tf.raw_ops.PadV2
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Pads a tensor.
tf.raw_ops.PadV2(
input, paddings, constant_values, name=None
)
This operation pads input according to the paddings and constant_values
you specify. paddings is an integer tensor with shape [Dn, 2], where n is
the rank of input. For each dimension D of input, paddings[D, 0] indicates
how many padding values to add before the contents of input in that dimension,
and paddings[D, 1] indicates how many padding values to add after the contents
of input in that dimension. constant_values is a scalar tensor of the same
type as input that indicates the value to use for padding input.
The padded size of each dimension D of the output is:
paddings(D, 0) + input.dim_size(D) + paddings(D, 1)
For example:
# 't' is [[1, 1], [2, 2]]
# 'paddings' is [[1, 1], [2, 2]]
# 'constant_values' is 0
# rank of 't' is 2
pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
[0, 0, 1, 1, 0, 0]
[0, 0, 2, 2, 0, 0]
[0, 0, 0, 0, 0, 0]]
Args |
|---|
input
|
A Tensor.
|
paddings
|
A Tensor. Must be one of the following types: int32, int64.
|
constant_values
|
A Tensor. Must have the same type as input.
|
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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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.PadV2\n\n\u003cbr /\u003e\n\nPads a 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.PadV2`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/PadV2)\n\n\u003cbr /\u003e\n\n tf.raw_ops.PadV2(\n input, paddings, constant_values, name=None\n )\n\nThis operation pads `input` according to the `paddings` and `constant_values`\nyou specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is\nthe rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates\nhow many padding values to add before the contents of `input` in that dimension,\nand `paddings[D, 1]` indicates how many padding values to add after the contents\nof `input` in that dimension. `constant_values` is a scalar tensor of the same\ntype as `input` that indicates the value to use for padding `input`.\n\nThe padded size of each dimension D of the output is:\n\n`paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`\n\n#### For example:\n\n # 't' is [[1, 1], [2, 2]]\n # 'paddings' is [[1, 1], [2, 2]]\n # 'constant_values' is 0\n # rank of 't' is 2\n pad(t, paddings) ==\u003e [[0, 0, 0, 0, 0, 0]\n [0, 0, 1, 1, 0, 0]\n [0, 0, 2, 2, 0, 0]\n [0, 0, 0, 0, 0, 0]]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------|-------------------------------------------------------------------|\n| `input` | A `Tensor`. |\n| `paddings` | A `Tensor`. Must be one of the following types: `int32`, `int64`. |\n| `constant_values` | A `Tensor`. Must have the same type as `input`. |\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"]]
