Convolutional Neural Networks On Ninapro datasets

-. usefulFcns.py 1. SplitOnColumn. split a 2D numpy array on its Column to a 3D numpy array 2. BuildNewlyDir. newly build an empty directory. if it exists, delete it, and then build a new one. or, build a new one.

-. FeaturesExtraction.py many features extraction functions sets and an extractSlidingWindow methods to extract [feStr] with sliding window of [LI-LW]

-. DataPreparation.py 1. getRawDict from [.mat] file 2. getRmsImagesLabels from rawDict, to construct two generalized numpy arrays 3D[Images]mx16x30 and 2D[Labels]mx8

-. scriptDataPreparation.py a work-through script 1. read from .mat file 2. extract [RMS] feature 3. write 3D[Images]mx16x30 and 2D[Labels]mx8 to file 'ninaRmsImagesLabels.pkl' with pickle module 4. read 3D[Images]mx16x30 and 2D[Labels]mx8 from file 'ninaRmsImagesLabels.pkl' with pickle module 5. write == read ? checking 6. time duration computation for every part.

-. classNinapro.py 1. read [Images]&[Labels] from .pkl file 2. split [Images]&[Labels] to Train-Test-Validate parts with a proportion 3. next_batch for usage during CNN training.