Invalid training data. The output size (2) of the last layer does not match the number of classes (6).

% TRAIN THE IMAGE

layers =[imageInputLayer([90 120 1])

%CONVOLUTION FILTER

convolution2dLayer(5,20)

reluLayer

%GET MAXIMUM VALUE FROM LAYER

maxPooling2dLayer(2,'stride',2)

%CONVOLUTION FILTER

convolution2dLayer(5,20)

reluLayer

%GET MAXIMUM VALUE FROM LAYER

maxPooling2dLayer(2,'stride',2)

fullyConnectedLayer(2)

softmaxLayer

classificationLayer()]

%% CLASSIFICATION

im = imresize(im,[90,120]);

options=trainingOptions('sgdm','MaxEpochs',15,'initialLearnRate',0.0001);

convnet=trainNetwork(Data,layers,options);

output=classify(convnet,im);

tf1=[];

for ii=1:2

st=int2str(ii);

tf=ismember(output,st);

tf1=[tf1 tf];

end

output=find(tf1==1);

Answer 1

[Athul Prakash]

Hi Aswin,

The number of outputs from your network would be determined by the last fullyConnectedLayer you have used. Since it has 2 neurons, the final output from the classificationLayer would also have 2.

If you want to classify into 6 categories, you may use fullyConnectedLayer(6) instead.

Hope it helps!