Pads a tensor.
代码语言:javascript复制tf.compat.v1.pad(
tensor,
paddings,
mode='CONSTANT',
name=None,
constant_values=0
)
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:
代码语言:javascript复制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)name
: A name for the operation (optional).constant_values
: In "CONSTANT" mode, the scalar pad value to use. Must be same type astensor
.
Returns:
- A
Tensor
. Has the same type astensor
.
Raises:
ValueError
: When mode is not one of "CONSTANT", "REFLECT", or "SYMMETRIC".