Lenet是一个 7 层的神经网络，包含 3 个卷积层，2 个池化层，1 个全连接层。其中所有卷积层的所有卷积核都为 5x5，步长 strid=1，池化方法都为全局 pooling，激活函数为 Sigmoid，网络结构如下：

import torch
from torch import nn

# 定义一个网络模型: MyLeNet5
class MyLeNet5(nn.Module):
    def __init__(self):  # 正确初始化方法名为 '__init__'
        super(MyLeNet5, self).__init__()  # 正确调用父类初始化方法

        # 这里补充MyLeNet5的具体层定义，以匹配原始LeNet-5结构
        self.c1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, padding=2 )  #[1,6,28,28]
        self.Sigmoid = nn.Sigmoid()
        self.s2 = nn.AvgPool2d(kernel_size=2, stride=2)
        self.c3 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5)  #[6,16,10,10]
        self.s4 = nn.AvgPool2d(kernel_size=2, stride=2)
        self.c5 = nn.Conv2d(in_channels=16, out_channels=120, kernel_size=5)  #[16,120,1,1]

        self.flatten = nn.Flatten()
        self.f6 = nn.Linear(120, 84)
        self.output = nn.Linear(84, 10)

    # 定义前向传播过程
    def forward(self, x):
        x = self.Sigmoid(self.c1(x))
        x = self.s2(x)
        x = self.Sigmoid(self.c3(x))
        x = self.s4(x)
        x = self.c5(x)
        x = self.flatten(x)
        x = self.f6(x)
        x = self.output(x)
        return x

if __name__=="__main__":
    x = torch.rand([1, 1, 28, 28])
    model = MyLeNet5()
    print(model)
    y = model(x)