Android app development isn’t exclusive to sweet little restaurant bill splitters (doesn’t it seem to be everyone’s “genius app concept,” or am I the only one?). Android is a powerful medium that is backed by one of the world’s largest and most powerful corporations. An organization that calls itself “AI-first” and is at the frontier of machine learning.
Developers can use TensorFlow Lite for Android to incorporate sophisticated machine learning into their apps. This massively expands an app’s capabilities and opens up a slew of fresh possibilities. It also teaches priceless abilities that can only become more in demand in the coming years.
Let’s get started with this excellent guide to machine learning.
What Is Tensorflow, and How Does It Work?
Let’s start at the beginning: what exactly is TensorFlow Lite? To address that, let’s start with TensorFlow itself. The Google Brain Team’s TensorFlow is an open-source machine learning tool that is “end-to-end” (i.e., all-in-one). TensorFlow is a machine learning software library that is open-source.
Any challenge that necessitates pattern recognition using algorithms and vast volumes of data is referred to as a machine learning task. This is AI, but not in the context of Hal from 2001: A Space Odyssey: There’s a hint of a Space Odyssey here.
Examples of Application
Computer vision is an example of a machine learning application. Computers may identify features in a snapshot or a live camera broadcast using this technology. To do this, the software must first be “trained” by viewing thousands of images of the entity. The software never fully comprehends the entity, but it does learn to search for certain data trends (such as variations in comparison, angles, or curves) that are likely to fit the object. The software improves its accuracy at detecting the item over time.
As an Android developer, machine vision opens up a world of possibilities, if you choose to use facial recognition as a protection tool, make an augmented reality system that highlights objects in the room, or create the new “Reface” app. This is until we mention the plethora of other applications for machine learning models, such as speech recognition, optical character recognition, enemy AI, and so on.
For a sole creator, creating and integrating these styles of models from scratch will be a monumental challenge, which is why ready-made libraries are so valuable.
TensorFlow can operate on a variety of CPUs and GPUs, but it works very well with Google’s Tensor Processing Units (TPUs). Through outsourcing machine learning operations to Google’s servers, developers will take advantage of the Google Cloud Platform’s strength.
What Exactly Is Tensorflow Lite?
TensorFlow Lite takes Tensor Flow to mobile devices as an on-board (that is, it operates on the mobile device itself) application. The TFLite software stack, which was announced in 2017, is developed specifically for mobile production. TensorFlow Lite “Micro,” on the other side, is a microcontroller-specific edition that recently merged with ARM’s uTensor.
Any developers may be wondering what the difference is between TensorFlow Lite and ML Kit. Although there is some overlap, TensorFlow Lite is more open and low-level. More specifically, TensorFlow Lite is device-agnostic, while ML Kit necessitates a Firebase account and an active internet link. ML Kit also uses TensorFlow “under the hood,” hence Google’s perplexing nomenclature. Similarly, Firebase is a kind of Google Cloud Platform project.
TensorFlow Lite is available for Android and iOS developers through a C++ API and a Java wrapper. The library will also use the Android Neural Networks API for hardware acceleration on devices that allow it.
Which method should you employ for your projects? That is highly dependent on your goal. If you don’t mind using a third-party cloud provider, ML Kit might make your life simpler. TensorFlow Lite is the way to go if you want the code to run natively or if you need a bit more customization and compatibility.
Tensorflow Lite: How To Use It
When using machine learning to solve a query, developers use “models.” Statistical models are stored in ML models archives. This files have been programmed to detect those trends. Training entails providing data samples to the model in order for it to increase its performance rate by improving the behaviors it employs.
As a result, a computer vision model can begin with a few simple assumptions about how an object appears. It can get more specific when you display more photos, but still broadening the spectrum of what it is searching for.
You’ll come across “pre-trained models” who have been fed all of this data to optimize their algorithms. As a result, this model is “good to go.” It will execute tasks such as recognizing feelings based on facial expressions or dragging a robot arm across space automatically.
These files are named “TensorFlow Lite Model Files” in TensorFlow Lite and have the extension “.tflite” or “.lite” in TensorFlow Lite. The labels that the file is conditioned with (for example, “happy” or “sad” for facial recognition models) are stored in the labels files.
Training Machine Learning (ML) Models
Other kinds of files that are included in the training phase can often be encountered. GraphDef Files (.pb or.pbtxt) provide information about the graph that can be accessed by other programs. The TXT format is also intended to be accessible by humans. TensorFlow may also be used to build these.
The Checkpoint File illustrates the learning method by listing serialized variables and displaying how their values shift with time. The Frozen Graph Def then transforms these values to constants and reads them through the graph from fixed checkpoints. The TOCO is then used to construct the TFlite model from the frozen graph (Tensor Flow Optimizing Converter Tool). This provides us with a good “pre-trained” file that we can use in our applications.
The good news is that the TensorFlow Task Library has a number of efficient and straightforward libraries that use pre-trained models. These are capable of performing a wide range of activities, including answering questions, remembering names, and more. As a result, newcomers won’t have to think about Checkpoint Files or preparation!
Using TFLite Files
You will get pre-trained TensorFlow Lite Model Files for your app in a variety of ways. Starting with the official TensorFlow site is a good place to go.
For example, you can download a starter model capable of simple image classification by clicking this link. The article also contains instructions for using it with the TensorFlow Lite Task Library. If you choose to implement your own inference pipeline, you might use the TensorFlow Lite Support Library instead (i.e. look for new stuff).
You’ll save the file to your assets directory after you’ve downloaded it. You must tell the program not to compress the image. To accomplish this, include the following code in your module build.gradle.
Getting Your Android Studio Project up and Running
You must apply the following dependency to your “build.gradle” file in order to use TensorFlow Lite in your app:
After that, you would import your translator. This is the code that loads the model and allows you to run it.
You’ll then build an instance of the Interpreter in your Java file and use it to evaluate the data you need. You should, for example, enter photos and receive results.
The outcomes would be presented as performance probabilities. Models would never be able to say for sure what an entity is. A image of a pet, for example, may be 0.75 dog and 0.25 cat.
Import the TensorFlow Support Library and transform the picture to tensor format as an alternative.
These pre-trained models may recognize thousands of different picture groups. However, there are a variety of model “architectures” that change how the model determines the “layers” involved in the learning cycle, as well as the steps involved in converting raw data into training data.
MobileNet and Inception are two well-known model architectures. Your task is to find the best option for the work. MobileNet, for example, is built to support light and quick models over deep and complicated ones. Complex models are more accurate, but they come at the expense of size and speed.
Increasing Your Knowledge
Although this is a difficult subject for learners, I hope that this article has provided you with a good understanding of the fundamentals so that you can properly appreciate future tutorials. Choosing a challenge and then learning the requisite steps to achieve that assignment is the perfect way to develop some new ability.