def __init__(self):

self.name = self.__class__.__name__.lower()

# Input shape

self.img_rows = 128

self.img_cols = 128

self.channels = 3

self.img_shape = (self.img_rows, self.img_cols, self.channels)

self.latent_dim = 100

optimizer = Adam(0.0002, 0.5)

# Build and compile the discriminator

self.discriminator = self.build_discriminator()

self.discriminator.compile(loss='binary_crossentropy',

optimizer=optimizer,

metrics=['accuracy'])

# Build the generator

self.generator = self.build_generator()

# The generator takes noise as input and generates imgs

z = Input(shape=(100,))

img = self.generator(z)

# For the combined model we will only train the generator

self.discriminator.trainable = False

# The discriminator takes generated images as input and determines validity

valid = self.discriminator(img)

# The combined model (stacked generator and discriminator)

# Trains the generator to fool the discriminator

self.combined = Model(z, valid)

self.combined.compile(loss='binary_crossentropy', optimizer=optimizer)

filename_weight = ''

self.weight_path = 'weight/%s' % self.name

self.file_weight = os.path.join(self.weight_path, filename_weight)

self._load_weight()

def build_generator(self):

model = Sequential()

model.add(Dense(8 * 128 * 128, activation="relu", input_dim=self.latent_dim))

model.add(Reshape((128, 128, 8)))

# model.add(UpSampling2D())

# model.add(Conv2D(128, kernel_size=3, padding="same"))

model.add(Conv2DTranspose(128, kernel_size=3, padding="same"))

model.add(BatchNormalization(momentum=0.8))

model.add(LeakyReLU(alpha=0.2))

# # model.add(UpSampling2D())

# # model.add(Conv2D(128, kernel_size=3, padding="same"))

# model.add(Conv2DTranspose(128, kernel_size=3, padding="same"))

# model.add(BatchNormalization(momentum=0.8))

# model.add(LeakyReLU(alpha=0.2))

# model.add(UpSampling2D())

# model.add(Conv2D(64, kernel_size=3, padding="same"))

model.add(Conv2DTranspose(64, kernel_size=3, padding="same"))

model.add(BatchNormalization(momentum=0.8))

model.add(LeakyReLU(alpha=0.2))

# model.add(Conv2D(self.channels, kernel_size=3, padding="same"))

model.add(Conv2DTranspose(self.channels, kernel_size=3, padding="same"))

model.add(Activation("tanh"))

model.summary()

noise = Input(shape=(self.latent_dim,))

img = model(noise)

return Model(noise, img)

def build_discriminator(self):

# inception = keras.applications.Xception(input_shape=self.img_shape, weights=None, include_top=False,

# pooling='avg')

# x = Dense(1, activation='sigmoid')(inception.output)

# return Model(inception.input, x)

model = Sequential()

model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))

model.add(LeakyReLU(alpha=0.2))

model.add(Dropout(0.25))

model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))

model.add(ZeroPadding2D(padding=((0, 1), (0, 1))))

model.add(BatchNormalization(momentum=0.8))

model.add(LeakyReLU(alpha=0.2))

model.add(Dropout(0.25))

model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))

model.add(BatchNormalization(momentum=0.8))

model.add(LeakyReLU(alpha=0.2))

model.add(Dropout(0.25))

model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))

model.add(BatchNormalization(momentum=0.8))

model.add(LeakyReLU(alpha=0.2))

model.add(Dropout(0.25))

model.add(Flatten())

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

model.summary()

img = Input(shape=self.img_shape)

validity = model(img)

return Model(img, validity)

def _load_weight(self):

if os.path.exists(self.file_weight) and os.path.isfile(self.file_weight):

self.combined.load_weights(self.file_weight, by_name=True)

print('Load weight [%s] successfully.' % self.file_weight)

else:

print('No weight loaded.')

def _save_weight(self, epoch, d_loss):

filename_weight = '%s_%05d-%.4f.h5' % (self.name, epoch, d_loss)

file_weight = os.path.join(self.weight_path, filename_weight)

if not os.path.exists(self.weight_path):

os.makedirs(self.weight_path)

self.combined.save_weights(file_weight, overwrite=True)

def train(self, epochs, batch_size=128, save_interval=50):

# Load the dataset

# (X_train, _), (_, _) = mnist.load_data()

generator = load_data(batch_size, self.img_rows, self.img_cols)

# Rescale -1 to 1

# X_train = X_train / 127.5 - 1.

# X_train = np.expand_dims(X_train, axis=3)

# Adversarial ground truths

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

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

for epoch in range(epochs):

# ---------------------

# Train Discriminator

# ---------------------

# Select a random half of images

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

# imgs = X_train[idx]

imgs, _ = next(generator)

imgs = imgs / 127.5 - 1.

if imgs.shape[0] != batch_size:

continue

# Sample noise and generate a batch of new images

noise = np.random.uniform(-1, 1, (batch_size, self.latent_dim))

gen_imgs = self.generator.predict(noise)

# Train the discriminator (real classified as ones and generated as zeros)

# X = np.concatenate((imgs, gen_imgs))

# y = np.concatenate((valid, fake))

# d_loss = self.discriminator.train_on_batch(X, y)

d_loss_real = self.discriminator.train_on_batch(imgs, valid)

d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)

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

# ---------------------

# Train Generator

# ---------------------

# if epoch < 200:

# print('Train %d, d_loss: %f' % (epoch, d_loss[0]))

# else:

# Train the generator (wants discriminator to mistake images as real)

self.discriminator.trainable = False

g_loss = self.combined.train_on_batch(noise, valid)

self.discriminator.trainable = True

# Plot the progress

print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100 * d_loss[1], g_loss))

# If at save interval => save generated image samples

if epoch % save_interval == 0:

self.save_imgs(epoch)

self._save_weight(epoch, d_loss[0])

def save_imgs(self, epoch):

r, c = 5, 5

noise = np.random.uniform(-1, 1, (r * c, self.latent_dim))

gen_imgs = self.generator.predict(noise)

# Rescale images 0 - 1

gen_imgs = 0.5 * gen_imgs + 0.5

fig, axs = plt.subplots(r, c)

cnt = 0

for i in range(r):

for j in range(c):

axs[i, j].imshow(gen_imgs[cnt, :, :, :])

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

cnt += 1

save_dir = 'images/%s' % self.name

if not os.path.exists(save_dir):

os.makedirs(save_dir)

fig.savefig("%s/%d.png" % (save_dir, epoch))