<p>The channel they are talking about is the depth of the layer L.</p>

<p><a href="https://i.sstatic.net/bM582.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/bM582.jpg" alt="enter image description here"></a></p>

<p>In this image, the channel is 5. There are 5 $3*2$ filters</p>

<p>They optimize an image from noise, to better respond to the filter F, in the layer L. Then you obtain this kind of image, and you can try to interpret the prupose of that filter (it learns to detect eyes, faces, wheels, and so on)</p>

<p>EDIT: To be more precise, you visualise the channel only if you optimise images for every filters, otherwise, you obtain a filter visualisation (if you do the optimisation one time, for one filter)</p>

<p>They call it "channel", because in a colored image, you have 3 dimensions : witdh, height, and channel (for color), as layers which have also 3 dimensions.</p>

CC BY-SA 4.0
2018-08-29T08:24:56.330Z
2018-08-29T16:55:14.630Z
2018-08-29T16:55:14.630Z
Jérémy Blain
<p>Tensorflow/Lucid is able to visualize what a "channel" of a layer of a neural network (image recognition, Inception-v1) responds to. Even after studying the tutorial, the source code, the three research papers on lucid and comments by the authors on Hacker News, I'm still not clear on how "channels" are supposed to be defined and individuated. Can somebody shed some light on this? Thank you.</p>

<p><a href="https://github.com/tensorflow/lucid" rel="nofollow noreferrer">https://github.com/tensorflow/lucid</a> <br/>
<a href="https://news.ycombinator.com/item?id=15649456" rel="nofollow noreferrer">https://news.ycombinator.com/item?id=15649456</a></p>

CC BY-SA 4.0
2018-08-29T08:04:36.117Z
I guess they're talking about filter. They optimize an image to make a high respond on the filter (channel)
Jérémy Blain
CC BY-SA 4.0
2018-08-29T08:06:04.653Z
(I don't read the papers, I don't see them on the github page. Moreover, they don't seem to talk about channel on the github page you've linked)
Jérémy Blain
CC BY-SA 4.0
2018-08-29T08:08:50.517Z
They talk about them in the tutorial and the linked articles. https://colab.research.google.com/github/tensorflow/lucid/blob/master/notebooks/tutorial.ipynb https://distill.pub/2018/building-blocks/ https://distill.pub/2017/feature-visualization/
Jonas
CC BY-SA 4.0
2018-08-29T08:09:49.600Z
Ohh, my bad, I didn't see there are two links ^^" (I edited your post to better see the two links)
Jérémy Blain
<tensorflow><computer-vision>
What exactly does "channel" refer to in tensorflow/lucid?
Jonas
# What exactly does "channel" refer to in tensorflow/lucid?

(Asked by: username_0 on 2018-08-29)

Tensorflow/Lucid is able to visualize what a "channel" of a layer of a neural network (image recognition, Inception-v1) responds to. Even after studying the tutorial, the source code, the three research papers on lucid and comments by the authors on Hacker News, I'm still not clear on how "channels" are supposed to be defined and individuated. Can somebody shed some light on this? Thank you.

<https://github.com/tensorflow/lucid>   
<https://news.ycombinator.com/item?id=15649456>

**Tags:** tensorflow, computer-vision

> **Comments:**
> **username_1**: I guess they're talking about filter. They optimize an image to make a high respond on the filter (channel)  
> **username_1**: (I don't read the papers, I don't see them on the github page. Moreover, they don't seem to talk about channel on the github page you've linked)  
> **username_0**: They talk about them in the tutorial and the linked articles. https://colab.research.google.com/github/tensorflow/lucid/blob/master/notebooks/tutorial.ipynb https://distill.pub/2018/building-blocks/ https://distill.pub/2017/feature-visualization/  
> **username_1**: Ohh, my bad, I didn't see there are two links ^^" (I edited your post to better see the two links)  

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## 1 answers

### Answer by username_1 on 2018-08-29. Score: 3 | Accepted answer

The channel they are talking about is the depth of the layer L.

[![enter image description here](https://i.sstatic.net/bM582.jpg)](https://i.sstatic.net/bM582.jpg)

In this image, the channel is 5. There are 5 $3\*2$ filters

They optimize an image from noise, to better respond to the filter F, in the layer L. Then you obtain this kind of image, and you can try to interpret the prupose of that filter (it learns to detect eyes, faces, wheels, and so on)

EDIT: To be more precise, you visualise the channel only if you optimise images for every filters, otherwise, you obtain a filter visualisation (if you do the optimisation one time, for one filter)

They call it "channel", because in a colored image, you have 3 dimensions : witdh, height, and channel (for color), as layers which have also 3 dimensions.


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