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:

• numpy.

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)