import keras from keras import layers import numpy as np
latent_dim = 32 height = 32 width = 32 channels =3
The Generator
generator_input = keras.Input(shape=(latent_dim,))
x = layers.Dense(128 * 16* 16) (generator_input) x = layers.LeakyReLU()(x) x = layers.Reshape((16, 16, 128))(x)
x = layers.Conv2D(256, 5, padding='same') (x) x = layers.LeakyReLU()(x)
x = layers.Conv2DTranspose(256, 4, strides=2, padding='same')(x) x = layers.LeakyReLU()(x)
x = layers.Conv2D(256, 5, padding='same')(x) x = layers.LeakyReLU()(x) x = layers.Conv2D(256, 5, padding='same')(x) x = layers.LeakyReLU()(x)
x = layers.Conv2D(channels, 7, activation='tanh', padding='same')(x) generator = keras.models.Model(generator_input, x) #generator.summary()
The Discriminator
discriminator_input = layers.Input(shape=(height, width, channels))
x = layers.Conv2D(128, 3)(discriminator_input)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, 4, strides=2)(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, 4, strides=2)(x)
x = layers.LeakyReLU()(x)
x = layers.Conv2D(128, 4, strides=2)(x)
x = layers.LeakyReLU()(x)
x = layers.Flatten()(x)
x =layers.Dropout(0.4)(x)
x = layers.Dense(1, activation='sigmoid')(x)
discriminator = keras.models.Model(discriminator_input, x) #discriminator.summary()
discriminator_optimizer = keras.optimizers.RMSprop(learning_rate=0.0008, clipvalue=1.0, decay=1e-8)
discriminator.compile(optimizer=discriminator_optimizer, loss='binary_crossentropy')
Adversarial network
discriminator.trainable = False
gan_input = keras.Input(shape=(latent_dim,)) gan_output = discriminator(generator(gan_input)) gan = keras.models.Model(gan_input, gan_output)
gan_optimizer = keras.optimizers.RMSprop(learning_rate=0.0004, clipvalue=1.0, decay=1e-8) gan.compile(optimizer=gan_optimizer, loss='binary_crossentropy')
Implementing GAN training
import os import tensorflow as tf from keras.preprocessing import image
(x_train, y_train), (_, _) = tf.keras.datasets.cifar10.load_data()
x_train = x_train[y_train.flatten() == 5]
x_train = x_train.reshape((x_train.shape[0],) + (height, width, channels)).astype('float32')/255
iterations = 10000 batch_size = 20 save_dir = 'dog_dir' os.mkdir(save_dir)
start = 0 for step in range(iterations): random_latent_vectors = np.random.normal(size=(batch_size, latent_dim))
generated_images = generator.predict(random_latent_vectors)
stop = start + batch_size real_images = x_train[start: stop] combined_images = np.concatenate([generated_images, real_images])
labels = np.concatenate([np.ones((batch_size, 1)), np.zeros((batch_size, 1))])
labels += 0.05 * np.random.random(labels.shape)
d_loss = discriminator.train_on_batch(combined_images, labels)
random_latent_vectors = np.random.normal(size= (batch_size, latent_dim))
misleading_targets = np.zeros((batch_size, 1))
a_loss = gan.train_on_batch(random_latent_vectors, misleading_targets)
start += batch_size if start > len(x_train) - batch_size: start = 0
if step % 1000 == 0: gan.save_weights('gan.h5')
print('discriminator loss:', d_loss) print('adversarial loss:', a_loss)
img = image.array_to_img(generated_images[0] * 255., scale=False) img.save(os.path.join(save_dir, 'generated_dog' + str(step) + '.png'))
img = image.array_to_img(real_images[0] * 255., scale=False) img.save(os.path.join(save_dir, 'real_dog' + str(step) + '.png'))