Why validation error is lower than training error?
A lower validation than training error can be caused by fluctuations associated with dropout or else, but if it persists in the long run this may indicate that the training and validation datasets were not actually drawn from the same statistical ensembles.
What happens when validation loss is less 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 validation loss smaller than training loss?
The same is available for LSTM/sequence classification data. It may happen in the early phase of the training that the validation loss is smaller than the training loss/validation accuracy is bigger than the training accuracy. During validation, dropout is not enabled, leading to greater results on the validation set.
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.
Which is better validation accuracy CNN or LSTM?
I have been training both CNNs and LSTMs for a while, and, depending on the construction/structure of the training/validation set, you can have a better validation accuracy/smaller validation loss on the validation set than on the training set.
What is the validation loss for epoch 20 / 20-14?
Epoch 20/20 – 14s – loss: 0.0510 – val_loss: 188.9537 I assume I must be doing something obvious wrong, but can’t realize it since I’m a newbie. I am hoping to either get some useful validation loss achieved (compared to training), or know that my data observations are simply not large enough for useful LSTM modeling.