Feed forward NN is taking much time with my current dataset and output is not matching with target

0 down vote favorite I am trying to develop a feedforward NN in MATLAB

1.I have a dataset of 12 inputs and 1 output with 46998 samples. It's giving results soon for one sample, but for the complete dataset it's taking much time.

Even with one row of data set the output is not matching with the target. can anyone help me with this?

%%Clear Variables, Close Current Figures, and Create Results Directory 
clc;
clear all;
close all;
mkdir('Results//'); %Directory for Storing Results

%%Configurations/Parameters
load 'Heave_dataset'
% dataFileName = 'InputANN.txt'; 
nbrOfNeuronsInEachHiddenLayer = [24]; 
nbrOfOutUnits = 1;
unipolarBipolarSelector = -1; %0 for Unipolar, -1 for Bipolar
learningRate = 0.08;
nbrOfEpochs_max = 46998;

for j = 1:nbrOfEpochs_max

      Sample = Heave_dataset(1,:);

%%Read Data
Input = Sample(:, 1:length(Sample(1,:))-1);
TargetClasses = Sample(:, length(Sample(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];

%%Adding the Bias as Nodes with a fixed Activation of 1

% bias matrix for hidden layer

bias1 = ones(1,1);

% bias matrix for output layer

bias2 = ones(1,1);

%%Initialize Random Wieghts Matrices

% weights from input layer

weights_input = rand(nbrOfInputNodes,nbrOfNeuronsInEachHiddenLayer);

% weights from hidden layer

weights_hidden = rand(nbrOfNeuronsInEachHiddenLayer,nbrOfOutUnits);

% weights from hidden layer

weights_bias1 = rand(1,nbrOfNeuronsInEachHiddenLayer);

% weights from hidden layer

weights_bias2 = rand(1,nbrOfOutUnits);

zeroRMSReached = 0;
nbrOfEpochs_done = 0;

for Epoch = 1:nbrOfEpochs_max

   %%Backpropagation Training

   %Forward Pass

%  net input of each hidden neurons

for i = 1:nbrOfNeuronsInEachHiddenLayer

     netinput_hidden{i} = Input*weights_input(:,i) + bias1*weights_bias1(:,i); 

     % squashing net input of hidden neurons to get output using bipolar sigmoid transfer function

     x = netinput_hidden{i};

     out_hidden{i} = Activation_func_hidden(x, unipolarBipolarSelector);

     %  net input to output neurons 

     netinput_out(i,:) = out_hidden{i}*weights_hidden(i,:) + bias2*weights_bias2;

end  

% squashing net input of output neurons to get output using linear transfer functio

     y = sum(netinput_out);

     out_output = Activation_func_output(y, unipolarBipolarSelector);

%%Calculating Error

     E_total = (0.5*(TargetClasses-out_output)^2);

% %Backward Pass

% %  output layer

% main objective is to find, how the concerned weight affects the total
% error. In order to do this, we have to find we will use gradient method,

% 1. Find change in error w.r.t output
% 2. Find change in output w.r.t input from hidden layer to the output
% 3. Find change in input w.r.t to weights

% % 1. Find change in error w.r.t output

       deltaEtotal_out = (out_output-TargetClasses);

% % 2. Find change in output w.r.t input from hidden layer to the output

       deltaout_input = ones(length(TargetClasses),1);

% % 3. Find change in input w.r.t to weights

for i = 1:nbrOfNeuronsInEachHiddenLayer

       deltainput_wh_i =  out_hidden{i};

       deltaEtotal_wh_i = deltaEtotal_out * deltaout_input * deltainput_wh_i;

       deltaEtotal_wh(i,:) = deltaEtotal_wh_i;
  end

%      To decrease the error, we subtract these value from initial assumed
%      weights(multiplied by learning rate)

      weights_hidden_updated =  weights_hidden - (learningRate*deltaEtotal_wh);

% %      Hidden Layer

% % follow the same procedure as previous

% % 1. Find change in error w.r.t output of hidden neurons

% % i) change in error w.r.t to output 

       deltaEtotal_out = (out_output-TargetClasses);

%   ii) change in output w.r.t input to the output layer from hidden layer

      deltaout_input = ones(length(TargetClasses),1);

%  iii) change in input to the output layer from hidden layer w.r.t output from hidden layer

for i = 1:nbrOfNeuronsInEachHiddenLayer

      deltainputoutput_hiddenoutput{i} = weights_hidden(i,:);

      deltaEtotal_hiddenout{i} = deltaEtotal_out * deltaout_input * deltainputoutput_hiddenoutput{i};

      % % 2. Find change in output w.r.t input of hidden layer

          fx = out_hidden{i};

       deltahout_hin{i} = Activation_func_drev(fx, unipolarBipolarSelector);

      % 3. Find change in input w.r.t to weights

       deltainput_wi(i,:) =  Input;

       deltaEtotal_wi_i = deltaEtotal_hiddenout{i} * deltahout_hin{i} * deltainput_wi(i,:);

       deltaEtotal_wi(i,:) = deltaEtotal_wi_i;

end

deltaEtotal_wrtinputweights = deltaEtotal_wi';

%      To decrease the error, we subtract these value from initial assumed
%      weights(multiplied by learning rate)

      weights_input_updated =  weights_input - (learningRate*deltaEtotal_wrtinputweights);

   %%Evaluation

   E_total(Epoch) = E_total;

if (E_total(Epoch) == 1e-25)
        zeroRMSReached = 1;
    end

end
end

Answer 1

[Greg Heath]

With "I"nput and "O"utput target matrices with sizes

 [ I N ] = size(input)
 [ O N ] = size(target)

Remove rows from BOTH matrices if EITHER input or target contains NaNs

Hope this helps.

Thank you for formally accepting my answer

Greg

Answer 2

[Greg Heath]

The next time think twice before posting, WITHOUT WARNING, a MEGABYTE data set with NaNs and zero variance rows.

[Tulasi Ram Tammu]

 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.

[Tulasi Ram Tammu]

I tried to remove them using `

Heave_dataset(isnan(Heave_dataset))=[];,

but my dataset is getting converted into a column matrix of (1*610964). could you please suggest me what should I do?