Contents
emlearn-micropython
Status
Features
Examples
Documentation
Citations
Developing
User Guide
1. Getting started on PC (Linux/MacOS/Windows)
1.1. Prerequisites
1.2. Install scikit-learn
1.3. Install emlearn
1.4. Install MicroPython Unix port
1.5. Install emlearn-micropython modules
1.6. Create model in Python
1.7. Use in MicroPython code
1.8. Try it out
1.9. Next
2. Getting started on device (ESP32/RP2/STM32/etc)
2.1. Prerequisites
2.2. Install mpremote
2.3. Install emlearn-micropython modules
2.4. Create model in Python
2.5. Use in MicroPython code
2.6. Try it out
2.7. Next
3. Getting started for browser
3.1. Prerequisites
3.2. emlearn-micropython build for browser
3.3. Setup web page
3.4. Try it out
3.5. Serving from device
4. Supported versions
4.1. Supported MicroPython versions
4.2. Supported hardware
5. Native modules
5.1. Supported versions
5.2. Prebuilt native modules
5.3. Installing using mip
6. External modules
6.1. Supported versions
6.2. Prerequisites
6.3. Include external modules in build
Examples
Soundlevel using IIR filters (soundlevel_iir)
Human Activity Detection using classification trees (har_trees)
Digits recognition using Convolutional Neural Networks (mnist_cnn)
XOR classification using trees (xor_trees)
API reference
emlearn_trees - Decision tree ensembles
Model
load_model()
new()
emlearn_linreg - Linear regression
Model
new()
train()
emlearn_cnn - Convolutional Neural Networks
Model
new()
emlearn_neighbors - K Nearest Neighbors (KNN)
Model
new()
emlearn_fft - Fast Fourier Transform
FFT
fill()
emlearn_iir - Infinite Impulse Reponse filters
IIR
new()
emlearn_arrayutils - Efficient utilities for array.array
linear_map()
More
emlearn documentation
Presentations
Microcontrollers + Machine Learning in 1-2-3
MicroPython - Python for microcontrollers and embedded linux
Sensor data processing on microcontrollers with MicroPython and emlearn
Machine Learning on microcontrollers using MicroPython and emlearn
Other resources
The MIT License
emlearn-micropython
Examples
View page source
Examples
Soundlevel using IIR filters (soundlevel_iir)
Example on Github
.
Human Activity Detection using classification trees (har_trees)
Example on Github
.
Digits recognition using Convolutional Neural Networks (mnist_cnn)
Example on Github
.
XOR classification using trees (xor_trees)
Example on Github
.