v0.4.1

The major change for this version is to support the RNN layers.

New RNN layers

• Support options: return_sequence, stateful and go_backewards
• Support RNN cells: Simple RNN, LSTM and GRU
• GRU and LSTM run on 16bit internally.

New Activations

• AdvanceReLU -> is an equivalence to Keras ReLU with any argment(s) i.e. slope, threshold and max. Also those predefine ReLU such as ReLU6
• Hard TanH and Hard Sigmoid -> currently only in backend.

New Examples

• New uci-har-rnn demonstrate the usage of new RNN layers.
• Add an RNN model in kws example.

Minor:

• Completed the Q format calculation for input/output tensor. Add new method model_io_format() to print the layer io info.

Bugs:

• Fixed concatenate on 2D input.