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简介

使用minst手写数字数据集训练变分自编码器,使之能够在编码解码后能够自动生成手写数字
具体原理图如下

引入库

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
import mpimg
import cv2
import matplotlib.pyplot as plt

设备配置

torch.cuda.set_device(0) # 这句用来设置pytorch在哪块GPU上运行，这里假设使用序号为1的这块GPU.
device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)

在当前目录，创建不存在的目录ave_samples

sample_dir = ‘ave_samples’
if not os.path.exists(sample_dir):
os.makedirs(sample_dir)

定义一些超参数

image_size = 784
h_dim = 400
z_dim = 20
num_epochs = 200
batch_size = 128
learning_rate = 0.001

下载MNIST训练集，这里因已下载，故download=False

dataset = torchvision.datasets.MNIST(root=‘data’,
train=True,
transform=transforms.ToTensor(),
download=True)

数据加载

data_loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True)

定义AVE模型

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)

开始训练模型

for epoch in range(num_epochs):
model.train()
for i, (x, _) in enumerate(data_loader):
# 前向传播
model.zero_grad()
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())

    # 反向传播及优化器
    loss = reconst_loss + kl_div
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (i + 1) % 100 == 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():
    # 保存采样图像，即潜在向量Z通过解码器生成的新图像
    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)))

    # 保存重构图像，即原图像通过解码器生成的图像
    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)))

reconsPath = ‘./ave_samples/reconst-30.png’
Image = cv2.imread(reconsPath)
plt.imshow(Image) # 显示图片
plt.axis(‘off’) # 不显示坐标轴
plt.show()
genPath = ‘./ave_samples/sampled-30.png’
Image = cv2.imread(genPath)
plt.imshow(Image) # 显示图片
plt.axis(‘off’) # 不显示坐标轴
plt.show()`
[4]: http://adrai.github.io/flowchart.js/