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

What is the difference between 'SAME' and 'VALID' padding in `tf.nn.max_pool`

of `tensorflow`

?

In my opinion, 'VALID' means there will be no zero padding outside the edges when we do max pool.

According to A guide to convolution arithmetic for deep learning, it says that there will be no padding in pool operator, i.e. just use 'VALID' of `tensorflow`

. But what is 'SAME' padding of max pool in `tensorflow`

?

I'll give an example to make it clearer:

`x`

: input image of shape [2, 3], 1 channel`valid_pad`

: max pool with 2x2 kernel, stride 2 and VALID padding.`same_pad`

: max pool with 2x2 kernel, stride 2 and SAME padding (this is the**classic**way to go)

The output shapes are:

`valid_pad`

: here, no padding so the output shape is [1, 1]`same_pad`

: here, we pad the image to the shape [2, 4] (with`-inf`

and then apply max pool), so the output shape is [1, 2]

```
x = tf.constant([[1., 2., 3.],
[4., 5., 6.]])
x = tf.reshape(x, [1, 2, 3, 1]) # give a shape accepted by tf.nn.max_pool
valid_pad = tf.nn.max_pool(x, [1, 2, 2, 1], [1, 2, 2, 1], padding='VALID')
same_pad = tf.nn.max_pool(x, [1, 2, 2, 1], [1, 2, 2, 1], padding='SAME')
valid_pad.get_shape() == [1, 1, 1, 1] # valid_pad is [5.]
same_pad.get_shape() == [1, 1, 2, 1] # same_pad is [5., 6.]
```

From: stackoverflow.com/q/37674306

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