Author: Petra Vidnerová, The Czech Academy of Sciences, Institute of Computer Science

RBF layer for Keras

You need rbflayer.py to use RBF layers in keras. See test.py for very simple example.

Feel free to use or modify the code.

Keras, Tensorflow, Scikit-learn, optionally Matplotlib (only for test.py)

  # creating RBF network
  rbflayer = RBFLayer(10,
                      initializer=InitCentersRandom(X),
                      betas=2.0,
                      input_shape=(num_inputs,))

  model = Sequential()
  model.add(rbflayer)
  model.add(Dense(n_outputs))

or using KMeans clustering for RBF centers

  # creating RBFLayer with centers found by KMeans clustering
  rbflayer = RBFLayer(10,
                      initializer=InitCentersKMeans(X),
                      betas=2.0,
                      input_shape=(num_inputs,))

Because you have created Keras model with a custom layer, you need to take it into account if you need to save it to file and load it. Saving is no problem:

model.save("some_fency_file_name.h5")

but while loading you have to specify your custom object RBFLayer:

rbfnet = load_model("some_fency_file_name.h5", custom_objects={'RBFLayer': RBFLayer})

Issue #1: For hint how to implement different radii for different dimensions.

If you need help, do not hesitate to contact me via petra@cs.cas.cz or write an Issue.

In case you use this RBF layer for any experiments that result in publication, please consider citing it. Thanks ❤️

Vidnerová, Petra. RBF-Keras: an RBF Layer for Keras Library. 2019. Available at https://github.com/PetraVidnerova/rbf_keras

Thanks to the author of the very first citation: Lukas Brausch, et al. Towards a wearable low-cost ultrasound device for classification of muscle activity and muscle fatigue. 2019 doi:10.1145/3341163.3347749

This work was partially supported by the Czech Grant Agency grant 18-23827S and institutional support of the Institute of Computer Science RVO 67985807.