Variational Auto-encoder(VAE)变分自编码器-Pytorch

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

# 配置GPU或CPU设置
# Device configuration
device = torch.device(‘cuda‘ if torch.cuda.is_available() else ‘cpu‘)

# 创建目录
# Create a directory if not exists
sample_dir = ‘samples‘
if not os.path.exists(sample_dir):
    os.makedirs(sample_dir)

# 超参数设置
# Hyper-parameters
image_size = 784
h_dim = 400
z_dim = 20
num_epochs = 15
batch_size = 128
learning_rate = 1e-3

# 获取数据集
# 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)

# 定义VAE类
# 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)

    # 编码
    def encode(self, x):
        h = F.relu(self.fc1(x))
        return self.fc2(h), self.fc3(h)

    # 参数重表示
    def reparameterize(self, mu, log_var):
        std = torch.exp(log_var / 2)
        eps = torch.randn_like(std)
        return mu + eps * std
    # 解码
    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

# 构造VAE实例对象
model = VAE().to(device)
print(model)
# 选择优化器
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
#开始训练
for epoch in range(num_epochs):
    for i, (x, _) in enumerate(data_loader):
        # 前向传播
        x = x.to(device).view(-1, image_size)# 将batch_size*1*28*28 ---->batch_size*image_size  其中，image_size=1*28*28=784
        x_reconst, mu, log_var = model(x)# 将batch_size*748的x输入模型进行前向传播计算

        # 计算重构损失和KL散度
        # 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())

        # 反向传播与优化
        # 计算误差(重构误差和KL散度值)
        loss = reconst_loss + kl_div
        # 清空上一步的残余更新参数值
        optimizer.zero_grad()
        # 误差反向传播, 计算参数更新值
        loss.backward()
        # 将参数更新值施加到VAE model的parameters上
        optimizer.step()
        # 每迭代一定步骤，打印结果值
        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
        # 保存采样值
        # 生成随机数 z
        z = torch.randn(batch_size, z_dim).to(device)# z的大小为batch_size * z_dim = 128*20
        # 对随机数 z 进行解码decode输出
        out = model.decode(z).view(-1, 1, 28, 28)
        # 保存结果值
        save_image(out, os.path.join(sample_dir, ‘sampled-{}.png‘.format(epoch + 1)))

        # Save the reconstructed images
        # 保存重构值
        # 将batch_size*748的x输入模型进行前向传播计算，获取重构值out
        out, _, _ = model(x)
        # 将输入与输出拼接在一起输出保存  batch_size*1*28*（28+28）=batch_size*1*28*56
        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)))

大概长这么个样子：

附上一张结果图：

原文地址：https://www.cnblogs.com/jeshy/p/11437547.html