Advanced Deep Learning with Keras

Generative Adversarial Networks (GANs)
• Use of Leaky ReLU in all layers of the discriminator. Unlike ReLU, instead of
zeroing out all outputs when the input is less than zero, Leaky ReLU generates
a small gradient equal to alpha × input. In the following example, alpha = 0.2.
Figure 4.2.1: A DCGAN model
The generator learns to generate fake images from 100-dim input vectors ([-1.0, 1.0]
range 100-dim random noise with uniform distribution). The discriminator classifies
real from fake images but inadvertently coaches the generator how to generate real
images when the adversarial network is trained. The kernel size used in our DCGAN
implementation is 5, this is to allow it to increase the coverage and expressive power
of the convolution.
The generator accepts the 100-dim z-vector generated by a uniform distribution
with a range of -1.0 to 1.0. The first layer of the generator is a 7 × 7 ×128 = 6,272 -
unit Dense layer. The number of units is computed based on the intended ultimate
dimensions of the output image (28 × 28 × 1, 28 is a multiple of 7) and the number
of filters of the first Conv2DTranspose, which is equal to 128. We can imagine
transposed CNNs (Conv2DTranspose) as the reversed process of CNN. In a
simple example, if a CNN converts an image to feature maps, a transposed CNN
will produce an image given feature maps. Hence, transposed CNNs were used
in the decoder in the previous chapter and here on generators.
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