* feature_addition : Mean squared loss layer for multiple output (#404)
* Added loss_mean_squared_multioutput layer to support multiple outputs.
* Also added a corresponding test case to test a single variable regression
with multiple outputs.
* Added error checks on truth argument
Added assert statements to check that truth argument in
compute_loss_value_and_gradient() method contains matrices
of correct dimension relative to the output tensor's size.
Also the requirements on argument truth to the abstract
documentation.
* GCC/Clang compatible vector extension SIMD code
* Minimal modifications to dlib for the simd_vec code to work, a few include changes and ifdefs
* Changed tabbing to spaces
* Allow type inference to binary ops on different types of same size
* Added cmake option USE_AUTO_VECTOR, and fixed up preprocessor checks.
It is required to build with gcc/clang auto vectorization
* Changed to intrinsic version due to poor auto vectorization results.
The simd8*_vec are just copies of the C code right now.
* Removed _vec variants, added to existing defines. simd_check.h back in place and removed from dlib/simd.h
thread_pool's destructor. The previous implementation used dlib's global
thread pooling to allocate threads to dlib::thread_pool, however, this
sometimes caused annoying behavior when used as part of a MATLAB mex file.
* cmake script to suppress randlib warnings about *.a having no symbols on
MacOSX
* Moved script code into the main CMakeLists.txt file to suppress the
superfluous ranlib warnings all the time.
* Fixed issue with uninitialised variables. There are 2 places where std::exception_ptr eptr is not initialised.
* running_gradient.h needs to qualify erfc with 'std::' to avoid compilation error.
Found when compiling dnn_mmod_face_detection_ex.cpp with RadStudio and Clang compiler.
