Body: <p>Imagine I have a tensorflow CNN model with good accuracy but maybe too many filters:</p>
  
  <ul>
  <li><p><strong>Is there a way to determine which filters have more impact in output?</strong> I think it should be possible. At least, if a filter A has a 0, that only multiples the output of a filter B, then filter B is not related to filter A. In particular, I'm thinking in 2d data where 1 dimension is time-related and the other feature related (like one-hot char).</p></li>
  <li><p><strong>Is there a way to eliminate the less relevant filters from a trained model</strong>, and leave the rest of the model intact?</p></li>
  <li><p><strong>Is it useful or there are better methods?</strong></p></li>
  </ul>
  
Tags: <deep-learning><convolutional-neural-networks><tensorflow><convolution>
Title: Is it useful to eliminate the less relevant filters from a trained CNN?
UserDisplayName: ESL
ThreadText: # Is it useful to eliminate the less relevant filters from a trained CNN?
  
  (Asked by: username_0 on 2019-09-06)
  
  Imagine I have a tensorflow CNN model with good accuracy but maybe too many filters:
  
  * **Is there a way to determine which filters have more impact in output?** I think it should be possible. At least, if a filter A has a 0, that only multiples the output of a filter B, then filter B is not related to filter A. In particular, I'm thinking in 2d data where 1 dimension is time-related and the other feature related (like one-hot char).
  * **Is there a way to eliminate the less relevant filters from a trained model**, and leave the rest of the model intact?
  * **Is it useful or there are better methods?**
  
  **Tags:** deep-learning, convolutional-neural-networks, tensorflow, convolution
  
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  ## 0 answers
  
  