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)
输出结果
明天接着详细分析。
