Джейд Картер "120 практических задач"

image_size = (128, 128) # Размер изображения для нейронной сети

def load_images(image_dir, image_size):

images = []

for filename in os.listdir(image_dir):

if filename.endswith(".jpg") or filename.endswith(".png"):

img_path = os.path.join(image_dir, filename)

img = Image.open(img_path).resize(image_size)

img = np.array(img)

images.append(img)

return np.array(images)

images = load_images(image_dir, image_size)

images = (images – 127.5) / 127.5 # Нормализация изображений в диапазон [-1, 1]

train_images, test_images = train_test_split(images, test_size=0.2)

```

2. Построение модели GAN

Генеративно-состязательная сеть состоит из двух частей: генератора и дискриминатора.

```python

import tensorflow as tf

from tensorflow.keras import layers

# Генератор

def build_generator():

model = tf.keras.Sequential()

model.add(layers.Dense(256, activation='relu', input_shape=(100,)))

model.add(layers.BatchNormalization())

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

model.add(layers.BatchNormalization())

model.add(layers.Dense(1024, activation='relu'))

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)

def sample_images(generator, image_grid_rows=4, image_grid_columns=4):

noise = np.random.normal(0, 1, (image_grid_rows * image_grid_columns, latent_dim))

gen_images = generator.predict(noise)

gen_images = 0.5 * gen_images + 0.5

fig, axs = plt.subplots(image_grid_rows, image_grid_columns, figsize=(10, 10))

cnt = 0

for i in range(image_grid_rows):

for j in range(image_grid_columns):

axs[i,j].imshow(gen_images[cnt])

axs[i,j].axis('off')

cnt += 1

plt.show()

```

4. Генерация изображений

После завершения обучения, можно использовать генератор для создания новых изображений ландшафтов.

```python

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

generated_image = generator.predict(noise)

generated_image = 0.5 * generated_image + 0.5 # Возвращение значений к диапазону [0, 1]

plt.imshow(generated_image[0])

plt.axis('off')

plt.show()

```