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* Fix error of version_info.cc not being generated on windows if building ↵Gravatar Erik Smistad2017-05-05
| | | | python bindings is disabled in CMake (#9660)
* Misspellings on the saved_model_cli.py (#9684)Gravatar Chris Hoyean Song2017-05-05
| | | information => information
* Merge pull request #9677 from vrv/branch_155159972Gravatar Vijay Vasudevan2017-05-04
|\ | | | | Branch 155159972
* | This fixes some issue introduced in the previous version where RTTI (#9671)Gravatar Guenther Schmuelling2017-05-04
| | | | | | | | | | | | was removed from the exclusion list. Because of this the number of symbols in the def file was close to 64K for gpu builds and yesterday a few added symbols pushed us over the 64K limit for the windows linker. Adding RTTI back to the exclusion list.
| * Merge commit for internal changesGravatar Vijay Vasudevan2017-05-04
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| * Implement ClusterSpec Propagation in TF MasterGravatar Brennan Saeta2017-05-04
| | | | | | | | | | | | | | | | | | | | | | ClusterSpec propagation is a capability upgrade for TensorFlow that should make it much easier to (1) build distributed TensorFlow clusters, and (2) handle node failures. The ClusterSpec propagation capability allows TensorFlow workers to be booted independently of each other, and with no knowledge about others. The client can then construct a ClusterDef (ClusterSpec), and then send it to the TF master at session creation. The master in turn then propagates the ClusterDef along to all of the workers. Change: 155159972
| * Android demo: Allow DetectorActivity to gracefully degrade if no native ↵Gravatar Andrew Harp2017-05-04
| | | | | | | | | | | | | | ObjectTracker support is found. If libtensorflow_demo.so is not found in the APK, rendered boxes will simply be stationary and will be replaced whenever new results come in. Partially addresses #6385 Change: 155159326
| * RNN checkpoint migration toolGravatar Shanqing Cai2017-05-04
| | | | | | | | Change: 155158477
| * Add `categorical_column_with_vocabulary_list`.Gravatar A. Unique TensorFlower2017-05-04
| | | | | | | | Change: 155158042
| * Automated rollback of change 155136555Gravatar Justine Tunney2017-05-04
| | | | | | | | Change: 155156366
* | replace sleep macro with an inline function (#9663)Gravatar Erik Smistad2017-05-04
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| * Include c++ gradients in c_api build rule.Gravatar Suharsh Sivakumar2017-05-04
| | | | | | | | | | | | #6268 #9150 Change: 155146664
| * [TF] optimization: SparseTensorDenseMatMul GPU kernel rewritten in pure cuda.Gravatar Eugene Brevdo2017-05-04
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Also added an additional GPU int32::max check that was missing. Performance seems to be between 1x-10x faster on average. The likely culprit on CPU slowdown was probably the unnecessary temp allocation for scratch space. Performance on a k40, compiled -c opt --config cuda --copt=-mavx: **BEFORE** Matrix sizes: A sparse [m, k] with % nonzero values between 1% and 80% B dense [k, n] % nnz n gpu m k dt(dense) dt(sparse) dt(sparse)/dt(dense) 0.01 50 True 100 100 0.000319954 0.000275495 0.861045 0.01 50 True 100 1000 0.000469565 0.000290895 0.619497 0.01 50 True 1000 100 0.000572815 0.000271131 0.473331 0.01 50 True 1000 1000 0.00133119 0.00042006 0.315554 0.01 50 False 100 100 0.00034191 0.000289171 0.845751 0.01 50 False 100 1000 0.0004796 0.00028483 0.593891 0.01 50 False 1000 100 0.000632371 0.000300461 0.475134 0.01 50 False 1000 1000 0.00134726 0.000576285 0.427746 0.01 100 True 100 100 0.000353755 0.00027729 0.783849 0.01 100 True 100 1000 0.000536649 0.00028337 0.528036 0.01 100 True 1000 100 0.000661941 0.00027933 0.421987 0.01 100 True 1000 1000 0.0014109 0.0006698 0.474732 0.01 100 False 100 100 0.00039546 0.00030159 0.762631 0.01 100 False 100 1000 0.00054909 0.00027276 0.49675 0.01 100 False 1000 100 0.000631344 0.00028231 0.447157 0.01 100 False 1000 1000 0.00141789 0.000657049 0.463398 0.2 50 True 100 100 0.00033689 0.000280155 0.831591 0.2 50 True 100 1000 0.000563495 0.00064159 1.13859 0.2 50 True 1000 100 0.00058635 0.00067611 1.15308 0.2 50 True 1000 1000 0.00153552 0.00486242 3.16662 0.2 50 False 100 100 0.000333545 0.000267555 0.802154 0.2 50 False 100 1000 0.000544 0.00066272 1.21824 0.2 50 False 1000 100 0.00058253 0.000670955 1.15179 0.2 50 False 1000 1000 0.00153017 0.00480928 3.14298 0.2 100 True 100 100 0.00036919 0.000288659 0.781872 0.2 100 True 100 1000 0.00067063 0.00110059 1.64113 0.2 100 True 1000 100 0.00066443 0.00108547 1.63369 0.2 100 True 1000 1000 0.00180991 0.00961579 5.31286 0.2 100 False 100 100 0.00040061 0.000325365 0.812174 0.2 100 False 100 1000 0.00066774 0.00111843 1.67494 0.2 100 False 1000 100 0.000696205 0.00108078 1.55239 0.2 100 False 1000 1000 0.00179788 0.00960569 5.34278 0.5 50 True 100 100 0.00034819 0.00033425 0.959963 0.5 50 True 100 1000 0.00075176 0.00134084 1.78359 0.5 50 True 1000 100 0.000642445 0.00133641 2.08019 0.5 50 True 1000 1000 0.00233791 0.0124282 5.31597 0.5 50 False 100 100 0.000345069 0.000334586 0.96962 0.5 50 False 100 1000 0.00071701 0.00135879 1.89508 0.5 50 False 1000 100 0.000632119 0.00134036 2.12043 0.5 50 False 1000 1000 0.00240216 0.0126202 5.25368 0.5 100 True 100 100 0.000393934 0.00040344 1.02413 0.5 100 True 100 1000 0.000957675 0.002709 2.82873 0.5 100 True 1000 100 0.000756125 0.00242428 3.20619 0.5 100 True 1000 1000 0.00298202 0.0241416 8.09572 0.5 100 False 100 100 0.000395606 0.000433675 1.09623 0.5 100 False 100 1000 0.000963565 0.00248293 2.57682 0.5 100 False 1000 100 0.00079523 0.0024281 3.05333 0.5 100 False 1000 1000 0.00299668 0.0242615 8.09614 0.8 50 True 100 100 0.00036806 0.00040923 1.11186 0.8 50 True 100 1000 0.00091419 0.00207383 2.26848 0.8 50 True 1000 100 0.000684329 0.00196612 2.87307 0.8 50 True 1000 1000 0.00302433 0.0199798 6.60637 0.8 50 False 100 100 0.000368149 0.000615025 1.67058 0.8 50 False 100 1000 0.0008786 0.00205821 2.3426 0.8 50 False 1000 100 0.00067889 0.00195498 2.87967 0.8 50 False 1000 1000 0.00290009 0.0191242 6.59434 0.8 100 True 100 100 0.000452549 0.00063767 1.40906 0.8 100 True 100 1000 0.00126929 0.00391422 3.08378 0.8 100 True 1000 100 0.000919235 0.00386167 4.20096 0.8 100 True 1000 1000 0.00423295 0.0431824 10.2015 0.8 100 False 100 100 0.000428261 0.000626891 1.46381 0.8 100 False 100 1000 0.00120801 0.00395877 3.27711 0.8 100 False 1000 100 0.00080466 0.00385143 4.78641 0.8 100 False 1000 1000 0.00370808 0.0403527 10.8824 **AFTER** Matrix sizes: A sparse [m, k] with % nonzero values between 1% and 80% B dense [k, n] % nnz n gpu m k dt(dense) dt(sparse) dt(sparse)/dt(dense) 0.01 50 True 100 100 0.000312485 0.00020528 0.656927 0.01 50 True 100 1000 0.0004655 0.00020095 0.431686 0.01 50 True 1000 100 0.000567449 0.000203935 0.359389 0.01 50 True 1000 1000 0.00132323 0.00027171 0.205339 0.01 50 False 100 100 0.000319945 0.000197511 0.617328 0.01 50 False 100 1000 0.000466419 0.000210185 0.450635 0.01 50 False 1000 100 0.0005581 0.000199865 0.358117 0.01 50 False 1000 1000 0.00129479 0.000451496 0.348702 0.01 100 True 100 100 0.000364131 0.000196835 0.540561 0.01 100 True 100 1000 0.00053398 0.000206494 0.386708 0.01 100 True 1000 100 0.00062722 0.000203185 0.323946 0.01 100 True 1000 1000 0.00138674 0.000335904 0.242227 0.01 100 False 100 100 0.000361339 0.000195 0.53966 0.01 100 False 100 1000 0.000531831 0.000207155 0.389513 0.01 100 False 1000 100 0.00062245 0.000197015 0.316515 0.01 100 False 1000 1000 0.0014007 0.000328825 0.234757 0.2 50 True 100 100 0.00033185 0.000262895 0.792209 0.2 50 True 100 1000 0.00054391 0.000586189 1.07773 0.2 50 True 1000 100 0.000581805 0.000531535 0.913597 0.2 50 True 1000 1000 0.00153913 0.00142783 0.927687 0.2 50 False 100 100 0.00033572 0.000266831 0.794803 0.2 50 False 100 1000 0.000534315 0.000585151 1.09514 0.2 50 False 1000 100 0.000580961 0.00033344 0.573947 0.2 50 False 1000 1000 0.0015055 0.00143968 0.956284 0.2 100 True 100 100 0.000371666 0.00026337 0.708621 0.2 100 True 100 1000 0.000667235 0.00056811 0.851439 0.2 100 True 1000 100 0.000671356 0.000400575 0.596666 0.2 100 True 1000 1000 0.00178568 0.00250393 1.40222 0.2 100 False 100 100 0.000370425 0.000254935 0.688223 0.2 100 False 100 1000 0.000661175 0.000601134 0.909191 0.2 100 False 1000 100 0.0006944 0.00039817 0.573401 0.2 100 False 1000 1000 0.00176969 0.0024947 1.40968 0.5 50 True 100 100 0.000346885 0.000263295 0.759028 0.5 50 True 100 1000 0.00073113 0.00107669 1.47263 0.5 50 True 1000 100 0.000672774 0.000493085 0.732914 0.5 50 True 1000 1000 0.00260436 0.003335 1.28054 0.5 50 False 100 100 0.00036242 0.000273196 0.753809 0.5 50 False 100 1000 0.000753295 0.00107086 1.42157 0.5 50 False 1000 100 0.00064886 0.000501654 0.773132 0.5 50 False 1000 1000 0.00241105 0.0033146 1.37475 0.5 100 True 100 100 0.000401269 0.00027831 0.693573 0.5 100 True 100 1000 0.00094245 0.00111468 1.18275 0.5 100 True 1000 100 0.00075719 0.00074962 0.990003 0.5 100 True 1000 1000 0.00297528 0.00601445 2.02147 0.5 100 False 100 100 0.000408576 0.00026246 0.642377 0.5 100 False 100 1000 0.00094272 0.00112762 1.19613 0.5 100 False 1000 100 0.000762925 0.00074343 0.974446 0.5 100 False 1000 1000 0.00314936 0.00604122 1.91824 0.8 50 True 100 100 0.00036589 0.000331376 0.905669 0.8 50 True 100 1000 0.00086403 0.00171248 1.98197 0.8 50 True 1000 100 0.00067048 0.000715261 1.06679 0.8 50 True 1000 1000 0.00284684 0.00527865 1.85422 0.8 50 False 100 100 0.000357161 0.000540144 1.51233 0.8 50 False 100 1000 0.000884765 0.00170428 1.92625 0.8 50 False 1000 100 0.000666975 0.000737065 1.10509 0.8 50 False 1000 1000 0.0028149 0.00530442 1.88441 0.8 100 True 100 100 0.00041237 0.00034323 0.832335 0.8 100 True 100 1000 0.00122102 0.00179725 1.47192 0.8 100 True 1000 100 0.000807976 0.00111246 1.37684 0.8 100 True 1000 1000 0.00379081 0.00968211 2.5541 0.8 100 False 100 100 0.000426315 0.000339085 0.795386 0.8 100 False 100 1000 0.00144096 0.00179819 1.2479 0.8 100 False 1000 100 0.000951196 0.0011155 1.17274 0.8 100 False 1000 1000 0.0039524 0.00980128 2.47983 Change: 155142876
| * Fix documentation in supervisor.Gravatar Yutaka Leon2017-05-04
| | | | | | | | Change: 155140112
* | Added identity op and a fix for LRN (#9641)Gravatar Vivek Rane2017-05-04
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * relu grad and maxpooling grad fixes for perf * Graph layout pass and conversion pass changes This commit makes following changes: - Enables support for ReluGrad and BiasAddGrad - Adds support for detecting depthwise/batchwise pooling - Adds more unit tests for Graph rewrite pass - Improvements to handling control-flow edges - Bug fixes * Defaulting to Eigen when LRN depth_radius!=2 * Fixed mkl_conv_grad_filter.cc for conv_ops_tests.py * Style fix to mkl_matmul and remove unnecessary 'MKL' label on matmul kernel * Style fixes based on clang-format to mkl_conv_* and mkl_matmul * Bug fixes * Adding OP_REQUIRES_OK check in Concat * Making some style changes * Enabled the configuration of MKL settings * relu grad and maxpooling grad fixes for perf * Graph layout pass and conversion pass changes This commit makes following changes: - Enables support for ReluGrad and BiasAddGrad - Adds support for detecting depthwise/batchwise pooling - Adds more unit tests for Graph rewrite pass - Improvements to handling control-flow edges - Bug fixes * Defaulting to Eigen when LRN depth_radius!=2 * Fixed mkl_conv_grad_filter.cc for conv_ops_tests.py * Style fix to mkl_matmul and remove unnecessary 'MKL' label on matmul kernel * Style fixes based on clang-format to mkl_conv_* and mkl_matmul * Bug fixes * Adding OP_REQUIRES_OK check in Concat * Making some style changes * Enabled the configuration of MKL settings * Fixing graph unit tests with Mkl op name change to _Mkl; Fixed missing _ in MklToTf op * Fixed missing libdl.so.2 in BUILD file * Fixes for unit test build failures. * Changes in mkl_conv_grad_filter_ops.cc for Google code style * Fixes to remove dead code * removed the dead code and added a TODO for mkl implementation to handle this case in the future * Enabling MklIdentityOp * Calling MKL for all values of depth radius in LRN * Fixed buildifier sanity check error * Adding support for google's CI automation * Updated link to new MKL version * Enabling MklIdentityOp * Calling MKL for all values of depth radius in LRN * Fix for missing locate binary * Fix for missing locate command in CI * Adding updatedb to populate the database after installing mlocate * Fixed buildifier issue * setting tf_need_mkl=0 in libtf files * Added third_party/mkl/* to .gitignore * Added third_party/eigen3/mkl_include to .gitignore * In configured, set MKL-enabling options only for Linux. * Enabling MklIdentityOp * Calling MKL for all values of depth radius in LRN * Making style fix in LRN * Fixed Indentation
| * Fix mac build.Gravatar Yutaka Leon2017-05-04
| | | | | | | | Change: 155140054
| * Clean up TensorBoard build and fix sync processGravatar Justine Tunney2017-05-04
| | | | | | | | Change: 155136555
| * Added traceback_with_start_lines property to op that includes function start ↵Gravatar Anna R2017-05-04
| | | | | | | | | | | | line number as the last element in each traceback tuple. Change: 155136334
| * Internal change.Gravatar Anna R2017-05-04
| | | | | | | | Change: 155135670
| * [tf distributions] Move conditional back to contrib (not slated for core); ↵Gravatar Eugene Brevdo2017-05-04
| | | | | | | | | | | | | | | | | | | | | | move transformed into core. (I accidentally moved the wrong file in the previous change!) Also move the Identity bijector & test into core. I can't move the TransformedDistribution test into core since it relies on linalg. Change: 155130709
| * Upgrade TypeScript to 2.3.1Gravatar Justine Tunney2017-05-04
| | | | | | | | Change: 155130334
| * Open sourced op level cost predictionGravatar Benoit Steiner2017-05-04
| | | | | | | | Change: 155123817
| * Go: Update generated wrapper functions for TensorFlow ops.Gravatar A. Unique TensorFlower2017-05-04
| | | | | | | | Change: 155122342
| * [XLA] Adapt to interface changes in llvm r302060.Gravatar A. Unique TensorFlower2017-05-04
| | | | | | | | Change: 155121754
| * Add sparse_recall_at_top_k which takes top-k class indices instead of class ↵Gravatar A. Unique TensorFlower2017-05-04
| | | | | | | | | | | | logits. Change: 155121560
| * Android demo: Add YUV -> RGB conversion Java implementation as fallback if ↵Gravatar Andrew Harp2017-05-04
| | | | | | | | | | | | | | | | | | | | | | native implementation is not found. This means that compiling libtensorflow_demo.so will only be strictly necessary for the Detection example (which uses native object tracking). A followup change will add graceful degradation in that case too. Java conversion may be slower depending on the device, but should still be acceptable for demo purposes as the majority of the compute time will still be spent on TF inference passes. Note that this has no effect on the necessity of libtensorflow_inference.so, which provides the actual TF support. However libtensorflow_inference.so may be added to applications via the prebuilt AAR, so no native compilation is necessary. Partially addresses #6385 Change: 155121431
* | Enable MKL in configure and various bug fixes (#9580)Gravatar Vivek Rane2017-05-04
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * relu grad and maxpooling grad fixes for perf * Graph layout pass and conversion pass changes This commit makes following changes: - Enables support for ReluGrad and BiasAddGrad - Adds support for detecting depthwise/batchwise pooling - Adds more unit tests for Graph rewrite pass - Improvements to handling control-flow edges - Bug fixes * Defaulting to Eigen when LRN depth_radius!=2 * Fixed mkl_conv_grad_filter.cc for conv_ops_tests.py * Style fix to mkl_matmul and remove unnecessary 'MKL' label on matmul kernel * Style fixes based on clang-format to mkl_conv_* and mkl_matmul * Bug fixes * Adding OP_REQUIRES_OK check in Concat * Making some style changes * Enabled the configuration of MKL settings * relu grad and maxpooling grad fixes for perf * Graph layout pass and conversion pass changes This commit makes following changes: - Enables support for ReluGrad and BiasAddGrad - Adds support for detecting depthwise/batchwise pooling - Adds more unit tests for Graph rewrite pass - Improvements to handling control-flow edges - Bug fixes * Defaulting to Eigen when LRN depth_radius!=2 * Fixed mkl_conv_grad_filter.cc for conv_ops_tests.py * Style fix to mkl_matmul and remove unnecessary 'MKL' label on matmul kernel * Style fixes based on clang-format to mkl_conv_* and mkl_matmul * Bug fixes * Adding OP_REQUIRES_OK check in Concat * Making some style changes * Enabled the configuration of MKL settings * Fixing graph unit tests with Mkl op name change to _Mkl; Fixed missing _ in MklToTf op * Fixed missing libdl.so.2 in BUILD file * Fixes for unit test build failures. * Changes in mkl_conv_grad_filter_ops.cc for Google code style * Fixes to remove dead code * removed the dead code and added a TODO for mkl implementation to handle this case in the future * Fixed buildifier sanity check error * Adding support for google's CI automation * Updated link to new MKL version * Fix for missing locate command in CI * Adding updatedb to populate the database after installing mlocate * Fixed buildifier issue * setting tf_need_mkl=0 in libtf files * Added third_party/mkl/* to .gitignore * Added third_party/eigen3/mkl_include to .gitignore * In configured, set MKL-enabling options only for Linux.
* | Java API to get the size of output list operations (#9640)Gravatar KB Sriram2017-05-04
| | | | | | | | | | | | * Java API to get the size of output list operations * Java API to get the size of output list operations
| * Organize the lookup table ops into it's own lookup_ops.cc file instead of ↵Gravatar Yutaka Leon2017-05-04
| | | | | | | | | | | | data_flow_ops.cc Change: 155119120
| * Docs: Fix broken link in contrib.layers.initializersGravatar Asim Shankar2017-05-04
| | | | | | | | Change: 155117831
| * Docs: Stop advertising GPU support for Mac OS X.Gravatar Asim Shankar2017-05-04
| | | | | | | | | | | | | | | | | | | | Starting with the next release (1.2) GPU support for Mac will be dropped in release binaries since the configuration of NVIDIA GPUs on a Mac is somewhat esoteric (and we currently do not have the bandwidth to debug test failures on that platform). While at it, change to version 1.1.0 from 1.1.0-rc2 Change: 155117808
* | Fix Bazel CI / TensorFlow project. (#9659)Gravatar László Csomor2017-05-04
| | | | | | | | | | | | | | | | | | | | | | | | | | This change: 1. updates common_env.sh to export PYTHON_LIB_PATH along with PYTHON_BIN_PATH so the configure scripts doesn't have to guess 2. writes these paths to bazelrc with quotes around, to guard agains spaces in the path (e.g. "C:/Program Files/Anaconda3/python") Fixes https://github.com/bazelbuild/bazel/issues/2892
| * Automated rollback of change 155092161Gravatar Peter Hawkins2017-05-04
| | | | | | | | Change: 155115134
| * Don't declare the ReadyNodeManager class in an anonymous namespace since ↵Gravatar Benoit Steiner2017-05-04
| | | | | | | | | | | | some compilers don't like that. Change: 155115034
| * Github #9633: enforce an omitted shape check in sparse_add() op.Gravatar Zongheng Yang2017-05-04
| | | | | | | | | | | | | | Previously, the code made the assumption that the two operands have matching shapes, but did not enforce the equality. This could lead to invalid memory access in some cases. Change: 155109464
| * Don't repeatedly serialize the function library in MasterSession.Gravatar Peter Hawkins2017-05-04
| | | | | | | | Change: 155102455
| * Fix an error.Gravatar Yao Zhang2017-05-04
| | | | | | | | Change: 155098574
| * Automated rollback of change 154868460Gravatar A. Unique TensorFlower2017-05-04
| | | | | | | | Change: 155096835
| * Making the pip smoke test warning clearer.Gravatar A. Unique TensorFlower2017-05-04
| | | | | | | | Change: 155094164
| * RNNCells' trainable_weights and non_trainable_weights parameters return ↵Gravatar Eugene Brevdo2017-05-04
| | | | | | | | | | | | valid values. Change: 155094112
| * Update build rules for contrib/opt.Gravatar A. Unique TensorFlower2017-05-04
| | | | | | | | Change: 155092161
* | Add input function for training and testing (#9617) (#9658)Gravatar xiaoyaozhuzi2017-05-04
| | | | | | | | | | | | | | * Add input function for training and testing Estimator is decoupled from Scikit Learn interface by moving into separate class SKCompat. Arguments x, y and batch_size are only available in the SKCompat class, Estimator will only accept input_fn * remove extra comma
| * Make contrib real_valued_column cross compatible with core feature_column ↵Gravatar Mustafa Ispir2017-05-04
| | | | | | | | | | | | builders. Change: 155090692
| * Add an option to not convert layout if GEMM is used internally in Conv2D, ↵Gravatar Yao Zhang2017-05-04
| | | | | | | | | | | | | | Conv2DBackpropFilter, and Conv2DBackpropInput, because in such cases, NHWC is usually faster than NCHW. The cost of enabling this option is the overhead of more non-cancellable layout conversion nodes. We added auto tuning to choose a better option by estimating the overhead using the number of added layout conversion nodes. Don't Convert the layout for Sum, because reduction along dimension 0, 2, 3 (in NCHW) is about 10x slower than along 0, 1, 2 (in NHWC). Change: 155089805
| * Use $opt defined in the loop rather than raw string.Gravatar Patrick Nguyen2017-05-04
| | | | | | | | | | Fixes #9651. Change: 155089799
| * Adjust getting started guide to use a training and eval data set.Gravatar Andrew Selle2017-05-04
| | | | | | | | Change: 155089162
| * Use six.string_types instead of str in estimator/export.Gravatar A. Unique TensorFlower2017-05-04
| | | | | | | | Change: 155087824
* | Group LSTM cell (#9606)Gravatar Oleksii Kuchaiev2017-05-04
| | | | | | | | | | | | | | | | | | | | * GLSTM cell from https://openreview.net/forum?id=ByxWXyNFg&noteId=ByxWXyNFg * Responding to comments on PR#9606 * Update comments according to review. * More fixes on users' behalf.
| * Add `categorical_column_with_vocabulary_file`.Gravatar A. Unique TensorFlower2017-05-04
| | | | | | | | | | Move lookup_ops implementation from tensorflow/contrib/lookup to tensorflow/python/feature_column. Change: 155079825
| * Change Unique op to use gtl::FlatMap instead of std::unordered_map<>.Gravatar Peter Hawkins2017-05-04
| | | | | | | | Change: 155070869
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