Interpret the output of neural network in matlab

问题

I have build a neural network model, with 3 classes. I understand that the best output for a classification process is the boolean 1 for a class and boolean zeros for the other classes , for example the best classification result for a certain class, where the output of a classifire that lead on how much this data are belong to this class is the first element in a vector is [1 , 0 , 0]. But the output of the testing data will not be like that,instead it will be a rational numbers like [2.4 ,-1 , .6] ,So how to interpret this result? How to decide to which class the testing data belong?

I have tried to take the absolute value and turn the maximum element to 1 and the other to zeros, so is this correct?

回答1:

Learner.

It appears your neural network is bad designed.

Regardless your structure is -number of input-hidden-output- layers, when you are doing a multiple classification problem, you must ensure each of your output neurones are evaluating an individual class, that is, each them has a bounded output, in this case, between 0 and 1. Use almost any of the defined function on the output layer for performing this.

Nevertheles, for the Neural Network to work properly, you must strongly remember, that every single neuron loop -from input to output- operates as a classificator, this is, they define a region on your input space which is going to be classified.

Under this framework, every single neuron has a direct interpretable sense on the non-linear expansion the NN is defining, particularly when there are few hidden layers. This is ensured by the general expression of Neural Networks:

Y_out=F_n(Y_n-1*w_n-t_n) ... Y_1=F_0(Y_in-1*w_0-t_0)

For example, with radial basis neurons -i.e. F_n=sqrt(sum(Yni-Rni)^2) and w_n=1 (identity):

Yn+1=sqrt(sum(Yni-Rni)^2)

a dn-dim spherical -being dn the dimension of the n-1 layer- clusters classification is induced from the first layer. Similarly, elliptical clusters are induced. When two radial basis neuron layers are added under that structure of spherical/elliptical clusters, unions and intersections of spherical/elliptical clusters are induced, three layers are unions and intersections of the previous, and so on.

When using linear neurons -i.e. F_n=(.) (identity), linear classificators are induced, that is, the input space is divided by dn-dim hyperplanes, and when adding two layers, union and intersections of hyperplanes are induced, three layers are unions and intersections of the previous, and so on.

Hence, you can realize the number of neurons per layer is the number of classificators per each class. So if the geometry of the space is -lets put this really graphically- two clusters for the class A, one cluster for the class B and three clusters for the class C, you will need at least six neurons per layer. Thus, assuming you could expect anything, you can consider as a very rough approximate, about n neurons per class per layer, that is, n neurons to n^2 minumum neurons per class per layer. This number can be increased or decreased according the topology of the classification.

Finally, the best advice here is for n outputs (classes), r inputs:

1. Have r good classificator neurons on the first layers, radial or linear, for segmenting the space according your expectations,
2. Have n to n^2 neurons per layer, or as per the dificulty of your problem,
3. Have 2-3 layers, only increase this number after getting clear results,
4. Have n thresholding networks on the last layer, only one layer, as a continuous function from 0 to 1 (make the crisp on the code)

Cheers...

来源：https://stackoverflow.com/questions/29394027/interpret-the-output-of-neural-network-in-matlab