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.

the behavior of dlib's add_prev layers.

when the converter is run.

Noticed compiler warning C4800: 'double': forcing value to bool 'true' or 'false' (performance warning) (#538)

- suggesting to change the test to what perhaps was the original intention

Moved shape_predictor_trainer to its own header in order to support the
use of shape_predictor with DLIB_ISO_CPP_ONLY defined (i.e. without
threading API wrappers).

So I just removed the error check that was preventing the conversion from
proceeding in that case.  I also added more useful output messages about
setting input tensor dimensions.

* Convert unique_ptr

* Fix passing unique_ptr as copy by value

* Remove scoped_ptr implementation

* Fix missed files

* Move bool cast into tester macro

* Reexport scoped_ptr alias from sockets

simplified script a little.

* Replace shared_ptr/weak_ptr with stdlib counterparts

* Fix ptr usage through tests compilation

* Bring back dlib smart ptrs as legacy

* Include scoped_ptr directly

* Add explanation about smart_ptr deprecation

* Replace shared_ptr_thread_safe

* Fix missed old include

* Removed missed old include

* Replace shared_ptr/weak_ptr with stdlib counterparts

* Fix ptr usage through tests compilation

* Bring back dlib smart ptrs as legacy

* Include scoped_ptr directly

* Add explanation about smart_ptr deprecation