https://github.com/PetraVidnerova/rbf_for_tf2
RBF Layer for tf.keras using Tensorflow 2.0 (work in progress!)
https://github.com/PetraVidnerova/rbf_for_tf2
Last synced: 5 months ago
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RBF Layer for tf.keras using Tensorflow 2.0 (work in progress!)
- Host: GitHub
- URL: https://github.com/PetraVidnerova/rbf_for_tf2
- Owner: PetraVidnerova
- License: mit
- Created: 2019-10-29T15:39:22.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-31T19:31:55.000Z (almost 6 years ago)
- Last Synced: 2024-11-13T03:34:54.303Z (11 months ago)
- Language: Python
- Homepage:
- Size: 28.3 KB
- Stars: 47
- Watchers: 3
- Forks: 16
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Tensorflow2 - PetraVidnerova/rbf_for_tf2
README
# rbf_for_tf2
*Author: Petra Vidnerová, The Czech Academy of Sciences, Institute of Computer Science*
RBF layer for Tensorflow 2.0 (as custom layer derived from tf.keras.layers.Layer)
You need rbflayer.py to use RBF layers in your code. See test.py for
**very simple** examples.
Feel free to use or modify the code.
## Requirements:
Tensorflow, Scikit-learn, optionally Matplotlib (only for the toy example in test.py)
## Usage:
```
# 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, use_bias=False))
```
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,))
```
If you need any other setup of centers or widhts, you can very easily define your own initializer,
just write your subclass of `tensorflow.keras.initializers.Initializer`.
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})
```
You can also load weights (centers or widhts) from file (.npy file with an numpy array of the right shape),
see IntFromFile in initializer.py and
example in test.py.
## See also:
**[Old repo](https://github.com/PetraVidnerova/rbf_keras/)** that was written
in 2017 for Keras.
## Contact:
If you need help, do not hesitate to contact me via petra@cs.cas.cz or write an Issue.
## How to cite:
In case you use this RBF layer for any experiments that result in publication, please consider citing it. Thanks :heart:
*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](https://doi.org/10.1145/3341163.3347749)
## Acknowledgement:
This work was partially supported by the Czech Grant Agency grant 18-23827S
and institutional support of the Institute of Computer Science RVO 67985807.