tensorflow:: אופס:: ParallelDynamicStitch
#include <data_flow_ops.h> שזור את הערכים מטנסור data לטנזור בודד.
תַקצִיר
בונה טנזור ממוזג כזה
merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]
לדוגמה, אם כל indices[m] הוא סקלארי או וקטור, יש לנו
# Scalar indices: merged[indices[m], ...] = data[m][...]
# Vector indices: merged[indices[m][i], ...] = data[m][i, ...]
כל data[i].shape חייב להתחיל עם indices[i].shape , ושאר data[i].shape חייב להיות קבוע wrt i . כלומר, יש לנו data[i].shape = indices[i].shape + constant . במונחים של constant זה, צורת הפלט היא
merged.shape = [max(indices)] + constant
ניתן למזג ערכים במקביל, כך שאם מופיע אינדקס גם indices[m][i] וגם indices[n][j] , התוצאה עלולה להיות לא חוקית. זה שונה מאופרטור DynamicStitch הרגיל שמגדיר את ההתנהגות במקרה זה.
לְדוּגמָה:
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]] ניתן להשתמש בשיטה זו למיזוג מחיצות שנוצרו על ידי dynamic_partition כפי שמוצג בדוגמה הבאה:
# 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.
טיעונים:
- scope: אובייקט Scope
החזרות:
-
Output: הטנזור הממוזג.
בנאים והורסים | |
|---|---|
ParallelDynamicStitch (const :: tensorflow::Scope & scope, :: tensorflow::InputList indices, :: tensorflow::InputList data) |
תכונות ציבוריות | |
|---|---|
merged | |
operation | |
תפקידים ציבוריים | |
|---|---|
node () const | ::tensorflow::Node * |
operator::tensorflow::Input () const | |
operator::tensorflow::Output () const | |
תכונות ציבוריות
התמזגו
::tensorflow::Output merged
מִבצָע
Operation operation
תפקידים ציבוריים
ParallelDynamicStitch
ParallelDynamicStitch( const ::tensorflow::Scope & scope, ::tensorflow::InputList indices, ::tensorflow::InputList data )
צוֹמֶת
::tensorflow::Node * node() const
מפעיל::tensorflow::קלט
operator::tensorflow::Input() const
אופרטור::tensorflow::פלט
operator::tensorflow::Output() const
אלא אם צוין אחרת, התוכן של דף זה הוא ברישיון Creative Commons Attribution 4.0 ודוגמאות הקוד הן ברישיון Apache 2.0. לפרטים, ניתן לעיין במדיניות האתר Google Developers. Java הוא סימן מסחרי רשום של חברת Oracle ו/או של השותפים העצמאיים שלה.
עדכון אחרון: 2025-07-27 (שעון UTC).
[null,null,["עדכון אחרון: 2025-07-27 (שעון UTC)."],[],[],null,["# tensorflow::ops::ParallelDynamicStitch Class Reference\n\ntensorflow::ops::ParallelDynamicStitch\n======================================\n\n`#include \u003cdata_flow_ops.h\u003e`\n\nInterleave the values from the `data` tensors into a single tensor.\n\nSummary\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\nFor example, if each `indices[m]` is 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\nEach `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 \n\n```gdscript\nmerged.shape = [max(indices)] + constant\n```\n\n\u003cbr /\u003e\n\nValues 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](/versions/r2.2/api_docs/cc/class/tensorflow/ops/dynamic-stitch#classtensorflow_1_1ops_1_1_dynamic_stitch) operator that defines the behavior in that case.\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\nThis method can be used to merge partitions created by `dynamic_partition` as 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\nArguments:\n\n- scope: A [Scope](/versions/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): 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/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::InputList](/versions/r2.2/api_docs/cc/class/tensorflow/input-list#classtensorflow_1_1_input_list)` indices, ::`[tensorflow::InputList](/versions/r2.2/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/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n| [operation](#classtensorflow_1_1ops_1_1_parallel_dynamic_stitch_1a339e540a99d7624dfdc0236dcaaa7fd0) | [Operation](/versions/r2.2/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```"]]