Hands-On Generative Adversarial Networks with PyTorch 1.x

Working on forward pass and backpropagation

Now, let's create our generator and discriminator networks. We put the code in the same simple_gan.py file as well:

1. Define the parameters of the generator network:

class Generator(object):    def __init__(self):        self.z = None        self.w1 = weight_initializer(Z_DIM, G_HIDDEN)        self.b1 = weight_initializer(1, G_HIDDEN)        self.x1 = None        self.w2 = weight_initializer(G_HIDDEN, G_HIDDEN)        self.b2 = weight_initializer(1, G_HIDDEN)        self.x2 = None        self.w3 = weight_initializer(G_HIDDEN, X_DIM)        self.b3 = weight_initializer(1, X_DIM)        self.x3 = None        self.x = None

We keep track of the inputs and outputs of all the layers because we need them to calculate the derivatives to update the parameters later.

2. Define the forward ...