What is validation data used for in a Keras Sequential model?

Question

My question is simple, what is the validation data passed to model.fit in a Sequential model used for?

And, does it affect how the model is trained (normally a validation set is used, for example, to choose hyper-parameters in a model, but I think this does not happen here)?

I am talking about the validation set that can be passed like this:

# Create model model = Sequential() # Add layers model.add(...)  # Train model (use 10% of training set as validation set) history = model.fit(X_train, Y_train, validation_split=0.1)  # Train model (use validation data as validation set) history = model.fit(X_train, Y_train, validation_data=(X_test, Y_test)) 

I investigated a bit, and I saw that keras.models.Sequential.fit calls keras.models.training.fit, which creates variables like val_accand val_loss (which can be accessed from Callbacks). keras.models.training.fit also calls keras.models.training._fit_loop, which adds the validation data to the callbacks.validation_data, and also calls keras.models.training._test_loop, which will loop the validation data in batches on the self.test_function of the model. The result of this function is used to fill the values of the logs, which are the values accessible from the callbacks.

After seeing all this, I feel that the validation set passed to model.fit is not used to validate anything during training, and its only use is to get feedback on how the trained model will perform in every epoch for a completely independent set. Therefore, it would be okey to use the same validation and test set, right?

Could anyone confirm if the validation set in model.fit has any other goal besides being read from the callbacks?

Answer 1

If you want to build a solid model you have to follow that specific protocol of splitting your data into three sets: One for training, one for validation and one for final evaluation, which is the test set.

The idea is that you train on your training data and tune your model with the results of metrics (accuracy, loss etc) that you get from your validation set.

Your model doesn't "see" your validation set and isn't in any way trained on it, but you as the architect and master of the hyperparameters tune the model according to this data. Therefore it indirectly influences your model because it directly influences your design decisions. You nudge your model to work well with the validation data and that can possibly bring in a tilt.

Exactly that is the reason you only evaluate your model's final score on data that neither your model nor you yourself has used – and that is the third chunk of data, your test set.

Only this procedure makes sure you get an unaffected view of your models quality and ability to generalize what is has learned on totally unseen data.