Getting NaN values in neural network weight matrices

7 ビュー (過去 30 日間)
Tulasi Ram Tammu
Tulasi Ram Tammu 2016 年 10 月 23 日
コメント済み: Tulasi Ram Tammu 2016 年 10 月 24 日
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
but my dataset is getting converted into a column matrix of (1*610964).
can anyone help me with this ?
clear all;
close all;
mkdir('Results//'); %Directory for Storing Results
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;


Greg Heath
Greg Heath 2016 年 10 月 24 日
Remove all input and corresponding output vectors if EITHER OR BOTH contain NaNs.
Hope this helps
Thank you for formally accepting my answer
  1 件のコメント
Tulasi Ram Tammu
Tulasi Ram Tammu 2016 年 10 月 24 日
Thank You


その他の回答 (1 件)

Teja Muppirala
Teja Muppirala 2016 年 10 月 24 日
編集済み: Teja Muppirala 2016 年 10 月 24 日
As of R2016b, you could use the RMMISSING function.
Heave_dataset = rmmissing(Heave_dataset);
Or you can do it like this.
You might want to make this two lines, just to be more readable.
missingRows = any(isnan(Heave_dataset),2);
Heave_dataset(missingRows,:) = [];
The way you have written now, it removes the NaN values, but not the rows, so it has to turn the matrix into a long vector of all the values.
  1 件のコメント
Tulasi Ram Tammu
Tulasi Ram Tammu 2016 年 10 月 24 日
Thank You



Find more on Deep Learning Toolbox in Help Center and File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by