newapp

app.py.py

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+# -*- coding: utf-8 -*-
+"""Untitled3.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1gWAA1NHcuSs1JrZSG9sQIrcozcWOaaZL
+"""
+
+
+
+import tensorflow as tf
+from tensorflow.keras.datasets import mnist #Загрузка датасета mnist:
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+x_train = x_train / 255
+x_test = x_test / 255
+y_train = tf.keras.utils.to_categorical(y_train, num_classes=10) # Преобразование меток в бинарные векторы
+y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)
+
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+model = Sequential()
+# Добавление слоев
+model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
+model.add(MaxPooling2D((2, 2)))
+model.add(Flatten())
+model.add(Dense(64, activation='relu'))
+model.add(Dense(10, activation='softmax'))
+model.summary()
+# Размеры тренировочного, валидационного и тестового датасетов
+train_size = x_train.shape[0]
+val_size = int(train_size * 0.1)
+test_size = x_test.shape[0]
+print("Размер тренировочного датасета:", train_size)
+print("Размер валидационного датасета:", val_size)
+print("Размер тестового датасета:", test_size)
+tf.keras.utils.plot_model(model, show_shapes= True, show_layer_names= True, show_layer_activations= True)
+
+model.save('my_model')
+
+from google.colab import drive
+drive.mount('/content/drive')
+
+model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_test, y_test)) # Обучение модели
+
+loss, accuracy = model.evaluate(x_test, y_test)
+print(f'Loss: {loss}, Accuracy: {accuracy}') # Оценка модели на тестовых данных
+
+import numpy as np
+index = np.random.randint(len(x_test)) # возьмем случайное изображение
+image = x_test[index]
+image = np.expand_dims(image, axis=0)
+prediction = model.predict(image) # найдем метки
+predicted_digit = np.argmax(prediction)
+remainder = predicted_digit % 2 # Вычисление остатка на 2
+print(f'Predicted Digit: {predicted_digit}, Remainder: {remainder}')