easily pass arguments to any optional parameters of a loss layer's to_tensor()
routine.  For instance, it makes it more convenient to set loss_mmod_'s
adjust_threshold parameter.

between.  This should make syncing more robust to sudden hardware failure that
happens right when saving to disk.

reallocation and copying inside conv_'s backward pass.  Doing this
required adding an add_to_output boolean option to the methods of
tensor_conv.

refactored interface to reduce complexity so conv and convt layers forward passes have to call setup explicit now and there is only one ()-operator

histogram is equalized or unmodified.  This way, if you are looking at
particularly dark or badly contrasted images you can toggle this mode and maybe
get a better view of what you are labeling.

min and max object height, it's now min and max object size.  This way, if you
have objects that are short and wide (i.e. objects where the relevant dimension
is width rather than height) you will get sensible behavior out of the random
cropper.

everything in it, it now makes a python file as before but an additional binary
file with all the weights in it.  This way, if you are working with a network
with a very large number of weights you won't end up with a crazy large python
script.

integers.

object so that users of the deep learning MMOD tool don't get spurious errors
about impossibly labeled objects during training.

objects that also provide a .clean() method.