This repository is an example on how to add a custom learning block to Edge Impulse. This repository contains a small fully-connected model built in Keras & TensorFlow. If you want to see a more complex example, see efficientnet. Or if you're looking for the PyTorch version of this repository, see edgeimpulse/example-custom-ml-block-pytorch.

As a primer, read the Custom learning blocks page in the Edge Impulse docs.

You run this pipeline via Docker. This encapsulates all dependencies and packages for you.

1. Install Docker Desktop.

2. Install the Edge Impulse CLI v1.16.0 or higher.

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

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

5. Open a command prompt or terminal window.

6. Initialize the block:

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

7. Fetch new data via:

   $ edge-impulse-blocks runner --download-data data/
   

8. Build the container:

   $ docker build -t custom-ml-keras .
   

9. Run the container to test the script (you don't need to rebuild the container if you make changes):

   $ docker run --rm -v $PWD:/app custom-ml-keras --data-directory /app/data --epochs 30 --learning-rate 0.01 --out-directory out/
   

10. This creates two .tflite files and a saved model ZIP file in the 'out' directory.

If you have extra packages that you want to install within the container, add them to requirements.txt and rebuild the container.

To add new arguments, see Custom learning blocks > Arguments to your script.

To get up-to-date data from your project:

1. Install the Edge Impulse CLI v1.16 or higher.

2. Open a command prompt or terminal window.

3. Fetch new data via:

   $ edge-impulse-blocks runner --download-data data/
   

You can also push this block back to Edge Impulse, that makes it available like any other ML block so you can retrain your model when new data comes in, or deploy the model to device. See Docs > Adding custom learning blocks for more information.

1. Push the block:

   $ edge-impulse-blocks push
   

2. The block is now available under any of your projects via Create impulse > Add new learning block.