Машинное обучение — Пример сверточной сети

import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical

# Загружаем данные
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(-1, 28, 28, 1).astype('float32') / 255.0
X_test = X_test.reshape(-1, 28, 28, 1).astype('float32') / 255.0
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# Создаем модель
model = Sequential([
    Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)),
    MaxPooling2D(pool_size=(2, 2)),
    Conv2D(64, kernel_size=(3, 3), activation='relu'),
    MaxPooling2D(pool_size=(2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

# Компилируем модель
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Обучаем модель
model.fit(X_train, y_train, epochs=10, batch_size=128, validation_split=0.2)

# Оцениваем модель
loss, accuracy = model.evaluate(X_test, y_test)
print(f"Точность CNN: {accuracy:.2f}")