tf.raw_ops.ReverseSequence
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Reverses variable length slices.
View aliases
Compat aliases for migration
See
Migration guide for
more details.
tf.compat.v1.raw_ops.ReverseSequence
tf.raw_ops.ReverseSequence(
input, seq_lengths, seq_dim, batch_dim=0, name=None
)
This op first slices input along the dimension batch_dim, and for each
slice i, reverses the first seq_lengths[i] elements along
the dimension seq_dim.
The elements of seq_lengths must obey seq_lengths[i] <= input.dims[seq_dim],
and seq_lengths must be a vector of length input.dims[batch_dim].
The output slice i along dimension batch_dim is then given by input
slice i, with the first seq_lengths[i] slices along dimension
seq_dim reversed.
For example:
# Given this:
batch_dim = 0
seq_dim = 1
input.dims = (4, 8, ...)
seq_lengths = [7, 2, 3, 5]
# then slices of input are reversed on seq_dim, but only up to seq_lengths:
output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]
# while entries past seq_lens are copied through:
output[0, 7:, :, ...] = input[0, 7:, :, ...]
output[1, 2:, :, ...] = input[1, 2:, :, ...]
output[2, 3:, :, ...] = input[2, 3:, :, ...]
output[3, 2:, :, ...] = input[3, 2:, :, ...]
In contrast, if:
# Given this:
batch_dim = 2
seq_dim = 0
input.dims = (8, ?, 4, ...)
seq_lengths = [7, 2, 3, 5]
# then slices of input are reversed on seq_dim, but only up to seq_lengths:
output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]
# while entries past seq_lens are copied through:
output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]
Args |
|---|
input
|
A Tensor. The input to reverse.
|
seq_lengths
|
A Tensor. Must be one of the following types: int32, int64.
1-D with length input.dims(batch_dim) and
max(seq_lengths) <= input.dims(seq_dim)
|
seq_dim
|
An int. The dimension which is partially reversed.
|
batch_dim
|
An optional int. Defaults to 0.
The dimension along which reversal is performed.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor. Has the same type as input.
|
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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.ReverseSequence\n\n\u003cbr /\u003e\n\nReverses variable length slices.\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.ReverseSequence`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/ReverseSequence)\n\n\u003cbr /\u003e\n\n tf.raw_ops.ReverseSequence(\n input, seq_lengths, seq_dim, batch_dim=0, name=None\n )\n\nThis op first slices `input` along the dimension `batch_dim`, and for each\nslice `i`, reverses the first `seq_lengths[i]` elements along\nthe dimension `seq_dim`.\n\nThe elements of `seq_lengths` must obey `seq_lengths[i] \u003c= input.dims[seq_dim]`,\nand `seq_lengths` must be a vector of length `input.dims[batch_dim]`.\n\nThe output slice `i` along dimension `batch_dim` is then given by input\nslice `i`, with the first `seq_lengths[i]` slices along dimension\n`seq_dim` reversed.\n\n#### For example:\n\n # Given this:\n batch_dim = 0\n seq_dim = 1\n input.dims = (4, 8, ...)\n seq_lengths = [7, 2, 3, 5]\n\n # then slices of input are reversed on seq_dim, but only up to seq_lengths:\n output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]\n output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]\n output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]\n output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]\n\n # while entries past seq_lens are copied through:\n output[0, 7:, :, ...] = input[0, 7:, :, ...]\n output[1, 2:, :, ...] = input[1, 2:, :, ...]\n output[2, 3:, :, ...] = input[2, 3:, :, ...]\n output[3, 2:, :, ...] = input[3, 2:, :, ...]\n\nIn contrast, if: \n\n # Given this:\n batch_dim = 2\n seq_dim = 0\n input.dims = (8, ?, 4, ...)\n seq_lengths = [7, 2, 3, 5]\n\n # then slices of input are reversed on seq_dim, but only up to seq_lengths:\n output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]\n output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]\n output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]\n output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]\n\n # while entries past seq_lens are copied through:\n output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]\n output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]\n output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]\n output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `input` | A `Tensor`. The input to reverse. |\n| `seq_lengths` | A `Tensor`. Must be one of the following types: `int32`, `int64`. 1-D with length `input.dims(batch_dim)` and `max(seq_lengths) \u003c= input.dims(seq_dim)` |\n| `seq_dim` | An `int`. The dimension which is partially reversed. |\n| `batch_dim` | An optional `int`. Defaults to `0`. The dimension along which reversal is performed. |\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 `input`. ||\n\n\u003cbr /\u003e"]]
