深度学习面试题24:在每个深度上分别卷积(depthwise卷积)

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


 

举例

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.]]]]
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单个张量与多个卷积核在深度上分别卷积

 

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.]]]]
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参考资料

《图解深度学习与神经网络:从张量到TensorFlow实现》_张平

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posted @ 2019-07-26 14:59  黎明程序员  阅读(1773)  评论(1编辑  收藏  举报