While modelling in keras, often I see the usage of
validation_data=(x_val, y_val) in model.fit_generator where
(x_val, y_val) generally forms 10% of the dataset. While training, is it that the model takes hint from the validation loss calculated on
(x_val, y_val) and I need to create another test_data for measuring accuracies in the end? Or, I can use the same
(x_val, y_val) in input of
model.fit_generator and measuring the accuracy in the end.
The confusion arises from the fact that we are often advised to create training, validation, test datasets while modelling. If the validation dataset has been used to judge some parameters like when to stop(early stopping), etc; wouldn't it be unfair to use the validation_data to calculate the various measures of accuracy?