MATLAB: I get an error when creating newff please help me ..

Deep Learning Toolboxneural network

the code is attached

the excel file has the dataset. it has 24 columns. first 12 columns has the target value 1. the last 12 colums have the target value 0. i wanted to design a NN for classification into 1 or 0. the data was copied to dat file. i selected all the 198 rowsand 24 colums to input.dat . single rown with 24 colums as target.dat and i get the following error . please rectify.

Error:

Error using newff (line 107)
Input ranges is not a two column matrix.
Error in signature1 (line 17)
net=newff(input_n,target_n,13);

Best Answer

It looks like you are using an obsolete version of NEWFF where the first input is minmax(input_n) instead of input_n.

However, even the latest version of NEWFF is obsolete.

What version of MATLAB do you have? Type ver into the command line

The current versions of MLP are

 Regression/curve-fitting: FITNET(calls FEEDFORWARDNET) instead of NEWFIT(calls NEWFF)
 Classification/pattern-recognition: PATTERNNET(calls FEEDFORWARDNET)instead of NEWPR(calls FEEDFORWARDNET)

Hope this helps.

Thank you for formally accepting my answer

Greg

Related Solutions

MATLAB: How to calculate the error in Neural Network Back Propagation Network

Use as many defaults as possible

 MSEgoal = 0.01*mean(var(target',1))
 MinGrad = MSEgoal/100
 net = newff(input,target);
 net.trainParam.goal     = MSEgoal;
 net.trainParam.min_grad = MinGrad;
 [ net tr output error ] = train(net, input, target);

For details, search ANSWERS using the search word msegoal

Hope this helps.

Greg

Thank you for formally accepting my answer

Greg

MATLAB: Neural network problem in newff

close all ; clc; clf; clear all;

% Load the Closing stock index Data

load wedinput.dat; load wedtarget.dat;

% Input and Target Data

input1=wedinput(1:24,1:198); target1=wedtarget(1:24,1);

1. INCORRECT. INPUT AND TARGETS SHOULD HAVE THE SAME NUMBER OF COLUMNS.

2. INPUT DIMENSIONALITY REDUCTION RECOMMENDED

% Preprocessing the input and target data % y = (ymax-ymin)*(x-xmin)/(xmax-xmin) + ymin;[input_n,input_n_struct]=mapminmax(input1'); [target_n,target_n_struct]=mapminmax(target1');

3. INEFFICIENT. TRY MAPSTD ON INPUT WITH TANSIG HIDDEN NODES. REMOVE OR MODIFY OUTLIERS BEFORE TRAINING. IF NOT CLASSIFIER, USE MAPSTD ON OUTPUT ALSO.

P.S. EASIER TO USE ZSCORE

 HELP ZSCORE
 DOC  ZSCORE

disp('Input versus target After Preprocessing this data used for NN training');

4. PLOT 198 INPUTS ?

%[input_n target_n]; % Stote MSE,RMSE,MAE,MAPE and THEIL'U value of Train/Val/T

5. SPELLING

net=newff(input_n,target_n,12);

6. minmax(input_n) FOR YOUR OBSOLETE VERSION?

7. WHY H = 12?

%training the network net. %net.trainParam.epochs=1 net.divideFcn='divideblock';

net.divideParam.trainRatio=80; net.divideParam.valRatio=10; net.divideParam.testRatio=10;

8. Divide above by 100. BUT WHY NOT ACCEPT DEFAULTS?

net.trainFcn='trainlm';

9. DELETE ABOVE. IS A DEFAULT.

    [net tr Y E]=train(net,input_n,target_n);
 10. NMSE = mse(E)/mean(var(target_n',1)
 11. R2 = 1-NMSE
 12. USE tr TO OBTAIN VALUES FOR TRN/VAL/TST

% Simulation disp('Simulation Output for the given input'); simoutput=sim(net,input_n);

13. REDUNDANT simoutput = Y

[input_n ; simoutput];

14. DELETE ABOVE LINE. USELESS FOR N = 198

15. I'M STOPPING HERE.

%Output Normalized Version

disp('Normalized Simulation Output'); Norm_input=mapminmax('reverse',input_n,input_n_struct); Norm_simoutput=mapminmax('reverse',simoutput',target_n_struct);

%[Norm_input Norm_simoutput]; [Norm_input]; [Norm_simoutput] [target1 Norm_simoutput]

% Calculating Mean Square Error

perfmse=mse(E); fprintf('Mean Square Error:%d \n',perfmse);

Best Answer

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MATLAB: Neural network problem in newff

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