def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.sigmoid(self.fc2(x)) return x
Here is a simple code implementation of a GAN in PyTorch: 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) def forward(self, x): x = torch
Another popular resource is the , which provides a wide range of pre-trained GAN models and code implementations. self).__init__() self.fc1 = nn.Linear(784
# Define the loss function and optimizer criterion = nn.BCELoss() optimizer_g = torch.optim.Adam(generator.parameters(), lr=0.001) optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=0.001)
# Initialize the generator and discriminator generator = Generator() discriminator = Discriminator()
import torch import torch.nn as nn import torchvision