pytorch定义一个简单的神经网络

刚学习pytorch，简单记录一下

"""
    test Funcition
"""

import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    ''' a neural network with pytorch'''
    def __init__(self):
        # 父类的构造方法
        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_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()
# 查看网络
print(net)

# 查看模型需要学习的参数
params = list(net.parameters())
print(len(params))
for param in params:
    print(param.size())

# 输入数据
input = Variable(torch.randn(1,1,32,32))
print(input)
out = net(input)
print(out)

# 损失函数
target = Variable(torch.arange(1, 11, dtype=torch.float32))
print(target)
criterion = nn.MSELoss()
loss = criterion(out, target)
print(loss)

输出结果：