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TensorFlow Lite on Andriod for beginners



TensorFlow Lite

Android development isn’t limited to cute little apps that split the bill in restaurants (that seems to be everyone’s ‘genius app idea,’ or is it just me?). Android is a powerful platform backed by one of the largest and most influential companies in the world. A company that is at the forefront of machine learning and sees itself as ‘AI-first’.

Learning TensorFlow Lite for Android allows developers to implement advanced machine learning in their creations. This vastly expands the capabilities of an app and introduces numerous new potential use cases. It also teaches invaluable skills that will only grow in demand in the years to come.

Also see: Is your job safe? Jobs that AI will destroy in the next 10-20 years

This is the perfect introduction to machine learning, so let’s get started!


What is TensorFlow?

Let’s start with the basics: what is TensorFlow Lite? To answer that, we must first look at TensorFlow itself. TensorFlow is an “end-to-end” (meaning all-in-one), open source machine learning platform from the Google Brain Team. TensorFlow is an open-source software library that enables machine learning tasks.

A machine learning task is any problem that requires pattern recognition, powered by algorithms and large amounts of data. This is AI, but not Hal van 2001: A Space Odyssey sense.

Also see: Artificial Intelligence vs Machine Learning: What’s the Difference?

Use cases

An example of a machine learning application is computer vision. It allows computers to recognize objects in a photo or a live camera feed. To do this, the program must first be “trained” by getting thousands of images of that object. The program never understands the object, but learns to search for certain data patterns (changes in contrast, certain angles or curves) that are likely to match the object. Over time, the program becomes more and more accurate at recognizing that object.

 machine learning

As an Android developer, computer vision creates many possibilities: whether you want to use facial recognition as a security feature, create an AR program that can highlight elements in the environment, or build the next “Reface” app. This is before we look at the myriad of other uses of machine learning models: speech recognition, OCR, enemy AI and much more.

Creating and implementing these types of models from scratch would be an extremely arduous task for a single developer, which is why it is so convenient to have access to ready-made libraries.

Also see: What is Google Cloud?

TensorFlow can run on a wide variety of CPUs and GPUs, but works particularly well with Google’s own Tensor Processing Units (TPUs). Developers can also leverage the power of the Google Cloud Platform by outsourcing machine learning operations to Google’s servers.

What is TensorFlow Lite?

TensorFlow Lite brings on-board (meaning it runs on the mobile device itself) Tensor Flow to mobile devices. The TFLite software stack, announced in 2017, is specifically designed for mobile development. TensorFlow Lite “Micro”, on the other hand, is a version specific to Microcontrollers, recently merged with ARM’s uTensor.

Some developers may now be wondering what the difference is between ML Kit and TensorFlow Lite. While there is certainly some overlap, TensorFlow Lite is more low and open. More importantly, TensorFlow Lite works off the device, while ML Kit requires Firebase registration and an active internet connection. Keep in mind that, despite Google’s confusing naming convention, ML Kit still uses TensorFlow ‘under the hood’. Firebase is also just another type of Google Cloud Platform project.

Also see: Build a face detection app with machine learning and Firebase ML Kit

TensorFlow Lite is available on Android and iOS via a C ++ API and a Java wrapper for Android developers. On devices that support it, the library can also take advantage of the Android Neural Networks API for hardware acceleration.

Which one should you use for your projects? That depends a lot on your goal. If you don’t mind relying on a third-party cloud service, ML Kit can make your life a little bit easier. If you want the code to run natively, or if you need a little more customization and flexibility, go for TensorFlow Lite.


How to use TensorFlow Lite

Developers rely on ‘models’ to solve a problem with machine learning. ML models are files that contain statistically models. These files are trained to recognize specific patterns. Training essentially means feeding the model with data samples so it can improve its success rate by refining the patterns it uses.

Also see: ML Kit Image Labeling: Determine the content of an image with machine learning

So a computer vision model can start with a few basic assumptions about what an object looks like. As you show it more and more images, it will become more and more precise while broadening the scope of what it seeks.

Train FFLite models

You will encounter “pre-trained models” who have been given all this data to refine their algorithms. This type of model is therefore “ready to go”. It can automatically perform a task such as identifying emotions based on facial expressions or moving a robotic arm through space.

In TensorFlow Lite, these files are called “TensorFlow Lite Model Files” and have the extension “.tflite” or “.lite”. Label files contain the labels the file was trained for (eg, “Happy” or “Sad” for facial recognition models).

Train ML models

You may also encounter other types of files used in the training process. GraphDef files (.pb or .pbtxt) describe your graph and can be read by other processes. The TXT version is also designed to be human readable. You can also build these with TensorFlow.

The Checkpoint file shows you the learning process by listing serial variables so you can see how the values ​​change over time. The Frozen Graph Def then converts these values ​​to constants and reads them from set checkpoints through the graph. The TFlite model is then built from the frozen graph using the TOCO (Tensor Flow Optimizing Converter Tool). This provides us with a nice “pre-trained” file that we can then implement in our apps.

These libraries can handle a variety of common tasks, such as responding to questions, recognizing faces, and more.

Discussing how to train and import models is beyond the scope of this post, although you can find a great tutorial here.

The good news is that the TensorFlow job library contains many powerful and simple libraries that rely on pre-trained models. These can handle many common tasks such as responding to questions, recognizing faces, and more. This means those starting out don’t have to worry about Checkpoint Files or training!

Using TFLite files

There are plenty of ways you can get your hands on pre-trained TensorFlow Lite Model Files for your app. I recommend starting with the official TensorFlow site.

For example, if you follow this link, you can download a starter model capable of basic image classification. The page also includes some details on how to use it through the TensorFlow Lite job library. You can also use the TensorFlow Lite support library if you want to add your own inference pipeline (i.e. looking for new stuff).

After downloading the file, put it in your assets folder. You must indicate that the file should not be compressed. To do this, add the following to your build.gradle module:

android {
    // Other settings

    // Specify tflite file should not be compressed for the app apk
    aaptOptions {
        noCompress "tflite"
    }

}

Set up your Android Studio project

To use TensorFlow Lite in your app, you need to add the following dependency to your build.gradle file:

compile ‘org.tensorflow:tensorflow-lite:+’

Next, you need to import your interpreter. This is the code that actually loads the model and lets you run it.

In your Java file, you then create an instance of the interpreter and use it to analyze the data you need. For example, you can enter images and this will produce results.

Results are provided in the form of output opportunities. Models can never say with certainty what an object is. So a picture of a cat can be 0.75 dog and 0.25 cat. Your code must

You can also import the TensorFlow support library and convert the image to the tensor format.

These pre-trained models are able to recognize thousands of image classes. However, many different model architectures exist that change the way the model defines the “layers” involved in the learning cycle, as well as the steps taken to convert raw data into training data.

Popular model architectures include MobileNet and Inception. Your job is to choose the optimal solution for the job. MobileNet, for example, is designed to favor light and fast models over deep and complex ones. Complex models have higher accuracy, but this comes at the expense of size and speed.


Learn more

While this is a complex topic for beginners, I hope this post has given you an idea of ​​the basics so you can better understand future tutorials. The best way to learn a new skill is to choose a project and then learn the necessary steps to complete that task.

Introduction to TensorFlow Lite Android

For a more in-depth understanding, we highly recommend Machine Learning With TensorFlow. This course contains 19 lessons that show you how to implement commonly used commercial solutions. Android authority readers now get a 91% discount, lowering the price from $ 124 to $ 10.


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