TensorFlow.js 0.11.1 releases!

TensorFlow team has released a new version of TensorFlow.js - a browser-based JavaScript library - for training and deploying machine learning models. This new version 0.11.1 has brought notable features in their armory to ease WebGL accelerated browser-based machine learning.

TensorFlow.js is an open source JavaScript library which allows you to build machine learning models in the browser. It provides you flexible and intuitive high-level APIs to build, train and run models from scratch. This means you can run and retrain pre-existing TensorFlow and Keras models right in the browser.

Some of the noteworthy changes available in TensorFlow.js 0.11:

Now you can save and load tf.models using various media - Thanks to the new capabilities added
- Browser IndexedDB
- Browser local storage
- HTTP requests
- Browser file downloads and uploads

In order to know more about each medium used to save and load models in TensorFlow.js, you can refer the tutorials page.

There are a set of new features added to both TensorFlow.js Core API and TensorFlow.js Layers API:

TensorFlow.js Core API (0.8.3 ==> 0.11.0)

TensorFlow.js Core API provides low-level, hardware-accelerated linear algebra operations. It also provides an eager API for carrying out automatic differentiation.

Breaking changes

From now on ES5 tf-core.js bundle users will have to use symbol tf instead of tfc

Now you can export GPGPUContext and add getCanvas() to the WebGLBackend

Performance and development changes

They have optimized CPU conv2dDerInput on CPU to get 100x faster.

Loading quantized weight support added to reduce the model size and improve model download time.

New serialization infrastructure added to the core API

New helper methods and basic types added to support model exporting

New features added to the Core API

Added tf.losses.logLoss support which allows you to add a log loss term to the training procedure

They have also added tf.losses.cosineDistance which allows you to add a cosine-distance loss to the training procedure

Added tensor.round() which rounds the value of a tensor to the nearest integer, element-wise.

They have added tf.cumsum support which allows you to compute the cumulative sum of the tensor x along the axis.

They have added tf.losses.hinge_loss support which allows you to add a hinge loss to the training procedure.

For the complete list of new features, documentation changes, a plethora of bug fixes and other miscellaneous changes added to the Core API you can refer the release notes.

TensorFlow.js Layers API (0.5.2 ==> 0.6.1)

TensorFlow.js Layers API is a high-level machine learning model API built on TensorFlow.js Core. This API can be used to build, train and execute deep learning models in the browser.

Breaking changes