MATLAB: Getting NaN values in neural network weight matrices

back propagationfeed forwardMATLABneural network

I am trying to develop a feedforward NN in MATLAB. I have a dataset of 12 inputs and 1 output with 46998 samples. I have some NaN values in last rows of Matrix, because some inputs are accelerations & velocities which are 1 & 2 steps less respectively than displacements. With this current data set I am getting w1_grad & w2_grad as NaN matrices. I tried to remove them using
Heave_dataset(isnan(Heave_dataset))=[];
but my dataset is getting converted into a column matrix of (1*610964).
can anyone help me with this ?
clc;
clear all;
close all;
mkdir('Results//'); %Directory for Storing Results
%%Configurations/Parameters
load 'Heave_dataset'
% Heave_dataset(isnan(Heave_dataset))=[];
nbrOfNeuronsInEachHiddenLayer = 24; 
nbrOfOutUnits = 1;
unipolarBipolarSelector = -1; %0 for Unipolar, -1 for Bipolar
learningRate = 0.08;
nbrOfEpochs_max = 50000;
%%Read Data
Input = Heave_dataset(:, 1:length(Heave_dataset(1,:))-1);
TargetClasses = Heave_dataset(:, length(Heave_dataset(1,:)));
%%Calculate Number of Input and Output NodesActivations
nbrOfInputNodes = length(Input(1,:)); %=Dimention of Any Input Samples
nbrOfLayers = 2 + length(nbrOfNeuronsInEachHiddenLayer);
nbrOfNodesPerLayer = [nbrOfInputNodes nbrOfNeuronsInEachHiddenLayer nbrOfOutUnits];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Forward Pass %%%%%%%%%%%
%%Adding the Bias to Input layer
Input = [ones(length(Input(:,1)),1) Input];
%%Weights leading from input layer to hidden layer is w1

w1 = rand(nbrOfNeuronsInEachHiddenLayer,(nbrOfInputNodes+1));
%%Input & output of hidde layer

hiddenlayer_input = Input*w1';
hiddenlayer_output = -1 + 2./(1 + exp(-(hiddenlayer_input)));
%%Adding the Bias to hidden layer
hiddenlayer_output = [ones(length(hiddenlayer_output(:,1)),1) hiddenlayer_output];
%%Weights leading from input layer to hidden layer is w1
w2 = rand(nbrOfOutUnits,(nbrOfNeuronsInEachHiddenLayer+1));
%%Input & output of hidde layer
outerlayer_input = hiddenlayer_output*w2';
outerlayer_output = outerlayer_input;
%%Error Calculation
TotalError = 0.5*(TargetClasses-outerlayer_output).^2;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%Backward Pass %%%%%%%%%%%
d3 = outerlayer_output - TargetClasses;
d2 = (d3*w2).*hiddenlayer_output.*(1-hiddenlayer_output);
d2 = d2(:,2:end);
D1 = d2' * Input;
D2 = d3' * hiddenlayer_output;
w1_grad = D1/46998 + learningRate*[zeros(size(w1,1),1) w1(:,2:end)]/46998;
w2_grad = D2/46998 + learningRate*[zeros(size(w2,1),1) w2(:,2:end)]/46998;

Best Answer

Remove all input and corresponding output vectors if EITHER OR BOTH contain NaNs.
Hope this helps
Thank you for formally accepting my answer
Greg
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