Advanced Deep Learning with Keras

Disentangled Representation GANs
# ACGAN labels
# concatenate z noise vector and one-hot labels
inputs = [inputs, labels]
else:
# infoGAN codes
# concatenate z noise vector, one-hot labels,
# and codes 1 & 2
inputs = [inputs, labels] + codes
x = concatenate(inputs, axis=1)
elif codes is not None:
# generator 0 of StackedGAN
inputs = [inputs, codes]
x = concatenate(inputs, axis=1)
else:
# default input is just 100-dim noise (z-code)
x = inputs
x = Dense(image_resize * image_resize * layer_filters[0])(x)
x = Reshape((image_resize, image_resize, layer_filters[0]))(x)
for filters in layer_filters:
# first two convolution layers use strides = 2
# the last two use strides = 1
if filters > layer_filters[-2]:
strides = 2
else:
strides = 1
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2DTranspose(filters=filters,
kernel_size=kernel_size,
strides=strides,
padding='same')(x)
if activation is not None:
x = Activation(activation)(x)
# generator output is the synthesized image x
return Model(inputs, x, name='generator')
The preceding listing shows the discriminator and Q-Network with the original
default GAN output. The three auxiliary outputs corresponding to discrete code
(for one-hot label) softmax prediction and the continuous codes probabilities
given the input MNIST digit image are highlighted.
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