参考：
https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py

Step1准备数据输入

按需制作训练集

Step2 设计神经网络

注意forward()函数是override method，名字不能改

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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(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        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 = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

##Step3 定义损失函数和优化方法

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import torch.optim as optim

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

Step4 训练网络

4.1 循环更新参数

  • for epoch in range(times):
    • 参数的梯度置零
    • 前向传播获取神经网络的输出
    • 比较输出与标签的差距并计算损失
    • 损失反向传播
    • 优化器更新参数
    • 累计损失
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for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

Step4 保存训练结果

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torch.save(net.state_dict(), PATH)

Step5 恢复并评估模型

5.1恢复模型

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net = Net()
net.load_state_dict(torch.load(PATH))

5.2 测试集前向传播得到输出

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with torch.no_grad():
  outputs = net(images)