{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","mount_file_id":"1bbVMysVGcsTFqIt6MuC4au-eAoPm6ccx","authorship_tag":"ABX9TyPxXSxO2w1/b3Roa81PFZ0r"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","source":["# импорт модулей\n","import os\n","os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab3')\n","\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\n"],"metadata":{"id":"deohyGvD2Aax","executionInfo":{"status":"ok","timestamp":1765216722444,"user_tz":-180,"elapsed":2,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["# загрузка датасета\n","from keras.datasets import mnist\n","(X_train, y_train), (X_test, y_test) = mnist.load_data()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2CQR5-Rl4w-y","executionInfo":{"status":"ok","timestamp":1765216725348,"user_tz":-180,"elapsed":2902,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"0eee5160-a10b-4556-fa60-1817ac6bed11"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n"]}]},{"cell_type":"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 = 23)\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)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6FSKMZhi4zp4","executionInfo":{"status":"ok","timestamp":1765216725442,"user_tz":-180,"elapsed":86,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"c0171a43-8944-4dbf-f351-3d0b572d6390"},"execution_count":8,"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)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Pg0IU4ez5kYp","executionInfo":{"status":"ok","timestamp":1765216725588,"user_tz":-180,"elapsed":144,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"be885f59-6028-496c-9cbd-cdf2b39c8d39"},"execution_count":9,"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()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"fLpm_MXA53q9","executionInfo":{"status":"ok","timestamp":1765216729000,"user_tz":-180,"elapsed":2476,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"452e5cc4-ce60-4d40-83b5-ed6c791dfe8c"},"execution_count":10,"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)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"on8tduvA6Hps","executionInfo":{"status":"ok","timestamp":1765216754501,"user_tz":-180,"elapsed":25502,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"1b940a02-1268-47f5-e2b2-9b8cc927ff94"},"execution_count":11,"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[1m9s\u001b[0m 40ms/step - accuracy: 0.6012 - loss: 1.2802 - val_accuracy: 0.9483 - val_loss: 0.1785\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: 0.9392 - loss: 0.2017 - val_accuracy: 0.9675 - val_loss: 0.1072\n","Epoch 3/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9605 - loss: 0.1282 - val_accuracy: 0.9728 - val_loss: 0.0857\n","Epoch 4/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9684 - loss: 0.1051 - val_accuracy: 0.9792 - val_loss: 0.0735\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: 0.9721 - loss: 0.0891 - val_accuracy: 0.9815 - val_loss: 0.0629\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: 0.9759 - loss: 0.0796 - val_accuracy: 0.9823 - val_loss: 0.0598\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: 0.9779 - loss: 0.0708 - val_accuracy: 0.9845 - val_loss: 0.0574\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: 0.9810 - loss: 0.0614 - val_accuracy: 0.9850 - val_loss: 0.0526\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: 0.9809 - loss: 0.0618 - val_accuracy: 0.9845 - val_loss: 0.0534\n","Epoch 10/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9815 - loss: 0.0565 - val_accuracy: 0.9847 - val_loss: 0.0497\n","Epoch 11/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9828 - loss: 0.0538 - val_accuracy: 0.9845 - val_loss: 0.0502\n","Epoch 12/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9840 - loss: 0.0504 - val_accuracy: 0.9860 - val_loss: 0.0481\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: 0.9841 - loss: 0.0483 - val_accuracy: 0.9872 - val_loss: 0.0468\n","Epoch 14/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9856 - loss: 0.0464 - val_accuracy: 0.9868 - val_loss: 0.0434\n","Epoch 15/15\n","\u001b[1m106/106\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9861 - loss: 0.0432 - val_accuracy: 0.9868 - val_loss: 0.0438\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":11}]},{"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])\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"m96G7zNB66ca","executionInfo":{"status":"ok","timestamp":1765216756411,"user_tz":-180,"elapsed":1906,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"281c6c0c-3320-4614-9bd2-5a53f9437584"},"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 - accuracy: 0.9900 - loss: 0.0318\n","Loss on test data: 0.03606646880507469\n","Accuracy on test data: 0.9894999861717224\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))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"wfzh1a3F6-j7","executionInfo":{"status":"ok","timestamp":1765216757181,"user_tz":-180,"elapsed":772,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"92ce6419-d42f-4431-a289-7447a8a37db9"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 401ms/step\n","NN output: [[3.3479900e-08 4.7786756e-14 9.9999976e-01 2.1413245e-08 1.2324374e-11\n"," 1.0817477e-09 6.3094833e-12 4.4141193e-10 2.3786414e-07 8.6100585e-11]]\n"]},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark: 2\n","NN answer: 2\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n","NN output: [[2.7364138e-09 7.8356831e-13 1.9827752e-08 7.4724680e-06 1.7721488e-06\n"," 2.3284849e-07 1.7225671e-11 2.4611420e-05 6.3226136e-05 9.9990261e-01]]\n"]},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark: 9\n","NN answer: 9\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()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":771},"id":"BJzUruLZ7Grd","executionInfo":{"status":"ok","timestamp":1765216758870,"user_tz":-180,"elapsed":1679,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"2245a6d3-ffbf-4602-8ad1-7448067e59bd"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"," precision recall f1-score support\n","\n"," 0 0.99 1.00 0.99 997\n"," 1 1.00 0.99 1.00 1164\n"," 2 0.99 0.98 0.99 1030\n"," 3 0.99 0.99 0.99 1031\n"," 4 0.99 0.99 0.99 967\n"," 5 0.98 0.99 0.99 860\n"," 6 0.99 1.00 0.99 977\n"," 7 0.98 0.99 0.99 1072\n"," 8 0.99 0.98 0.99 939\n"," 9 0.99 0.98 0.98 963\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 ['test3.png', 'test5.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))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":914},"id":"pFVJZwuk7QAK","executionInfo":{"status":"ok","timestamp":1765216761006,"user_tz":-180,"elapsed":2149,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"71c2e110-746f-4616-d677-493d3b64b3de"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n","I think it's 3\n"]},{"output_type":"display_data","data":{"text/plain":["
"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/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":255},"id":"Plyx39Cm72c7","executionInfo":{"status":"ok","timestamp":1765216762816,"user_tz":-180,"elapsed":1804,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"eebaf793-5f1b-4fff-912b-6ad55cee9d62"},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_10\"\u001b[0m\n"],"text/html":["
Model: \"sequential_10\"\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_23 (\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_24 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m) │ \u001b[38;5;34m5,050\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_25 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m510\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ dense_23 (Dense)                │ (None, 100)            │        78,500 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_24 (Dense)                │ (None, 50)             │         5,050 │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_25 (Dense)                │ (None, 10)             │           510 │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m84,062\u001b[0m (328.37 KB)\n"],"text/html":["
 Total params: 84,062 (328.37 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m84,060\u001b[0m (328.36 KB)\n"],"text/html":["
 Trainable params: 84,060 (328.36 KB)\n","
\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"],"text/html":["
 Non-trainable params: 0 (0.00 B)\n","
\n"]},"metadata":{}},{"output_type":"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 = 23)\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)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Gl-SOZoHHkie","executionInfo":{"status":"ok","timestamp":1765216797621,"user_tz":-180,"elapsed":240,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"2c7babb2-99de-44fd-85b6-39e1f5a80aa7"},"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])\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NKMJJLIUHsoj","executionInfo":{"status":"ok","timestamp":1765216808331,"user_tz":-180,"elapsed":2878,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"8132ade5-55c7-4dc2-d7bc-b45146f75302"},"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: 0.9576 - loss: 0.1293\n","Loss on test data: 0.13758081197738647\n","Accuracy on test data: 0.9567000269889832\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()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bNlYmpBUH1nD","executionInfo":{"status":"ok","timestamp":1765216911865,"user_tz":-180,"elapsed":16710,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"81c84db4-0773-4ea1-8fa5-1876f2ffa569"},"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[1m13s\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 = 23)\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)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ww_CL1uRIK9V","executionInfo":{"status":"ok","timestamp":1765216926509,"user_tz":-180,"elapsed":365,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"25b525a7-0fe7-4da0-9dad-b04b5cce18b7"},"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()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":826},"id":"vn81SpnTINSC","executionInfo":{"status":"ok","timestamp":1765216937490,"user_tz":-180,"elapsed":1611,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"2a69ec40-9b23-4e87-c233-a823bf9cc269"},"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)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NUwOVYuCIPIy","executionInfo":{"status":"ok","timestamp":1765216948692,"user_tz":-180,"elapsed":590,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"2832ca08-854f-4213-d588-af752444d015"},"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()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"VhXXpAUKIg5z","executionInfo":{"status":"ok","timestamp":1765217432833,"user_tz":-180,"elapsed":539,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"ba4ea6b3-933f-4135-8449-c83c4ab42f39"},"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)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1MmpBaHFLM9S","executionInfo":{"status":"ok","timestamp":1765218101408,"user_tz":-180,"elapsed":368146,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"6619dad6-9783-4fad-fb5c-6495a11bcebf"},"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[1m33s\u001b[0m 23ms/step - accuracy: 0.2713 - loss: 2.0996 - val_accuracy: 0.4600 - val_loss: 1.4698\n","Epoch 2/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.4710 - loss: 1.4611 - val_accuracy: 0.5876 - val_loss: 1.2134\n","Epoch 3/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.5671 - loss: 1.2243 - val_accuracy: 0.6042 - val_loss: 1.2126\n","Epoch 4/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.6242 - loss: 1.0786 - val_accuracy: 0.6838 - val_loss: 0.8650\n","Epoch 5/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.6618 - loss: 0.9714 - val_accuracy: 0.7076 - val_loss: 0.8535\n","Epoch 6/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.7015 - loss: 0.8811 - val_accuracy: 0.7096 - val_loss: 0.8280\n","Epoch 7/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.7175 - loss: 0.8198 - val_accuracy: 0.7428 - val_loss: 0.7633\n","Epoch 8/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.7368 - loss: 0.7710 - val_accuracy: 0.7632 - val_loss: 0.6851\n","Epoch 9/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.7579 - loss: 0.7130 - val_accuracy: 0.7674 - val_loss: 0.6738\n","Epoch 10/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7730 - loss: 0.6751 - val_accuracy: 0.8030 - val_loss: 0.5984\n","Epoch 11/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.7847 - loss: 0.6376 - val_accuracy: 0.7694 - val_loss: 0.7044\n","Epoch 12/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7970 - loss: 0.5956 - val_accuracy: 0.7886 - val_loss: 0.6338\n","Epoch 13/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.8038 - loss: 0.5751 - val_accuracy: 0.7992 - val_loss: 0.6106\n","Epoch 14/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8139 - loss: 0.5532 - val_accuracy: 0.8152 - val_loss: 0.5782\n","Epoch 15/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8206 - loss: 0.5221 - val_accuracy: 0.8126 - val_loss: 0.5920\n","Epoch 16/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8288 - loss: 0.5039 - val_accuracy: 0.8266 - val_loss: 0.5345\n","Epoch 17/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8327 - loss: 0.4915 - val_accuracy: 0.8188 - val_loss: 0.5628\n","Epoch 18/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8415 - loss: 0.4732 - val_accuracy: 0.8248 - val_loss: 0.5356\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: 0.8486 - loss: 0.4533 - val_accuracy: 0.8232 - val_loss: 0.5351\n","Epoch 20/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8525 - loss: 0.4387 - val_accuracy: 0.8304 - val_loss: 0.5254\n","Epoch 21/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.8520 - loss: 0.4297 - val_accuracy: 0.7862 - val_loss: 0.7218\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: 0.8589 - loss: 0.4169 - val_accuracy: 0.8384 - val_loss: 0.5035\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: 0.8636 - loss: 0.4034 - val_accuracy: 0.8192 - val_loss: 0.5844\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: 0.8716 - loss: 0.3834 - val_accuracy: 0.8250 - val_loss: 0.5500\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: 0.8709 - loss: 0.3822 - val_accuracy: 0.8364 - val_loss: 0.5000\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: 0.8786 - loss: 0.3592 - val_accuracy: 0.8500 - val_loss: 0.4872\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: 0.8708 - loss: 0.3783 - val_accuracy: 0.8492 - val_loss: 0.5018\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: 0.8819 - loss: 0.3502 - val_accuracy: 0.8498 - val_loss: 0.4784\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: 0.8866 - loss: 0.3325 - val_accuracy: 0.8434 - val_loss: 0.5009\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: 0.8878 - loss: 0.3299 - val_accuracy: 0.8450 - val_loss: 0.4781\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: 0.8902 - loss: 0.3252 - val_accuracy: 0.8550 - val_loss: 0.4734\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: 0.8907 - loss: 0.3245 - val_accuracy: 0.8434 - val_loss: 0.5142\n","Epoch 33/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8927 - loss: 0.3160 - val_accuracy: 0.8498 - val_loss: 0.5049\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: 0.8983 - loss: 0.3004 - val_accuracy: 0.8432 - val_loss: 0.5066\n","Epoch 35/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8984 - loss: 0.3011 - val_accuracy: 0.8394 - val_loss: 0.5416\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: 0.9025 - loss: 0.2877 - val_accuracy: 0.8502 - val_loss: 0.4972\n","Epoch 37/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9016 - loss: 0.2897 - val_accuracy: 0.8468 - val_loss: 0.5086\n","Epoch 38/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9011 - loss: 0.2889 - val_accuracy: 0.8528 - val_loss: 0.4809\n","Epoch 39/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 10ms/step - accuracy: 0.9063 - loss: 0.2709 - val_accuracy: 0.8506 - val_loss: 0.5207\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: 0.9082 - loss: 0.2689 - val_accuracy: 0.8546 - val_loss: 0.4983\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: 0.9040 - loss: 0.2784 - val_accuracy: 0.8522 - val_loss: 0.5021\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: 0.9055 - loss: 0.2769 - val_accuracy: 0.8520 - val_loss: 0.5108\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: 0.9098 - loss: 0.2575 - val_accuracy: 0.8470 - val_loss: 0.5368\n","Epoch 44/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.9134 - loss: 0.2487 - val_accuracy: 0.8542 - val_loss: 0.4839\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: 0.9147 - loss: 0.2523 - val_accuracy: 0.8494 - val_loss: 0.5282\n","Epoch 46/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.9162 - loss: 0.2465 - val_accuracy: 0.8538 - val_loss: 0.4990\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: 0.9169 - loss: 0.2459 - val_accuracy: 0.8584 - val_loss: 0.4915\n","Epoch 48/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.9178 - loss: 0.2409 - val_accuracy: 0.8602 - val_loss: 0.4881\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: 0.9168 - loss: 0.2516 - val_accuracy: 0.8578 - val_loss: 0.5049\n","Epoch 50/50\n","\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.9204 - loss: 0.2299 - val_accuracy: 0.8524 - val_loss: 0.4976\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])\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8DFXlHIFLRmW","executionInfo":{"status":"ok","timestamp":1765218104302,"user_tz":-180,"elapsed":2888,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"217f2436-0f57-4ee6-b65b-82b5868add12"},"execution_count":25,"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: 0.8549 - loss: 0.5187\n","Loss on test data: 0.518433153629303\n","Accuracy on test data: 0.8521999716758728\n"]}]},{"cell_type":"code","source":["# вывод двух тестовых изображений и результатов распознавания\n","\n","for n in [1,10]:\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))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"XFNJlrvzMupw","executionInfo":{"status":"ok","timestamp":1765218332815,"user_tz":-180,"elapsed":1260,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"cfe6de90-c202-4c27-d199-f0c58e3e236b"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step\n","NN output: [[5.0897655e-05 1.5590033e-06 1.1971882e-07 5.6473659e-10 3.7635881e-09\n"," 1.3418226e-10 1.1458374e-08 1.3129012e-10 9.9994743e-01 1.9529903e-08]]\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark: 8\n","NN answer: 8\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step\n","NN output: [[3.7840240e-05 8.8557690e-06 1.5065701e-02 6.9747168e-01 3.3291127e-03\n"," 6.2251734e-03 1.4282507e-02 2.6306707e-01 7.0983755e-05 4.4104352e-04]]\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Real mark: 3\n","NN answer: 3\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()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":898},"id":"757ub76BNjY4","executionInfo":{"status":"ok","timestamp":1765218354931,"user_tz":-180,"elapsed":11282,"user":{"displayName":"Чиёми Анзай","userId":"17549274460477558773"}},"outputId":"f5cac39f-9212-44a7-af3c-ed0c369438ba"},"execution_count":32,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step\n"," precision recall f1-score support\n","\n"," airplane 0.86 0.88 0.87 986\n"," automobile 0.94 0.93 0.94 971\n"," bird 0.75 0.85 0.80 1043\n"," cat 0.83 0.64 0.72 1037\n"," deer 0.78 0.90 0.83 969\n"," dog 0.78 0.75 0.76 979\n"," frog 0.83 0.92 0.87 1025\n"," horse 0.92 0.83 0.87 948\n"," ship 0.93 0.93 0.93 1003\n"," truck 0.94 0.90 0.92 1039\n","\n"," accuracy 0.85 10000\n"," macro avg 0.86 0.85 0.85 10000\n","weighted avg 0.86 0.85 0.85 10000\n","\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]}]}