Hello community,
I have novice in ANN, so please bare with me.
I have a image[mxnxp]which i want to classify with ANN back propagation. I have ground truth data for my 5 class. How would i represent my input vector? Is input vector will be AxP where A is total number of my samples with all classes! How would i represent my output vector? Since i have five classes, i think my output matrix would be Ax5.
In my understanding p and no. of classes certainly could be different. But when i want to train my ANN, with following cmd it says
net=newff(input_norm,output, [NUM_NEURON,5],{'logsig','logsig'},'traingd','learngdm','mse');
Error using traingd (line 102) Inputs and targets have different numbers of samples.
Though my no. of rows of both input and output matrix is same, no. of columns is different ( input column 4, output column 5), which i think is right.
But error " Inputs and targets have different numbers of samples" is always occuring.
any guidance, how i should build my input and output matrix?
In addition, please guide me, how would classify my image after training my ANN ?
sincerely Yours Sukuchha
Best Answer