Contents
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.
When is training a deep learning model should the validation?
If your validation loss is lower than the training loss, it means you have not split the training data correctly. Correctly here means, the distribution of training and validation set is different. Ideally, it should be the same.
Which is higher valid _ loss or train _ loss?
Remember that the train_loss generally is lower than the valid_loss. Because the validation dataset is used to validate de model with data that the model has never seen. For this reason, the valid_loss generally is higher compared with the train_loss. If you are a lucky guy the values could be similar.
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.
How can underfit LSTM model be diagnosed from a plot?
This can be diagnosed from a plot where the training loss is lower than the validation loss, and the validation loss has a trend that suggests further improvements are possible. A small contrived example of an underfit LSTM model is provided below.
How is log loss and accuracy measured in LSTM?
For example, if your model was compiled to optimize the log loss ( binary_crossentropy) and measure accuracy each epoch, then the log loss and accuracy will be calculated and recorded in the history trace for each training epoch. Each score is accessed by a key in the history object returned from calling fit ().