I have created and trained a neural network using the following code .I want to know how to get the training testing and validation errors/mis-classifications the way we get using the matlab GUI.
trainFcn = 'trainscg'; % Scaled conjugate gradient backpropagation. % Create a Pattern Recognition Network hiddenLayerSize = 25; net = patternnet(hiddenLayerSize); % Setup Division of Data for Training, Validation, Testing net.divideParam.trainRatio = trainper/100; net.divideParam.valRatio = valper/100; net.divideParam.testRatio = testper/100; % Train the Network [net,tr] = train(net,x,t);
ANSWER
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BOTH documentation commands
help patternnet
and
doc patternnet
have the following sample code for CLASSIFICATION & PATTERN-RECOGNITION:
[x,t] = iris_dataset;
net = patternnet(10);
net = train(net,x,t);
view(net)
y = net(x);
perf = perform(net,t,y);
classes = vec2ind(y);
However, the following are missing
1. Dimensions of x and t
2. Plots of x, t, and t vs x
3. Minimum possible number of hidden nodes
4. Initial state of the RNG (Needed for duplication)
5. Training record, tr
Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.
BOTH documentation commands
help patternnet and doc patternnet
have the following sample code for CLASSIFICATION & PATTERN-RECOGNITION:
[x,t] = iris_dataset; net = patternnet(10); net = train(net,x,t); view(net) y = net(x); perf = perform(net,t,y); classes = vec2ind(y);
However, the following are missing
1. Dimensions of x and t 2. Plots of x, t, and t vs x 3. Minimum possible number of hidden nodes 4. Initial state of the RNG (Needed for duplication) 5. Training record, tr
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