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From: Greg Heath on 8 Nov 2009 05:44
On Nov 2, 9:24 pm, "Kishore " <kishore3...(a)yahoo.co.in> wrote:
> Ok, one more thing i need to tell,
>
> The network assumes 2 layers, but i have implemented equations ( back propagation for 1 layer - so this is the problem i guess).

NO. You have 2 layers. Note the N-1 in the documentation.

> But  how do i do the testing using standard function.
>
> ( i have testing data as well, which function should i  pass it to?)

See my previous post.

Hope this helps.

Greg
From: Greg Heath on 8 Nov 2009 05:59
On Nov 2, 10:08 pm, "ade77 " <ade1...(a)gmail.com> wrote:
> I did exactly the same project last year. Your code seems correct. I will assume the following based on the description of the problem:
>
> 1. You have 20 rows of input features.
> 2. You need to classify the problem into 4 possible outcomes.
> unless you need to pass the weights into another program, the codes you have written:
>  W = net.IW{1};> V = net.LW{2};
>
> > W =W';
> > V = V'
>
> is completely unnecessay, since MATLAB will stote the weights in the network(net).
>
> Back to the problem, once you have the network trained, all you have to do is test the network.
>
> test = sim(net,new_input).
> The trick here is that your output will produce 4 elements, the one that is closest to 1 is your classification.

No.

The maximum output is the winner.

> For example if your classification is [red green blue orange], and you get
> [1.2  2.4  5.6  3] , then red is your classification. because 1.2 is closest to 1

No.

The correct class is 3.

That is why it is better to use LOGSIG. Then you can choose
take the maximum probability( or maximum risk by considering
prior probabilities and misclassification costs ...See
the NN book by Duda et al ).

> In most cases, you will get negative, you need to decide based on your input features, if you will use absoulte values.

No, No, No!

> For example,
> test = sim(net, new_inputs) gives [0.7  1.5  -0.8  3.2], if you take absolute value,
> then -0.8 is closest to 1, hence blue is your classification.

No!

> Finally, I have assumed that you correctly put the inputs and outputs in your
> training, and you normalize the inputs.
>
> if you are still confused, feel free to let me know

No comment.

> One last thing, you can just let MATLAB use its default parameters, and all you
> need to change is the number of neurons, and try 2 or more hidden layers,

No!

One hidden layer is sufficient.

> since your input features are many.

Hope this is not too late.

Greg

From: Greg Heath on 8 Nov 2009 06:18
On Nov 8, 5:42 am, Greg Heath <he...(a)alumni.brown.edu> wrote:
> On Nov 2, 8:45 pm, "Kishore " <kishore3...(a)yahoo.co.in> wrote:
>
> > Hello,
>
> > I am trying to classify a 4 class problem (each class has
> > 20 features ) using neural network.
> > So, in order to reduce the complexity, i used newff function to
> > get the weights.
>
> > The  problem is i am not very familiar with newff function usage
> > ( the samples are not classified properly- same sample set is being
> > classified welll using k nearest neighbour and bayesian techniques).
>
> > It would be great if i can get feed back on the usage of  this
> > newff sequence.
>
> > %%%%%%%%
>
> > net = newff(training_data',group',no_hiddenLayer);
>
> help newff
> doc newff
>
> You have accepted the PURELIN output default. For classification,
> LOGSIG is superior. The output then represents the posterior
> probability (conditional on the input) for class "1".

You have accepted the PURELIN output default. For classification,
LOGSIG is superior. The ith output then represents the posterior
probability (conditional on the input) for class "i".

Hope this helps.

Greg
From: Greg Heath on 8 Nov 2009 06:23
On Nov 8, 5:59 am, Greg Heath <he...(a)alumni.brown.edu> wrote:
> On Nov 2, 10:08 pm, "ade77 " <ade1...(a)gmail.com> wrote:
>
>
>
>
>
> > I did exactly the same project last year. Your code seems correct. I will assume the following based on the description of the problem:
>
> > 1. You have 20 rows of input features.
> > 2. You need to classify the problem into 4 possible outcomes.
> > unless you need to pass the weights into another program, the codes you have written:
> >  W = net.IW{1};> V = net.LW{2};
>
> > > W =W';
> > > V = V'
>
> > is completely unnecessay, since MATLAB will stote the weights in the network(net).
>
> > Back to the problem, once you have the network trained, all you have to do is test the network.
>
> > test = sim(net,new_input).
> > The trick here is that your output will produce 4 elements, the one that is closest to 1 is your classification.
>
> No.
>
> The maximum output is the winner.
>
> > For example if your classification is [red green blue orange], and you get
> > [1.2  2.4  5.6  3] , then red is your classification. because 1.2 is closest to 1
>
> No.
>
> The correct class is 3.
>
> That is why it is better to use LOGSIG. Then you can choose
> take the maximum probability( or maximum risk by considering
> prior probabilities and misclassification costs ...See
> the NN book by Duda et al ).

Then you can choose the class with the maximum posterior
probability( or minimum risk by considering prior probabilities
and misclassification costs ...See the NN book by Duda et al ).

Hope this helps.

Greg
From: Kishore on 9 Nov 2009 00:16
Hi Greg,
thanks for your suggestions..

1) Yes, i am considering the maximum of the 4 outputs for classification.

2) I am using 'logsig' function at the output layer,instead of the default 'purelin'.
The hidden layer has 10 neurons. -i am also experimenting with variations of this,but this factor does not have major effect .(i tried 8 - 15 -20 etc)

3) The function now looks like

[net,tr] = train(net,training_data,group);

ALSO - training_data and group were transposed before this step

one observation - after the simulation i.e test, the results seems to be biased towards class1
( i.e i get o/p as [1 0.5 0.5 0.5] for class1 test data ( which is fine)
and
[0.5 0.5 0.5 0.5] for the rest. i.e data belonging to class 2/3/4 .

so,everything is getting classified to class1.

4) But, if i use the default purelin,
altleast this bias does not seem to exist although the misclassifcation rate is high.( same data was classified at a good rate using the Baye's method.)

5) Test was done using
test = sim(net,test_data);

I could

Thanks again.
Kishore.
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