emlearn 0.11.5

Machine learning for microcontrollers and embedded systems

emlearn

Machine learning for microcontroller and embedded systems. Train in Python, then do inference on any device with a C99 compiler.

Key features

Embedded-friendly Inference

• Portable C99 code
• No libc required
• No dynamic allocations
• Support integer/fixed-point math
• Single header file include

Convenient Training

• Using Python with scikit-learn or Keras
• The generated C classifier is also accessible in Python

MIT licensed

Can be used as an open source alternative to MATLAB Classification Trees, Decision Trees using MATLAB Coder for C/C++ code generation. fitctree, fitcensemble, TreeBagger, ClassificationEnsemble, CompactTreeBagger

Status

Minimally useful

Classifiers:

• eml_trees: sklearn.RandomForestClassifier, sklearn.ExtraTreesClassifier, sklearn.DecisionTreeClassifier
• eml_net: sklearn.MultiLayerPerceptron, Keras.Sequential with fully-connected layers
• eml_bayes: sklearn.GaussianNaiveBayes

Feature extraction:

• eml_audio: Melspectrogram

Tested running on AVR Atmega, ESP8266, Linux and Windows. Mac OS should also work fine, please file a bug report if it does not.

Installing

Install from PyPI

pip install --user emlearn

Usage

1. Train your model in Python

from sklearn.ensemble import RandomForestClassifier
estimator = RandomForestClassifier(n_estimators=10, max_depth=10)
estimator.fit(X_train, Y_train)
...

2. Convert it to C code

import emlearn
cmodel = emlearn.convert(estimator, method='inline')
cmodel.save(file='sonar.h')

3. Use the C code

#include "sonar.h"

const int32_t length = 60;
int32_t values[length] = { ... };
const int32_t predicted_class = sonar_predict(values, length):

For full example code, see examples/digits.py

Contributors

Jon Nordby
Mark Cooke

Citations

If you use emlearn in an academic work, please reference it using:

@misc{emlearn,
  author       = {Jon Nordby},
  title        = {{emlearn: Machine Learning inference engine for 
                   Microcontrollers and Embedded Devices}},
  month        = mar,
  year         = 2019,
  doi          = {10.5281/zenodo.2589394},
  url          = {https://doi.org/10.5281/zenodo.2589394}
}