120 практических задач

Автор

Джейд Картер

Год написания книги

2024

Теги

<< 1 ... 40 41 42 43 44 45 46 >>

На страницу:

Перейти

44 из 46

Настройки чтения

Размер шрифта

Высота строк

Поля

model.add(layers.BatchNormalization())

model.add(layers.Dense(np.prod(image_size) * 3, activation='tanh'))

model.add(layers.Reshape((image_size[0], image_size[1], 3)))

return model

# Дискриминатор

def build_discriminator():

model = tf.keras.Sequential()

model.add(layers.Flatten(input_shape=image_size + (3,)))

model.add(layers.Dense(512, activation='relu'))

model.add(layers.Dense(256, activation='relu'))

model.add(layers.Dense(1, activation='sigmoid'))

return model

# Сборка модели GAN

generator = build_generator()

discriminator = build_discriminator()

discriminator.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

gan_input = layers.Input(shape=(100,))

generated_image = generator(gan_input)

discriminator.trainable = False

gan_output = discriminator(generated_image)

gan = tf.keras.Model(gan_input, gan_output)

gan.compile(optimizer='adam', loss='binary_crossentropy')

```

3. Обучение модели

```python

import tensorflow as tf

# Гиперпараметры

epochs = 10000

batch_size = 64

sample_interval = 200

latent_dim = 100

# Генерация меток

real_labels = np.ones((batch_size, 1))

fake_labels = np.zeros((batch_size, 1))

for epoch in range(epochs):

# Обучение дискриминатора

idx = np.random.randint(0, train_images.shape[0], batch_size)

real_images = train_images[idx]

noise = np.random.normal(0, 1, (batch_size, latent_dim))

fake_images = generator.predict(noise)

d_loss_real = discriminator.train_on_batch(real_images, real_labels)

d_loss_fake = discriminator.train_on_batch(fake_images, fake_labels)

d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

# Обучение генератора

noise = np.random.normal(0, 1, (batch_size, latent_dim))

g_loss = gan.train_on_batch(noise, real_labels)

# Печать прогресса

if epoch % sample_interval == 0:

print(f"{epoch} [D loss: {d_loss[0]}, acc.: {100*d_loss[1]}] [G loss: {g_loss}]")

sample_images(generator)