Advanced Deep Learning with Keras

Disentangled Representation GANs
# first output is probability that feature1 is real
f1_source = Dense(1)(x)
f1_source = Activation('sigmoid', name='feature1_source')
(f1_source)
# z1 reonstruction (Q1 network)
z1_recon = Dense(z_dim)(x)
z1_recon = Activation('tanh', name='z1')(z1_recon)
discriminator_outputs = [f1_source, z1_recon]
dis1 = Model(inputs, discriminator_outputs, name='dis1')
return dis1
With all builder functions available, StackedGAN is assembled in Listing 6.2.4. Before
training StackedGAN, the encoder is pretrained. Note that we already incorporated
the three generator loss functions (adversarial, conditional, and entropy) in the
adversarial model training. The Q-Network shares some common layers with
the discriminator model. Therefore, its loss function is also incorporated in the
discriminator model training.
Listing 6.2.4, stackedgan-mnist-6.2.1.py. Building StackedGAN in Keras:
def build_and_train_models():
# load MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# reshape and normalize images
image_size = x_train.shape[1]
x_train = np.reshape(x_train, [-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
x_test = np.reshape(x_test, [-1, image_size, image_size, 1])
x_test = x_test.astype('float32') / 255
# number of labels
num_labels = len(np.unique(y_train))
# to one-hot vector
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
model_name = "stackedgan_mnist"
# network parameters
batch_size = 64
train_steps = 40000
[ 190 ]