tf.pad

Pads a tensor.

tf.pad( tensor, paddings, mode='CONSTANT', constant_values=0, name=None )

Used in the notebooks

Used in the guide Used in the tutorials

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]]

tensor A Tensor.
paddings A Tensor of type int32.
mode One of "CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive)
constant_values In "CONSTANT" mode, the scalar pad value to use. Must be same type as tensor.
name A name for the operation (optional).

A Tensor. Has the same type as tensor.

ValueError When mode is not one of "CONSTANT", "REFLECT", or "SYMMETRIC".