tensorflow_conv2d_max_pool卷积池化padding参数为SAME和VALID的区别

卷积：conv2

• "VALID" = without padding:

     inputs:         1  2  3  4  5  6  7  8  9  10 11 (12 13)                  |________________|                dropped                                 |_________________|

• "SAME" = with zero padding:

                 pad|                                      |pad   inputs:      0 |1  2  3  4  5  6  7  8  9  10 11 12 13|0  0               |________________|                              |_________________|                                             |________________|

In this example:

• Input width = 13
• Filter width = 6
• Stride = 5

• "VALID" only ever drops the right-most columns (or bottom-most rows).
• "SAME" tries to pad evenly left and right, but if the amount of columns to be added is odd, it will add the extra column to the right, as is the case in this example (the same logic applies vertically: there may be an extra row of zeros at the bottom).

The TensorFlow Convolution example gives an overview about the difference betweenSAME and VALID :

• For the SAME padding, the output height and width are computed as:

  out_height = ceil(float(in_height) / float(strides[1]))

  out_width = ceil(float(in_width) / float(strides[2]))

And

• For the VALID padding, the output height and width are computed as:

  out_height = ceil(float(in_height - filter_height + 1) / float(strides[1]))

  out_width = ceil(float(in_width - filter_width + 1) / float(strides[2]))

池化：max_pool

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 theclassic 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_poolvalid_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.]