Using pure numpy to construct deep learning computational graph framework.
note：To view the code, please switch to the master branch
- pycharm
- python: >= 3.7
- Based on computational graph, can be used to build common machine learning models.
- Support automatic gradient.
- Support common optimization methods (such as GD, Momentum, Adagrad, RMSprop, Adam, etc.)
- Support common evaluation methods (such as Accuracy, Precision, AUC, F1_score, etc.)
- Support model save and load
- Support drawing calculate graph by pyecharts
- Support model serving by grpc
- Support model export
- Support distribute trainning.
To be added