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59738ba32d
Now that model.h is in the repo, it no longer is a build dependency. This allows compilation of the test without having emlearn installed, which is useful e.g. for build testing. |
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| .. | ||
| tests | ||
| .gitignore | ||
| digit | ||
| generate_digit.py | ||
| generate_model.py | ||
| main.c | ||
| Makefile | ||
| Makefile.ci | ||
| model | ||
| model.h | ||
| README.md | ||
| train_model.py | ||
Emlearn package test application
This application shows how to use a machine learning model with emlearn on RIOT in order to predict a value from a hand written digit image. The model is a Scikit-Learn random forest estimator trained on the MNIST dataset.
Expected output
The default digit to predict is a hand-written '6', so the application output is the following:
Predicted digit: 6
Use the Python scripts
The application comes with 3 Python scripts:
generate_digit.pyis used to generate a new digit file. This file is embedded in the firmware image and is used as input for the inference engine. Use the-ioption to select a different digit. For example, the following command:
will generate a digit containing a '9'. The digit is displayed at the end of the script so one knows which digit is stored. Note that each time a new digit is generated, the firmware image must be rebuilt to include this new digit.$ ./generate_digit.py -i 1train_model.pyis used to train a new Scikit-Learn Random Forest estimator. The trained model is stored in themodelbinary file.
will just train the model.$ ./train_model.pygenerate_model.pyis used to generate thesonar.hheader file from themodelbinary file. The script is called automatically by the build system when themodelbinary file is updated.