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TensorFlow – How to add padding to a tensor

  • Last Updated : 01 Aug, 2020

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning  neural networks.

Padding means adding values before and after Tensor values.

Method Used:

  • tf.pad: This method accepts input tensor and padding tensor with other optional arguments and returns a Tensor with added padding and same type as input Tensor. Padding tensor is a Tensor with shape(n, 2).

Example 1: This example uses constant padding mode i.e. value at all the padded indices will be constant.

Python3






# importing the library
import tensorflow as tf
  
# Initializing the Input
input = tf.constant([[1, 2], [3, 4]])
padding = tf.constant([[2, 2], [2, 2]])
  
# Printing the Input
print("Input: ", input)
print("Padding: ", padding)
  
# Generating padded Tensor
res = tf.pad(input, padding, mode ='CONSTANT')
  
# Printing the resulting Tensors
print("Res: ", res )

Output:

Input:  tf.Tensor(
[[1 2]
 [3 4]], shape=(2, 2), dtype=int32)
Padding:  tf.Tensor(
[[2 2]
 [2 2]], shape=(2, 2), dtype=int32)
Res:  tf.Tensor(
[[0 0 0 0 0 0]
 [0 0 0 0 0 0]
 [0 0 1 2 0 0]
 [0 0 3 4 0 0]
 [0 0 0 0 0 0]
 [0 0 0 0 0 0]], shape=(6, 6), dtype=int32)

Example 2: This example uses REFLECT padding mode. For this mode to work paddings[D, 0] and paddings[D, 1] must be less than or equal to tensor.dim_size(D) – 1.

Python3




# importing the library
import tensorflow as tf
  
# Initializing the Input
input = tf.constant([[1, 2, 5], [3, 4, 6]])
padding = tf.constant([[1, 1], [2, 2]])
  
# Printing the Input
print("Input: ", input)
print("Padding: ", padding)
  
# Generating padded Tensor
res = tf.pad(input, padding, mode ='REFLECT')
  
# Printing the resulting Tensors
print("Res: ", res )

Output:

Input:  tf.Tensor(
[[1 2 5]
 [3 4 6]], shape=(2, 3), dtype=int32)
Padding:  tf.Tensor(
[[1 1]
 [2 2]], shape=(2, 2), dtype=int32)
Res:  tf.Tensor(
[[6 4 3 4 6 4 3]
 [5 2 1 2 5 2 1]
 [6 4 3 4 6 4 3]
 [5 2 1 2 5 2 1]], shape=(4, 7), dtype=int32)

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