clear all; close all; clc;
load mfcc_S6_8_01.mat;
load mfcc_S6_8_02.mat;
load mfcc_S6_9_01.mat;
load mfcc_S6_9_02.mat;
[s1 s2]=size(sg_S6_8_01);
%YTrain_data=[sg_S6_8_01 sg_S6_8_02 sg_S6_9_01 sg_S6_9_02];
cmb_8_01=[];
for ii=1:s2
cmb_sg_8=sg_S6_8_01{ii};
cmb_8_01=[cmb_8_01; cmb_sg_8];
end
[s1 s2]=size(sg_S6_8_02)
cmb_8_02=[];
for ii=1:s2
cmb_sg_8_2=sg_S6_8_02{ii};
cmb_8_02=[cmb_8_02; cmb_sg_8_2];
end
[s1 s2]=size(sg_S6_9_01)
cmb_9_01=[];
for ii=1:s2
cmb_sg_9_01=sg_S6_9_01{ii};
cmb_9_01=[cmb_9_01; cmb_sg_9_01];
end
[s1 s2]=size(sg_S6_9_02)
cmb_9_02=[];
for ii=1:s2
cmb_sg_9_02=sg_S6_9_02{ii};
cmb_9_02=[cmb_9_02; cmb_sg_9_02];
end
comb_train_data={cmb_8_01;cmb_8_02;cmb_9_01;cmb_9_02;};
index2=1;
files={sg_S6_8_01; sg_S6_8_02;sg_S6_9_01;sg_S6_9_02};
for ii=1:length(files)
chk_files=files{ii};
for jj=1:length(chk_files)
comb_data=chk_files{jj};
all_frames{:,:,index2}=comb_data;
index2=index2+1;
end
end
YTrain_lbl=categorical([8*ones(1,50) 8*ones(1,51) 9*ones(1,47) 9*ones(1,47)]);
%YTrain_lbl=categorical(YTrain1_lbl);
for kk=1:195
if (kk<=1 && kk>=101)
label1{kk}=8;
else
label1{kk}=9;
end
end
YTrain=all_frames;
classWeights = 1./countcats(YTrain_lbl);
classWeights = classWeights'/mean(classWeights);
numClasses = numel(categories(YTrain_lbl));
numHops=129;numBands=7;
timePoolSize = ceil(numHops/8);
dropoutProb = 0.2;
numF = 12;
layers = [
imageInputLayer([numHops numBands])
convolution2dLayer(3,numF,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(3,'Stride',2,'Padding','same')
convolution2dLayer(3,2*numF,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(3,'Stride',2,'Padding','same')
convolution2dLayer(3,4*numF,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer(3,'Stride',2,'Padding','same')
convolution2dLayer(3,4*numF,'Padding','same')
batchNormalizationLayer
reluLayer
convolution2dLayer(3,4*numF,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer([timePoolSize,1])
dropoutLayer(dropoutProb)
fullyConnectedLayer(numClasses)
softmaxLayer
weightedClassificationLayer(classWeights)];
miniBatchSize = 128;
%validationFrequency = floor(numel(YTrain)/miniBatchSize);
options = trainingOptions('adam', ...
'InitialLearnRate',3e-4, ...
'MaxEpochs',25, ...
'MiniBatchSize',miniBatchSize, ...
'Shuffle','every-epoch', ...
'Plots','training-progress', ...
'Verbose',false, ...
'LearnRateSchedule','piecewise', ...
'LearnRateDropFactor',0.1, ...
'LearnRateDropPeriod',20)
trainedNet = trainNetwork(all_frames,label1',layers,options);
ERROR:
Error using trainNetwork (line 184)
Invalid training data. Predictors must be a numeric array, a datastore, or a table. For networks with
sequence input, predictors can also be a cell array of sequences.
Error in JAN31_2023_EX (line 113)
trainedNet = trainNetwork(all_frames,label1',layers,options);
Sir/ Madam
I have written code for digit recognition. Input is MFCC matrix only for digit 8 & 9 in terms of frames. In this code, digit 8 frames are 101 and digit 9 frames are 94 frames. i have declared label variable according to that. Please guide to resolve above error
