Science and technology

What is TensorFlow? |

TensorFlow is an open supply software program library for numerical computation utilizing data-flow graphs. It was initially developed by the Google Brain Team inside Google’s Machine Intelligence analysis group for machine studying and deep neural networks analysis, however the system is normal sufficient to be relevant in all kinds of different domains as properly. It reached version 1.0 in February 2017, and has continued speedy improvement, with 21,000+ commits to date, many from outdoors contributors. This article introduces TensorFlow, its open supply neighborhood and ecosystem, and highlights some fascinating TensorFlow open sourced fashions.

TensorFlow is cross-platform. It runs on practically every thing: GPUs and CPUs—together with cell and embedded platforms—and even tensor processing items (TPUs), that are specialised to do tensor math on. They aren’t broadly accessible but, however we’ve got just lately launched an alpha program.

The TensorFlow distributed execution engine abstracts away the various supported gadgets and offers a excessive performance-core applied in C++ for the TensorFlow platform.

On high of that sit the Python and C++ frontends (with extra to come back). The Layers API offers an easier interface for generally used layers in deep studying fashions. On high of that sit higher-level APIs, together with Keras (extra on the site) and the Estimator API, which makes coaching and evaluating distributed fashions simpler.

And lastly, plenty of generally used fashions are prepared to make use of out of the field, with extra to come back.

TensorFlow execution mannequin


Machine studying can get complicated rapidly, and deep studying fashions can change into massive. For many mannequin graphs, you want distributed coaching to have the ability to iterate inside an inexpensive timeframe. And, you may usually need the fashions you develop to deploy to a number of platforms.

With the present model of TensorFlow, you write code to construct a computation graph, then execute it. The graph is a knowledge construction that totally describes the computation you need to carry out. This has numerous benefits:

  • It’s moveable, because the graph will be executed instantly or saved to make use of later, and it could run on a number of platforms: CPUs, GPUs, TPUs, cell, embedded. Also, it may be deployed to manufacturing with out having to depend upon any of the code that constructed the graph, solely the runtime essential to execute it.
  • It’s transformable and optimizable, because the graph will be remodeled to provide a extra optimum model for a given platform. Also, reminiscence or compute optimizations will be carried out and trade-offs made between them. This is helpful, for instance, in supporting quicker cell inference after coaching on bigger machines.
  • Support for distributed execution

TensorFlow’s high-level APIs, along side computation graphs, allow a wealthy and versatile improvement setting and highly effective manufacturing capabilities in the identical framework.

Eager execution

An upcoming addition to TensorFlow is eager execution, an crucial type for writing TensorFlow. When you allow keen execution, you may be executing TensorFlow kernels instantly, slightly than setting up graphs that will probably be executed later.

Why is that this necessary? Four main causes:

  • You can examine and debug intermediate values in your graph simply.
  • You can use Python management circulate inside TensorFlow APIs—loops, conditionals, features, closures, and many others.
  • Eager execution ought to make debugging extra easy.
  • Eager’s “define-by-run” semantics will make constructing and coaching dynamic graphs simple.

Once you’re happy along with your TensorFlow code operating eagerly, you’ll be able to convert it to a graph routinely. This will make it simpler to avoid wasting, port, and distribute your graphs.

This interface is in its early (pre-alpha) phases. Follow alongside on GitHub.

TensorFlow and the open supply software program neighborhood

TensorFlow was open sourced largely to permit the neighborhood to enhance it with contributions. The TensorFlow crew has arrange processes to handle pull requests, assessment and route points filed, and reply Stack Overflow and mailing list questions.

So far, we have had greater than 890 exterior contributors add to the code, with every thing from small documentation fixes to massive additions like OS X GPU support or the OpenCL implementation. (The broader TensorFlow GitHub group has had practically 1,000 distinctive non-Googler contributors.)

Tensorflow has greater than 76,000 stars on GitHub, and the variety of different repos that use it’s rising each month—as of this writing, there are greater than 20,000.

Many of those are community-created tutorials, fashions, translations, and initiatives. They is usually a nice supply of examples in case you’re getting began on a machine studying activity.

Stack Overflow is monitored by the TensorFlow crew, and it is a good way to get questions answered (with eight,000+ answered to date).

The exterior model of TensorFlow internally isn’t any totally different than inner, past some minor variations.  These embrace the interface to Google’s inner infrastructure (it will be no assist to anybody), some paths, and elements that are not prepared but.  The core of TensorFlow, nonetheless, is similar.  Pull requests to inner will seem externally inside round a day and a half and vice-versa.

In the TensorFlow GitHub org, yow will discover not solely TensorFlow itself, however a helpful ecosystem of different repos, together with models, serving, TensorBoard, Project Magenta, and plenty of extra. (A couple of of those are described beneath). You may also discover TensorFlow APIs in multiple languages (Python, C++, Java, and Go); and the neighborhood has developed other bindings, together with C#, Haskell, Julia, Ruby, Rust, and Scala.

Performance and benchmarking

TensorFlow has excessive requirements round measurement and transparency. The crew has developed a set of detailed benchmarks and has been very cautious to incorporate all vital particulars to breed. We’ve not but run comparative benchmarks, however would welcome for others to publish complete and reproducible benchmarks.

There’s a section of the TensorFlow web site with info particularly for performance-minded builders. Optimization can usually be model-specific, however there are some normal tips that may usually make a giant distinction.

TensorFlow’s open supply fashions

The TensorFlow crew has open sourced numerous fashions. You can discover them within the tensorflow/models repo. For many of those, the launched code consists of not solely the mannequin graph, but in addition educated mannequin weights. This means which you can attempt such fashions out of the field, and you may tune lots of them additional utilizing a course of known as transfer learning.

Here are only a few of the just lately launched fashions (there are a lot of extra):

  • tf-seq2seq: Google beforehand introduced Google Neural Machine Translation (GNMT), a sequence-to-sequence (seq2seq) mannequin that’s now utilized in Google Translate manufacturing programs. tf-seq2seq is an open supply seq2seq framework in TensorFlow that makes it simple to experiment with seq2seq fashions and obtain state-of-the-art outcomes.
  • ParseySaurus is a set of pretrained fashions that replicate an improve to SyntaxNet. The new fashions use a character-based enter illustration and are significantly better at predicting the that means of recent phrases primarily based each on their spelling and the way they’re utilized in context. They are far more correct than their predecessors, notably for languages the place there will be dozens of kinds for every phrase and plenty of of those kinds may by no means be noticed throughout coaching, even in a really massive corpus.
  • Multistyle Pastiche Generator from the Magenta Project: “Style transfer” is what’s taking place underneath the hood with these enjoyable apps that apply the type of a portray to one in all your images. This Magenta mannequin extends picture type switch by creating a single network that may carry out multiple stylization of a picture, optionally on the similar time. (Try enjoying with the sliders for the canine photographs on this blog post.)

Transfer studying

Many of the TensorFlow models embrace educated weights and examples that present how you need to use them for transfer learning, e.g. to be taught your personal classifications. You usually do that by deriving details about your enter knowledge from the penultimate layer of a educated mannequin—which encodes helpful abstractions—then use that as enter to coach your personal a lot smaller neural internet to foretell your personal lessons. Because of the facility of the realized abstractions, the extra coaching usually doesn’t require massive knowledge units.

For instance, you need to use switch studying with the Inception picture classification mannequin to coach a picture classifier that makes use of your specialised picture knowledge.

For examples of utilizing switch studying for medical analysis by coaching a neural internet to detect specialised lessons of photographs, see the next articles:

And, you are able to do the identical to learn your own (potentially goofy) picture classifications too.

The Object Detection API code is designed to assist switch studying as properly. In the tensorflow/models repo, there’s an example of how you need to use switch studying to bootstrap this educated mannequin to build a pet detector, utilizing a (considerably restricted) knowledge set of canine and cat breed examples. And, in case you want raccoons greater than canine and cats, see this tutorial too.

Using TensorFlow on cell gadgets

Mobile is a good use case for TensorFlow—cell is smart when there’s a poor or lacking community connection or the place sending steady knowledge to a server could be too costly. But, as soon as you’ve got educated your mannequin and also you’re ready to start using it, you don’t need the on-device mannequin footprint to be too large.

TensorFlow is working to assist builders make lean mobile apps, each by persevering with to cut back the code footprint and by supporting quantization.

(And though it is early days, see additionally Accelerated Linear Algebra [XLA], a domain-specific compiler for linear algebra that optimizes TensorFlow computations.)

One of the TensorFlow initiatives, MobileNet, is creating a set of laptop imaginative and prescient fashions which are notably designed to tackle the velocity/accuracy trade-offs that have to be thought-about on cell gadgets or in embedded functions. The MobileInternet fashions will be discovered within the TensorFlow models repo as properly.

One of the newer Android demos, TF Detect, makes use of a MobileInternet mannequin educated utilizing the Tensorflow Object Detection API.

And in fact we might be remiss in not mentioning “How HBO’s ‘Silicon Valley’ built ‘Not Hotdog’ with mobile TensorFlow, Keras, and React Native.”

The TensorFlow ecosystem

The TensorFlow ecosystem consists of many instruments and libraries that can assist you work extra successfully. Here are just a few.


TensorBoard is a set of internet functions for inspecting, visualizing, and understanding your TensorFlow runs and graphs. You can use TensorBoard to view your TensorFlow mannequin graphs and zoom in on the main points of graph subsections.

You can plot metrics like loss and accuracy throughout a coaching run; present histogram visualizations of how a tensor is altering over time; present further knowledge, like photographs; gather runtime metadata for a run, corresponding to whole reminiscence utilization and tensor shapes for nodes; and extra.

TensorBoard works by studying TensorFlow recordsdata that include summary information concerning the coaching course of. You can generate these recordsdata when operating TensorFlow jobs.

You can use TensorBoard to match coaching runs, gather runtime stats, and generate histograms.

A very mesmerizing function of TensorBoard is its embeddings visualizer. Embeddings are ubiquitous in machine studying, and within the context of TensorFlow, it is usually pure to view tensors as factors in house, so nearly any TensorFlow mannequin will give rise to numerous embeddings.


Jupyter notebooks are a straightforward strategy to interactively discover your knowledge, outline TensorFlow fashions, and kick off coaching runs. If you are utilizing Google Cloud Platform instruments and merchandise as a part of your workflow—perhaps utilizing Google Cloud Storage or BigQuery on your datasets, or Apache Beam for data preprocessing—then Google Cloud Datalab offers a Jupyter-based setting with all of those instruments (and others like NumPy, pandas, scikit-learn, and Matplotlib), together with TensorFlow, preinstalled and bundled collectively. Datalab is open source, so if you wish to additional modify its pocket book setting, it is easy to do.


Machine studying’s energy comes from its capability to be taught patterns from massive quantities of knowledge, so understanding your knowledge will be essential to constructing a robust machine studying system.

Facets is a just lately launched open source data visualization tool that helps you perceive your machine studying datasets and get a way of the form and traits of every function and see at a look how the options work together with one another. For instance, you’ll be able to view your coaching and check datasets (as is completed right here with some Census knowledge), evaluate the traits of every function, and type the options by “distribution distance.”

Cloud Datalab consists of Facets integration. This GitHub link has a small instance of loading a NHTSA Traffic Fatality BigQuery public dataset and viewing it with Facets.

In Facets’ Dive view we will rapidly see which states have probably the most site visitors fatalities and that the distribution of collusion sort seems to alter because the variety of fatalities per accident will increase.

And extra…

Another helpful diagnostic instrument is the TensorFlow debugger, tfdbg, which helps you to view the interior construction and states of operating TensorFlow graphs throughout coaching and inference.

Once you’ve got educated a mannequin that you simply’re pleased with, the following step is to determine how you may serve it so as to scalably assist predictions on the mannequin. TensorFlow Serving is a high-performance serving system for machine-learned fashions, designed for manufacturing environments. It has recently moved to model

There are many different instruments and libraries that we do not have room to cowl right here, however see the TensorFlow GitHub org repos to study them.

The TensorFlow site has many getting started guides, examples, and tutorials. (A enjoyable new tutorial is this audio recognition instance.)

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