padding的规则
· padding=‘VALID’时，输出的宽度和高度的计算公式（下图gif为例）
输出宽度：output_width = （in_width-filter_width+1）/strides_width =（5-3+1）/2=1.5【向上取整=2】
输出高度：output_height = （in_height-filter_height+1）/strides_height =（5-3+1）/2=1.5【向上取整=2】
输出的形状[1,2,2,1]
import tensorflow as tf image = [0,1.0,1,2,2,0,1,1,0,0,1,1,0,1,0,1,0,1,1,1,0,2,0,1,0] input = tf.Variable(tf.constant(image,shape=[1,5,5,1])) ##1通道输入 fil1 = [-1.0,0,1,-2,0,2,-1,0,1] filter = tf.Variable(tf.constant(fil1,shape=[3,3,1,1])) ##1个卷积核对应1个featuremap输出 op = tf.nn.conv2d(input,filter,strides=[1,2,2,1],padding='VALID') ##步长2，VALID不补0操作 init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) # print('input:\n', sess.run(input)) # print('filter:\n', sess.run(filter)) print('op:\n',sess.run(op)) ##输出结果 ''' [[[[ 2.] [-1.]] [[-1.] [ 0.]]]] '''
如果strides=[1,3,3,1]的情况又是如何呢？
输出宽度：output_width = （in_width-filter_width+1）/strides_width =（5-3+1）/3=1
输出高度：output_height = （in_height-filter_height+1）/strides_height =（5-3+1）/3=1
输出的形状[1,1,1,1]，因此输出的结果只有一个
import tensorflow as tf image = [0,1.0,1,2,2,0,1,1,0,0,1,1,0,1,0,1,0,1,1,1,0,2,0,1,0] input = tf.Variable(tf.constant(image,shape=[1,5,5,1])) ##1通道输入 fil1 = [-1.0,0,1,-2,0,2,-1,0,1] filter = tf.Variable(tf.constant(fil1,shape=[3,3,1,1])) ##1个卷积核对应1个featuremap输出 op = tf.nn.conv2d(input,filter,strides=[1,3,3,1],padding='VALID') ##步长2，VALID不补0操作 init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) # print('input:\n', sess.run(input)) # print('filter:\n', sess.run(filter)) print('op:\n',sess.run(op)) ##输出结果 ''' op: [[[[ 2.]]]] '''
padding=‘SAME’时，输出的宽度和高度的计算公式
输出宽度：output_width = in_width/strides_width=5/2=2.5【向上取整3】
输出高度：output_height = in_height/strides_height=5/2=2.5【向上取整3】
则输出的形状：[1,3,3,1]
那么padding补0的规则又是如何的呢？【先确定输出形状，再计算补多少0】
pad_width = max（（out_width-1）*strides_width+filter_width-in_width,0）=max（（3-1）*2+3-5,0）=2
pad_height = max（（out_height-1）*strides_height+filter_height-in_height,0）=max（（3-1）*2+3-5,0）=2
pad_top = pad_height/2=1
pad_bottom = pad_height-pad_top=1
pad_left = pad_width/2=1
pad_right = pad_width-pad_left=1
import tensorflow as tf image = [0,1.0,1,2,2,0,1,1,0,0,1,1,0,1,0,1,0,1,1,1,0,2,0,1,0] input = tf.Variable(tf.constant(image,shape=[1,5,5,1])) ##1通道输入 fil1 = [-1.0,0,1,-2,0,2,-1,0,1] filter = tf.Variable(tf.constant(fil1,shape=[3,3,1,1])) ##1个卷积核对应1个featuremap输出 op = tf.nn.conv2d(input,filter,strides=[1,2,2,1],padding='SAME') ##步长2，VALID不补0操作 init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) # print('input:\n', sess.run(input)) # print('filter:\n', sess.run(filter)) print('op:\n',sess.run(op)) ##输出结果 ''' op: [[[[ 3.] [ 1.] [-4.]] [[ 3.] [ 0.] [-3.]] [[ 4.] [-1.] [-3.]]]] '''
如果步长为3呢？补0的规则又如何？
输出宽度：output_width = in_width/strides_width=5/3=2
输出高度：output_height = in_height/strides_height=5/3=2
则输出的形状：[1,2,2,1]
那么padding补0的规则又是如何的呢？【先确定输出形状，再计算补多少0】
pad_width = max（（out_width-1）*strides_width+filter_width-in_width,0）=max（（2-1）*3+3-5,0）=1
pad_height = max（（out_height-1）*strides_height+filter_height-in_height,0）=max（（2-1）*3+3-5,0）=1
pad_top = pad_height/2=0【向下取整】
pad_bottom = pad_height-pad_top=1
pad_left = pad_width/2=0【向下取整】
pad_right = pad_width-pad_left=1
import tensorflow as tf print(3/2) image = [0,1.0,1,2,2,0,1,1,0,0,1,1,0,1,0,1,0,1,1,1,0,2,0,1,0] input = tf.Variable(tf.constant(image,shape=[1,5,5,1])) ##1通道输入 fil1 = [-1.0,0,1,-2,0,2,-1,0,1] filter = tf.Variable(tf.constant(fil1,shape=[3,3,1,1])) ##1个卷积核对应1个featuremap输出 op = tf.nn.conv2d(input,filter,strides=[1,3,3,1],padding='SAME') ##步长2，VALID不补0操作 init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) # print('input:\n', sess.run(input)) # print('filter:\n', sess.run(filter)) print('op:\n',sess.run(op)) ##输出结果 ''' op: [[[[ 2.] [-3.]] [[ 0.] [-3.]]]] '''