Pads a tensor.
Aliases:
tf.compat.v2.pad
tf.pad(
tensor,
paddings,
mode='CONSTANT',
constant_values=0,
name=None
)
This operation pads a tensor
according to the paddings
you specify. paddings
is an integer tensor
with shape [n, 2]
, where n is the rank of tensor
. For each dimension D of input
, paddings
[D, 0] indicates how many values to add before the contents of tensor
in that dimension, and paddings
[D, 1] indicates how many values to add after the contents of tensor
in that dimension. If mode
is "REFLECT" then both paddings
[D, 0] and paddings
[D, 1] must be no greater than tensor
.dim_size(D) - 1. If mode
is "SYMMETRIC" then both paddings
[D, 0] and paddings
[D, 1] must be no greater than tensor
.dim_size(D).
The padded size of each dimension D of the output is:
paddings[D, 0] + tensor.dim_size(D) + paddings[D, 1]
For example:
t = tf.constant([[1, 2, 3], [4, 5, 6]])
paddings = tf.constant([[1, 1,], [2, 2]])
# 'constant_values' is 0.
# rank of 't' is 2.
tf.pad(t, paddings, "CONSTANT") # [[0, 0, 0, 0, 0, 0, 0],
# [0, 0, 1, 2, 3, 0, 0],
# [0, 0, 4, 5, 6, 0, 0],
# [0, 0, 0, 0, 0, 0, 0]]
tf.pad(t, paddings, "REFLECT") # [[6, 5, 4, 5, 6, 5, 4],
# [3, 2, 1, 2, 3, 2, 1],
# [6, 5, 4, 5, 6, 5, 4],
# [3, 2, 1, 2, 3, 2, 1]]
tf.pad(t, paddings, "SYMMETRIC") # [[2, 1, 1, 2, 3, 3, 2],
# [2, 1, 1, 2, 3, 3, 2],
# [5, 4, 4, 5, 6, 6, 5],
# [5, 4, 4, 5, 6, 6, 5]]
Args:
tensor
: ATensor
.paddings
: ATensor
of typeint32
.mode
: One of "CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive)constant_values
: In "CONSTANT"mode
, the scalar pad value to use. Must be same type astensor
.name
: Aname
for the operation (optional).
Returns:
A Tensor
. Has the same type as tensor
.
Raises:
ValueError
: When mode is not one of "CONSTANT", "REFLECT", or "SYMMETRIC".