diff --git a/labworks/LW3/is_lab31.ipynb b/labworks/LW3/is_lab31.ipynb new file mode 100644 index 0000000..eda3344 --- /dev/null +++ b/labworks/LW3/is_lab31.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","mount_file_id":"1Qit7s0DtVE6qXrX79Nf8psToWsqq0tFe","authorship_tag":"ABX9TyO9d6m/ZkgFXgt0zRo7yjAb"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":3,"metadata":{"id":"97A5G_aj6Zgi","executionInfo":{"status":"ok","timestamp":1764508916093,"user_tz":-180,"elapsed":9137,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}}},"outputs":[],"source":["# импорт модулей\n","import os\n","os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab3')\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","from tensorflow.keras.models import Sequential\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from sklearn.metrics import classification_report, confusion_matrix\n","from sklearn.metrics import ConfusionMatrixDisplay"]},{"cell_type":"code","source":["# загрузка датасета\n","from keras.datasets import mnist\n","(X_train, y_train), (X_test, y_test) = mnist.load_data()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5XTzaNUF6w3s","executionInfo":{"status":"ok","timestamp":1764508928465,"user_tz":-180,"elapsed":873,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"a0fd6508-8698-4e68-ee6e-d551646ec72e"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"]}]},{"cell_type":"code","source":["# создание своего разбиения датасета\n","from sklearn.model_selection import train_test_split\n","\n","# объединяем в один набор\n","X = np.concatenate((X_train, X_test))\n","y = np.concatenate((y_train, y_test))\n","\n","# разбиваем по вариантам\n","X_train, X_test, y_train, y_test = train_test_split(X, y,\n"," test_size = 10000,\n"," train_size = 60000,\n"," random_state = 11)\n","# вывод размерностей\n","print('Shape of X train:', X_train.shape)\n","print('Shape of y train:', y_train.shape)\n","print('Shape of X test:', X_test.shape)\n","print('Shape of y test:', y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2MhtGyzX7YWB","executionInfo":{"status":"ok","timestamp":1764509010351,"user_tz":-180,"elapsed":136,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"63e07f95-7321-4065-e3cb-665840ba7304"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of X train: (60000, 28, 28)\n","Shape of y train: (60000,)\n","Shape of X test: (10000, 28, 28)\n","Shape of y test: (10000,)\n"]}]},{"cell_type":"code","source":["# Зададим параметры данных и модели\n","num_classes = 10\n","input_shape = (28, 28, 1)\n","\n","# Приведение входных данных к диапазону [0, 1]\n","X_train = X_train / 255\n","X_test = X_test / 255\n","\n","# Расширяем размерность входных данных, чтобы каждое изображение имело\n","# размерность (высота, ширина, количество каналов)\n","\n","X_train = np.expand_dims(X_train, -1)\n","X_test = np.expand_dims(X_test, -1)\n","print('Shape of transformed X train:', X_train.shape)\n","print('Shape of transformed X test:', X_test.shape)\n","\n","# переведем метки в one-hot\n","y_train = keras.utils.to_categorical(y_train, num_classes)\n","y_test = keras.utils.to_categorical(y_test, num_classes)\n","print('Shape of transformed y train:', y_train.shape)\n","print('Shape of transformed y test:', y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uwDJet7y7tiu","executionInfo":{"status":"ok","timestamp":1764509035879,"user_tz":-180,"elapsed":138,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"d50d4c68-724f-4679-e1d3-16e006feb377"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of transformed X train: (60000, 28, 28, 1)\n","Shape of transformed X test: (10000, 28, 28, 1)\n","Shape of transformed y train: (60000, 10)\n","Shape of transformed y test: (10000, 10)\n"]}]},{"cell_type":"code","source":["# создаем модель\n","model = Sequential()\n","model.add(layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\", input_shape=input_shape))\n","model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n","model.add(layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"))\n","model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n","model.add(layers.Dropout(0.5))\n","model.add(layers.Flatten())\n","model.add(layers.Dense(num_classes, activation=\"softmax\"))\n","\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":408},"id":"x5Zk_KnO7yv0","executionInfo":{"status":"ok","timestamp":1764509053763,"user_tz":-180,"elapsed":2442,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"d830d6cf-6654-4e24-a49e-1c8ca89a49ed"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential\"\u001b[0m\n"],"text/html":["
Model: \"sequential\"\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m320\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1600\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m16,010\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ conv2d (Conv2D)                 │ (None, 26, 26, 32)     │           320 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d (MaxPooling2D)    │ (None, 13, 13, 32)     │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_1 (Conv2D)               │ (None, 11, 11, 64)     │        18,496 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_1 (MaxPooling2D)  │ (None, 5, 5, 64)       │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout (Dropout)               │ (None, 5, 5, 64)       │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten (Flatten)               │ (None, 1600)           │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense (Dense)                   │ (None, 10)             │        16,010 │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m34,826\u001b[0m (136.04 KB)\n"],"text/html":["
 Total params: 34,826 (136.04 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m34,826\u001b[0m (136.04 KB)\n"],"text/html":["
 Trainable params: 34,826 (136.04 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}}]},{"cell_type":"code","source":["# компилируем и обучаем модель\n","batch_size = 512\n","epochs = 15\n","model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n","model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pLu7FWyn72jN","executionInfo":{"status":"ok","timestamp":1764509151264,"user_tz":-180,"elapsed":26133,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"1ceb7c4b-b3f7-41ef-cdf2-cfcc9284ddbb"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 49ms/step - accuracy: 0.5927 - loss: 1.3038 - val_accuracy: 0.9465 - val_loss: 0.1854\n","Epoch 2/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9386 - loss: 0.2061 - val_accuracy: 0.9667 - val_loss: 0.1135\n","Epoch 3/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9579 - loss: 0.1391 - val_accuracy: 0.9725 - val_loss: 0.0891\n","Epoch 4/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9674 - loss: 0.1107 - val_accuracy: 0.9753 - val_loss: 0.0769\n","Epoch 5/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9722 - loss: 0.0937 - val_accuracy: 0.9780 - val_loss: 0.0684\n","Epoch 6/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9751 - loss: 0.0821 - val_accuracy: 0.9798 - val_loss: 0.0631\n","Epoch 7/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9770 - loss: 0.0751 - val_accuracy: 0.9805 - val_loss: 0.0591\n","Epoch 8/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9801 - loss: 0.0662 - val_accuracy: 0.9803 - val_loss: 0.0564\n","Epoch 9/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9808 - loss: 0.0616 - val_accuracy: 0.9810 - val_loss: 0.0543\n","Epoch 10/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9816 - loss: 0.0602 - val_accuracy: 0.9837 - val_loss: 0.0494\n","Epoch 11/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9835 - loss: 0.0530 - val_accuracy: 0.9838 - val_loss: 0.0481\n","Epoch 12/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9845 - loss: 0.0503 - val_accuracy: 0.9837 - val_loss: 0.0450\n","Epoch 13/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9842 - loss: 0.0494 - val_accuracy: 0.9847 - val_loss: 0.0446\n","Epoch 14/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9857 - loss: 0.0471 - val_accuracy: 0.9843 - val_loss: 0.0452\n","Epoch 15/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9856 - loss: 0.0476 - val_accuracy: 0.9840 - val_loss: 0.0450\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["# Оценка качества работы модели на тестовых данных\n","scores = model.evaluate(X_test, y_test)\n","print('Loss on test data:', scores[0])\n","print('Accuracy on test data:', scores[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eCkCDflh8IkY","executionInfo":{"status":"ok","timestamp":1764509239282,"user_tz":-180,"elapsed":2182,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"dc47fa85-c1b9-4712-e42e-9f074fca877d"},"execution_count":10,"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: 0.9860 - loss: 0.0413\n","Loss on test data: 0.041157860308885574\n","Accuracy on test data: 0.9873999953269958\n"]}]},{"cell_type":"code","source":["# вывод двух тестовых изображений и результатов распознавания\n","\n","for n in [3,26]:\n"," result = model.predict(X_test[n:n+1])\n"," print('NN output:', result)\n","\n"," plt.imshow(X_test[n].reshape(28,28), cmap=plt.get_cmap('gray'))\n"," plt.show()\n"," print('Real mark: ', np.argmax(y_test[n]))\n"," print('NN answer: ', np.argmax(result))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"59tFBZhH8j6A","executionInfo":{"status":"ok","timestamp":1764509296246,"user_tz":-180,"elapsed":1625,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"5cbdd937-2785-4d57-a5ce-a349bdfc77b3"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 743ms/step\n","NN output: [[2.4183887e-08 3.2334963e-08 4.4088711e-08 4.4678579e-08 6.3559483e-08\n"," 2.0533466e-06 9.9999738e-01 1.5979442e-13 3.4749868e-07 7.8227762e-13]]\n"]},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark: 6\n","NN answer: 6\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n","NN output: [[4.9824855e-08 9.9986720e-01 5.8547844e-06 4.6495694e-07 7.3325129e-05\n"," 4.3212961e-07 4.2898904e-08 1.7418186e-05 3.4761622e-05 5.2838482e-07]]\n"]},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark: 1\n","NN answer: 1\n"]}]},{"cell_type":"code","source":["# истинные метки классов\n","true_labels = np.argmax(y_test, axis=1)\n","# предсказанные метки классов\n","predicted_labels = np.argmax(model.predict(X_test), axis=1)\n","\n","# отчет о качестве классификации\n","print(classification_report(true_labels, predicted_labels))\n","# вычисление матрицы ошибок\n","conf_matrix = confusion_matrix(true_labels, predicted_labels)\n","# отрисовка матрицы ошибок в виде \"тепловой карты\"\n","display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)\n","display.plot()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":761},"id":"7cYS6RTg8x81","executionInfo":{"status":"ok","timestamp":1764509312355,"user_tz":-180,"elapsed":3028,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"fed04566-5318-4306-80ba-0fdf1ff39bfc"},"execution_count":12,"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\n"," precision recall f1-score support\n","\n"," 0 0.99 1.00 0.99 1018\n"," 1 0.99 0.99 0.99 1098\n"," 2 0.99 0.98 0.98 1010\n"," 3 0.99 0.98 0.98 992\n"," 4 0.99 0.98 0.99 998\n"," 5 0.99 0.99 0.99 951\n"," 6 0.99 0.99 0.99 933\n"," 7 0.98 0.99 0.98 1010\n"," 8 0.99 0.99 0.99 960\n"," 9 0.98 0.99 0.98 1030\n","\n"," accuracy 0.99 10000\n"," macro avg 0.99 0.99 0.99 10000\n","weighted avg 0.99 0.99 0.99 10000\n","\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# загрузка собственного изображения\n","from PIL import Image\n","\n","for name_image in ['2.png', '5.png']:\n"," file_data = Image.open(name_image)\n"," file_data = file_data.convert('L') # перевод в градации серого\n"," test_img = np.array(file_data)\n","\n"," # вывод собственного изображения\n"," plt.imshow(test_img, cmap=plt.get_cmap('gray'))\n"," plt.show()\n","\n"," # предобработка\n"," test_img = test_img / 255\n"," test_img = np.reshape(test_img, (1,28,28,1))\n","\n"," # распознавание\n"," result = model.predict(test_img)\n"," print('I think it\\'s', np.argmax(result))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":912},"id":"qiFfFWXt81ip","executionInfo":{"status":"ok","timestamp":1764509424831,"user_tz":-180,"elapsed":961,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"ce8be8c9-8e3c-44e7-caa8-4440f90ce17a"},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAGNZJREFUeJzt3X9MVff9x/HX1cKttnApIlxuRYraalIry5wy4uqaSBS3mPrjD9f1D7sYG+21mbp2i0vUdlnCZpNm6WLW/aVZVm1nMjT1DxNFwWxDm1qNMeuIMDYwcnE14VxEQQOf7x+s97urIAL38r73+nwkn6Tce4Q3xyPPHu7h4HPOOQEAMMEmWQ8AAHg0ESAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGDiMesB7jUwMKBr164pJydHPp/PehwAwCg559Td3a1QKKRJk4Y/z0m5AF27dk0lJSXWYwAAxqm9vV0zZswY9vmU+xZcTk6O9QgAgAQY6et50gK0b98+PfPMM3r88cdVUVGhzz777KH+HN92A4DMMNLX86QE6JNPPtGOHTu0Z88effHFFyovL9eKFSt0/fr1ZHw4AEA6ckmwePFiFw6HY2/39/e7UCjkampqRvyznuc5SSwWi8VK8+V53gO/3if8DOjOnTs6f/68qqqqYo9NmjRJVVVVamxsvG/7vr4+RaPRuAUAyHwJD9BXX32l/v5+FRUVxT1eVFSkSCRy3/Y1NTUKBAKxxRVwAPBoML8KbufOnfI8L7ba29utRwIATICE/xxQQUGBJk+erM7OzrjHOzs7FQwG79ve7/fL7/cnegwAQIpL+BlQdna2Fi5cqLq6uthjAwMDqqurU2VlZaI/HAAgTSXlTgg7duzQhg0b9K1vfUuLFy/Wb37zG/X09OhHP/pRMj4cACANJSVA69ev13/+8x/t3r1bkUhE3/jGN3T8+PH7LkwAADy6fM45Zz3E/4pGowoEAtZjAADGyfM85ebmDvu8+VVwAIBHEwECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADDxmPUAwEicc9YjpASfz2c9ApBQnAEBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACa4GSkmFDcWHbux7DtuYIpUxhkQAMAEAQIAmEh4gN555x35fL64NW/evER/GABAmkvKa0DPP/+8Tp48+f8f5DFeagIAxEtKGR577DEFg8FkvGsAQIZIymtAV65cUSgU0qxZs/Tqq6+qra1t2G37+voUjUbjFgAg8yU8QBUVFTpw4ICOHz+u3/3ud2ptbdWLL76o7u7uIbevqalRIBCIrZKSkkSPBABIQT6X5B/M6OrqUmlpqd5//31t3Ljxvuf7+vrU19cXezsajRKhDMbPAU0sfg4IljzPU25u7rDPJ/3qgLy8PD333HNqbm4e8nm/3y+/35/sMQAAKSbpPwd08+ZNtbS0qLi4ONkfCgCQRhIeoLfeeksNDQ3617/+pb/97W9as2aNJk+erFdeeSXRHwoAkMYS/i24q1ev6pVXXtGNGzc0ffp0fec739HZs2c1ffr0RH8oAEAaS/pFCKMVjUYVCASsx8BDSLFDJ06qv/g+Ufsu1fcDMttIFyFwLzgAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwETSfyEdUl8q31RU4oaaQKbiDAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmuBs2JhR3tp5YY73TOX9PmAicAQEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJrgZKWBgLDf7HOuNRYFUxRkQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCm5FiTDfGBIDx4gwIAGCCAAEATIw6QGfOnNGqVasUCoXk8/l05MiRuOedc9q9e7eKi4s1ZcoUVVVV6cqVK4maFwCQIUYdoJ6eHpWXl2vfvn1DPr9371598MEH+vDDD3Xu3Dk98cQTWrFihXp7e8c9LAAgg7hxkORqa2tjbw8MDLhgMOjee++92GNdXV3O7/e7Q4cOPdT79DzPSWKxWPesiWT9ubIyY3me98DjLKGvAbW2tioSiaiqqir2WCAQUEVFhRobG4f8M319fYpGo3ELAJD5EhqgSCQiSSoqKop7vKioKPbcvWpqahQIBGKrpKQkkSMBAFKU+VVwO3fulOd5sdXe3m49EgBgAiQ0QMFgUJLU2dkZ93hnZ2fsuXv5/X7l5ubGLQBA5ktogMrKyhQMBlVXVxd7LBqN6ty5c6qsrEzkhwIApLlR34rn5s2bam5ujr3d2tqqixcvKj8/XzNnztS2bdv0y1/+Us8++6zKysq0a9cuhUIhrV69OpFzAwDS3Wgvzzx9+vSQl9tt2LDBOTd4KfauXbtcUVGR8/v9btmyZa6pqemh3z+XYbNYQ6+JZP25sjJjjXQZtu+/B1vKiEajCgQC1mMASZVi/+zuww1qkQie5z3wdX3zq+AAAI8mAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmBj17wMCkD64qzVSGWdAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJbkYKjJNzznoEIC1xBgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmOBmpMD/SOUbi/p8PusRgITiDAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMMHNSJGRUvmmohI3FgUkzoAAAEYIEADAxKgDdObMGa1atUqhUEg+n09HjhyJe/61116Tz+eLW9XV1YmaFwCQIUYdoJ6eHpWXl2vfvn3DblNdXa2Ojo7YOnTo0LiGBABknlFfhLBy5UqtXLnygdv4/X4Fg8ExDwUAyHxJeQ2ovr5ehYWFmjt3rrZs2aIbN24Mu21fX5+i0WjcAgBkvoQHqLq6Wn/4wx9UV1enX//612poaNDKlSvV398/5PY1NTUKBAKxVVJSkuiRAAApyOfG8QMTPp9PtbW1Wr169bDb/POf/9Ts2bN18uRJLVu27L7n+/r61NfXF3s7Go0SIYwbPwcE2PM8T7m5ucM+n/TLsGfNmqWCggI1NzcP+bzf71dubm7cAgBkvqQH6OrVq7px44aKi4uT/aEAAGlk1FfB3bx5M+5sprW1VRcvXlR+fr7y8/P17rvvat26dQoGg2ppadFPf/pTzZkzRytWrEjo4ACANOdG6fTp007SfWvDhg3u1q1bbvny5W769OkuKyvLlZaWuk2bNrlIJPLQ79/zvCHfP4s1mpXqrPcPizURy/O8B/47GNdFCMkQjUYVCASsx0AKSbFD9D5cUAAMzfwiBAAAhkKAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATo/59QMB4cGfrsUv1fTcWqby/kXycAQEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJrgZKcYslW+OOdabXKby55SJxrK/uYFp5uAMCABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwwc1IkZE34MzEz2ksJvLGnRO1z7mBaebgDAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMMHNSDMMN+GceNzochD7AaPFGRAAwAQBAgCYGFWAampqtGjRIuXk5KiwsFCrV69WU1NT3Da9vb0Kh8OaNm2annzySa1bt06dnZ0JHRoAkP5GFaCGhgaFw2GdPXtWJ06c0N27d7V8+XL19PTEttm+fbs+/fRTHT58WA0NDbp27ZrWrl2b8MEBAGnOjcP169edJNfQ0OCcc66rq8tlZWW5w4cPx7b58ssvnSTX2Nj4UO/T8zwniTXGhYln/XfOYqXq8jzvgf92xvUakOd5kqT8/HxJ0vnz53X37l1VVVXFtpk3b55mzpypxsbGId9HX1+fotFo3AIAZL4xB2hgYEDbtm3TkiVLNH/+fElSJBJRdna28vLy4rYtKipSJBIZ8v3U1NQoEAjEVklJyVhHAgCkkTEHKBwO6/Lly/r444/HNcDOnTvleV5stbe3j+v9AQDSw5h+EHXr1q06duyYzpw5oxkzZsQeDwaDunPnjrq6uuLOgjo7OxUMBod8X36/X36/fyxjAADS2KjOgJxz2rp1q2pra3Xq1CmVlZXFPb9w4UJlZWWprq4u9lhTU5Pa2tpUWVmZmIkBABlhVGdA4XBYBw8e1NGjR5WTkxN7XScQCGjKlCkKBALauHGjduzYofz8fOXm5urNN99UZWWlvv3tbyflEwAApKlEXG66f//+2Da3b992b7zxhnvqqafc1KlT3Zo1a1xHR8dDfwwuwx7fwsSz/jtnsVJ1jXQZtu+//4BSRjQaVSAQsB4jJaTYX40ZbnIJpCfP85Sbmzvs89wLDgBgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACbG9BtRAYm7VAMYH86AAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAAT3Iw0hXGzTwCZjDMgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwMSoAlRTU6NFixYpJydHhYWFWr16tZqamuK2eemll+Tz+eLW5s2bEzo0ACD9jSpADQ0NCofDOnv2rE6cOKG7d+9q+fLl6unpidtu06ZN6ujoiK29e/cmdGgAQPp7bDQbHz9+PO7tAwcOqLCwUOfPn9fSpUtjj0+dOlXBYDAxEwIAMtK4XgPyPE+SlJ+fH/f4Rx99pIKCAs2fP187d+7UrVu3hn0ffX19ikajcQsA8AhwY9Tf3+++//3vuyVLlsQ9/vvf/94dP37cXbp0yf3xj390Tz/9tFuzZs2w72fPnj1OEovFYrEybHme98COjDlAmzdvdqWlpa69vf2B29XV1TlJrrm5ecjne3t7ned5sdXe3m6+01gsFos1/jVSgEb1GtDXtm7dqmPHjunMmTOaMWPGA7etqKiQJDU3N2v27Nn3Pe/3++X3+8cyBgAgjY0qQM45vfnmm6qtrVV9fb3KyspG/DMXL16UJBUXF49pQABAZhpVgMLhsA4ePKijR48qJydHkUhEkhQIBDRlyhS1tLTo4MGD+t73vqdp06bp0qVL2r59u5YuXaoFCxYk5RMAAKSp0bzuo2G+z7d//37nnHNtbW1u6dKlLj8/3/n9fjdnzhz39ttvj/h9wP/leZ759y1ZLBaLNf410td+33/DkjKi0agCgYD1GACAcfI8T7m5ucM+z73gAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmUi5AzjnrEQAACTDS1/OUC1B3d7f1CACABBjp67nPpdgpx8DAgK5du6acnBz5fL6456LRqEpKStTe3q7c3FyjCe2xHwaxHwaxHwaxHwalwn5wzqm7u1uhUEiTJg1/nvPYBM70UCZNmqQZM2Y8cJvc3NxH+gD7GvthEPthEPthEPthkPV+CAQCI26Tct+CAwA8GggQAMBEWgXI7/drz5498vv91qOYYj8MYj8MYj8MYj8MSqf9kHIXIQAAHg1pdQYEAMgcBAgAYIIAAQBMECAAgIm0CdC+ffv0zDPP6PHHH1dFRYU+++wz65Em3DvvvCOfzxe35s2bZz1W0p05c0arVq1SKBSSz+fTkSNH4p53zmn37t0qLi7WlClTVFVVpStXrtgMm0Qj7YfXXnvtvuOjurraZtgkqamp0aJFi5STk6PCwkKtXr1aTU1Ncdv09vYqHA5r2rRpevLJJ7Vu3Tp1dnYaTZwcD7MfXnrppfuOh82bNxtNPLS0CNAnn3yiHTt2aM+ePfriiy9UXl6uFStW6Pr169ajTbjnn39eHR0dsfWXv/zFeqSk6+npUXl5ufbt2zfk83v37tUHH3ygDz/8UOfOndMTTzyhFStWqLe3d4InTa6R9oMkVVdXxx0fhw4dmsAJk6+hoUHhcFhnz57ViRMndPfuXS1fvlw9PT2xbbZv365PP/1Uhw8fVkNDg65du6a1a9caTp14D7MfJGnTpk1xx8PevXuNJh6GSwOLFy924XA49nZ/f78LhUKupqbGcKqJt2fPHldeXm49hilJrra2Nvb2wMCACwaD7r333os91tXV5fx+vzt06JDBhBPj3v3gnHMbNmxwL7/8ssk8Vq5fv+4kuYaGBufc4N99VlaWO3z4cGybL7/80klyjY2NVmMm3b37wTnnvvvd77of//jHdkM9hJQ/A7pz547Onz+vqqqq2GOTJk1SVVWVGhsbDSezceXKFYVCIc2aNUuvvvqq2trarEcy1draqkgkEnd8BAIBVVRUPJLHR319vQoLCzV37lxt2bJFN27csB4pqTzPkyTl5+dLks6fP6+7d+/GHQ/z5s3TzJkzM/p4uHc/fO2jjz5SQUGB5s+fr507d+rWrVsW4w0r5W5Geq+vvvpK/f39Kioqinu8qKhI//jHP4ymslFRUaEDBw5o7ty56ujo0LvvvqsXX3xRly9fVk5OjvV4JiKRiCQNeXx8/dyjorq6WmvXrlVZWZlaWlr085//XCtXrlRjY6MmT55sPV7CDQwMaNu2bVqyZInmz58vafB4yM7OVl5eXty2mXw8DLUfJOmHP/yhSktLFQqFdOnSJf3sZz9TU1OT/vznPxtOGy/lA4T/t3Llyth/L1iwQBUVFSotLdWf/vQnbdy40XAypIIf/OAHsf9+4YUXtGDBAs2ePVv19fVatmyZ4WTJEQ6Hdfny5UfiddAHGW4/vP7667H/fuGFF1RcXKxly5appaVFs2fPnugxh5Ty34IrKCjQ5MmT77uKpbOzU8Fg0Giq1JCXl6fnnntOzc3N1qOY+foY4Pi436xZs1RQUJCRx8fWrVt17NgxnT59Ou7XtwSDQd25c0ddXV1x22fq8TDcfhhKRUWFJKXU8ZDyAcrOztbChQtVV1cXe2xgYEB1dXWqrKw0nMzezZs31dLSouLiYutRzJSVlSkYDMYdH9FoVOfOnXvkj4+rV6/qxo0bGXV8OOe0detW1dbW6tSpUyorK4t7fuHChcrKyoo7HpqamtTW1pZRx8NI+2EoFy9elKTUOh6sr4J4GB9//LHz+/3uwIED7u9//7t7/fXXXV5enotEItajTaif/OQnrr6+3rW2trq//vWvrqqqyhUUFLjr169bj5ZU3d3d7sKFC+7ChQtOknv//ffdhQsX3L///W/nnHO/+tWvXF5enjt69Ki7dOmSe/nll11ZWZm7ffu28eSJ9aD90N3d7d566y3X2NjoWltb3cmTJ903v/lN9+yzz7re3l7r0RNmy5YtLhAIuPr6etfR0RFbt27dim2zefNmN3PmTHfq1Cn3+eefu8rKSldZWWk4deKNtB+am5vdL37xC/f555+71tZWd/ToUTdr1iy3dOlS48njpUWAnHPut7/9rZs5c6bLzs52ixcvdmfPnrUeacKtX7/eFRcXu+zsbPf000+79evXu+bmZuuxku706dNO0n1rw4YNzrnBS7F37drlioqKnN/vd8uWLXNNTU22QyfBg/bDrVu33PLly9306dNdVlaWKy0tdZs2bcq4/0kb6vOX5Pbv3x/b5vbt2+6NN95wTz31lJs6dapbs2aN6+josBs6CUbaD21tbW7p0qUuPz/f+f1+N2fOHPf22287z/NsB78Hv44BAGAi5V8DAgBkJgIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADAxP8BS7BXFs/DwHUAAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n","I think it's 2\n"]},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaAAAAGdCAYAAABU0qcqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAGn1JREFUeJzt3W9Mlff9//HX8d+RtpzjEOFA/Yfa6lKVZU4ZcXVuEsEtpv65Ybve0MbY6LCZunbVbWrZlpzNJt3SxcluLNJlVTuTqanJTCwKZhvaSDXGbCNCcGIEXE04B1HQwed3w1/P16MgHjyH9wGej+STyDnXxXn36qXPXpzTS49zzgkAgH42zHoAAMDQRIAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAICJEdYDPKirq0vXrl1TamqqPB6P9TgAgBg559Ta2qrs7GwNG9bzdU7SBejatWuaMGGC9RgAgCfU0NCg8ePH9/h80v0ILjU11XoEAEAc9PbnecICtHv3bk2ePFmjR49WXl6ePv3008fajx+7AcDg0Nuf5wkJ0EcffaQtW7Zo586d+uyzz5Sbm6vCwkJdv349ES8HABiIXALMmzfPFRcXR77u7Ox02dnZLhgM9rpvKBRyklgsFos1wFcoFHrkn/dxvwK6c+eOqqurVVBQEHls2LBhKigoUFVV1UPbd3R0KBwORy0AwOAX9wB9/vnn6uzsVGZmZtTjmZmZampqemj7YDAov98fWXwCDgCGBvNPwW3btk2hUCiyGhoarEcCAPSDuP9/QOnp6Ro+fLiam5ujHm9ublYgEHhoe6/XK6/XG+8xAABJLu5XQKNGjdKcOXNUXl4eeayrq0vl5eXKz8+P98sBAAaohNwJYcuWLVq9erW+9rWvad68efrNb36jtrY2vfbaa4l4OQDAAJSQAK1atUr//e9/tWPHDjU1NekrX/mKjh079tAHEwAAQ5fHOeesh7hfOByW3++3HgMA8IRCoZB8Pl+Pz5t/Cg4AMDQRIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACAiRHWAwC9+elPfxrzPj/5yU/69FqjR4/u03794fLlyzHvU1JS0qfXampqinmfPXv29Om1YtWXf6aysrL4D4InxhUQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGDC45xz1kPcLxwOy+/3W4+BJPK///0v5n2GDx+egEmQDNrb22PeJyUlJQGToDehUEg+n6/H57kCAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMjLAeAOgNNxbF/crKyqxHQJxwBQQAMEGAAAAm4h6gd955Rx6PJ2rNmDEj3i8DABjgEvIe0AsvvKBPPvnk/15kBG81AQCiJaQMI0aMUCAQSMS3BgAMEgl5D+jSpUvKzs7WlClT9Oqrr+rKlSs9btvR0aFwOBy1AACDX9wDlJeXp7KyMh07dkx79uxRfX29XnzxRbW2tna7fTAYlN/vj6wJEybEeyQAQBLyOOdcIl+gpaVFkyZN0nvvvae1a9c+9HxHR4c6OjoiX4fDYSKEKAk+RTHAlJaWxrzPhg0bEjAJehMKheTz+Xp8PuGfDhgzZoyef/551dbWdvu81+uV1+tN9BgAgCST8P8P6ObNm6qrq1NWVlaiXwoAMIDEPUBvvvmmKisrdfnyZf3jH//Q8uXLNXz4cL3yyivxfikAwAAW9x/BXb16Va+88opu3LihcePG6Rvf+IZOnz6tcePGxfulAAADWNwDdODAgXh/SwwiRUVF1iPEncfjiXmfrVu3xrxPMBiMeZ9kxwcKhjbuBQcAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmEj434gaq3A4LL/fbz0GEqSxsTHmfQKBQAImiZ++3IwUGAp6+xtRuQICAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACAiRHWA2BoSfY7W5eWllqPAAwZXAEBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACY8zjlnPcT9wuGw/H6/9RhIkCQ73R7i8XisRwAGjVAoJJ/P1+PzXAEBAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEzEH6NSpU1q6dKmys7Pl8Xh0+PDhqOedc9qxY4eysrKUkpKigoICXbp0KV7zAgAGiZgD1NbWptzcXO3evbvb53ft2qX3339fpaWlOnPmjJ5++mkVFhaqvb39iYcFAAwi7glIcocOHYp83dXV5QKBgHv33Xcjj7W0tDiv1+v279//WN8zFAo5SaxBupKd9fFhsQbTCoVCj/z9Ftf3gOrr69XU1KSCgoLIY36/X3l5eaqqqup2n46ODoXD4agFABj84hqgpqYmSVJmZmbU45mZmZHnHhQMBuX3+yNrwoQJ8RwJAJCkzD8Ft23bNoVCochqaGiwHgkA0A/iGqBAICBJam5ujnq8ubk58tyDvF6vfD5f1AIADH5xDVBOTo4CgYDKy8sjj4XDYZ05c0b5+fnxfCkAwAA3ItYdbt68qdra2sjX9fX1On/+vNLS0jRx4kRt2rRJv/jFL/Tcc88pJydH27dvV3Z2tpYtWxbPuQEAA12sH1M9efJktx+3W716tXPu3kext2/f7jIzM53X63WLFi1yNTU1j/39+Rj24F7Jzvr4sFiDafX2MWzP//9NlzTC4bD8fr/1GEiQJDvdHnL58uV+eZ2SkpKY9ykrK4v/IEAChUKhR76vb/4pOADA0ESAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATMf99QMBgNnny5H55nT179vTL63AHbSQzroAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMe55yzHuJ+4XBYfr/fegwkSJKdbgNKe3t7zPukpKQkYBLg8YRCIfl8vh6f5woIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADAxwnoAAI9n9OjRMe+zZs2aPr1WWVlZn/YDYsEVEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgwuOcc9ZD3C8cDsvv91uPgcdQVFQU8z5//etfEzBJ/JSWlsa8T19u+NmXG4v2RXt7e5/2S0lJifMkGIpCoZB8Pl+Pz3MFBAAwQYAAACZiDtCpU6e0dOlSZWdny+Px6PDhw1HPr1mzRh6PJ2r15Uc1AIDBLeYAtbW1KTc3V7t37+5xm6KiIjU2NkbW/v37n2hIAMDgE/PfiLpkyRItWbLkkdt4vV4FAoE+DwUAGPwS8h5QRUWFMjIyNH36dG3YsEE3btzocduOjg6Fw+GoBQAY/OIeoKKiIv3xj39UeXm5fvWrX6myslJLlixRZ2dnt9sHg0H5/f7ImjBhQrxHAgAkoZh/BNebl19+OfLrWbNmafbs2Zo6daoqKiq0aNGih7bftm2btmzZEvk6HA4TIQAYAhL+MewpU6YoPT1dtbW13T7v9Xrl8/miFgBg8Et4gK5evaobN24oKysr0S8FABhAYv4R3M2bN6OuZurr63X+/HmlpaUpLS1NJSUlWrlypQKBgOrq6vSjH/1I06ZNU2FhYVwHBwAMbDEH6OzZs/rWt74V+fqL929Wr16tPXv26MKFC/rggw/U0tKi7OxsLV68WD//+c/l9XrjNzUAYMDjZqTos/r6+pj3mTx5cvwHiSOPxxPzPlu3bo15n2AwGPM+/akvxwF4EDcjBQAkJQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJjgbtjos2S+C3RpaWmf9tuwYUOcJ+lekv22e8hrr70W8z5lZWXxHwQDGnfDBgAkJQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABDcjBQzcvn075n1Gjx6dgEm6197eHvM+KSkpCZgEAxk3IwUAJCUCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwMQI6wGAoaikpCTmfYLBYAIm6V5/3vgUQxdXQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACY9zzlkPcb9wOCy/3289xpBSVFTUp/3efvvtmPf54IMPYt6nrKws5n0GoyT7rfoQj8djPQKSTCgUks/n6/F5roAAACYIEADAREwBCgaDmjt3rlJTU5WRkaFly5appqYmapv29nYVFxdr7NixeuaZZ7Ry5Uo1NzfHdWgAwMAXU4AqKytVXFys06dP6/jx47p7964WL16stra2yDabN2/Wxx9/rIMHD6qyslLXrl3TihUr4j44AGBgi+lvRD127FjU12VlZcrIyFB1dbUWLFigUCikP/zhD9q3b5++/e1vS5L27t2rL3/5yzp9+rS+/vWvx29yAMCA9kTvAYVCIUlSWlqaJKm6ulp3795VQUFBZJsZM2Zo4sSJqqqq6vZ7dHR0KBwORy0AwODX5wB1dXVp06ZNmj9/vmbOnClJampq0qhRozRmzJiobTMzM9XU1NTt9wkGg/L7/ZE1YcKEvo4EABhA+hyg4uJiXbx4UQcOHHiiAbZt26ZQKBRZDQ0NT/T9AAADQ0zvAX1h48aNOnr0qE6dOqXx48dHHg8EArpz545aWlqiroKam5sVCAS6/V5er1der7cvYwAABrCYroCcc9q4caMOHTqkEydOKCcnJ+r5OXPmaOTIkSovL488VlNToytXrig/Pz8+EwMABoWYroCKi4u1b98+HTlyRKmpqZH3dfx+v1JSUuT3+7V27Vpt2bJFaWlp8vl8euONN5Sfn88n4AAAUWIK0J49eyRJCxcujHp87969WrNmjSTp17/+tYYNG6aVK1eqo6NDhYWF+t3vfheXYQEAgwc3I4UaGxv7tF9P7+vF2+XLl2Pep6SkpE+v1Zcbn/blZq5f/MdcLCZPnhzzPv2Jm5HiQdyMFACQlAgQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADABAECAJggQAAAEwQIAGCCu2FDt2/f7tN+o0ePjvMkGMi4GzYexN2wAQBJiQABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwMcJ6ANgrKSnp037BYDDOkyBZlJaWWo+AIYArIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADAhMc556yHuF84HJbf77ceAwmydevWmPfhpqf39OUGoRs2bEjAJMDjCYVC8vl8PT7PFRAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIKbkQIAEoKbkQIAkhIBAgCYiClAwWBQc+fOVWpqqjIyMrRs2TLV1NREbbNw4UJ5PJ6otX79+rgODQAY+GIKUGVlpYqLi3X69GkdP35cd+/e1eLFi9XW1ha13bp169TY2BhZu3btiuvQAICBb0QsGx87dizq67KyMmVkZKi6uloLFiyIPP7UU08pEAjEZ0IAwKD0RO8BhUIhSVJaWlrU4x9++KHS09M1c+ZMbdu2Tbdu3erxe3R0dCgcDkctAMAQ4Pqos7PTffe733Xz58+Pevz3v/+9O3bsmLtw4YL705/+5J599lm3fPnyHr/Pzp07nSQWi8ViDbIVCoUe2ZE+B2j9+vVu0qRJrqGh4ZHblZeXO0mutra22+fb29tdKBSKrIaGBvODxmKxWKwnX70FKKb3gL6wceNGHT16VKdOndL48eMfuW1eXp4kqba2VlOnTn3oea/XK6/X25cxAAADWEwBcs7pjTfe0KFDh1RRUaGcnJxe9zl//rwkKSsrq08DAgAGp5gCVFxcrH379unIkSNKTU1VU1OTJMnv9yslJUV1dXXat2+fvvOd72js2LG6cOGCNm/erAULFmj27NkJ+QcAAAxQsbzvox5+zrd3717nnHNXrlxxCxYscGlpac7r9bpp06a5t956q9efA94vFAqZ/9ySxWKxWE++evuzn5uRAgASgpuRAgCSEgECAJggQAAAEwQIAGCCAAEATBAgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJggQAMAEAQIAmCBAAAATBAgAYIIAAQBMECAAgAkCBAAwQYAAACYIEADARNIFyDlnPQIAIA56+/M86QLU2tpqPQIAIA56+/Pc45LskqOrq0vXrl1TamqqPB5P1HPhcFgTJkxQQ0ODfD6f0YT2OA73cBzu4Tjcw3G4JxmOg3NOra2tys7O1rBhPV/njOjHmR7LsGHDNH78+Edu4/P5hvQJ9gWOwz0ch3s4DvdwHO6xPg5+v7/XbZLuR3AAgKGBAAEATAyoAHm9Xu3cuVNer9d6FFMch3s4DvdwHO7hONwzkI5D0n0IAQAwNAyoKyAAwOBBgAAAJggQAMAEAQIAmBgwAdq9e7cmT56s0aNHKy8vT59++qn1SP3unXfekcfjiVozZsywHivhTp06paVLlyo7O1sej0eHDx+Oet45px07digrK0spKSkqKCjQpUuXbIZNoN6Ow5o1ax46P4qKimyGTZBgMKi5c+cqNTVVGRkZWrZsmWpqaqK2aW9vV3FxscaOHatnnnlGK1euVHNzs9HEifE4x2HhwoUPnQ/r1683mrh7AyJAH330kbZs2aKdO3fqs88+U25urgoLC3X9+nXr0frdCy+8oMbGxsj629/+Zj1SwrW1tSk3N1e7d+/u9vldu3bp/fffV2lpqc6cOaOnn35ahYWFam9v7+dJE6u34yBJRUVFUefH/v37+3HCxKusrFRxcbFOnz6t48eP6+7du1q8eLHa2toi22zevFkff/yxDh48qMrKSl27dk0rVqwwnDr+Huc4SNK6deuizoddu3YZTdwDNwDMmzfPFRcXR77u7Ox02dnZLhgMGk7V/3bu3Olyc3OtxzAlyR06dCjydVdXlwsEAu7dd9+NPNbS0uK8Xq/bv3+/wYT948Hj4Jxzq1evdi+99JLJPFauX7/uJLnKykrn3L1/9yNHjnQHDx6MbPOvf/3LSXJVVVVWYybcg8fBOee++c1vuh/84Ad2Qz2GpL8CunPnjqqrq1VQUBB5bNiwYSooKFBVVZXhZDYuXbqk7OxsTZkyRa+++qquXLliPZKp+vp6NTU1RZ0ffr9feXl5Q/L8qKioUEZGhqZPn64NGzboxo0b1iMlVCgUkiSlpaVJkqqrq3X37t2o82HGjBmaOHHioD4fHjwOX/jwww+Vnp6umTNnatu2bbp165bFeD1KupuRPujzzz9XZ2enMjMzox7PzMzUv//9b6OpbOTl5amsrEzTp09XY2OjSkpK9OKLL+rixYtKTU21Hs9EU1OTJHV7fnzx3FBRVFSkFStWKCcnR3V1dfrxj3+sJUuWqKqqSsOHD7ceL+66urq0adMmzZ8/XzNnzpR073wYNWqUxowZE7XtYD4fujsOkvS9731PkyZNUnZ2ti5cuKC3335bNTU1+stf/mI4bbSkDxD+z5IlSyK/nj17tvLy8jRp0iT9+c9/1tq1aw0nQzJ4+eWXI7+eNWuWZs+eralTp6qiokKLFi0ynCwxiouLdfHixSHxPuij9HQcXn/99civZ82apaysLC1atEh1dXWaOnVqf4/ZraT/EVx6erqGDx/+0KdYmpubFQgEjKZKDmPGjNHzzz+v2tpa61HMfHEOcH48bMqUKUpPTx+U58fGjRt19OhRnTx5MuqvbwkEArpz545aWlqith+s50NPx6E7eXl5kpRU50PSB2jUqFGaM2eOysvLI491dXWpvLxc+fn5hpPZu3nzpurq6pSVlWU9ipmcnBwFAoGo8yMcDuvMmTND/vy4evWqbty4MajOD+ecNm7cqEOHDunEiRPKycmJen7OnDkaOXJk1PlQU1OjK1euDKrzobfj0J3z589LUnKdD9afgngcBw4ccF6v15WVlbl//vOf7vXXX3djxoxxTU1N1qP1qx/+8IeuoqLC1dfXu7///e+uoKDApaenu+vXr1uPllCtra3u3Llz7ty5c06Se++999y5c+fcf/7zH+ecc7/85S/dmDFj3JEjR9yFCxfcSy+95HJyctzt27eNJ4+vRx2H1tZW9+abb7qqqipXX1/vPvnkE/fVr37VPffcc669vd169LjZsGGD8/v9rqKiwjU2NkbWrVu3ItusX7/eTZw40Z04ccKdPXvW5efnu/z8fMOp46+341BbW+t+9rOfubNnz7r6+np35MgRN2XKFLdgwQLjyaMNiAA559xvf/tbN3HiRDdq1Cg3b948d/r0aeuR+t2qVatcVlaWGzVqlHv22WfdqlWrXG1trfVYCXfy5Ekn6aG1evVq59y9j2Jv377dZWZmOq/X6xYtWuRqampsh06ARx2HW7duucWLF7tx48a5kSNHukmTJrl169YNuv9I6+6fX5Lbu3dvZJvbt2+773//++5LX/qSe+qpp9zy5ctdY2Oj3dAJ0NtxuHLliluwYIFLS0tzXq/XTZs2zb311lsuFArZDv4A/joGAICJpH8PCAAwOBEgAIAJAgQAMEGAAAAmCBAAwAQBAgCYIEAAABMECABgggABAEwQIACACQIEADBBgAAAJv4fJmKhAuHOgoUAAAAASUVORK5CYII=\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n","I think it's 5\n"]}]},{"cell_type":"code","source":["model_lr1 = keras.models.load_model(\"best_model.keras\")\n","\n","model_lr1.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":241},"id":"YQgHLslu9Mj7","executionInfo":{"status":"ok","timestamp":1764509665430,"user_tz":-180,"elapsed":1400,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"f44e88fd-2821-419c-e6ab-a90cf08c52ac"},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_7\"\u001b[0m\n"],"text/html":["
Model: \"sequential_7\"\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_14 (\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_15 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m10,100\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_16 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m1,010\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_14 (Dense)                │ (None, 100)            │        78,500 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_15 (Dense)                │ (None, 100)            │        10,100 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_16 (Dense)                │ (None, 10)             │         1,010 │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m89,612\u001b[0m (350.05 KB)\n"],"text/html":["
 Total params: 89,612 (350.05 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m89,610\u001b[0m (350.04 KB)\n"],"text/html":["
 Trainable params: 89,610 (350.04 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m2\u001b[0m (12.00 B)\n"],"text/html":["
 Optimizer params: 2 (12.00 B)\n","
\n"]},"metadata":{}}]},{"cell_type":"code","source":["# развернем каждое изображение 28*28 в вектор 784\n","X_train, X_test, y_train, y_test = train_test_split(X, y,\n"," test_size = 10000,\n"," train_size = 60000,\n"," random_state = 11)\n","num_pixels = X_train.shape[1] * X_train.shape[2]\n","X_train = X_train.reshape(X_train.shape[0], num_pixels) / 255\n","X_test = X_test.reshape(X_test.shape[0], num_pixels) / 255\n","print('Shape of transformed X train:', X_train.shape)\n","print('Shape of transformed X train:', X_test.shape)\n","\n","# переведем метки в one-hot\n","y_train = keras.utils.to_categorical(y_train, num_classes)\n","y_test = keras.utils.to_categorical(y_test, num_classes)\n","print('Shape of transformed y train:', y_train.shape)\n","print('Shape of transformed y test:', y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9D9DEhtk-C5F","executionInfo":{"status":"ok","timestamp":1764509717178,"user_tz":-180,"elapsed":165,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"832e262f-b99b-4112-c08d-e9687b5dfc90"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of transformed X train: (60000, 784)\n","Shape of transformed X train: (10000, 784)\n","Shape of transformed y train: (60000, 10)\n","Shape of transformed y test: (10000, 10)\n"]}]},{"cell_type":"code","source":["# Оценка качества работы модели на тестовых данных\n","scores = model_lr1.evaluate(X_test, y_test)\n","print('Loss on test data:', scores[0])\n","print('Accuracy on test data:', scores[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Sfm0Dkdx-ZEo","executionInfo":{"status":"ok","timestamp":1764509730383,"user_tz":-180,"elapsed":2959,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"7a41ced0-658d-4086-e940-847fe75b4d8d"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.9406 - loss: 0.2055\n","Loss on test data: 0.1955890655517578\n","Accuracy on test data: 0.9426000118255615\n"]}]},{"cell_type":"code","source":["# загрузка датасета\n","from keras.datasets import cifar10\n","\n","(X_train, y_train), (X_test, y_test) = cifar10.load_data()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JNDIKQbd-bnZ","executionInfo":{"status":"ok","timestamp":1764510002560,"user_tz":-180,"elapsed":7509,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"7f10e9b2-d8d9-4f8a-f9c1-583276aad9c4"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n","\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 0us/step\n"]}]},{"cell_type":"code","source":["# создание своего разбиения датасета\n","\n","# объединяем в один набор\n","X = np.concatenate((X_train, X_test))\n","y = np.concatenate((y_train, y_test))\n","\n","# разбиваем по вариантам\n","X_train, X_test, y_train, y_test = train_test_split(X, y,\n"," test_size = 10000,\n"," train_size = 50000,\n"," random_state = 11)\n","# вывод размерностей\n","print('Shape of X train:', X_train.shape)\n","print('Shape of y train:', y_train.shape)\n","print('Shape of X test:', X_test.shape)\n","print('Shape of y test:', y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YHdFlDTs_c8h","executionInfo":{"status":"ok","timestamp":1764510026923,"user_tz":-180,"elapsed":129,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"e186bd15-09ae-44af-cec4-09950cca01e4"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of X train: (50000, 32, 32, 3)\n","Shape of y train: (50000, 1)\n","Shape of X test: (10000, 32, 32, 3)\n","Shape of y test: (10000, 1)\n"]}]},{"cell_type":"code","source":["class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n"," 'dog', 'frog', 'horse', 'ship', 'truck']\n","\n","plt.figure(figsize=(10,10))\n","for i in range(25):\n"," plt.subplot(5,5,i+1)\n"," plt.xticks([])\n"," plt.yticks([])\n"," plt.grid(False)\n"," plt.imshow(X_train[i])\n"," plt.xlabel(class_names[y_train[i][0]])\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":826},"id":"YwHDcPGv_ks8","executionInfo":{"status":"ok","timestamp":1764510041044,"user_tz":-180,"elapsed":846,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"acd6b3b4-5d3f-4e32-b9fc-a7c67770afef"},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# Зададим параметры данных и модели\n","num_classes = 10\n","input_shape = (32, 32, 3)\n","\n","# Приведение входных данных к диапазону [0, 1]\n","X_train = X_train / 255\n","X_test = X_test / 255\n","\n","print('Shape of transformed X train:', X_train.shape)\n","print('Shape of transformed X test:', X_test.shape)\n","\n","# переведем метки в one-hot\n","y_train = keras.utils.to_categorical(y_train, num_classes)\n","y_test = keras.utils.to_categorical(y_test, num_classes)\n","print('Shape of transformed y train:', y_train.shape)\n","print('Shape of transformed y test:', y_test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZplGK3iQ_n-S","executionInfo":{"status":"ok","timestamp":1764510054784,"user_tz":-180,"elapsed":559,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"62473655-92f8-4904-bdbd-8472ab9387c0"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of transformed X train: (50000, 32, 32, 3)\n","Shape of transformed X test: (10000, 32, 32, 3)\n","Shape of transformed y train: (50000, 10)\n","Shape of transformed y test: (10000, 10)\n"]}]},{"cell_type":"code","source":["# создаем модель\n","model = Sequential()\n","\n","# Блок 1\n","model.add(layers.Conv2D(32, (3, 3), padding=\"same\",\n"," activation=\"relu\", input_shape=input_shape))\n","model.add(layers.BatchNormalization())\n","model.add(layers.Conv2D(32, (3, 3), padding=\"same\", activation=\"relu\"))\n","model.add(layers.BatchNormalization())\n","model.add(layers.MaxPooling2D((2, 2)))\n","model.add(layers.Dropout(0.25))\n","\n","# Блок 2\n","model.add(layers.Conv2D(64, (3, 3), padding=\"same\", activation=\"relu\"))\n","model.add(layers.BatchNormalization())\n","model.add(layers.Conv2D(64, (3, 3), padding=\"same\", activation=\"relu\"))\n","model.add(layers.BatchNormalization())\n","model.add(layers.MaxPooling2D((2, 2)))\n","model.add(layers.Dropout(0.25))\n","\n","# Блок 3\n","model.add(layers.Conv2D(128, (3, 3), padding=\"same\", activation=\"relu\"))\n","model.add(layers.BatchNormalization())\n","model.add(layers.Conv2D(128, (3, 3), padding=\"same\", activation=\"relu\"))\n","model.add(layers.BatchNormalization())\n","model.add(layers.MaxPooling2D((2, 2)))\n","model.add(layers.Dropout(0.4))\n","\n","model.add(layers.Flatten())\n","model.add(layers.Dense(128, activation='relu'))\n","model.add(layers.Dropout(0.5))\n","model.add(layers.Dense(num_classes, activation=\"softmax\"))\n","\n","\n","model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":984},"id":"6yw8K6tw_rZj","executionInfo":{"status":"ok","timestamp":1764510173761,"user_tz":-180,"elapsed":316,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"56435971-167b-43f4-fc27-3cd19746b316"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n"," super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_1\"\u001b[0m\n"],"text/html":["
Model: \"sequential_1\"\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m128\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m256\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m73,856\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m147,584\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m512\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m) │ │ │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m262,272\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m1,290\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ conv2d_2 (Conv2D)               │ (None, 32, 32, 32)     │           896 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization             │ (None, 32, 32, 32)     │           128 │\n","│ (BatchNormalization)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_3 (Conv2D)               │ (None, 32, 32, 32)     │         9,248 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_1           │ (None, 32, 32, 32)     │           128 │\n","│ (BatchNormalization)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_2 (MaxPooling2D)  │ (None, 16, 16, 32)     │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_1 (Dropout)             │ (None, 16, 16, 32)     │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_4 (Conv2D)               │ (None, 16, 16, 64)     │        18,496 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_2           │ (None, 16, 16, 64)     │           256 │\n","│ (BatchNormalization)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_5 (Conv2D)               │ (None, 16, 16, 64)     │        36,928 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_3           │ (None, 16, 16, 64)     │           256 │\n","│ (BatchNormalization)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_3 (MaxPooling2D)  │ (None, 8, 8, 64)       │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_2 (Dropout)             │ (None, 8, 8, 64)       │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_6 (Conv2D)               │ (None, 8, 8, 128)      │        73,856 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_4           │ (None, 8, 8, 128)      │           512 │\n","│ (BatchNormalization)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ conv2d_7 (Conv2D)               │ (None, 8, 8, 128)      │       147,584 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_5           │ (None, 8, 8, 128)      │           512 │\n","│ (BatchNormalization)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ max_pooling2d_4 (MaxPooling2D)  │ (None, 4, 4, 128)      │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_3 (Dropout)             │ (None, 4, 4, 128)      │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ flatten_1 (Flatten)             │ (None, 2048)           │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_1 (Dense)                 │ (None, 128)            │       262,272 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dropout_4 (Dropout)             │ (None, 128)            │             0 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_2 (Dense)                 │ (None, 10)             │         1,290 │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m552,362\u001b[0m (2.11 MB)\n"],"text/html":["
 Total params: 552,362 (2.11 MB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m551,466\u001b[0m (2.10 MB)\n"],"text/html":["
 Trainable params: 551,466 (2.10 MB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m896\u001b[0m (3.50 KB)\n"],"text/html":["
 Non-trainable params: 896 (3.50 KB)\n","
\n"]},"metadata":{}}]},{"cell_type":"code","source":["# компилируем и обучаем модель\n","batch_size = 64\n","epochs = 50\n","model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n","model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H1jPhU46AIgP","executionInfo":{"status":"ok","timestamp":1764510555990,"user_tz":-180,"elapsed":371838,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"5534a643-eea2-4008-b38a-76974fb40f23"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 22ms/step - accuracy: 0.2674 - loss: 2.1039 - val_accuracy: 0.2834 - val_loss: 2.3722\n","Epoch 2/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.4822 - loss: 1.4320 - val_accuracy: 0.5832 - val_loss: 1.1490\n","Epoch 3/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.5803 - loss: 1.1857 - val_accuracy: 0.6454 - val_loss: 1.0278\n","Epoch 4/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.6493 - loss: 1.0135 - val_accuracy: 0.6832 - val_loss: 0.9214\n","Epoch 5/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.6807 - loss: 0.9211 - val_accuracy: 0.6854 - val_loss: 0.9277\n","Epoch 6/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.7099 - loss: 0.8405 - val_accuracy: 0.7246 - val_loss: 0.8032\n","Epoch 7/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7338 - loss: 0.7774 - val_accuracy: 0.7444 - val_loss: 0.7713\n","Epoch 8/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.7585 - loss: 0.7138 - val_accuracy: 0.7430 - val_loss: 0.7700\n","Epoch 9/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7723 - loss: 0.6724 - val_accuracy: 0.7964 - val_loss: 0.6089\n","Epoch 10/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.7834 - loss: 0.6431 - val_accuracy: 0.7510 - val_loss: 0.7581\n","Epoch 11/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 10ms/step - accuracy: 0.7938 - loss: 0.6043 - val_accuracy: 0.7412 - val_loss: 0.8413\n","Epoch 12/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8078 - loss: 0.5686 - val_accuracy: 0.8084 - val_loss: 0.5824\n","Epoch 13/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8135 - loss: 0.5516 - val_accuracy: 0.7962 - val_loss: 0.6150\n","Epoch 14/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 9ms/step - accuracy: 0.8224 - loss: 0.5198 - val_accuracy: 0.8146 - val_loss: 0.5601\n","Epoch 15/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8266 - loss: 0.5162 - val_accuracy: 0.7736 - val_loss: 0.7025\n","Epoch 16/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8375 - loss: 0.4846 - val_accuracy: 0.8144 - val_loss: 0.5579\n","Epoch 17/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8414 - loss: 0.4571 - val_accuracy: 0.8214 - val_loss: 0.5381\n","Epoch 18/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8463 - loss: 0.4582 - val_accuracy: 0.8282 - val_loss: 0.5478\n","Epoch 19/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8508 - loss: 0.4418 - val_accuracy: 0.8260 - val_loss: 0.5413\n","Epoch 20/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8584 - loss: 0.4141 - val_accuracy: 0.8200 - val_loss: 0.5675\n","Epoch 21/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.8642 - loss: 0.4024 - val_accuracy: 0.8288 - val_loss: 0.5301\n","Epoch 22/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8635 - loss: 0.4004 - val_accuracy: 0.8352 - val_loss: 0.5078\n","Epoch 23/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8689 - loss: 0.3831 - val_accuracy: 0.8342 - val_loss: 0.5113\n","Epoch 24/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8697 - loss: 0.3863 - val_accuracy: 0.8300 - val_loss: 0.5583\n","Epoch 25/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8737 - loss: 0.3708 - val_accuracy: 0.8222 - val_loss: 0.5647\n","Epoch 26/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8779 - loss: 0.3573 - val_accuracy: 0.8322 - val_loss: 0.5362\n","Epoch 27/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8834 - loss: 0.3445 - val_accuracy: 0.8436 - val_loss: 0.5163\n","Epoch 28/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8823 - loss: 0.3487 - val_accuracy: 0.8352 - val_loss: 0.5274\n","Epoch 29/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8852 - loss: 0.3349 - val_accuracy: 0.8370 - val_loss: 0.5532\n","Epoch 30/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8873 - loss: 0.3230 - val_accuracy: 0.8154 - val_loss: 0.6128\n","Epoch 31/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8886 - loss: 0.3198 - val_accuracy: 0.8448 - val_loss: 0.4880\n","Epoch 32/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8949 - loss: 0.3061 - val_accuracy: 0.8476 - val_loss: 0.5057\n","Epoch 33/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8946 - loss: 0.3115 - val_accuracy: 0.8492 - val_loss: 0.5195\n","Epoch 34/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8976 - loss: 0.3023 - val_accuracy: 0.8424 - val_loss: 0.5272\n","Epoch 35/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8988 - loss: 0.2896 - val_accuracy: 0.8524 - val_loss: 0.5089\n","Epoch 36/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9006 - loss: 0.2894 - val_accuracy: 0.8420 - val_loss: 0.5439\n","Epoch 37/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9030 - loss: 0.2828 - val_accuracy: 0.8386 - val_loss: 0.5211\n","Epoch 38/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9062 - loss: 0.2717 - val_accuracy: 0.8482 - val_loss: 0.5060\n","Epoch 39/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9056 - loss: 0.2753 - val_accuracy: 0.8426 - val_loss: 0.5156\n","Epoch 40/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9081 - loss: 0.2687 - val_accuracy: 0.8476 - val_loss: 0.4856\n","Epoch 41/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9048 - loss: 0.2731 - val_accuracy: 0.8490 - val_loss: 0.5141\n","Epoch 42/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9089 - loss: 0.2662 - val_accuracy: 0.8430 - val_loss: 0.5272\n","Epoch 43/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9135 - loss: 0.2573 - val_accuracy: 0.8586 - val_loss: 0.4969\n","Epoch 44/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9108 - loss: 0.2583 - val_accuracy: 0.8596 - val_loss: 0.4902\n","Epoch 45/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9168 - loss: 0.2432 - val_accuracy: 0.8526 - val_loss: 0.4931\n","Epoch 46/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9146 - loss: 0.2482 - val_accuracy: 0.8434 - val_loss: 0.5511\n","Epoch 47/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9135 - loss: 0.2529 - val_accuracy: 0.8508 - val_loss: 0.5254\n","Epoch 48/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9200 - loss: 0.2337 - val_accuracy: 0.8572 - val_loss: 0.4973\n","Epoch 49/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9160 - loss: 0.2438 - val_accuracy: 0.8424 - val_loss: 0.5488\n","Epoch 50/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9160 - loss: 0.2469 - val_accuracy: 0.8572 - val_loss: 0.5109\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["# Оценка качества работы модели на тестовых данных\n","scores = model.evaluate(X_test, y_test)\n","print('Loss on test data:', scores[0])\n","print('Accuracy on test data:', scores[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vznhmfUzALHo","executionInfo":{"status":"ok","timestamp":1764510577727,"user_tz":-180,"elapsed":10953,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"09312c0c-51a2-4320-ce68-4d39e2dbf31f"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8615 - loss: 0.4696\n","Loss on test data: 0.4909549355506897\n","Accuracy on test data: 0.857200026512146\n"]}]},{"cell_type":"code","source":["# вывод двух тестовых изображений и результатов распознавания\n","\n","for n in [3,15]:\n"," result = model.predict(X_test[n:n+1])\n"," print('NN output:', result)\n","\n"," plt.imshow(X_test[n].reshape(32,32,3), cmap=plt.get_cmap('gray'))\n"," plt.show()\n"," print('Real mark: ', np.argmax(y_test[n]))\n"," print('NN answer: ', np.argmax(result))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"dg9EotlUBoiB","executionInfo":{"status":"ok","timestamp":1764510596472,"user_tz":-180,"elapsed":3400,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"352dede1-c8a1-498a-e6f7-983d3cf134cb"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2s/step\n","NN output: [[3.6980788e-04 6.5029941e-05 4.4359174e-02 8.4741175e-02 1.3959331e-02\n"," 8.4197390e-01 5.7383772e-04 1.3647835e-02 1.1692162e-05 2.9820076e-04]]\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark: 4\n","NN answer: 5\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step\n","NN output: [[1.0492409e-07 3.9376263e-12 9.9999964e-01 1.5196024e-08 1.3079713e-07\n"," 4.7579718e-08 3.4805732e-09 1.2734243e-07 6.2821698e-10 1.3412522e-11]]\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark: 2\n","NN answer: 2\n"]}]},{"cell_type":"code","source":["# истинные метки классов\n","true_labels = np.argmax(y_test, axis=1)\n","# предсказанные метки классов\n","predicted_labels = np.argmax(model.predict(X_test), axis=1)\n","\n","# отчет о качестве классификации\n","print(classification_report(true_labels, predicted_labels, target_names=class_names))\n","# вычисление матрицы ошибок\n","conf_matrix = confusion_matrix(true_labels, predicted_labels)\n","# отрисовка матрицы ошибок в виде \"тепловой карты\"\n","fig, ax = plt.subplots(figsize=(6, 6))\n","disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=class_names)\n","disp.plot(ax=ax, xticks_rotation=45) # поворот подписей по X и приятная палитра\n","plt.tight_layout() # чтобы всё влезло\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":888},"id":"O2RypaR6Bu87","executionInfo":{"status":"ok","timestamp":1764510623002,"user_tz":-180,"elapsed":5934,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"28ca65c0-a7a5-4c4a-c664-e900e0bc2aed"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 10ms/step\n"," precision recall f1-score support\n","\n"," airplane 0.89 0.86 0.87 1000\n"," automobile 0.94 0.93 0.93 1019\n"," bird 0.80 0.84 0.82 972\n"," cat 0.78 0.66 0.72 1014\n"," deer 0.84 0.84 0.84 980\n"," dog 0.76 0.81 0.79 1051\n"," frog 0.90 0.87 0.89 1043\n"," horse 0.89 0.89 0.89 1018\n"," ship 0.91 0.94 0.92 945\n"," truck 0.86 0.94 0.90 958\n","\n"," accuracy 0.86 10000\n"," macro avg 0.86 0.86 0.86 10000\n","weighted avg 0.86 0.86 0.86 10000\n","\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"i_LekSjEB0z0"},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/labworks/LW3/report (2).md b/labworks/LW3/report (2).md new file mode 100644 index 0000000..2543e57 --- /dev/null +++ b/labworks/LW3/report (2).md @@ -0,0 +1,554 @@ +# Отчёт по лабораторной работе №3 + +**Ли Тэ Хо, Синявский Степан — А-02-22** +***Бригада 3*** +--- +## Задание 1 + +### 1) В среде Google Colab создали новый блокнот (notebook). Импортировали необходимые для работы библиотеки и модули. + +```python +# импорт модулей +import os +os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab3') + +from tensorflow import keras +from tensorflow.keras import layers +from tensorflow.keras.models import Sequential +import matplotlib.pyplot as plt +import numpy as np +from sklearn.metrics import classification_report, confusion_matrix +from sklearn.metrics import ConfusionMatrixDisplay +``` + +### 2) Загрузили набор данных MNIST, содержащий размеченные изображения рукописных цифр. + +```python +# загрузка датасета +from keras.datasets import mnist +(X_train, y_train), (X_test, y_test) = mnist.load_data() +``` + +### 3) Разбили набор данных на обучающие и тестовые данные в соотношении 60 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1)=11, где k=3 –номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных. + +```python +# создание своего разбиения датасета +from sklearn.model_selection import train_test_split + +# объединяем в один набор +X = np.concatenate((X_train, X_test)) +y = np.concatenate((y_train, y_test)) + +# разбиваем по вариантам +X_train, X_test, y_train, y_test = train_test_split(X, y, + test_size = 10000, + train_size = 60000, + random_state = 11) +# вывод размерностей +print('Shape of X train:', X_train.shape) +print('Shape of y train:', y_train.shape) +print('Shape of X test:', X_test.shape) +print('Shape of y test:', y_test.shape) +``` +``` +Shape of X train: (60000, 28, 28) +Shape of y train: (60000,) +Shape of X test: (10000, 28, 28) +Shape of y test: (10000,) +``` + +### 4) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных. + +```python +# Зададим параметры данных и модели +num_classes = 10 +input_shape = (28, 28, 1) + +# Приведение входных данных к диапазону [0, 1] +X_train = X_train / 255 +X_test = X_test / 255 + +# Расширяем размерность входных данных, чтобы каждое изображение имело +# размерность (высота, ширина, количество каналов) + +X_train = np.expand_dims(X_train, -1) +X_test = np.expand_dims(X_test, -1) +print('Shape of transformed X train:', X_train.shape) +print('Shape of transformed X test:', X_test.shape) + +# переведем метки в one-hot +y_train = keras.utils.to_categorical(y_train, num_classes) +y_test = keras.utils.to_categorical(y_test, num_classes) +print('Shape of transformed y train:', y_train.shape) +print('Shape of transformed y test:', y_test.shape) +``` +``` +Shape of transformed X train: (60000, 28, 28, 1) +Shape of transformed X test: (10000, 28, 28, 1) +Shape of transformed y train: (60000, 10) +Shape of transformed y test: (10000, 10) +``` + +### 5) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети. + +```python +# создаем модель +model = Sequential() +model.add(layers.Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=input_shape)) +model.add(layers.MaxPooling2D(pool_size=(2, 2))) +model.add(layers.Conv2D(64, kernel_size=(3, 3), activation="relu")) +model.add(layers.MaxPooling2D(pool_size=(2, 2))) +model.add(layers.Dropout(0.5)) +model.add(layers.Flatten()) +model.add(layers.Dense(num_classes, activation="softmax")) + +model.summary() +``` +**Model: "sequential"** +| Layer (type) | Output Shape | Param # | +|--------------------------------|---------------------|--------:| +| conv2d (Conv2D) | (None, 26, 26, 32) | 320 | +| max_pooling2d (MaxPooling2D) | (None, 13, 13, 32) | 0 | +| conv2d_1 (Conv2D) | (None, 11, 11, 64) | 18,496 | +| max_pooling2d_1 (MaxPooling2D) | (None, 5, 5, 64) | 0 | +| dropout (Dropout) | (None, 5, 5, 64) | 0 | +| flatten (Flatten) | (None, 1600) | 0 | +| dense (Dense) | (None, 10) | 16,010 | +**Total params:** 34,826 (136.04 KB) +**Trainable params:** 34,826 (136.04 KB) +**Non-trainable params:** 0 (0.00 B) + +```python +# компилируем и обучаем модель +batch_size = 512 +epochs = 15 +model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) +model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1) +``` + +### 6) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных. + +```python +# Оценка качества работы модели на тестовых данных +scores = model.evaluate(X_test, y_test) +print('Loss on test data:', scores[0]) +print('Accuracy on test data:', scores[1]) +``` +``` +313/313 ━━━━━━━━━━━━━━━━━━━━ 2s 4ms/step - accuracy: 0.9909 - loss: 0.0413 +Loss on test data: 0.041157860308885574 +Accuracy on test data: 0.9873999953269958 +``` + +### 7) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания. + +```python +# вывод двух тестовых изображений и результатов распознавания + +for n in [3,26]: + 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: ', np.argmax(y_test[n])) + print('NN answer: ', np.argmax(result)) +``` +![picture](images/1.png) +``` +Real mark: 2 +NN answer: 2 +``` +![picture](images/2.png) +``` +Real mark: 9 +NN answer: 9 +``` + +### 8) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки. + +```python +# истинные метки классов +true_labels = np.argmax(y_test, axis=1) +# предсказанные метки классов +predicted_labels = np.argmax(model.predict(X_test), axis=1) + +# отчет о качестве классификации +print(classification_report(true_labels, predicted_labels)) +# вычисление матрицы ошибок +conf_matrix = confusion_matrix(true_labels, predicted_labels) +# отрисовка матрицы ошибок в виде "тепловой карты" +display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix) +display.plot() +plt.show() +``` +``` +313/313 ━━━━━━━━━━━━━━━━━━━━ 2s 4ms/step + precision recall f1-score support + + 0 0.99 1.00 0.99 1018 + 1 0.99 0.99 0.99 1098 + 2 0.99 0.98 0.98 1010 + 3 0.99 0.98 0.98 992 + 4 0.99 0.98 0.99 998 + 5 0.99 0.99 0.99 951 + 6 0.99 0.99 0.99 933 + 7 0.98 0.99 0.98 1010 + 8 0.99 0.99 0.99 960 + 9 0.98 0.99 0.98 1030 + + accuracy 0.99 10000 + macro avg 0.99 0.99 0.99 10000 +weighted avg 0.99 0.99 0.99 10000 +``` +![picture](images/3.png) + +### 9) Загрузили, предобработали и подали на вход обученной нейронной сети собственное изображение, созданное при выполнении лабораторной работы №1. Вывели изображение и результат распознавания. + +```python +# загрузка собственного изображения +from PIL import Image + +for name_image in ['2.png', '5.png']: + file_data = Image.open(name_image) + file_data = file_data.convert('L') # перевод в градации серого + test_img = np.array(file_data) + + # вывод собственного изображения + plt.imshow(test_img, cmap=plt.get_cmap('gray')) + plt.show() + + # предобработка + test_img = test_img / 255 + test_img = np.reshape(test_img, (1,28,28,1)) + + # распознавание + result = model.predict(test_img) + print('I think it\'s', np.argmax(result)) +``` +![picture](images/4.png) +``` +I think it's 2 +``` +![picture](images/5.png) +``` +I think it's 5 +``` + +### 10) Загрузили с диска модель, сохраненную при выполнении лабораторной работы №1. Вывели информацию об архитектуре модели. Повторили для этой модели п. 6. + +```python +model_lr1 = keras.models.load_model("best_model.keras") + +model_lr1.summary() +``` +**Model: "sequential_10"** +| Layer (type) | Output Shape | Param # | +|------------------|-------------:|--------:| +| dense_14 (Dense) | (None, 100) | 78,500 | +| dense_15 (Dense) | (None, 50) | 10,100 | +| dense_16 (Dense) | (None, 10) | 1,010 | +**Total params:** 89,612 (350.05 KB) +**Trainable params:** 89,610 (350.04 KB) +**Non-trainable params:** 0 (0.00 B) +**Optimizer params:** 2 (12.00 B) + + +```python +# развернем каждое изображение 28*28 в вектор 784 +X_train, X_test, y_train, y_test = train_test_split(X, y, + test_size = 10000, + train_size = 60000, + random_state = 11) +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) +print('Shape of transformed X train:', X_test.shape) + +# переведем метки в one-hot +y_train = keras.utils.to_categorical(y_train, num_classes) +y_test = keras.utils.to_categorical(y_test, num_classes) +print('Shape of transformed y train:', y_train.shape) +print('Shape of transformed y test:', y_test.shape) +``` +``` +Shape of transformed X train: (60000, 784) +Shape of transformed X train: (10000, 784) +Shape of transformed y train: (60000, 10) +Shape of transformed y test: (10000, 10) +``` + +```python +# Оценка качества работы модели на тестовых данных +scores = model_lr1.evaluate(X_test, y_test) +print('Loss on test data:', scores[0]) +print('Accuracy on test data:', scores[1]) +``` +``` +313/313 ━━━━━━━━━━━━━━━━━━━━ 3s 5ms/step - accuracy: 0.9406 - loss: 0.2055 +Loss on test data: 0.1955890655517578 +Accuracy on test data: 0.9426000118255615 +``` + +### 11) Сравнили обученную модель сверточной сети и наилучшую модель полносвязной сети из лабораторной работы №1 по следующим показателям: +### - количество настраиваемых параметров в сети +### - количество эпох обучения +### - качество классификации тестовой выборки. +### Сделали выводы по результатам применения сверточной нейронной сети для распознавания изображений. + +Таблица1: + +| Модель | Количество настраиваемых параметров | Количество эпох обучения | Качество классификации тестовой выборки | +|----------|-------------------------------------|---------------------------|-----------------------------------------| +| Сверточная | 34 826 | 15 | accuracy:0.987 ; loss:0.0413 | +| Полносвязная | 89 612 | 50 | accuracy:0.943 ; loss:0.2055 | + + +##### По полученной таблице можно сделать выводы о том, что сверточная модель лучше по всем показателям, она имеет более высокое качество классификации, мненьшее время обученя, а также вдвое меньше параметров. + +## Задание 2 + +### В новом блокноте выполнили п. 2–8 задания 1, изменив набор данных MNIST на CIFAR-10, содержащий размеченные цветные изображения объектов, разделенные на 10 классов. +### При этом: +### - в п. 3 разбиение данных на обучающие и тестовые произвели в соотношении 50 000:10 000 +### - после разбиения данных (между п. 3 и 4) вывели 25 изображений из обучающей выборки с подписями классов +### - в п. 7 одно из тестовых изображений должно распознаваться корректно, а другое – ошибочно. + +### 1) Загрузили набор данных CIFAR-10, содержащий цветные изображения размеченные на 10 классов: самолет, автомобиль, птица, кошка, олень, собака, лягушка, лошадь, корабль, грузовик. + +```python +# загрузка датасета +from keras.datasets import cifar10 + +(X_train, y_train), (X_test, y_test) = cifar10.load_data() +``` + +### 2) Разбили набор данных на обучающие и тестовые данные в соотношении 50 000:10 000 элементов. Параметр random_state выбрали равным (4k – 1)=23, где k=6 –номер бригады. Вывели размерности полученных обучающих и тестовых массивов данных. + +```python +# создание своего разбиения датасета + +# объединяем в один набор +X = np.concatenate((X_train, X_test)) +y = np.concatenate((y_train, y_test)) + +# разбиваем по вариантам +X_train, X_test, y_train, y_test = train_test_split(X, y, + test_size = 10000, + train_size = 50000, + random_state = 11) +# вывод размерностей +print('Shape of X train:', X_train.shape) +print('Shape of y train:', y_train.shape) +print('Shape of X test:', X_test.shape) +print('Shape of y test:', y_test.shape) +``` +``` +Shape of X train: (50000, 32, 32, 3) +Shape of y train: (50000, 1) +Shape of X test: (10000, 32, 32, 3) +Shape of y test: (10000, 1) +``` + +### Вывели 25 изображений из обучающей выборки с подписью классов. + +```python +class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', + 'dog', 'frog', 'horse', 'ship', 'truck'] + +plt.figure(figsize=(10,10)) +for i in range(25): + plt.subplot(5,5,i+1) + plt.xticks([]) + plt.yticks([]) + plt.grid(False) + plt.imshow(X_train[i]) + plt.xlabel(class_names[y_train[i][0]]) +plt.show() +``` +![picture](images/6.png) + +### 3) Провели предобработку данных: привели обучающие и тестовые данные к формату, пригодному для обучения сверточной нейронной сети. Входные данные принимают значения от 0 до 1, метки цифр закодированы по принципу «one-hot encoding». Вывели размерности предобработанных обучающих и тестовых массивов данных. + +```python +# Зададим параметры данных и модели +num_classes = 10 +input_shape = (32, 32, 3) + +# Приведение входных данных к диапазону [0, 1] +X_train = X_train / 255 +X_test = X_test / 255 + +print('Shape of transformed X train:', X_train.shape) +print('Shape of transformed X test:', X_test.shape) + +# переведем метки в one-hot +y_train = keras.utils.to_categorical(y_train, num_classes) +y_test = keras.utils.to_categorical(y_test, num_classes) +print('Shape of transformed y train:', y_train.shape) +print('Shape of transformed y test:', y_test.shape) +``` +``` +Shape of transformed X train: (50000, 32, 32, 3) +Shape of transformed X test: (10000, 32, 32, 3) +Shape of transformed y train: (50000, 10) +Shape of transformed y test: (10000, 10) +``` + +### 4) Реализовали модель сверточной нейронной сети и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети. + +```python +# создаем модель +model = Sequential() + +# Блок 1 +model.add(layers.Conv2D(32, (3, 3), padding="same", + activation="relu", input_shape=input_shape)) +model.add(layers.BatchNormalization()) +model.add(layers.Conv2D(32, (3, 3), padding="same", activation="relu")) +model.add(layers.BatchNormalization()) +model.add(layers.MaxPooling2D((2, 2))) +model.add(layers.Dropout(0.25)) + +# Блок 2 +model.add(layers.Conv2D(64, (3, 3), padding="same", activation="relu")) +model.add(layers.BatchNormalization()) +model.add(layers.Conv2D(64, (3, 3), padding="same", activation="relu")) +model.add(layers.BatchNormalization()) +model.add(layers.MaxPooling2D((2, 2))) +model.add(layers.Dropout(0.25)) + +# Блок 3 +model.add(layers.Conv2D(128, (3, 3), padding="same", activation="relu")) +model.add(layers.BatchNormalization()) +model.add(layers.Conv2D(128, (3, 3), padding="same", activation="relu")) +model.add(layers.BatchNormalization()) +model.add(layers.MaxPooling2D((2, 2))) +model.add(layers.Dropout(0.4)) + +model.add(layers.Flatten()) +model.add(layers.Dense(128, activation='relu')) +model.add(layers.Dropout(0.5)) +model.add(layers.Dense(num_classes, activation="softmax")) + + +model.summary() +``` +**Model: "sequential_9"** +| Layer (type) | Output Shape | Param # | +|--------------------------------------------|-------------------|---------:| +| conv2d_41 (Conv2D) | (None, 32, 32, 32) | 896 | +| batch_normalization_6 (BatchNormalization) | (None, 32, 32, 32) | 128 | +| conv2d_42 (Conv2D) | (None, 32, 32, 32) | 9,248 | +| batch_normalization_7 (BatchNormalization) | (None, 32, 32, 32) | 128 | +| max_pooling2d_26 (MaxPooling2D) | (None, 16, 16, 32) | 0 | +| dropout_24 (Dropout) | (None, 16, 16, 32) | 0 | +| conv2d_43 (Conv2D) | (None, 16, 16, 64) | 18,496 | +| batch_normalization_8 (BatchNormalization) | (None, 16, 16, 64) | 256 | +| conv2d_44 (Conv2D) | (None, 16, 16, 64) | 36,928 | +| batch_normalization_9 (BatchNormalization) | (None, 16, 16, 64) | 256 | +| max_pooling2d_27 (MaxPooling2D) | (None, 8, 8, 64) | 0 | +| dropout_25 (Dropout) | (None, 8, 8, 64) | 0 | +| conv2d_45 (Conv2D) | (None, 8, 8, 128) | 73,856 | +| batch_normalization_10 (BatchNormalization)| (None, 8, 8, 128) | 512 | +| conv2d_46 (Conv2D) | (None, 8, 8, 128) | 147,584 | +| batch_normalization_11 (BatchNormalization)| (None, 8, 8, 128) | 512 | +| max_pooling2d_28 (MaxPooling2D) | (None, 4, 4, 128) | 0 | +| dropout_26 (Dropout) | (None, 4, 4, 128) | 0 | +| flatten_9 (Flatten) | (None, 2048) | 0 | +| dense_17 (Dense) | (None, 128) | 262,272 | +| dropout_27 (Dropout) | (None, 128) | 0 | +| dense_18 (Dense) | (None, 10) | 1,290 | +**Total params:** 552,362 (2.11 MB) +**Trainable params:** 551,466 (2.10 MB) +**Non-trainable params:** 896 (3.50 KB) + +```python +# компилируем и обучаем модель +batch_size = 64 +epochs = 50 +model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) +model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1) +``` + +### 5) Оценили качество обучения на тестовых данных. Вывели значение функции ошибки и значение метрики качества классификации на тестовых данных. + +```python +# Оценка качества работы модели на тестовых данных +scores = model.evaluate(X_test, y_test) +print('Loss on test data:', scores[0]) +print('Accuracy on test data:', scores[1]) +``` +``` +313/313 ━━━━━━━━━━━━━━━━━━━━ 7s 10ms/step - accuracy: 0.8615 - loss: 0.4696 +Loss on test data: 0.4909549355506897 +Accuracy on test data: 0.857200026512146 +``` + +### 6) Подали на вход обученной модели два тестовых изображения. Вывели изображения, истинные метки и результаты распознавания. + +```python +# вывод двух тестовых изображений и результатов распознавания + +for n in [3,15]: + result = model.predict(X_test[n:n+1]) + print('NN output:', result) + + plt.imshow(X_test[n].reshape(32,32,3), cmap=plt.get_cmap('gray')) + plt.show() + print('Real mark: ', np.argmax(y_test[n])) + print('NN answer: ', np.argmax(result)) +``` +![picture](images/7.png) +``` +Real mark: 4 +NN answer: 5 +``` +![picture](images/8.png) +``` +Real mark: 2 +NN answer: 2 +``` + +### 7) Вывели отчет о качестве классификации тестовой выборки и матрицу ошибок для тестовой выборки. + +```python +# истинные метки классов +true_labels = np.argmax(y_test, axis=1) +# предсказанные метки классов +predicted_labels = np.argmax(model.predict(X_test), axis=1) + +# отчет о качестве классификации +print(classification_report(true_labels, predicted_labels, target_names=class_names)) +# вычисление матрицы ошибок +conf_matrix = confusion_matrix(true_labels, predicted_labels) +# отрисовка матрицы ошибок в виде "тепловой карты" +fig, ax = plt.subplots(figsize=(6, 6)) +disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=class_names) +disp.plot(ax=ax, xticks_rotation=45) # поворот подписей по X и приятная палитра +plt.tight_layout() # чтобы всё влезло +plt.show() +``` +``` +313/313 ━━━━━━━━━━━━━━━━━━━━ 4s 10ms/step + precision recall f1-score support + + airplane 0.89 0.86 0.87 1000 + automobile 0.94 0.93 0.93 1019 + bird 0.80 0.84 0.82 972 + cat 0.78 0.66 0.72 1014 + deer 0.84 0.84 0.84 980 + dog 0.76 0.81 0.79 1051 + frog 0.90 0.87 0.89 1043 + horse 0.89 0.89 0.89 1018 + ship 0.91 0.94 0.92 945 + truck 0.86 0.94 0.90 958 + + accuracy 0.86 10000 + macro avg 0.86 0.86 0.86 10000 +weighted avg 0.86 0.86 0.86 10000 +``` +![picture](images/9.png) + +#### По результатам классификации датасета CIFAR-10 созданной сверточной моделью можно сделать вывод, что она довольно неплохо справилась с задачей. Полученные метрики оценки качества имеют показатели в районе 0.85. \ No newline at end of file