* 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.

already enabled.  This also makes the global destruction order a little more
deterministic.

early stopping based on observing the loss on held out data.

that really needs it off.