Fork of the STT fork of Tensorflow
22586bdf90
* Enable grappler to propagate shapes through queues.
Change: 154789133
* Add whitelist support in uid of RunConfig.
Change: 154794859
* Fix a bunch of bad links and missing docs in contrib.
Change: 154820641
* Don't try to refine the shapes for a node if its inference context wasn't
successfully built by the AddNode() method.
Change: 154838211
* Fix issue related to empty bazel.rc file.
Change: 154840138
* Remove overly precise CHECK when rendering debug output for a function.
An `_Arg` node can have more than three attrs, because the runtime may
(and does) add system-defined attrs (viz. "_output_shapes") that do
not change the meaning of the op.
Change: 154850526
* Port makefile build breakage
Change: 154855106
* [TF:XLA] Try to incorporate Tensorflow node structure for large HLO GraphDefs.
This change assumes that a TF subgraph/op does not cross the boundary of a HLO
computation and always put top-level TF subgraphs/ops under HLO computations.
Change: 154855884
* Added a unit test to check what happens when 2 shapes with known rank but
unknown dimensions are merged
Change: 154856675
* [XLA] Refactor constant folding operations into a dedicated module
Refactor constant folding operations into a dedicated module, and added a new
ReplaceInstruction() API to collapse { computation->ReplaceInstruction();
changed=true}.
Change: 154857025
* Java: Docs: Update instructions for Windows.
Inspired by
http://stackoverflow.com/questions/43741775/tensorflow-in-java-running-failed
Change: 154859066
* Add more documentation for features and labels.
Change: 154859649
* Added link to high-performance models
Change: 154860213
* Navigation and index for new performance section documents.
Change: 154862215
* Fix shape mismatch between loss and weights.
Change: 154862650
* Add examples to TensorShape documentation and ran autoformatter.
Change: 154862667
* Move linking of cudnn_plugin, cublas_plugin and cufft_plugin from
stream_executor to the ops that need them.
Change: 154863520
* Properly track the persistent memory usage of lookup tables.
Change: 154866686
* Reset the inputs to ShapeRefiner::RunShapeFn so that it behaves the same every time it's called.
To properly handle queues that have populated by several enqueue ops, merge the shapes of the inputs to all the enqueue ops before calling InferenceContext::set_output_handle_shape(). This ensures that we detect incorrect queue setups (where the 2 enqueue ops might generate tensors with incompatible shapes), and that we take all the known shape information instead of that of just one of the enqueue ops.
Change: 154866747
* Making sure an error message will be produced by session_manager when a non-tensor object is passed in.
Otherwise the 'name' property is missing.
Change: 154868022
* Don't needlessly synchronize the CUDA stream in CropAndResize.
Make the op Async so we don't block an executor thread while waiting for the result of the box bounds check to be copied back to the host.
Change: 154868460
* Add contribution guidelines and standards section to CONTRIBUTING.md
Several parts are largely based on the post by @yaroslavvb at: #7443#issuecomment-279182613
Fixes #7443
Change: 154876045
* Final draft
Change: 154876563
* Final draft
Change: 154876646
* Fix losses documentation.
Fix documentation of get_total_loss() to be correct.
And add a helpful comment about a common pitfall.
Change: 154876822
* [XLA] Second change for HLO interpreter.
Extends HloEvaluator to allow evaluation of HLO Computation or single HLO instruction
with non-constant operands, by traversing the instruction in post order and keeps track of
each instruction along the way as evaluated literals.
Change: 154877580
* [tf distributions] Move the remaining whitelisted distributions to core.
Change: 154878206
* Add shape to error message.
Change: 154880260
* Revert "Fix build issue when `/usr/bin/python` path is not available (#9547)"
This reverts commit
|
||
|---|---|---|
| tensorflow | ||
| third_party | ||
| tools | ||
| util/python | ||
| .gitignore | ||
| .gitmodules | ||
| .mention-bot | ||
| ACKNOWLEDGMENTS | ||
| ADOPTERS.md | ||
| AUTHORS | ||
| bower.BUILD | ||
| BUILD | ||
| configure | ||
| CONTRIBUTING.md | ||
| ISSUE_TEMPLATE.md | ||
| LICENSE | ||
| models.BUILD | ||
| README.md | ||
| RELEASE.md | ||
| WORKSPACE | ||
Linux CPU |
Linux GPU |
Mac OS CPU |
Windows CPU |
Android |
|---|---|---|---|---|
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 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'd like to contribute to TensorFlow, be sure to review the contribution guidelines.
We use GitHub issues for tracking requests and bugs, but please see Community for general questions and discussion.
Installation
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:
- Linux CPU-only: Python 2 (build history) / Python 3.4 (build history) / Python 3.5 (build history)
- Linux GPU: Python 2 (build history) / Python 3.4 (build history) / Python 3.5 (build history)
- Mac CPU-only: Python 2 (build history) / Python 3 (build history)
- Mac GPU: Python 2 (build history) / Python 3 (build history)
- Windows CPU-only: Python 3.5 64-bit (build history)
- Windows GPU: Python 3.5 64-bit (build history)
- Android: demo APK, native libs (build history)
Try your first TensorFlow program
$ 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
The TensorFlow community has created amazing things with TensorFlow, please see the resources section of tensorflow.org for an incomplete list.