TensorFlow Lite: A Learning Solution

What is TensorFlow Mobile?

TensorFlow Mobile is used for a mobile platform such as iOS and Android. This is for those developers who have a successful TensorFlow model and want to integrate their model into a mobile environment. This is also for those who are not able to use TensorFlow Lite. Basic challenges one can find in integrating their desktop environment model into the mobile environment are:

• To see how to use TensorFlow mobile.
• Building their model for a mobile platform.
• Adding the TensorFlow libraries into their mobile application.
• Preparing the model file.
• Optimising binary size, file size, RAM usage etc.

Let us see how to install TensorFlow quickly and easily 

Cases for using Mobile Machine Learning

Generally, the developer’s associated with TensorFlow use it on high powered GPU’s. But it is very time consuming and a very expensive way to send all device data to across a network connection, running it on a mobile can be an easy way to do it.

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Commonly used cases for on-device deep learning:

a. Image Recognition

A useful way to detect or get a sense of the image captured with a mobile. If the users are taking photos getting to know what’s in there can be a way to apply appropriate filters or label them so, as to find them whenever necessary. TensorFlow comes with wide range of examples of detecting the types of objects inside of images. It also consists of a variety of pre-trained models which can be used to run on mobile devices.

b. Speech Recognition

There are various applications which can build with a speech-driven interface. Many times a user won’t be giving instructions so streaming it continuously to a server would create a lot of problems. To solve this, it’s good to have a neural network running on a device for a particular word rather than listening to the whole conversation.

Tensorflow Features | Community, Visualizer & Event Logger

c. Gesture Recognition

It is useful to control applications with the help of hands or other gestures, through analysing sensor data. You can do this with the help of TensorFlow.

Other examples are Optical Character Recognition (OCR), Translation, Text classification, Voice recognition etc.

What is TensorFlow Lite?

TensorFlow Lite is the lightweight version which is specifically designed for the mobile platform and embedded devices. It provides machine learning solution to mobile with low latency and small binary size.

TensorFlow supports set of core operators which have been tuned for mobile platforms. It also supports custom operations in models.

TensorFlow Lite tutorial defines a new file format based on FlatBuffers which is an open source platform serialization library. It consists of a new mobile interpreter which is used to keep apps small and faster. It uses custom memory allocator for minimal load and execution latency.

a. TensorFlow Lite Architecture

The above diagram you see is of TensorFlow architecture. The trained TensorFlow model on the disk will convert into TensorFlow Lite file format (.tflite) using the TensorFlow Lite converter. Then we can use that converted file in the mobile application.

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For deploying TensorFlow Lite model file:

• Java API: A wrapper around C++ API on Android.
• C++ API: It loads the TensorFlow Lite model and calls the interpreter.
• Interpreter: It executes the model. It uses selective kernel loading which is a unique feature of TensorFlow Lite.

You can also implement custom kernels using the C++ API.

Some of the highlights of TensorFlow Lite are as follows:

• It supports set of core operators which have been tuned for mobile platforms. TensorFlow also supports custom operations in models.
• A new file format based on FlatBuffers.
• On device interpreter which uses selective loading technique.
• When all supported operators are linked TensorFlow Lite is smaller than 300kb.
• Java and C++ API support.

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See Also-

• TensorFlow Quiz – 1
• TensorFlow Quiz – 2
• TensorFlow Quiz – 3