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83 lines
2.6 KiB
Markdown
83 lines
2.6 KiB
Markdown
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uTensor sample application
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==========================
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This application shows how to setup utensor and use it with RIOT: an MLP
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(Multi-Layer Perceptron) model is trained with TensorFlow on the
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[MNIST dataset](http://yann.lecun.com/exdb/mnist/) in order to be able to
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recognize hand written digits in an image.
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The code of this application comes from the following blog post:
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https://www.hackster.io/news/simple-neural-network-on-mcus-a7cbd3dc108c
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Build
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-----
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```
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make BOARD=<board of your choice> all term
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```
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Expected output
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---------------
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The digit to recognize is stored in the `digit` binary file of the application
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and is automatically added to the application firmware as a C array via the
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blob mechanism of the build system.
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By default, the digit contains the first test image of the MNIST dataset, a
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hand-written `7`.
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So by default the expected output should be:
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```
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Predicted label: 7
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```
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Use the `generate_digit.py` script provided with this application to update
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the digit file from another test image of the MNIST dataset:
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```
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./generate_digit.py --index 1
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```
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Each selected digit is displayed at the end of the script to allow a "visual"
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comparison with the value predicted by the firmware.
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For each new digit generated, the firmware must be rebuilt: the image is
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statically embedded as a blob in the firmware image.
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Training the model
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------------------
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The application contains a pre-trained model in the `models` external module.
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Except the Makefiles, all C++ files (model + weights) were generated using the
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`utensor-cli` tool from a model trained with TensorFlow.
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Here are the steps required to train a new model and update the C++ files in the
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`models` external module within the application:
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1. Install Python3 dependencies
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```
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pip3 install --user utensor_cgen graphviz
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pip3 install --user tensorflow -U
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```
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Note that utensor_cgen is only compatible with tensorflow 1 for the moment.
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2. Clone the utensor-mnist-demo repository: it contains the Python script used
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to train the MLP model on the MNIST dataset.
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```
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cd /tmp
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git clone https://github.com/uTensor/utensor-mnist-demo
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```
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3. Train the MLP model:
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```
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cd /tmp/utensor-mnist-demo
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python3 /tmp/utensor-mnist-demo/tensorflow-models/deep_mlp.py
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```
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The model is stored in `/tmp/utensor-mnist-demo/mnist_model/deep_mlp.pb`
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using the protocol buffer format.
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4. Generate the C++ model files that will be included later in the RIOT build:
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```
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cd $RIOTBASE/tests/pkg_utensor
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utensor-cli convert /tmp/utensor-mnist-demo/mnist_model/deep_mlp.pb --target utensor --output-nodes=y_pred
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```
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