Deep Learning with R Cookbook

How it works...

In step 1, we defined the shape of the input images and the number of channels. Since the images that were used were colourful, we specified the number of channels as 3, meaning RGB mode. We also specified the latent space dimension. In step 2, we constructed a generator network. The job of this generator is to map random normal vectors of shape latent_dim to images of shape (32, 32, 3). 

Use normal distribution to generate points from latent space rather than a uniform distribution for robust results.

We used a deep convolutional network as the generator network in our example. The layer_conv_2d_transpose() function is used to upsample the image data. We used tanh as the activation function in the last layer of the generator ...