Multiple Inputs for Neural Network binary classification (Error Input/Target of different numbers of samples)

Hi,

I cannot seem to get the following code to run with 3 matrix inputs (IN_1, IN_2, IN_3) and binary output (0, 1). Each of the input matrix is 30,000 x 2,000 where the row is my input/signal. Below is what I have:

x = 3 x 1 cell of 30,000 x 2000

t = 1 x 30,000

Error using network/train (line 347)
Inputs and targets have different numbers of samples.
[net,tr] = train(net,x,t);

Below is what I have:

% Solve a Pattern Recognition Problem with a Neural Network
% Script generated by Neural Pattern Recognition app

inputData = {IN_1, IN_2, IN_3};


x = inputData';
t = target';

% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'traincgf';  % Scaled conjugate gradient backpropagation. traincgf


% Create a Pattern Recognition Network
hiddenLayerSize = 50;
net = patternnet(hiddenLayerSize, trainFcn);

net.numinputs = 3;
net = configure(net,x);
net.inputConnect = [1 1 1; 0 0 0];

% Train the Network
[net,tr] = train(net,x,t);

Answer 1

[Wookie]

inputData = {IN_1'; IN_2'; IN_3'};

x = inputData';
t = target';

trainFcn = 'traincgf';

% Create a Pattern Recognition Network
hiddenLayerSize = 50;
net = patternnet(hiddenLayerSize, trainFcn);

net.numinputs = 3;
net = configure(net,x);
net.inputConnect = [1 1 1; 0 0 0];

% Train the Network
[net,tr] = train(net,x,t);