tensorflow::ops::MirrorPad

#include <array_ops.h>

Pads a tensor with mirrored values.

Summary

This operation pads a input with mirrored values according to the paddings you specify. paddings is an integer tensor with shape [n, 2], where n is the rank of input. For each dimension D of input, paddings[D, 0] indicates how many values to add before the contents of input in that dimension, and paddings[D, 1] indicates how many values to add after the contents of input in that dimension. Both paddings[D, 0] and paddings[D, 1] must be no greater than input.dim_size(D) (or input.dim_size(D) - 1) if copy_border is true (if false, respectively).

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, 2, 3], [4, 5, 6]].

'paddings' is [[1, 1]], [2, 2]].

'mode' is SYMMETRIC.

rank of 't' is 2.

pad(t, paddings) ==> [[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]] ```

Arguments:

  • scope: A Scope object
  • input: The input tensor to be padded.
  • paddings: A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of input.
  • mode: Either REFLECT or SYMMETRIC. In reflect mode the padded regions do not include the borders, while in symmetric mode the padded regions do include the borders. For example, if input is [1, 2, 3] and paddings is [0, 2], then the output is [1, 2, 3, 2, 1] in reflect mode, and it is [1, 2, 3, 3, 2] in symmetric mode.

Returns:

Constructors and Destructors

MirrorPad(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input paddings, StringPiece mode)

Public attributes

output

Public functions

node() const
::tensorflow::Node *
operator::tensorflow::Input() const
operator::tensorflow::Output() const

Public attributes

output

::tensorflow::Output output

Public functions

MirrorPad

 MirrorPad(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input paddings,
  StringPiece mode
)

node

::tensorflow::Node * node() const 

operator::tensorflow::Input

 operator::tensorflow::Input() const 

operator::tensorflow::Output

 operator::tensorflow::Output() const