In this notebook, we will review the basics of TF Lite. The primary goal of this notebook is to describe two things:
1. How can you convert a TensorFlow model to TF Lite
2. How can you extract model architecture and weights from a TF Lite model
TensorFlow Lite is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and small binary size. It consists of two primary components.
Let’s look at the overall workflow of using TensorFlow Lite.
The workflow for using TensorFlow Lite involves the following steps:
Bring your own TensorFlow model, find a model online, or use a pre-trained model to drop in or retrain.
If you’re using a custom model, use the TensorFlow Lite converter and a few lines of Python to convert it to the TensorFlow Lite format.
Run your model on-device with the TensorFlow Lite interpreter, with APIs in many languages.
Use Google’s Model Optimization Toolkit to reduce your model’s size and increase its efficiency with minimal impact on accuracy.
Let’s answer the second bullet point with the first primary component. To convert a TensorFlow model to TFLITE, we use the TensorFlow Lite Converter. This will introduce optimizations to improve binary size and performance. The TF Lite Converter will create a TensorFlow Lite model (an optimized FlatBuffer format identified by the .tflite file extension).
It is recommended to use Google’s Python API to interact with the converter. Suppose you have trained a model in TensorFlow. You can save it as a _SavedModel _or a single HDF5 file through
## You should save it as a SavedModel by specifying save_format=‘tf’ tf.keras.Model.save(filepath, save_format=None)
Now that you have a SavedModel, you can convert it as follows:
import tensorflow as tf ## Convert the model converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) ## path to the SavedModel directory tflite_model = converter.convert() ## Save the model. with open(‘model.tflite’, ‘wb’) as f: f.write(tflite_model)
You can find more details about from_saved_model() here.
#deep-learning #tensorflow #developer #python
I have not created the Object Detection model, I have just merely cloned Google’s Tensor Flow Lite model and followed their Raspberry Pi Tutorial which they talked about in the Readme! You don’t need to use this article if you understand everything from the Readme. I merely talk about what I did!
I have used a Raspberry Pi 3 Model B and PI Camera Board (3D printed a case for camera board). **I had this connected before starting and did not include this in the 90 minutes **(plenty of YouTube videos showing how to do this depending on what Pi model you have. I used a video like this a while ago!)
I have used my Apple Macbook which is Linux at heart and so is the Raspberry Pi. By using Apple you don’t need to install any applications to interact with the Raspberry Pi, but on Windows you do (I will explain where to go in the article if you use windows)
#raspberry-pi #object-detection #raspberry-pi-camera #tensorflow-lite #tensorflow #tensorflow lite object detection using raspberry pi and pi camera
Tensorflow Lite is one of my favourite software packages. It enables easy and fast deployment on a range of hardware and now comes with a wide range of delegates to accelerate inference — GPU, Core ML and Hexagon, to name a few. One drawback of Tensorflow Lite however is that it’s been designed with mobile applications in mind, and therefore isn’t optimised for Intel & AMD x86 processors. Better x86 support is on the Tensorflow Lite development roadmap, but for now Tensorflow Lite mostly relies on converting ARM Neon instructions to SSE via the Neon_2_SSE bridge.
There is however a new Tensorflow Lite delegate for CPU-based floating-point computations, XNNPACK, that does feature x86 AVX and AVX-512 optimizations. In this post I’ll walk you through using XNNPACK and show some benchmarks.
#tensorflow-lite #machine-learning #tensorflow #xnnpack
Deploying machine learning-based Android apps is gaining prominence and momentum with frameworks like TensorFlow Lite, and there are quite a few articles that describe how to develop mobile apps for computer vision tasks like text classification and image classification.
But there’s very much less that exists about working with audio-based ML tasks in mobile apps, and this blog is meant to address that gap — specifically, I’ll describe the steps and code required to perform audio classification in Android apps.
Tensorflow Lite Model on Android to make audio classification
This article covers different technologies required to develop ML apps in mobile and deals with audio processing techniques. As such, the following are the pre-requisite to get the complete understanding of the article:
→ Familiarity with deep learning, Keras, and convolutional neural networks
→ Experience with Python and Jupyter Notebooks
→ Basic understanding of audio processing and vocal classification concepts
→ Basics of Android app development with Kotlin
Note: If you’re new to audio processing concepts and would like to understand what MFCC [‘Mel Frequency Cepstral Coefficient’] is — pls refer this other blog of mine, where I have explained some of these concepts in detail.
I’ve provided detailed info with regard to various steps and processing involved, and have commented on the code extensively in GitHub for easier understanding. Still, if you have any queries, please feel free to post them as comments.
One major challenge with regard to development of audio-based ML apps in Android is the lack of libraries in Java that perform audio processing.
I was surprised to find that there are no libraries available in Java for Android that help with the calculation of MFCC and other features required for audio classification. Most of my time with regard to this article has been spent towards developing a Java components that generates MFCC values just like Librosa does — which is very critical to a model’s ability to make predictions.
At the end of the tutorial, you’ll have developed an Android app that helps you classify audio files present in your mobile sdcard directory into any one of the noise type of the Urbancode Challenge dataset. Your app should more or less look like below:
#tensorflow #heartbeat #tensorflow-lite #audio-classification #android #android app
If you need to deploy a machine learning model to a mobile device, it becomes challenging, as there’s limited space and processing power on the device. There’s no doubt that machine learning models suffer from such heavy model sizes and high latency when targeting mobile devices.
However, there are techniques to reduce size or increase performance so that they do fit and work on mobile (see the links below for more on these techniques). It should be noted that, despite these challenges, ML models are currently being deployed to mobile devices.
In this article, we’re going to discuss how to implement a housing price prediction machine learning model for mobile using TensorFlow Lite. We’ll learn how to train a TensorFlow Lite neural network for regression that provides a continuous value prediction, specifically in the context of housing prices.
TensorFlow Lite is an open source deep learning framework for mobile device inference. It is essentially a set of tools to help us run TensorFlow models on mobile, embedded, and IoT devices. TensorFlow Lite enables on-device machine learning inference with low latency and a small binary size.
There are two main components of TensorFlow Lite:
#machine-learning #tensorflow #tensorflow-lite #heartbeat #mobile-app-development
It’s currently not possible to implement some of the most compute-intensive machine learning models on mobile and IoT devices. Generally speaking, this is because mobile devices have significant computation limits, less available space, and lower processing power. Although the biggest and most powerful models won’t work on mobile devices, we can still implement a wide range ML features on today’s mobile devices.
This article will explain how to reduce the size of an image classification machine learning model for mobile using TensorFlow Lite, in order to make it fit and work on mobile devices.
TensorFlow Lite is a lighter version of TensorFlow, an open-source machine learning framework developed by Google. TensorFlow Lite is designed to run machine learning models on mobile and embedded devices. It’slikeaset of tools that help to build and optimize TensorFlow models to run on mobile and IoT devices.
TensorFlow Lite has two major components: an **interpreter and a converter. **By running models directly on mobile devices themselves (i.e. close to the input data), we see several unique benefits:
Lower latency for model predictions
Privacy for on-device data
Connectivity — no internet is required
Power consumption — less power required
TensorFlow Lite interpreter: The interpreter runs optimized Lite models on many different hardware types, including mobile phones, embedded devices, and microcontrollers.
TensorFlow Lite Converter: The converter basically converts TensorFlow models into an efficient form to be used by the interpreter. This can introduce optimizations to improve binary size as well as performance
#heartbeat #machine-learning #tensorflow-lite #tensorflow