How does data normalization work in keras during prediction?

Question

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 exampl

Answer 1

I also had the same issue and I solved it using the same functionality, that the ImageDataGenerator used:

# Load Cifar-10 dataset
(trainX, trainY), (testX, testY) = cifar10.load_data()
generator = ImageDataGenerator(featurewise_center=True, 
                               featurewise_std_normalization=True)

# Calculate statistics on train dataset
generator.fit(trainX)
# Apply featurewise_center to test-data with statistics from train data
testX -= generator.mean
# Apply featurewise_std_normalization to test-data with statistics from train data
testX /= (generator.std + K.epsilon())

# Do your regular fitting
model.fit_generator(..., validation_data=(testX, testY), ...)

Note that this is only possible if you have a reasonable small dataset, like CIFAR-10. Otherwise the solution proposed by Marcin sounds good more reasonable.