Documentation Version:
TensorLayerX is a deep learning library designed for researchers and engineers that is compatible with multiple deep learning frameworks such as TensorFlow, MindSpore and PaddlePaddle, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend. It provides popular DL and RL modules that can be easily customized and assembled for tackling real-world machine learning problems. More details can be found here.
TensorLayerX is a multi-backend AI framework, which can run on almost all operation systems and AI hardwares, and support hybrid-framework programming. The currently version supports TensorFlow, MindSpore, PaddlePaddle and PyTorch(partial) as the backends.
Note
If you got problem to read the docs online, you could download the repository on TensorLayerX, then go to /docs/_build/html/index.html to read the docs offline. The _build folder can be generated in docs using make html.
The TensorLayerX user guide explains how to install TensorFlow, CUDA and cuDNN, how to build and train neural networks using TensorLayerX, and how to contribute to the library as a developer.
user/installation user/examples user/contributing user/faq
user/get_start_model user/get_start_advance
If you are looking for information on a specific function, class or method, this part of the documentation is for you.
modules/activation modules/losses modules/metrics modules/dataflow modules/files modules/nn modules/model modules/vision modules/initializers modules/ops modules/optimizers
TensorLayerX provides a handy command-line tool tlx to perform some common tasks.
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