renamed the objects and generally improved documentation.

code from java.

libpng than they built against.

member area of the class.  This way, we avoid a potential error where the
buffers are reallocated while cuDNN is still using them in the background.

* added shared workspace

* rewrite shared workspace code

* rename and device-based buffer allocation

* fix cudnn_device_buffer constructors

next using dlib::correlation_tracker.

* Make http_client compile in C++11 and above

* Optimize file reading

* Add per-pixel mean square loss

* Add documentation of loss_mean_squared_per_pixel_

* Add test case for per-pixel mean square loss: a simple autoencoder

* Review fix: reorder params of function tensor_index, so that the order corresponds to the convention used in the rest of the dlib code base

* Review fix: add breaks as intended, and change the rest of the test accordingly

* Again a case where the tests already work locally for me, but not on AppVeyor/Travis - this commit is a blindfolded attempt to fix the problem
(and it also fixes a compiler warning)

Hopefully improved compiler error diagnostic when using gcc and a user misconfigures their build environment.

* remove linking to libpython on linux

* add OSX libpython free building

* add automatic discovery of include python dir back in

* make the libs non required for building on manylinux

Test on a given video like this cv::VideoCapture cap("Sample.avi") may be broken when the video frames are not enough before the main window is closed by the user.

without to make sure both types of lookup work.

* Add new loss for weighted pixel inputs (may be useful e.g. to emphasize rare classes)

* Deduplicate method loss_multiclass_log_per_pixel_(weighted_)::to_label

* Add a simple test case for weighted inputs
(also, fix a typo in test_tensor_resize_bilienar's name)

* Add loss_multiclass_log_per_pixel_weighted_ to loss_abstract.h

* Decrease the amount of weighting

* There's no need to train for a very long time

matlab.

*  Added check to see if __ARM_NEON__ is defined. Now we can use the following command: cmake --build --config Release ..

* Rename to use_arm_neon.cmake to check_if_neon_available.cmake for clarity, minor tidying up of script, and simplifying try_compile() code for ARM NEON.