I see that the imageDataGenerator allows me to specify different styles of data normalization, e.g. featurewise_center, samplewise_center, etc.
I see from the examples that if I specify one of these options, then I need to call the fit method on the generator in order to allow the generator to compute statistics like the mean image on the generator.
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
datagen = ImageDataGenerator(
# compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied)
# fits the model on batches with real-time data augmentation:
model.fit_generator(datagen.flow(X_train, Y_train, batch_size=32),
My question is, how does prediction work if I have specified data normalization during training? I can't see how in the framework I would even pass the knowledge of the training set mean/std deviation along to predict to allow me to normalize my test data myself, but I also don't see in the training code where this information is stored.
Are the image statistics needed for normalization stored in the model so that they can be used during prediction?