https://github.com/vertexclique/evren
Machine Learning model optimizer and exporter for embedded systems and mobile platforms
https://github.com/vertexclique/evren
android android-ndk core-ml ios machine-learning mlkit model-export
Last synced: 2 months ago
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Machine Learning model optimizer and exporter for embedded systems and mobile platforms
- Host: GitHub
- URL: https://github.com/vertexclique/evren
- Owner: vertexclique
- License: mit
- Created: 2018-02-06T11:45:49.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-02-07T17:31:45.000Z (over 7 years ago)
- Last Synced: 2025-01-28T12:36:21.346Z (4 months ago)
- Topics: android, android-ndk, core-ml, ios, machine-learning, mlkit, model-export
- Language: Python
- Size: 16.6 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Evren
[](https://travis-ci.org/vertexclique/evren)
[](https://codecov.io/gitlab/vertexclique/evren?branch=master)
**NOTE:** Project is in experimental phase.
**Evren** is pre-optimizer and exporter for machine learning models to embedded systems, android and iOS platforms.
Currently Android and iOS supported, for iOS it can export `caffe` and `keras` models, for Android it exports `keras` models.
Keras models should use Tensorflow backend.
## Installation
After cloning use `tox` to setup environment.
```bash
$ tox
```
## Usage
You can find examples in `examples` directory. Documentation will be made available soon.
Minimal export code can be:
```python
MODEL_NAME = "dense_android_test"
# Define Keras model
model = Sequential()
model.add(Dense(10, input_shape=(10, 1)))
model.add(Dense(10))
model.add(Dense(10))
model.add(Dense(10))
model.add(Dense(10))
model.add(Dense(10))
model.add(Dense(1, activation='sigmoid'))
# Train it in here, just because it is demonstration we ignore that
# . . . . . . TRAINING . . . . . .
# If you know the output nodes you
# don't need to call underlying output nodes list
output_nodes = get_output_node_names()
# Write Keras model with weights as protobuf file
# for use with Tensorflow Lite and Android NDK
# It will produce `dense_android_test.pb` with weights embedded in.
export_keras_to_file(model_name=MODEL_NAME,
input_names=map(str, range(1, 10)),
output_names=output_nodes,
out_file_name=MODEL_NAME)
```
## TODO
* Caffee export for Android
* Extra optimization techniques for Android and iOS.
* Travis setup with Tox.
## Contributing
Read [CONTRIBUTING](CONTRIBUTING.md).