RIOT/tests/pkg_utensor

uTensor sample application

This application shows how to setup utensor and use it with RIOT: an MLP (Multi-Layer Perceptron) model is trained with TensorFlow on the MNIST dataset in order to be able to recognize hand written digits in an image.

The code of this application comes from the following blog post: https://www.hackster.io/news/simple-neural-network-on-mcus-a7cbd3dc108c

Build

make BOARD=<board of your choice> all term

Expected output

The digit to recognize is stored in the digit binary file of the application and is automatically added to the application firmware as a C array via the blob mechanism of the build system. By default, the digit contains the first test image of the MNIST dataset, a hand-written 7.

So by default the expected output should be:

Predicted label: 7

Use the generate_digit.py script provided with this application to update the digit file from another test image of the MNIST dataset:

./generate_digit.py --index 1

Each selected digit is displayed at the end of the script to allow a "visual" comparison with the value predicted by the firmware. For each new digit generated, the firmware must be rebuilt: the image is statically embedded as a blob in the firmware image.

Training the model

The application contains a pre-trained model in the models external module. Except the Makefiles, all C++ files (model + weights) were generated using the utensor-cli tool from a model trained with TensorFlow.

Here are the steps required to train a new model and update the C++ files in the models external module within the application:

1. Install Python3 dependencies

   pip3 install --user utensor_cgen graphviz
   pip3 install --user tensorflow -U
   

Note that utensor_cgen is only compatible with tensorflow 1 for the moment.

2. Clone the utensor-mnist-demo repository: it contains the Python script used to train the MLP model on the MNIST dataset.

   cd /tmp
   git clone https://github.com/uTensor/utensor-mnist-demo
   

3. Train the MLP model:

   cd /tmp/utensor-mnist-demo
   python3 /tmp/utensor-mnist-demo/tensorflow-models/deep_mlp.py
   

   The model is stored in /tmp/utensor-mnist-demo/mnist_model/deep_mlp.pb using the protocol buffer format.

4. Generate the C++ model files that will be included later in the RIOT build:

   cd $RIOTBASE/tests/pkg_utensor
   utensor-cli convert /tmp/utensor-mnist-demo/mnist_model/deep_mlp.pb --target utensor --output-nodes=y_pred