nnkit 1.3.2

NNKit: A Python framework for creating dynamic neural networks.

NNKit: A Python framework for creating dynamic neural networks

NNKit is a framework for creating and training neural network models.

It is based on the concept of dynamic computation graphs, meaning that graph topology can change at runtime. This approach allows for faster iteration and experimentation and is especially well suited to tasks with inputs of varying length, like those in sequential models (i.e. RNNs). NNKit graphs are implicitly recreated on each forward pass, as a consequence of feeding nodes into other nodes to describe a computation.

See this article 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 net nodes and optimizers in the framework, 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)

normalization:

• Batch Normalization (1.0)

regularization:

• L2 (1.0)

• Dropout (1.0)

training:

• Gradient descent w/ momentum (1.0)

• Adam / RMSProp (1.0)