TensorFlow Weekly Issue 7

TensorFlow Weekly Issue 7 — June 20, 2019

Hi there, these are the top TensorFlow links from my weekly curation. If you're having trouble viewing this email, please read the online version.

Introducing TF.Text — TF.Text is a TensorFlow 2.0 library that can be easily installed using PIP and is designed to ease this problem by providing ops to handle the preprocessing regularly found in text-based models, and other features useful for language modeling not provided by core TensorFlow...
Robby Neale

Train TensorFlow models on YARN in just a few lines of code! — tf-yarn is a Python library we have built at Criteo for training TensorFlow models on a YARN cluster...It supports running on one worker or on multiple workers with different distribution strategies, it can run on CPUs or GPUs and also runs with the recently added standalone client mode, and this with just a few lines of code...Its API provides an easy entry point for working with Estimators. Keras is currently supported via the model_to_estimator conversion function, and low-level distributed TensorFlow via standalone client mode API...
Fabian Höring

TensorWatch: a library for monitoring and visualizing learning processes — TensorWatch is a tool for debugging and visualizing the learning process of models. The library was developed by Microsoft Research . The main functionality of the tool is to monitor the process of learning models in real time in Jupyter Notebook...
Vlad Tămaș

PoseNet for iOS, Android and Flutter using TensorFlow  — PoseNet is a well-known pose estimation model in TensorFlow.js. There’s a tflite version of the model provided here on TensorFlow’s webpage but no official tutorial yet on how to use it...I ported the code of PoseNet for TensorFlow.js to Android and iOS in the Flutter tflite plugin. In this post I will share the native code used to run the model, and the Flutter code to use the plugin...
Sha Qian

A Simple Image Classification Walkthrough With Tensorflow 2.0 — This article isn’t going to show you how to build the best model, but my hope is that you walk away with a deepened understanding and a richer appreciation of neural networks...For this, we will work through the dog versus cats dataset. The goal is to build a deep learning model that will take in an image and differentiate between a cat image and a dog image. In this article, the criteria for a good model is a high accuracy. There is also multiclass classification, e.g. differentiating between different dog species, and the interested learner can delve deeper into that after covering the foundations. But for simplicity’s sake, we will work through a binary classification problem...
Jagerynn Ting Verano

Announcing TensorFlow 2.0 Beta — We’re delighted to announce the release of the beta of TensorFlow 2.0 today...In this beta release, we have completed renaming and deprecating symbols for the 2.0 API. This means the current API is final and is also available as a v2 compatibility module inside the TensorFlow 1.14 release. (A list of all symbol changes can be found here.) We have also added 2.0 support for Keras features like model subclassing, simplified the API for custom training loops, added distribution strategy support for most kinds of hardware, and lots more...Core components of TensorFlow product ecosystem such as TensorBoard, TensorFlow Hub, TensorFlow Lite, and TensorFlow.js work with the Beta...
TensorFlow
 

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Cheers,
Sebastian