<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>

<deep-learning><convolutional-neural-networks><tensorflow><convolution>
Is it useful to eliminate the less relevant filters from a trained CNN?
ESL
# 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

