pytorch中图的训练和验证损失

Question

我正在使用pytorch来训练我的CNN网络。我想绘制我的训练和验证损失曲线来可视化模型的性能。如何绘制两条曲线?

我有以下代码

# create a function (this my favorite choice)
def RMSELoss(predicted,target):
return torch.sqrt(torch.mean((predicted-target)**2))
criterion = RMSELoss
# loss = torch.sqrt(criterion(x, y))
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
epochs = 300
n_total_steps = len(train_dataset)
trainingEpoch_loss = []
validationEpoch_loss = []
for epoch in range(epochs):
step_loss = []
model.train()
for i, data in enumerate(train_dataset):
feature,target = data['data'].type(torch.FloatTensor),torch.tensor(data['target']).type(torch.FloatTensor)

# Clear the gradients
optimizer.zero_grad()
# Forward Pass
outputs = model(feature)
# Find the Loss
training_loss = criterion(outputs, target)
# Calculate gradients
training_loss.backward()
# Update Weights
optimizer.step()
# Calculate Loss
step_loss.append(training_loss.item())
if (i+1) % 1 == 0:
print (f'Epoch [{epoch+1}/{epochs}], Step [{i+1}/{n_total_steps}], Loss: {training_loss.item():.4f}')
trainingEpoch_loss.append(np.array(step_loss).mean())

model.eval()     # Optional when not using Model Specific layer
for i, data in enumerate(val_dataset):
validationStep_loss = []
feature,target = data['data'].type(torch.FloatTensor),torch.tensor(data['target']).type(torch.FloatTensor)

# Forward Pass
outputs = model(feature)
# Find the Loss
validation_loss = criterion(outputs, target)
# Calculate Loss
validationStep_loss.append(validation_loss.item())
validationEpoch_loss.append(np.array(validationStep_loss).mean())

你能让我知道我做得对吗?也请让我知道如何绘制训练和验证损失?

Answer 1

您收集trainingEpoch_loss和validationEpoch_loss列表中的历元损失是正确的。现在，在训练之后，添加代码来绘制损失:

from matplotlib import pyplot as plt
plt.plot(trainingEpoch_loss, label='train_loss')
plt.plot(validationEpoch_loss,label='val_loss')
plt.legend()
plt.show

阅读matplotlib文档获取更多奇特的绘图功能。