<p>As far as I know, more than 3 channel is perfectly fine, since, 3 channels are what we use for images and that's enough since we can only see this many colors, but I don't see why more than that wouldn't work</p>

<p>Your 2nd question is like asking whether or not you will be good at a sport... Just try it</p>

<p>For your 3rd question, I've never seen any language AI using CNN instead they all use RNN, not sure if that's what you meant though</p>

CC BY-SA 4.0
2019-08-13T10:15:07.347Z
2019-08-13T10:15:07.347Z
Kenivia
<p>I am using a CNN for function approximation using geospatial data. 
The input of the function I am trying to approximate consists of all the spatial distances between N location on a grid and all the other points in the grid. </p>

<p>As of now I implemented a CNN that takes an "image" as input. The image has N channels, one for each location of interest. Each i-th channel is a matrix representing my grid, where the pixel values are the distance between each point in the grid and the i-th location of interest. The labels are the N values computed via the actual function I want to approximate. N can be up to 100. </p>

<p><strong>Here an example input of the first layer</strong>:</p>

<p><a href="https://i.sstatic.net/m6fMT.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/m6fMT.png" alt="enter image description here"></a></p>

<p>So far I could see the train and validation loss go down, but since it is a bit of a unusual application for a CNN (to my knowledge the input channels are at most 3, RGB) I was wondering:</p>

<ul>
<li>does this many-channel-input approach have any pitfalls?</li>
<li>will I be able to obtain a good accuracy or are there any hard limits I am not aware of?</li>
<li>are there any other similar application in literature?</li>
</ul>

CC BY-SA 4.0
2019-09-12T13:30:49.833Z
It is possible to train a CNN with multiple channels, for example i've seen Wavenet like structures applied to traffic datasets, where they have multiple channels such as: average speed, lane occupancy, number of cars etc. The only downside that I could see is the large parameter space that it might require.
razvanc92
<convolutional-neural-networks><architecture><function-approximation><image-processing>
Tweaking a CNN for large number of input channels
JacoSolari
# Tweaking a CNN for large number of input channels

(Asked by: username_0 on 2019-08-12)

I am using a CNN for function approximation using geospatial data.
The input of the function I am trying to approximate consists of all the spatial distances between N location on a grid and all the other points in the grid.

As of now I implemented a CNN that takes an "image" as input. The image has N channels, one for each location of interest. Each i-th channel is a matrix representing my grid, where the pixel values are the distance between each point in the grid and the i-th location of interest. The labels are the N values computed via the actual function I want to approximate. N can be up to 100.

**Here an example input of the first layer**:

[![enter image description here](https://i.sstatic.net/m6fMT.png)](https://i.sstatic.net/m6fMT.png)

So far I could see the train and validation loss go down, but since it is a bit of a unusual application for a CNN (to my knowledge the input channels are at most 3, RGB) I was wondering:

* does this many-channel-input approach have any pitfalls?
* will I be able to obtain a good accuracy or are there any hard limits I am not aware of?
* are there any other similar application in literature?

**Tags:** convolutional-neural-networks, architecture, function-approximation, image-processing

> **Comments:**
> **username_1**: It is possible to train a CNN with multiple channels, for example i've seen Wavenet like structures applied to traffic datasets, where they have multiple channels such as: average speed, lane occupancy, number of cars etc. The only downside that I could see is the large parameter space that it might require.  

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## 1 answers

### Answer by username_2 on 2019-08-13. Score: 1

As far as I know, more than 3 channel is perfectly fine, since, 3 channels are what we use for images and that's enough since we can only see this many colors, but I don't see why more than that wouldn't work

Your 2nd question is like asking whether or not you will be good at a sport... Just try it

For your 3rd question, I've never seen any language AI using CNN instead they all use RNN, not sure if that's what you meant though


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