batch_size and the input_shape dependency

[cancan101]

There is currently a non-transparent / non-intuitive dependency between the batch_size and the input_shape.

Currently the default batch_iterator is BatchIterator(batch_size=128). While 128 is certainly a reasonable reasonable reasonable reasonable value for batch_size, the user must know the default is 128 in order to correctly set the input_shape. Ideally there would be some way for the user to change the batch_size without having to remember to update the input shape. One idea would be some sort of lazily resolved BATCH_SIZE constant that could be used in the input shape. The iterator could then have an additional method get_batch_size which is used by the NeuralNet to set the BATCH_SIZE constant.

[dnouri]

This is luckily a non-issue now, because it turns out you can leave the batch size unspecified (None) when setting the input layer's shape. See #8 (comment)

[jseabold]

IMO it's still a bit confusing since it doesn't go the other way from input_shape -> batch size. I specified the input shape to be (1, num_features) to do online learning, but I still get the default 128 sample batch size without also setting this explicitly.

[dnouri]

So you'll need to use BatchIterator(batch_size=1) and set input_shape to be (None, num_features): not repeating yourself.

[jseabold]

Sure, that's what I settled on, but it wasn't obvious to me without reading the source. Just some feedback.