How to plot the loss function

1

Can someone show me how to plot the train and valid loss? what’s the best way to visualize it? perhaps, some explanations for your visualization, please.

import numpy as np
batch_size = 32
epochs = 3
min_valid_loss = np.inf

for e in range(epochs):
    train_loss = 0.0
    model.train()     # Optional when not using Model Specific layer
    for data, target in train_loader:
         # Transfer Data to GPU if available
        if torch.cuda.is_available():
            data, target = data.cuda(), target.cuda()
            print("targets.data", targets.item())
        
        
         # Clear the gradients
        optimizer.zero_grad()
        
        # Forward Pass
        output = (model(data))
        squeezed_output = torch.squeeze(output)

        
        # Find the Loss
        loss = criterion(squeezed_output.view(-1,1),target.view(-1,1))
        
        # Calculate gradients 
        loss.backward()
        # Update Weights
        optimizer.step()
        # Calculate Loss
        train_loss += loss.item()
    
    valid_loss = 0.0
    model.eval()     # Optional when not using Model Specific layer
    for data, target in valid_loader:
        if torch.cuda.is_available():
            data, target = data.cuda(), target.cuda()
        
        output = model(data)
        squeezed_output = torch.squeeze(output)
        
        loss = criterion(squeezed_output.view(-1,1),target.view(-1,1))
        valid_loss = loss.item() * data.size(0)
        

    print(f'Epoch {e+1} \t\t Training Loss: {train_loss / len(train_loader)} \t\t Validation Loss: {valid_loss / len(valid_loader)}')
   

    if min_valid_loss > valid_loss:
        print(f'Validation Loss Decreased({min_valid_loss:.6f}--->{valid_loss:.6f}) \t Saving The Model')
        min_valid_loss = valid_loss
2

I think you should calculate the mean train and val losses (mean over the batch) for each epoch, append them to a list, and plot the training and validation error curves over the number of epochs. That’s a starting point.

3

Could you share with me a piece of code that I can use to calculate it?