TensorFlow Framework
(Redirected from TensorFlow Library)
Jump to navigation
Jump to search
A TensorFlow Framework is an open source Python-based numerical analysis platform.
- Context:
- It can (typically) make use of Data Flow Graphs.
- It can (typically) support TensorFlow Tensors (n-dimensional arrays).
- It can (typically) support TensorFlow Graphs (to descrine computations).
- It can (often) be a Deep Learning Framework.
- It can include TensorFlow API's such as tf.contrib.learn ML System[1].
- It can be used to specify a TensorFlow Program (to develop complex machine learning systems).
- It can be managed by a Google Project.
- ...
- Example(s):
- TensorFlow v2.13.0 (~2023-08-03) [2].
- TensorFlow v1.6.0 (~2018-02-28) [3].
- TensorFlow v0.12 (~2016-12-19) [4]
- TensorFlow v0.8 (~2016-04-13).
https://github.com/tensorflow/tensorflow/releases
- TensorFlow Demo: http://playground.tensorflow.org
- ...
- Counter-Example(s):
- See: Distributed TensorFlow, Keras, DeepMind, Google Brain, TensorFlow Serving, TensorFlow Cluster.
References
2017
- (TensorFlow Dev Summit - 2017)." Feb, 2017
2017
Software | Creator | Software license | Open source | Platform | Written in | Interface | OpenMP support | OpenCL support | CUDA support | Automatic differentiation | Has pretrained models | Recurrent nets | Convolutional nets | RBM/DBNs | Parallel execution (multi node) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TensorFlow | Google Brain team | Apache 2.0 | Yes | Linux, Mac OS X, Windows^{[1]} | C++, Python | Python, C/C++, Java, Go | No | No^{[2]}^{[3]} | Yes | Yes^{[4]} | Yes^{[5]} | Yes | Yes | Yes | Yes |
2016
- https://www.tensorflow.org/versions/r0.10/get_started/basic_usage.html#overview
- QUOTE: TensorFlow is a programming system in which you represent computations as graphs. Nodes in the graph are called ops (short for operations). An op takes zero or more Tensors, performs some computation, and produces zero or more Tensors. A Tensor is a typed multi-dimensional array. For example, you can represent a mini-batch of images as a 4-D array of floating point numbers with dimensions [batch, height, width, channels].
A TensorFlow graph is a description of computations. To compute anything, a graph must be launched in a Session. A Session places the graph ops onto Devices, such as CPUs or GPUs, and provides methods to execute them. These methods return tensors produced by ops as numpy ndarray objects in Python, and as tensorflow::Tensor instances in C and C++.
- QUOTE: TensorFlow is a programming system in which you represent computations as graphs. Nodes in the graph are called ops (short for operations). An op takes zero or more Tensors, performs some computation, and produces zero or more Tensors. A Tensor is a typed multi-dimensional array. For example, you can represent a mini-batch of images as a 4-D array of floating point numbers with dimensions [batch, height, width, channels].
2016
- https://www.tensorflow.org/versions/r0.10/get_started/basic_usage.html#tensors
- QUOTE: TensorFlow programs use a tensor data structure to represent all data -- only tensor are passed between operations in the computation graph. You can think of a TensorFlow tensor as an n-dimensional array or list. A tensor has a static type, a rank, and a shape. To learn more about how TensorFlow handles these concepts, see the Rank, Shape, and Type reference.
2016
- (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/TensorFlow Retrieved:2016-5-2.
- TensorFlow is an open source software library for machine learning in various kinds of perceptual and language understanding tasks.^{[6]} It is a second-generation API which is currently used for both research and production by 50 different teams in dozens of commercial Google products, such as speech recognition, Gmail, Google Photos, and Search. These teams had previously used DistBelief, a first-generation API. TensorFlow was originally developed by the Google Brain team for Google's research and production purposes and later released under the Apache 2.0 open source license on November 9, 2015.
- ↑ https://developers.googleblog.com/2016/11/tensorflow-0-12-adds-support-for-windows.html
- ↑ "tensorflow/roadmap.md at master · tensorflow/tensorflow · GitHub". GitHub. January 23, 2017. https://github.com/tensorflow/tensorflow/blob/master/tensorflow/docs_src/about/roadmap.md. Retrieved May 21, 2017.
- ↑ "OpenCL support · Issue #22 · tensorflow/tensorflow". GitHub. https://github.com/tensorflow/tensorflow/issues/22.
- ↑ https://www.tensorflow.org/
- ↑ https://github.com/tensorflow/models
- ↑ "TensorFlow: Open source machine learning" "It is machine learning software being used for various kinds of perceptual and language understanding tasks" — Jeffrey Dean, minute 0:47 / 2:17 from Youtube clip
2016
- http://googleresearch.blogspot.tw/2016/05/announcing-syntaxnet-worlds-most.html
- QUOTE: ... Parsey McParseface and other SyntaxNet models are some of the most complex networks that we have trained with the TensorFlow framework ...
2016
- http://googleresearch.blogspot.com/2016/04/deepmind-moves-to-tensorflow.html
- QUOTE: With Google’s recent open source release of TensorFlow, we initiated a project to test its suitability for our research environment. Over the last six months, we have re-implemented more than a dozen different projects in TensorFlow to develop a deeper understanding of its potential use cases and the tradeoffs for research. Today we are excited to announce that DeepMind will start using TensorFlow for all our future research. We believe that TensorFlow will enable us to execute our ambitious research goals at much larger scale and an even faster pace, providing us with a unique opportunity to further accelerate our research programme.
2016
- (Abadi et al., 2016) ⇒ Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. (2016). “TensorFlow: A System for Large-scale Machine Learning.” In: Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation. ISBN:978-1-931971-33-1
- (Abadi et. al., 2016a) ⇒ Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng. (2016). “TensorFlow - Large-Scale Machine Learning on Heterogeneous Distributed Systems.” In: arXiv 1603.04467 Journal.