TensorFlow Lite for Microcontrollers docs update
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Daniel Situnayake
TensorFlower Gardener
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@ -32,11 +32,17 @@ Microcontrollers.
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### Build the code
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To compile and test this example on a desktop Linux or MacOS machine, download
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[the TensorFlow source code](https://github.com/tensorflow/tensorflow), `cd`
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into the source directory from a terminal, and then run the following command:
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To compile and test this example on a desktop Linux or macOS machine, first
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clone the TensorFlow repository from GitHub to a convenient place:
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```bash
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git clone --depth 1 https://github.com/tensorflow/tensorflow.git
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```
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Next, `cd` into the source directory from a terminal, and then run the following
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command:
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```bash
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make -f tensorflow/lite/experimental/micro/tools/make/Makefile test_hello_world_test
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```
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@ -9,6 +9,11 @@ This document explains the process of converting a TensorFlow model to run on
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microcontrollers. It also outlines the supported operations and gives some
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guidance on designing and training a model to fit in limited memory.
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For an end-to-end, runnable example of building and converting a model, see the
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following Jupyter notebook:
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<a class="button button-primary" href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro/examples/hello_world/create_sine_model.ipynb">create_sine_model.ipynb</a>
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## Model conversion
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To convert a trained TensorFlow model to run on microcontrollers, you should use
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@ -3,12 +3,54 @@
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This document will help you start working with TensorFlow Lite for
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Microcontrollers.
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## Sample code
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Start by reading through and running our [Examples](#examples).
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To get started, you can explore the following example:
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Note: If you need a device to get started, we recommend the
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[SparkFun Edge Powered by TensorFlow](https://www.sparkfun.com/products/15170).
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It was designed in conjunction with the TensorFlow Lite team to offer a flexible
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platform for experimenting with deep learning on microcontrollers.
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<a class="button button-primary" href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro/examples/micro_speech">Micro
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speech example</a>
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For a walkthrough of the code required to run inference, see the *Run inference*
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section below.
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## Examples
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There are several examples that demonstrate how to build embedded machine
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learning applications with TensorFlow Lite:
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### Hello World example
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This example is designed to demonstrate the absolute basics of using TensorFlow
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Lite for Microcontrollers. It includes the full end-to-end workflow of training
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a model, converting it for use with TensorFlow Lite, and running inference on a
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microcontroller.
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In the example, a model is trained to replicate a sine function. When deployed
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to a microcontroller, its predictions are used to either blink LEDs or control
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an animation.
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<a class="button button-primary" href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro/examples/hello_world">Hello
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World example</a>
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The example code includes a Jupyter notebook that demonstrates how the model is
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trained and converted:
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<a class="button button-primary" href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro/examples/hello_world/create_sine_model.ipynb">create_sine_model.ipynb</a>
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The process of building and converting a model is also covered in the guide
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[Build and convert models](build_convert.md).
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To see how inference is performed, take a look at
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[hello_world_test.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/experimental/micro/examples/hello_world/hello_world_test.cc).
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The example is tested on the following platforms:
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- [SparkFun Edge Powered by TensorFlow (Apollo3 Blue)](https://www.sparkfun.com/products/15170)
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- [Arduino MKRZERO](https://store.arduino.cc/usa/arduino-mkrzero)
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- [STM32F746G Discovery Board](https://www.st.com/en/evaluation-tools/32f746gdiscovery.html)
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- Mac OS X
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### Micro Speech example
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This example uses a simple
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[audio recognition model](https://www.tensorflow.org/tutorials/sequences/audio_recognition)
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@ -16,48 +58,43 @@ to identify keywords in speech. The sample code captures audio from a device's
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microphones. The model classifies this audio in real time, determining whether
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the word "yes" or "no" has been spoken.
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The sample works end-to-end (including audio capture and inference) on the
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following platforms:
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<a class="button button-primary" href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro/examples/micro_speech">Micro
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Speech example</a>
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The [Run inference](#run_inference) section walks through the code of the Micro
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Speech sample and explains how it works.
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The example is tested on the following platforms:
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- [SparkFun Edge Powered by TensorFlow (Apollo3 Blue)](https://www.sparkfun.com/products/15170)
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- [STM32F746G Discovery Board](https://www.st.com/en/evaluation-tools/32f746gdiscovery.html)
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- Mac OS X
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### SparkFun Edge
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If you need a device to get started, we recommend the
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[SparkFun Edge Powered by TensorFlow](https://www.sparkfun.com/products/15170).
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It was designed in conjunction with the TensorFlow Lite team to offer a flexible
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platform for experimenting with deep learning on microcontrollers.
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To get started using the Edge board, we recommend following
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Note: To get started using the SparkFun Edge board, we recommend following
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[Machine learning on a microcontroller with SparkFun TensorFlow](https://codelabs.developers.google.com/codelabs/sparkfun-tensorflow),
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a codelab that introduces you to the development workflow.
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a codelab that introduces you to the development workflow using the Micro Speech
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example.
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## Workflow
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### Micro Vision example
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Using TensorFlow Lite for Microcontrollers involves four major steps:
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This example shows how you can use TensorFlow Lite to run a 250 kilobyte neural
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network to recognize people in images captured by a camera. It is designed to
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run on systems with small amounts of memory such as microcontrollers and DSPs.
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1. Create or find a model architecture.
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2. Train a model.
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3. Convert the model.
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4. Write code to run inference.
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<a class="button button-primary" href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro/examples/micro_vision">Micro
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Vision example</a>
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The first three steps are covered in the guide
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[Build and convert models](build_convert.md). The sample code comes with a
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pretrained model, and includes scripts to train a model that recognizes
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different spoken words. Instructions on training are in
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[README.md](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/experimental/micro/examples/micro_speech/README.md#creating-your-own-model).
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The example is tested on the following platforms:
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In this document, we will focus on the code that will feed processed audio data
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into the model and execute it, resulting in a prediction of which word was
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spoken. This process is called *inference*.
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- [SparkFun Edge Powered by TensorFlow (Apollo3 Blue)](https://www.sparkfun.com/products/15170)
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- [STM32F746G Discovery Board](https://www.st.com/en/evaluation-tools/32f746gdiscovery.html)
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- Mac OS X
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## Run inference
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The sample's
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The following section walks through the [Micro Speech](#micro_speech) sample's
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[main.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/experimental/micro/examples/micro_speech/main.cc)
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contains the code that runs inference. We'll now walk through the key parts.
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and explains how it used TensorFlow Lite for Microcontrollers to run inference.
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### Includes
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@ -277,48 +314,9 @@ recognition results across a number of frames. This is defined in
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The same technique can be used to improve reliability when processing any
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continuous stream of data.
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## Build the sample
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The sample contains build scripts that will download all required dependencies
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and compile a binary that can be run on a device.
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Note: The build process has been tested on MacOS and Linux, but not on Windows.
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To build the sample, take the following steps:
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1. Clone the TensorFlow repository from GitHub to a convenient place.
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```bash
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git clone --depth 1 https://github.com/tensorflow/tensorflow.git
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```
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1. Enter the directory that was created in the previous step.
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```bash
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cd tensorflow
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```
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1. If you are using MacOS, run the following command. If you are using Linux,
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you do not need to do this.
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```bash
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PATH=tensorflow/lite/experimental/micro/tools/make/downloads/gcc_embedded/bin/:$PATH
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```
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1. To download all of the required dependencies and initiate the build process,
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issue the following command. You can set `TARGET` depending on which
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platform you want to build for. Explore
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[`targets/`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro/tools/make/targets)
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for the current options.
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```bash
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make -f tensorflow/lite/experimental/micro/tools/make/Makefile
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TARGET=sparkfun_edge micro_speech_bin
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```
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## Next steps
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Once you have built and run the sample, read the following documents:
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Once you have built and run the samples, read the following documents:
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* Learn how to work with models in
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[Build and convert models](build_convert.md).
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