Mia  Marquardt

Mia Marquardt

1622173500

TensorFlow Lite Text Classification models with Model Maker

Generate TF Lite models from custom data using Model Maker

In this article, let’s look at how you can use TensorFlow Model Maker to create a custom text classification model. Currently, the TF Lite model maker supports image classification, question answering, and text classification models. It uses transfer learning for shortening the amount of time required to build TF Lite models.

#text-classification #tflite #model-makers #heartbeat

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TensorFlow Lite Text Classification models with Model Maker
Mia  Marquardt

Mia Marquardt

1622173500

TensorFlow Lite Text Classification models with Model Maker

Generate TF Lite models from custom data using Model Maker

In this article, let’s look at how you can use TensorFlow Model Maker to create a custom text classification model. Currently, the TF Lite model maker supports image classification, question answering, and text classification models. It uses transfer learning for shortening the amount of time required to build TF Lite models.

#text-classification #tflite #model-makers #heartbeat

Trevor  Russel

Trevor Russel

1616514360

TensorFlow Lite Image Classification models with Model Maker

TensorFlow is one of the greatest gifts to the machine learning community by Google. An end-to-end open-source framework for machine learning with a comprehensive ecosystem of tools, libraries and community resources, TensorFlow  lets researchers push the state-of-the-art in ML and developers can easily build and deploy ML-powered applications. Ever since its release to the public back in November 2015, TensorFlow has grown to become one of the most popular deep learning frameworks. This month, TensorFlow  turned five, and in this article, we take a look at its popular libraries.

#model-makers #tensorflow #heartbeat #image-classification #tflite

Mia  Marquardt

Mia Marquardt

1622878440

Custom Text Classification on Android using TensorFlow Lite

A lot of social media platforms have been using AI these days to classify vulgar and offensive posts and automatically take them down. I thought why not try doing something similar; and so, I’ve come up with this end-to-end tutorial that will help you build your own corpus for training a text classification model, and later export and deploy it on an Android app for you to use. All this, absolutely on a custom dataset of your choice.

So, are you excited to build your own text classifier app? If yes, let’s begin the show.

Now, before we begin, let me tell you that we’ll be doing all the model’s hyperparameter configuration and training on Google Colab. To build the Android app, we’ll need to have Android Studio. If you haven’t installed it yet, find it here.

#nlp #machine-learning #android #tensorflow-lite #text-classification

Mckenzie  Osiki

Mckenzie Osiki

1621931885

How TensorFlow Lite Fits In The TinyML Ecosystem

TensorFlow Lite has emerged as a popular platform for running machine learning models on the edge. A microcontroller is a tiny low-cost device to perform the specific tasks of embedded systems.

In a workshop held as part of Google I/O, TensorFlow founding member Pete Warden delved deep into the potential use cases of TensorFlow Lite for microcontrollers.

Further, quoting the definition of TinyML from a blog, he said:

“Tiny machine learning is capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of ways-on-use-case and targeting battery operated devices.”

#opinions #how to design tinyml #learn tinyml #machine learning models low cost #machine learning models low power #microcontrollers #tensoflow latest #tensorflow lite microcontrollers #tensorflow tinyml #tinyml applications #tinyml models

Chaz  Homenick

Chaz Homenick

1594782660

Audio Classification in an Android App with TensorFlow Lite

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.

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Tensorflow Lite Model on Android to make audio classification

Intended Audience and Pre-requisites:

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.

A Major Challenge

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.

What We’ll Build

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