model.py

import torch
import torch.nn as nn

"""
Network Architectures
The following are the discriminator and generator architectures
"""

class discriminator(nn.Module):
    def __init__(self):
        super(discriminator, self).__init__()
        self.fc1 = nn.Linear(784, 512)
        self.fc2 = nn.Linear(512, 1)
        self.activation = nn.LeakyReLU(0.1)

    def forward(self, x):
        x = x.view(-1, 784)
        x = self.activation(self.fc1(x))
        x = self.fc2(x)
        return nn.Sigmoid()(x)


class generator(nn.Module):
    def __init__(self):
        super(generator, self).__init__()
        self.fc1 = nn.Linear(128, 1024)
        self.fc2 = nn.Linear(1024, 2048)
        self.fc3 = nn.Linear(2048, 784)
        self.activation = nn.ReLU()

    def forward(self, x):
        x = self.activation(self.fc1(x))
        x = self.activation(self.fc2(x))
        x = self.fc3(x)
        x = x.view(-1, 1, 28, 28)
        return nn.Tanh()(x)