"""

    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)