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NNKit: A Python framework for creating dynamic neural networks

NNKit is a framework for creating and training neural network models, based on dynamic computation graphs. See this post for more info on how the framework works.

Dependencies:

Installation:

You can pip install nnkit, in which case Numpy will also be installed. Otherwise you can download the source and manually install numpy if necessary.

Modules:

The following is a list of modules, nodes and optimizers, along with the framework version in which they were added.

activation:

  • ReLU (1.0)
  • LReLU (1.0)
  • Sigmoid (1.0)
  • Tanh (1.0)
  • Softmax (1.0)

arithmetic:

  • Multiply (1.0)
  • Add (1.0)

loss:

  • L1 (1.0)
  • L2 (1.0)
  • Cross Entropy (1.0)
  • Huber (1.4.0)

normalization:

  • Batch Normalization (1.0)

regularization:

  • L2 (1.0)
  • Dropout (1.0)

optimization:

  • Gradient descent / momentum (1.0)
  • Adam / RMSProp (1.0)

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A Python framework for creating dynamic neural networks

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