Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-08-13T10:15:07.347Z
Answers.LastActivityDate: 2019-08-13T10:15:07.347Z
Answers.UserDisplayName: Kenivia
Body: <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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-12T13:30:49.833Z
Comments.Text: 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.
Comments.UserDisplayName: razvanc92
Tags: <convolutional-neural-networks><architecture><function-approximation><image-processing>
Title: Tweaking a CNN for large number of input channels
UserDisplayName: JacoSolari
ThreadText: # 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.  
  
  ---
  
  ## 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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