MATLAB Answers

4

How to apply Matlab CNN code on an input image with 6 channels

Asked by Chandrama Sarker on 25 Jul 2017
Latest activity Commented on by Walter Roberson
on 15 Mar 2019
I have currently applied the Matbal CNN function to train my research data. Unlike, the Matlab 'lettersTrainSet'with a size of 28x28x1x1500 (4-D array), the input train data of my experiment have a size of 7x7x6x30,000. The problem I have encountered is that while running the 'trainNetwork' function, Matlab shows me an error: *Error using trainNetwork>iAssertValidImageArray (line 575) X must be a 4-D array of images.
Error in trainNetwork>iParseInput (line 329) iAssertValidImageArray( X );
Error in trainNetwork (line 68) [layers, opts, X, Y] = iParseInput(varargin{:});*
However, the same training data with 3 channels or 1 channels I can run the CNN code without any error message. It will be a great help if anyone can suggest how to use image data with more than 3 channels in Matlab for CNN classification.

  1 Comment

Hi, all I have the same problem, Please remove this limitation

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5 Answers

Answer by Kristen Amaddio on 27 Jul 2017
 Accepted Answer

Currently, CNN exclusively supports single and RGB channel imagery. Due to this limitation, the ability to use CNNs with image data with more than 3 channels is not available at this time.
I work at MathWorks, so I have forwarded this feedback to the relevant development team.

  7 Comments

The purposes of PCA and PLS are to reduce dimensionality subject to the constraints of minimizing the loss of regression information (PCA) or classification information(PLS).
It may definitely be worth doing before trying to preserve the dimensionality.
Hope this helps
Greg
I have currently applied the Matbal CNN function to train my research data. Unlike, the Matlab 'lettersTrainSet'with a size of 28x28x1x1500 (4-D array), the input train data of my experiment have a size of 7x7x2500. The problem I have encountered is that while running the 'trainNetwork' function, Matlab shows me an error: *Error using trainNetwork>iAssertValidImageArray (line 575) X must be a 4-D array of images.
Error in trainNetwork>iParseInput (line 329) iAssertValidImageArray( X );
Error in trainNetwork (line 68) [layers, opts, X, Y] = iParseInput(varargin{:});*
please help me in this regard
reshape your data to 7 by 7 by 1 by 2500

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Answer by jim peyton on 1 Nov 2017
Edited by jim peyton on 1 Nov 2017

If the development team is prioritizing by market need, this is a deal-breaker for a few of our applications too:
Using XYZRGB (6ch), or XYZ+Gray(4ch), or XYZ+normals+gray(7ch), or two stereo channels with multiple exposures/textures each (up to 24ch)...

  1 Comment

Yes, I agree with you, Jim, that is why I have to shift from Matlab to Python in order to utilize the information from all the 6 channels of the image. In some cases specifically in the field of remote sensing, the number of channels would never be limited to 3 channel data and it may be higher than 6 channels too. Regards

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Answer by Zhiyi TANG on 27 Mar 2018

  1 Comment

Hi Zhiyi,
Thanks very much for the link. I will try that with my 6 channel data and will update the outcome.

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Answer by Carole
on 21 Feb 2018

This is the same for me. I wanted to implement a deconvolutional neural network and thus meed to have an input layer with more than 3 channels (to input the feature map and also to modify them as all needed layers for this are not yet implemented). Is there any workaround, or will this fixed in the next release? I will have to switch to Python otherwise. Is it in the plans of the development team? Cheers.

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Answer by Hang-Rai Kim on 17 Apr 2018

I want to apply CNN in 3D images (MRI data). I am planning to use 3D images as 2D x z stacks thus need to work in 2D CNN with multi channels. Please let me know what should i do.. Thank you.

  9 Comments

Sorry, I do not know. Probably, but I am not certain at all.
oh ok~ Thank you! Goodday!!

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