**Generative Adversarial Networks (GANs) for Image Synthesis

Generative Adversarial Networks (GANs) for Image Synthesis

Here's a compact code snippet for generating synthetic images using a Conditional GAN (CGAN) in PyTorch:

import torch
import torch.nn as nn
import torchvision

# Define generator and discriminator networks
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            nn.ConvTranspose2d(100, 64 * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(64 * 8),
            nn.ReLU(True),
            nn.ConvTranspose2d(64 * 8, 64 * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64 * 4),
            nn.ReLU(True),
            nn.ConvTranspose2d(64 * 4, 64 * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64 * 2),
            nn.ReLU(True),
            nn.ConvTranspose2d(64 * 2, 1, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, x):
        return self.model(x)

# Train the network
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
generator = Generator().to(device)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(generator.parameters(), lr=0.001)

This code snippet defines a basic generator network using a transposed convolutional architecture. The generator takes a 100-dimensional latent vector and produces a synthetic image with shape (1, 28, 28). The network is optimized using the Adam algorithm with a learning rate of 0.001. The MSELoss criterion is used to measure the difference between the generated image and the target image. This code snippet serves as a starting point for training a CGAN model, which can be used to generate synthetic images for various applications, such as data augmentation or image-to-image translation.

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