Should validation loss be lower than training loss?
Training Loss. If your training loss is much lower than validation loss then this means the network might be overfitting . Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on.
Why is train loss higher than validation loss?
Reason #2: Training loss is measured during each epoch while validation loss is measured after each epoch. The second reason you may see validation loss lower than training loss is due to how the loss value are measured and reported: Training loss is measured during each epoch.
Why is validation loss lower than train loss?
As we see in the plot, validation loss is lower than the train loss which is totally weird. Based on the post Validation loss lower than training loss, I understood that it is because of the dropout layer in my model. So I ran the model with dropout layer.
What to do about validation loss in machine learning?
Really a fundamental question in machine learning. If validation loss >> training loss you can call it overfitting. If validation loss > training loss you can call it some overfitting. If validation loss < training loss you can call it some underfitting. If validation loss << training loss you can call it underfitting.
Can a validation error be less than a training error?
Generally speaking though, training error will almost always underestimate your validation error. However it is possible for the validation error to be less than the training. You can think of it two ways: Your training set had many ‘hard’ cases to learn. Your validation set had mostly ‘easy’ cases to predict.
Which is better training data or validation data?
As I understand, a model usually fits better training data than validation data but I am getting the opposite results. Loss is lower and accuracy is higher for validation set. As I have read in other answers, this can be because of dropout not being applied during validation.