Get started with microcontrollers

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This document explains how to train a model and run inference using a microcontroller.

The Hello World example

The Hello World example is designed to demonstrate the absolute basics of using TensorFlow Lite for Microcontrollers. We train and run a model that replicates a sine function, i.e, it takes a single number as its input, and outputs the number's sine value. When deployed to the microcontroller, its predictions are used to either blink LEDs or control an animation.

The end-to-end workflow involves the following steps:

  1. Train a model (in Python): A jupyter notebook to train, convert and optimize a model for on-device use.
  2. Run inference (in C++ 11): An end-to-end unit test that runs inference on the model using the C++ library.

Get a supported device

The example application we'll be using has been tested on the following devices:

Learn more about supported platforms in TensorFlow Lite for Microcontrollers.

Train a model

Use Google Colaboratory to train your own model. For more details, refer to the

Hello World Training

Run inference

To run the model on your device, we will walk through the instructions in the

Hello World

The following sections walk through the example's, unit test which demonstrates how to run inference using TensorFlow Lite for Microcontrollers. It loads the model and runs inference several times.

1. Include the library headers

To use the TensorFlow Lite for Microcontrollers library, we must include the following header files:

#include "tensorflow/lite/micro/all_ops_resolver.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"

2. Include the model header

The TensorFlow Lite for Microcontrollers interpreter expects the model to be provided as a C++ array. The model is defined in model.h and files. The header is included with the following line:

#include "tensorflow/lite/micro/examples/hello_world/model.h"

3. Include the unit test framework header

In order to create a unit test, we include the TensorFlow Lite for Microcontrollers unit test framework by including the following line:

#include "tensorflow/lite/micro/testing/micro_test.h"

The test is defined using the following macros:


TF_LITE_MICRO_TEST(LoadModelAndPerformInference) {
  . // add code here


We now discuss the code included in the macro above.

4. Set up logging

To set up logging, a tflite::ErrorReporter pointer is created using a pointer to a tflite::MicroErrorReporter instance:

tflite::MicroErrorReporter micro_error_reporter;
tflite::ErrorReporter* error_reporter = &micro_error_reporter;

This variable will be passed into the interpreter, which allows it to write logs. Since microcontrollers often have a variety of mechanisms for logging, the implementation of tflite::MicroErrorReporter is designed to be customized for your particular device.

5. Load a model

In the following code, the model is instantiated using data from a char array, g_model, which is declared in model.h. We then check the model to ensure its schema version is compatible with the version we are using:

const tflite::Model* model = ::tflite::GetModel(g_model);
if (model->version() != TFLITE_SCHEMA_VERSION) {
      "Model provided is schema version %d not equal "
      "to supported version %d.\n",
      model->version(), TFLITE_SCHEMA_VERSION);

6. Instantiate operations resolver

An AllOpsResolver instance is declared. This will be used by the interpreter to access the operations that are used by the model:

tflite::AllOpsResolver resolver;

The AllOpsResolver loads all of the operations available in TensorFlow Lite for Microcontrollers, which uses a lot of memory. Since a given model will only use a subset of these operations, it's recommended that real world applications load only the operations that are needed.

This is done using a different class, MicroMutableOpResolver. You can see how to use it in the Micro speech example's

7. Allocate memory

We need to preallocate a certain amount of memory for input, output, and intermediate arrays. This is provided as a uint8_t array of size tensor_arena_size:

const int tensor_arena_size = 2 * 1024;
uint8_t tensor_arena[tensor_arena_size];

The size required will depend on the model you are using, and may need to be determined by experimentation.

8. Instantiate interpreter

We create a tflite::MicroInterpreter instance, passing in the variables created earlier:

tflite::MicroInterpreter interpreter(model, resolver, tensor_arena,
                                     tensor_arena_size, error_reporter);

9. Allocate tensors

We tell the interpreter to allocate memory from the tensor_arena for the model's tensors:


10. Validate input shape

The MicroInterpreter instance can provide us with a pointer to the model's input tensor by calling .input(0), where 0 represents the first (and only) input tensor:

  // Obtain a pointer to the model's input tensor
  TfLiteTensor* input = interpreter.input(0);

We then inspect this tensor to confirm that its shape and type are what we are expecting:

// Make sure the input has the properties we expect
TF_LITE_MICRO_EXPECT_NE(nullptr, input);
// The property "dims" tells us the tensor's shape. It has one element for
// each dimension. Our input is a 2D tensor containing 1 element, so "dims"
// should have size 2.
TF_LITE_MICRO_EXPECT_EQ(2, input->dims->size);
// The value of each element gives the length of the corresponding tensor.
// We should expect two single element tensors (one is contained within the
// other).
TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[0]);
TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[1]);
// The input is a 32 bit floating point value
TF_LITE_MICRO_EXPECT_EQ(kTfLiteFloat32, input->type);

The enum value kTfLiteFloat32 is a reference to one of the TensorFlow Lite data types, and is defined in common.h.

11. Provide an input value

To provide an input to the model, we set the contents of the input tensor, as follows:

input->data.f[0] = 0.;

In this case, we input a floating point value representing 0.

12. Run the model

To run the model, we can call Invoke() on our tflite::MicroInterpreter instance:

TfLiteStatus invoke_status = interpreter.Invoke();
if (invoke_status != kTfLiteOk) {
  TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed\n");

We can check the return value, a TfLiteStatus, to determine if the run was successful. The possible values of TfLiteStatus, defined in common.h, are kTfLiteOk and kTfLiteError.

The following code asserts that the value is kTfLiteOk, meaning inference was successfully run.

TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, invoke_status);

13. Obtain the output

The model's output tensor can be obtained by calling output(0) on the tflite::MicroInterpreter, where 0 represents the first (and only) output tensor.

In the example, the model's output is a single floating point value contained within a 2D tensor:

TfLiteTensor* output = interpreter.output(0);
TF_LITE_MICRO_EXPECT_EQ(2, output->dims->size);
TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[0]);
TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[1]);
TF_LITE_MICRO_EXPECT_EQ(kTfLiteFloat32, output->type);

We can read the value directly from the output tensor and assert that it is what we expect:

// Obtain the output value from the tensor
float value = output->data.f[0];
// Check that the output value is within 0.05 of the expected value
TF_LITE_MICRO_EXPECT_NEAR(0., value, 0.05);

14. Run inference again

The remainder of the code runs inference several more times. In each instance, we assign a value to the input tensor, invoke the interpreter, and read the result from the output tensor:

input->data.f[0] = 1.;
value = output->data.f[0];
TF_LITE_MICRO_EXPECT_NEAR(0.841, value, 0.05);

input->data.f[0] = 3.;
value = output->data.f[0];
TF_LITE_MICRO_EXPECT_NEAR(0.141, value, 0.05);

input->data.f[0] = 5.;
value = output->data.f[0];
TF_LITE_MICRO_EXPECT_NEAR(-0.959, value, 0.05);

15. Read the application code

Once you have walked through this unit test, you should be able to understand the example's application code, located in It follows a similar process, but generates an input value based on how many inferences have been run, and calls a device-specific function that displays the model's output to the user.