| commit | fa095c5db0ac9cfe2328a19b32ae208e58e3746a | [log] [tgz] |
|---|---|---|
| author | Manjunath Kudlur <keveman@gmail.com> | Wed Dec 02 09:12:45 2015 -0800 |
| committer | Manjunath Kudlur <keveman@gmail.com> | Wed Dec 02 09:19:48 2015 -0800 |
| tree | 0ea34b3110247357202386d91aac9a0083b0b9cf | |
| parent | f586a5ee94465f8f482964c6bbcc1206f160af35 [diff] |
TensorFlow: upstream changes to git Change 109195845 Fix TensorFlow for build against Bazel 0.1.2rc2 Two things are currently broken with TensorFlow and Bazel 0.1.2: - Bazel now use sandboxing by default on Linux and have fixed it for cc_* rules. Undeclared headers are not mounted in the sandbox which make several cc_* rules fails. - Bazel now enforce strict header checking and some target were missing headers even though the headers were mounted in the sandbox. This change adds a "strict_headers" target that globs every headers of the core library and add it to the `tf_cc_tests` targets. Change 109162708 Fix various website issues - Fix headline in os_setup.md - Fix #anchor links Change 109162129 Fix numbers in mnist tutorial, fixes #362 Change 109158967 Fix typo in word2vec tutorial, fixes #347 Change 109151855 Fix tile and its gradient for scalars on GPUs Eigen doesn't handle scalars on GPUs in all cases. Fortunately, both tile and its gradient are the identity for scalars, so we can just copy the input to the output. Fixes https://github.com/tensorflow/tensorflow/issues/391. Change 109140763 Support int32 and int64 in tf.random_uniform This requires a new RandomUniformInt op on the C++ side since the op needs to know minval and maxval. Fixes https://github.com/tensorflow/tensorflow/issues/364. Change 109140738 Fix spacing in docs. Change 109140030 Fix content nav to not hide the bottom 100 or so px. Change 109139967 Add license files to TensorBoard files, fix mnist_with_summaries test Change 109138333 Fix typos in docstring Change 109138098 Fix some missing resources in the website. Fixes #366. Change 109123771 Make sparse_to_dense's default_value default to 0 Nearly all uses of sparse_to_dense use 0 as the default. The same goes for sparse_tensor_to_dense. Base CL: 109198336
#TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph 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 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.
Note: Currently we do not accept pull requests on github -- see CONTRIBUTING.md for information on how to contribute code changes to TensorFlow through tensorflow.googlesource.com
We use github issues for tracking requests and bugs, but please see Community for general questions and discussion.
To install the CPU version of TensorFlow using a binary package, see the instructions below. For more detailed installation instructions, including installing from source, GPU-enabled support, etc., see here.
The TensorFlow Python API currently requires Python 2.7: we are working on adding support for Python 3.
The simplest way to install TensorFlow is using pip for both Linux and Mac.
For the GPU-enabled version, or if you encounter installation errors, or for more detailed installation instructions, see here.
# For CPU-only version $ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
# Only CPU-version is available at the moment. $ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
$ 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 >>>
##For more information