Introduction:

Note is an efficient and flexible machine learning library that allows developers to easily build and train neural networks. It supports deep learning and reinforcement learning, enabling developers to easily perform distributed training.

Installation:

To use Note, you need to download it from https://github.com/NoteDance/Note and then unzip it to the site-packages folder of your Python environment.

dependent packages:

tensorflow>=2.16.1

pytorch>=2.3.1

gym<=0.25.2

matplotlib>=3.8.4

einops>=0.8.0

python requirement:

python>=3.10

Deep learning:

Note.nn.layer package contains many layer modules, you can use them to build neural networks. Note’s layer modules are implemented based on TensorFlow, which means they are compatible with TensorFlow’s API. The layer modules allow you to build neural networks in the style of PyTorch or Keras. You can use the layer modules to build neural networks trained with TensorFlow.

https://github.com/NoteDance/Note/tree/Note-7.0/Note/nn/layer

Documentation: https://github.com/NoteDance/Note-documentation/tree/layer-7.0

Note.models.tf package contains neural networks implemented with Note’s layer module that can be trained with TensorFlow. You can also consider these models as examples using the Note. The documentation shows how to train, test, find best lr, find best optimizer, save, and restore models built with Note.

https://github.com/NoteDance/Note/tree/Note-7.0/Note/models/tf

Documentation: https://github.com/NoteDance/Note-documentation/tree/Model-7.0

Reinforcement learning：

You just need to have your agent class inherit from the RL or RL_pytorch class, and you can easily train your agent built with Note, Keras or PyTorch. You can learn how to build an agent from the examples here. The documentation shows how to train, save, and restore agent built with Note, Keras or PyTorch.

Documentation: https://github.com/NoteDance/Note-documentation/tree/RL-7.0

Function modules:

Documentation: https://github.com/NoteDance/Note-documentation/tree/function-7.0

Note.nn.Model.Model:

Model class manages the parameters and layers of neural network.

Model's function documentation: https://github.com/NoteDance/Note-documentation/tree/function-7.0

Note.nn.initializer.initializer:

This function is used to initialize the parameters of the neural network, it returns a TensorFlow variable and stores the variable in trainable parameters list(Model.param).

Note.nn.initializer.initializer_:

This function is used to initialize the parameters of the neural network, and it returns a TensorFlow variable.

Note.nn.parameter.Parameter:

Its function is similar to torch.nn.parameter.Parameter.

Note.nn.Sequential.Sequential:

This class is used similarly to tf.keras.Sequential and torch.nn.Sequential.

Kernel:

You can use Note's kernel module, which is based on Python's multiprocessing module, to train models in parallel. The documentation for the kernel module is provided below.

https://github.com/NoteDance/Note-documentation/tree/kernel-7.0

https://github.com/NoteDance/Note-documentation/tree/kernel-other-7.0

Patreon:

You can support this project on Patreon.

https://www.patreon.com/NoteDance

Contact:

If you have any issues with the use, or you have any suggestions, you can contact me.

E-mail: [email protected]