（刘二大人）PyTorch深度学习实践-卷积网络（Residual）

1. Residual Block 的实现

1.1 代码展示

import torch
import torch.nn.functional as F
 
class Residual_Block(torch.nn.Module):
    def __init__(self,channels):
        super(Residual_Block, self).__init__()
        self.channels = channels #因为输入的x与输出的y要进行加法，需要保证他们的 C、W、H都一样
 
        self.conv_1 = torch.nn.Conv2d(channels,channels,kernel_size=3,padding=1)
        self.conv_2 = torch.nn.Conv2d(channels,channels,kernel_size=3,padding=1)
 
    def forward(self,x):
        y = F.relu(self.conv_1(x))
        y = self.conv_2(y)
        return F.relu(y+x) #对x和y的和再做激活
    

 2. 使用Residual Model 对 Minist数据集进行训练

2.1 代码展示

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