What is the difference between 'SAME' and 'VALID' padding in tf.nn.max_pool of tensorflow?

VALID Padding: it means no padding and it assumes that all the dimensions are valid so that the input image gets fully covered by a filter and the stride specified by you.

SAME Padding: it applies padding to the input image so that the input image gets fully covered by the filter and specified stride.It is called SAME because, for stride 1 , the output will be the same as the input.

 Let’s see an example:

 Here,

a is the input image of shape [2, 3], 1 channel

validPad refers to max pool having 2x2 kernel, stride=2 and VALID padding.

samePad refers to max pool having 2x2 kernel, stride=2 and SAME padding.

a = tf.constant([[1., 2., 3.],
                         [4., 5., 6.]])
 a = tf.reshape(x, [1, 2, 3, 1])  
validPad = tf.nn.max_pool(x, [1, 2, 2, 1], [1, 2, 2, 1], padding='VALID')
samePad = tf.nn.max_pool(x, [1, 2, 2, 1], [1, 2, 2, 1], padding='SAME')
 validPad.get_shape() == [1, 1, 1, 1]  
samePad.get_shape() == [1, 1, 2, 1]   
Output shapes are-
validPad : output shape is [1,1]
samePad: output shape is [1,2]