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TensorFlow for Mobile and IoT and TensorFlow.js
TensorFlow.js
TensorFlow.js is a JavaScript library for machine learning models that can work
either in vanilla mode or via Node.js. In this section we are going to review both
of them.
Vanilla TensorFlow.js
TensorFlow.js is a JavaScript library for training and using Machine Learning (ML)
models in a browser. It is derived from deeplearn.js, an open source, hardwareaccelerated
library for doing Deep Learning (DL) in JavaScript, and is now a
companion library to TensorFlow.
The most common use of TensorFlow.js is to make pretrained ML/DL models
available on the browser. This can help in situations where it may not be feasible to
send client data back to the server due to network bandwidth or security concerns.
However, TensorFlow.js is a full stack ML platform, and it is possible to build and
train an ML/DL model from scratch, as well as fine-tune an existing pretrained
model with new client data.
An example of a TensorFlow.js application is the TensorFlow Projector (https://
projector.tensorflow.org), which allows a client to visualize their own data (as
word vectors) in 3-dimensional space, using one of several dimensionality reduction
algorithms provided. There are a few other examples of TensorFlow.js applications
listed on the TensorFlow.js demo page (https://www.tensorflow.org/js/demos).
Similarly to TensorFlow, TensorFlow.js also provides two main APIs – the Ops
API, which exposes low-level tensor operations such as matrix multiplication, and
the Layers API, which exposes Keras-style high-level building blocks for neural
networks.
At the time of writing, TensorFlow.js runs on three different backends. The fastest
(and also the most complex) is the WebGL backend, which provides access to
WebGL's low-level 3D graphics APIs and can take advantage of GPU hardware
acceleration. The other popular backend is the Node.js backend, which allows the
use of TensorFlow.js in server-side applications. Finally, as a fallback, there is the
CPU-based implementation in plain JavaScript that will run in any browser.
In order to gain a better understanding of how to write a TensorFlow.js
application, we will walk through an example of classifying MNIST digits using
a Convolutional Neural Network (CNN) provided by the TensorFlow.js team
(https://storage.googleapis.com/tfjs-examples/mnist/dist/index.html).
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