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Chapter 6
Figure 1: Visualizing the architecture of a generator
The discriminator network takes in the images (either generated by the generator
or from the real dataset), and the images undergo convolution followed by batch
normalization. At each convolution step the images get downsampled using strides.
The final output of the convolutional layer is flattened and feeds a one-neuron
classifier layer. In the following diagram, you can see the discriminator:
Figure 2: Visualizing the architecture of a discriminator
The generator and the discriminator are combined together to form the DCGAN. The
training follows in the same manner as before; that is, we first train the discriminator
on a mini-batch, then freeze the discriminator and train the generator. The process is
repeated iteratively for a few thousand epochs. The authors found that we get more
stable results with the Adam optimizer and a learning rate of 0.002.
Next, we'll implement a DCGAN for generating handwritten digits.
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