I have a question that normally, when we are making a training set and a final test set,
we would compute the mean and standard deviation for preprocessing using the training data and use it to standardize (transform) the test data.
So, when we are making a training data, a cross-validation data and a final test data, shouldn't we compute mean and std for preprocessing using the training data alone and then standardize the validation set and test set using it? And, if I'm right how can we do it in keras, since in keras like in the below code, taken from kaggle( by Francois Chollet MNIST tutorial):
# pre-processing: divide by max and substract mean scale = np.max(X_train) X_train /= scale X_test /= scale mean = np.std(X_train) X_train -= mean X_test -= mean model.fit(X_train, y_train, nb_epoch=10, batch_size=16, validation_split=0.1, show_accuracy=True, verbose=2)
The validation set is created using the fit method and thus, it is also used in computing the preprocessing mean and std. So, how to create validation set before and use only the training data for feature scaling and normalization? And which method is the right method?
Also, in the above code scale and mean are being computed for each column respectively and is being used for normalization , should we compute mean and scale(standard deviation or range) for each pixel or for the entire image? How does that affect?