Welcome to TensorLayerX¶
Documentation Version: 0.5.8
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.
User Guide¶
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.
API Reference¶
If you are looking for information on a specific function, class or method, this part of the documentation is for you.
Stable Functionalities
- API - Activations
- API - Losses
- Softmax cross entropy
- Sigmoid cross entropy
- Binary cross entropy
- Mean squared error (L2)
- Normalized mean square error
- Absolute difference error (L1)
- Dice coefficient
- Hard Dice coefficient
- IOU coefficient
- Cross entropy for sequence
- Cross entropy with mask for sequence
- Cosine similarity
- Regularization functions
- API - Metrics
- API - Dataflow
- API - Files
- API - NN
- API - Model Training
- API - Vision
- API - Initializers
- API - Operations
- API - Optimizers
Command-line Reference¶
TensorLayerX provides a handy command-line tool tlx to perform some common tasks.