gan.py

# import keras
# from keras import layers
# from keras.preprocessing import image

from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.preprocessing import image
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)

import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"


latent_dim = 32
height = 32
width = 32
channels = 3

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()

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(lr=0.0008, clipvalue=1.0, decay=1e-8)
discriminator.compile(optimizer=discriminator_optimizer, loss='binary_crossentropy')

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(lr=0.0004, clipvalue=1.0, decay=1e-8)
gan.compile(optimizer=gan_optimizer, loss='binary_crossentropy')

(x_train, y_train), (_, _) = keras.datasets.cifar10.load_data()

x_train = x_train[y_train.flatten() == 7]
x_train = x_train.reshape(
    (x_train.shape[0],) +
    (height, width, channels)).astype('float32') / 255.
print(x_train.shape)
iterations = 10000
batch_size = 32
save_dir = 'tmp'
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))])
    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 % 100 == 0:
        print('discriminator loss:', d_loss)
        print('adversarial loss:', a_loss)
        img = image.array_to_img(generated_images[-1] * 255., scale=False)
        img.save(os.path.join(save_dir, 'generated' + str(step) + '.png'))
        # img = image.array_to_img(real_images[-1] * 255., scale=False)
        # img.save(os.path.join(save_dir, 'real' + str(step) + '.png'))

gan.save_weights('gan.h5')