tf.nn.conv2d 和 tf.nn.max_pool 中 padding 分別為 'VALID' 和 'SAME' 的直覺上的經驗和測試代碼

這個地方一開始是迷糊的，寫代碼做比較分析，總結出直覺上的經驗．
某人若想看精准的解釋，移步這個網址(http://blog.csdn.net/fireflychh/article/details/73743849)，但我覺得直覺上的經驗更有用，如下:

直覺上的經驗：

1. 一件確定的事: padding　無論取 'SAME' 還是取 'VALID', 它在 conv2d　和　max_pool 上的表現是一致的;
2. padding = 'SAME' 時，輸出並不一定和原圖size一致，但會保證覆蓋原圖所有像素，不會舍棄邊上的莫些元素;
3. padding = 'VALID' 時，輸出的size總比原圖的size小，有時不會覆蓋原圖所有元素(既，可能舍棄邊上的某些元素).

# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np


def pooling_show():
    a = tf.Variable(tf.random_normal(X))
    pooling = tf.nn.max_pool(a, pooling_filter, pooling_strides, padding=pad)
    # VALID (1, 2, 2, 7)
    # SAME (1, 3, 3, 7)

    init = tf.global_variables_initializer()

    with tf.Session() as sess:
        sess.run(init)

        print 'image: '
        image = sess.run(a)
        print image.shape

        print 'pooling result: '
        res = sess.run(pooling)
        print res.shape


def conv2d_padding_show():
    # [1, 13, 13, 2] ---> [m, height, width, channel]
    input = tf.Variable(tf.random_normal(X))
    # [6, 6, 2, 7] ---> [height, width, prev_channel, output_channel]
    filter = tf.Variable(tf.random_normal(conv2d_filter))

    op = tf.nn.conv2d(input, filter, strides=conv2d_strides, padding=pad)
    # VALID (1, 2, 2, 7)
    # SAME  (1, 3, 3, 7)

    init = tf.global_variables_initializer()

    with tf.Session() as sess:
        sess.run(init)

        print 'image: '
        image = sess.run(input)
        print image.shape

        print 'result: '
        res = sess.run(op)
        print res.shape


pad = 'VALID'

# X ---> [m, height, width, channel]
# X = [1, 13, 13, 7]
X = [1, 8, 8, 3]

# ---> [1, f, f, 1]
# pooling_filter = [1, 6, 6, 1]
pooling_filter = [1, 2, 2, 1]

# ---> [1, s, s, 1]
# pooling_strides = [1, 5, 5, 1]
pooling_strides = [1, 2, 2, 1]

# ---> [height, width, prev_channel, output_channel]
# conv2d_filter = [6, 6, 7, 7]
conv2d_filter = [2, 2, 3, 3]

# ---> [1, s, s, 1]
# conv2d_strides = [1, 5, 5, 1]
conv2d_strides = [1, 2, 2, 1]

# 自己改改 X, fileter, strides 的值，配合直覺經驗，會有更好的理解
conv2d_padding_show()
pooling_show()