RIOT/tests/pkg_tensorflow-lite

TensorFlow Lite sample application

This application contains several examples of usage of TensorFlow Lite for microcontrollers:

• The default example, mnist contains a complete example to perform hand-written digit recognition: it shows how to train a very simple MLP (Multi-Layer Perceptron) model and how to reuse it in a RIOT application. The code of this example is provided as an external module in the mnist directory.
• The other example, Hello World, taken as-is from TensorFlow Lite code, simply replicates a sine function from a trained model.

To get started with TensorFlow Lite on microcontrollers, please refer to this page.

Usage

Simply run the application on the board of your choice using:

make BOARD=<board of your choice> -C tests/pkg_tensorflow-lite flash term

Set EXAMPLE=hello_world from the command line to try the upstream hello_world example.

Then type 's' to start the application.

Examples details

mnist

expected output

Digit prediction: 7

scripts usage

First, install tensorflow:

pip3 install --user tensorflow

The scripts require TensorFlow >= 2, so a fairly recent version of pip is required.

The mnist_mlp example comes with 2 Python scripts:

• mnist_mlp.py is used to train and store the model. To minimize the size of the generated model, the script uses post-training quantization. The quantized model is stored in the model.tflite file in the FlatBuffers format and is embedded in the application using the BLOB mechanism.
• generate_digit.py is used to generate the digit from the MNIST dataset test data. The default digit generated corresponds to a 7. Use the -i option to choose another digit. The script displays the generated digit so you can compare with the prediction made by the RIOT application. Note that after a new digit is generated the firmware has to be rebuilt so that it embeds the array containing the pixel values.

hello_world

expected output

The application prints the values of the sine function:

x_value: 1.0*2^-127, y_value: 1.9783614*2^-8

x_value: 1.2566366*2^-2, y_value: 1.3910355*2^-2

x_value: 1.2566366*2^-1, y_value: 1.1282844*2^-1

x_value: 1.8849551*2^-1, y_value: 1.5455950*2^-1

x_value: 1.2566366*2^0, y_value: 1.8238020*2^-1

x_value: 1.5707957*2^0, y_value: 1.8701699*2^-1

x_value: 1.8849551*2^0, y_value: 1.8547139*2^-1

x_value: 1.995567*2^1, y_value: 1.4683149*2^-1

x_value: 1.2566366*2^1, y_value: 1.1128282*2^-1

x_value: 1.4137159*2^1, y_value: 1.819164*2^-2

x_value: 1.5707957*2^1, y_value: -1.2364758*2^-5

x_value: 1.7278753*2^1, y_value: -1.6074185*2^-2

x_value: 1.8849551*2^1, y_value: -1.2982997*2^-1

x_value: 1.210171*2^2, y_value: -1.7928901*2^-1

x_value: 1.995567*2^2, y_value: -1.46367*2^0

x_value: 1.1780966*2^2, y_value: -1.46367*2^0