119 lines
2.7 KiB
ReStructuredText
119 lines
2.7 KiB
ReStructuredText
.. _tensorflow_magic_wand:
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TensorFlow Lite Micro Magic Wand sample
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#######################################
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Overview
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********
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This sample application shows how to use TensorFlow Lite Micro
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to run a 20 kilobyte neural network model that recognizes gestures
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from an accelerometer.
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.. Note::
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This README and sample have been modified from
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`the TensorFlow Magic Wand sample for Zephyr`_ and
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`the Antmicro tutorial on Renode emulation for TensorFlow`_.
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.. _the TensorFlow Magic Wand sample for Zephyr:
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https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/examples/magic_wand
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.. _the Antmicro tutorial on Renode emulation for TensorFlow:
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https://github.com/antmicro/litex-vexriscv-tensorflow-lite-demo
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Building and Running
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********************
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The application can be built for the :ref:`litex-vexriscv` for
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emulation in Renode as follows:
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.. zephyr-app-commands::
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:zephyr-app: samples/tensorflow/magic_wand
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:host-os: unix
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:board: litex_vexriscv
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:goals: build
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:compact:
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Once the application is built, `download and install Renode 1.12 or higher as a package`_
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following the instructions in the `Renode GitHub README`_ and
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start the emulator:
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.. code-block:: console
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renode -e "set zephyr_elf @./build/zephyr/zephyr.elf; s @./renode/litex-vexriscv-tflite.resc"
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.. _download and install Renode 1.12 or higher as a package:
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https://github.com/renode/renode/releases/
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.. _Renode GitHub README:
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https://github.com/renode/renode/blob/master/README.rst
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Sample Output
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=============
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The Renode-emulated LiteX/VexRiscv board is fed data that the
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application recognizes as a series of alternating ring and slope
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gestures.
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.. code-block:: console
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Got accelerometer, label: accel-0
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RING:
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*
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* *
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* *
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* *
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* *
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* *
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*
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SLOPE:
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*
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*
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*
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*
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*
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*
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*
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* * * * * * * *
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RING:
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*
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* *
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* *
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* *
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* *
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* *
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*
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SLOPE:
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*
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*
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*
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*
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*
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*
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*
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* * * * * * * *
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Modifying Sample for Your Own Project
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*************************************
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It is recommended that you copy and modify one of the two TensorFlow
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samples when creating your own TensorFlow project. To build with
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TensorFlow, you must enable the below Kconfig options in your :file:`prj.conf`.
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:file:`prj.conf`:
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.. code-block:: console
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CONFIG_CPLUSPLUS=y
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CONFIG_NEWLIB_LIBC=y
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CONFIG_TENSORFLOW_LITE_MICRO=y
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Training
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********
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Follow the instructions in the :file:`train/` directory to train your
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own model for use in the sample.
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