It is still improving. The main design of this framework it to simplify the structure. I devide all stuff common in deep learning models into 3 categories:
- Layer (
LayerLib.py
): perform some transform on input and have parameters to be optimize (such as Dense, Conv, and so on). The api is following Keras somewhat. - Op (
OpLib.py
): operation on input but with no parameters, such as add, multiply, maxpool, non-linear activation and so on. - Loss (
LossLib.py
): The loss function used to minimize. such as MSE, L1Loss, CrossEntropy and so on.
A MLP example has been provide in example/
dir.