Pytorch学习笔记

自动求导：

https://zhuanlan.zhihu.com/p/84812085

Pytorch入门教程：

https://github.com/fendouai/PyTorchDocs/blob/master/SecondSection/training_a_classifier.md

Pytorch中文手册：

https://ptorch.com/docs/1/optim

卷积神经网络模版

class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 设置卷积层
        self.conv1 = nn.Conv2d(3, 6, 3)
        self.conv2 = nn.Conv2d(6, 16, 5)
        # 设置全连接层
        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)
        # 将多行的Tensor拼接成一行
        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

训练的迭代中执行的代码：

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

for epoch in range(2):

        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            inputs, labels = data
# 初始化梯度
            optimizer.zero_grad()
# 得到网络输出
            outputs = net(inputs)
#计算损失函数
            loss = criterion(outputs, labels)
#计算梯度（自动求导）
            loss.backward()
#反向传播
            optimizer.step()

            running_loss += loss.item()
            if i % 2000 == 1999:
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0