一、Lenet实现网络图

二、创建网络

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


# TODO: 构建神经网络
class LeNet(nn.Module):

    def __init__(self):
        super(LeNet, self).__init__()

        self.conv1 = nn.Conv2d(1, 6, (5, 5))
        self.conv2 = nn.Conv2d(6, 16, (5, 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):
        # 卷积 -> 激活 ->池化
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv1(x)), 2)

        x = x.view(x.size()[0], -1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)

        return x


net = LeNet()
print(net)

网络的可学习参数通过 net.parameters() 返回，net.named_parameters 可同时返回可学习的参数及名称。

net = LeNet()
print(len(list(net.parameters())))

for name, para in net.named_parameters():
    print(name, ":", para.size())

# 输出测试
input = torch.randn(1, 1, 32, 32)
out = net(input)
print(out)

# 梯度归零 和 反向传播
net.zero_grad()
out.backward(torch.ones_like(out))

三、定义损失函数

nn实现了神经网络中大多数的损失函数，例如nn.MSELoss用来计算均方误差，nn.CrossEntropyLoss用来计算交叉滴损失。

# 输出测试
input = torch.randn(1, 1, 32, 32)
out = net(input)

# 定义损失函数
criterion = nn.MSELoss()

# 随机生成标签
target = torch.randn(10)
target = target.view(1, -1)

loss = criterion(out, target)

print(loss)
loss.backward()

# 反向传播
net.zero_grad()
loss.backward()

四、定义优化器

更新网络参数，选择优化算法 SGD

# 传统SGD实现
learning_rate = 0.01
for f in net.parameters():
    f.data.sub_(f.grad.data*learning_rate)

# pytorch
import torch.optim as optim

# 初始化优化器
optimizer = optim.SGD(net.parameters(), lr=0.01)

# 梯度清零
optimizer.zero_grad()
output = net(input)

# 反向传播
loss = criterion(out, target)
loss.backward()
# 参数更新
optimizer.step()