDAMHDiQoKIj27ds/d//Kli2bpfhzYt68eVhbW9OrV68M88qUKUNYWBhhYWH6tlu3bnHnzh2DbRcvXpylS5fy1VdfYWtrazDuyqlTp9DpdEyfPp06depQqlQpIiIiXjruF5FT8QWJT93018D+8Tb+sWFstJhEj0ef8+78NP7oXxt/V3mLjCh4kpOT9f9YPnnyhDlz5hAXF0e7du0AKFmyJLdv32b16tXUrFmTLVu26I8kIf3IafXq1cycOTPTl0H9W0hICLdv3yYkJCTT+devX2fq1Kl06NAhw53yUVFRpKSk4ODggJOTE7/++iseHh7cvn2bL774ItP1paSkkJSUpP+sKAqpqamkpaXpT4mvXLmS6tWrP/PO8lGjRjFhwgRKlChBlSpVWLJkCUFBQbl+pOzt7Y25uTk///wzH330ERcuXGDy5MkZ+o0cORKVSsXMmTMxMzPDxsYmQ67+G3+XLl2oWrUqTZs2ZdOmTfz999/s3r3boJ9Wq9XnKi0tDZ1Op//8rJvzpk2bxqZNmzIdt71Zs2aULVuWHj16MHPmTCD95rkqVarQuHFjfT8bGxv9H0FLly6lVq1avPPOO7zxxhv4+/uj1Wr5+eefadeuHQcPHtQ/mvlKKSKD6OhoBVCio6Nfel0pKSnKhg0blJSUlFyILIse31SUn6oqygRb5cnEYkrrL+YoVb/eqZy/E/X6YnhJRslbAZCTvCUmJiqXLl1SEhMTX2Fkr0afPn0UQD/Z2NgoNWvWVNatW2fQb9SoUYqTk5NibW2tdO3aVZk5c6ZiZ2enKIqiHD9+XPH19VW+++47JS0tTb/Mvn37FEB58uSJoiiKMmHCBAVQfvzxx2f28fHxMYjnv9O+ffsURVGUXbt2KWXLllU0Go1SqVIlJSAgQAGU9evXK4qiKDdu3HjuepYsWaIoiqK89dZbikqlUk6cOKGPacKECUrlypX1n9PS0pSJEycqRYsWVczMzJTKlSsr27Zt089/uq0zZ84Y5MzHx0eZOXOm/vO/43u63vr16yuffPKJvm3lypWKr6+votFolLp16yr//POPwbpXrlypuLm5KXfv3jX4GXbo0EF5nl9++UUpXry4YmZmppQqVUpZvny5wfzn5erf09M4nv7c2rZtm2E9/97H69evK23atFGsrKwUa2trpVOnTsqdO3f08/+ba0VRlK+//lrx9/dX4uPjFUVRlBkzZigeHh6KpaWl0rx5c2XevHkG35n/et7vY1ZrU555u1teEhMTg52d3QvfoJMVWq2WrVu30rp169f7MpO4B7DiHQgPIk5VhB5JX3BDU5olfWtSwzfr77s3FqPlLZ/LSd6SkpK4ceMGfn5+hfLtbjqdjpiYGGxtbV/6ZSa+vr4EBATg6+ubYV7Hjh0zPF+dE8OHD6dKlSr07dv3pdbzsnIzb4VFVnL2vN/HrNYm+WkUVNYu0GcTeNXGWolntcUUSiZfotei4xwLlYFshHgVXFxcDO4a/zcHB4dced+9mZnZM7chBMg19oLNwhbe+xtWdsHq1mFWWkyld9Io3l8KS/vVopZf3j9yFyI/OXHixDPn5da11ad3VQvxLHLEXtBprKHnWvB7Cwslid8tplFee5F+S47Lq1+FEKIAksJeGJgXgR5roEQTNEoyyy1+pLj2Gn0Wn+DM7Zw/ZiOEECLvkcJeWJhZQrcV4NMASyWBFZbT8Ei5Se/Fx7l0L8bY0Yk8QO6jFcL4cuP3UAp7YWJmCT1WQ9Hq2Opi+NNyCo7Jd+iz5Di3H8kgNoXV07vnMxtCVAjxej39PXyZp4Hk5rnCRmMDPdfB0rY43L/InxZTaRc7gd6LA1n7UT1cbDTGjlC8Zmq1Gnt7e/0oYVZWVpkO2FFQ6XQ6/UAw8thW1knesu95OVMUhYSEBO7fv4+9vf1LPfkghb0wsnKE3htgcUvcHl/nD8sfefvRV/RdcpzVA+tgYyHPjRc2T8dHz2wI0IJOURQSExP17wcXWSN5y76s5Mze3v6F7yt4ESnshZW1K7z3FyxqRqn4G/xmMZv37n3Gh7+fYmm/Wpibyl/ghYlKpcLDwwNXV1f960sLC61Wy4EDB3jzzTdlMKRskLxl34tylltjFEhhL8wc/aDHn7C0LXW055iu+Y1h1z9k7IbzfN+5kvwVXgip1epCN/iJWq0mNTUVCwsLKVDZIHnLvteVMzksK+yKVoMuy0ClpoPqACNN1/LnyTssOBBq7MiEEELkgBR2ASWbQbtZAHxsuoH2Jkf4fvsVtl949a8XFEIIkbuksIt01XpD/eEA/KhZSDluMnzNGc7fiTZuXEIIIbJFCrv4nybjwb8Z5koyy6xmUUT7hAHLT/IwLvN3GQshhMh7pLCL/zFRQ+ffwLEEzmn3WVxkDg9j4vh45RlS03TGjk4IIUQWSGEXhiztofsqMLehctpFvtas4GjoI37YGWzsyIQQQmSBFHaRkUtp6LwQgB6qHbQ1OcqC/aFsOx9u5MCEEEK8iBR2kbnSreCNkQBMt1iEnyqcz9aeJeR+nJEDE0II8TxS2MWzNfwSfBqg0SWwtMgcUlMSGbziFEnaNGNHJoQQ4hmksItnU5vCO4ugiAs+qTeYavkHVyPj+GbLJWNHJoQQ4hmksIvns3FPv1MeFZ2U3XQ0OcQfx27L4DVCCJFHSWEXL1a8ITT8AoDvLZbipYrk87/OcS8q0bhxCSGEyEAKu8iaN0eBd100ugQWFFlIbGIyw1cHkaZTjB2ZEEKIf5HCLrLGRA2dFoC5DeVSL/GJ+WaO33zML/tCjB2ZEEKIf5HCLrLOwQda/wDAJ+p1VFCF8tOea1y8J+PJCyFEXiGFXWRP5W5QriMmSiq/WS/AVJfEyD/PkpIqQ84KIUReIIVdZI9KBW1ngo0H7towJlj+yZWIWH7ee83YkQkhhEAKu8gJK0foMBeAbsp2aqiu8EvAdc7diTJuXEIIIYxb2KdMmULNmjWxsbHB1dWVjh07Ehz84peNrF27ljJlymBhYUHFihXZunWrwXxFURg/fjweHh5YWlrStGlTrl2TI8pc5d8Eqr6HCoW51ksw1SUz8s+zMiqdEEIYmVEL+/79+xkyZAjHjh1j165daLVamjdvTnx8/DOXOXLkCN27d+eDDz7gzJkzdOzYkY4dO3LhwgV9n2nTpjF79mzmz59PYGAgRYoUoUWLFiQlJb2O3So8mn8L1u64acMYY7mBa/fj5JS8EEIYmVEL+/bt2+nbty/ly5encuXKLF26lNu3b3Pq1KlnLvPTTz/RsmVLRo0aRdmyZZk8eTLVqlVjzpw5QPrR+qxZsxg7diwdOnSgUqVKLF++nHv37rFhw4bXtGeFhKV9+vV2oDebqKgKZcH+UIIjYo0blxBCFGKmxg7g36Kj0x+bcnR0fGafo0ePMmLECIO2Fi1a6Iv2jRs3iIiIoGnTpvr5dnZ21K5dm6NHj9KtW7cM60xOTiY5OVn/OSYmBgCtVotWq83x/jxdx7//W+CUaIa6XCdMLq1nnvUiGsVO4ou/zrK6fy1MTFQ5Xm2Bz9srInnLPslZzkjesu9lc5bV5fJMYdfpdAwfPpz69etToUKFZ/aLiIjAzc3NoM3NzY2IiAj9/Kdtz+rzX1OmTGHSpEkZ2nfu3ImVlVW29uNZdu3alSvryYvMTZrQ2HQ3xbQ3GGy6iZ/COjF26XYauL/8qHQFOW+vkuQt+yRnOSN5y76c5iwhISFL/fJMYR8yZAgXLlzg0KFDr33bY8aMMTgLEBMTg5eXF82bN8fW1val1q3Vatm1axfNmjXDzMzsZUPNs1Ql1LBxEB+b/8NfafXZds+TYe/Uw83WIkfrKyx5y22St+yTnOWM5C37XjZnT88mv0ieKOxDhw5l8+bNHDhwgGLFij23r7u7O5GRkQZtkZGRuLu76+c/bfPw8DDoU6VKlUzXqdFo0Gg0GdrNzMxy7Qubm+vKk6p0h7MrMb15kBk2K+kSM5zvtl/ll57VX2q1BT5vr4jkLfskZzkjecu+nOYsq8sY9eY5RVEYOnQo69evZ+/evfj5+b1wmbp167Jnzx6Dtl27dlG3bl0A/Pz8cHd3N+gTExNDYGCgvo94BVQqaDMdTMyolXKc5upTbD0fwd4rkS9eVgghRK4xamEfMmQIf/zxBytXrsTGxoaIiAgiIiJITPzf60B79+7NmDFj9J+HDRvG9u3bmT59OleuXGHixImcPHmSoUOHAqBSqRg+fDjffPMN//zzD+fPn6d37954enrSsWPH172LhYtLaaiX/nOYVmQFliTx9aZLJKfKs+1CCPG6GLWwz5s3j+joaBo2bIiHh4d+WrNmjb7P7du3CQ8P13+uV68eK1eu5Ndff6Vy5cqsW7eODRs2GNxwN3r0aD7++GMGDhxIzZo1iYuLY/v27VhY5Ox6r8iGN0eBnRf2KRGMttrMzUcJ/HbwhrGjEkKIQsOo19gV5cV3TQcEBGRoe/fdd3n33XefuYxKpeLrr7/m66+/fpnwRE6YF4GWU2FNT/oo//CHqi5z9qp5u1pRPOwsjR2dEEIUeDJWvMh9ZdpAyeaYKKn8YLOGRG0a3229YuyohBCiUJDCLnKfSgUtpoCJGdVSTtJQHcSms/c4FvrI2JEJIUSBJ4VdvBrO/lD7QwCmWa/BlFQm/nOR1DR5b7sQQrxKUtjFq/PmKLBywjX5FgMs9nElIpbVJ8KMHZUQQhRoUtjFq2NpD43HAjDc9C/siWXW7qvEJsnY0kII8apIYRevVrU+4FYBTWoME2w28jAuhQX7Q40dlRBCFFhS2MWrZaKGllMA6JC6A3/VHRYeDCU8OvEFCwohhMgJKezi1fN7E0q3wURJY4rt3ySn6vhhR7CxoxJCiAJJCrt4PZpOBJWamsnHqKG6wvozd7lwN9rYUQkhRIEjhV28Hi6loFovAKbZ/42iKHy75XKWRh8UQgiRdVLYxevz1hdgaknxxAu0MjvD0dBHBFx9YOyohBCiQJHCLl4fWw+oOxiAyUXWoiaNH7YHo9PJUbsQQuQWKezi9ao/DCwdcU66xXuaQ1wKj2HL+fAXLyeEECJLpLCL18vCLn1EOmCU5m8sSGbGrqsy1KwQQuQSKezi9av5Adh7Y53ygA8t93HjYTzrTt0xdlRCCFEgSGEXr5+pBt76HICPTDdRhER+2nONJG2akQMTQoj8Twq7MI5K3cCxBJbaJ3xcZA/h0Un8ceyWsaMSQoh8Twq7MA61KTQcA8D7qs3YEs/cfSHEJacaOTAhhMjfpLAL46nwNriUxTw1hs9sdvEkQcuyIzeNHZUQQuRrUtiF8ZioodGXAHRXtuBADL8eCJXXugohxEuQwi6Mq2w7cK+EWWo8n9vuJDpRy9LDN40dlRBC5FtS2IVxqVTQeCwA76RtxZloFh6Uo3YhhMgpKezC+Eo2h6LVMU1LYpTtTmKSUll65LaxoxJCiHxJCrswPpVK/1x757TtOBLDkqO3SJAb5IUQItuksIu8oWRz8KyKaVoin9vtIjYplYBw+XoKIUR2yb+cIm/491F76jYciGF/uIqYRLnWLoQQ2SGFXeQdpVqCeyVM0xIYZbuHpDQVy4/JtXYhhMgOKewi7/jXUfu7uq3YEceyo7dlNDohhMgGKewibynTBtwqYpYazycWW4lK1LJCxpAXQogsk8Iu8haVCt5Kf197T9UObEhg4cFQElPkzW9CCJEVUthF3lOmHYpzaSyURIZa7+VhXAqrT8i1diGEyAop7CLvMTEhrd4wAPqotmJBMgv2h5KcKkftQgjxIlLYRZ6klH+beHMXLLRRDCxyiIiYJNadumPssIQQIs+Twi7yJhNTrrm1AeBD082Ykcq8gOukpumMHJgQQuRtUthFnhXm2ADF2o0iyZH0sjzKnSeJbD4XbuywhBAiT5PCLvIsnYk5ujpDAPhEsxkTdMwLuI5Opxg5MiGEyLuMWtgPHDhAu3bt8PT0RKVSsWHDhuf279u3LyqVKsNUvnx5fZ+JEydmmF+mTJlXvCfiVdFV7Q2WDtgnhfG25iTBkbHsvXLf2GEJIUSeZdTCHh8fT+XKlZk7d26W+v/000+Eh4frp7CwMBwdHXn33XcN+pUvX96g36FDh15F+OJ1MLeG2oMAGFVkM6DwS0AIiiJH7UIIkRlTY268VatWtGrVKsv97ezssLOz03/esGEDT548oV+/fgb9TE1NcXd3z7U4hZHVGgCHf8ItIYQmZufZc7sSgTceU6e4k7EjE0KIPMeohf1lLVq0iKZNm+Lj42PQfu3aNTw9PbGwsKBu3bpMmTIFb2/vZ64nOTmZ5ORk/eeYmBgAtFotWu3LvV3s6fIvu57CxiBvZjaYVO2F+vh8vrLdzp5HlZi79xrVvWyNHGXeI9+37JOc5YzkLfteNmdZXU6l5JFzmiqVivXr19OxY8cs9b937x7e3t6sXLmSLl266Nu3bdtGXFwcpUuXJjw8nEmTJnH37l0uXLiAjY1NpuuaOHEikyZNytC+cuVKrKyscrQ/IndZpDyi2cXPMCGNTsmTOKOUZFSlVIoVMXZkQgjxeiQkJNCjRw+io6OxtX32gU2+LexTpkxh+vTp3Lt3D3Nz82f2i4qKwsfHhxkzZvDBBx9k2iezI3YvLy8ePnz43ORlhVarZdeuXTRr1gwzM7OXWldhklne1JuGYnJuNWetG9Dh4WBaV3Djp66VjRxp3iLft+yTnOWM5C37XjZnMTExODs7v7Cw58tT8YqisHjxYnr16vXcog5gb29PqVKlCAkJeWYfjUaDRqPJ0G5mZpZrX9jcXFdhYpC3Bp/CudVUijtMcVVHtl+EezEp+DjJYft/yfct+yRnOSN5y76c5iyry+TL59j3799PSEjIM4/A/y0uLo7r16/j4eHxGiITr5RrGSjVChUKE5x2o1Pgt4M3jB2VEELkKUYt7HFxcQQFBREUFATAjRs3CAoK4vbt9Dd5jRkzht69e2dYbtGiRdSuXZsKFSpkmPfZZ5+xf/9+bt68yZEjR+jUqRNqtZru3bu/0n0Rr0mD4QC8kbAXV57w58kwHsUlP38ZIYQoRIxa2E+ePEnVqlWpWrUqACNGjKBq1aqMHz8egPDwcH2Rfyo6Opq//vrrmUfrd+7coXv37pQuXZouXbrg5OTEsWPHcHFxebU7I14P7zrgVQcTXQpfOOwlOVXHsiM3jR2VEELkGUa9xt6wYcPnDjSydOnSDG12dnYkJCQ8c5nVq1fnRmgiL2swHFZ1o712OxNoxbKjt/jwrRIU0eTLW0aEECJX5ctr7KKQK9kCnEthmhrPYJuDRCdq+fNkmLGjEkKIPEEKu8h/TEyg3scA9DHZhhmp/HbwBlp5pasQQkhhF/lUpa5g7YZV8n16WB3nblQiW+SVrkIIIYVd5FOmGqj9IQAfW24HFBYcCJWXwwghCj0p7CL/qvE+mBXBOT6E5uYXuBwew6GQh8aOSgghjEoKu8i/LB2geh8AvrDbCcCvB0KNGZEQQhidFHaRv9UZBCo1xWNPUdHkBgevPeTivWhjRyWEEEYjhV3kb/beUOFtACY47QFkmFkhROEmhV3kf///6Fv1uP0U5QGbzt7jXlSikYMSQgjjkMIu8j+PyuD3Fiolja+cAkjVKSw+JEftQojCSQq7KBjqfwJA8+Qd2BLHquO3iU7UGjkoIYR4/aSwi4KhRBNwLYdpagKf2B8mPiWNlYG3X7ycEEIUMFLYRcGgUumvtfdUtmJGKkuP3CAlVYaZFUIULlLYRcFR4R2w8cAy+QHvFTlOZEwy/5y9Z+yohBDitZLCLgoOU3P9MLNDLdKHmV0ow8wKIQoZKeyiYKneD8ytcYoPoYX5BYIjYzlwTYaZFUIUHlLYRcFiaQ/V0oeZ/dxuNwALZZhZIUQhIoVdFDx1Pvr/YWZPUEF9i0MhD7lwV4aZFUIUDlLYRcFj7w3lOwIwwWkvAL8dlKN2IUThIIVdFEx1hwJQI3Yf7jxi07lw7sows0KIQkAKuyiYilYD3zdQKal85XyANJ3CEhlmVghRCEhhFwXX/w9Y0yp5G9YkyDCzQohCQQq7KLj8m4FzaUy1cXxsf4T4lDRWH5dhZoUQBZsUdlFwmZhA3SEAvKfahimpLDl8U4aZFUIUaFLYRcFWqSsUcaFIYjjdi5wmIiaJTTLMrBCiAJPCLgo2MwuolT7M7CeW/z/M7EEZZlYIUXBJYRcFX80PwMwKl7grNDa/zJUIGWZWCFFwSWEXBZ+VI1R9D4Av7WWYWSFEwSaFXRQOdQaDygT/mGOUVYfJMLNCiAJLCrsoHBz9oGx7ACb+/zCzv8pRuxCiAJLCLgqP+p8AUCt2D+48Ysv5cMIeJxg5KCGEyF1S2EXhUbQ6+DRApaQyziV9mNlFMsysEKKAkcIuCpf/H2a2RdI2bEhgzYkwnsSnGDkoIYTIPVLYReFSsrl+mNlhDkdI1Kbx+7Fbxo5KCCFyjRR2UbiYmOiP2nsqmzEjlWVHbpKkTTNyYEIIkTuksIvCp1IXsHbHMuk+fa1P8Cg+hXWn7hg7KiGEyBVS2EXhY6qBOoMAGKzZggodCw+GkqaTYWaFEPmfUQv7gQMHaNeuHZ6enqhUKjZs2PDc/gEBAahUqgxTRESEQb+5c+fi6+uLhYUFtWvX5vjx469wL0S+VKMfmNvgEB9KW8sL3HqUwPYLES9eTggh8jijFvb4+HgqV67M3Llzs7VccHAw4eHh+snV1VU/b82aNYwYMYIJEyZw+vRpKleuTIsWLbh//35uhy/yMwu79OIOfG6zA4B5+0Pk5TBCiHzPqIW9VatWfPPNN3Tq1Clby7m6uuLu7q6fTEz+txszZsxgwIAB9OvXj3LlyjF//nysrKxYvHhxbocv8rs6g8DEjGIxZ6htdp0Ld2M4HPLI2FEJIcRLMTV2ADlRpUoVkpOTqVChAhMnTqR+/foApKSkcOrUKcaMGaPva2JiQtOmTTl69Ogz15ecnExycrL+c0xMDABarRatVvtSsT5d/mXXU9i8lrxZuqCu8A4m51Yx3nE3bSJL8Mu+a9T2tXt123zF5PuWfZKznJG8Zd/L5iyry+Wrwu7h4cH8+fOpUaMGycnJ/PbbbzRs2JDAwECqVavGw4cPSUtLw83NzWA5Nzc3rly58sz1TpkyhUmTJmVo37lzJ1ZWVrkS+65du3JlPYXNq86bTUpFGrOKctEHKGHSjiOhMP/PrXhbv9LNvnLyfcs+yVnOSN6yL6c5S0jI2hDY+aqwly5dmtKlS+s/16tXj+vXrzNz5kx+//33HK93zJgxjBgxQv85JiYGLy8vmjdvjq2t7UvFrNVq2bVrF82aNcPMzOyl1lWYvM686f7cj8m1HXzjspfuke9xUVeUj1pXfqXbfFXk+5Z9krOckbxl38vm7OnZ5BfJV4U9M7Vq1eLQoUMAODs7o1ariYyMNOgTGRmJu7v7M9eh0WjQaDQZ2s3MzHLtC5ub6ypMXkve3hgJ13ZQJ3YXbrRmxyUIi0qmuEv+PWyX71v2Sc5yRvKWfTnNWVaXyffPsQcFBeHh4QGAubk51atXZ8+ePfr5Op2OPXv2ULduXWOFKPI679rgXQ+VTsvXrvtRFHmlqxAi/zLqEXtcXBwhISH6zzdu3CAoKAhHR0e8vb0ZM2YMd+/eZfny5QDMmjULPz8/ypcvT1JSEr/99ht79+5l586d+nWMGDGCPn36UKNGDWrVqsWsWbOIj4+nX79+r33/RD7yxghYcYSmCVuwoxl/n77L8KalcLezMHZkQgiRLUYt7CdPnqRRo0b6z0+vc/fp04elS5cSHh7O7du39fNTUlIYOXIkd+/excrKikqVKrF7926DdXTt2pUHDx4wfvx4IiIiqFKlCtu3b89wQ50QBvybglsF1JEX+NL5IJ8/bMXCg6GMa1vO2JEJIUS2GLWwN2zY8LkDgixdutTg8+jRoxk9evQL1zt06FCGDh36suGJwkSlggafwl8f8LZ2MxNpxMrA2wxp5I9jEXNjRyeEEFmW76+xC5FrynUEB1/Mkp8w3DGQRG0aSw7fMHZUQgiRLVLYhXhKbQr1PgGgt/IPZqSy9MhNYpNkAA4hRP4hhV2If6vSE4q4YpkYzkD7k8QmpfL7sVvGjkoIIbIsR4V92bJlbNmyRf959OjR2NvbU69ePW7dkn8ERT5mZgH1PgbgI/VGTNCx6OANElPSjByYEEJkTY4K+3fffYelpSUAR48eZe7cuUybNg1nZ2c+/fTTXA1QiNeuxvtg6YBN/C162ZzmUXwKf54MM3ZUQgiRJTkq7GFhYfj7+wOwYcMGOnfuzMCBA5kyZQoHDx7M1QCFeO001lB7EADDNZtQoWP+/uskp8pRuxAi78tRYbe2tubRo/TXW+7cuZNmzZoBYGFhQWJiYu5FJ4Sx1B4I5jY4xF2js/UFwqOT+OvUXWNHJYQQL5Sjwt6sWTP69+9P//79uXr1Kq1btwbg4sWL+Pr65mZ8QhiHpQPU6g/A51abAYW5+0LQpumMG5cQQrxAjgr73LlzqVu3Lg8ePOCvv/7CyckJgFOnTtG9e/dcDVAIo6kzBEwtcYm5QGurYO5GJbL+tBy1CyHythyNPGdvb8+cOXMytGf2TnMh8i1rF6jeBwLnM852C1sTyjBnXwhvVyuKqVqeFBVC5E05+tdp+/bt+lelQvoRfJUqVejRowdPnjzJteCEMLp6n4DaHI+oUzSzusbtxwlsDLpn7KiEEOKZclTYR40apX/h+/nz5xk5ciStW7fmxo0b+he5CFEg2BWFqr0AmGi7CYA5+0JI0z37HQdCCGFMOSrsN27coFy59Lde/fXXX7Rt25bvvvuOuXPnsm3btlwNUAija/ApmJhRNOokTSyvceNhPJvPyVG7ECJvylFhNzc3JyEhAYDdu3fTvHlzABwdHfVH8kIUGPZeUPU9ACbabQbgpz3X5KhdCJEn5aiwN2jQgBEjRjB58mSOHz9OmzZtALh69SrFihXL1QCFyBPeGAEmZnhFnaChZQihD+L556zcIS+EyHtyVNjnzJmDqakp69atY968eRQtWhSAbdu20bJly1wNUIg8wd4bqvYEYLJ9+nsSftp9jVR5rl0Ikcfk6HE3b29vNm/enKF95syZLx2QEHlWgxFw5g+8ngTSyCqUfY+Ks/7MXd6t4WXsyIQQQi9HhR0gLS2NDRs2cPnyZQDKly9P+/btUavVuRacEHmKg0/6a11PL2Oy3SYaJAxj9t5rdKxaFDN5rl0IkUfk6F+jkJAQypYtS+/evfn777/5+++/ee+99yhfvjzXr1/P7RiFyDveGAkmZhR7EkjzIiGEPU7kr1N3jB2VEELo5aiwf/LJJ5QoUYKwsDBOnz7N6dOnuX37Nn5+fnzyySe5HaMQeYeDD1TrDcDXNhsAhZ/3hpCSKtfahRB5Q44K+/79+5k2bRqOjo76NicnJ6ZOncr+/ftzLTgh8qQ3PwO1Bveo07Qtkj6G/Bp5X7sQIo/IUWHXaDTExsZmaI+Li8Pc3PylgxIiT7P1hJofADChyN+Aws97rpGYIu9rF0IYX44Ke9u2bRk4cCCBgYEoioKiKBw7doyPPvqI9u3b53aMQuQ9DT4FMytcYi7wrs1F7scms+zoTWNHJYQQOSvss2fPpkSJEtStWxcLCwssLCyoV68e/v7+zJo1K5dDFCIPsnaFWgMB+NLyb1TomBdwnehErZEDE0IUdjl+bevGjRsJCQnRP+5WtmxZ/P39czU4IfK0+sPgxCIcYq7Qz+E8i59UZuGBUD5rUdrYkQkhCrEsF/YXvbVt3759+v+fMWNGziMSIr+wcoS6g2H/94wwXcsyKrD48A361PPFxUZj7OiEEIVUlgv7mTNnstRPpVLlOBgh8p26Q+H4QqxjQ/nU+QQ/PqzD3H0hTGxf3tiRCSEKqSwX9n8fkQsh/p+Fbfrjbzu+ZIDuT36mGisCb/FBAz+8HK2MHZ0QohCScTCFeFk1PgA7LzQJEUx0O4g2TWH6zmBjRyWEKKSksAvxsswsoNGXAHRJXIstcWwIuseFu9FGDkwIURhJYRciN1TqCq7lUKfEMMMz/bLVd1svoyiKkQMTQhQ2UtiFyA0mamgyAYDG0evxUj/hyPVHBFx9YOTAhBCFjRR2IXJLqRbgXReTtCTmeGwFYOrWK6Tp5KhdCPH6SGEXIreoVNBsMgCVHm6lpsVdgiNj5bWuQojXSgq7ELnJqyaU74QKhZkO6wCF6buC5QUxQojXRgq7ELmt6URQm1PsSSDv2F4mMiaZXw+EGjsqIUQhYdTCfuDAAdq1a4enpycqlYoNGzY8t//ff/9Ns2bNcHFxwdbWlrp167Jjxw6DPhMnTkSlUhlMZcqUeYV7IcR/OPhC7Q8BmKBZhZo05u+/TkR0knHjEkIUCkYt7PHx8VSuXJm5c+dmqf+BAwdo1qwZW7du5dSpUzRq1Ih27dplGO62fPnyhIeH66dDhw69ivCFeLY3PgNLB2xirzPK5TiJ2jS+337F2FEJIQqBHL3dLbe0atWKVq1aZbn/f18J+91337Fx40Y2bdpE1apV9e2mpqa4u7vnVphCZJ+lPbz1BWz/nA+0q/iZKqw/c5fedX2o6u1g7OiEEAWYUQv7y9LpdMTGxuLo6GjQfu3aNTw9PbGwsKBu3bpMmTIFb2/vZ64nOTmZ5ORk/eeYmBgAtFotWu3LvV/76fIvu57CpkDkrUovTI8vwOxxKLOL7uGDu22ZtOkifw6o9cpellQg8vaaSc5yRvKWfS+bs6wup1LyyNBYKpWK9evX07FjxywvM23aNKZOncqVK1dwdXUFYNu2bcTFxVG6dGnCw8OZNGkSd+/e5cKFC9jY2GS6nokTJzJp0qQM7StXrsTKSl7kIXLOLfoMdUJnkqYypXnyNK7r3Onln0YNlzzxayeEyEcSEhLo0aMH0dHR2NraPrNfvi3sK1euZMCAAWzcuJGmTZs+s19UVBQ+Pj7MmDGDDz74INM+mR2xe3l58fDhw+cmLyu0Wi27du2iWbNmmJmZvdS6CpMCkzdFQb26Cyah+7ju+CZN7n2Em62GncPqY2We+yfMCkzeXiPJWc5I3rLvZXMWExODs7PzCwt7vjwVv3r1avr378/atWufW9QB7O3tKVWqFCEhIc/so9Fo0Gg0GdrNzMxy7Qubm+sqTApE3lp9D7/UpcTjA3S0bcSGmNL8eugWo1q8uqc1CkTeXjPJWc5I3rIvpznL6jL57jn2VatW0a9fP1atWkWbNm1e2D8uLo7r16/j4eHxGqITIhMupaHWQAC+sViBKaksPHCD0AdxRg5MCFEQGbWwx8XFERQURFBQEAA3btwgKCiI27dvAzBmzBh69+6t779y5Up69+7N9OnTqV27NhEREURERBAd/b/XY3722Wfs37+fmzdvcuTIETp16oRaraZ79+6vdd+EMNDwc7BywjomhIkex0hJ0zFx0yV5+5sQItcZtbCfPHmSqlWr6h9VGzFiBFWrVmX8+PEAhIeH64s8wK+//kpqaipDhgzBw8NDPw0bNkzf586dO3Tv3p3SpUvTpUsXnJycOHbsGC4uLq9354T4N0sHaDwWgO4JK3BTx3Hg6gN2XIw0cmBCiILGqNfYGzZs+NwjlqVLlxp8DggIeOE6V69e/ZJRCfGKVOsDJxejjjjPb0U30e52dyZvvsSbpZxfyY10QojCKd9dYxci3zJRQ5sZAFS8v4mWtje5G5XI3H3PvrFTCCGySwq7EK+TVy2oln7fyA+WS1GTxsIDNwi5LzfSCSFyhxR2IV63ppPA0hGb6Kt843GYlDQdX60/LzfSCSFyhRR2IV43K0dolj7SYde43/ExiybwxmPWnrxj5MCEEAWBFHYhjKHKe1CsFibaeBZ7rAfg262XeRiX/IIFhRDi+aSwC2EMJibQdgaoTChxfye9na8Snajlm82XjB2ZECKfk8IuhLG4V4Q6gwEYqyzEWpXEhqB7HLz2wMiBCSHyMynsQhhToy/B3hvz+Lv8Vmw7AF+tv0BCSqqRAxNC5FdS2IUwJvMi0HYWALUfrKWJzW1uP07gxx1XjRuXECLfksIuhLH5N4FK3VCh8JPVEsxIZcmRG5y8+djYkQkh8iEp7ELkBS2+S39JTHQws70PoCgwet05krRpxo5MCJHPSGEXIi8o4gQtvweg5aPl1La+T+jDeGbullPyQojskcIuRF5R8R0o2QJVWgq/2i76/+FmQwkKizJ2ZEKIfEQKuxB5hUoF7WaBhR12j88zy+sAOgVGrT0rp+SFEFkmhV2IvMTWU39Kvu3jZdQuEsm1+3H8uCPYyIEJIfILKexC5DWVu0GplqjSUlj4/6fkFx2+wdHrj4wdmRAiH5DCLkReo1KlP9tuYY/tkwv84pN+l/xna88Sk6Q1dnRCiDxOCrsQeZGtB7SaBkDzB0tpah/O3ahEJv0jY8kLIZ5PCrsQeVWlLlC2PSqdlp81v2ClSuav03fYfiHC2JEJIfIwKexC5FUqFbT7CazdsYy+zu/eWwAY8/c5IqKTjBycECKvksIuRF5m5QgdfwGgeuQ6+rpc5UmCluFrzpCmU4wcnBAiL5LCLkRe598Eag8CYGzaXIqax3Ms9DHzAkKMHJgQIi+Swi5EftB0AriUxTThAWs9VgAKM3df49QteVGMEMKQFHYh8gMzS+i8ENTmeEYGMMP7KGk6hU9WBRGdKI/ACSH+Rwq7EPmFe0Vo/i0AnR4uoIn9Pe5GJfLFX+dQFLneLoRIJ4VdiPyk1gAo0xaVTssvZj/joE5k24UIlhy+aezIhBB5hBR2IfITlQo6zAE7LzSxt/jLay2g8N3Wy5y69cTY0Qkh8gAp7ELkN5YO0HkRqNQUj9jOt96nSdUpDF15msfxKcaOTghhZFLYhciPvGtD47EA9Hg0hxYO4YRHJ/HZuvPI4+1CFG5S2IXIr+oPh1KtUKUl87PpLNzMEjgY8ogdd+TXWojCTP4FECK/MjGBTvPBwRfz2DA2eCxDhY7td0zYc/m+saMTQhiJFHYh8jNLe+j6B5ha4HH/IL/57AVg5F/nCbkfZ9zYhBBGIYVdiPzOvWL6+9uBxpFL6F7kDPHJaQz8/aS8v12IQkgKuxAFQZXuUOMDVChMUuZSy+YhoQ/iGbEmCJ3cTSdEoSKFXYiCouVUdMVqY65LYLnlLBxNk9h9+T4zdl01dmRCiNdICrsQBYWpOWmdl5Bo5ohFTChbPJdhgo45+0JYf+aOsaMTQrwmUtiFKEisXQksPgzF1AKP+/tZUXwXAJ+vO8/Jm/ImOCEKA6MW9gMHDtCuXTs8PT1RqVRs2LDhhcsEBARQrVo1NBoN/v7+LF26NEOfuXPn4uvri4WFBbVr1+b48eO5H7wQeVS0lR9pbWYBUPfeMsZ6XyAlTceHv58i7HGCcYMTQrxyRi3s8fHxVK5cmblz52ap/40bN2jTpg2NGjUiKCiI4cOH079/f3bs2KHvs2bNGkaMGMGECRM4ffo0lStXpkWLFty/L8/1isJDqfBO+gA2wAePfuQdlzs8ik/h/aUn5E55IQo4oxb2Vq1a8c0339CpU6cs9Z8/fz5+fn5Mnz6dsmXLMnToUN555x1mzpyp7zNjxgwGDBhAv379KFeuHPPnz8fKyorFixe/qt0QIm9qMiH9TXBpKUxL/Z5qNk+4dj+OQX+cIiVVZ+zohBCviKmxA8iOo0eP0rRpU4O2Fi1aMHz4cABSUlI4deoUY8aM0c83MTGhadOmHD169JnrTU5OJjk5Wf85JiYGAK1Wi1b7ckc3T5d/2fUUNpK3nMmQt/a/oI6+g0l4ECvtptMo+SsOhzzisz+D+PGdCqhUKiNGmzfIdy1nJG/Z97I5y+py+aqwR0RE4ObmZtDm5uZGTEwMiYmJPHnyhLS0tEz7XLly5ZnrnTJlCpMmTcrQvnPnTqysrHIl9l27duXKegobyVvO/DtvGqf3efPhRKyiQ1lt9SPNU77gn3PhJDy8SzsfOXJ/Sr5rOSN5y76c5iwhIWv3yOSrwv6qjBkzhhEjRug/x8TE4OXlRfPmzbG1tX2pdWu1Wnbt2kWzZs0wMzN72VALDclbzjwzb5GVUZa3wSfpMjuKr6FRaA923zOhQbVy9KztbbyA8wD5ruWM5C37XjZnT88mv0i+Kuzu7u5ERkYatEVGRmJra4ulpSVqtRq1Wp1pH3d392euV6PRoNFoMrSbmZnl2hc2N9dVmEjeciZD3opVgS7LYGVXfO9tZV0JJzpfb8XXW67gamdF64oeRos1r5DvWs5I3rIvpznL6jL56jn2unXrsmfPHoO2Xbt2UbduXQDMzc2pXr26QR+dTseePXv0fYQotPybQof0J1Cq3/2duX5H0CkwbPUZDl57YOTghBC5xaiFPS4ujqCgIIKCgoD0x9mCgoK4ffs2kH6KvHfv3vr+H330EaGhoYwePZorV67wyy+/8Oeff/Lpp5/q+4wYMYKFCxeybNkyLl++zKBBg4iPj6dfv36vdd+EyJMqd4NmXwPQJnwO430uok1T+PD3U5y+/cTIwQkhcoNRC/vJkyepWrUqVatWBdKLctWqVRk/fjwA4eHh+iIP4Ofnx5YtW9i1axeVK1dm+vTp/Pbbb7Ro0ULfp2vXrvz444+MHz+eKlWqEBQUxPbt2zPcUCdEoVXvE6gzGIB+D75naLFQElLS6LfkBMERsUYOTgjxsox6jb1hw4YoyrPfPJXZqHINGzbkzJkzz13v0KFDGTp06MuGJ0TBpFJB828h7j6qC+sYGfUNUe6T+CPCi16LAvnzw7r4OhcxdpRCiBzKV9fYhRC5xMQEOs2HUi1RpSYxOf5r2juFcz82mR4Lj8nQs0LkY1LYhSis1Gbw7jLwexOVNp5ZaZNp6viAe9FJdF94jHtRicaOUAiRA1LYhSjMzCyg2yooVhOTpCgW8A1vOjzmzpNEeiw8RmRMkrEjFEJkkxR2IQo7jTX0XAvuFVEnPGCJyWTq2j/h5qMEuktxFyLfkcIuhABLB+i1EVzLo46P5Hf1N9SyjSL0QTzdfj1GeLSclhciv5DCLoRIV8QJem8ElzKYxoez0vwbatlFc+NhPF0XHOPOE7mhToj8QAq7EOJ/rF2gzyZwLoVp3D1Wmn9DPYdobj9OoOuCY9x+JMVdiLxOCrsQwpC1a3pxdyqJaexdfjeZSEPHx9yNSqTLgqOE3JdBbITIy6SwCyEysnGHflv119wXKxNo4fSAiJgk3p1/lHN3oowdoRDiGaSwCyEyZ+0KfTeDR2VMEh8xL3U877hF8CRBS4+FgRwLfWTsCIUQmZDCLoR4NitH6P0PFKuFSXI0PySOY0DRW8Qlp9Jn8XF2X4p88TqEEK+VFHYhxPNZ2kOv9eD3FqqUeL6MmsDn3ldITtUx8PeTrD5++4WrEEK8PlLYhRAv9nQQm3IdUKWl8NH9yUz3O4lOgS/+Ps+s3Vef+0InIcTrI4VdCJE1php4ZwlU74cKhc7hM1jpvxdQmLX7Gl+uP09qms7YUQpR6ElhF0JknYka2s6EN0cDUO/Ob+wu8SfmqlRWHQ+j//KTxCZpjRykEIWbFHYhRPaoVND4q/QCrzLB/+5GjngvwMksiYDgB7w7/yh35c1wQhiNFHYhRM7UeB+6rwazIjhHHuaQ8zTKW8dxJSKWDnMOExQWZewIhSiUpLALIXKuVAvotwWs3bB8coV/NONo5xzBw7hkui44yqaz94wdoRCFjhR2IcTL8awK/XeDaznU8ZHMTvqKUV7pj8N9vOoM07ZfIU0nd8wL8bpIYRdCvDx7b3h/B/g3Q5WayJAHX7PMPwBQ+CXgOgOWnyRGbqoT4rWQwi6EyB0WtunX3GsPAuCtO79yqMQf2JumsPfKfTrNPUzI/TgjBylEwSeFXQiRe9Sm0Gpq+h3zJqYUu7uNoy5TqW4bxfUH8XSYc4ht58ONHaUQBZoUdiFE7qvxPvTZDEVcsXxyhbUmXzLQM5T4lDQGrTjNd1svy2A2QrwiUtiFEK+GT10YGABFq2OSFMWYx+NYViIAFTp+PRBKz98CuR+TZOwohShwpLALIV4du6LQbxtU640Khbfu/spxn/kUM48n8MZjWs8+yMFrD4wdpRAFihR2IcSrZaqB9j9Dh1/A1BKXyEME2IzjbecwHsal0HvxcX7cESyn5oXIJVLYhRCvR9WeMGAPOJXEND6C6fFfMt93Pyg65uwLofvCY9x5kmDsKIXI96SwCyFeH7fyMHAfVHgHlZJGy4gFHPeai48mjhM3n9Dqp4P8I6PVCfFSpLALIV4vjQ10/i399LypJS4PjrLX6kv6uV0nNimVT1adYcSaIHlLnBA5JIVdCPH6qVRQrXf6XfOu5VEnPmRC9DjW+23EUpXC32fu0uqngxwLfWTsSIXId6SwCyGMx7VM+nX3mgMAqBq+htOu39DY7h53niTSfeExJm++RJI2zciBCpF/SGEXQhiXmSW0+RF6rkt/S1x0CIu0X7DAdx8mShqLDt2gzeyD8hpYIbJICrsQIm8o2QwGHYWy7VDpUmkRsZAznj9Qy/oB1x/E8/Yvh5my9bIcvQvxAlLYhRB5RxEn6PI7dJwPGjtsH59jjTKaWV4HQdGx4EAorX46yImbj40dqRB5lhR2IUTeolJBle4w+Cj4N0WVlkzHB/M4XXQ6tW0ecONhPF0WHGXchgvyKlghMiGFXQiRN9kVTb/u3v5nMLfB/tEZVutGMd97H2olld+P3aLZjP1sOx+OoijGjlaIPCNPFPa5c+fi6+uLhYUFtWvX5vjx48/s27BhQ1QqVYapTZs2+j59+/bNML9ly5avY1eEELnp6WNxQ45Byeao0lJoeX8hQR5TaGF/j8iYZAatOM2A5Sdl1Doh/p/RC/uaNWsYMWIEEyZM4PTp01SuXJkWLVpw//79TPv//fffhIeH66cLFy6gVqt59913Dfq1bNnSoN+qVatex+4IIV4Fu2LQ4094eyFYOmL95DLzk0ax3nc9juoEdl++T9MZ+5m7L4TkVLm5ThRuRi/sM2bMYMCAAfTr149y5coxf/58rKysWLx4cab9HR0dcXd310+7du3CysoqQ2HXaDQG/RwcHF7H7gghXhWVCip1gSHHoWIXVChUjVjLcdsvGOl2hiRtGj/sCKblrIMcuCpvjBOFl6kxN56SksKpU6cYM2aMvs3ExISmTZty9OjRLK1j0aJFdOvWjSJFihi0BwQE4OrqioODA40bN+abb77Byckp03UkJyeTnJys/xwTEwOAVqtFq325m3OeLv+y6ylsJG85UyjyprGH9r+gqtQd9fbRmD66xseJP9DNszofR/fg2EMPei8+TrOyrnzRshTejlbPXV2hyNkrIHnLvpfNWVaXUylGvOvk3r17FC1alCNHjlC3bl19++jRo9m/fz+BgYHPXf748ePUrl2bwMBAatWqpW9fvXo1VlZW+Pn5cf36db788kusra05evQoarU6w3omTpzIpEmTMrSvXLkSK6vn/6MghDAelS4V//vbKBWxEVMlBR0m7NY0ZVT0O0RjjVql0MhToVlRHRYZf/WFyFcSEhLo0aMH0dHR2NraPrNfvi7sH374IUePHuXcuXPP7RcaGkqJEiXYvXs3TZo0yTA/syN2Ly8vHj58+NzkZYVWq2XXrl00a9YMMzOzl1pXYSJ5y5lCm7foMNS7x2NyZRMAqRZOLLXoyZSImqShxtVGw/Am/rxd1RO1icpg0UKbs5ckecu+l81ZTEwMzs7OLyzsRj0V7+zsjFqtJjIy0qA9MjISd3f35y4bHx/P6tWr+frrr1+4neLFi+Ps7ExISEimhV2j0aDRaDK0m5mZ5doXNjfXVZhI3nKm0OXNuTh0+wOu74NtozF9eJX+SbPp6laKSUndWRddmi83XOT3wDC+al2WBiWdM6yi0OUsl0jesi+nOcvqMka9ec7c3Jzq1auzZ88efZtOp2PPnj0GR/CZWbt2LcnJybz33nsv3M6dO3d49OgRHh4eLx2zECIPK9EIPjoMLaeChT020Vf5MXkSBz3nUNXiHpfDY3hvUSB9lxznSkSMsaMV4pUw+l3xI0aMYOHChSxbtozLly8zaNAg4uPj6devHwC9e/c2uLnuqUWLFtGxY8cMN8TFxcUxatQojh07xs2bN9mzZw8dOnTA39+fFi1avJZ9EkIYkak51BkEn5yBOkPAxAyvx0f4WzWav4utwsMkioDgB7T66SAj1gRx50misSMWIlcZ9VQ8QNeuXXnw4AHjx48nIiKCKlWqsH37dtzc3AC4ffs2JiaGf38EBwdz6NAhdu7cmWF9arWac+fOsWzZMqKiovD09KR58+ZMnjw509PtQogCysoRWn4HNT+A3RNRXf6Hag83cdhqN9tsuzD63pv8feYum87do66LCTVjk/F0lFPKIv8zemEHGDp0KEOHDs10XkBAQIa20qVLP3MISUtLS3bs2JGb4Qkh8jOnEtD1d7gdCDvHYnLnOG0eL6OF/WZWWXRhckQdDkSY0WTmQXrX8+WjN0vgUMTc2FELkWNGPxUvhBCvhXdt+GAnvLsMHEtgmvSIXlHzOOv0JQOsDpCsTWXB/lDemLaPGTuDiUpIMXbEQuSIFHYhROGhUkH5jjAkENrOBGt3LOPv8pVuPuddxjHY6TQJySnM3htCg+/38eOOYJ7ES4EX+YsUdiFE4aM2gxrvwydnSGs0nhR1EYrE3mB0/I+ccxnHIMdTJCYnM2dfCA2+38vUbVd4EJv84vUKkQdIYRdCFF7mVujqfcKu8jNIa/gVWDpgHXuDzxOmc855LMMcj5GSksz8/ddp8P1eJmy8wN0ouYte5G1S2IUQhV6q2hJd/U9h2DloPBYsHSkSd4tPE2ZzzvFzxjgfRJWayLKjt3hr2j4+W3uWq5Gxxg5biExJYRdCiKcsbOHNUTD8PDT/FqzdsEwI58O4eZyzG8n3rjuw0sWy7tQdms88wAdLTxAY+uiZT+kIYQxS2IUQ4r801lBvaPoRfJvpYO+DefJjusYs44z1pyx0X4+H6hF7rtyn66/H6Dj3MP+cvYc2TWfsyIWQwi6EEM9kZgE1+8PHp6HzInCrgDo1nmZRazli+Sl/eyyngukdzt6J5pNVZ3hr2j4W7L9OdIK8ylQYjxR2IYR4EbUpVHwHPjoEPdeB7xuodKlUe7Kdzaaj2e8+m45W5wiPTmDKtivUmbKHMX+fJzhCrsOL1y9PjDwnhBD5gkoFJZulT3dPweHZcPkffKKOMYtjTHb0YqWuOXOj6rDqeBqrjt+mTnFHetf1pVk5N8zUciwlXj0p7EIIkRNFq0OXZfDkJpz4DU4vxyYhjA9ZRH/rlRy2asqUR29wLBSOhT7G1UZDt1redK/lhYedpbGjFwWY/PkohBAvw8EXmn8DIy5D21ngWg51aiJvxmxim9loDrhOp6vVKR7HxjN7zzUafL+PActPsu/KfdJ0cje9yH1yxC6EELnBvAjU6AfV+8KtwxC4AK5swTvmFN9zikl2zmw2a870h3XZdUlh16VIitpb0qWGF+/UKEZRezmKF7lDCrsQQuQmlQp8G6RP0Xfg5BI4vRyL+Pu8k7ySzparuWZbh1+i67M5qiIzd19l1p6rvFHShS41itGsnBsaU7Wx90LkY1LYhRDiVbErBk3GwVufw5XNcHIxqpsHKRV9hFkcYaqdEzvNGjPrUW0OXIUDVx9gZ2lG+8qedK5ejMrF7FCpVMbeC5HPSGEXQohXzdQcKrydPj28Bmd+h6CVWMQ/oH3yWtpr1nLXphLLEhuwIq46vx/T8vuxW/i7WvN2taJ0rFIUTzlVL7JICrsQQrxOziWh2dfQeBwEb4OgFXBtJ0Vjz/El5/i8iAVnijRgwZPq7L1fgWnb4/hhRzC1/RzpVLUoLSt4YGdpZuy9EHmYFHYhhDAGtRmUa58+xYTDudVwZgXqR9eoEbObGurdJFk6ss/sDeY/rs6x0BIcC33MuA0Xeau0C+0re9K0rBuW5nI9XhiSwi6EEMZm6wENPoX6w+HuaTi3Bi78hUXCQ1qlbKSVZiNRlt78o2vA4pjq7LqkY9elSKzM1TQp60bbSh68VcoFCzMp8kIKuxBC5B0qFRSrnj61+Bau74Vzf8KVLdgn3qY3K+mtWUlkkdL8nVKbFXHV2XQ2jU1n72GtMaVZOTdaV/TgjZLOUuQLMSnsQgiRF6nNoFSL9Ck5Fq5sSS/yoQG4xQcziGAGaZZzt0g5/kqqwZ8J1Vl/JpX1Z+5SxFxNozKutKrgQcPSLhTRyD/1hYn8tIUQIq/T2EDlbulT/EO4/A9c+BtuHqJo/CU+4RKfaJZz16oM/yRXZ11CFTafS2XzuXDMTU14w9+ZFuXdaVLWFSdrjbH3RrxiUtiFECI/KeIMNd5Pn2Ij04v8pY1w6zBFE64wiCsM0qzgscaLHbrqrI2rwt4r/uy5ch8TFVT3caBZOTealXPHz7mIsfdGvAJS2IUQIr+ycYNaA9KnuAfpg+AEb4XQAByTw+hOGN01G4gzd2a/qhYrYytz/GZZTtx8wndbr1DCpQhNyrrRqLQrNXwd5O1zBYQUdiGEKAisXdLHqq/RL/2afMie9EJ/dQfWyQ9pw1bamG8lxdSa02bVWRtbnj0PKvPrg3h+PRCKjYUpb5ZyoVFpV94q5YKLjZyyz6+ksAshREGjsYHyHdOn1GS4cTD9lH3wVszjH1AndT91TPejmJpww7I8W5IqsiWpElvOadlyLhyAikXtaFjahTdLuVDVyx5TOZrPN6SwCyFEQWaqgZJN0yfdLLh7Cq5ug+BtqO5fonjieT7mPB9rVhJr7soRVVXWx5blyN3y/Hw3mp/3hmCjMaWevxNvlnLhDX8XvJ2sjL1X4jmksAshRGFhYgJeNdOnJuMh6jZc2wnXdkHofmxS7tOCHbQw34FOpeaGRVl2JpVjZ1J5dl8szo6LkQB4O1pRv4QjFtEq6iVocbGTIW7zEinsQghRWNl7Q83+6ZM2EW4dgZDdELIbk4dXKZF4gUFcYJAGkkxtOKuuxJb40hx8Up5VJ+IBNUun7qOchy31/Z2pW9yJmn6OWMtz80Yl2RdCCAFmluDfJH1iCjy5lT7y3fW9cGM/FknR1E49TG3Tw2AKUWauHNSWZVdKBY7cq8Cv92L49UAoahMVFYvaUbeEE3WKO1HDx0EGyHnNJNtCCCEycvD53132aalw7wzcCIDQ/RAWiL32Pu24Tzvz/QDcsyjBodSy7E3w50RYaYLCopgXcF1f6GsXd6S2nyPVfRzl7XSvmBR2IYQQz6c2/d+1+TdHQUoCqaEHubF3Cf6qO6giz+OZdJ0uXKeLefoikebeHEsrzd7EkgSGlWVBWBQL9oeiUkFZd1tq+TlS3ceBGr4OeNjJu+ZzkxR2IYQQ2WNuhVKiMZeCk/Bt3Rqz5Ci4eSD9Gv2tI3D/Em4pt+nAbTqY7wLgkZknJ5XSBCQW52REaZaFe7L0SPojdEXtLfVFvpq3A2XcbeTxupcghV0IIcTLsXaBCp3TJ4CEx3D7aHqRv3kIIs7hpL1HC+7RwmwfAIlqG86blCEgsQQnokux42xx/jl7DwArczWVi9lTzceeql4OVPG2x1nGuM8yKexCCCFyl5UjlGmTPgEkRUPYCQg7BrePwd1TWGpjqZV2glqmJ8AU0lSm3DTz52iyH8e1JTh9oyRHQ50BFZD+iF1lL3uq/P9U3tNWXk37DFLYhRBCvFoWdv8bJAcgTQsR5+B2oL7Yq+MiKZFyhRKqK7z3/9fpY00dOUdJDiX6cSbKnz2Pi7PprAUApiYqSrvbUKmYPZWL2VGpmD0l3axlvHuksAshhHjd1GZQtHr6VHcwKApE3YI7J+HOCQg7DhHnsEl9TH0CqW8WCIAOE+6Y+3FSW5xjKb6cDy/O2ntFWXU8vZRpTE0o52lLxaJ2VChqRwVPu0JZ7PNEYZ87dy4//PADERERVK5cmZ9//platWpl2nfp0qX069fPoE2j0ZCUlKT/rCgKEyZMYOHChURFRVG/fn3mzZtHyZIlX+l+CCGEyAGVChx806eK76S3aRMh/Fx6ob9zHO6cwiTmDt4p1/HmOm///xNzqSpzbpr6cVLrwymtL+fDirPidlHSSD9Nb25qQll3G8p52lHe05ZynraUdbfF0rzgnsY3emFfs2YNI0aMYP78+dSuXZtZs2bRokULgoODcXV1zXQZW1tbgoOD9Z9VKpXB/GnTpjF79myWLVuGn58f48aNo0WLFly6dAkLC4tXuj9CCCFygZkleNdOn56Kufe/o/p7ZyD8HKbJ0fhrg/EnmG7/X+y1KnNumhbnVIo3QaneXLjry193vFhFegcTFfg5F6Gcpx3lPGwp62FDOQ9bXGw0GepJfmT0wj5jxgwGDBigPwqfP38+W7ZsYfHixXzxxReZLqNSqXB3d890nqIozJo1i7Fjx9KhQwcAli9fjpubGxs2bKBbt24ZlklOTiY5OVn/OSYmBgCtVotWq32p/Xu6/Muup7CRvOWM5C37JGc5Y5S8WbpAyVbpE4Cigyc3UIWfRRVxFlV4EKqIc5glx1JSe4WSqiv6Yp+mUnPX1JuzqT6cSfHi8iNvDj3wYtNZW/3qHazMKONuQxl3G0q5WVPazZqSrta5dpPey+Ysq8upFEVRcrSFXJCSkoKVlRXr1q2jY8eO+vY+ffoQFRXFxo0bMyyzdOlS+vfvT9GiRdHpdFSrVo3vvvuO8uXLAxAaGkqJEiU4c+YMVapU0S/31ltvUaVKFX766acM65w4cSKTJk3K0L5y5UqsrOQtRkIIkW8oOookR2KXeAv7hFvYJd7EPuEm5mnxmXZ/pHIgWPHiXKo3l3XeXFG8CVU80f7/ca8KBRcLcLdS8LACD0sFdysFVwt43ZfuExIS6NGjB9HR0dja2j6zn1GP2B8+fEhaWhpubm4G7W5ubly5ciXTZUqXLs3ixYupVKkS0dHR/Pjjj9SrV4+LFy9SrFgxIiIi9Ov47zqfzvuvMWPGMGLECP3nmJgYvLy8aN68+XOTlxVarZZdu3bRrFkzzMxkGMWskrzljOQt+yRnOZOv8qYoaGPuooo4hyryPKrIC6juX0YVdRMn5Qn1eEI903P67mmouWdajEupRbmg9eRqihdXkr04/9gV5f+v3ZuaqPB1sqKka/pRfQmXIvi7FsHHqQga08wr/svm7OnZ5Bcx+qn47Kpbty5169bVf65Xrx5ly5ZlwYIFTJ48OUfr1Gg0aDQZBz8wMzPLtS9sbq6rMJG85YzkLfskZzmTb/Lm7Jc+Vejwv7bkWLh/GSIvQORF/aROjsEr9RZe3KLFv3YtRWXBTbU3F7SeXE71JORRUc4/LMr2i84opBdztYkKH0crirtY4++aPlUqZkcpNxv9enKas6wuY9TC7uzsjFqtJjIy0qA9MjLymdfQ/8vMzIyqVasSEhICoF8uMjISDw8Pg3X++9S8EEKIQk5jA1610qenFAWi78CDK3D/EkReSv/vg2DM05IolXqVUqqrYFDwNYSpi3E51YNLWk+uPy5KyCNPAi67kYopnasVY3qXyq9tt4xa2M3NzalevTp79uzRX2PX6XTs2bOHoUOHZmkdaWlpnD9/ntatWwPg5+eHu7s7e/bs0RfymJgYAgMDGTRo0KvYDSGEEAWFSgX2XulTyWb/a09LhcehcP8iPAj+3/ToGuZpyZRIvU4JrtP2XwU/DTWRph7c13YECklhBxgxYgR9+vShRo0a1KpVi1mzZhEfH6+/S753794ULVqUKVOmAPD1119Tp04d/P39iYqK4ocffuDWrVv0798fSL9jfvjw4XzzzTeULFlS/7ibp6enwQ16QgghRJapTcGlVPr0b2mp6YPrPLjy/9P/F/yH11Br4/FMvYOn8+sN1eiFvWvXrjx48IDx48cTERFBlSpV2L59u/7mt9u3b2Ni8r8bEZ48ecKAAQOIiIjAwcGB6tWrc+TIEcqVK6fvM3r0aOLj4xk4cCBRUVE0aNCA7du3yzPsQgghcpfaFJxKpE9Px8aH9FP6MXfh4VWw8Xj28q+A0Qs7wNChQ5956j0gIMDg88yZM5k5c+Zz16dSqfj666/5+uuvcytEIYQQIutUKrArlj69ZoVrAF0hhBCigJPCLoQQQhQgUtiFEEKIAkQKuxBCCFGASGEXQgghChAp7EIIIUQBIoVdCCGEKECksAshhBAFiBR2IYQQogCRwi6EEEIUIFLYhRBCiAJECrsQQghRgEhhF0IIIQoQKexCCCFEAZInXtua1yiKAkBMTMxLr0ur1ZKQkEBMTAxmZmYvvb7CQvKWM5K37JOc5YzkLfteNmdPa9LTGvUsUtgzERsbC4CXl5eRIxFCCCEMxcbGYmdn98z5KuVFpb8Q0ul03Lt3DxsbG1Qq1UutKyYmBi8vL8LCwrC1tc2lCAs+yVvOSN6yT3KWM5K37HvZnCmKQmxsLJ6enpiYPPtKuhyxZ8LExIRixYrl6jptbW3ly58Dkreckbxln+QsZyRv2fcyOXvekfpTcvOcEEIIUYBIYRdCCCEKECnsr5hGo2HChAloNBpjh5KvSN5yRvKWfZKznJG8Zd/rypncPCeEEEIUIHLELoQQQhQgUtiFEEKIAkQKuxBCCFGASGEXQgghChAp7K/Y3Llz8fX1xcLCgtq1a3P8+HFjh5RnTJkyhZo1a2JjY4OrqysdO3YkODjYoE9SUhJDhgzByckJa2trOnfuTGRkpJEiznumTp2KSqVi+PDh+jbJWebu3r3Le++9h5OTE5aWllSsWJGTJ0/q5yuKwvjx4/Hw8MDS0pKmTZty7do1I0ZsfGlpaYwbNw4/Pz8sLS0pUaIEkydPNhirXPIGBw4coF27dnh6eqJSqdiwYYPB/Kzk6PHjx/Ts2RNbW1vs7e354IMPiIuLy1lAinhlVq9erZibmyuLFy9WLl68qAwYMECxt7dXIiMjjR1antCiRQtlyZIlyoULF5SgoCCldevWire3txIXF6fv89FHHyleXl7Knj17lJMnTyp16tRR6tWrZ8So847jx48rvr6+SqVKlZRhw4bp2yVnGT1+/Fjx8fFR+vbtqwQGBiqhoaHKjh07lJCQEH2fqVOnKnZ2dsqGDRuUs2fPKu3bt1f8/PyUxMREI0ZuXN9++63i5OSkbN68Wblx44aydu1axdraWvnpp5/0fSRvirJ161blq6++Uv7++28FUNavX28wPys5atmypVK5cmXl2LFjysGDBxV/f3+le/fuOYpHCvsrVKtWLWXIkCH6z2lpaYqnp6cyZcoUI0aVd92/f18BlP379yuKoihRUVGKmZmZsnbtWn2fy5cvK4By9OhRY4WZJ8TGxiolS5ZUdu3apbz11lv6wi45y9znn3+uNGjQ4JnzdTqd4u7urvzwww/6tqioKEWj0SirVq16HSHmSW3atFHef/99g7a3335b6dmzp6IokrfM/LewZyVHly5dUgDlxIkT+j7btm1TVCqVcvfu3WzHIKfiX5GUlBROnTpF06ZN9W0mJiY0bdqUo0ePGjGyvCs6OhoAR0dHAE6dOoVWqzXIYZkyZfD29i70ORwyZAht2rQxyA1Izp7ln3/+oUaNGrz77ru4urpStWpVFi5cqJ9/48YNIiIiDPJmZ2dH7dq1C3Xe6tWrx549e7h69SoAZ8+e5dChQ7Rq1QqQvGVFVnJ09OhR7O3tqVGjhr5P06ZNMTExITAwMNvblJfAvCIPHz4kLS0NNzc3g3Y3NzeuXLlipKjyLp1Ox/Dhw6lfvz4VKlQAICIiAnNzc+zt7Q36urm5ERERYYQo84bVq1dz+vRpTpw4kWGe5CxzoaGhzJs3jxEjRvDll19y4sQJPvnkE8zNzenTp48+N5n9vhbmvH3xxRfExMRQpkwZ1Go1aWlpfPvtt/Ts2RNA8pYFWclRREQErq6uBvNNTU1xdHTMUR6lsIs8YciQIVy4cIFDhw4ZO5Q8LSwsjGHDhrFr1y4sLCyMHU6+odPpqFGjBt999x0AVatW5cKFC8yfP58+ffoYObq8688//2TFihWsXLmS8uXLExQUxPDhw/H09JS85WFyKv4VcXZ2Rq1WZ7gbOTIyEnd3dyNFlTcNHTqUzZs3s2/fPoPX5bq7u5OSkkJUVJRB/8Kcw1OnTnH//n2qVauGqakppqam7N+/n9mzZ2Nqaoqbm5vkLBMeHh6UK1fOoK1s2bLcvn0bQJ8b+X01NGrUKL744gu6detGxYoV6dWrF59++ilTpkwBJG9ZkZUcubu7c//+fYP5qampPH78OEd5lML+ipibm1O9enX27Nmjb9PpdOzZs4e6desaMbK8Q1EUhg4dyvr169m7dy9+fn4G86tXr46ZmZlBDoODg7l9+3ahzWGTJk04f/48QUFB+qlGjRr07NlT//+Ss4zq16+f4VHKq1ev4uPjA4Cfnx/u7u4GeYuJiSEwMLBQ5y0hIQETE8MyoVar0el0gOQtK7KSo7p16xIVFcWpU6f0ffbu3YtOp6N27drZ32iOb/0TL7R69WpFo9EoS5cuVS5duqQMHDhQsbe3VyIiIowdWp4waNAgxc7OTgkICFDCw8P1U0JCgr7PRx99pHh7eyt79+5VTp48qdStW1epW7euEaPOe/59V7yiSM4yc/z4ccXU1FT59ttvlWvXrikrVqxQrKyslD/++EPfZ+rUqYq9vb2yceNG5dy5c0qHDh0K3WNb/9WnTx+laNGi+sfd/v77b8XZ2VkZPXq0vo/kLf0plTNnzihnzpxRAGXGjBnKmTNnlFu3bimKkrUctWzZUqlataoSGBioHDp0SClZsqQ87pZX/fzzz4q3t7dibm6u1KpVSzl27JixQ8ozgEynJUuW6PskJiYqgwcPVhwcHBQrKyulU6dOSnh4uPGCzoP+W9glZ5nbtGmTUqFCBUWj0ShlypRRfv31V4P5Op1OGTdunOLm5qZoNBqlSZMmSnBwsJGizRtiYmKUYcOGKd7e3oqFhYVSvHhx5auvvlKSk5P1fSRvirJv375M/y3r06ePoihZy9GjR4+U7t27K9bW1oqtra3Sr18/JTY2NkfxyGtbhRBCiAJErrELIYQQBYgUdiGEEKIAkcIuhBBCFCBS2IUQQogCRAq7EEIIUYBIYRdCCCEKECnsQgghRAEihV0IIYQoQKSwCyHyBJVKxYYNG4wdhhD5nhR2IQR9+/ZFpVJlmFq2bGns0IQQ2STvYxdCANCyZUuWLFli0KbRaIwUjRAip+SIXQgBpBdxd3d3g8nBwQFIP00+b948WrVqhaWlJcWLF2fdunUGy58/f57GjRtjaWmJk5MTAwcOJC4uzqDP4sWLKV++PBqNBg8PD4YOHWow/+HDh3Tq1AkrKytKlizJP//8o5/35MkTevbsiYuLC5aWlpQsWTLDHyJCCCnsQogsGjduHJ07d+bs2bP07NmTbt26cfnyZQDi4+Np0aIFDg4OnPi/9u4epJEtDAPwO/EHkkFBiUqsLIQQBS1UJP4UEhAiCIGITZDBRhJDsLERRWNhJ2o3ELBTDKQQRI2ilgFxQfwBo502EhRsjGCafLcQ5jK77K6scvc6vA8MzDknmfnONG8mZ2C+fUMqlcLh4aEpuHVdRzQaxdjYGC4vL7G1tYXGxkbTOebn5zE8PIyLiwsMDAwgFArh6enJOP/V1RXS6TSy2Sx0XYfT6fzvLgDRV/Gxl9URkRVomiYlJSWiqqppW1hYEJG3V+yGw2HTdzo7OyUSiYiISCKRkKqqKsnn88b4zs6O2Gw2yeVyIiJSX18v09PTP60BgMzMzBjtfD4vACSdTouIyODgoIyOjn7OhIksjGvsRAQA6Ovrg67rpr7q6mpj3+v1msa8Xi/Ozs4AANlsFq2trVBV1Rjv7u5GsVjEzc0NFEXB/f09fD7fL2toaWkx9lVVRWVlJR4eHgAAkUgEwWAQp6en6O/vRyAQQFdX1x/NlcjKGOxEBOAtSL//a/yz2O32d32urKzM1FYUBcViEQDg9/txd3eH3d1dHBwcwOfzIRqNYnFx8dPrJfrKuMZORO9yfHz8Q9vj8QAAPB4Pzs/P8fLyYoxnMhnYbDa43W5UVFSgoaEBR0dHH6qhpqYGmqZhbW0NKysrSCQSHzoekRXxjp2IAACFQgG5XM7UV1paajyglkql0N7ejp6eHqyvr+Pk5ASrq6sAgFAohLm5OWiahng8jsfHR8RiMYyMjKCurg4AEI/HEQ6HUVtbC7/fj+fnZ2QyGcRisXfVNzs7i7a2NjQ3N6NQKGB7e9v4YUFE/2KwExEAYG9vDy6Xy9TndrtxfX0N4O2J9WQyifHxcbhcLmxsbKCpqQkA4HA4sL+/j4mJCXR0dMDhcCAYDGJpack4lqZpeH19xfLyMiYnJ+F0OjE0NPTu+srLyzE1NYXb21vY7Xb09vYimUx+wsyJrEUREfnbRRDR/5uiKNjc3EQgEPjbpRDRb3CNnYiIyEIY7ERERBbCNXYi+i2u2BF9HbxjJyIishAGOxERkYUw2ImIiCyEwU5ERGQhDHYiIiILYbATERFZCIOdiIjIQhjsREREFvIPy+43PKVSgIsAAAAASUVORK5CYII=\n"},"metadata":{}}],"source":["plt.figure(figsize=(12, 5))\n","\n","plt.subplot(1, 2, 1)\n","plt.plot(H_01_300_50.history['loss'], label='Обучающая ошибка')\n","plt.plot(H_01_300_50.history['val_loss'], label='Валидационная ошибка')\n","plt.title('Функция ошибки по эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"]},{"cell_type":"code","execution_count":32,"metadata":{"id":"pP5dd_4LMZqn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485317073,"user_tz":-180,"elapsed":2667,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"7a0b3c25-c98e-456a-d8e9-51ccba27cd0b"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.8761 - loss: 0.4844\n","Loss on test data: 0.4881931245326996\n","Accuracy on test data: 0.8740000128746033\n"]}],"source":["scores_01_300_50=model_01_300_50.evaluate(X_test,y_test)\n","print('Loss on test data:',scores_01_300_50[0])\n","print('Accuracy on test data:',scores_01_300_50[1])"]},{"cell_type":"code","execution_count":33,"metadata":{"id":"2KGjZmc1NVSI","colab":{"base_uri":"https://localhost:8080/","height":238},"executionInfo":{"status":"ok","timestamp":1758485320784,"user_tz":-180,"elapsed":134,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"16a357cb-5197-4fcf-c024-09c14787637c"},"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_6\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_6\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m)            │       \u001b[38;5;34m235,500\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_12 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m30,100\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_13 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,010\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">300</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">235,500</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">30,100</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,010</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m266,610\u001b[0m (1.02 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">266,610</span> (1.02 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m266,610\u001b[0m (1.02 MB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">266,610</span> (1.02 MB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n","</pre>\n"]},"metadata":{}}],"source":["model_01_300_100 = Sequential()\n","model_01_300_100.add(Dense(units=300, input_dim=num_pixels, activation='sigmoid'))\n","model_01_300_100.add(Dense(units=100, activation='sigmoid'))\n","model_01_300_100.add(Dense(units=num_classes, activation='softmax'))\n","model_01_300_100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n","model_01_300_100.summary()"]},{"cell_type":"code","execution_count":34,"metadata":{"id":"_t8LqaEiOiqw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485391291,"user_tz":-180,"elapsed":61754,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"9686c162-0c30-42d2-a4f1-ab91ca97559e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 19ms/step - accuracy: 0.1070 - loss: 2.3687 - val_accuracy: 0.1328 - val_loss: 2.2869\n","Epoch 2/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.1536 - loss: 2.2832 - val_accuracy: 0.1165 - val_loss: 2.2717\n","Epoch 3/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.1559 - loss: 2.2679 - val_accuracy: 0.1893 - val_loss: 2.2564\n","Epoch 4/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.2401 - loss: 2.2525 - val_accuracy: 0.2463 - val_loss: 2.2406\n","Epoch 5/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.2753 - loss: 2.2360 - val_accuracy: 0.3690 - val_loss: 2.2241\n","Epoch 6/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.3593 - loss: 2.2189 - val_accuracy: 0.4323 - val_loss: 2.2067\n","Epoch 7/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4321 - loss: 2.2018 - val_accuracy: 0.4517 - val_loss: 2.1882\n","Epoch 8/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 2.0709\n","Epoch 13/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - accuracy: 0.5436 - loss: 2.0629 - val_accuracy: 0.5803 - val_loss: 2.0408\n","Epoch 14/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.5827 - loss: 2.0324 - val_accuracy: 0.5922 - val_loss: 2.0079\n","Epoch 15/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.5978 - loss: 1.9996 - val_accuracy: 0.5823 - val_loss: 1.9725\n","Epoch 16/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5958 - loss: 1.9629 - val_accuracy: 0.6122 - val_loss: 1.9342\n","Epoch 17/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6200 - loss: 1.9249 - val_accuracy: 0.6068 - val_loss: 1.8931\n","Epoch 18/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.6200 - loss: 1.8817 - val_accuracy: 0.6195 - val_loss: 1.8492\n","Epoch 19/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6285 - loss: 1.8358 - val_accuracy: 0.6505 - val_loss: 1.8028\n","Epoch 20/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6448 - loss: 1.7902 - val_accuracy: 0.6435 - val_loss: 1.7546\n","Epoch 21/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6468 - loss: 1.7447 - val_accuracy: 0.6480 - val_loss: 1.7047\n","Epoch 22/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 1.4521\n","Epoch 27/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7064 - loss: 1.4433 - val_accuracy: 0.7098 - val_loss: 1.4044\n","Epoch 28/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7130 - loss: 1.3961 - val_accuracy: 0.7142 - val_loss: 1.3587\n","Epoch 29/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7169 - loss: 1.3499 - val_accuracy: 0.7298 - val_loss: 1.3147\n","Epoch 30/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7278 - loss: 1.3082 - val_accuracy: 0.7313 - val_loss: 1.2729\n","Epoch 31/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7352 - loss: 1.2638 - val_accuracy: 0.7373 - val_loss: 1.2332\n","Epoch 32/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7420 - loss: 1.2247 - val_accuracy: 0.7425 - val_loss: 1.1955\n","Epoch 33/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7468 - loss: 1.1908 - val_accuracy: 0.7470 - val_loss: 1.1600\n","Epoch 34/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7526 - loss: 1.1557 - val_accuracy: 0.7595 - val_loss: 1.1263\n","Epoch 35/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7569 - loss: 1.1232 - val_accuracy: 0.7653 - val_loss: 1.0945\n","Epoch 36/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.9606\n","Epoch 41/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7838 - loss: 0.9628 - val_accuracy: 0.7887 - val_loss: 0.9379\n","Epoch 42/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7869 - loss: 0.9390 - val_accuracy: 0.7933 - val_loss: 0.9162\n","Epoch 43/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7921 - loss: 0.9161 - val_accuracy: 0.7962 - val_loss: 0.8955\n","Epoch 44/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7966 - loss: 0.8950 - val_accuracy: 0.7988 - val_loss: 0.8759\n","Epoch 45/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8002 - loss: 0.8727 - val_accuracy: 0.8028 - val_loss: 0.8575\n","Epoch 46/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8020 - loss: 0.8602 - val_accuracy: 0.8050 - val_loss: 0.8397\n","Epoch 47/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8066 - loss: 0.8408 - val_accuracy: 0.8083 - val_loss: 0.8229\n","Epoch 48/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8055 - loss: 0.8284 - val_accuracy: 0.8122 - val_loss: 0.8068\n","Epoch 49/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8113 - loss: 0.8099 - val_accuracy: 0.8112 - val_loss: 0.7913\n","Epoch 50/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.7234\n","Epoch 55/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8268 - loss: 0.7266 - val_accuracy: 0.8293 - val_loss: 0.7114\n","Epoch 56/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8308 - loss: 0.7069 - val_accuracy: 0.8328 - val_loss: 0.6999\n","Epoch 57/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8295 - loss: 0.7043 - val_accuracy: 0.8357 - val_loss: 0.6889\n","Epoch 58/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8309 - loss: 0.6982 - val_accuracy: 0.8367 - val_loss: 0.6784\n","Epoch 59/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8373 - loss: 0.6804 - val_accuracy: 0.8402 - val_loss: 0.6682\n","Epoch 60/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8363 - loss: 0.6759 - val_accuracy: 0.8412 - val_loss: 0.6583\n","Epoch 61/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8404 - loss: 0.6558 - val_accuracy: 0.8425 - val_loss: 0.6489\n","Epoch 62/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8392 - loss: 0.6574 - val_accuracy: 0.8430 - val_loss: 0.6399\n","Epoch 63/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8417 - loss: 0.6455 - val_accuracy: 0.8468 - val_loss: 0.6313\n","Epoch 64/100\n","\u001b[1m106/106\u001b[0m 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val_loss: 0.5921\n","Epoch 69/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8507 - loss: 0.5999 - val_accuracy: 0.8553 - val_loss: 0.5851\n","Epoch 70/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8554 - loss: 0.5851 - val_accuracy: 0.8557 - val_loss: 0.5786\n","Epoch 71/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8527 - loss: 0.5844 - val_accuracy: 0.8568 - val_loss: 0.5719\n","Epoch 72/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8574 - loss: 0.5742 - val_accuracy: 0.8583 - val_loss: 0.5656\n","Epoch 73/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8554 - loss: 0.5724 - val_accuracy: 0.8595 - val_loss: 0.5595\n","Epoch 74/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8562 - loss: 0.5662 - val_accuracy: 0.8598 - val_loss: 0.5538\n","Epoch 75/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8580 - loss: 0.5593 - val_accuracy: 0.8608 - val_loss: 0.5482\n","Epoch 76/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8605 - loss: 0.5549 - val_accuracy: 0.8623 - val_loss: 0.5428\n","Epoch 77/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8618 - loss: 0.5465 - val_accuracy: 0.8632 - val_loss: 0.5373\n","Epoch 78/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8612 - loss: 0.5462 - val_accuracy: 0.8652 - val_loss: 0.5321\n","Epoch 79/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8649 - loss: 0.5372 - val_accuracy: 0.8655 - val_loss: 0.5272\n","Epoch 80/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8635 - loss: 0.5326 - val_accuracy: 0.8682 - val_loss: 0.5224\n","Epoch 81/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8665 - loss: 0.5271 - val_accuracy: 0.8683 - val_loss: 0.5177\n","Epoch 82/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.8641 - loss: 0.5286 - val_accuracy: 0.8693 - val_loss: 0.5132\n","Epoch 83/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8683 - loss: 0.5145 - val_accuracy: 0.8693 - val_loss: 0.5086\n","Epoch 84/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8694 - loss: 0.5124 - val_accuracy: 0.8705 - val_loss: 0.5044\n","Epoch 85/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8692 - loss: 0.5131 - val_accuracy: 0.8710 - val_loss: 0.5003\n","Epoch 86/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8731 - loss: 0.5021 - val_accuracy: 0.8718 - val_loss: 0.4963\n","Epoch 87/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8708 - loss: 0.5041 - val_accuracy: 0.8730 - val_loss: 0.4924\n","Epoch 88/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8707 - loss: 0.5002 - val_accuracy: 0.8733 - val_loss: 0.4885\n","Epoch 89/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8720 - loss: 0.4959 - val_accuracy: 0.8737 - val_loss: 0.4849\n","Epoch 90/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8737 - loss: 0.4936 - val_accuracy: 0.8738 - val_loss: 0.4812\n","Epoch 91/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8743 - loss: 0.4866 - val_accuracy: 0.8755 - val_loss: 0.4777\n","Epoch 92/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8738 - loss: 0.4834 - val_accuracy: 0.8772 - val_loss: 0.4744\n","Epoch 93/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8730 - loss: 0.4854 - val_accuracy: 0.8765 - val_loss: 0.4710\n","Epoch 94/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8760 - loss: 0.4751 - val_accuracy: 0.8772 - val_loss: 0.4679\n","Epoch 95/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8773 - loss: 0.4742 - val_accuracy: 0.8775 - val_loss: 0.4647\n","Epoch 96/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8781 - loss: 0.4704 - val_accuracy: 0.8780 - val_loss: 0.4616\n","Epoch 97/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8746 - loss: 0.4729 - val_accuracy: 0.8785 - val_loss: 0.4588\n","Epoch 98/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8787 - loss: 0.4652 - val_accuracy: 0.8797 - val_loss: 0.4558\n","Epoch 99/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8778 - loss: 0.4651 - val_accuracy: 0.8798 - val_loss: 0.4530\n","Epoch 100/100\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.8780 - loss: 0.4608 - val_accuracy: 0.8803 - val_loss: 0.4502\n"]}],"source":["H_01_300_100 = model_01_300_100.fit(\n","    X_train, y_train,\n","    validation_split=0.1,\n","    epochs=100,\n","    batch_size=512\n",")"]},{"cell_type":"code","execution_count":35,"metadata":{"id":"uvTQqGxRO38O","colab":{"base_uri":"https://localhost:8080/","height":487},"executionInfo":{"status":"ok","timestamp":1758485399049,"user_tz":-180,"elapsed":487,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"019606fc-7864-484c-d7d5-c769db23d523"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 1 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\n"},"metadata":{}}],"source":["plt.figure(figsize=(12, 5))\n","\n","plt.subplot(1, 2, 1)\n","plt.plot(H_01_300_100.history['loss'], label='Обучающая ошибка')\n","plt.plot(H_01_300_100.history['val_loss'], label='Валидационная ошибка')\n","plt.title('Функция ошибки по эпохам')\n","plt.xlabel('Epochs')\n","plt.ylabel('loss')\n","plt.legend()\n","plt.grid(True)"]},{"cell_type":"code","execution_count":36,"metadata":{"id":"RGl_z5fmPG7t","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485404216,"user_tz":-180,"elapsed":2727,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"bdfb1abf-1ca4-4eb5-9fbd-c940c2977569"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8814 - loss: 0.4605\n","Loss on test data: 0.4638420343399048\n","Accuracy on test data: 0.8795999884605408\n"]}],"source":["scores_01_300_100=model_01_300_100.evaluate(X_test,y_test)\n","print('Loss on test data:',scores_01_300_100[0])\n","print('Accuracy on test data:',scores_01_300_100[1])"]},{"cell_type":"code","execution_count":37,"metadata":{"id":"fVMBwoRJVXlZ","executionInfo":{"status":"ok","timestamp":1758485422219,"user_tz":-180,"elapsed":571,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"outputs":[],"source":["model_01_300.save(filepath='best_model.keras')\n"]},{"cell_type":"code","execution_count":38,"metadata":{"id":"yFKJ50yqW0jD","executionInfo":{"status":"ok","timestamp":1758485424981,"user_tz":-180,"elapsed":98,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"outputs":[],"source":["from keras.models import load_model\n","model = load_model('best_model.keras')"]},{"cell_type":"code","execution_count":39,"metadata":{"id":"WXonZCRTWLEr","colab":{"base_uri":"https://localhost:8080/","height":517},"executionInfo":{"status":"ok","timestamp":1758485427550,"user_tz":-180,"elapsed":450,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"53e59c68-4e34-4403-fa10-030ef090bc98"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 251ms/step\n","NN output: [[6.7044683e-03 6.5092892e-05 8.0898860e-03 3.6560427e-04 4.4942164e-04\n","  1.0991883e-02 9.6887839e-01 3.7091802e-06 4.2457585e-03 2.0581596e-04]]\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark:  6\n","NN answer:  6\n"]}],"source":["n = 123\n","result = model.predict(X_test[n:n+1])\n","print('NN output:', result)\n","plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n","plt.show()\n","print('Real mark: ', str(np.argmax(y_test[n])))\n","print('NN answer: ', str(np.argmax(result)))"]},{"cell_type":"code","execution_count":40,"metadata":{"id":"cKmqXXcOX80K","colab":{"base_uri":"https://localhost:8080/","height":517},"executionInfo":{"status":"ok","timestamp":1758485432276,"user_tz":-180,"elapsed":243,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"214bd2f9-bbb2-4bbb-a2e3-049385771de7"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n","NN output: [[3.7733166e-04 3.6412096e-04 1.4499854e-03 9.2658949e-01 5.1390834e-04\n","  5.4276615e-02 3.5510810e-05 8.6189411e-04 1.2458544e-02 3.0724849e-03]]\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark:  3\n","NN answer:  3\n"]}],"source":["n = 765\n","result = model.predict(X_test[n:n+1])\n","print('NN output:', result)\n","plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n","plt.show()\n","print('Real mark: ', str(np.argmax(y_test[n])))\n","print('NN answer: ', str(np.argmax(result)))"]},{"cell_type":"code","execution_count":41,"metadata":{"id":"43z4eLyxiLs5","executionInfo":{"status":"ok","timestamp":1758485438169,"user_tz":-180,"elapsed":433,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"outputs":[],"source":["from PIL import Image\n","file_07_data = Image.open('7.png')\n","file_07_data = file_07_data.convert('L')\n","test_07_img = np.array(file_07_data)"]},{"cell_type":"code","execution_count":42,"metadata":{"id":"oZR5iCUtiYAh","colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"status":"ok","timestamp":1758485444072,"user_tz":-180,"elapsed":188,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"ed0cf700-05cd-41b6-c22c-85934b6474f1"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.imshow(test_07_img, cmap=plt.get_cmap('gray'))\n","plt.show()"]},{"cell_type":"code","execution_count":43,"metadata":{"id":"PdnQPo_ziduu","executionInfo":{"status":"ok","timestamp":1758485446770,"user_tz":-180,"elapsed":48,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"outputs":[],"source":["test_07_img = test_07_img / 255\n","test_07_img = test_07_img.reshape(1, num_pixels)"]},{"cell_type":"code","execution_count":44,"metadata":{"id":"PJydX_A6ih1c","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485448538,"user_tz":-180,"elapsed":111,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"273ebf5b-6609-4992-c390-52afe8e38dab"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n","I think it's  7\n"]}],"source":["result = model.predict(test_07_img)\n","print('I think it\\'s ', np.argmax(result))"]},{"cell_type":"code","execution_count":45,"metadata":{"id":"-vu4le7Ii1kD","executionInfo":{"status":"ok","timestamp":1758485451506,"user_tz":-180,"elapsed":468,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"outputs":[],"source":["from PIL import Image\n","file_05_data = Image.open('5.png')\n","file_05_data = file_05_data.convert('L') # перевод в градации серого\n","test_05_img = np.array(file_05_data)"]},{"cell_type":"code","execution_count":46,"metadata":{"id":"d9ddUAozi7Xe","colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"status":"ok","timestamp":1758485452928,"user_tz":-180,"elapsed":179,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"23af732e-e652-46f5-eedb-2ed070e3d881"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.imshow(test_05_img, cmap=plt.get_cmap('gray'))\n","plt.show()"]},{"cell_type":"code","execution_count":47,"metadata":{"id":"Cg9NgtUZi-pN","executionInfo":{"status":"ok","timestamp":1758485456201,"user_tz":-180,"elapsed":47,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"outputs":[],"source":["test_05_img = test_05_img / 255\n","test_05_img = test_05_img.reshape(1, num_pixels)"]},{"cell_type":"code","execution_count":48,"metadata":{"id":"ZPBBAH1fjFEi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485457453,"user_tz":-180,"elapsed":109,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"3bafabf1-4a3a-4d9c-da9c-db401fb1054d"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n","I think it's  5\n"]}],"source":["result = model.predict(test_05_img)\n","print('I think it\\'s ', np.argmax(result))"]},{"cell_type":"code","execution_count":49,"metadata":{"id":"1Prj83mdlIqH","executionInfo":{"status":"ok","timestamp":1758485460398,"user_tz":-180,"elapsed":483,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"outputs":[],"source":["from PIL import Image\n","file_07_90_data = Image.open('7-90.png')\n","file_07_90_data = file_07_90_data.convert('L')\n","test_07_90_img = np.array(file_07_90_data)"]},{"cell_type":"code","execution_count":50,"metadata":{"id":"J-h0NoPblWHP","colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"status":"ok","timestamp":1758485461709,"user_tz":-180,"elapsed":93,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"1bcc94ab-eb24-4153-9539-7ed2343754b5"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.imshow(test_07_90_img, cmap=plt.get_cmap('gray'))\n","plt.show()"]},{"cell_type":"code","execution_count":51,"metadata":{"id":"XDyQEozwlaan","executionInfo":{"status":"ok","timestamp":1758485464505,"user_tz":-180,"elapsed":4,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"outputs":[],"source":["test_07_90_img = test_07_90_img / 255\n","test_07_90_img = test_07_90_img.reshape(1, num_pixels)"]},{"cell_type":"code","execution_count":52,"metadata":{"id":"jTT1aO7HlgA0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485466008,"user_tz":-180,"elapsed":130,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"ba458c09-2bc8-47cf-c8ff-e5fa95111baf"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n","I think it's  2\n"]}],"source":["result = model.predict(test_07_90_img)\n","print('I think it\\'s ', np.argmax(result))"]},{"cell_type":"code","execution_count":53,"metadata":{"id":"r151rpruli75","executionInfo":{"status":"ok","timestamp":1758485468248,"user_tz":-180,"elapsed":435,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"outputs":[],"source":["from PIL import Image\n","file_05_90_data = Image.open('5-90.png')\n","file_05_90_data = file_05_90_data.convert('L')\n","test_05_90_img = np.array(file_05_90_data)"]},{"cell_type":"code","execution_count":54,"metadata":{"id":"voOAg_CslqvJ","colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"status":"ok","timestamp":1758485469423,"user_tz":-180,"elapsed":254,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"9be9b21c-493c-4b96-e512-a54a33f0c3c6"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.imshow(test_05_90_img, cmap=plt.get_cmap('gray'))\n","plt.show()"]},{"cell_type":"code","execution_count":55,"metadata":{"id":"goK2uhgwlvAF","executionInfo":{"status":"ok","timestamp":1758485472543,"user_tz":-180,"elapsed":59,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}}},"outputs":[],"source":["test_05_90_img = test_05_90_img / 255\n","test_05_90_img = test_05_90_img.reshape(1, num_pixels)"]},{"cell_type":"code","execution_count":56,"metadata":{"id":"uqtsomP7lxTu","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1758485473980,"user_tz":-180,"elapsed":146,"user":{"displayName":"Mi Ri","userId":"10370067304318068772"}},"outputId":"b7ceb4a9-05d2-432e-d147-8b757532c0f4"},"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","I think it's  4\n"]}],"source":["result = model.predict(test_05_90_img)\n","print('I think it\\'s ', np.argmax(result))"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[],"mount_file_id":"150ibV5FVGjhfi9jyOYyFoMLj6A0FBqNq","authorship_tag":"ABX9TyNZQfksCD0/vXsU8GuGWaJ1"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/labworks/LW1/Report.md b/labworks/LW1/Report.md
new file mode 100644
index 0000000..f996192
--- /dev/null
+++ b/labworks/LW1/Report.md
@@ -0,0 +1,688 @@
+# Отчёт по лабораторной работе №1
+## по теме: "Архитектура и обучение глубоких нейронных сетей"
+
+---
+Выполнили: Бригада 2, Мачулина Д.В., Бирюкова А.С.
+
+---
+### 1. Создаyние  блокнота в Google Collab и настройка директории
+```python
+import os 
+os.chdir('/content/drive/MyDrive/Colab Notebooks')
+```
+**Импорт библиотек** 
+```python
+from tensorflow import keras 
+import matplotlib.pyplot as plt 
+import numpy as np 
+import sklearn
+```
+
+---
+### 2. Загрузка набора данных MNIST
+```python
+from keras.datasets import mnist 
+(X_train, y_train), (X_test, y_test) = mnist.load_data()
+```
+___
+### 3. Разбиение набора данных на обучающие и тестовые данные
+```python
+from sklearn.model_selection import train_test_split 
+```
+**Объединение обучающих и тестовых данных в один набор**
+```python
+X = np.concatenate((X_train, X_test)) 
+y = np.concatenate((y_train, y_test))
+```
+**Разбиение набора случайным образом (номер бригады - 2)**
+```python
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 10000, train_size = 60000, random_state = 7) 
+```
+**Вывод размерностей**
+```python
+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
+```python
+fig, axes = plt.subplots(1, 4, figsize=(10, 3))
+
+for i in range(4):
+    axes[i].imshow(X_train[i], cmap=plt.get_cmap('gray'))
+    axes[i].set_title(f'Label: {y_train[i]}')  
+
+plt.show()
+```
+![3576](data:image/png;base64,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)
+
+---
+### 5. Предобработка данных
+**Преобразование данных из массива в вектор**
+```python
+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)*
+
+**Кодировка метод цифр по принципу one-hot encoding**
+```python
+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 модели нейронной сети
+```python
+from keras.models import Sequential 
+from keras.layers import Dense 
+```
+
+**Создание и компиляция модели**
+```python
+model_01 = Sequential()
+model_01.add(Dense(units=num_classes,input_dim=num_pixels, activation='softmax'))
+model_01.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) 
+model_01.summary()
+```
+*Model: "sequential_5"*
+<table>
+    <thead>
+        <tr>
+            <th>Layer (type)</th>
+            <th>Output Shape</th>
+            <th>Param #</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td rowspan=1 align="center">dense_10 (Dense)</td>
+            <td rowspan=2 align="center">(None, 10)</td>
+            <td align="center">7,850</td>
+        </tr>
+    </tbody>
+</table>
+
+*Total params: 7,850 (30.66 KB)*
+
+*Trainable params: 7,850 (30.66 KB)*
+
+*Non-trainable params: 0 (0.00 B)*
+
+**Обучение модели**
+```python
+H = model_01.fit(
+    X_train, y_train,
+    validation_split=0.1,
+    epochs=100,
+    batch_size = 512
+)
+```
+**Вывод графика ошибки**
+```python
+plt.figure(figsize=(12, 5))
+
+plt.subplot(1, 2, 1)
+plt.plot(H.history['loss'], label='Обучающая ошибка')
+plt.plot(H.history['val_loss'], label='Валидационная ошибка')
+plt.title('Функция ошибки по эпохам')
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend()
+plt.grid(True)
+```
+![](data:image/png;base64,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)
+
+### 7. Применение модели к тестовым данным
+```python
+scores=model_01.evaluate(X_test,y_test)
+print('Loss on test data:', scores[0]) 
+print('Accuracy on test data:', scores[1])
+```
+*Loss on test data: 0.3511466085910797;*
+
+*Accuracy on test data: 0.9067999720573425*
+
+### 8. Повторные эксперименты с добавлением первого скрытого слоя
+**100 нейронов в первом скрытом слое:**
+```python
+model_01_100 = Sequential()
+model_01_100.add(Dense(units=100,input_dim=num_pixels, activation='sigmoid'))
+model_01_100.add(Dense(units=num_classes, activation='softmax'))
+model_01_100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+model_01_100.summary()
+```
+*Model: "sequential_6"*
+<table>
+    <thead>
+        <tr>
+            <th>Layer (type)</th>
+            <th>Output Shape</th>
+            <th>Param #</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td align="center">dense_11 (Dense)</td>
+            <td align="center">(None, 100)</td>
+            <td align="center">78,500</td>
+        </tr>
+        <tr>
+        <td align="center">dense_12(Dense)</td>
+        <td align="center">(None,10)</td>
+        <td align="center">1,010</td>
+        </tr>
+    </tbody>
+</table>
+
+*Total params: 79,510 (310.59 KB)*
+
+*Trainable params: 79,510 (310.59 KB)*
+
+*Non-trainable params: 0 (0.00 B)*
+```python
+H_01_100 = model_01_100.fit(
+    X_train, y_train,
+    validation_split=0.1,
+    epochs=100,
+    batch_size = 512
+)
+```
+```python
+plt.figure(figsize=(12, 5))
+
+plt.subplot(1, 2, 1)
+plt.plot(H_01_100.history['loss'], label='Обучающая ошибка')
+plt.plot(H_01_100.history['val_loss'], label='Валидационная ошибка')
+plt.title('Функция ошибки по эпохам')
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend()
+plt.grid(True)
+```
+![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfYAAAHWCAYAAACFR6uKAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAgCtJREFUeJzt3Xd4FFX3wPHvpm167xASIPQuTYoK0puCvhZADSioCCqCqLwixYY/G1gQbBQVxNcCKE0CUqQjEDqBBEgCpJDek032/v5YsrIkgTTYlPN5nnmSnbkze+aknJ2ZO3c0SimFEEIIIWoFC3MHIIQQQoiqI4VdCCGEqEWksAshhBC1iBR2IYQQohaRwi6EEELUIlLYhRBCiFpECrsQQghRi0hhF0IIIWoRKexCiNtGr9eTmJjIuXPnzB2KELWWFHYhxC0VFxfH5MmTCQwMxMbGBi8vL1q2bEl6erq5QxOiVrIydwBClGTMmDH88ssvZGZmmjsUUQkRERH07t0bnU7HCy+8wB133IGVlRV2dnY4ODiYOzwhaiUp7KLaSEpKYvny5fz999/s2LGDnJwcBg4cSIcOHXj44Yfp0KGDuUMU5fTMM89gY2PD3r17qVevnrnDEaJO0MhDYER1sHLlSsaPH09mZiZBQUHodDri4uLo0KEDR44cQafTERISwldffYWNjY25wxVlcPDgQTp16sSmTZvo16+fucMRos6Qa+zC7Hbt2sVjjz2Gr68vu3bt4vz58/Tt2xdbW1sOHDjA5cuXGTlyJMuWLeOll14CQClFUFAQ999/f7Ht5ebm4uLiwjPPPAPAtm3b0Gg0/PLLL8XaOjo6MmbMGOPrpUuXotFouHDhgnHeiRMncHNzY+jQoRQUFJi0++eff0y2l5iYiEajYfbs2SbzS5r3wQcfoNFo6NWrl8n8c+fO8dBDD+Hv74+FhQUajQaNRkPr1q1vlEYACgoKeOutt2jcuDFarZagoCD++9//kpeXZ9IuKCiIoUOHmsybNGkSGo3GZN7mzZvRaDSsXbvWOK9Xr17FYj5w4IAxziJ79+7F1taWyMhIWrVqhVarxdfXl2eeeYbk5GST9Uva5jvvvIOFhQUrVqwo93uXplevXsa2JU3X/twBvvjiC2Ps/v7+TJw4kdTU1Bu+R0ZGBuPGjSMwMBCtVkv9+vV59tlniY+PN2lX9DtU2nT978vhw4cZNGgQzs7OODo60qdPH/bu3WtcrpSid+/eeHl5kZCQYJyfn59PmzZtaNy4MVlZWQBERUXx3HPP0axZM+zs7PDw8OChhx4qtv9FMdrY2HDlyhWTZXv27DHGev3fgTAvORUvzO69995Dr9ezcuVKOnbsWGy5p6cn3333HSdPnuTLL79k1qxZeHt789hjj/H++++TnJyMu7u7sf0ff/xBeno6jz32WKVji4mJYeDAgTRv3pz//e9/WFlVzZ9Mamoqc+fOLTa/sLCQ++67j6ioKCZPnkzTpk3RaDS88847ZdruuHHjWLZsGf/5z3+YOnUq+/btY+7cuZw6dYpVq1ZVSewlefXVV4vNS0pKIjc3lwkTJnDvvffy7LPPEhkZyYIFC9i3bx/79u1Dq9WWuL0lS5YwY8YMPvroI0aNGlXu976R+vXrF8v9+vXr+fHHH03mzZ49mzlz5tC3b18mTJhAeHg4Cxcu5MCBA+zatQtra+sSt5+cnMzRo0cZN24cvr6+REREsGjRIjZu3Mj+/fvx9vY2af/mm2/SsGFD4+vMzEwmTJhg0ubEiRPcddddODs788orr2Btbc2XX35Jr1692L59O127dkWj0bB48WLatm3Ls88+y2+//QbArFmzOHHiBNu2bTP2azhw4AC7d+/m0UcfpX79+ly4cIGFCxfSq1cvTp48ib29vcn7W1pa8sMPPxg/WIPhZ2Rra0tubm5Z0i5uJyWEmbm7u6vAwECTeSEhIcrBwcFk3htvvKEA9ccffyillAoPD1eAWrhwoUm7++67TwUFBSm9Xq+UUmrr1q0KUD///HOx93ZwcFAhISHG10uWLFGAOn/+vEpOTlYtW7ZUzZo1U4mJiSbrFbU7cOCAyfwrV64oQM2aNctk/vXzXnnlFeXt7a06duyo7rnnHuP8on2aO3euyfr33HOPatWqVbH4rxUWFqYANW7cOJP5L7/8sgLUX3/9ZZwXGBiohgwZYtJu4sSJ6vp/CaGhoSY5L4rl2pjXr1+vADVw4ECT9WfNmqUA1adPH1VQUGCcX5S7zz77rMRtrlu3TllZWampU6cW28eyvndpSsvjBx98YPy5K6VUQkKCsrGxUf3791eFhYXGdp9//rkC1OLFi2/6Xtc6fvy40mq16sknnzTOK8/v0PDhw5WNjY2KjIw0zrt8+bJycnJSd999t8n6X375pQLUDz/8oPbu3assLS3V5MmTTdpkZ2cXi3HPnj0KUN99912xGEeOHKnatGljnJ+VlaWcnZ3VqFGjStwHYV5yKl6YXUZGRrGjmJL4+PgAGG+Tatq0KV27dmX58uXGNsnJyWzYsIHRo0cXOzWbkZFBYmKiyVSa3Nxc7rvvPq5cucLGjRvx8PCoyK6V6NKlS3z22We88cYbODo6FosRqND7rV+/HoApU6aYzJ86dSoA69atq0i4N6SUYvr06Tz44IN07dq1xDZTpkzB0tLS+Prxxx/Hx8enxHj279/Pww8/zIMPPsgHH3xQ6feuqM2bN5Ofn8/kyZOxsPj33+T48eNxdna+aS6L7tcvmnx8fBg8eDC//vorer2+XLEUFhayadMmhg8fTqNGjYzz/fz8GDVqFDt37jS5dfDpp59mwIABPP/88zz++OM0btyYd99912SbdnZ2xu91Oh1JSUkEBwfj6urKoUOHisXw+OOPc/r0aeMp919//RUXFxf69OlTrn0Rt4cUdmF2/v7+REZG3rRdREQEgEnv6ieeeIJdu3YRFRUFwM8//4xOp+Pxxx8vtv6TTz6Jl5eXyVR0zfF6Y8eOZefOnWRkZBivq1eVWbNm4e/vb+wDcK1mzZrh5ubGRx99xK5du7hy5QqJiYnodLqbbjcqKgoLCwuCg4NN5vv6+uLq6mrMUVVavnw5J06cKFY4AOMHq+bNm5vMt7S0pEmTJsWu5166dIkhQ4aQlZVFUlLSTa+Z3+i9K6soV82aNTOZb2NjQ6NGjW6ay+jo6GK/a6tWrSItLe2GHyhLcuXKFbKzs4vFAtCiRQv0ej0xMTEm87/99luys7M5e/YsS5cuNSnkADk5OcycOZOAgAC0Wi2enp54eXmRmppKWlpasffx8vJiyJAhLF68GIDFixcTEhJi8qFHVB/yUxFmN3ToUJKTk/n2229LbRMfH8+yZcvw8vLizjvvNM5/9NFHsba2Nh61//DDD3Tq1KnEf4IzZ84kNDTUZLK1tS3x/Q4dOsSaNWvw8vLi6aefruQe/uvUqVMsXbqUt99+u8RrtI6Ojvz0009kZWXRs2dPvL298fLyYvfu3WV+j7J0IqsK+fn5vPHGGzz11FM0bdq02PLri8nNRERE0KBBA77//ns2b97MsmXLKvze5ubr61vsd23kyJG37f23bdtm7DB57NixYsuff/553nnnHR5++GH+97//sWnTJkJDQ/Hw8Cj1jMKTTz7Jjz/+yKlTp9ixY4dJp1NRvUjnOWF2M2bMYPXq1UyYMIHTp08zatQoCgsLAcORz5YtW5g5cyYpKSmsWLHCpMOVu7s7Q4YMYfny5YwePZpdu3Yxf/78Et+nTZs29O3b12TetaeIr/XNN99w3333YWlpydChQ/n222956qmnKr2v06dPp3379jzyyCOltunXrx/vv/8+o0ePZtGiRTRq1IipU6cac1KawMBA9Ho9Z8+epUWLFsb58fHxpKamEhgYWOn4r/XFF1+QkJBQrPd2kaIOYeHh4SankItivH5cAj8/P9avX4+Pjw9r1qxh6tSpDB48GC8vr3K/d2UV5er62PPz8413bdyIra1tsTaffvopzs7OeHp6lisWLy8v7O3tCQ8PL7bs9OnTWFhYEBAQYJwXGxvL888/T//+/bGxseHll19mwIABJj//X375hZCQED766CPjvNzc3Bv2+B80aBC2trY8+uij9OzZk8aNG/P333+Xa1/E7SFH7MLsfH192bNnD4MGDeKjjz7ijjvu4IcffiArK4vAwECefPJJ7Ozs+OOPP0o86nn88cc5efIk06ZNw9LSkkcffbTSMd11110ADBkyhEcffZRp06YVu12pvPbs2cOaNWt47733bnhUHRMTw3PPPccLL7zA008/Td++fXFzc7vp9gcPHgxQ7IPNxx9/DBj2papkZGTwzjvv8NJLL+Hr61timz59+qDVavn0009NjgKXL19OfHx8sdvtmjZtauxH8dlnn6HX63nxxRcr9N6V1bdvX2xsbPj0009R1wz18e2335KWlnbDXJZ0xHv48GE2bNjA8OHDy3362tLSkv79+7NmzRqTyxfx8fGsWLGCnj174uzsbJw/fvx49Ho93377LV999RVWVlY89dRTJvthaWlp8hoMOb/Rh0crKyueeOIJjh49ypNPPlmufRC3lxyxi2ohICCANWvWEBsby65du/jggw8ICwtj0aJFtG/fnvbt25daDIcMGYKHhwc///wzgwYNKlNHvPL45JNPaNGiBc8//zz/+9//TJbt2bPH5JppUSemiIgI9u/fT5cuXYzLigZqudHRnl6v5/HHH6d+/fq899575YqzXbt2xkF8UlNTueeee9i/fz/Lli1j+PDh9O7d26R9UcfAItHR0QAm88LCwkp8r0OHDuHp6ckrr7xSajzu7u7MmDGDN954gwEDBnD//fdz7tw5Pv/8c9q1a8e4ceNKXdfX15cPPviAcePG8dhjjxk/tJT1vSvLy8uL6dOnM2fOHAYOHMh9991HeHg4X3zxBZ07d77hrZTR0dEMGTKEhx56iHr16nH8+HG+/vprPD09K9wf4O233yY0NJSePXvy3HPPYWVlxZdffkleXh7vv/++sd2SJUtYt24dS5cupX79+oChYD/22GMsXLiQ5557DjBc/vr+++9xcXGhZcuW7Nmzh82bN9+00+Zbb73FtGnTyvRBU5iRWfvkC1GKkm53u5HnnntOAWrFihXFllX0drdrLVu2TAHq999/N2l3o+na27IApdFo1MGDB022e/3tW++++67SarXqyJEjxdrd7HY3pZTS6XRqzpw5qmHDhsra2loFBASo6dOnq9zcXJN2gYGBN43/2un6290ANW/ePJNtFt3edr0FCxao5s2bK2tra+Xj46OeeeYZlZSUdMM8FLn33ntVgwYNVEZGRoXe+3plvd2tyOeff24S+4QJE1RKSsoN3yMjI0ONHz9eBQYGKhsbG+Xl5aUef/xxFRUVZdKuvLdMHjp0SA0YMEA5Ojoqe3t71bt3b7V7927j8piYGOXi4qKGDRtWLKYRI0YoBwcHde7cOaWUUikpKWrs2LHK09NTOTo6qgEDBqjTp0+rwMDAEv8eSrud7WbLhXnIkLKiVnjppZf49ttviYuLKza4hjnMnj2bbdu2sW3bNnOHIoSoY+Qau6jxcnNz+eGHH3jwwQerRVEXQghzkmvsosZKSEhg8+bN/PLLLyQlJZXY0cpcgoODyc7ONncYQog6SE7Fixpr27Zt9O7dG29vb9544w0mTZpk7pCEEMLspLALIYQQtYhcYxdCCCFqESnsQgghRC0inedKoNfruXz5Mk5OTrdt3G0hhBDiRpRSZGRk4O/vf8MRDKWwl+Dy5csmYy8LIYQQ1UVMTIxxZMGSSGEvgZOTE2BI3rVjMFeETqdj06ZN9O/fv8SneYmSSd4qRvJWfpKzipG8lV9lc5aenk5AQICxRpVGCnsJik6/Ozs7V0lht7e3x9nZWX75y0HyVjGSt/KTnFWM5K38qipnN7tELJ3nhBBCiFpECrsQQghRi0hhF0IIIWoRucYuhAAMt9IUFBRQWFho7lBuK51Oh5WVFbm5uXVu3ytD8lZ+N8uZpaUlVlZWlb7NWgq7EIL8/HxiY2Pr5INrlFL4+voSExMj41aUg+St/MqSM3t7e/z8/LCxsanw+0hhF6KO0+v1nD9/HktLS/z9/bGxsalT/6j1ej2ZmZk4OjrecNAPYUryVn43yplSivz8fK5cucL58+dp0qRJhfMqhV2IOi4/Px+9Xk9AQECdfJ69Xq8nPz8fW1tbKVDlIHkrv5vlzM7ODmtra6KiooztKkJ+GkIIAPnnLEQ1UBV/h/KXLIQQQtQiUtiFEHWWTqczdwiiAuTndmNS2IUQdUZYWBghISE0bdoUNzc3nJ2dSUtLM3dY4ibOnTvHhAkTaNmyJR4eHtjZ2XH69Glzh1VtSWEXQtRoMTExPPnkk8Ye/YGBgbz44oskJSWZtNu2bRs9e/bE19eXlStXcuDAASIiInBxcTFT5KIsTp06RceOHSkoKGDx4sXs27ePyMhImjdvbu7Qqi3pFS+EqLHOnTtHt27daNq0KT/++CMNGzbkxIkTTJs2jQ0bNrB3717c3d1RSjF+/Hjmz5/PuHHjTLah1+vNFL0oi0mTJjFx4kTefvttc4dSY8gR+y02+X9HeeuQJScup5s7FCHKTClFdn6BWSalVJnjnDhxIjY2NmzatIl77rmHBg0aMGjQIDZv3sylS5d4/fXXATh9+jRRUVFEREQQGBiIra0td955Jzt37jTub9OmTfnwww9Nth8WFoZGoyEiIoJt27ah0WhITU01Lh8zZgzDhw83vt64cSM9e/bE1dUVDw8Phg4dSmRkpHH5hQsX0Gg0hIWFAXDp0iUeeughvL29cXJyYsSIEVy8eNHYfvbs2bRv3974OjU1FY1Gw7Zt20qNITIykvvvvx8fHx8cHR3p3LkzmzdvNtmv2NhYHnjgATw8PNBoNMbp2n273rFjx7j33nuxs7PDw8ODZ555hszMzFLjKMrdhQsXjPN69erF5MmTja+DgoKYP3++8fWWLVvQaDTG7WRlZbF161by8/Np0qQJtra2tGnThjVr1pSa07y8PPr27Uvfvn3Jy8sD4MCBA/Tr1w9PT09cXFy45557OHToUKn7WtPJEfstdjk1h8Q8DTEpObQPNHc0QpRNjq6QljP/NMt7n3xzAPY2N//XlJyczJ9//sk777yDnZ2dyTJfX19Gjx7NTz/9xBdffMGVK1fQ6XR8//33fP311zRs2JBPPvmEgQMHEh4ejoODA2PHjmXJkiW8/PLLxu0sWbKEu+++m+DgYJOCW5qsrCymTJlC27ZtyczMZObMmYwYMYKwsLBitzHpdDoGDx6MtbU1f/zxB9bW1rz44osMHz6cAwcOVHiQoMzMTAYPHsw777yDVqvlu+++Y9iwYYSHh9OgQQMApk6dypkzZ9i4cSMBAQHs3r2bBx988Ib7NWDAALp168aBAwdISEhg3LhxZGVl8cMPP1Qozuvp9XqmTp2Ko6OjcV5SUhJKKb788ksWLVpEx44dWbFiBQ888AAHDx40+dADUFhYyKOPPkpmZiabN29Gq9UCkJGRQUhICJ999hlKKT766CMGDx7M2bNnb/ps85pIjthvMX9Xwz+cS6k5Zo5EiNrl7NmzKKVo0aJFictbtGhBSkoKV65cMZ5u/+CDDxg8eDAtWrTgiy++wN/fny+++AKAkJAQwsPD2b9/P2AovCtWrODJJ58EMH54yMkp/W/5wQcf5IEHHiA4OJj27duzePFijh07xsmTJ4u13bx5M0ePHuW7776ja9eu3HHHHSxfvpywsDC2bNlS4by0a9eOZ555htatW9OkSRPeeustGjduzO+//25sExYWxqhRo+jcuTO+vr64u7vfcJsrVqwgNzeX7777jtatW3Pvvffy6aef8tNPPxEfH1/hWK+1bNky8vLyuP/++43zin5ur776KiNHjqRp06bMnj2b3r17Fzu7opRi7NixREREsH79epMPCPfeey+PPfYYzZs3p0WLFnz11VdkZ2ezffv2Kom9upEj9lusvrGw55o5EiHKzs7akpNvDjDbe5dHeU7d9+jRw/i9hYUF3bt3NxZdf39/hgwZwuLFi+nSpQt//PEHeXl5PPTQQwA0adIEGxsbfvzxR6ZMmVLi9s+ePcvMmTPZt28fiYmJxsIUHR1N69atje26d+9OYWEhrq6utGzZ0ji/QYMGBAQEcPLkSfr27Vv2JFwjMzOT2bNns27dOmJjYykoKCAnJ4fo6Ghjm4YNG7J+/XqeffbZmxZ1MHRga9euHQ4ODsZ5PXr0QK/XEx4ejp+fX4ViLZKdnc2MGTNYtGgRv/76a7Hl1/7cAHr27GnyQQVg2rRpbNmyhbFjxxbbp/j4eGbMmMG2bdtISEigsLCQ7Oxsk5zUJnLEfovVczMMCXgpRY7YRc2h0Wiwt7Eyy1TWU9DBwcFoNBpOnTpV4vJTp07h5uaGl5cXbm5uN9zXIuPGjWPlypXk5OSwZMkSHnnkEeMwu+7u7nz88ce89tpr2NnZ4ejoyPLly022NWzYMJKTk/n666/Zt28f+/btAwzD9l7rp59+4q233ipTTOX18ssvs2rVKt59913+/vtvwsLCaNOmjUkM8+bNIy8vD09PTxwdHRk0aFCF368qfPDBBzRr1oxhw4aZzC/rzw0MP+8NGzawcuVK/vzT9DJSSEgIYWFhfPLJJ+zevZuwsDA8PDyK/VxqC7MW9rlz59K5c2ecnJzw9vZm+PDhhIeH33Cdr7/+mrvuugs3Nzfc3Nzo27ev8dRZkTFjxph0CNFoNAwcOPBW7kqp6smpeCFuCQ8PD/r168cXX3xR7PR4XFwcy5cv55FHHkGj0dC4cWOsrKzYtWuXsY1er2f37t0mR8yDBw/GwcGBhQsXsnHjRuNp+CITJ04kLS2N48ePExYWxn333WdclpSURHh4ODNmzKBPnz7GSwElCQgIoGfPnqSmppqcpo+JiSEmJsYkpvLatWsXY8aMYcSIEbRp0wZfX1+TDmwATZs2ZcyYMQQFBbFv3z6++eabG26zRYsWHDlyhKysLJP3sbCwoFmzZhWOFQwd+T766CM++uijYstcXFzw9fU1+bkB7Ny5s1iOvv/+ewYOHMhbb73F+PHjSU//t8Pyrl27eOGFFxg8eDCtWrVCq9WSmJhYqbirM7MW9u3btzNx4kT27t1LaGgoOp2O/v37m/zyXG/btm2MHDmSrVu3smfPHgICAujfvz+XLl0yaTdw4EBiY2ON048//nird6dE9a45FV+eU4ZCiJv7/PPPycvLY8CAAezYsYOYmBg2btxIv379qFevHu+88w4Ajo6OjB8/nmnTprF+/XpOnTrFc889x+XLl5kwYYJxe5aWlowZM4bp06fTpEkTunXrVuw97ezsaNy4McHBwSYdr9zc3PDw8OCrr74iIiKCv/76q9RT9mA4Hd+1a1eeeOIJ9u/fz6FDhxg9ejTt27fn3nvvNbZTSpGbm0tubq6xl3d+fr5xXmFhIXq93jgaW5MmTfjtt98ICwvjyJEjjBo1qtgtfXv37uW///0vv/zyC61ataJevXo3zPPo0aOxtbUlJCSE48ePs3XrVl588UUeeeQRfHx8jO30er0xrqKj4by8POO8km4tXLBgASNGjKBDhw4lvvdLL73E//3f/7Fy5UrOnDnD7Nmz2bp1q0knR8B4+v2ll14iICDAJPdNmjTh+++/59SpU+zbt4/Ro0cX63BZq6hqJCEhQQFq+/btZV6noKBAOTk5qWXLlhnnhYSEqPvvv7/CcaSlpSlApaWlVXgbxm1l5qjAV9eqwFfXqtSs/Epvr67Iz89Xq1evVvn5krPyqEjecnJy1MmTJ1VOTs4tjOzWuXDhggoJCVE+Pj7K2tpaBQQEqOeff14lJiaatMvKylLPPfec8vT0VDY2NurOO+9UO3fuVIWFhSolJUUVFhYqpZSKjIxUgHr//fdv+t7X/68JDQ1VLVq0UFqtVrVt21Zt27ZNAWrVqlVKKaXOnz+vAHX48GGllFIXL15Uw4cPV46OjsrR0VGNGDFCxcTEGLc3a9YsBZRpCgkJMb5H7969lZ2dnQoICFCff/65uueee9SLL76olDL8n61fv7765ptvjO+zdetWBaiUlJRS9/Xo0aOqd+/eytbWVrm7u6tx48apmJgYY95CQkLKFGdRHEopFRgYqOzs7Ez2+fqcFhQUqBkzZih/f39lbW2t2rRpo1avXm1cfn1OlVIqPDxc2dnZqT///FMppdShQ4dUp06dlK2trWrSpIn6+eefVWBgoJo3b16p+3srXP+7VpIb/T2WtTZplKo+h5ERERE0adKEY8eOmXQ0uZGMjAy8vb35+eefGTp0KGA4Fb969WpsbGxwc3Pj3nvv5e2338bDw6PEbeTl5Rk/CQOkp6cTEBBAYmIizs7OldonnU5Hl7l/kanTsHrCnbTyr9z26gqdTkdoaCj9+vXD2tra3OHUGBXJW25uLjExMQQFBVX4MZE1mVKKjIwMnJyc0Gg0/P333/Tr14+oqCiTo9HqbPXq1axZs4YlS5bctve8Pm/i5sqSs9zcXC5cuEBAQECxv8f09HQ8PT1JS0u7YW2qNr3i9Xo9kydPpkePHmUu6mC4DcLf39+kB+nAgQN54IEHaNiwIZGRkfz3v/9l0KBB7NmzB0vL4j1u586dy5w5c4rN37RpU5U8n9pDa0mmDv74axdR7tXmc1SNEBoaau4QaqTy5M3KygpfX18yMzNrbWeiskhMTCQxMZGZM2dy//33Y2dnZ3KdtjorOjAxR7wZGRm3/T1ruhvlLD8/n5ycHHbs2EFBQYHJsuzs7DJtv9ocsU+YMIENGzawc+dO6tevX6Z13nvvPd5//322bdtG27ZtS2137tw5GjduzObNm+nTp0+x5bf6iH3Ugi2EJVnw30HNGNtdRqkpCzlirxg5Yi+/oqOo3377jfHjx9O+fXtWr1590+vOdZ0csZdfnTpinzRpEmvXrmXHjh1lLuoffvgh7733Hps3b75hUQdo1KgRnp6eRERElFjYtVqtcYSia1lbW1dJUXG/uunY9DwpUuVUVT+DuqY8eSssLESj0WBhYVFsdLS6oKhD15gxY4r1ghelK8pb0e+OuLmy5MzCwgKNRlPi33BZ/6bNWtiVUjz//POsWrWKbdu20bBhwzKt9/777/POO+/w559/0qlTp5u2v3jxIklJSZUeRKGi3LWGkyJyL7sQQohbzawfsyZOnMgPP/zAihUrcHJyIi4ujri4OJN7Up944gmmT59ufP1///d/vPHGGyxevJigoCDjOkUPI8jMzGTatGns3buXCxcusGXLFu6//36Cg4MZMMA8I2kVHbFflMIuhBDiFjNrYV+4cCFpaWn06tULPz8/4/TTTz8Z20RHRxMbG2uyTn5+Pv/5z39M1ikaN9jS0pKjR49y33330bRpU5566ik6duzI33//XeLp9tvBeMQug9QIIYS4xcx+Kv5mrn08IVBsBKXr2dnZFRtO0NyKjtjTcnSk5+pwtpVrxkIIIW4N6fFwG2gtwc3eUMzlOrsQQohbSQr7bWIcWlYKuxBC1FhFQ/dWZ1LYbxN/V8P9iBdTyjbAgBBCCPNbtWoVQ4YMISgoCEdHR+666y5zh3RTUthvAwu9jgAXG0B6xgtRla5/kqOHhwcDBw7k6NGj5g5N1AJz585l/PjxDB06lHXr1hEWFsb69evNHdZNVYsBamozy++GMiRmPwWdvwMspGe8EFVs4MCBxjHS4+LimDFjBkOHDiU6OtrMkYma7Ny5c7z77rvs3buXVq1amTuccpEj9lvNwhIL9DTWG/7JyBG7qBGUgvws80zlHOVaq9Xi6+uLr68v7du357XXXiMmJoYrV64Y27z66qs0bdoUe3t7GjVqxBtvvFHsWumFCxdMjv6LptTUVABmz55N+/btje3z8/MJDg42aVMkKCio2HZWr15tXL5x40Z69uyJq6srHh4eDB06lMjIyGKxhIWFFdvu/Pnzja979erF5MmTja/Dw8OxtrY2iVOv1/Pmm29Sv359tFot7du3Z+PGjeV+r+v3AWDo0KG89NJLxtfff/89nTp1wsnJCV9fX0aNGkVCQoLJOmvXrqVdu3bY2dkZczN8+HBuZOHChTRu3BgbGxuaNWvG999/b7L8+tgmT55Mr169St3Hbdu2Ffu5Pf744ybb+fPPP2ncuDHvvPMOXl5eODk58cADD3Dx4kXjOtf/Thw6dAhXV1eT59t//PHHtGnTBgcHBwIDA5k6dapx3JVbRY7YbzHl1QKiduGXfx4IkmvsombQZcO7/uZ57/9eBhuHCq2amZnJDz/8QHBwsMnTHJ2cnFi6dCn+/v4cO3aM8ePH4+TkxCuvvGJsU3T77ebNm2nVqhW7d+/mwQcfLPW9Pv/8c+Lj40td/uabbzJ+/HiAYqNeZmVlMWXKFNq2bUtmZiYzZ85kxIgRhIWFVWp41mnTphUbX/yTTz7ho48+4ssvv6RDhw4sXryY++67jxMnTtCkSZMKv1dJdDodb731Fs2aNSMhIYEpU6YwZswY4+nr1NRUHnnkEcaNG8fq1auxs7PjxRdfNHlWx/VWrVrFiy++yPz58+nbty9r165l7Nix1K9fn969e1dJ3AcPHuT33383mXflyhWOHDmCk5MTGzZsAODFF19k+PDhHDhwoNhY76dPn2bAgAHMmDGDcePGGedbWFjw6aef0rBhQyIiInjuued49dVXWbhwYZXEXhIp7LeaV3MAXDIigN6kZOvIyivAQSupF6IqrF27FkdHR8BQMP38/Fi7dq1JgZwxY4bx+6CgIF5++WVWrlxpUtiLjuCLjv7d3d1Lfc/k5GTefvttXn31Vd54441iy/Py8nB3d8fX17fE9a//wLB48WK8vLw4efJkuZ5uea2tW7eye/duxo0bx9atW43zP/zwQ1599VUeffRRwDB659atW5k/fz4LFiyo0HuV5tqx9hs1asSnn35K586dyczMxNHRkTNnzpCdnW18KicYxh65UWH/8MMPGTNmDM899xwAU6ZMYe/evXz44YdVVtinTJnCtGnTTH6Wer0eS0tLVqxYQUBAAAArVqygcePGbNmyxeSJolFRUfTr14+nn36al19+2WTb155RadCgAa+//jpTp06Vwl6TKa8WAFgnheNsa0V6bgGXUnNo6uNk5siEuAFre8ORs7neuxx69+5t/CeZkpLCF198waBBg9i/fz+BgYanKf700098+umnREZGkpmZSUFBQbGnYxU98tTB4eZnC95880169+5Nz549S1yenJx8w6dvnT17lpkzZ7Jv3z4SExONDweJjo6uUGFXSjF16lRmzZpFUlKScX56ejqXL1+mR48eJu179OjBkSNHTOZ1797d5MNQSY8IHTlypMmjr3NycujYsaPx9cGDB5k9ezZHjhwhJSXFZL9atmxJQEAAVlZW/Pjjj7z00ktlOjtx6tQpnn766WLxf/LJJzddtyxWr17NuXPnmDp1arEPaQEBAcaiDhAYGEj9+vU5efKksbCnpqbSt29fLl68WOKw5Zs3b2bu3LmcPn2a9PR0CgoKyM3NJTs7u0oeC14SucZ+i6mrR+yajMs0dTH8ksu97KLa02gMp8PNMZXzEaAODg4EBwcTHBxM586d+eabb8jKyuLrr78GYM+ePYwePZrBgwezdu1aDh8+zOuvv17s2fOXL1/GwsKi1KPsImfPnuWbb77h//7v/0pcfvHiRfLz82/4UKthw4aRnJzM119/zb59+9i3bx9AsZjK6rvvviMrK4tnn322QuuD4cNPWFiYcSo6or7WvHnzjMsPHTpEhw4djMuysrIYMGAAzs7OLF++nAMHDrBq1Srg3/3y8/Nj4cKFvPvuu9ja2uLo6Mjy5csrHHNl6XQ6XnnlFd555x3s7OxMlrm5uZW63rWn4aOioujatSuzZ8/mySefNPlAdOHCBYYOHUrbtm359ddfOXDgAB988AFQ8Z91WUhhv9VsXci2NpzS6+RguB4n19mFuHWKHolZ9DCp3bt3ExgYyOuvv06nTp1o0qQJUVFRxdb7559/aN68+U2fSf/qq68ybtw4goODS1y+fft27OzsSn3yZFJSEuHh4cyYMYM+ffrQokULUlJSyrmX/8rOzub111/n//7v/4o91tPZ2Rl/f3927dplMn/Xrl20bNnSZF5AQIDxA1JwcDBWVsVP6Pr6+pq0uTZXp0+fJikpiffee4+77rqL5s2bF+s4BxASEkLz5s15+umnCQsL47777rvh/rVo0aJM8VfEwoULcXR05PHHHy+2rHnz5sTExBATE2OcFxUVxcWLF03eu1GjRixdupTXX38dZ2dnk4eWHTx4EL1ez0cffcSdd95J06ZNiYuLq3TcNyOn4m+DDLv62OuSaW11CfCSnvFCVKG8vDzjP8uUlBQ+//xzMjMzGTZsGABNmjQhOjqalStX0rlzZ9atW2c8kgTDkdPKlSuZN28ec+bMueF7RUREEB0dTURERInLIyMjee+997j//vuL9ZRPTU0lPz8fNzc3PDw8+Oqrr/Dz8yM6OprXXnutxO3l5+eTm5trfK2UoqCggMLCQuMp8RUrVtCxY8dSe5ZPmzaNWbNm0bhxY9q3b8+SJUsICwur8iPlBg0aYGNjw2effcazzz7L8ePHeeutt4q1mzp1KhqNhnnz5mFtbY2Tk1OxXF0f/8MPP0yHDh3o27cvf/zxB7/99hubN282aafT6Yy5KiwsRK/XG1+Xdg3//fff548//ijWEQ6gX79+tGjRglGjRjFv3jzA0Hmuffv23HvvvcZ2Tk5Oxg9BS5cupUuXLvznP//hrrvuIjg4GJ1Ox2effcawYcP4+++/jbdm3lJKFJOWlqYAlZaWVult5efnqzMLHlVqlrM6/vV4FfjqWvXc8oNVEGXtlp+fr1avXq3y8/PNHUqNUpG85eTkqJMnT6qcnJxbGNmtERISogDj5OTkpDp37qx++eUXk3bTpk1THh4eytHRUT3yyCNq3rx5ysXFRSml1P79+1VQUJB69913VWFhoXGdrVu3KkClpKQopZSaNWuWAtSHH35YapvAwECTeK6ftm7dqpRSKjQ0VLVo0UJptVrVtm1btW3bNgWoVatWKaWUOn/+/A23s2TJEqWUUvfcc4/SaDTqwIEDxphmzZql2rVrZ3xdWFioZs+ererVq6esra1Vu3bt1IYNG4zLi97r8OHDJjkLDAxU8+bNM76+Nr6i7fbo0UO98MILxnkrVqxQQUFBSqvVqm7duqnff//dZNsrVqxQPj4+6tKlSyY/w/vvv1/dyBdffKEaNWqkrK2tVdOmTdV3331nsvxGubp2Koqj6Oc2dOjQYtu5dh8jIyPVkCFDlL29vXJ0dFQjRoxQFy9eNC6/PtdKKfXmm2+q4OBglZWVpZRS6uOPP1Z+fn7Kzs5O9e/fXy1cuNDkd+Z6N/p7LGtt0lzdGXGN9PR0XFxcSEtLu2EHmLLQ6XQc+/6/3BH9FcmeXbjj4mTaBbiyZmKPm69ch+l0OtavX8/gwYOLnV4UpatI3nJzczl//jwNGza86Wno2kiv15Oeno6zs3OlbjUDQ4/7bdu2ERQUVGzZ8OHDi91fXRGTJ0+mffv2jBkzplLbqayqzFtdUZac3ejvsay1SX4at0G6XX0AnDPOAopLco1diFrJy8vLpNf4tdzc3LCxsan0e1hbW5f6HkKAXGO/LTJs/VFosMpLwYs0rmS6kqsrxNZa/jiFqE0OHDhQ6rKqurZa1KtaiNLIEfttoLewAXfDrS/ttIZ7g2OS5ahdCCFE1ZPCfpsoL8PtEXc6Gm7/CI/PMGc4Qgghaikp7LdJ0UA17WwuARAeJ4VdVC/Sj1YI86uKv0Mp7LeJ8jYMLRt09Slvp2KlsIvqoaj3fElDiAohbq+iv8PK3A0kneduk6Ix492zzqFBz+m4dDNHJISBpaUlrq6uxlHC7O3tSxywo7bS6/XGgWDktq2yk7yV341yppQiOzubhIQEXF1dK3XngxT228WtIVjaYFmQRT1NEhdTLMjI1eFkK/doC/MrGh+9pCFAazulFDk5Ocbng4uykbyVX1ly5urqetPnFdyMFPbbxdIaPJtC/HHudIjjl0wvzsRn0DGw9EdDCnG7aDQa/Pz88Pb2Nj6+tK7Q6XTs2LGDu+++WwZDKgfJW/ndLGdVNUaBFPbbybsFxB+nq0M8v2S24VSsFHZRvVhaWta5wU8sLS0pKCjA1tZWClQ5SN7K73blTC6M3E7ehlveWlpdBJDr7EIIIaqcFPbb6Wphr68zPDJSbnkTQghR1aSw305Xb3lzyjyPFQWcjs2Qe4eFEEJUKSnst5NLANg4YqHPJ9ginoy8Ai6lyrPZhRBCVB0p7LeThYXxdHwv13hATscLIYSoWlLYbzff1gB0sjUMLXtaCrsQQogqJIX9dvMxFPamGDrQnYqVnvFCCCGqjhT22823DQA+ORGAnIoXQghRtaSw327eLQEN2pwEPEjjXGIWubpCc0clhBCilpDCfrtpHcG9EWC4zl6oV0QkZJo5KCGEELWFFHZzuNqBrqdTHCAd6IQQQlQdsxb2uXPn0rlzZ5ycnPD29mb48OGEh4ffdL2ff/6Z5s2bY2trS5s2bVi/fr3JcqUUM2fOxM/PDzs7O/r27cvZs2dv1W6Un4/hOntb6xgAwmVoWSGEEFXErIV9+/btTJw4kb179xIaGopOp6N///5kZWWVus7u3bsZOXIkTz31FIcPH2b48OEMHz6c48ePG9u8//77fPrppyxatIh9+/bh4ODAgAEDyM3NvR27dXNXj9gDdecAOWIXQghRdcz6dLeNGzeavF66dCne3t4cPHiQu+++u8R1PvnkEwYOHMi0adMAeOuttwgNDeXzzz9n0aJFKKWYP38+M2bM4P777wfgu+++w8fHh9WrV/Poo48W22ZeXh55eXnG1+nphiNonU5X6UdYFq1vsh2P5lgDzpnnsUHHqdj0OveozJspMW/ipiRv5Sc5qxjJW/lVNmdlXa9aPbY1LS0NAHf30h9lumfPHqZMmWIyb8CAAaxevRqA8+fPExcXR9++fY3LXVxc6Nq1K3v27CmxsM+dO5c5c+YUm79p0ybs7e0rsivFhIaG/vtCKQZZOmBTmEUTzUVOZDZk5er1ONtUyVvVKiZ5E2UmeSs/yVnFSN7Kr6I5y87OLlO7alPY9Xo9kydPpkePHrRu3brUdnFxcfj4+JjM8/HxIS4uzri8aF5pba43ffp0kw8L6enpBAQE0L9/f5ydnSu0P0V0Oh2hoaH069fP5Pm7lintIWoXdznFcSK9IfVadeGuJp6Veq/apLS8iRuTvJWf5KxiJG/lV9mcFZ1NvplqU9gnTpzI8ePH2blz521/b61Wi1arLTbf2tq6yn5hi23Lty1E7aKLfSyL0iE8IZt7W8ofx/Wq8mdQl0jeyk9yVjGSt/KraM7Kuk61uN1t0qRJrF27lq1bt1K/fv0btvX19SU+Pt5kXnx8PL6+vsblRfNKa1Mt+BYNLXsBgJMytKwQQogqYNbCrpRi0qRJrFq1ir/++ouGDRvedJ1u3bqxZcsWk3mhoaF069YNgIYNG+Lr62vSJj09nX379hnbVAtXx4z3yY4AFCcup5k3HiGEELWCWU/FT5w4kRUrVrBmzRqcnJyM18BdXFyws7MD4IknnqBevXrMnTsXgBdffJF77rmHjz76iCFDhrBy5Ur++ecfvvrqKwA0Gg2TJ0/m7bffpkmTJjRs2JA33ngDf39/hg8fbpb9LJFXc9BYYp2fii/JnE/UkJ1fgL1Ntbk6IoQQogYy6xH7woULSUtLo1evXvj5+Rmnn376ydgmOjqa2NhY4+vu3buzYsUKvvrqK9q1a8cvv/zC6tWrTTrcvfLKKzz//PM8/fTTdO7cmczMTDZu3Iitre1t3b8bsrYFr2YAdHO4jFJwKlbuZxdCCFE5Zj08VErdtM22bduKzXvooYd46KGHSl1Ho9Hw5ptv8uabb1YmvFvPpzUknKSnUyyrstpwMjadjoFu5o5KCCFEDVYtOs/VWVc70LWyMAwte/KydKATQghROVLYzelqB7r6+ZEAnJQOdEIIISpJemqZk6/hYTAOmVHYk8vpOAsKCvVYWcrnLSGEEBUjFcScHL3B0RcNig42F8kr0HM+sfQH4AghhBA3I4Xd3PzaAdDbxXCr3wm5zi6EEKISpLCbm19bADpYRwMyAp0QQojKkcJubleP2BsVRADICHRCCCEqRQq7uV0t7C6Zkdig4+Tl9DLd3y+EEEKURAq7ubkEgK0rFnodLSwvkZKtIy4919xRCSGEqKGksJubRvNvBzrnywCcuCTX2YUQQlSMFPbq4Gph72R7EZAOdEIIISpOCnt1cLWwNyk0jEAnHeiEEEJUlBT26uBqYffMOosFermXXQghRIVJYa8O3BuDjSOWhbk00lzmYkoOadk6c0clhBCiBpLCXh1YWBgfCHOPk6ED3XE5HS+EEKICpLBXF1dPx3e3N3SgO3ZJCrsQQojyk8JeXVwt7M25AEhhF0IIUTFS2KuLq2PG+2SFA4rjUtiFEEJUgBT26sKrOVjaYKXLIECTQFRSNmk50oFOCCFE+Uhhry4srcGnFfBvBzq5n10IIUR5SWGvTnwNp+N7OlwCkNPxQgghyk0Ke3VytQNdC2MHOhmoRgghRPlIYa9O/NobvmSfRjrQCSGEqAgp7NWJT0uwsMImLxk/kjmfmEVGrnSgE0IIUXZS2KsTazvwbgFALyfDQDUybrwQQojykMJe3fh3AOAuhxhAOtAJIYQoHyns1c3V6+ytNecAGYFOCCFE+Uhhr26uHrH7ZRk60ElhF0IIUR5S2Ksbn1ZgYY11fir1NYmcT8wiM6/A3FEJIYSoIaSwVzdWWuMIdHc7xKAUnJQOdEIIIcpICnt1ZOxAJ49wFUIIUT5S2Kujq4W91dUOdNIzXgghRFlJYa+OjB3oTgGKoxdTzRqOEEKImkMKe3Xk3QIstVjrMgjUxHNORqATQghRRmYt7Dt27GDYsGH4+/uj0WhYvXr1DduPGTMGjUZTbGrVqpWxzezZs4stb968+S3ekypmaQ2+bQC4x/EiSsl1diGEEGVj1sKelZVFu3btWLBgQZnaf/LJJ8TGxhqnmJgY3N3deeihh0zatWrVyqTdzp07b0X4t9bV0/F3X+1Ad/SiFHYhhBA3Z2XONx80aBCDBg0qc3sXFxdcXFyMr1evXk1KSgpjx441aWdlZYWvr2+VxWkWVwt7SyIBOBKTasZghBBC1BRmLeyV9e2339K3b18CAwNN5p89exZ/f39sbW3p1q0bc+fOpUGDBqVuJy8vj7y8POPr9HTDfeM6nQ6drnLXtovWL/d2vNtgDXhnnkaDniMxqZWOpSapcN7qOMlb+UnOKkbyVn6VzVlZ19MopVSF3qGKaTQaVq1axfDhw8vU/vLlyzRo0IAVK1bw8MMPG+dv2LCBzMxMmjVrRmxsLHPmzOHSpUscP34cJyenErc1e/Zs5syZU2z+ihUrsLe3r9D+VJZGFTL46DNY6fPpk/cBkaoeb3UswNnGLOEIIYQws+zsbEaNGkVaWhrOzs6ltquxR+zLli3D1dW12AeBa0/tt23blq5duxIYGMj//vc/nnrqqRK3NX36dKZMmWJ8nZ6eTkBAAP37979h8spCp9MRGhpKv379sLa2Lte6FokL4eJ++rpcJjK1Ht4tOnNvM69KxVNTVCZvdZnkrfwkZxUjeSu/yuas6GzyzdTIwq6UYvHixTz++OPY2Nz4ENbV1ZWmTZsSERFRahutVotWqy0239rausp+YSu0rXp3wMX99LCP4cvUzpy4nMGA1v5VEk9NUZU/g7pE8lZ+krOKkbyVX0VzVtZ1auR97Nu3byciIqLUI/BrZWZmEhkZiZ+f322IrIpd7UDXXG/4UHJEesYLIYS4CbMW9szMTMLCwggLCwPg/PnzhIWFER0dDRhOkT/xxBPF1vv222/p2rUrrVu3Lrbs5ZdfZvv27Vy4cIHdu3czYsQILC0tGTly5C3dl1vC/w4APDNOY0UBRy6mUk26RAghhKimzHoq/p9//qF3797G10XXuUNCQli6dCmxsbHGIl8kLS2NX3/9lU8++aTEbV68eJGRI0eSlJSEl5cXPXv2ZO/evXh51cBr0x7BoHXBIi+NVpaXOJIdSExyDg08zNOhTwghRPVn1sLeq1evGx6BLl26tNg8FxcXsrOzS11n5cqVVRFa9WBhAfU6wLltDHC9xJGkQI5cTJXCLoQQolQ18hp7nVKvEwBdtecBGahGCCHEjUlhr+7qGwp7cP5pQIaWFUIIcWM18na3OqVeRwCcM8/hSDbHLllSUKjHylI+kwkhhChOqkN15+gNLg3QoOiijSZHV0jElUxzRyWEEKKaksJeE9Q3HLX3czbcISDX2YUQQpRGCntNcPV0/B1Whg50YTFynV0IIUTJpLDXBFd7xgfmnASUHLELIYQolRT2msCvHWgssc29gh/JnI5LJyuvwNxRCSGEqIaksNcENvbg0xKAXo7R6JXc9iaEEKJkUthriqun4+91MnSgOxSdYs5ohBBCVFNS2GuKqx3oWquzAByOTjVjMEIIIaorKew1xdUR6LwzT2FJIYejU+RJb0IIIYqRwl5TeDYFGycsC3JoYXmJpKx8YpJzzB2VEEKIakYKe01hYQn+7QEY5HYZkOvsQgghipPCXpNcPR3f7eqT3g5LYRdCCHEdKew1Sf3OAATnnwLgkHSgE0IIcR0p7DVJ/S4AOGdE4EwWp2LTyckvNHNQQgghqhMp7DWJoxe4NwKgl0MUBXrFsUsyUI0QQoh/SWGvaQK6AjDA+QIg19mFEEKYksJe01wt7O1VOCA944UQQpiSwl7TXC3svpknsKSQQ9GpMlCNEEIIIynsNY1Xc9A6Y1mQTSvLGK5k5HEpVQaqEUIIYSCFvaaxsDDe9jbINQaQceOFEEL8Swp7TXT1dHwPmwgADkbJdXYhhBAGUthrogaGwh6cdwKQwi6EEOJfUthronodQWOBffZlfEjmxOU0MvMKzB2VEEKIakAKe02kdQKfVgD0dYpCryBMrrMLIYRACnvNdfU6ez/HCwAcuJBsxmCEEEJUF1LYa6qrhb2V/jQA/0RJYRdCCCGFveYKMDwQxjPjNFryORSViq5Qb+aghBBCmJsU9prKNRAcfdHodXSzjSZHV8jJy+nmjkoIIYSZSWGvqTQa41H7UNcoQK6zCyGEkMJeszW4E4BOlmcA+OeC3M8uhBB1nRT2miywOwD108OwQM8/UcnyQBghhKjjpLDXZD5twMYJK10GbawukpiZz4WkbHNHJYQQwozMWth37NjBsGHD8Pf3R6PRsHr16hu237ZtGxqNptgUFxdn0m7BggUEBQVha2tL165d2b9//y3cCzOytDIOLzvc7QIg19mFEKKuM2thz8rKol27dixYsKBc64WHhxMbG2ucvL29jct++uknpkyZwqxZszh06BDt2rVjwIABJCQkVHX41cPV0/HdrcMBOHBeCrsQQtRlVuZ880GDBjFo0KByr+ft7Y2rq2uJyz7++GPGjx/P2LFjAVi0aBHr1q1j8eLFvPbaayWuk5eXR15envF1errhtjGdTodOpyt3fNcqWr+y2ymNpl5XrICGWUcAxYELybfsvW6nW5232kryVn6Ss4qRvJVfZXNW1vXMWtgrqn379uTl5dG6dWtmz55Njx49AMjPz+fgwYNMnz7d2NbCwoK+ffuyZ8+eUrc3d+5c5syZU2z+pk2bsLe3r5KYQ0NDq2Q717PQ6xisscYmL5nGmstEJtVj5er1ONvckre77W5V3mo7yVv5Sc4qRvJWfhXNWXZ22fpQ1ajC7ufnx6JFi+jUqRN5eXl888039OrVi3379nHHHXeQmJhIYWEhPj4+Juv5+Phw+vTpUrc7ffp0pkyZYnydnp5OQEAA/fv3x9nZuVIx63Q6QkND6devH9bW1pXaVmk0qUsgaifDXM8zP6Uerk06MrCVz81XrMZuR95qI8lb+UnOKkbyVn6VzVnR2eSbqVGFvVmzZjRr1sz4unv37kRGRjJv3jy+//77Cm9Xq9Wi1WqLzbe2tq6yX9iq3FYxQT0haie9bSOYT08ORqcxrH39W/Net9ktzVstJnkrP8lZxUjeyq+iOSvrOjX+drcuXboQEREBgKenJ5aWlsTHx5u0iY+Px9fX1xzh3R5XO9A1zT0KKPaeSzJvPEIIIcymxhf2sLAw/Pz8ALCxsaFjx45s2bLFuFyv17Nlyxa6detmrhBvvfqdwcIKu5w46muucDoug6TMvJuvJ4QQotYx66n4zMxM49E2wPnz5wkLC8Pd3Z0GDRowffp0Ll26xHfffQfA/PnzadiwIa1atSI3N5dvvvmGv/76i02bNhm3MWXKFEJCQujUqRNdunRh/vz5ZGVlGXvJ10o29uB/B1zcz/2uF1iQ4s2+88kMbuNn7siEEELcZmYt7P/88w+9e/c2vi7qwBYSEsLSpUuJjY0lOjrauDw/P5+pU6dy6dIl7O3tadu2LZs3bzbZxiOPPMKVK1eYOXMmcXFxtG/fno0bNxbrUFfrBHaHi/vpYx/BgpQu7D2XJIVdCCHqILMW9l69et1wbPOlS5eavH7llVd45ZVXbrrdSZMmMWnSpMqGV7ME9oBd82mWdwyAPZFynV0IIeqiGn+NXVzVoCtoLHDIjMJHk8LZhEyuZMh1diGEqGuksNcWti7g2waAEe4XAKR3vBBC1EFS2GuTwJ4A9LMzPJ99jxR2IYSoc6Sw1yYN7wagRe5hAPbKdXYhhKhzpLDXJoHdQWOJfWY09TVXOJeYRXx6rrmjEkIIcRtJYa9NbJ2hficAHnKPBOQ6uxBC1DVS2GubhvcA0Ed7CpDb3oQQoq6Rwl7bNDIU9ibZhwElHeiEEKKOkcJe29TvDFZ2aHMTaW5xkaikbC6n5pg7KiGEELeJFPbaxkoLgYYH3jzoZrjOvisi0ZwRCSGEuI2ksNdGV6+z97YxXGffKYVdCCHqDCnstdHV6+xBmYexpJC/zyai15c+Jr8QQojaQwp7beTbFmxdsdJl0sXmAslZ+ZyMTTd3VEIIIW4DKey1kYUlNLwLgIc9zgGw4+wVc0YkhBDiNqlQYV+2bBnr1q0zvn7llVdwdXWle/fuREVFVVlwohIa9QKgG4bHuO44I4VdCCHqggoV9nfffRc7OzsA9uzZw4IFC3j//ffx9PTkpZdeqtIARQU17AWAT9pRbMnjYFQKWXkFZg1JCCHErVehwh4TE0NwcDAAq1ev5sEHH+Tpp59m7ty5/P3331UaoKggj8bgXA+NPp/BLhfQFSoZXlYIIeqAChV2R0dHkpIMRWLTpk3069cPAFtbW3JyZDCUakGjgUa9ARjhfBqAv8/KbW9CCFHbVaiw9+vXj3HjxjFu3DjOnDnD4MGDAThx4gRBQUFVGZ+ojCZ9AeiQewCQDnRCCFEXVKiwL1iwgG7dunHlyhV+/fVXPDw8ADh48CAjR46s0gBFJTTqDRpLHDPO0cAikXNXsriYkm3uqIQQQtxCVhVZydXVlc8//7zY/Dlz5lQ6IFGF7FwhoCtE7+Yxj3DeveLJ32cTGdmlgbkjE0IIcYtU6Ih948aN7Ny50/h6wYIFtG/fnlGjRpGSklJlwYkqcPV0fF+ro4Dc9iaEELVdhQr7tGnTSE83jGR27Ngxpk6dyuDBgzl//jxTpkyp0gBFJTXpD0Bgxj9oyWdXRCIFhXozByWEEOJWqVBhP3/+PC1btgTg119/ZejQobz77rssWLCADRs2VGmAopJ8WoOTH5YFOdxjG0F6bgFHLqaaOyohhBC3SIUKu42NDdnZhk5Ymzdvpn9/w1Ghu7u78UheVBMaDQT3AeBRV8Ntb5tPJZgzIiGEELdQhQp7z549mTJlCm+99Rb79+9nyJAhAJw5c4b69etXaYCiClw9Hd+54CAAW07FmzMaIYQQt1CFCvvnn3+OlZUVv/zyCwsXLqRevXoAbNiwgYEDB1ZpgKIKNOoFFlY4ZZ4nyCKBM/GZxCTLbW9CCFEbVeh2twYNGrB27dpi8+fNm1fpgMQtYOtiuO0tahePe57hrQRvNp+KZ2yPhuaOTAghRBWrUGEHKCwsZPXq1Zw6dQqAVq1acd9992FpaVllwYkqFNwXonbRz/oYb9GTLacSpLALIUQtVKFT8REREbRo0YInnniC3377jd9++43HHnuMVq1aERkZWdUxiqpw9Tp7/bQDaMln3/kkMnJ1Zg5KCCFEVatQYX/hhRdo3LgxMTExHDp0iEOHDhEdHU3Dhg154YUXqjpGURV8WoGTPxYFuTzgGomuULHjjDwURgghapsKFfbt27fz/vvv4+7ubpzn4eHBe++9x/bt26ssOFGFNBpoNgiAh5wMo9BJ73ghhKh9KlTYtVotGRkZxeZnZmZiY2NT6aDELdLccFti64xdWKBna3gChXpl5qCEEEJUpQoV9qFDh/L000+zb98+lFIopdi7dy/PPvss9913X1XHKKpK0F2gdcYmN5GethdIydZxKFrG9hdCiNqkQoX9008/pXHjxnTr1g1bW1tsbW3p3r07wcHBzJ8/v8zb2bFjB8OGDcPf3x+NRsPq1atv2P63336jX79+eHl54ezsTLdu3fjzzz9N2syePRuNRmMyNW/evAJ7WQtZ2Rg70YW4Hwdgs5yOF0KIWqVChd3V1ZU1a9Zw5swZfvnlF3755RfOnDnDqlWrcHV1LfN2srKyaNeuHQsWLChT+x07dtCvXz/Wr1/PwYMH6d27N8OGDePw4cMm7Vq1akVsbKxxuvZJdHVei6EAdM3bDSg2n5TCLoQQtUmZ72O/2VPbtm7davz+448/LtM2Bw0axKBBg8oaQrGzAe+++y5r1qzhjz/+oEOHDsb5VlZW+Pr6lnm7dUpwX7C0wTErmhaWlzh1pT6RVzJp7OVo7siEEEJUgTIX9uuPikuj0WgqHEx56fV6MjIyTHrnA5w9exZ/f39sbW3p1q0bc+fOpUGDBqVuJy8vj7y8POProgfZ6HQ6dLrK3etdtH5lt1NlLGyxDLobi8jNjPU4wSsJ9Vl35BIT7mlk7shMVLu81RCSt/KTnFWM5K38Kpuzsq6nUUpVi27RGo2GVatWMXz48DKv8/777/Pee+9x+vRpvL29AcN49ZmZmTRr1ozY2FjmzJnDpUuXOH78OE5OTiVuZ/bs2cyZM6fY/BUrVmBvb1+h/anOAhO30j5mCTHWDbkr4x3q2SteaVdo7rCEEELcQHZ2NqNGjSItLQ1nZ+dS29XYwr5ixQrGjx/PmjVr6Nu3b6ntUlNTCQwM5OOPP+app54qsU1JR+wBAQEkJibeMHllodPpCA0NpV+/flhbW1dqW1UmMwGrT1qhQdEz/3Mu6t3ZPLkngR7V50NMtcxbDSB5Kz/JWcVI3sqvsjlLT0/H09PzpoW9wmPFm9PKlSsZN24cP//88w2LOhg6+jVt2pSIiIhS22i1WrRabbH51tbWVfYLW5XbqjS3eoaHwsTsZbz3KWbF9eDPU1eY2DvY3JEVU63yVoNI3spPclYxkrfyq2jOyrpOhXrFm9OPP/7I2LFj+fHHH43Pgb+RzMxMIiMj8fPzuw3R1SBXB6sZYPkPAOuPxZozGiGEEFXErIU9MzOTsLAwwsLCADh//jxhYWFER0cDMH36dJ544glj+xUrVvDEE0/w0Ucf0bVrV+Li4oiLiyMtLc3Y5uWXX2b79u1cuHCB3bt3M2LECCwtLRk5cuRt3bdq72ph90n+Bw+LTE5cTicqKcvMQQkhhKgssxb2f/75hw4dOhhvVZsyZQodOnRg5syZAMTGxhqLPMBXX31FQUEBEydOxM/Pzzi9+OKLxjYXL15k5MiRNGvWjIcffhgPDw/27t2Ll5fX7d256s6jMfi0RqMKmeB9AoD1x+LMHJQQQojKMus19l69enGjvntLly41eb1t27abbnPlypWVjKoOaf0gxB9nqMVu3qYr64/FMqFXY3NHJYQQohJq3DV2UYVaPwgYTsf7aFI4dimN6KRsMwclhBCiMqSw12VugVC/MxoUE72PAbDhuHSiE0KImkwKe13X+j8ADGIXIL3jhRCippPCXte1GgEaC7zSjhGoiefIxTTOJ0rveCGEqKmksNd1Tj6G57QDk7yPArDq0EVzRiSEEKISpLALaGM4Hd9fGU7H/3b4Enp9tRhpWAghRDlJYRfQYhhYWOOSfoZ22lgupuTwT1SKuaMSQghRAVLYBdi5GZ7TDrzgHQbAb3I6XgghaiQp7MLg6un4HjnbAcW6o7Hk6uRRrkIIUdNIYRcGzQaBjSO2mdEMdr5ARl4Bm0/FmzsqIYQQ5SSFXRjYOECr4QBMcNkLwG+HLpkxICGEEBUhhV38q8PjALRK2YI9uWw/c4UrGXlmDkoIIUR5SGEX/wroCh7BWBRk86zXUQr1ij+OXDZ3VEIIIcpBCrv4l0YD7UcD8IjldgB+OXjxhk/gE0IIUb1IYRem2o0EjQU+qYdpahXHydh0jl5MM3dUQgghykgKuzDl7Ge8p32az0EAVuyLNmdEQgghykEKuyiuw2MA3JOzGQv0/H7kMum5OjMHJYQQoiyksIvimg4Cew9ssuMZ6X6WHF0hqw/LrW9CCFETSGEXxVnZQNtHABjvaHgwzPK90dKJTgghagAp7KJkV0/HByZuo4F1KuHxGRyUB8MIIUS1J4VdlMynFTTojkZfwAzf/YB0ohNCiJpACrsoXZfxAPTOXIc1Baw9FktKVr6ZgxJCCHEjUthF6VoMAyc/rHOuMM7jGPkFen6Vx7kKIUS1JoVdlM7SGjo9CcCT1qEAfL83ikK9dKITQojqSgq7uLE7QsDCGq/UMLraxhCVlE3oSXmcqxBCVFdS2MWNOfkYH+f6utffAHy785wZAxJCCHEjUtjFzXV5BoA2yaF4W2Zw4EIKYTGp5o1JCCFEiaSwi5ur3wn82qMpzGNmPcP48V//LUftQghRHUlhFzen0UCXpwEYkPU71hSw4VgsMcnZZg5MCCHE9aSwi7Jp8x/DrW9ZcUzzO4JewdLdF8wdlRBCiOtIYRdlY6WFOycAMLpwFRr0/HQgRp76JoQQ1YwUdlF2HceC1gWH9HM84XaSzLwCfpRhZoUQolqRwi7KztYZOj8FwAvatYDi67/Pk6srNG9cQgghjKSwi/K5cwJYavFIPcoQ53MkZubJw2GEEKIakcIuysfRGzqMBuB1lz8BWLQ9Uo7ahRCimjBrYd+xYwfDhg3D398fjUbD6tWrb7rOtm3buOOOO9BqtQQHB7N06dJibRYsWEBQUBC2trZ07dqV/fv3V33wdVn350Fjgf+VndzjHEdCRh4r98tRuxBCVAdmLexZWVm0a9eOBQsWlKn9+fPnGTJkCL179yYsLIzJkyczbtw4/vzzT2Obn376iSlTpjBr1iwOHTpEu3btGDBgAAkJCbdqN+oe90bQ8n4A3nTfCMBCOWoXQohqwcqcbz5o0CAGDRpU5vaLFi2iYcOGfPTRRwC0aNGCnTt3Mm/ePAYMGADAxx9/zPjx4xk7dqxxnXXr1rF48WJee+21Erebl5dHXl6e8XV6ejoAOp0Ona5yt3MVrV/Z7VQ73SZjfWIVgXGb6Ok4gJ3pfvy47wKPdW1QJZuvtXm7xSRv5Sc5qxjJW/lVNmdlXc+shb289uzZQ9++fU3mDRgwgMmTJwOQn5/PwYMHmT59unG5hYUFffv2Zc+ePaVud+7cucyZM6fY/E2bNmFvb18lsYeGhlbJdqqTjq5dqZ+6j+mWPzCEaXyy6RTOV45jVYXngWpj3m4HyVv5Sc4qRvJWfhXNWXZ22Ub7rFGFPS4uDh8fH5N5Pj4+pKenk5OTQ0pKCoWFhSW2OX36dKnbnT59OlOmTDG+Tk9PJyAggP79++Ps7FypmHU6HaGhofTr1w9ra+tKbavaSWqC+rIHrfIO08chii1ZgaR7ta6So/ZanbdbSPJWfpKzipG8lV9lc1Z0NvlmalRhv1W0Wi1arbbYfGtr6yr7ha3KbVUbvi2h3UgIW85brmvYkvUCC7ad46HOgThqq+ZXq1bm7TaQvJWf5KxiJG/lV9GclXWdGnW7m6+vL/Hx8Sbz4uPjcXZ2xs7ODk9PTywtLUts4+vreztDrTvueRUsrPFP2stw10gSM/P5eoc8+U0IIcylRhX2bt26sWXLFpN5oaGhdOvWDQAbGxs6duxo0kav17NlyxZjG1HF3AKhYwgAMx1+wzAa3TkS0nPNG5cQQtRRZi3smZmZhIWFERYWBhhuZwsLCyM62nBP9PTp03niiSeM7Z999lnOnTvHK6+8wunTp/niiy/43//+x0svvWRsM2XKFL7++muWLVvGqVOnmDBhAllZWcZe8uIWuOtlsLLFPekw43zOkp1fyPwtZ80dlRBC1ElmLez//PMPHTp0oEOHDoChKHfo0IGZM2cCEBsbayzyAA0bNmTdunWEhobSrl07PvroI7755hvjrW4AjzzyCB9++CEzZ86kffv2hIWFsXHjxmId6kQVcvaDLuMBmKpZjiWF/HQghoiEDDMHJoQQdY9ZO8/16tULpVSpy0saVa5Xr14cPnz4htudNGkSkyZNqmx4ojzumgqHl2OXepY36x3g9Ut38n8bw/n6iU7mjkwIIeqUGnWNXVRjdm7Q+78APJr1PW4WWYSejGfvuSQzByaEEHWLFHZRdTqOBa/mWOam8Hm9zQDMWnMCXaHezIEJIUTdIYVdVB1LKxjwLgDdk36lnd0VwuMz+G5PlJkDE0KIukMKu6hawX2gyQA0+gK+8FoFwLzQM8TL7W9CCHFbSGEXVa//22BhRb2EbYzxiSQzr4B3158yd1RCCFEnSGEXVc+rKXR5GoDp6htsNfmsCbvMnkjpSCeEELeaFHZxa/SaDk7+aNOj+DLAMBLgzDXHyS+QjnRCCHErSWEXt4atMwz+AIC7E3+ks30cZxMyWbQ90syBCSFE7SaFXdw6LYZCsyFo9AUscv0eDXo+++ssp+PK9uhBIYQQ5SeFXdxag98HG0c8kg/zZv1/0BUqXv75iNzbLoQQt4gUdnFrudSHe98AYHTGYoJtMzh+KZ2v5NGuQghxS0hhF7del/Hg3wGLvHR+8P4BUHyy+Sxn4uUhMUIIUdWksItbz8IShi8CSy2+CX8zp94B8gv1TPv5CAVySl4IIaqUFHZxe3g3h76zAHg8/Sta2CZy5GIan8pz24UQokpJYRe3T9cJENgTC102y92XYoGez7ZGyMA1QghRhaSwi9vHwgKGfwE2TrgnH+KzwJ0oBS/9FEZKVr65oxNCiFpBCru4vdwCYdB7AAxOXMwA9zji0nN55dejKKXMHJwQQtR8UtjF7dd+NDQfiqYwn88s5+NumUvoyXh+2CuPdxVCiMqSwi5uP40G7v8cXBpgkxHNr/VWAIq31p3i6MVUc0cnhBA1mhR2YR52bvDQUrCwpmHCZt6pt5f8Aj3Pfn+QpMw8c0cnhBA1lhR2YT71O0L/twAYlfolA90uczktl+d/PCz3twshRAVJYRfm1fXZf6+3W32Kv002uyOT+DBU7m8XQoiKkMIuzEujgfsXgFsQ1hnR/OHzNVYU8O2uKA4maswdnRBC1DhS2IX52bnCoz8angJ3ZR8/Bf4OwI8RFoTFpJo1NCGEqGmksIvqwaclPPA1oKFj/C/M9tuLTml4ZvlhYpKzzR2dEELUGFLYRfXRfDDcOwOAkLQvGGp/kuQsHWOW7CctW2fm4IQQomaQwi6ql7umQusH0egL+Egzj65OV4i8ksUzP/xDfoH0lBdCiJuRwi6ql6ud6fT+HdEWZvG9zf/RUJvO3nPJvPLLEfR6GXZWCCFuRAq7qH6s7Sh8eDmZWh9ssi7zh9t8XCxyWB12mdl/nJAx5YUQ4gaksIvqycGTPY1fQTl445h6ms3+X6HV6PhuTxQfbgo3d3RCCFFtSWEX1Va21ouCR1eCjSNeifvYFLgcSwpZsDWSRdsjzR2eEEJUS1LYRfXm2xYe+R4srAmM28T6wJVo0PPehtN8L0+DE0KIYqSwi+qv8b3w0BLQWNIsfh2rGvwCKN5YfZxluy+YOzohhKhWpLCLmqHFMHjgK9BY0D5hNT8HrgEUs34/wTd/nzN3dEIIUW1Ui8K+YMECgoKCsLW1pWvXruzfv7/Utr169UKj0RSbhgwZYmwzZsyYYssHDhx4O3ZF3Ept/mMYVx7oHP8/fm64DlC8ve4UX8o1dyGEAKpBYf/pp5+YMmUKs2bN4tChQ7Rr144BAwaQkJBQYvvffvuN2NhY43T8+HEsLS156KGHTNoNHDjQpN2PP/54O3ZH3GrtR8GQjwHoHLuCNUG/oUHP3A2nmb/5jNwKJ4So88xe2D/++GPGjx/P2LFjadmyJYsWLcLe3p7FixeX2N7d3R1fX1/jFBoair29fbHCrtVqTdq5ubndjt0Rt0Pnp+C+zwAN7eJ+ZUODFVhSyPzNZ5m55gSFMoiNEKIOszLnm+fn53Pw4EGmT59unGdhYUHfvn3Zs2dPmbbx7bff8uijj+Lg4GAyf9u2bXh7e+Pm5sa9997L22+/jYeHR4nbyMvLIy8vz/g6PT0dAJ1Oh05XuTHKi9av7Hbqmpvmrc1INBY2WK55juYJ69nSII/+MSF8vzeKxIxcPvhPG7RWZv/cetvJ71v5Sc4qRvJWfpXNWVnX0ygznru8fPky9erVY/fu3XTr1s04/5VXXmH79u3s27fvhuvv37+frl27sm/fPrp06WKcv3LlSuzt7WnYsCGRkZH897//xdHRkT179mBpaVlsO7Nnz2bOnDnF5q9YsQJ7e/tK7KG41XxTD9LpwgIsVQGR2lY8kD6ZNOVAUxc9TzXVY2vWj65CCFF1srOzGTVqFGlpaTg7O5farkYX9meeeYY9e/Zw9OjRG7Y7d+4cjRs3ZvPmzfTp06fY8pKO2AMCAkhMTLxh8spCp9MRGhpKv379sLa2rtS26pLy5E1zbhuWv4Sg0WWR6dqMYcmTOZ/vQlNvR758rAP13exuU9TmJ79v5Sc5qxjJW/lVNmfp6el4enretLCb9XjG09MTS0tL4uPjTebHx8fj6+t7w3WzsrJYuXIlb7755k3fp1GjRnh6ehIREVFiYddqtWi12mLzra2tq+wXtiq3VZeUKW/N+sHY9bD8IRxTw/nT6S2eyJvG3gT4z5f7+OqJTnQMrFt9LOT3rfwkZxUjeSu/iuasrOuY9SKkjY0NHTt2ZMuWLcZ5er2eLVu2mBzBl+Tnn38mLy+Pxx577Kbvc/HiRZKSkvDz86t0zKKa8m8P4zaDRxNssi6zwnIWozzPkZSVz8iv97Im7JK5IxRCiNvC7L2LpkyZwtdff82yZcs4deoUEyZMICsri7FjxwLwxBNPmHSuK/Ltt98yfPjwYh3iMjMzmTZtGnv37uXChQts2bKF+++/n+DgYAYMGHBb9kmYiVsgPLUJAu7EIi+dd7JmMbfebvILCnlxZRhz15+ioFCe6S6EqN3M3rXokUce4cqVK8ycOZO4uDjat2/Pxo0b8fHxASA6OhoLC9PPH+Hh4ezcuZNNmzYV256lpSVHjx5l2bJlpKam4u/vT//+/XnrrbdKPN0uahl7d3hiDaydjObIj4xM+pzmATE8HPMgX+44x7FLaXw2sgMejvK7IISoncxe2AEmTZrEpEmTSly2bdu2YvOaNWtW6kAkdnZ2/Pnnn1UZnqhprG1h+ELwbgmhM+lwZQ17611kxJWn2R0JQz/bycLHOtI+wNXckQohRJUz+6l4IW4JjQZ6vACj/gdaZzySDvKX4xuMcD1HbFouDy/aw+Kd52WkOiFErSOFXdRuTfvDuC3g1QKr7AQ+zpvJfP/N6AoLeHPtScZ/9w/JWfnmjlIIIaqMFHZR+3k1hfFboP1oNErP8OTF7Kz3BX5W6Ww+lcCgT3awJzLJ3FEKIUSVkMIu6gYbBxj+heHpcFZ21Evazd9Ob/CwWzjx6XmM+mYv76w7Sa6u0NyRCiFEpUhhF3VLh8dg/F/g3RKrnCu8nzOH7+uvwVrp+Prv8wz7bCfHLqaZO0ohhKgwKeyi7vFpaSjuXZ4G4K7Enzjo+x53OsRyNiGTEV/sYl7oGfIK5OhdCFHzSGEXdZO1HQz+AEb+BPYeOKWe4kf1Gp/W24LSF/DJlrMM/XQnB6NSzB2pEEKUixR2Ubc1GwgT9kCzIWj0Ou5L+paDvu/R1SGOswmZ/GfRbmatOU5mXoG5IxVCiDKRwi6Ekw88uhwe+BpsXXFNPcFK9Srf1t+Ajcpn2Z4o+n60nT+OXJb73oUQ1Z4UdiHAMKBN24fhub3QbDAavY4+id8T5jmTB13CiUvP5fkfDzP6m31EJGSYO1ohhCiVFHYhruXsB4+ugEd+ACd/7DKj+ShvDn/WX0KgVTK7I5MYOP9v3l57krQcnbmjFUKIYqSwC3E9jQZaDINJ+6HrBNBY0CwxlK2205jnG4qlPo9vdp6n1wdb+W7PBXTyxDghRDUihV2I0midYNB78PQ2aNANi4IcRqQuIcxjBmPdjpKSnc/MNScYOH8HoSfj5fq7EKJakMIuxM34tYOxG+DBbw2n57MuMivnPfb5fchd9lFEXsli/Hf/8ODC3ew7J0PTCiHMSwq7EGWh0UCb/8CkA3D3NLCywyflMN/rp7O+3hKCra9wKDqVR77ay5gl+2X0OiGE2UhhF6I8tI5w7wx44RC0fwzQ0DIplFDrqfyv3k/Us0hhW/gVhn2+k3HL/uH4JSnwQojbSwq7EBXh7A/DF8Czf0PjPmj0BXRJWsNOuyks9V+FtyaNzafiGfrZTp7+7h+OXkw1d8RCiDpCCrsQleHbBh7/zXANvkF3NIV59Er+mb32L7HM71f8NElsOhnPfZ/v4rFv9rE7IlE62Qkhbikp7EJUhcDuMHY9jP4V6nXCojCXe1J+ZbfdVFb4rSTIIoGdEYmM+mYfw7/YzbqjsRTIbXJCiFtACrsQVUWjgSZ9YdxmeHwVBPZAo8+ne8rvbNVO4Xe/JbS1iuFITCoTVxyi14fbWLzzvIxDL4SoUlLYhahqGg00vtdwBD9mPQT3RaP0tE0J5XerV/nLbwED7U9zMSWbN9eepNu7W5jzxwnOJ2aZO3IhRC1gZe4AhKjVgnoYptgjsHM+nFxNo5RdLGIXqZ7BfK0byDdpnViy6wJLdl2gVzMvQroFcXdTLywtNOaOXghRA0lhF+J28GsHDy2BpBmwbxEcXo5rZgTT+JzJzm6E2vbn3Svd2RYO28KvUM/Vjkc7B/Bw5wB8nG3NHb0QogaRU/FC3E4ejWHwBzDlJPR/G1waYJ2fwuD0n/jb9iX+9F3IILsTXE7N4qPQM3R/7y+e/u4fNp+Ml852QogykSN2IczBzhW6P294yMzZP2H/V2jObaNZ6t8s5G+y3OuxxqIv8xK7sOmkYtPJeLyctDx4R30e6lSfxl6O5t4DIUQ1JYVdCHOytILmQwxT4lk48A0c+RGH7EuMYhkj7X7gjEsPFqXfyR8ZrVm0PZJF2yNpV9+FER3qMaydPx6OWnPvhRCiGpHCLkR14dkEBv0f9JkFJ1fDwaVoYvbRLHUH89jBey7ubNf24rOkThy5GMiRi2m8ve4UdzXx5L72/vRr6YtWLq4JUedJYReiurGxh/ajDFP8STiyAo78hDYrgf55v9Hf+jdSXBuzRt3FouSObA1XbA2/gq31MXo39cJXp6F3fiHW1tbm3hMhhBlIYReiOvNpaehk12c2RG6BsOUQvgG3rEjGEEmI7TIuOnfg17zO/JDWng0n9IAlK9/byr0tfBjc2o9ezbxw0MqfuhB1hfy1C1ETWFpB0wGGKSfVcKr+yE9ooncTkH6IyRziRTsLLjh24Mf0tqzK68y6o3rWHY1Fa2XBXU086d/Kl74tfHB3sDH33gghbiEp7ELUNHau0HGMYUqNhpNr4MQqNJcO0jDjIP/VHGS67VIuOrVldW5HVma0Y/MpPZtPJWChgY6BbvRt4UOfFj409nJAo5GBcISoTaSwC1GTuTYw3DbX/XlIuUDhsd9I2/sD7tmRBGQc4XmO8LwtJDg048/CTixPa8OBC4oDF1KYu+E0QR729G7uTe9m3nRp6I6ttaW590gIUUlS2IWoLdyC0Hd7nr9TGjO4Zzusz26AU39A9B68s8J5nHAe10KmrR97rTqxMrUFfye1ZMmubJbsuoCdtSXdGntwdxNP7m7qRUNPOZoXoiaSwi5EbeRcD+6cYJiyEuHMRji1Fs5txTE3lr78QV+rPyjU2nLGvgNrs1uxJrs1f50u5K/TCQDUd7PjriZe9Az2pHtjD9zk2rwQNYIUdiFqOwdP6PCYYcrPhgt/Gwr9mT+xTL9Ei4w9tGAP07SQahfIXov2/JbWlJ0pLfhxfw4/7o9Go4FW/s50b+xJt8YedA5yx1F62gtRLVWL4SwWLFhAUFAQtra2dO3alf3795fadunSpWg0GpPJ1tb0IRlKKWbOnImfnx92dnb07duXs2fP3urdEKL6s7E39KwfOg9eOgETdkPf2RDYAzSWuOZEMTBrDV9ZfcBx+2fY7vk+b7qs5Q7COX0pma92nGPskgO0m7OJEV/s4r0Np9kankBGrs7ceyaEuMrsH7l/+uknpkyZwqJFi+jatSvz589nwIABhIeH4+3tXeI6zs7OhIeHG19ffx3w/fff59NPP2XZsmU0bNiQN954gwEDBnDy5MliHwKEqLM0GvBpZZh6vmS4je7C3xD5F0RswSI1isDMMJ4gjCe0UGBpR6RdW/7Ka8bGzGCORjfkcHQqi7ZHYqGB1vVc6BToTucgNzoGueHtJH9rQpiD2Qv7xx9/zPjx4xk7diwAixYtYt26dSxevJjXXnutxHU0Gg2+vr4lLlNKMX/+fGbMmMH9998PwHfffYePjw+rV6/m0UcfLbZOXl4eeXl5xtfp6ekA6HQ6dLrKHYkUrV/Z7dQ1kreKqVTerBwgeKBhGgCknEdzfgcWF3agidqJVXYSzTL30Yx9TNCCzsqBSG1LduQ1YUtWY8IuNuboxTQW7zoPQAN3Ozo2cKV9gCt3NHClibdjtXzGvPyuVYzkrfwqm7OyrqdRSqkKvUMVyM/Px97enl9++YXhw4cb54eEhJCamsqaNWuKrbN06VLGjRtHvXr10Ov13HHHHbz77ru0atUKgHPnztG4cWMOHz5M+/btjevdc889tG/fnk8++aTYNmfPns2cOXOKzV+xYgX29vaV31EhajqlxznnIp6ZJ/HMPI1H5mlsCrNNmhRgyXnLhuwrbMb2/GYc0DcjFSfjcq2lItBREegIQY6KQCeFk4x6K0SZZWdnM2rUKNLS0nB2di61nVmP2BMTEyksLMTHx8dkvo+PD6dPny5xnWbNmrF48WLatm1LWloaH374Id27d+fEiRPUr1+fuLg44zau32bRsutNnz6dKVOmGF+np6cTEBBA//79b5i8stDpdISGhtKvXz8Zu7scJG8Vc9vypi9El3ACi5h9aGL2oonZi1VmPE0KI2hCBI/ZrAMgyTaQE5ombMsKZJ+uMafTAjiT9u+98v4utrSp50ybei60re9Ma39nnGxv789bftcqRvJWfpXNWdHZ5Jsx+6n48urWrRvdunUzvu7evTstWrTgyy+/5K233qrQNrVaLVpt8UdfWltbV9kvbFVuqy6RvFXMrc+bNQR0NEw8B0pBygWI2QdRuyF6LySG45Ebxd1EcbcFcPU6fZS2GQcKGrM1qwFH0xrxZ1oOf55MMG65oacDbeq50KaeC63qOdPKzwUX+1v/OyC/axUjeSu/iuasrOuYtbB7enpiaWlJfHy8yfz4+PhSr6Ffz9ramg4dOhAREQFgXC8+Ph4/Pz+TbV57al4IUYU0GnBvaJjaXe3Hkp0Mlw7CxQNXp3+wykuncXYYjQnjUWvAGrKt3TlnHcyB/EB2ZTfgaGIjfk/M4vcjl42bD3C3o5WfCy38nGnpb5j8XWxlAB0hSmDWwm5jY0PHjh3ZsmWL8Rq7Xq9ny5YtTJo0qUzbKCws5NixYwwePBiAhg0b4uvry5YtW4yFPD09nX379jFhwoRbsRtCiJLYu0OTfoYJQK+HxDNXi/x+uHQIEk5hr0umtW4/rdnP2Ktj4GTZeBBp1YRD+QHsya7HiZQgNiZ7sfHEv5fTnG2taO7nTAtfJ5r7OdPM14lmPk7yJDtR55n9L2DKlCmEhITQqVMnunTpwvz588nKyjL2kn/iiSeoV68ec+fOBeDNN9/kzjvvJDg4mNTUVD744AOioqIYN24cYOgxP3nyZN5++22aNGlivN3N39/fpIOeEOI2s7AA7+aG6Y7HDfN0ORB/Ai4fhsthhq9XTuGQn0Tb/CTaspcxV4t9npUjMTbBHC8IYE+2PyfyAjhyvj77zyebvE2Aux3NfJxp6uNIUx8nmvg40tjLUcbBF3WG2Qv7I488wpUrV5g5cyZxcXG0b9+ejRs3Gju/RUdHY2Hx7zg6KSkpjB8/nri4ONzc3OjYsSO7d++mZcuWxjavvPIKWVlZPP3006SmptKzZ082btwo97ALUd1Y20H9ToapSH42xB2DuKMQGwaxRyHhFNqCTIILwggmjOFWgBUoLEi2rU+kpgFhef4czvXjTEp9tib7svnUv4XcQgMN3O0J9jYU+ibehmLf2NsRbbUYpkuIqmPW292qq/T0dFxcXG56S0FZ6HQ61q9fz+DBg6WDSTlI3iqm1uatIN9wGj/u2L9FP+EkZCeV2LxQY80VbQMiqc/hPD+O5fkRofyJUj4UXHc84+OsxUWTQ+dmgQT7ONHIy5FGng74u9pVy/vuq4ta+7t2C1U2Z2WtTWY/YhdCiJuysgHf1oaJkYZ5SkFmAiScgPiTcOUUJJyChNNY6rLwzY3El0h6AFw9nV+osSJJW5/z1OdEvjfHcn04l+HHOeXP8v0xJm9pY2VBoLs9DT0daOjpQJCnA0Eehu99nLXScU9UW1LYhRA1k0YDTj6GqfG9/87X6yEtBq6cNhzVJ5w2FP3Es1jqsvHOvYA3F+gKxoIPkGnlzmWr+pwt9OVYrhcRhb6cv+LL1gQfNl33r9LW2oJAdwcaeNgT5GFPAw8HGrjbE+huTz03O6wt5fy+MB8p7EKI2sXCAtwCDVPTAf/O1+sh/SJcOQOJ4ZB4FpIiUFfC0WQl4FiQTNOCZJpylCGWwNVL9HosSLXx5ZKFHxEF3pzI9eRCoTdRCT7siPcmFNPH2VpowM/Fjgbu9gS4G77WdzN8X9/NHi9HLRZyil/cQlLYhRB1g4UFuDYwTE36GmcX6HRs+uMXBnRshFValLHgG6ZILHRZuOdfxp3LtAFGXPdfM93akzgLX6L0XpzO8+BcgRfRad5EpHqz55wrYFrEbawsqOdqR303O+q5Xp3c/v3q62yLlRzxi0qQwi6EqPMKLO1R/ndAYFfTBUpBRhwknzNMKechKdLwNfkC5KXhrEvEmUSaAv0sMDm9r7PQkmTtx2W8OF/gQXieOzF6Ly4leXIy0ZO/ceb6wm+hAV9nW/xd7fB3tcPP1RZ/Fzv8XAzz/FxscXewkWv8olRS2IUQojQaDTj7GaagHqbLlIKcFEg+D6kXDEPqply4+joK0i5irc/DN+8CvlzgDij2H7fAQkuylTdxGi+iC92JzHMjRu/OpQxPLqd7cCzKnbzrTvWD4ajf19kWPxdbfK9Ofs6Grz5Xv3o5auXIv46Swi6EEBWh0RhG17N3h/odiy8v1Bk68aVcgNTof6eUKMP8jDis9Hl458fgTQxtwXBd/7pxdDKt3Ei28CAOd6ILXLmQ78plvTuxqR7EprhzRLmTS/FnXWg04OmoxdfZFh9nLd7Otvg42eLtrDW8vvq9h4NWbuurZaSwCyHErWBpDe6NDFNJCvINnflSYyD9EqRdNBT+tIuG16kxUJCDY0EKjqTQAOgCJf7XzrF0IsXSnQTlzqVCVy7oXIjVuxGf5UZcpjtHL7mRiAt6ih/BW2jAw1GLt5Nh8iqaHLV4Odn++9pJi4ONpVwCqAGksAshhDlY2dy48CtleJBO+iXIiDV8Tb98dboEaZcMX3XZ2BVmYFeYgT9RtIcSj/z1WFw9+ncnXrlyucCFaJ0zV5QLCVmuXMl05cxlV3bjUuLpfzDc5ufhoMXTSYuHvTU5KRac3nwWb2c7PB21eDjaGL462OBqbyNnAsxECrsQQlRHGg04eBgmv7Ylt1EK8tIhPRYyLl/9WjTFGT4EZMRBZjwWqhDngiScSSKoaP1SKkCOpSNpFm4k40K83pnLBU7EFjiTqHchKd2ZxDQXzuBCknJmT8L5ErdhoQF3Bxvj5HG14LvZ2+DheM18By3uDja42VtLn4AqIoVdCCFqKo0GbF0Mk3fz0tvpCyEr0VD8M+IhM+7fr5kJV4t/guF1YT52hZnYFWbiSwwtwdBxv5QRUPMtbEm3cCNZ40Ki3om4QidiCxxJVs4kZjuTnO1McoIz55UzKTiRX9qGABc7a9wdbHC1t8bd3ga3qwXf7eoHAjd7a8PXq21c7WywsZIPA9eTwi6EELWdheW/o/TdiFKQm3a1yMdDVgJkXrn6NQGyrhi/qswENIV52Ohz8dTH4kksTYu2c4PKkmthT4aFCykaZ5L0zsQXOhJX4EiyciQl34mUPCdSkhw5jxMHlRPpOJTYN6CIo9bKUOSvFn0XO8NXV3trXOyscbW3wdXO2vja5epXrVXtfdqfFHYhhBAGGg3YuRomr6Y3bFqQn8+mtb/Rv+cdWOelGgp+dqKh+Gdd+zXx6vxEUIXY6rOx1WfjRey/G7tBJVJoyLZ0JkPjRBqGDwCJhQ4kFNiTohxJLXAkNe3qpBy4gANpyoEM7FE3+EBgZ215tfBb42x3tejbWeNsW/S9FS72/752vma5rbVFte5EKIVdCCFE+Wk0FFjagVtDKMuTyvR6yE01dAjMvq7gZycZvuYkX12eZBgjIC8dDQqHwjQcSMP32u3dpHrpsSDb0pHMqx8IUpUDyYV2JBbaka7sSdU7kpblQFqmI2k4EKMcOa4cSMOBbLRcP3DQtawtNTjbGoq9s60VznbWONlaGec5aU3nBXkaHhl8u0hhF0IIcetZWPx73z/BZVunIN9Q4IsKfU7y1dfJ171OMXzNTTV81WVjgR7HwnQcSTf9QFCGM/AFGityLBzJ1DiQoRxIU3ak6O1JLrQjVdmToexJz7UnI8eeDOzIUPYkYk+6sicDezKxM7l8MLJLA+Y+0KYcyaocKexCCCGqJyubsvUNuF5BHuSk/lv4c1IMr3PTrhb/1H+/5lz7oSAV9DqsVAFOhak4kYrftdstx2X5HAt7sjQOZGLPlcwhwNzy7UMlSGEXQghRu1hpK/aBQCnQZV/zIeDqB4Hc9OteX/0+r2h++tXv06EwDwA7fTZ2ZOMJBHnpq3gHb0wKuxBCCAGGzoM2DobJpV7FtlGQd02hv1r8HX1vvl4VksIuhBBCVBUrLTh6GSYzkTv7hRBCiFpECrsQQghRi0hhF0IIIWoRKexCCCFELSKFXQghhKhFpLALIYQQtYgUdiGEEKIWkcIuhBBC1CJS2IUQQohaRAq7EEIIUYtIYRdCCCFqESnsQgghRC0ihV0IIYSoRaSwCyGEELWIPLa1BEopANLT0yu9LZ1OR3Z2Nunp6VhbW1d6e3WF5K1iJG/lJzmrGMlb+VU2Z0U1qahGlUYKewkyMjIACAgIMHMkQgghhKmMjAxcXFxKXa5RNyv9dZBer+fy5cs4OTmh0Wgqta309HQCAgKIiYnB2dm5iiKs/SRvFSN5Kz/JWcVI3sqvsjlTSpGRkYG/vz8WFqVfSZcj9hJYWFhQv379Kt2ms7Oz/PJXgOStYiRv5Sc5qxjJW/lVJmc3OlIvIp3nhBBCiFpECrsQQghRi0hhv8W0Wi2zZs1Cq9WaO5QaRfJWMZK38pOcVYzkrfxuV86k85wQQghRi8gRuxBCCFGLSGEXQgghahEp7EIIIUQtIoVdCCGEqEWksN9iCxYsICgoCFtbW7p27cr+/fvNHVK1MXfuXDp37oyTkxPe3t4MHz6c8PBwkza5ublMnDgRDw8PHB0defDBB4mPjzdTxNXPe++9h0ajYfLkycZ5krOSXbp0icceewwPDw/s7Oxo06YN//zzj3G5UoqZM2fi5+eHnZ0dffv25ezZs2aM2PwKCwt54403aNiwIXZ2djRu3Ji33nrLZKxyyRvs2LGDYcOG4e/vj0ajYfXq1SbLy5Kj5ORkRo8ejbOzM66urjz11FNkZmZWLCAlbpmVK1cqGxsbtXjxYnXixAk1fvx45erqquLj480dWrUwYMAAtWTJEnX8+HEVFhamBg8erBo0aKAyMzONbZ599lkVEBCgtmzZov755x915513qu7du5sx6upj//79KigoSLVt21a9+OKLxvmSs+KSk5NVYGCgGjNmjNq3b586d+6c+vPPP1VERISxzXvvvadcXFzU6tWr1ZEjR9R9992nGjZsqHJycswYuXm98847ysPDQ61du1adP39e/fzzz8rR0VF98sknxjaSN6XWr1+vXn/9dfXbb78pQK1atcpkeVlyNHDgQNWuXTu1d+9e9ffff6vg4GA1cuTICsUjhf0W6tKli5o4caLxdWFhofL391dz5841Y1TVV0JCggLU9u3blVJKpaamKmtra/Xzzz8b25w6dUoBas+ePeYKs1rIyMhQTZo0UaGhoeqee+4xFnbJWcleffVV1bNnz1KX6/V65evrqz744APjvNTUVKXVatWPP/54O0KsloYMGaKefPJJk3kPPPCAGj16tFJK8laS6wt7WXJ08uRJBagDBw4Y22zYsEFpNBp16dKlcscgp+Jvkfz8fA4ePEjfvn2N8ywsLOjbty979uwxY2TVV1paGgDu7u4AHDx4EJ1OZ5LD5s2b06BBgzqfw4kTJzJkyBCT3IDkrDS///47nTp14qGHHsLb25sOHTrw9ddfG5efP3+euLg4k7y5uLjQtWvXOp237t27s2XLFs6cOQPAkSNH2LlzJ4MGDQIkb2VRlhzt2bMHV1dXOnXqZGzTt29fLCws2LdvX7nfUx4Cc4skJiZSWFiIj4+PyXwfHx9Onz5tpqiqL71ez+TJk+nRowetW7cGIC4uDhsbG1xdXU3a+vj4EBcXZ4Yoq4eVK1dy6NAhDhw4UGyZ5Kxk586dY+HChUyZMoX//ve/HDhwgBdeeAEbGxtCQkKMuSnp77Uu5+21114jPT2d5s2bY2lpSWFhIe+88w6jR48GkLyVQVlyFBcXh7e3t8lyKysr3N3dK5RHKeyiWpg4cSLHjx9n586d5g6lWouJieHFF18kNDQUW1tbc4dTY+j1ejp16sS7774LQIcOHTh+/DiLFi0iJCTEzNFVX//73/9Yvnw5K1asoFWrVoSFhTF58mT8/f0lb9WYnIq/RTw9PbG0tCzWGzk+Ph5fX18zRVU9TZo0ibVr17J161aTx+X6+vqSn59PamqqSfu6nMODBw+SkJDAHXfcgZWVFVZWVmzfvp1PP/0UKysrfHx8JGcl8PPzo2XLlibzWrRoQXR0NIAxN/L3amratGm89tprPProo7Rp04bHH3+cl156iblz5wKSt7IoS458fX1JSEgwWV5QUEBycnKF8iiF/RaxsbGhY8eObNmyxThPr9ezZcsWunXrZsbIqg+lFJMmTWLVqlX89ddfNGzY0GR5x44dsba2NslheHg40dHRdTaHffr04dixY4SFhRmnTp06MXr0aOP3krPievToUexWyjNnzhAYGAhAw4YN8fX1Nclbeno6+/btq9N5y87OxsLCtExYWlqi1+sByVtZlCVH3bp1IzU1lYMHDxrb/PXXX+j1erp27Vr+N61w1z9xUytXrlRarVYtXbpUnTx5Uj399NPK1dVVxcXFmTu0amHChAnKxcVFbdu2TcXGxhqn7OxsY5tnn31WNWjQQP3111/qn3/+Ud26dVPdunUzY9TVz7W94pWSnJVk//79ysrKSr3zzjvq7Nmzavny5cre3l798MMPxjbvvfeecnV1VWvWrFFHjx5V999/f527bet6ISEhql69esbb3X777Tfl6empXnnlFWMbyZvhLpXDhw+rw4cPK0B9/PHH6vDhwyoqKkopVbYcDRw4UHXo0EHt27dP7dy5UzVp0kRud6uuPvvsM9WgQQNlY2OjunTpovbu3WvukKoNoMRpyZIlxjY5OTnqueeeU25ubsre3l6NGDFCxcbGmi/oauj6wi45K9kff/yhWrdurbRarWrevLn66quvTJbr9Xr1xhtvKB8fH6XValWfPn1UeHi4maKtHtLT09WLL76oGjRooGxtbVWjRo3U66+/rvLy8oxtJG9Kbd26tcT/ZSEhIUqpsuUoKSlJjRw5Ujk6OipnZ2c1duxYlZGRUaF45LGtQgghRC0i19iFEEKIWkQKuxBCCFGLSGEXQgghahEp7EIIIUQtIoVdCCGEqEWksAshhBC1iBR2IYQQohaRwi6EEELUIlLYhRDVgkajYfXq1eYOQ4gaTwq7EIIxY8ag0WiKTQMHDjR3aEKIcpLnsQshABg4cCBLliwxmafVas0UjRCiouSIXQgBGIq4r6+vyeTm5gYYTpMvXLiQQYMGYWdnR6NGjfjll19M1j927Bj33nsvdnZ2eHh48PTTT5OZmWnSZvHixbRq1QqtVoufnx+TJk0yWZ6YmMiIESOwt7enSZMm/P7778ZlKSkpjB49Gi8vL+zs7GjSpEmxDyJCCCnsQogyeuONN3jwwQc5cuQIo0eP5tFHH+XUqVMAZGVlMWDAANzc3Dhw4AA///wzmzdvNincCxcuZOLEiTz99NMcO3aM33//neDgYJP3mDNnDg8//DBHjx5l8ODBjB49muTkZOP7nzx5kg0bNnDq1CkWLlyIp6fn7UuAEDVF5R5WJ4SoDUJCQpSlpaVycHAwmd555x2llOERu88++6zJOl27dlUTJkxQSin11VdfKTc3N5WZmWlcvm7dOmVhYaHi4uKUUkr5+/ur119/vdQYADVjxgzj68zMTAWoDRs2KKWUGjZsmBo7dmzV7LAQtZhcYxdCANC7d28WLlxoMs/d3d34fbdu3UyWdevWjbCwMABOnTpFu3btcHBwMC7v0aMHer2e8PBwNBoNly9fpk+fPjeMoW3btsbvHRwccHZ2JiEhAYAJEybw4IMPcujQIfr378/w4cPp3r17hfZViNpMCrsQAjAU0utPjVcVOzu7MrWztrY2ea3RaNDr9QAMGjSIqKgo1q9fT2hoKH369GHixIl8+OGHVR6vEDWZXGMXQpTJ3r17i71u0aIFAC1atODIkSNkZWUZl+/atQsLCwuaNWuGk5MTQUFBbNmypVIxeHl5ERISwg8//MD8+fP56quvKrU9IWojOWIXQgCQl5dHXFycyTwrKytjB7Wff/6ZTp060bNnT5YvX87+/fv59ttvARg9ejSzZs0iJCSE2bNnc+XKFZ5//nkef/xxfHx8AJg9ezbPPvss3t7eDBo0iIyMDHbt2sXzzz9fpvhmzpxJx44dadWqFXl5eaxdu9b4wUII8S8p7EIIADZu3Iifn5/JvGbNmnH69GnA0GN95cqVPPfcc/j5+fHjjz/SsmVLAOzt7fnzzz958cUX6dy5M/b29jz44IN8/PHHxm2FhISQm5vLvHnzePnll/H09OQ///lPmeOzsbFh+vTpXLhwATs7O+666y5WrlxZBXsuRO2iUUopcwchhKjeNBoNq1atYvjw4eYORQhxE3KNXQghhKhFpLALIYQQtYhcYxdC3JRcsROi5pAjdiGEEKIWkcIuhBBC1CJS2IUQQohaRAq7EEIIUYtIYRdCCCFqESnsQgghRC0ihV0IIYSoRaSwCyGEELXI/wPf3mugCEtiAgAAAABJRU5ErkJggg==)
+```python
+scores_01_100=model_01_100.evaluate(X_test,y_test)
+print('Loss on test data:', scores_01_100[0]) 
+print('Accuracy on test data:', scores_01_100[1])
+```
+*Loss on test data: 0.3824511766433716*
+
+*Accuracy on test data: 0.9000999927520752*
+
+**300 нейронов в первом скрытом слое**
+```python
+model_01_300 = Sequential()
+model_01_300.add(Dense(units=300,input_dim=num_pixels, activation='sigmoid'))
+model_01_300.add(Dense(units=num_classes, activation='softmax'))
+model_01_300.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+model_01_300.summary()
+```
+*Model: "sequential_7"*
+<table>
+    <thead>
+        <tr>
+            <th>Layer (type)</th>
+            <th>Output Shape</th>
+            <th>Param #</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td align="center">dense_13 (Dense)</td>
+            <td align="center">(None, 300)</td>
+            <td align="center">235,500</td>
+        </tr>
+        <tr>
+        <td align="center">dense_14(Dense)</td>
+        <td align="center">(None,10)</td>
+        <td align="center">3,010</td>
+        </tr>
+    </tbody>
+</table>
+
+*Total params: 238,510 (931.68 KB)*
+
+*Trainable params: 238,510 (931.68 KB)*
+
+*Non-trainable params: 0 (0.00 B)*
+
+```python
+H_01_300 = model_01_300.fit(
+    X_train, y_train,
+    validation_split=0.1,
+    epochs=100,
+    batch_size = 512
+)
+```
+```python
+plt.figure(figsize=(12, 5))
+
+plt.subplot(1, 2, 1)
+plt.plot(H_01_300.history['loss'], label='Обучающая ошибка')
+plt.plot(H_01_300.history['val_loss'], label='Валидационная ошибка')
+plt.title('Функция ошибки по эпохам')
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend()
+plt.grid(True)
+```
+![](data:image/png;base64,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)
+```python
+scores_01_300=model_01_300.evaluate(X_test,y_test)
+print('Loss on test data:', scores_01_300[0]) 
+print('Accuracy on test data:', scores_01_300[1])
+```
+
+*Loss on test data: 0.37091827392578125*
+
+*Accuracy on test data: 0.9013000130653381*
+
+**500 нейронов в первом скрытом слое**
+```python
+model_01_500 = Sequential()
+model_01_500.add(Dense(units=500,input_dim=num_pixels, activation='sigmoid'))
+model_01_500.add(Dense(units=num_classes, activation='softmax'))
+model_01_500.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+model_01_500.summary()
+```
+*Model: "sequential_8"*
+<table>
+    <thead>
+        <tr>
+            <th>Layer (type)</th>
+            <th>Output Shape</th>
+            <th>Param #</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td align="center">dense_15 (Dense)</td>
+            <td align="center">(None, 500)</td>
+            <td align="center">392,500</td>
+        </tr>
+        <tr>
+        <td align="center">dense_16(Dense)</td>
+        <td align="center">(None,10)</td>
+        <td align="center">5,010</td>
+        </tr>
+    </tbody>
+</table>
+ 
+*Total params: 397,510 (1.52 MB)*
+
+*Trainable params: 397,510 (1.52 MB)*
+
+*Non-trainable params: 0 (0.00 B)*
+
+```python
+H_01_500 = model_01_500.fit(
+    X_train, y_train,
+    validation_split=0.1,
+    epochs=100,
+    batch_size = 512
+)
+```
+```python
+plt.figure(figsize=(12, 5))
+
+plt.subplot(1, 2, 1)
+plt.plot(H_01_500.history['loss'], label='Обучающая ошибка')
+plt.plot(H_01_500.history['val_loss'], label='Валидационная ошибка')
+plt.title('Функция ошибки по эпохам')
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend()
+plt.grid(True)
+```
+![](data:image/png;base64,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)
+```python
+scores_01_500=model_01_500.evaluate(X_test,y_test)
+print('Loss on test data:',scores_01_500[0]) 
+print('Accuracy on test data:',scores_01_500[1]) 
+```
+*Loss on test data: 0.36660370230674744*
+
+*Accuracy on test data: 0.9010000228881836*
+
+Таким образом, наиболее точной архитектурой со скрытым слоем является архитектура со 300 нейронами в скрытом слое. Для дальнейшей работы будем использовать её.
+
+### 9. Повторные эксперименты с добавлением второго скрытого слоя
+**50 нейронов во втором скрытом слое**
+```python
+model_01_300_50 = Sequential()
+model_01_300_50.add(Dense(units=300, input_dim=num_pixels, activation='sigmoid'))
+model_01_300_50.add(Dense(units=50, activation='sigmoid'))
+model_01_300_50.add(Dense(units=num_classes, activation='softmax'))
+model_01_300_50.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+model_01_300_50.summary()
+```
+*Model: "sequential_11"*
+<table>
+    <thead>
+        <tr>
+            <th>Layer (type)</th>
+            <th>Output Shape</th>
+            <th>Param #</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td align="center">dense_23 (Dense)</td>
+            <td align="center">(None, 300)</td>
+            <td align="center">235,500</td>
+        </tr>
+        <tr>
+        <td align="center">dense_24(Dense)</td>
+        <td align="center">(None,50)</td>
+        <td align="center">15,050</td>
+        </tr>
+        <tr>
+        <td align="center">dense_25 (Dense)</td>
+        <td align="center">(None,10)</td>
+        <td align="center">510</td>
+        </tr>
+    </tbody>
+</table>
+
+*Total params: 251,060 (328.36 KB)*
+
+*Trainable params: 251,060 (328.36 KB)*
+
+*Non-trainable params: 0 (0.00 B)*
+
+```python
+H_01_300_50 = model_01_300_50.fit(
+    X_train, y_train,
+    validation_split=0.1,
+    epochs=100,
+    batch_size=512
+)
+```
+```python
+plt.figure(figsize=(12, 5))
+
+plt.subplot(1, 2, 1)
+plt.plot(H_01_300_50.history['loss'], label='Обучающая ошибка')
+plt.plot(H_01_300_50.history['val_loss'], label='Валидационная ошибка')
+plt.title('Функция ошибки по эпохам')
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend()
+plt.grid(True)
+```
+![](data:image/png;base64,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)
+```python
+scores_01_300_50=model_01_300_50.evaluate(X_test,y_test)
+print('Loss on test data:',scores_01_300_50[0])
+print('Accuracy on test data:',scores_01_300_50[1])
+```
+*Loss on test data: 0.4881931245326996*
+
+*Accuracy on test data: 0.8740000128746033*
+
+**100 нейронов во втором скрытом слое**
+```python
+model_01_300_100 = Sequential()
+model_01_300_100.add(Dense(units=300, input_dim=num_pixels, activation='sigmoid'))
+model_01_300_100.add(Dense(units=100, activation='sigmoid'))
+model_01_300_100.add(Dense(units=num_classes, activation='softmax'))
+model_01_300_100.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
+model_01_300_100.summary()
+```
+*Model: "sequential_12"*
+<table>
+    <thead>
+        <tr>
+            <th>Layer (type)</th>
+            <th>Output Shape</th>
+            <th>Param #</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td align="center">dense_26 (Dense)</td>
+            <td align="center">(None, 300)</td>
+            <td align="center">235,500</td>
+        </tr>
+        <tr>
+        <td align="center">dense_27(Dense)</td>
+        <td align="center">(None,100)</td>
+        <td align="center">30,100</td>
+        </tr>
+        <tr>
+        <td align="center">dense_28 (Dense)</td>
+        <td align="center">(None,10)</td>
+        <td align="center">1,010</td>
+        </tr>
+    </tbody>
+</table>
+
+*Total params: 266,610 (350.04 KB)*
+
+*Trainable params: 266,610 (350.04 KB)*
+ 
+*Non-trainable params: 0 (0.00 B)*
+
+```python
+H_01_300_100 = model_01_300_100.fit(
+    X_train, y_train,
+    validation_split=0.1,
+    epochs=100,
+    batch_size=512
+)
+```
+```python
+plt.figure(figsize=(12, 5))
+
+plt.subplot(1, 2, 1)
+plt.plot(H_01_300_100.history['loss'], label='Обучающая ошибка')
+plt.plot(H_01_300_100.history['val_loss'], label='Валидационная ошибка')
+plt.title('Функция ошибки по эпохам')
+plt.xlabel('Epochs')
+plt.ylabel('loss')
+plt.legend()
+plt.grid(True)
+```
+![](data:image/png;base64,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)
+```python
+scores_01_100_100=model_01_300_100.evaluate(X_test,y_test)
+print('Lossontestdata:',scores_01_300_100[0])
+print('Accuracyontestdata:',scores_01_300_100[1])
+```
+*Loss on test data: 0.4638420343399048*
+
+*Accuracy on test data: 0.8795999884605408*
+
+**Сведём результаты в таблицу**
+<table>
+    <thead>
+        <tr>
+            <th>Количество скрытых слоёв (type)</th>
+            <th>Количество нейронов в первом скрытом слое</th>
+            <th>Количество нейронов во втором скрытом слое </th>
+            <th>Значение метрики качества классификации</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td align="center">0</td>
+            <td align="center">-</td>
+            <td align="center">-</td>
+            <td align="center">0.9067999720573425</td>
+        </tr>
+        <tr>
+        <td rowspan = 3 align="center">1</td>
+        <td align="center">100</td>
+        <td rowspan =3 align="center">-</td>
+        <td align="center">0.9000999927520752</td>
+        </tr>
+        <tr>
+        <td align="center">300</td>
+        <td align="center">0.9013000130653381</td>
+        </tr>
+        <tr>
+        <td align="center">500</td>
+        <td align="center">0.9010000228881836</td>
+        </tr>
+        <tr>
+        <td rowspan = 2 align="center">2</td>
+        <td rowspan = 2 align="center">300</td>
+        <td  align="center">50</td>
+        <td  align="center">0.8740000128746033</td>
+        </tr>
+        <tr>
+        <td  align="center">100</td>
+        <td  align="center">0.8795999884605408</td>
+        </tr>
+    </tbody>
+</table>
+
+### 11.Сохранение лучшей модели на диск
+```python
+model_01_300.save(filepath='best_model.keras')
+```
+
+### 12. Вывод тестовых изображений
+**Загрузка лучшей модели с диска**
+```python
+from keras.models import load_model 
+model = load_model('best_model.keras') 
+```
+**Вывод изображений**
+```python
+n = 123
+result = model.predict(X_test[n:n+1]) 
+print('NN output:', result) 
+plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray')) 
+plt.show() 
+print('Real mark: ', str(np.argmax(y_test[n]))) 
+print('NN answer: ', str(np.argmax(result)))
+```
+![](data:image/png;base64,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)
+
+*Real mark:  6*
+
+*NN answer:  6*
+
+```python
+n = 765
+result = model.predict(X_test[n:n+1]) 
+print('NN output:', result) 
+plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray')) 
+plt.show() 
+print('Real mark: ', str(np.argmax(y_test[n]))) 
+print('NN answer: ', str(np.argmax(result)))
+```
+![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAG4pJREFUeJzt3X9sVfX9x/FXC/QC2l5WSnt7+VlAZZMf21C6KjAdHaUzDpQ4fy2BxWjQixOZutVM0G1ZJ9uc03S6PwydmfgzAoEsJFBt2VzBgBBmdB0lVUqgZZL03lKkdO3n+wfxfr1SwHO5t+97L89H8kl6zznvnjcfTvri3Hv4NMs55wQAwADLtm4AAHBxIoAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgYrB1A1/U19enw4cPKzc3V1lZWdbtAAA8cs6ps7NTwWBQ2dlnv89JuQA6fPiwxo4da90GAOACtba2asyYMWfdn3JvweXm5lq3AABIgPP9PE9aANXU1GjChAkaOnSoSktL9e67736pOt52A4DMcL6f50kJoFdffVUrV67U6tWr9d5772nGjBmqqKjQ0aNHk3E6AEA6ckkwa9YsFwqFoq97e3tdMBh01dXV560Nh8NOEoPBYDDSfITD4XP+vE/4HdCpU6e0e/dulZeXR7dlZ2ervLxcjY2NZxzf3d2tSCQSMwAAmS/hAfTJJ5+ot7dXRUVFMduLiorU1tZ2xvHV1dXy+/3RwRNwAHBxMH8KrqqqSuFwODpaW1utWwIADICE/z+ggoICDRo0SO3t7THb29vbFQgEzjje5/PJ5/Mlug0AQIpL+B1QTk6OZs6cqbq6uui2vr4+1dXVqaysLNGnAwCkqaSshLBy5UotWbJEV111lWbNmqWnn35aXV1d+tGPfpSM0wEA0lBSAujWW2/Vf//7X61atUptbW36+te/ri1btpzxYAIA4OKV5Zxz1k18XiQSkd/vt24DAHCBwuGw8vLyzrrf/Ck4AMDFiQACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAICJwdYNpKPBg71PW0NDg+eaa665xnNNPDZt2hRX3d69exPbSAIdPnw4rrq1a9d6runp6fFc09fX57kGyDTcAQEATBBAAAATCQ+gxx9/XFlZWTFjypQpiT4NACDNJeUzoCuvvFLbtm37/5PE8ZkJACCzJSUZBg8erEAgkIxvDQDIEEn5DGj//v0KBoOaOHGi7rzzTh08ePCsx3Z3dysSicQMAEDmS3gAlZaWqra2Vlu2bNFzzz2nlpYWzZkzR52dnf0eX11dLb/fHx1jx45NdEsAgBSU8ACqrKzULbfcounTp6uiokJ/+9vf1NHRoddee63f46uqqhQOh6OjtbU10S0BAFJQ0p8OGDFihC6//HI1Nzf3u9/n88nn8yW7DQBAikn6/wM6fvy4Dhw4oOLi4mSfCgCQRhIeQA899JAaGhr00Ucf6Z///KduuukmDRo0SLfffnuiTwUASGMJfwvu0KFDuv3223Xs2DGNGjVKs2fP1o4dOzRq1KhEnwoAkMaynHPOuonPi0Qi8vv91m2cU05OjueaeB4vHzJkiOcaDLx169Z5rtm+fbvnmldeecVzzYkTJzzXSFJvb29cdcDnhcNh5eXlnXU/a8EBAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwwWKkA2T27Nmea2bOnJmEThLn+9//vuea6dOnJ6ETW+dabPFsBg9O+u+ClCQ9+uijcdXV1NR4rjl+/Hhc50LmYjFSAEBKIoAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYYDVs4AItWrTIc82cOXM814RCIc81Q4YM8VwjSW+88YbnmjvuuMNzTW9vr+capA9WwwYApCQCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmWIwUSBPxLHr67LPPxnWuYDDouebHP/6x55qamhrPNUgfLEYKAEhJBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATAy2bgDAl7Nt2zbPNSdOnEhCJ/376KOPBuxcyAzcAQEATBBAAAATngNo+/btuvHGGxUMBpWVlaUNGzbE7HfOadWqVSouLtawYcNUXl6u/fv3J6pfAECG8BxAXV1dmjFjxll/kdSaNWv0zDPP6Pnnn9fOnTt1ySWXqKKiQidPnrzgZgEAmcPzQwiVlZWqrKzsd59zTk8//bR+/vOfa+HChZKkF198UUVFRdqwYYNuu+22C+sWAJAxEvoZUEtLi9ra2lReXh7d5vf7VVpaqsbGxn5ruru7FYlEYgYAIPMlNIDa2tokSUVFRTHbi4qKovu+qLq6Wn6/PzrGjh2byJYAACnK/Cm4qqoqhcPh6GhtbbVuCQAwABIaQIFAQJLU3t4es729vT2674t8Pp/y8vJiBgAg8yU0gEpKShQIBFRXVxfdFolEtHPnTpWVlSXyVACANOf5Kbjjx4+rubk5+rqlpUV79+5Vfn6+xo0bpxUrVuhXv/qVLrvsMpWUlOixxx5TMBjUokWLEtk3ACDNeQ6gXbt26frrr4++XrlypSRpyZIlqq2t1SOPPKKuri7dc8896ujo0OzZs7VlyxYNHTo0cV0DANJelnPOWTfxeZFIRH6/37qNi0pOTk5cdRMmTPBc85///Ceuc6WyiRMneq4JhUKea2bNmuW55pprrvFcI0l///vfPdfccMMNnmu6uro81yB9hMPhc36ub/4UHADg4kQAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMOH51zEg8zzwwANx1T322GOea7Zt2xbXuVLZ7NmzPdeMHDkyCZ0kzu9+9zvPNaxsDa+4AwIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCxUihwYPjuww+/fRTzzULFy6M61wYWN/97nc913zyySeea9577z3PNadOnfJcg9TEHRAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATWc45Z93E50UiEfn9fus28CVceeWVnmuKi4uT0MmZRo8eHVfd0qVLE9uIsauuuiquuuHDhye4k/49/fTTnmt+9rOfea7p6enxXIMLFw6HlZeXd9b93AEBAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwwWKkQAabOXNmXHW///3vPdfMmTMnrnN59cc//tFzzSOPPBLXuf73v//FVYfTWIwUAJCSCCAAgAnPAbR9+3bdeOONCgaDysrK0oYNG2L2L126VFlZWTFjwYIFieoXAJAhPAdQV1eXZsyYoZqamrMes2DBAh05ciQ6Xn755QtqEgCQeQZ7LaisrFRlZeU5j/H5fAoEAnE3BQDIfEn5DKi+vl6FhYW64oordO+99+rYsWNnPba7u1uRSCRmAAAyX8IDaMGCBXrxxRdVV1enJ598Ug0NDaqsrFRvb2+/x1dXV8vv90fH2LFjE90SACAFeX4L7nxuu+226NfTpk3T9OnTNWnSJNXX12vevHlnHF9VVaWVK1dGX0ciEUIIAC4CSX8Me+LEiSooKFBzc3O/+30+n/Ly8mIGACDzJT2ADh06pGPHjqm4uDjZpwIApBHPb8EdP3485m6mpaVFe/fuVX5+vvLz8/XEE09o8eLFCgQCOnDggB555BFNnjxZFRUVCW0cAJDePAfQrl27dP3110dff/b5zZIlS/Tcc89p3759+stf/qKOjg4Fg0HNnz9fv/zlL+Xz+RLXNQAg7bEYKYAzjBo1ynPNG2+84blm9uzZnmvisWLFirjqnn322cQ2cpFhMVIAQEoigAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJhgNWwACVFQUOC55oMPPvBcM3LkSM81GzZs8FwjSbfccovnmr6+vrjOlYlYDRsAkJIIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYYDFSxC03N9dzza9//WvPNU8++aTnmkOHDnmuwcDbunWr55rvfOc7Seikf0OHDvVc09PTk4RO0hOLkQIAUhIBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATg60bQPr65S9/6bnmvvvu81xTV1fnuYbFSNPDm2++6blmIBcjRXJxBwQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEi5Eibh9//LF1C0hz3/jGN6xbgCHugAAAJgggAIAJTwFUXV2tq6++Wrm5uSosLNSiRYvU1NQUc8zJkycVCoU0cuRIXXrppVq8eLHa29sT2jQAIP15CqCGhgaFQiHt2LFDW7duVU9Pj+bPn6+urq7oMQ8++KA2bdqk119/XQ0NDTp8+LBuvvnmhDcOAEhvnh5C2LJlS8zr2tpaFRYWavfu3Zo7d67C4bBeeOEFrVu3LvpbC9euXauvfvWr2rFjh771rW8lrnMAQFq7oM+AwuGwJCk/P1+StHv3bvX09Ki8vDx6zJQpUzRu3Dg1Njb2+z26u7sViURiBgAg88UdQH19fVqxYoWuvfZaTZ06VZLU1tamnJwcjRgxIubYoqIitbW19ft9qqur5ff7o2Ps2LHxtgQASCNxB1AoFNL777+vV1555YIaqKqqUjgcjo7W1tYL+n4AgPQQ139EXb58uTZv3qzt27drzJgx0e2BQECnTp1SR0dHzF1Qe3u7AoFAv9/L5/PJ5/PF0wYAII15ugNyzmn58uVav3693nrrLZWUlMTsnzlzpoYMGaK6urrotqamJh08eFBlZWWJ6RgAkBE83QGFQiGtW7dOGzduVG5ubvRzHb/fr2HDhsnv9+uuu+7SypUrlZ+fr7y8PN1///0qKyvjCTgAQAxPAfTcc89Jkq677rqY7WvXrtXSpUslSX/4wx+UnZ2txYsXq7u7WxUVFfrTn/6UkGYBAJkjyznnrJv4vEgkIr/fb90GvoTLL7/cc82HH37ouaajo8NzTW1treca6fQ/oLw6dOhQXOfKNF/72tc817zzzjuea/Ly8jzX1NTUeK6RpAceeMBzTYr9SDUVDofP+ffFWnAAABMEEADABAEEADBBAAEATBBAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABOsho24DRo0yHPN5MmTPdds2rTJc82kSZM810hSZ2en55oXXnjBc02q/+r5adOmea75wQ9+4Llm+PDhnmv27Nnjuebxxx/3XCNJmzdvjqsOp7EaNgAgJRFAAAATBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADDBYqRIeRMmTPBc88QTT8R1rh/+8Idx1SE+A7WwKIuK2mAxUgBASiKAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCxUiRkbKz4/u31aBBgzzXLFmyxHPNqlWrPNeMHj3ac028/vWvf3mu2bhxo+eap556ynNNOBz2XAMbLEYKAEhJBBAAwAQBBAAwQQABAEwQQAAAEwQQAMAEAQQAMEEAAQBMEEAAABMEEADABAEEADBBAAEATLAYKQAgKViMFACQkgggAIAJTwFUXV2tq6++Wrm5uSosLNSiRYvU1NQUc8x1112nrKysmLFs2bKENg0ASH+eAqihoUGhUEg7duzQ1q1b1dPTo/nz56urqyvmuLvvvltHjhyJjjVr1iS0aQBA+hvs5eAtW7bEvK6trVVhYaF2796tuXPnRrcPHz5cgUAgMR0CADLSBX0G9Nmvxs3Pz4/Z/tJLL6mgoEBTp05VVVWVTpw4cdbv0d3drUgkEjMAABcBF6fe3l53ww03uGuvvTZm+5///Ge3ZcsWt2/fPvfXv/7VjR492t10001n/T6rV692khgMBoORYSMcDp8zR+IOoGXLlrnx48e71tbWcx5XV1fnJLnm5uZ+9588edKFw+HoaG1tNZ80BoPBYFz4OF8AefoM6DPLly/X5s2btX37do0ZM+acx5aWlkqSmpubNWnSpDP2+3w++Xy+eNoAAKQxTwHknNP999+v9evXq76+XiUlJeet2bt3rySpuLg4rgYBAJnJUwCFQiGtW7dOGzduVG5urtra2iRJfr9fw4YN04EDB7Ru3Tp973vf08iRI7Vv3z49+OCDmjt3rqZPn56UPwAAIE15+dxHZ3mfb+3atc455w4ePOjmzp3r8vPznc/nc5MnT3YPP/zwed8H/LxwOGz+viWDwWAwLnyc72c/i5ECAJKCxUgBACmJAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAIAJAggAYIIAAgCYIIAAACYIIACACQIIAGAi5QLIOWfdAgAgAc738zzlAqizs9O6BQBAApzv53mWS7Fbjr6+Ph0+fFi5ubnKysqK2ReJRDR27Fi1trYqLy/PqEN7zMNpzMNpzMNpzMNpqTAPzjl1dnYqGAwqO/vs9zmDB7CnLyU7O1tjxow55zF5eXkX9QX2GebhNObhNObhNObhNOt58Pv95z0m5d6CAwBcHAggAICJtAogn8+n1atXy+fzWbdiink4jXk4jXk4jXk4LZ3mIeUeQgAAXBzS6g4IAJA5CCAAgAkCCABgggACAJhImwCqqanRhAkTNHToUJWWlurdd9+1bmnAPf7448rKyooZU6ZMsW4r6bZv364bb7xRwWBQWVlZ2rBhQ8x+55xWrVql4uJiDRs2TOXl5dq/f79Ns0l0vnlYunTpGdfHggULbJpNkurqal199dXKzc1VYWGhFi1apKampphjTp48qVAopJEjR+rSSy/V4sWL1d7ebtRxcnyZebjuuuvOuB6WLVtm1HH/0iKAXn31Va1cuVKrV6/We++9pxkzZqiiokJHjx61bm3AXXnllTpy5Eh0/OMf/7BuKem6uro0Y8YM1dTU9Lt/zZo1euaZZ/T8889r586duuSSS1RRUaGTJ08OcKfJdb55kKQFCxbEXB8vv/zyAHaYfA0NDQqFQtqxY4e2bt2qnp4ezZ8/X11dXdFjHnzwQW3atEmvv/66GhoadPjwYd18882GXSfel5kHSbr77rtjroc1a9YYdXwWLg3MmjXLhUKh6Ove3l4XDAZddXW1YVcDb/Xq1W7GjBnWbZiS5NavXx993dfX5wKBgPvtb38b3dbR0eF8Pp97+eWXDTocGF+cB+ecW7JkiVu4cKFJP1aOHj3qJLmGhgbn3Om/+yFDhrjXX389esyHH37oJLnGxkarNpPui/PgnHPf/va33QMPPGDX1JeQ8ndAp06d0u7du1VeXh7dlp2drfLycjU2Nhp2ZmP//v0KBoOaOHGi7rzzTh08eNC6JVMtLS1qa2uLuT78fr9KS0svyuujvr5ehYWFuuKKK3Tvvffq2LFj1i0lVTgcliTl5+dLknbv3q2enp6Y62HKlCkaN25cRl8PX5yHz7z00ksqKCjQ1KlTVVVVpRMnTli0d1YptxjpF33yySfq7e1VUVFRzPaioiL9+9//NurKRmlpqWpra3XFFVfoyJEjeuKJJzRnzhy9//77ys3NtW7PRFtbmyT1e318tu9isWDBAt18880qKSnRgQMH9Oijj6qyslKNjY0aNGiQdXsJ19fXpxUrVujaa6/V1KlTJZ2+HnJycjRixIiYYzP5euhvHiTpjjvu0Pjx4xUMBrVv3z799Kc/VVNTk958803DbmOlfADh/1VWVka/nj59ukpLSzV+/Hi99tpruuuuuww7Qyq47bbbol9PmzZN06dP16RJk1RfX6958+YZdpYcoVBI77///kXxOei5nG0e7rnnnujX06ZNU3FxsebNm6cDBw5o0qRJA91mv1L+LbiCggINGjTojKdY2tvbFQgEjLpKDSNGjNDll1+u5uZm61bMfHYNcH2caeLEiSooKMjI62P58uXavHmz3n777Zhf3xIIBHTq1Cl1dHTEHJ+p18PZ5qE/paWlkpRS10PKB1BOTo5mzpypurq66La+vj7V1dWprKzMsDN7x48f14EDB1RcXGzdipmSkhIFAoGY6yMSiWjnzp0X/fVx6NAhHTt2LKOuD+ecli9frvXr1+utt95SSUlJzP6ZM2dqyJAhMddDU1OTDh48mFHXw/nmoT979+6VpNS6HqyfgvgyXnnlFefz+Vxtba374IMP3D333ONGjBjh2trarFsbUD/5yU9cfX29a2lpce+8844rLy93BQUF7ujRo9atJVVnZ6fbs2eP27Nnj5PknnrqKbdnzx738ccfO+ec+81vfuNGjBjhNm7c6Pbt2+cWLlzoSkpK3KeffmrceWKdax46OzvdQw895BobG11LS4vbtm2b++Y3v+kuu+wyd/LkSevWE+bee+91fr/f1dfXuyNHjkTHiRMnoscsW7bMjRs3zr311ltu165drqyszJWVlRl2nXjnm4fm5mb3i1/8wu3atcu1tLS4jRs3uokTJ7q5c+cadx4rLQLIOeeeffZZN27cOJeTk+NmzZrlduzYYd3SgLv11ltdcXGxy8nJcaNHj3a33nqra25utm4r6d5++20n6YyxZMkS59zpR7Efe+wxV1RU5Hw+n5s3b55ramqybToJzjUPJ06ccPPnz3ejRo1yQ4YMcePHj3d33313xv0jrb8/vyS3du3a6DGffvqpu++++9xXvvIVN3z4cHfTTTe5I0eO2DWdBOebh4MHD7q5c+e6/Px85/P53OTJk93DDz/swuGwbeNfwK9jAACYSPnPgAAAmYkAAgCYIIAAACYIIACACQIIAGCCAAIAmCCAAAAmCCAAgAkCCABgggACAJgggAAAJgggAICJ/wPv3ws6tzaNVAAAAABJRU5ErkJggg==)
+
+*Real mark:  3*
+
+*NN answer:  3*
+
+### 13. Тестирование на собственных изображениях
+**Загрузка собственного изображения**
+```python
+from PIL import Image 
+file_07_data = Image.open('7.png') 
+file_07_data = file_07_data.convert('L') 
+test_07_img = np.array(file_07_data) 
+```
+**Вывод изображения**
+```python
+plt.imshow(test_07_img, cmap=plt.get_cmap('gray')) 
+plt.show() 
+```
+![](data:image/png;base64,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)
+
+**Распознавание изображения**
+```python
+test_07_img = test_07_img / 255 
+test_07_img = test_07_img.reshape(1, num_pixels) 
+```
+*I think it's  7*
+
+**Второе изображение**
+```python
+from PIL import Image 
+file_05_data = Image.open('5.png') 
+file_05_data = file_05_data.convert('L') 
+test_05_img = np.array(file_05_data) 
+```
+```python
+plt.imshow(test_05_img, cmap=plt.get_cmap('gray')) 
+plt.show()
+```
+![](data:image/png;base64,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)
+
+```python
+test_05_img = test_05_img / 255 
+test_05_img = test_05_img.reshape(1, num_pixels) 
+```
+```python
+result = model.predict(test_05_img) 
+print('I think it\'s ', np.argmax(result))
+```
+*I think it's  5*
+Нейросеть распознала изображения корректно
+
+### 14. Тестирование на собственных перевёрнутых изображениях
+**Первое изображение**
+```python
+from PIL import Image 
+file_07_90_data = Image.open('7-90.png') 
+file_07_90_data = file_07_90_data.convert('L') 
+test_07_90_img = np.array(file_07_90_data) 
+```
+```python
+plt.imshow(test_07_90_img, cmap=plt.get_cmap('gray')) 
+plt.show() 
+```
+![](data:image/png;base64,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)
+
+```python
+test_07_90_img = test_07_90_img / 255 
+test_07_90_img = test_07_90_img.reshape(1, num_pixels) 
+```
+```python
+result = model.predict(test_07_90_img) 
+print('I think it\'s ', np.argmax(result))
+```
+*I think it's  2*
+
+**Второе изображение**
+```python
+from PIL import Image 
+file_05_90_data = Image.open('5-90.png') 
+file_05_90_data = file_05_90_data.convert('L') 
+test_05_90_img = np.array(file_05_90_data) 
+```
+```python
+plt.imshow(test_05_90_img, cmap=plt.get_cmap('gray')) 
+plt.show() 
+```
+![](data:image/png;base64,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)
+
+```python
+test_05_90_img = test_05_90_img / 255 
+test_05_90_img = test_05_90_img.reshape(1, num_pixels) 
+```
+```python
+result = model.predict(test_05_90_img) 
+print('I think it\'s ', np.argmax(result))
+```
+*I think it's  4*
+
+Нейросеть не смогла распознать изображения
+
+**Вывод по архитектуре**: анализируя полученные результаты, можем прийти к выводу, что с ростом количества нейронов точность сначала улучшается - сеть обучается лучше, а при 500 нейронах - немного падает качество классификации, что может свидетельствовать о том, что алгоритм «застревал» в каком-то локальном минимуме; либо слишком малое время обучения - сеть не успевает обучиться, из-за чего страдает качество конечного результата. В данном примере это не критично, так как переобучение не наблюдается, а сама по себе точность достаточно высокая.
+
+**Вывод по картинкам**: проанализировав результаты работы сети, делаем вывод, что нейросеть справилась только с прямыми изображениями, повёрнутые она распознать не смогла. Это логично, потому что обучали её только на прямых изображениях. Если необходимо, чтобы картинки распознавались в том числе перевёрнутыми, в обучающую выборку стоит включить изображения такого же характера.
\ No newline at end of file
diff --git a/labworks/LW1/model_01_500.png b/labworks/LW1/model_01_500.png
new file mode 100644
index 0000000..1fe031d
Binary files /dev/null and b/labworks/LW1/model_01_500.png differ