I have a question:
In the video at the 1:32:35 mark,Jeremy mentioned that augmentation should not be applied on the validation set. However at 1:54:50, the augmentation is also applied on the validation set. Am I missing something here?
Here is a snippet of the Data Augmentation section of mnist.ipynb
gen = image.ImageDataGenerator(rotation_range=8, width_shift_range=0.08, shear_range=0.3,
batches = gen.flow(X_train, y_train, batch_size=64)
test_batches = gen.flow(X_test, y_test, batch_size=64)
lm.fit_generator(batches, batches.N, nb_epoch=1,
In my opinion, with the augmentation we have access to pretty much infinite number of different images.
Would that mean that the choice of batches.N as number of batches per epoch to be actually quite meaningless, and the same goes to the choice of test_batches.N?
In the absence of augmentations, I’m thinking batches.N/batch_size might be more appropriate, it feels like more like an epoch, except some images get sampled more than once and some don’t get sampled at all.
I use Python 3 and Keras 2.0.2, and batches.n returns 60000 for me, and I assume batches.N is the same.
I hope I am not speaking gibberish here!