I am using Keras with a Tensorflow backend to train an Image Classification model on a GPU. I have read somewhere that training uses roughly twice (both forward and back props) the GPU memory of validating, so therefore the training batch size should be the half of the validation batch size.
However, on many blogs and tutorials, I see that people use the same batch size for training and validating.
Is it true that training uses twice the GPU memory, because of the forward and backward pass, or is this false?