what is x,t and y variables in this code?

[x,t] = simplefit_dataset;
net = feedforwardnet(10); 
net = train(net,x,t); 
view(net)
y = net(x); 
perf = perform(net,y,t)

Answer 1

[Greg Heath]

x is the input matrix with N I-dimensional input columns

t is the output target matrix with N O-dimensional output target columns

vart1 = mean(var(t',1)) is the mean target variance

y is the output matrix with N O-dimensional output columns

e = t - y is the error matrix

NMSE = mse(e)/vart1 is the normalized output mean-squared-error

I prefer NMSE < 0.01 as a goal for regression and pattern-recognition,

NMSEo < 0.001 as a goal for open-loop time-series and

NMSEc < 0.01 as a goal for closed-loop time-series

Hope this helps.

Thank you for formally accepting my answer

Greg

Answer 2

[Walter Roberson]

x is the data. t is the class information.

y is the result of running prediction using the input data. When the prediction for an input does not match the known target for the input then the Neural Net is not as good is it could be.

Answer 3

[Sadiq Akbar]

[x,t] = simplefit_dataset;

Using this how can I enter my own inputs and traget data. e.g. if my input=[1; 2; 3; 4; 5]; and my target=[1 2 3 4;2 4 6 8;3 6 9 12;4 8 12 16;5 10 15 20]; Now WhenI eneter my this data via command window and enter these commands also i.e.

input=[1; 2; 3; 4; 5];
target=[1 2 3 4;2 4 6  8;3 6 9 12;4 8 12 16;5 10 15 20];
net = fitnet(10);
view(net)
net = train(net,x,yes);
view(net)
yes = net(x);
perf = perform(net,yes,t)
net = fitnet(10,'trainbr');
net = train(net,x,t);
yes = net(x);
perf = perform(net,yes,t)

I get this error:

RefFitNetExample

Undefined function or variable 'x'.

Error in RefFitNetExample (line 19)

net = train(net,x,yes);

So how to tacke this problem.

[Walter Roberson]

net = train(net, input, target);

Caution: it is... unusual... to have your target data wider than your input data, especially when your input is vector.

[Sadiq Akbar]

Input data is always in the form of vector and output may be greater than input as in my case. You mean to say we cannot treat this with above code. If yes , then how can we tackle this problem please?

[Walter Roberson]

net = train(net, input.', target.');