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