深度學習面試題25：分離卷積(separable卷積)

目錄

　　舉例

　　單個張量與多個卷積核的分離卷積

　　參考資料

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舉例

分離卷積就是先在深度上分別卷積，而後再進行卷積，對應代碼爲：

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]
depthwise_filter = tf.reshape(tf.constant([3,1,-2,2,-1,-3,4,5], tf.float32),[2,2,2,1]) pointwise_filter = tf.reshape(tf.constant([-1,1], tf.float32),[1,1,2,1]) print(tf.Session().run(tf.nn.separable_conv2d(input,depthwise_filter,pointwise_filter,[1,1,1,1],"VALID"))) [[[[ 20.] [ 9.]] [[-24.] [ 29.]]]]

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單個張量與多個卷積核的分離卷積

 

對應代碼爲：

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]
depthwise_filter = 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]) pointwise_filter = tf.reshape(tf.constant([0,0,1,0,0,1,0,0,0,0,0,0], tf.float32),[1,1,6,2]) print(tf.Session().run(tf.nn.separable_conv2d(input,depthwise_filter,pointwise_filter,[1,1,1,1],"VALID"))) [[[[ 32.  -7.] [ 52.  -8.]] [[ 41. 0.] [ 11. -34.]]]]

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參考資料

《圖解深度學習與神經網絡：從張量到TensorFlow實現》_張平

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