Generating textures with a GAN

One of the things so rarely covered in advanced deep learning books is the specifics of shaping data to input into a network. Along with shaping data is the need to alter the internals of a network to accommodate the new data. The final version of this example is Chapter_3_3.py, but for this exercise, start with the Chapter_3_wgan.py file and follow these steps:

1. We will start by changing the training set of data from MNIST to CIFAR by swapping out the imports like so:

from keras.datasets import mnist  #remove or leave
from keras.datasets import cifar100  #add

2. At the start of the class, we will change the image size parameters from 28 x 28 grayscale to 32 x 32 color like so:

class WGAN():
  def __init__(self):
    self.img_rows = 32
    self.img_cols = 32
    self.channels = 3

3. Now, move down to the train function and alter the code as follows:
...