Skip to main content

Stretch the dynamic range of the given 8-bit grayscale image using MATL...

Post a Comment

Custom Layer- Incorrent number of outputs

 Hi,

 
I'm trying to create a custom intermediate layer that can split up data. When I use checkLayer to validate the functionality it throws the error: "Incorrect number of output arguments for 'predict' in Layer splitDataLayer. Expected to have 1, but instead it has 4." although I've set the number of Outputs to 4 in the constructor.
 
classdef splitDataLayer < nnet.layer.Layer
    
    methods
        function obj = splitDataLayer(name)
            
            obj.Name = name;
            obj.numOutputs = 4;
            obj.OutputNames = {'out1','out2','out3','out4'};
        end
        
        function [Z1, Z2, Z3, Z4] = predict(~, X)
            
            Z1 = X(1, :, :, :);
            Z2 = X(2, :, :, :);
            Z3 = X(3, :, :, :);
            Z4 = X(4, :, :, :);  
     
        end        

        function [dLdX] = backward(~,~,~,~,~,~,dLdZ1,dLdZ2,dLdZ3,dLdZ4,~)
            
            dLdX = cat(1, dLdZ1,...
                          dLdZ2,...
                          dLdZ3,...
                          dLdZ4);
            
        end
    end
end
As can be seen above, both the number of outputs as well as the output matrix in the predict function have been set correctly. So I don't know what could be wrong about the code and cause that error.



ANSWER



Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.


Multi-input/Multi-output custom layers are supported from R2019a. From your error message I suspect you are on a older release.
 
Your layer looks good though, apart from obj.numOutputs = 4; which should be obj.NumOutputs = 4;
 
When I correct for that all the checkLayer tests pass in R2019a:

layer = splitDataLayer('test');
validInputSize = [4 5 20 4]; % Some arbitrary dimensions
checkLayer(layer,validInputSize,'ObservationDimension',4)
Running nnet.checklayer.TestCase
.......... .......... ....
Done nnet.checklayer.TestCase
__________

Test Summary:
	 24 Passed, 0 Failed, 0 Incomplete, 0 Skipped.
	 Time elapsed: 1.3818 seconds.


Comments

Post a Comment

Popular posts from this blog

https://journals.worldnomads.com/scholarships/story/70330/Worldwide/Dat-shares-his-photos-from-Bhutan https://www.blogger.com/comment.g?blogID=441349916452722960&postID=9118208214656837886&page=2&token=1554200958385 https://todaysinspiration.blogspot.com/2016/08/lp-have-look-at-this-this-is-from.html?showComment=1554201056566#c578424769512920148 https://behaviorpsych.blogspot.com/p/goal-bank.html?showComment=1554201200695 https://billlumaye.blogspot.com/2012/10/tagg-romney-drops-by-bill-show.html?showComment=1550657710334#c7928008051819098612 http://blog.phdays.com/2014/07/review-of-waf-bypass-tasks.html?showComment=1554201301305#c6351671948289526101 http://www.readyshelby.org/blog/gifts-of-preparedness/#comment_form http://www.hanabilkova.svet-stranek.cz/nakup/ http://www.23hq.com/shailendrasingh/photo/21681053 http://blogs.stlawu.edu/jbpcultureandmedia/2013/11/18/blog-entry-10-guns-as-free-speech/comment-page-1443/#comment-198345 https://journals.worldnomads.com
6 comments
Read more

USING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR DETECTING SPAM AND NON-SPAM EMAILS

    ABSTRACT We know the increasing volume of unwanted volume of emails as spam. As per statistical analysis 40% of all messages are spam which about 15.4 billion email for every day and that cost web clients about $355 million every year. Spammers to use a few dubious techniques to defeat the filtering strategies like utilizing irregular sender addresses or potentially add irregular characters to the start or the finish of the message subject line. A particular calculation is at that point used to take in the order rules from these email messages. Machine learning has been contemplated and there are loads of calculations can be used in email filtering. To classify these mails as spam and non-spam mails implementation of machine learning algorithm  such as KNN, SVM, Bayesian classification  and ANN  to develop better filtering tool.   Contents ABSTRACT 2 1. INTRODUCTION 4 1.1 Objective : 5 2. Literature Review 5 2.1. Existing Machine learning technique. 6 2.2 Existing
Post a Comment
Read more

Why are Fourier series important? Are there any real life applications of Fourier series?

A  Fourier series  is a way of representing a periodic function as a (possibly infinite) sum of sine and cosine functions. It is analogous to a Taylor series, which represents functions as possibly infinite sums of monomial terms. A sawtooth wave represented by a successively larger sum of trigonometric terms. For functions that are not periodic, the Fourier series is replaced by the Fourier transform. For functions of two variables that are periodic in both variables, the trigonometric basis in the Fourier series is replaced by the spherical harmonics. The Fourier series, as well as its generalizations, are essential throughout the physical sciences since the trigonometric functions are eigenfunctions of the Laplacian, which appears in many physical equations. Real-life applications: Signal Processing . It may be the best application of Fourier analysis. Approximation Theory . We use Fourier series to write a function as a trigonometric polynomial. Control Theory . The F
3 comments
Read more