from numpy.core.fromnumeric import size
import torch
import torch.nn as nn
import torch.nn.functional as F

#定义网络类
class Net(nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        #定义第一层卷积层，输入维度=1，输出维度=6，卷积核大小3*3
        self.conv1=nn.Conv2d(1,6,3)
        #定义第二层卷积层，输入维度=6，输出维度=16，卷积核大小3*3
        self.conv2=nn.Conv2d(6,16,3)
        #定义三层全连接神经网络
        self.fc1= nn.Linear(16*6*6,120)
        self.fc2= nn.Linear(120,84)
        self.fc3= nn.Linear(84,10)

    def forward(self,x):
        #注意：任意卷积层后面要加激活层，池化层
        x= F.max_pool2d(F.relu(self.conv1(x)),(2,2))
        x= F.max_pool2d(F.relu(self.conv2(x),2))
        #经过卷积层的处理后，张量要进入全连接层，进入前调整张量的形状
        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
    
    def num_flat_features(self,x):
        size=x.size()[1:]
        num_features =1
        for s in size:
            num_features *=s
        return num_features

net=Net()
print(net)