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.