Github - Gans In Action Pdf

The key idea behind GANs is to train the generator network to produce synthetic data samples that are indistinguishable from real data samples, while simultaneously training the discriminator network to correctly distinguish between real and synthetic samples. This adversarial process leads to a minimax game between the two networks, where the generator tries to produce more realistic samples and the discriminator tries to correctly classify them.

import torch import torch.nn as nn import torchvision gans in action pdf github

Here is a simple code implementation of a GAN in PyTorch: The key idea behind GANs is to train

def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.sigmoid(self.fc2(x)) return x and in practice

# Train the generator optimizer_g.zero_grad() fake_logits = discriminator(generator(torch.randn(100))) loss_g = criterion(fake_logits, torch.ones_like(fake_logits)) loss_g.backward() optimizer_g.step() Note that this is a simplified example, and in practice, you may need to modify the architecture and training process of the GAN to achieve good results.