Generative adversarial networks with Python

Generative adversarial networks (GANs) are a type of deep learning model that consists of two neural networks - a generator and a discriminator. The generator generates new data instances, while the discriminator evaluates them for authenticity. Step 1: In the first step, we need to define the generator network. This network takes random noise as input and generates fake data samples that resemble the real data. We will use a simple feedforward neural network for the generator.

import torch
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(input_dim, 128),
            nn.ReLU(),
            nn.Linear(128, output_dim),
            nn.Tanh()
        )
    
    def forward(self, x):
        return self.model(x)

Step 2: Next, we need to define the discriminator network. This network takes input data samples and predicts whether they are real or fake. We will also use a simple feedforward neural network for the discriminator.

class Discriminator(nn.Module):
    def __init__(self, input_dim):
        super(Discriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Linear(input_dim, 128),
            nn.ReLU(),
            nn.Linear(128, 1),
            nn.Sigmoid()
        )
    
    def forward(self, x):
        return self.model(x)

Step 3: Now, we can train the GAN model by alternating between training the generator and discriminator networks. We will use the adversarial loss function to train the networks to compete against each other.

# Initialize the generator and discriminator
generator = Generator(input_dim, output_dim)
discriminator = Discriminator(input_dim)

# Define the loss function and optimizer
criterion = nn.BCELoss()
d_optimizer = torch.optim.Adam(discriminator.parameters(), lr=0.0002)
g_optimizer = torch.optim.Adam(generator.parameters(), lr=0.0002)

# Training loop
for epoch in range(num_epochs):
    for i, real_data in enumerate(data_loader):
        # Train the discriminator
        discriminator.zero_grad()
        real_data = real_data.view(-1, input_dim)
        real_output = discriminator(real_data)
        real_loss = criterion(real_output, torch.ones_like(real_output))
        
        noise = torch.randn(batch_size, input_dim)
        fake_data = generator(noise)
        fake_output = discriminator(fake_data.detach())
        fake_loss = criterion(fake_output, torch.zeros_like(fake_output))
        
        d_loss = real_loss + fake_loss
        d_loss.backward()
        d_optimizer.step()
        
        # Train the generator
        generator.zero_grad()
        fake_output = discriminator(fake_data)
        g_loss = criterion(fake_output, torch.ones_like(fake_output))
        g_loss.backward()
        g_optimizer.step()

In this way, we have successfully implemented a basic generative adversarial network in Python using PyTorch.