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gmls-nets
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GMLS-Nets - a Tensorflow implementation
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# GMLS-Nets This code is the Tensorflow implementation of N. Trask, R. G. Patel, B. J. Gross, and P. J. Atzberger, "GMLS-Nets: A Framework for Learning from Unstructured Data," arXiv:1909.05371, (2019). #### Installation `pip install gmlsnets-tensorflow` #### Requirements `Python >= 3.5` `numpy` `scipy` `matplotlib` `scikit-learn` `toolz` `tensorflow` #### Usage The three classes in `gmlsnets_tensorflow/__init__.py` provide Keras layers used to construct GMLS-Nets architectures. `MFLayer` creates layers that compute GMLS coefficients from functions sampled on a point cloud. `MFConvLayer` and `MFPoolLayer` create for point cloud data the equivalent to (strided) convolutional layers and pool layers, respectively. These classes use the weighting functions in `gmlsnets_tensorflow/weightfuncs.py` and the polynomial bases in `gmlsnets_tensorflow/bases.py`. See the examples folder for MNIST and PDE discovery examples. #### Additional information For the PyTorch implementation, see https://github.com/atzberg/gmls-nets.
