Contains classes for various network architectures which can be initialized to behave like polynomials. These classes are associated with a network described in this arxiv preprint: https://arxiv.org/abs/1905.10457
This class is contained in the file deep.py.
It can be used for function approximation in
For example, one can approximate a 1-d osciallting function. Here we plot the initialization of a network for approximating a 1-d function using our polynomial initialization and Xavier inititialization.
After training these networks have the following behavior. The blue points are the sample values of the target function used for training the network.
Clearly, the polynomial initialized network was better able to learn the behavior of the target function.
Check back soon for extensions of these initialization techniques and the inclusion of more examples.
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