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path: root/tensorflow/core/kernels/strided_slice_op.h
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* fix C++ header guards.Gravatar A. Unique TensorFlower2018-08-21
| | | | PiperOrigin-RevId: 209679086
* TF_CALL_ALL_TYPES should include variantGravatar Alexandre Passos2018-02-02
| | | | PiperOrigin-RevId: 184347081
* Make ResourceHandle not be a protoGravatar Geoffrey Irving2017-06-27
| | | | | | | I'm trying to make core/kernels independent of protos. Currently the dtype ResourceHandle is itself a proto. After this CL, ResourceHandle is a normal C++ type which gets converted to/from ResourceHandleProto at (de)serialization time. RELNOTES: n/a PiperOrigin-RevId: 160329002
* Registers kernels for resources on slice / stack.Gravatar A. Unique TensorFlower2016-12-21
| | | | Change: 142707321
* Create GPU implementation of StridedSliceAssignGravatar Andrew Selle2016-10-26
| | | | Change: 137329621
* Cleanup license headerGravatar A. Unique TensorFlower2016-09-29
| | | | Change: 134714467
* Add sliced assignment to variablesGravatar Andrew Selle2016-08-31
| | | | | | | | | | | | - Currently this does not support broadcasts, so the r-value's shape must match the shape of the slice. - Only assignments to variables are supported. e.g. of usage op = foo[3:5].assign(tf.constant([1,2])) sess.run(op) Change: 131837883
* Fixed the initialization of the gradients for the strided slice op on GPUGravatar Benoit Steiner2016-07-20
| | | | Change: 127951209
* Implement gradient for StridedSlice op.Gravatar Andrew Selle2016-06-24
| | | | | | | | | StridedSliceGrad op implements the gradient of StridedSlice. Also implement python benchmark for StridedSlice and simple Slice. Fix bugs in special case optimizations in StridedSlice. (Toward resolving bug #206) Change: 125789921
* Extended slicing in TensorFlow (similar to NumPy)Gravatar Andrew Selle2016-06-15
This extends slicing to allow a more complete set of the basic slicing in NumPy. For example, you can now do negative strides while zero strides are still disallowed. NOTE: This change does not yet hook up the new slicing behavior to the python getitem operator, because we do not yet support gradients (unlike the simpler slice we are extending). NOTE: This change does not introduce slicing using tensors as arguments (advanced indexing in NumPy). foo[5:1:-2] You can also do ellipsis foo[5:1:-2,...,3:4] In addition you can add new dimensions foo[5:, tf.newaxis, ...] Fixes part of #206. Change: 124998811
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