Fork of the STT fork of Tensorflow
18f3692716
* Internal cleanup
PiperOrigin-RevId: 167636242
* Move the Keras API to tf.keras.
PiperOrigin-RevId: 167638421
* Automated g4 rollback of changelist 167604306
PiperOrigin-RevId: 167639833
* Call HloComputation.Accept instead of HloInstruction.Accept to get all instructions profiled.
RELNOTES: n/a
PiperOrigin-RevId: 167640259
* Add fast math attributes to all generated methods when fast math enabled.
RELNOTES: n/a
PiperOrigin-RevId: 167646637
* Extended ScratchSpace to expose its underlying scratch tensor object.
PiperOrigin-RevId: 167649551
* Change zip(...)[1] to list(zip(...))[1], for python 3 compatibility.
PiperOrigin-RevId: 167654035
* Add scoped timer to log jit compile times.
RELNOTES: n/a
PiperOrigin-RevId: 167656720
* Verify that predictions are in the expected range for ops that use thresholds, e.g. tf.contrib.metrics.streaming_auc.
PiperOrigin-RevId: 167658134
* Internal change.
PiperOrigin-RevId: 167658401
* Fix list formatting
PiperOrigin-RevId: 167660250
* Enable java test.
PiperOrigin-RevId: 167660276
* Add shape functions on debug ops.
PiperOrigin-RevId: 167668811
* Increase session_bundle_test to a medium test.
PiperOrigin-RevId: 167672587
* Include layout of convolution input data in the op_profile.
PiperOrigin-RevId: 167680208
* Fix tf.sparse_add for SparseTensor with _ref typed values.
Example:
st = tf.SparseTensor(
indices=[[1]], values=tf.Variable([1.0]), dense_shape=[1])
tf.sparse_add(st, st)
PiperOrigin-RevId: 167681121
* Fix conversion to explicit scalar broadcast
The dimensions field of a broadcast HLO op is meant to be populated with the
dimensions that are broadcasted, which in case of a scalar is the empty vector.
Generally, the rank of the operand of a broadcast op should always equal the
size of the dimensions vector.
PiperOrigin-RevId: 167686946
* Add 'unknown shape' shape functions on deprecated linalg ops.
PiperOrigin-RevId: 167719029
* Be more careful in IsInitalized, and log when it is called on an unknown
node_id.
PiperOrigin-RevId: 167722344
* tfdbg: Refactor graph-processing code out of debug_data.py
The basic idea is to separate the code in debug_data.py that handles graph structures into its own module (debug_graphs.py). This tackles an existing TODO item to simplify the code debug_data.DebugDumpDir.
In a later CL, code will be added to debug_graphs.DebugGraph to allow reconstruction of the original GraphDef, i.e., the GraphDef without the Copy* and Debug* nodes inserted by tfdbg. This will be useful for, among other things, the TensorBoard Debugger Plugin.
PiperOrigin-RevId: 167726113
* internal
PiperOrigin-RevId: 167727508
* Update MaxPoolV2Shape to support NCHV_VECT_C.
PiperOrigin-RevId: 167732437
* Deleting tf.contrib.learn.dnn benchmark tests.
PiperOrigin-RevId: 167741308
* Fix off-by-one documentation error.
sequence_lengths is the actual length of the sequence and therefor should not be used as zero-based indexing.
The code is correct but the documentation was misleading.
PiperOrigin-RevId: 167742082
* contrib summaries work in eager-graph mode (with defun)
As a side effect fix issues related to using eager-defined variables in graph
mode.
PiperOrigin-RevId: 167744121
* Fix minor documentation error in ZlibInputStream.
PiperOrigin-RevId: 167745218
* Sets the distributed training related properties of RunConfig based on TF_CONFIG.
PiperOrigin-RevId: 167752997
* Improved documentation about eval ops in EstimatorSpec.
PiperOrigin-RevId: 167753099
* Automated g4 rollback of changelist 156748870
PiperOrigin-RevId: 167753805
* Make cuda_solvers_gpu.cu.cc compile with nvcc8.
PiperOrigin-RevId: 167754383
* Add csv dataset example to get_started/regression.
PiperOrigin-RevId: 167754634
* Switches to OrderedDict to make the dictionary order deterministic so we have less randomness from graph building.
PiperOrigin-RevId: 167755072
* Add int8 version of fused_conv2d_bias_activation operator for the forward phase,
and support side_input and scaling parameters in float and int8 versions.
PiperOrigin-RevId: 167763219
* Make the text summary write no plugin data content
This is actually a safe removal because no logic makes use of the content of text plugin data.
PiperOrigin-RevId: 167763880
* Avoid unnecessary buffer allocations & deallocations
Before this change, when we reached the end of a file, we would
(1) clear the existing buffer (which at large buffer sizes typically involved
deallocating it).
(2) reserve a buffer (which at large buffer sizes is non-trivial)
(3) realize we had reached EoF, and therefore clear the buffer, deallocating
it again.
With this change, whenever the buffered reader detects an EoF condition, we
remember it, so that we can short-circuit the above logic.
The above optimization results in a more than 25x performance improvement for
large buffers reading small files.
PiperOrigin-RevId: 167766751
* [TF:XLA] In Literal: correctly handle operands with zero elements in
Copy.
PiperOrigin-RevId: 167769308
* Reduce batch size for resampler backward pass test, to speed up test.
PiperOrigin-RevId: 167769539
* Remove `SimpleGraphExecutionState::costs_`, which is unused.
PiperOrigin-RevId: 167772120
* detecting cycles when users add a control edge to a graph
PiperOrigin-RevId: 167773598
* Make writer_test avoid setting content to a string
That content field of the PluginData proto is going to be converted into a bytes field, and setting it to a string makes the test fail. Furthermore, the purpose of this test is to make sure that correct data is written, so setting the name of the plugin suffices.
PiperOrigin-RevId: 167776457
* Propagate the original stack trace when exceptions caught be MonitoredSession
are re-raised.
PiperOrigin-RevId: 167781071
* Change trace.py to not access a graph as a default argument.
Checks for None and access via default graph inside the function.
PiperOrigin-RevId: 167788815
* Added custom metric support for tf.estimator.Estimator.
PiperOrigin-RevId: 167788891
* A eager Saver that allows restore on create.
PiperOrigin-RevId: 167789332
* Make content field of PluginData a bytes field
The content field had previously been a string field, which had been problematic because
string fields can only store UTF-8 strings.
This problem can manifest in various ways. For instance, take the precision-recall curve plugin. Its summary collects data that scales in size based on the number of thresholds. When the content field is a string, the summary logic serializes the relevant data proto just fine when we only have a few thresholds (about 100). However, for large numbers of thresholds (ie, around 200), the summary logic fails to serialize and throws a cryptic error.
ValueError: '\x10\xc8\x01' has type str, but isn't valid UTF-8 encoding. Non-UTF-8 strings must be converted to unicode objects before being added.
Changing the content field to a bytes field fixes this issue because bytes fields are not restricted to UTF-8 strings. I just happened to have needed a long enough string for the string to no longer be a valid UTF-8 one.
PiperOrigin-RevId: 167790594
* Temporarily disable tf_should_use wrapper, since it can cause
python Graph/Operation/Tensor memory leaks.
PiperOrigin-RevId: 167790657
* Ensure using "path" as a URI will keep working.
PiperOrigin-RevId: 167793848
* Fix typo in graph transforms error message
PiperOrigin-RevId: 167796563
* Merge changes from github.
END_PUBLIC
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Commit
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| tensorflow | ||
| third_party | ||
| tools | ||
| util/python | ||
| .gitignore | ||
| ACKNOWLEDGMENTS | ||
| ADOPTERS.md | ||
| arm_compiler.BUILD | ||
| AUTHORS | ||
| BUILD | ||
| CODE_OF_CONDUCT.md | ||
| CODEOWNERS | ||
| configure | ||
| configure.py | ||
| CONTRIBUTING.md | ||
| ISSUE_TEMPLATE.md | ||
| LICENSE | ||
| models.BUILD | ||
| README.md | ||
| RELEASE.md | ||
| WORKSPACE | ||
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.
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:
Nightly pip packages
- We are pleased to announce that TensorFlow now offers nightly pip packages
under the tf-nightly project on pypi.
Simply run
pip install tf-nightlyin 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
- 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)
- Windows CPU-only: Python 3.5 64-bit (build history) / Python 3.6 64-bit (build history)
- Windows GPU: Python 3.5 64-bit (build history) / Python 3.6 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
>>> sess.close()
For more information
- TensorFlow website
- TensorFlow White Papers
- TensorFlow Model Zoo
- TensorFlow MOOC on Udacity
- TensorFlow course at Stanford
Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.