2019-04-16 神经网络

该系列文集是基于官方网站[Deep Learning with PyTorch:A 60 Minute Blitz]的学习

1.定义网络

import torch as t
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    
    def __init__(self):
        super(Net,self).__init__()
        
        self.conv1 = nn.Conv2d(1,6,5)
        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(84,10)
        
    def forward(self,x):
        x = F.max_pool2d(F.relu(self.conv1(x)),(2,2))
        
        x = F.max.pool2d(F.relu(self.conv2(x)),2)
        x = x.view(-1,self.num_flat_feature(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)

明天接着详细分析。