Pytorch-pytorch深度学习教程之变分自动编码器.zip

import os import torch import torch.nn as nn import torch.nn.functional as F import torchvision from torchvision import transforms from torchvision.utils import save_image # Device configuration device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Create a directory if not exists sample_dir = 'samples' if not os.path.exists(sample_dir): os.makedirs(sample_dir) # Hyper-parameters image_size = 784 h_dim = 400 z_dim = 20 num_epochs = 15 batch_size = 128 learning_rate = 1e-3 # MNIST dataset dataset = torchvision.datasets.MNIST(root='../../data', train=True, transform=transforms.ToTensor(), download=True) # Data loader data_loader = torch.utils.data.DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True) # VAE model class VAE(nn.Module): def __init__(self, image_size=784, h_dim=400, z_dim=20): super(VAE, self).__init__() self.fc1 = nn.Linear(image_size, h_dim) self.fc2 = nn.Linear(h_dim, z_dim) self.fc3 = nn.Linear(h_dim, z_dim) self.fc4 = nn.Linear(z_dim, h_dim) self.fc5 = nn.Linear(h_dim, image_size) def encode(self, x): h = F.relu(self.fc1(x)) return self.fc2(h), self.fc3(h) def reparameterize(self, mu, log_var): std = torch.exp(log_var/2) eps = torch.randn_like(std) return mu + eps * std def decode(self, z): h = F.relu(self.fc4(z)) return F.sigmoid(self.fc5(h)) def forward(self, x): mu, log_var = self.encode(x) z = self.reparameterize(mu, log_var) x_reconst = self.decode(z) return x_reconst, mu, log_var model = VAE().to(device) optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # Start training for epoch in range(num_epochs): for i, (x, _) in enumerate(data_loader): # Forward pass x = x.to(device).view(-1, image_size) x_reconst, mu, log_var = model(x) # Compute reconstruction loss and kl divergence # For KL divergence, see Appendix B in VAE paper or http://yunjey47.tistory.com/43 reconst_loss = F.binary_cross_entropy(x_reconst, x, size_average=False) kl_div = - 0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp()) # Backprop and optimize loss = reconst_loss + kl_div optimizer.zero_grad() loss.backward() optimizer.step() if (i+1) % 10 == 0: print ("Epoch[{}/{}], Step [{}/{}], Reconst Loss: {:.4f}, KL Div: {:.4f}" .format(epoch+1, num_epochs, i+1, len(data_loader), reconst_loss.item(), kl_div.item())) with torch.no_grad(): # Save the sampled images z = torch.randn(batch_size, z_dim).to(device) out = model.decode(z).view(-1, 1, 28, 28) save_image(out, os.path.join(sample_dir, 'sampled-{}.png'.format(epoch+1))) # Save the reconstructed images out, _, _ = model(x) x_concat = torch.cat([x.view(-1, 1, 28, 28), out.view(-1, 1, 28, 28)], dim=3) save_image(x_concat, os.path.join(sample_dir, 'reconst-{}.png'.format(epoch+1)))