验证损失在几个 epoch 后增加

Question

我正在构建一个小型 CNN 模型，以使用 Plant Village 数据集预测植物作物病害。它由有病和无病的39类不同物种组成。

CNN 模型

class CropDetectCNN(nn.Module):
    # initialize the class and the parameters
    def __init__(self):
        super(CropDetectCNN, self).__init__()

        # convolutional layer 1 & max pool layer 1
        self.layer1 = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3),
            nn.MaxPool2d(kernel_size=2))

        # convolutional layer 2 & max pool layer 2
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=3, padding=1, stride=2),
            nn.MaxPool2d(kernel_size=2))

        #Fully connected layer
        self.fc = nn.Linear(32*28*28, 39)
        
        

    # Feed forward the network
    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out


model = CropDetectCNN()

培训

criterion = nn.CrossEntropyLoss()  # this include softmax + cross entropy loss
optimizer = torch.optim.Adam(model.parameters())

def batch_gd(model, criterion, train_loader, validation_loader, epochs):
    train_losses = np.zeros(epochs)
    test_losses = np.zeros(epochs)
    validation_losses = np.zeros(epochs)
    
    for e in range(epochs):
        t0 = datetime.now()
        train_loss = []
        model.train()
        for inputs, targets in train_loader:
            inputs, targets = inputs.to(device), targets.to(device)

            optimizer.zero_grad()

            output = model(inputs)

            loss = criterion(output, targets)

            train_loss.append(loss.item())  # torch to numpy world

            loss.backward()
            optimizer.step()
            
        
        train_loss = np.mean(train_loss)

        validation_loss = []

        for inputs, targets in validation_loader:
            
            model.eval()

            inputs, targets = inputs.to(device), targets.to(device)

            output = model(inputs)

            loss = criterion(output, targets)

            validation_loss.append(loss.item())  # torch to numpy world
        
        
        
        validation_loss = np.mean(validation_loss)

        train_losses[e] = train_loss
        validation_losses[e] = validation_loss

        dt = datetime.now() - t0

        print(
            f"Epoch : {e+1}/{epochs} Train_loss: {train_loss:.3f} Validation_loss: {validation_loss:.3f} Duration: {dt}"
        )

    return train_losses, validation_losses

# Running the function
train_losses, validation_losses = batch_gd(
    model, criterion, train_loader, validation_loader, 5
)

# And theses are results:
Epoch : 1/5 Train_loss: 1.164 Validation_loss: 0.861 Duration: 0:10:59.968168
Epoch : 2/5 Train_loss: 0.515 Validation_loss: 0.816 Duration: 0:10:49.199842
Epoch : 3/5 Train_loss: 0.241 Validation_loss: 1.007 Duration: 0:09:56.334155
Epoch : 4/5 Train_loss: 0.156 Validation_loss: 1.147 Duration: 0:10:12.625819
Epoch : 5/5 Train_loss: 0.135 Validation_loss: 1.603 Duration: 0:09:56.746308

验证损失不应该随着 epochs 减少吗？那为什么是先减后增呢？

我应该如何设置 epoch 的数量，为什么？

非常感谢任何帮助！

Answer 1

当您的验证损失减少后上升时，您将面临“过度拟合”的现象。你应该在那个时候停止训练并尝试使用一些技巧来避免过度拟合。

当你的梯度在推理过程中不断更新时，可能会得到不同的预测，所以try explicitly "stop" them from updating with torch.no_grad()