v0.2.1

• Backend fully supports CHW format. Ready for MCU with hardware accelerator.
• Flatten layer now flatten CHW format to HWC.
• Input layer auto-convert HWC image to CHW when using CHW format.
• Implement layer-wise KLD quantisation. (KLD only apply for convolution)
• Activation quantisation now uses 1000 samples for calibration instead of the whole dataset.
• nnom_predict() now provide probability as output. It now supports single neural output.
• fixed pointer address cut-off in Win64 platform
• Add a new example auto-test, for Travis CI and all PC platforms.
• Update other examples for new APIs.
• Fixed bugs in TanH, Sigmoid when the integer bit width is smaller than 0.
• Documentation

Known issues:

• Batch normalisation after depthwise convolution is not working properly.
  Temporary solution: use batch normalisation after the pointwise convolution (in a depthwise-pointwise structure).

• The script does not support implicitly defined activation. e.g. Dense(32, activation='relu').
  Temporary solution: use explicitly activation. e.g.

Dense(32)
ReLU()

Note:
KLD quantisation. Ref: http://on-demand.gputechconf.com/gtc/2017/presentation/s7310-8-bit-inference-with-tensorrt.pdf