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Chapter 6
The resultant generator model is as follows:
Figure 3: A summary of the resultant generator model
You can also experiment with the transposed convolution layer.
This layer not only upsamples the input image but also learns
how to fill in details during the training. Thus, you can replace
upsampling and convolution layers with a single transposed
convolution layer. The transpose convolutional layer performs
an inverse convolution operation. You can read about it in more
detail in the paper: A guide to convolution arithmetic for deep learning
(https://arxiv.org/abs/1603.07285).
Now that we have a generator, let us see the code to build the discriminator. The
discriminator is similar to a standard convolutional neural network but with one
major change: instead of maxpooling we use convolutional layers with strides of
2. We also add dropout layers to avoid overfitting, and batch normalization for
better accuracy and fast convergence. The activation layer is leaky ReLU. In the
following network we use three such convolutional layers, with filters of 32, 64, and
128 respectively. The output of the third convolutional layer is flattened and fed
to a dense layer with a single unit.
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