Posted by the TensorFlow team
took place, we published a lot of exciting new docs on , and the library. The TensorFlow Model Remediation library provides training-time techniques to intervene on the model such as changing the model itself by introducing or altering model objectives. Originally, model remediation launched with its first technique, . This is a technique that seeks to ensure that a model’s prediction doesn’t change when a sensitive attribute referenced in an example is either removed or replaced. For example, in a toxicity classifier, examples such as "I am a man" and "I am a lesbian" should be equal and not classified as toxic.
Check out the , and the expansion.
By decoupling the overall application from sharding directives, DTensor enables running the same application on a single device, multiple devices, or even multiple clients, while preserving its global semantics. If you remember Mesh TensorFlow from TF1, DTensor can address the same issue that Mesh addressed: training models that may be larger than a single core.
With TensorFlow 2.9, we made DTensor, that had been in nightly builds, visible on , the .
New in TensorFlow Lite
We made some big changes to the TensorFlow Lite site, including to the getting started docs.
Developer Journeys
First off, we now organize the developer journeys by platform (Android, iOS, and other edge devices) to make it easier to get started with your platform. Android gained a new . We also earlier added a guide to the new beta for , as recommended by our colleagues in Android developer relations!
If you want to immediately run an Android sample, one can now be imported directly from Android studio. When starting a new project, choose: New Project > Import Sample... and look for Artificial Intelligence > TensorFlow Lite in Play Services image classification example application. This is the sample that can help you find your mug...or other objects:
library simplifies the process of training a TensorFlow Lite model using custom datasets. It uses transfer learning to reduce the amount of training data required and reduce training time, and comes pre-built with seven common tasks including image classification, object detection, and text search.We added a new tutorial for .
TF Lite model page
Finding the right model for your use case can sometimes be confusing. We've written provides implementations of many state-of-the-art machine learning (ML) models for vision and natural language processing (NLP), as well as workflow tools to let you quickly configure and run those models on standard datasets. The Model Garden covers both vision and text tasks, and a flexible training loop library called Orbit. Models come with pre-built configs to train to state-of-the-art, as well as many useful specialized ops.
We're just getting started documenting all the great things you can do with the Model Garden. Your first stops should be the , and , read the in Colab yourself.
Also, there is a new tutorial on
And, of course, don't miss all the . Thank you!
SOCIAL SHARE CARD GENERATOR