这里W和H是输入的宽和高， F为滤波器维数， P是填充大小(即要填充的行数或列数)

对于相同的填充:

对于有效填充:

Tensorflow 2.0兼容答案:上面已经提供了关于“有效”和“相同”填充的详细解释。

但是，我将在Tensorflow 2中指定不同的池化函数和它们各自的命令。X(>= 2.0)，为社区的利益。

1.x中的函数:

tf.nn.max_pool

tf.keras.layers.MaxPool2D

tf中平均池值=>无。神经网络,tf.keras.layers.AveragePooling2D

2.x中的函数:

tf.nn。Max_pool如果在2中使用。如果从1迁移，则tf. compat_v1 .nn.max_pool_v2或tf. compat_v2 .nn.max_pool。X到2。X。

tf.keras.layers。MaxPool2D如果在2中使用。x和

tf. compat_v1 .keras.layers. maxpooling2d或tf. compat_v1 .keras.layers. maxpooling2d或tf. compat_v2 .keras.layers. maxpooling2d或tf. compat_v2 .keras.layers. maxpooling2d，如果从1迁移。X到2。X。

平均池=> tf.nn。Avg_pool2d或tf.keras.layers。如果在TF 2中使用AveragePooling2D。x和

tf. compat_v1 . dn .avg_pool_v2或tf. compat_v2 .v2. dn .avg_pool或tf. compat_v1 .keras.layers. averagepooling2d或tf. compat_v1 .keras.layers. avgpool2d或tf. compat_v2 .keras.layers. averagepooling2d或tf. compat_v2 .keras.layers. avgpool2d，如果从1迁移。X到2。X。

有关Tensorflow迁移的更多信息。X到2。请参考本迁移指南。

有效填充是没有填充。 相同的填充在某种程度上是输出与输入大小相同的填充。

我举个例子来说明:

X:输入形状[2,3]的图像，1通道 valid_pad: max pool with 2x2 kernel, stride 2和VALID padding。 same_pad: max pool with 2x2 kernel, stride 2和SAME padding(这是经典的方法)

输出形状为:

Valid_pad:这里没有填充，所以输出形状是[1,1] Same_pad:在这里，我们将图像填充到形状[2,4](使用-inf，然后应用Max pool)，因此输出形状是[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.]

Padding on/off. Determines the effective size of your input. VALID: No padding. Convolution etc. ops are only performed at locations that are "valid", i.e. not too close to the borders of your tensor. With a kernel of 3x3 and image of 10x10, you would be performing convolution on the 8x8 area inside the borders. SAME: Padding is provided. Whenever your operation references a neighborhood (no matter how big), zero values are provided when that neighborhood extends outside the original tensor to allow that operation to work also on border values. With a kernel of 3x3 and image of 10x10, you would be performing convolution on the full 10x10 area.

根据这里的解释和Tristan的回答，我通常使用这些快速函数进行完整性检查。

# a function to help us stay clean
def getPaddings(pad_along_height,pad_along_width):
    # if even.. easy..
    if pad_along_height%2 == 0:
        pad_top = pad_along_height / 2
        pad_bottom = pad_top
    # if odd
    else:
        pad_top = np.floor( pad_along_height / 2 )
        pad_bottom = np.floor( pad_along_height / 2 ) +1
    # check if width padding is odd or even
    # if even.. easy..
    if pad_along_width%2 == 0:
        pad_left = pad_along_width / 2
        pad_right= pad_left
    # if odd
    else:
        pad_left = np.floor( pad_along_width / 2 )
        pad_right = np.floor( pad_along_width / 2 ) +1
        #
    return pad_top,pad_bottom,pad_left,pad_right

# strides [image index, y, x, depth]
# padding 'SAME' or 'VALID'
# bottom and right sides always get the one additional padded pixel (if padding is odd)
def getOutputDim (inputWidth,inputHeight,filterWidth,filterHeight,strides,padding):
    if padding == 'SAME':
        out_height = np.ceil(float(inputHeight) / float(strides[1]))
        out_width  = np.ceil(float(inputWidth) / float(strides[2]))
        #
        pad_along_height = ((out_height - 1) * strides[1] + filterHeight - inputHeight)
        pad_along_width = ((out_width - 1) * strides[2] + filterWidth - inputWidth)
        #
        # now get padding
        pad_top,pad_bottom,pad_left,pad_right = getPaddings(pad_along_height,pad_along_width)
        #
        print 'output height', out_height
        print 'output width' , out_width
        print 'total pad along height' , pad_along_height
        print 'total pad along width' , pad_along_width
        print 'pad at top' , pad_top
        print 'pad at bottom' ,pad_bottom
        print 'pad at left' , pad_left
        print 'pad at right' ,pad_right

    elif padding == 'VALID':
        out_height = np.ceil(float(inputHeight - filterHeight + 1) / float(strides[1]))
        out_width  = np.ceil(float(inputWidth - filterWidth + 1) / float(strides[2]))
        #
        print 'output height', out_height
        print 'output width' , out_width
        print 'no padding'


# use like so
getOutputDim (80,80,4,4,[1,1,1,1],'SAME')