I readed this post and i get a little understanding of how to interpret
loss: 0.1350 - accuracy: 0.7074 - val_loss: 0.1711 - val_accuracy: 0.5299
but in my case loss decreases and accuracy increases but val_loss and val_accuracy just stay in the same range:
val_loss goes from 0.1511 to 0.2011
val_accuracy goes from 0.4011 to 0.7011
what does this means?
Loss and accuracy refer to how well your model performed on the training data. Whereas validation loss and validation accuracy refer to how well your model performed on the validation set, which is not part of the training data. You describe a situation where your model is doing better and better on training data, but not doing better and better on validation data. This means that your model is essentially learning to predict the training data instead of learning the underlying concept to generalize. You may need to consider tweaking the parameters of your model or choose a different model altogether.
It means your model is performing well on your training dataset, but not on your validation (test) dataset. This might mean your training dataset is not a very good sample for your optimisation task. Or that your training set and test set model different functions altogether.
