state to disk. If it detects that the loss has been going up then instead of
saving to disk it recalls the previously good state.  This way, if we hit a
bad mini-batch during training which negatively effects the model in a
significant way, the dnn_trainer will automatically revert back to an earlier
good state.

Merge branch 'skip_gui_examples' of git://github.com/miscellanea/dlib into miscellanea-skip_gui_examples

Conflicts:
	examples/CMakeLists.txt

load a dataset of high resolution files into a user requested lower resolution.

Now dlib can be compiled for android with -DANDROID_ABI="armeabi-v7a with NEON" with https://github.com/taka-no-me/android-cmake
However, when compiling for arm64-v8a, since __ARM_NEON__ is not defined,
neon code will not be actually enabled, which requires upgrading libpng a bit more

encountered a bad mini-batch that made the loss value suddenly jump up by a
larger than normal value it could make the trainer think we converged.  Now the
test is robust to recent spikes in loss value.

This was in some of the svd routines.  However, we already had a svd routine
that used a separate svd code that is better than the NRIC derived version.  So
that's what we use everywhere now.

Now PYTHON_INCLUDE_DIR and PYTHON_LIBRARY setting up correctly for both 32/64 bit architectures

imagenet examples compile.

it simply partitions the data and is an invertible operation.

avoid compiler warnings.

if the input xml file contained unannotated images.