How to Run TensorFlow Models on Android

How to Run TensorFlow Models on Android

In this blog post, we’ll show you how to run your TensorFlow models on Android. We’ll cover how to convert your models to the TensorFlow Lite format, how to compile and run them on your Android device, and how to debug your models if something goes wrong.

Click to see video:


In this tutorial, we’ll show you how to use TensorFlow Lite to run computer vision models on an Android device. We’ll use a instance segmentation model that can identify and locate multiple objects in an image.

##Heading: Prerequisites
To follow this tutorial, you’ll need:
– A physical Android device (or access to an emulator) running Android 5.0+ with Developer options enabled
– TensorFlow Lite switched on in Developer options (this will be covered in the next section)
– Android Studio 3.0+
– The latest version of the TensorFlow Lite Support Library

##Heading: Installing the Support Library

To install the TensorFlow Lite Support Library, open the build.gradle file for your app module and add the following lines under dependencies:

Why TensorFlow?

TensorFlow is an open source library for numerical computation that enables machine learning. It was originally developed by researchers and engineers working on the Google Brain team within Google’s Machine Intelligence research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains as well.

TensorFlow offers APIs for beginners and experts to develop for desktop, mobile, web, and cloud. See the sections below to get started.

What is TensorFlow?

TensorFlow is an open-source platform for machine learning. It was developed by Google Brain and released under the Apache 2.0 open-source license in 2015. TensorFlow is used by organizations such as Airbus, IBM, and Coca-Cola to build and train machine-learning models. TensorFlow can be used for a variety of tasks, including image classification, natural language processing, and predictive analytics.

TensorFlow offers APIs for Java, C++, and Python. In addition to the TensorFlow library, there is also a TensorFlow Lite library that can be used for running TensorFlow models on mobile devices.

There are two ways to run TensorFlow models on Android: using the TensorFlow Lite library or using the TensorFlow Mobile library.

TensorFlow Lite is the recommended way to run TensorFlow models on mobile devices. The TensorFlow Lite library has been designed specifically for running machine-learning models on mobile devices. It is smaller and faster than the full TensorFlow Mobile library, and it doesn’t require a specific device architecture (e.g., an ARM processor).

To use the TensorFlow Lite library, you need to convert your model into a format that can be run on mobile devices (e.g., using the tflite_convert command-line tool). You can then load the converted model into your app and use it to make predictions.

TensorFlow Mobile is the full TensorFlow platform, including all of the APIs and libraries that are available in the desktop version of TensorFlow. It also includes support for running models on mobile devices that have specific device architectures (e.g., an ARM processor).

To use the TensorFlow Mobile library, you need to compile your model using a cross-compiler (e.g., bazel). You can then load the compiled model into your app and use it to make predictions.

TensorFlow on Android

TensorFlow is an open-source software library for Machine Intelligence. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it is widely used for developing and training machine learning models, with a particular focus on deep neural networks.

TensorFlow can be used to run machine learning models on a variety of platforms, including Android. In this tutorial, we’ll show you how to set up TensorFlow on your Android device and run a simple example model.


Before you begin, you’ll need the following:

– A device running Android 4.1 (Jelly Bean) or higher
– The TensorFlow Android SDK
– A computer running Windows, macOS, or Linux

Once you have everything set up, you’re ready to get started!

Getting Started

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

This guide demonstrates how to get started with running TensorFlow models on an Android device. We will use the example of classifying images of flowers.

In order to run this guide, you will need:
-A compatible Android device that runs Android 5.0 (Lollipop) or higher and has at least 1 GB of RAM.
-Android Studio version 3.1 or higher.
-Java Development Kit (JDK) 8

Building Your Model

If you’re just getting started with TensorFlow, we recommend checking out the official documentation for [getting started with TensorFlow](

Once you have a basic understanding of how TensorFlow works, you can begin building your own models. For this tutorial, we’ll be using the [inception model](, which is a pre-trained image classification model that can be used to recognize objects in images.

To use the inception model with TensorFlow on Android, you’ll first need to convert it to the [TensorFlow Lite]( format. You can do this using the [TensorFlow Lite Converter]( tool.

Once you have converted the model to the TensorFlow Lite format, you can then run it on your Android device using the [TensorFlow Lite Interpreter](

Converting Your Model

TensorFlow for Mobile Poets
This guide is for people who have TensorFlow models that they want to run on mobile devices. If you already have a model built, and you just want to run it on mobile devices, this is the guide for you!

If you are looking to build and train a mobile-friendly model from scratch, check out our other guide: retraining a pre-trained TensorFlow model.

Either way, once you have a model that you want to run on a mobile device, the first step is to convert it into the desired format. TensorFlow provides two different APIs that you can use to convert your models: the TF Lite Converter and the TF Optimizer. Both of these tools are located in the tensorflow/python/tools folder.

The TF Lite Converter is the recommended tool for most users. It has a simple graphical interface that allows you view your model graphically and select which ops should be converted to TensorFlow Lite ops. It also performs some important optimizations that will reduce the size of your model and improve its performance on mobile devices.

The TF Optimizer is a more advanced tool that can be used to convert TensorFlow models into a variety of different formats. It can also be used to optimize your models for better performance on mobile devices. However, it does not have a graphical interface, so it may be more difficult to use if you are not familiar with the TensorFlow graph format.

Using the TensorFlow Lite Interpreter

The TensorFlow Lite Interpreter is a library that allows you to run TensorFlow Lite models on your device. The Interpreter is designed to be lean and fast, so it can be used in situations where CPU resources are limited, such as on mobile devices or embedded systems.

To use the Interpreter, you need to first convert your TensorFlow model into the TensorFlow Lite format. This can be done using the TensorFlow Lite Converter, which is a tool that converts TensorFlow models into the TensorFlow Lite format.

Once your model is in the TensorFlow Lite format, you can use the Interpreter to run it on your device. The Interpretertakes input in the form of Tensors, and outputs results as Tensors as well.

To use the Interpreter in your own code, you need to first create an instance of the Interpreter class. To do this, you need to specify the location of your model file (in either the Assets folder or external storage), as well as the type and shape of the input and output Tensors:

Interpreter tflite = new Interpreter(tfliteModel);

Once you have an instance of the Interpreter class, you can use it to run inference on your input data. To do this, you need to first provide values for each of the input Tensors:

float[][][][] input = new float[1][224][224][3]; // values for each image pixel (red, green, blue)

// fill input array with image data here; // performing inference

float[][] output = new float[1][1000]; // output will contain probabilities for each class

tflite.readOutputs(output); // reading results into output array


Benchmarking Your Model

The first step in running your TensorFlow model on Android is to benchmark it. This will help you understand how your model performs and what improvements you can make to optimize it for your specific application.

Android devices come in a variety of shapes and sizes, so it’s important to test your model on as many different devices as possible. You can use the Android Profiler tool to collect performance data on your devices.

Once you have collected performance data, you can use the TensorFlow Model Optimizer tool to optimize your model for inference. The Model Optimizer will help you identify which parts of your model are causing slowdowns and how you can improve them.

Wrap Up

Now that you’ve completed this tutorial, you know how to run TensorFlow models on Android. Congratulations!

Keyword: How to Run TensorFlow Models on Android

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