tf.raw_ops.DynamicStitch
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Interleave the values from the data tensors into a single tensor.
View aliases
Compat aliases for migration
See
Migration guide for
more details.
tf.compat.v1.raw_ops.DynamicStitch
tf.raw_ops.DynamicStitch(
indices, data, name=None
)
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) + 1] + constant
Values are merged in order, so if an index appears in both indices[m][i] and
indices[n][j] for (m,i) < (n,j) the slice data[n][j] will appear in the
merged result. If you do not need this guarantee, ParallelDynamicStitch might
perform better on some devices.
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 |
|---|
indices
|
A list of at least 1 Tensor objects with type int32.
|
data
|
A list with the same length as indices of Tensor objects with the same type.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor. Has the same type as data.
|
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.DynamicStitch\n\n\u003cbr /\u003e\n\nInterleave the values from the `data` tensors into a single tensor.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.DynamicStitch`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DynamicStitch)\n\n\u003cbr /\u003e\n\n tf.raw_ops.DynamicStitch(\n indices, data, name=None\n )\n\nBuilds a merged tensor such that \n\n merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...]\n\nFor example, if each `indices[m]` is scalar or vector, we have \n\n # Scalar indices:\n merged[indices[m], ...] = data[m][...]\n\n # Vector indices:\n merged[indices[m][i], ...] = data[m][i, ...]\n\nEach `data[i].shape` must start with the corresponding `indices[i].shape`,\nand the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we\nmust have `data[i].shape = indices[i].shape + constant`. In terms of this\n`constant`, the output shape is \n\n merged.shape = [max(indices) + 1] + constant\n\nValues are merged in order, so if an index appears in both `indices[m][i]` and\n`indices[n][j]` for `(m,i) \u003c (n,j)` the slice `data[n][j]` will appear in the\nmerged result. If you do not need this guarantee, ParallelDynamicStitch might\nperform better on some devices.\n\n#### For example:\n\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\nThis method can be used to merge partitions created by `dynamic_partition`\nas illustrated on the following example: \n\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\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------|----------------------------------------------------------------------------------|\n| `indices` | A list of at least 1 `Tensor` objects with type `int32`. |\n| `data` | A list with the same length as `indices` of `Tensor` objects with the same type. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor`. Has the same type as `data`. ||\n\n\u003cbr /\u003e"]]
