I was wondering if the fit_generator() in keras has any advantage in respect to memory usage over using the usual fit() method with the same batch_size as the generator yields. I've seen some examples similar to this:
def generator():
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# some data prep
...
while 1:
for i in range(1875): # 1875 * 32 = 60000 -> # of training samples
yield X_train[i*32:(i+1)*32], y_train[i*32:(i+1)*32]
If I pass this into the fit_generator() method or just pass all the data directly into the fit() method and define a batch_size of 32, would it make any difference regarding (GPU?)-memory whatsoever?