when the converter is run.

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

the example restarts correctly.

affine layers was changed so to use the same conventions as other layers that
support convolutional vs fully connected modes.

to caffe models.  Not all layers are supported yet, but it works on the net
from the first dlib dnn example program.  Will add the rest of the layers soon.

you to ask for attributes that don't exist and get a defined behavior (an
exception being thrown) rather than it being a contract violation.

* added basic AppVeyor config

* [AppVeyor] /verbosity:minimal