import torch.nn as nn     # 神经网络的层的实现：卷积层
import torch.nn.functional as fu

class LeNet(nn.Module):
    def __init__(self, cls_num=10):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5, padding=2)  
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5,  120)
        self.fc2 = nn.Linear(120, 84) 
        self.fc3 = nn.Linear(84, cls_num)
    
    def forward(self, x):
        y = fu.max_pool2d(fu.relu(self.conv1(x)), (2, 2))
        y = fu.max_pool2d(fu.relu(self.conv2(y)), (2, 2))
        # 格式转换
        y = y.view(y.size()[0], -1)
        y = fu.relu(self.fc1(y))
        y = fu.relu(self.fc2(y))
        y = self.fc3(y)
        return y