| commit | 2546f7930e4488dd7fa6f482ed1fb389d2d32774 | [log] [tgz] |
|---|---|---|
| author | Vivek Rane <vivek.v.rane@intel.com> | Mon Jul 10 06:16:36 2017 |
| committer | Vivek Rane <vivek.v.rane@intel.com> | Mon Aug 28 21:23:14 2017 |
| tree | 09cd587a3a2690801fbe1e885e039ed8fe5b6d01 | |
| parent | 668db64a5d612d5f96b5d87772ce6ff6531fc035 [diff] |
Added MKL element-wise ops that utilize eigen ops as their back-end. Also added an input-conversion op that ensures that shapes of both input tensors are compatible (same or broadcastable). Added SquaredDifference to layout pass, and fixed the test for layout pass (it assumed Add/Mul/Sub would not be substituted with Mkl ops) Fixed missing edge deletion for 2 edges Fixing condition for checking broadcast Added more sanity checks Changed check for incoming control edges to code that moves control edges from the elementwise node to the inputconversion node. Fixed bug in CHECK for input types, which did not consider output number of source while checking datatype Fixed unit test bugs and added SquaredDifference elementwise op for CycleGAN Fixed merge issue (code duplication) Ran buildifier and clang-format on changed code Fixed a couple of merge issues and clang-format changes outside the modified code
Linux CPU | Linux GPU | Mac OS CPU | Windows CPU | Android |
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TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.
We use GitHub issues for tracking requests and bugs. So please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.
See Installing TensorFlow for instructions on how to install our release binaries or how to build from source.
People who are a little more adventurous can also try our nightly binaries:
Nightly pip packages
pip install tf-nightly in a clean environment to install the nightly tensorflow build. We currently only support CPU-only packages on Linux and Mac. GPU packages on all platforms and Windows CPU-only packages will arrive soon!Individual whl files
$ python
>>> import tensorflow as tf >>> hello = tf.constant('Hello, TensorFlow!') >>> sess = tf.Session() >>> sess.run(hello) 'Hello, TensorFlow!' >>> a = tf.constant(10) >>> b = tf.constant(32) >>> sess.run(a + b) 42 >>> sess.close()
Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.