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https://github.com/keras-team/keras-contrib
Keras community contributions
https://github.com/keras-team/keras-contrib
data-science deep-learning keras machine-learning neural-networks tensorflow theano
Last synced: 3 months ago
JSON representation
Keras community contributions
- Host: GitHub
- URL: https://github.com/keras-team/keras-contrib
- Owner: keras-team
- License: mit
- Archived: true
- Created: 2017-01-25T02:26:49.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-10-21T19:02:46.000Z (about 2 years ago)
- Last Synced: 2024-09-06T14:01:33.770Z (4 months ago)
- Topics: data-science, deep-learning, keras, machine-learning, neural-networks, tensorflow, theano
- Language: Python
- Size: 848 KB
- Stars: 1,583
- Watchers: 80
- Forks: 650
- Open Issues: 191
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: CODEOWNERS
Awesome Lists containing this project
- awesome-datascience - keras-contrib
README
# keras-contrib : Keras community contributions
Keras-contrib is deprecated. Use [TensorFlow Addons](https://github.com/tensorflow/addons).
## The future of Keras-contrib:
We're migrating to [tensorflow/addons](https://github.com/tensorflow/addons). See the announcement [here](https://github.com/keras-team/keras-contrib/issues/519).
[](https://travis-ci.org/keras-team/keras-contrib)
This library is the official extension repository for the python deep learning library [Keras](http://www.keras.io). It contains additional layers, activations, loss functions, optimizers, etc. which are not yet available within Keras itself. All of these additional modules can be used in conjunction with core Keras models and modules.
As the community contributions in Keras-Contrib are tested, used, validated, and their utility proven, they may be integrated into the Keras core repository. In the interest of keeping Keras succinct, clean, and powerfully simple, only the most useful contributions make it into Keras. This contribution repository is both the proving ground for new functionality, and the archive for functionality that (while useful) may not fit well into the Keras paradigm.
---
## Installation
#### Install keras_contrib for keras-team/keras
For instructions on how to install Keras,
see [the Keras installation page](https://keras.io/#installation).
```shell
git clone https://www.github.com/keras-team/keras-contrib.git
cd keras-contrib
python setup.py install
```
Alternatively, using pip:
```shell
sudo pip install git+https://www.github.com/keras-team/keras-contrib.git
```
to uninstall:
```pip
pip uninstall keras_contrib
```
#### Install keras_contrib for tensorflow.keras
```shell
git clone https://www.github.com/keras-team/keras-contrib.git
cd keras-contrib
python convert_to_tf_keras.py
USE_TF_KERAS=1 python setup.py install
```
to uninstall:
```shell
pip uninstall tf_keras_contrib
```
For contributor guidelines see [CONTRIBUTING.md](https://github.com/keras-team/keras-contrib/blob/master/CONTRIBUTING.md)
---
## Example Usage
Modules from the Keras-Contrib library are used in the same way as modules within Keras itself.
```python
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
# I wish Keras had the Parametric Exponential Linear activation..
# Oh, wait..!
from keras_contrib.layers.advanced_activations import PELU
# Create the Keras model, including the PELU advanced activation
model = Sequential()
model.add(Dense(100, input_shape=(10,)))
model.add(PELU())
# Compile and fit on random data
model.compile(loss='mse', optimizer='adam')
model.fit(x=np.random.random((100, 10)), y=np.random.random((100, 100)), epochs=5, verbose=0)
# Save our model
model.save('example.h5')
```
### A Common "Gotcha"
As Keras-Contrib is external to the Keras core, loading a model requires a bit more work. While a pure Keras model is loadable with nothing more than an import of `keras.models.load_model`, a model which contains a contributed module requires an additional import of `keras_contrib`:
```python
# Required, as usual
from keras.models import load_model
# Recommended method; requires knowledge of the underlying architecture of the model
from keras_contrib.layers import PELU
from keras_contrib.layers import GroupNormalization
# Load our model
custom_objects = {'PELU': PELU, 'GroupNormalization': GroupNormalization}
model = load_model('example.h5', custom_objects)
```