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

GANs are a type of deep learning model that consists of two neural networks: a generator network and a discriminator network. The generator network takes a random noise vector as input and produces a synthetic data sample that aims to mimic the real data distribution. The discriminator network, on the other hand, takes a data sample (either real or synthetic) as input and outputs a probability that the sample is real.

Another popular resource is the , which provides a wide range of pre-trained GAN models and code implementations. gans in action pdf github

class Discriminator(nn.Module): def __init__(self): super(Discriminator, self).__init__() self.fc1 = nn.Linear(784, 128) self.fc2 = nn.Linear(128, 1)

Here is a simple code implementation of a GAN in PyTorch: def forward(self, z): x = torch

# Train the GAN for epoch in range(100): for i, (x, _) in enumerate(train_loader): # Train the discriminator optimizer_d.zero_grad() real_logits = discriminator(x) fake_logits = discriminator(generator(torch.randn(100))) loss_d = criterion(real_logits, torch.ones_like(real_logits)) + criterion(fake_logits, torch.zeros_like(fake_logits)) loss_d.backward() optimizer_d.step()

import torch import torch.nn as nn import torchvision Another popular resource is the , which provides

For those interested in implementing GANs, there are several resources available online. One popular resource is the PDF, which provides a comprehensive overview of GANs, including their architecture, training process, and applications.