目录

　　举例

　　单个张量与多个卷积核在深度上分别卷积

　　参考资料

如下张量x和卷积核K进行depthwise_conv2d卷积

结果为：

depthwise_conv2d和conv2d的不同之处在于conv2d在每一深度上卷积，然后求和，depthwise_conv2d没有求和这一步，对应代码为：

import tensorflow as tf

# [batch, in_height, in_width, in_channels]
input =tf.reshape( tf.constant([2,5,3,3,8,2,6,1,1,2,5,4,7,9,2,3,-1,3], tf.float32),[1,3,3,2])

# [filter_height, filter_width, in_channels, out_channels]
kernel = tf.reshape(tf.constant([3,1,-2,2,-1,-3,4,5], tf.float32),[2,2,2,1])

print(tf.Session().run(tf.nn.depthwise_conv2d(input,kernel,[1,1,1,1],"VALID")))
[[[[ -2.  18.]
   [ 12.  21.]]

  [[ 17.  -7.]
   [-13.  16.]]]]

返回目录

对应代码为：

import tensorflow as tf

# [batch, in_height, in_width, in_channels]
input =tf.reshape( tf.constant([2,5,3,3,8,2,6,1,1,2,5,4,7,9,2,3,-1,3], tf.float32),[1,3,3,2])

# [filter_height, filter_width, in_channels, out_channels]
kernel = tf.reshape(tf.constant([3,1,-3,1,-1,7,-2,2,-5,2,7,3,-1,3,1,-3,-8,6,4,6,8,5,9,-5], tf.float32),[2,2,2,3])

print(tf.Session().run(tf.nn.depthwise_conv2d(input,kernel,[1,1,1,1],"VALID")))
[[[[ -2.  32.  -7.  18.  26.  40.]
   [ 12.  52.  -8.  21.  31.  19.]]

  [[ 17.  41.   0.  -7. -32.  52.]
   [-13.  11. -34.  16.  29.  29.]]]]

返回目录

《图解深度学习与神经网络：从张量到TensorFlow实现》_张平

返回目录

原文地址：https://www.cnblogs.com/mfryf/p/11381401.html