Google Research Blog
The latest news from Research at Google
TensorFlow - Google’s latest machine learning system, open sourced for everyone
Monday, November 09, 2015
Posted by Jeff Dean, Senior Google Fellow, and Rajat Monga, Technical Lead
Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day. Our internal deep learning infrastructure
DistBelief
, developed in 2011, has allowed Googlers to build ever larger
neural networks
and scale training to thousands of cores in our datacenters. We’ve used it to demonstrate that
concepts like “cat”
can be learned from unlabeled YouTube images, to improve speech recognition in
the Google app
by 25%, and to build image search
in Google Photos
. DistBelief also trained the Inception model that won Imagenet’s
Large Scale Visual Recognition Challenge in 2014
, and drove our experiments in
automated image captioning
as well as
DeepDream
.
While DistBelief was very successful, it had some limitations. It was narrowly targeted to neural networks, it was difficult to configure, and it was tightly coupled to Google’s internal infrastructure - making it nearly impossible to share research code externally.
Today we’re proud to announce the open source release of
TensorFlow
-- our second-generation machine learning system, specifically designed to correct these shortcomings. TensorFlow is general, flexible, portable, easy-to-use, and completely open source. We added all this while improving upon DistBelief’s speed, scalability, and production readiness -- in fact, on some benchmarks, TensorFlow is twice as fast as DistBelief (see the
whitepaper
for details of TensorFlow’s programming model and implementation).
TensorFlow has extensive built-in support for deep learning, but is far more general than that -- any computation that you can express as a computational flow graph, you can compute with TensorFlow (see some
examples
). Any gradient-based machine learning algorithm will benefit from TensorFlow’s
auto-differentiation
and suite of first-rate optimizers. And it’s easy to express your new ideas in TensorFlow via the flexible Python interface.
Inspecting a model with TensorBoard, the visualization tool
TensorFlow is great for research, but it’s ready for use in real products too. TensorFlow was built from the ground up to be fast, portable, and ready for production service. You can move your idea seamlessly from training on your desktop GPU to running on your mobile phone. And you can get started quickly with powerful machine learning tech by using our state-of-the-art
example model architectures
. For example, we plan to release our complete, top shelf ImageNet computer vision model on TensorFlow soon.
But the most important thing about TensorFlow is that it’s yours. We’ve open-sourced TensorFlow as a standalone library and associated tools, tutorials, and examples with the Apache 2.0 license so you’re free to use TensorFlow at your institution (no matter where you work).
Our deep learning researchers all use TensorFlow in their experiments. Our engineers use it to infuse Google Search with
signals derived from deep neural networks
, and to power the
magic features of tomorrow
. We’ll continue to use TensorFlow to serve machine learning in products, and our research team is committed to sharing TensorFlow implementations of our published ideas. We hope you’ll join us at
www.tensorflow.org
.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
Android
API
App Engine
App Inventor
April Fools
Art
Audio
Australia
Automatic Speech Recognition
Awards
Cantonese
China
Chrome
Cloud Computing
Collaboration
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gmail
Google Books
Google Brain
Google Cloud Platform
Google Drive
Google Genomics
Google Science Fair
Google Sheets
Google Translate
Google Voice Search
Google+
Government
grants
Hardware
HCI
Health
High Dynamic Range Imaging
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
KDD
Klingon
Korean
Labs
Linear Optimization
localization
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
MapReduce
market algorithms
Market Research
ML
MOOC
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
Ngram
NIPS
NLP
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
ph.d. fellowship
PiLab
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum Computing
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
SIGCOMM
SIGMOD
Site Reliability Engineering
Software
Speech
Speech Recognition
statistics
Structured Data
Systems
TensorFlow
Translate
trends
TTS
TV
UI
University Relations
UNIX
User Experience
video
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
YouTube
Archive
2016
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Google
on
Follow @googleresearch
Give us feedback in our
Product Forums
.