Tensorlayerx

[TensorLayerX 0.5.6](https://github.com/tensorlayer/TensorLayerX) is a maintenance release . In this release , we have the following changes .

- Fixed Sequential mode ONNX node collection .
- Fixed bug with RNN LSTM GRU training parameters .
- Fixed the inconsistency of different backends parameters of DepthWiseConv2d.
- Fixed the bug of saving parameters to npz.
- Updated padding layers.

Feel free to use it and make suggestions!

[TensorLayerX 0.5.5](https://github.com/) is a maintenance release.In this release, we have the following changes.

- Added get_device, to_device operator.
- Changed the parameter name of the average pooling layer to (AvgPool1d, GlobalAvgPool1d, AdaptiveAvgPool1d, AvgPool2d, GlobalAvgPool2d Etc.)
- Fixed LSTM RNN GRU.
- Fixed a bug where ParameterList and ParameterDict training parameters on the TensorFlow backend were not collected.
- Fixed support for MindSpore1.7.0 version.

Feel free to use it and make suggestions!

[TensorLayerX 0.5.4](https://github.com/) is a maintenance release.In this release, we have the following changes.

- Added documentation for metric functions
- Add Einsum
- Fixed PyTorch back-end optimizers
- Fixed preprocessing when activation functions are used as parameters

Feel free to use it and make suggestions!

[TensorLayerX 0.5.3](https://github.com/) is a maintenance release.In this release, we have the following changes.

- Added kernel_size, stride, dilation parameters can be int or tuple.
- Added padding mode can be int, tuple, or str. str is "SAME" or "VALID".
- Added TensorLayerX model topology for ONNX model export, can generate topology by model.build_graph(inputs).
- Fix the problem of slow training speed due to MindSpore optimizer wrapping.

Feel free to use it and make suggestions!

[TensorLayerX 0.5.1](https://github.com/) is a maintenance release.In this release, we have updated a number of interface and parameter names to make them easier to use. Some examples have been improved.

Feel free to use it and make suggestions!

TensorLayerX 0.5.0 is a maintenance release，it supports TensorFlow、MindSpore and PaddlePaddle backends, and supports some PyTorch operator backends, allowing users to run the code on different hardware like Nvidia-GPU and Huawei-Ascend. Feel free to use it and make suggestions.