当 training=False 时仍然继续训练 - Python

在深度学习中，我们通常需要对训练模型进行测试以评估其性能直接。而在测试过程中，我们不希望更新模型的参数。因此，我们可以将 training=False 传递给模型的 forward 方法，以确保模型不会更新参数。但在某些情况下，我们需要在测试时仍然继续训练模型。在这个教程中，我们将了解如何实现这一目标。

方法

我们可以通过以下步骤来实现当 training=False 时仍然继续训练：

1. 在模型定义中定义一个标志位 continue_training 并设为 False。

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        ...
        self.continue_training = False

2. 重写模型的 forward 方法，根据 continue_training 的值来决定是否对模型进行训练。

class MyModel(nn.Module):
    def forward(self, x):
        ...
        if self.continue_training:
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        ...

3. 在测试时将 continue_training 的值设为 True。

model.continue_training = True
model.eval()
with torch.no_grad():
    for batch_idx, (data, target) in enumerate(test_loader):
        output = model(data)
        ...

完成上述步骤后，我们就可以在测试时对模型进行运行，并在不更新参数的同时让模型继续训练。

以下是完整的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(784, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 10)
        self.continue_training = False

    def forward(self, x):
        x = x.view(-1, 784)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        output = self.fc3(x)
        if self.continue_training:
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        return output

def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = nn.functional.cross_entropy(output, target)
        loss.backward()
        optimizer.step()

def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += nn.functional.cross_entropy(output, target, reduction='sum').item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
        test_loss, correct, len(test_loader.dataset), accuracy))

def main():
    batch_size = 64
    test_batch_size = 1000
    epochs = 10
    learning_rate = 0.01

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('./data', train=True, download=True,
                    transform=transforms.Compose([
                        transforms.ToTensor(),
                        transforms.Normalize((0.1307,), (0.3081,))
                    ])),
        batch_size=batch_size, shuffle=True)

    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('./data', train=False, transform=transforms.Compose([
                        transforms.ToTensor(),
                        transforms.Normalize((0.1307,), (0.3081,))
                    ])),
        batch_size=test_batch_size, shuffle=True)

    model = MyModel().to(device)
    optimizer = optim.SGD(model.parameters(), lr=learning_rate)

    for epoch in range(1, epochs + 1):
        train(model, device, train_loader, optimizer, epoch)
        test(model, device, test_loader)

    model.continue_training = True
    model.eval()
    with torch.no_grad():
        for batch_idx, (data, target) in enumerate(test_loader):
            output = model(data)

if __name__ == '__main__':
    main()

在此示例中，我们首先定义了一个名为 MyModel 的模型，并在其中添加了一个名为 continue_training 的标志变量。然后，我们重写了 forward 方法并根据 continue_training 的值来决定是否对模型进行训练。最后，我们在测试时将 continue_training 标志变量的值设为 True，并在不更新参数的同时让模型继续训练。