Pytorch实现LeNet

 实现代码如下：

import torch.functional as F


class LeNet(torch.nn.Module):

    def __init__(self):
        super(LeNet, self).__init__()
        # 1 input image channel (black & white), 6 output channels, 5x5 square convolution
        # kernel
        #其中 表示图像的channel，6表示output的channel维度，5表示卷积核的大小

        self.conv1 = torch.nn.Conv2d(1, 6, 5)
        self.conv2 = torch.nn.Conv2d(6, 16, 3)
        # an affine operation: y = Wx + b
         #在这里我们无需考虑batch这一个维度，只需要考虑我们6图像的大小，图像的长和宽根据公示计算出来为6*6，而一共有16个维度，因此这里放入16*16*6，
        #如果不想计算这个长和宽，则可以直接使用view操作

        self.fc1 = torch.nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension
        self.fc2 = torch.nn.Linear(120, 84)
        self.fc3 = torch.nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
    #这个函数是为了得到图像的长和宽，(一般来说还会得到图像的batch，因此我们将其舍弃)
    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features