I have an X matrix that contains on every row some features extracted from images (one image for one row) and a Y matrix that has on every row a classification criteria for every feature vector/row in matrix X (Y is a matrix with just one column). Now I want to feed these two matrices to a Neural Networks and train it and then I want to test a new feature vector of an image (new_ft) and be able to get one of the two classification criterias built in the Neural Network model. Basically I want to see what group does a new feature vector belong to. I tried training a network in the form: net = newpr(X,Y,numHiddenNeurons) and then use [net,tr] = train(net,Xx,Y); outputs = sim(net,Xx); but I get the an error "Dimensions of matrices being concatenated are not consistent." on the first line. My matrices have the sizes: X= 46 x 25750 double and Y = 46 x 1 cell. I tried giving Y the same size 46 x 25750, but I get the same problem.
Could you let me know what could be the problem here? Thank you!
Try transposing X and Y for the net: [net, tr] = train(net, X',Y')