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tensorflow::
#include <data_flow_ops.h>
Interleave the values from the
data
tensors into a single tensor.
Summary
Builds a merged tensor such that
merged [ indices[m ][ i, ..., j ] , ... ] = data [ m ][ i, ..., j, ... ]
For example, if each
indices[m]
is scalar or vector, we have
# Scalar indices :
merged [ indices[m ] , ... ] = data [ m ][ ... ]
# Vector indices :
merged [ indices[m ][ i ] , ... ] = data [ m ][ i, ... ]
Each
data[i].shape
must start with the corresponding
indices[i].shape
, and the rest of
data[i].shape
must be constant w.r.t.
i
. That is, we must have
data[i].shape = indices[i].shape + constant
. In terms of this
constant
, the output shape is
merged . shape = [ max ( indices )] + constant
Values may be merged in parallel, so if an index appears in both
indices[m][i]
and
indices[n][j]
, the result may be invalid. This differs from the normal
DynamicStitch
operator that defines the behavior in that case.
For example:
indices[0] = 6
indices[1] = [4, 1]
indices[2] = [[5, 2], [0, 3]]
data[0] = [61, 62]
data[1] = [[41, 42], [11, 12]]
data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]
merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],
[51, 52], [61, 62]]
This method can be used to merge partitions created by
dynamic_partition
as illustrated on the following example:
# Apply function (increments x_i) on elements for which a certain condition
# apply (x_i != -1 in this example).
x = tf . constant ([ 0.1 , - 1. , 5.2 , 4.3 , - 1. , 7.4 ])
condition_mask = tf . not_equal ( x , tf . constant ( - 1. ))
partitioned_data = tf . dynamic_partition (
x , tf . cast ( condition_mask , tf . int32 ) , 2 )
partitioned_data [ 1 ] = partitioned_data [ 1 ] + 1.0
condition_indices = tf . dynamic_partition (
tf . range ( tf . shape ( x )[ 0 ]), tf . cast ( condition_mask , tf . int32 ) , 2 )
x = tf . dynamic_stitch ( condition_indices , partitioned_data )
# Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain
# unchanged.
Args:
Returns:
Public attributes
Public functions
node
:: tensorflow :: Node * node () const
operator :: tensorflow :: Input () const
operator::tensorflow::Output
operator :: tensorflow :: Output () const
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license .
Last updated 2021-05-14 UTC.
[null,null,["Last updated 2021-05-14 UTC."],[],[],null,["# tensorflow::ops::ParallelDynamicStitch Class Reference\n\ntensorflow::\nops::\nParallelDynamicStitch\n========================================\n\n`\n#include \u003cdata_flow_ops.h\u003e\n`\n\n\nInterleave the values from the\n`\ndata\n`\ntensors into a single tensor.\n\nSummary\n-------\n\n\nBuilds a merged tensor such that\n\n\n```transact-sql\n merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]\n```\n\n\u003cbr /\u003e\n\n\nFor example, if each\n`\nindices[m]\n`\nis scalar or vector, we have\n\n\n```transact-sql\n # Scalar indices:\n merged[indices[m], ...] = data[m][...]\n```\n\n\u003cbr /\u003e\n\n\n```transact-sql\n # Vector indices:\n merged[indices[m][i], ...] = data[m][i, ...]\n```\n\n\u003cbr /\u003e\n\n\nEach\n`\ndata[i].shape\n`\nmust start with the corresponding\n`\nindices[i].shape\n`\n, and the rest of\n`\ndata[i].shape\n`\nmust be constant w.r.t.\n`\ni\n`\n. That is, we must have\n`\ndata[i].shape = indices[i].shape + constant\n`\n. In terms of this\n`\nconstant\n`\n, the output shape is \n\n```gdscript\nmerged.shape = [max(indices)] + constant\n```\n\n\u003cbr /\u003e\n\n\nValues may be merged in parallel, so if an index appears in both\n`\nindices[m][i]\n`\nand\n`\nindices[n][j]\n`\n, the result may be invalid. This differs from the normal\n[DynamicStitch](/versions/r2.5/api_docs/cc/class/tensorflow/ops/dynamic-stitch#classtensorflow_1_1ops_1_1_dynamic_stitch)\noperator that defines the behavior in that case.\n\n\nFor example:\n\n\n```text\n indices[0] = 6\n indices[1] = [4, 1]\n indices[2] = [[5, 2], [0, 3]]\n data[0] = [61, 62]\n data[1] = [[41, 42], [11, 12]]\n data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]]\n merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42],\n [51, 52], [61, 62]]\n```\n\n\u003cbr /\u003e\n\n\nThis method can be used to merge partitions created by\n`\ndynamic_partition\n`\nas illustrated on the following example:\n\n\n```gdscript\n # Apply function (increments x_i) on elements for which a certain condition\n # apply (x_i != -1 in this example).\n x=tf.constant([0.1, -1., 5.2, 4.3, -1., 7.4])\n condition_mask=tf.not_equal(x,tf.constant(-1.))\n partitioned_data = tf.dynamic_partition(\n x, tf.cast(condition_mask, tf.int32) , 2)\n partitioned_data[1] = partitioned_data[1] + 1.0\n condition_indices = tf.dynamic_partition(\n tf.range(tf.shape(x)[0]), tf.cast(condition_mask, tf.int32) , 2)\n x = tf.dynamic_stitch(condition_indices, partitioned_data)\n # Here x=[1.1, -1., 6.2, 5.3, -1, 8.4], the -1. values remain\n # unchanged.\n```\n\n\u003cbr /\u003e\n\n\n\u003cbr /\u003e\n\n\nArgs:\n\n- scope: A [Scope](/versions/r2.5/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n\n\u003cbr /\u003e\n\n\nReturns:\n\n- `\n `[Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)`\n ` : The merged tensor.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| ` `[ParallelDynamicStitch](#classtensorflow_1_1ops_1_1_parallel_dynamic_stitch_1a6d5464f1c148b04bc28b9bff03f884d3)` (const :: `[tensorflow::Scope](/versions/r2.5/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, :: `[tensorflow::InputList](/versions/r2.5/api_docs/cc/class/tensorflow/input-list#classtensorflow_1_1_input_list)` indices, :: `[tensorflow::InputList](/versions/r2.5/api_docs/cc/class/tensorflow/input-list#classtensorflow_1_1_input_list)` data) ` ||\n\n| ### Public attributes ||\n|-----------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|\n| ` `[merged](#classtensorflow_1_1ops_1_1_parallel_dynamic_stitch_1acf4ad6fe444ed11732637ae9f1951f16)` ` | ` :: `[tensorflow::Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)` ` |\n| ` `[operation](#classtensorflow_1_1ops_1_1_parallel_dynamic_stitch_1a339e540a99d7624dfdc0236dcaaa7fd0)` ` | ` `[Operation](/versions/r2.5/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation)` ` |\n\n| ### Public functions ||\n|---------------------------------------------------------------------------------------------------------------------------------------|--------------------------|\n| ` `[node](#classtensorflow_1_1ops_1_1_parallel_dynamic_stitch_1af337a4bfc6cb29dc5bf35e4158622436)` () const ` | ` ::tensorflow::Node * ` |\n| ` `[operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_parallel_dynamic_stitch_1aa13a376d3e19711dd994e37a3c97cbc8)` () const ` | ` ` |\n| ` `[operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_parallel_dynamic_stitch_1aa279ea721b609a0870436bf241c90c9f)` () const ` | ` ` |\n\nPublic attributes\n-----------------\n\n### merged\n\n```text\n::tensorflow::Output merged\n``` \n\n### operation\n\n```text\nOperation operation\n``` \n\nPublic functions\n----------------\n\n### ParallelDynamicStitch\n\n```gdscript\n ParallelDynamicStitch(\n const ::tensorflow::Scope & scope,\n ::tensorflow::InputList indices,\n ::tensorflow::InputList data\n)\n``` \n\n### node\n\n```gdscript\n::tensorflow::Node * node() const \n``` \n\n### operator::tensorflow::Input\n\n```gdscript\n operator::tensorflow::Input() const \n``` \n\n### operator::tensorflow::Output\n\n```gdscript\n operator::tensorflow::Output() const \n```"]]
