20180703b

Changelog:

1. Extensive support for HDF5 file format. SLP (v1 and v2) now support HDF5 files for both training and test files. Access speed is much much improved.
   1. TxtToHDF5 and HDF5ToTxt: New utilities to manage HDF5 (conversion to/from Txt). Existing Txt files can be easily converted into HDF5. Conversion to HDF5 can also provide built in full spectral normalization (so it doesn't need to be done within SLP2, speeding up the preprocessing).
   2. Added HDF5 support for: PlotData, PlotDataSplit, AddHorizontalOffset, AddLinearBackground, AddNoisyData, AddRelativeHorNoisyData, AddRelativeNoisyData, AddVerticalOffset,MixMakerRruff, XRange`
2. Substantially improved speed and capabilities: RruffDataMaker2, MakeCrossValidSet, RandomCrossValidMaker, AddNoisyData, MixMakerRruff. By using HDF5, it is now extremely efficient and fast.
3. Redesigned training data makers for less I/O usage (older versions available as legacy): RruffDataMaker and XmuDataMaker:
4. New Utility:
   1. PlotSingleDataRruffSpectra: Plot individual data from Rruff.
   2. AddSpectraToLearnFile: Add spectra to existing training files
   3. NormLearnFile: Normalize max spectral intensity to a specific value (if specified) or 1 (if not specified).
5. Binary .npy is no longer actively developed, although it is still supported. New utility TxtHDF5NpyConverter replaces existing ones and allow for interconversion between npy, txt, h5.
6. Simplification of learning matrix handling.
7. Added support for controlling GPU memory allocation.
8. Added profiler and evaluation hooks to dnntf.
9. Improved default parameters in SpectraKeras.
10. UI and bug fixes.
11. 20180703b has several hotfixes for Utilities, and it supersedes 20180623b and 20180703a. SPL itself us unchanged.