Kotlin 中 OkHttp 使用及解析

前言：

     结合手写数字识别的例子，实现以下AutoEncoder

     ae.py:  实现autoEncoder 网络

     main.py: 加载手写数字数据集，以及训练，验证，测试网络。

左图：原图像

右图：重构图像

目录：

     1： AE 实现

     2： main 实现

一  ae(AutoEncoder) 实现

  文件名: ae.py

               模型的搭建

   注意点：

            手写数字数据集 提供了 标签y，但是AutoEncoder 网络不需要，

它的标签就是输入的x, 需要重构本身

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

@author: chengxf2
"""

import torch
from torch import nn

#ae: AutoEncoder

class AE(nn.Module):
    
    def __init__(self,hidden_size=10):
        
        super(AE, 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]

        self.decoder = nn.Sequential(
             nn.Linear(in_features=hidden_size, 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 
        
            '''
      
            m= x.size(0)
            
            x = x.view(m, 784)
            
            hidden= self.encoder(x)
            x =   self.decoder(hidden)
            
            #reshape
            x = x.view(m,1,28,28)
            
            return x
        
    

二 main 实现

  文件名： main.py

  作用：

      加载数据集

     训练模型

     测试模型泛化能力

# -*- 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 ae import AE
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 =AE().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 = model(x)
           
           loss = criteon(x_hat, x)
           
           #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 = end - start
       print("\n 每轮训练时间 :",int(interval))
       print(epoch, 'loss:',loss.item())
       
       x,target = iter(test_data).next()
       x = x.to(device)
       with torch.no_grad():
           x_hat = 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()