[PyTorch][chapter 54][Variational Auto-Encoder 实战]

前言：

   
 

这里主要实现： Variational Autoencoders (VAEs) 变分自动编码器
其训练效果如下

 

训练的过程中要注意调节forward 中的kle ,调参。

整个工程两个文件：

    vae.py

   main.py

目录：

1.      vae
2.       main

---

一  vae

  文件名： vae.py

   作用：   Variational Autoencoders (VAE)

 训练的过程中加入一些限制，使它的latent space规则一点呢。于是就引入了variational autoencoder(VAE)，它被定义为一个有规律地训练以避免过度拟合的Autoencoder，可以确保潜在空间具有良好的属性从而实现内容的生成。
variational autoencoder的架构和Autoencoder差不多，区别在于不再是把输入当作一个点，而是把输入当成一个分布。

# -*- coding: utf-8 -*-
"""
Created on Wed Aug 30 14:19:19 2023

@author: chengxf2
"""

import torch
from torch import nn

#ae: AutoEncoder

class VAE(nn.Module):
    
    def __init__(self,hidden_size=20):
        
        super(VAE, self).__init__()
        
        self.encoder = nn.Sequential(
            nn.Linear(in_features=784, out_features=256),
            nn.ReLU(),
            nn.Linear(in_features=256, out_features=128),
            nn.ReLU(),
            nn.Linear(in_features=128, out_features=64),
            nn.ReLU(),
            nn.Linear(in_features=64, out_features=hidden_size),
            nn.ReLU()
            )
         # hidden [batch_size, 10]
         
        h_dim = int(hidden_size/2)
        self.hDim = h_dim

        self.decoder = nn.Sequential(
             nn.Linear(in_features=h_dim, out_features=64),
             nn.ReLU(),
             nn.Linear(in_features=64, out_features=128),
             nn.ReLU(),
             nn.Linear(in_features=128, out_features=256),
             nn.ReLU(),
             nn.Linear(in_features=256, out_features=784),
             nn.Sigmoid()
             )
        
        
    def forward(self, x):
            '''
            param x:[batch, 1,28,28]
            return 
        
            '''
      
            batchSz= x.size(0)
            #flatten
            x = x.view(batchSz, 784)
            
            #encoder
            h= self.encoder(x)
     
            #在给定维度上对所给张量进行分块,前一半的神经元看作u, 后一般的神经元看作sigma
            u, sigma = h.chunk(2,dim=1)
            
            #Reparameterize trick：
            #randn_like：产生一个正太分布 ~ N(0,1)
            #h.shape [batchSize,self.hDim]
            h = u+sigma* torch.randn_like(sigma)
           
            #kld :1e-8 防止sigma 平方为0
            kld = 0.5*torch.sum(
                torch.pow(u,2)+
                torch.pow(sigma,2)-
                torch.log(1e-8+torch.pow(sigma,2))-
                1
                )
            
            #MSE loss 是平均loss, 所以kld 也要算一个平均值
            kld = kld/(batchSz*32*32)
            xHat =   self.decoder(h)
            
            #reshape
            xHat = xHat.view(batchSz,1,28,28)
            
            return xHat,kld
        
    

---

二 main

文件名: main.py

作用： 训练，测试数据集

 

# -*- coding: utf-8 -*-
"""
Created on Wed Aug 30 14:24:10 2023

@author: chengxf2
"""

import torch
from torch.utils.data import DataLoader
from torchvision import transforms, datasets
import time
from torch import optim,nn
from vae import VAE
import visdom





def main():
   
   batchNum = 32
   lr = 1e-3
   epochs = 20
   device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
   torch.manual_seed(1234)
   viz = visdom.Visdom()
   viz.line([0],[-1],win='train_loss',opts =dict(title='train acc'))

    
   

   tf= transforms.Compose([ transforms.ToTensor()])
   mnist_train = datasets.MNIST('mnist',True,transform= tf,download=True)
   train_data = DataLoader(mnist_train, batch_size=batchNum, shuffle=True)
   
   mnist_test = datasets.MNIST('mnist',False,transform= tf,download=True)
   test_data = DataLoader(mnist_test, batch_size=batchNum, shuffle=True)
   global_step =0

   
   

   
  
   model =VAE().to(device)
   criteon = nn.MSELoss().to(device) #损失函数
   optimizer = optim.Adam(model.parameters(),lr=lr) #梯度更新规则
   
   print("\n ----main-----")
   for epoch in range(epochs):
       
       start = time.perf_counter()
       for step ,(x,y) in enumerate(train_data):
           #[b,1,28,28]
           x = x.to(device)
           x_hat,kld = model(x)
           
           loss = criteon(x_hat, x)
           
           if kld is not None:
              
               
               elbo = -loss -1.0*kld
               loss = -elbo
           #backprop
           optimizer.zero_grad()
           loss.backward()
           optimizer.step()
           viz.line(Y=[loss.item()],X=[global_step],win='train_loss',update='append')
           global_step +=1



    
       end = time.perf_counter()    
       interval = int(end - start)
  
       print("epoch: %d"%epoch, "\t 训练时间 %d"%interval, '\t 总loss: %4.7f'%loss.item(),"\t KL divergence: %4.7f"%kld.item())
       
       x,target = iter(test_data).next()
       x = x.to(device)
       with torch.no_grad():
           x_hat,kld = model(x)
       
       tip = 'hat'+str(epoch)
       viz.images(x,nrow=8, win='x',opts=dict(title='x'))
       viz.images(x_hat,nrow=8, win='x_hat',opts=dict(title=tip))
           
           
           
           
   

if __name__ == '__main__':
    
    main()

 参考：

 课时118 变分Auto-Encoder实战-2_哔哩哔哩_bilibili