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

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Chandrama Sarker 2017-7-25

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评论： Walter Roberson 2019-3-15

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.

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Armin Eskandari 2017-10-27

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

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Kristen Amaddio 2017-7-27

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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.

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$ 2019-3-15

Hi Kristen Amaddio

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

Walter Roberson 2019-3-15

reshape your data to 7 by 7 by 1 by 2500

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jim peyton 2017-11-1

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编辑：jim peyton 2017-11-1

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)...

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Chandrama Sarker 2017-11-1

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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Zhiyi TANG 2018-3-27

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Check the doc of imageInputLayer (2018a) below:

https://www.mathworks.com/help/nnet/ref/nnet.cnn.layer.imageinputlayer.html?s_tid=srchtitle

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Chandrama Sarker 2018-3-27

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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Carole 2018-2-21

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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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Hang-Rai Kim 2018-4-17

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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.