Make sure validation batch size smaller than validation dataset

[yangobeil]

By default the validation batch size is the same as the training batch size, but sometimes there are not many validation images and this gives an error when the batch size is larger than the number of validation images. I think we should add a check to make sure this does not happen.

Here is what I suggest. Add an extra value in the __init__ for the val_batch_size. If the batch size is smaller than the size of the validation dataset, then this value is the batch size. Otherwise we take something like half the size of the dataset. This will require to add the batch size as an argument in the _get_dataset method and use the correct one when we get a dataset.