Example-custom-ml-block-keras - Custom Keras ML block example for Edge Impulse

Overview

Custom Keras ML block example for Edge Impulse

This repository is an example on how to bring a custom transfer learning model into Edge Impulse. This repository contains a small fully-connected model built in Keras & TensorFlow. If you want to see a more complex PyTorch example, see edgeimpulse/yolov5.

As a primer, read the Adding custom transfer learning models page in the Edge Impulse docs.

To test this locally:

  1. Create a new Edge Impulse project, and add data from the continuous gestures dataset.

  2. Under Create impulse add a 'Spectral features' processing block, and a random ML block.

  3. Generate features for the DSP block.

  4. Then go to Dashboard and download the 'Spectral features training data' and 'Spectral features training labels' files.

  5. Create a new folder in this repository named home and copy the downloaded files in under the names: X_train_features.npy and y_train.npy.

  6. Build the container:

    $ docker build -t custom-ml .
    
  7. Run the container to test:

    $ docker run --rm -v $PWD/home:/home custom-ml --epochs 1 --learning-rate 0.01 --validation-set-size 0.2 --input-shape "(33)"
    
  8. This should have created two .tflite files in the 'home' directory.

Now you can initialize the block to Edge Impulse:

$ edge-impulse-blocks init
# Answer the questions, select "other" for 'What type of data does this model operate on?'

And push the block:

$ edge-impulse-blocks push

The block is now available under any project that's owned by your organization.

Owner
Edge Impulse
Enabling developers to create the next generation of intelligent device solutions through embedded Machine Learning
Edge Impulse
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