验证损失在几个时期后增加

Question

我正在使用植物村数据集构建一个小型CNN模型来预测作物病害。它由39类不同的物种组成，有病和无病。

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

验证损失不是应该随着时间的推移而减少吗？那么，为什么它是先减少后增加的呢？

我应该如何设置划时代的数量，为什么？

非常感谢您的帮助！

Answer 1

您正面临着"过拟合"；当您的验证损失在减少后又增加时。这时你应该停止训练，并尝试使用一些技巧来避免过度适应。

当你的梯度在推理过程中不断更新时，可能会出现不同的预测，所以请明确地尝试"；停止"；使用torch.no_grad((更新