How could I know the bias value of the MLP network? I have used the explore section of weka for MLP.
Related
I tried to get and set weight and bias of trained lstm in Matlab but I failed.
Does any one know how I can get and set weight and bias of a LSTM becuase function like getwb(net) did not work.
I am reading a lot of articles about neural networks and I found very different information. I understand that the supervised neural network can be also regression and classification. In both cases I can use the sigmoid function but what is the difference?
A single-layer neural network is essentially the same thing as linear regression. That's because of how neural networks work: Each input gets weighted with a weight factor to produce an output, and the weight factors are iteratively chosen in such a way that the error (the discrepancy between the outputs produced by the model and the correct output that should be produced for a given input) is minimised. Linear regression does the same thing. But in a neural network, you can stack several of such layers on top of each other.
Classification is a potential, but by far not the only, use case for neural networks. Conversely, there are classification algorithms that don't use neural networks (e.g. K-nearest neighbours). The sigmoid function is often used as an activation function for the last layer in a classifier neural network.
How can I use trained data of ANN with softmax as the output layer? Is it possible with Matlab inbuilt neural network tool (nnstart)?
Suppose I am using the MATLAB probabilistic neural network example for a classification problem, as given (click here)
The weights and the bias for the network can be determined as follows;
weights = net.LW
biases = net.b
My question is, how do I get an equations describing the model. I am a beginner in neural network, any detailed explanation/sample code would be most helpful.
Thanks!
I have read this line about neural networks :
"Although the perceptron rule finds a successful weight vector when
the training examples are linearly separable, it can fail to converge
if the examples are not linearly separable.
My data distribution is like this :The features are production of rubber ,consumption of rubber , production of synthetic rubber and exchange rate all values are scaled
My question is that the data is not linearly separable so should i apply ANN on it or not? is this a rule that it should be applied on linerly separable data only ? as i am getting good results using it (0.09% MAPE error) . I have also applied SVM regression (fitrsvm function in MATLAB)so I have to ask can SVM be used in forecasting /prediction or it is used only for classification I haven't read anywhere about using SVM to forecast , and the results for SVM are also not good what can be the possible reason?
Neural networks are not perceptrons. Perceptron is on of the oldest ideas, which is at most a single building block of neural networks. Perceptron is designed for binary, linear classification and your problem is neither the binary classification nor linearly separable. You are looking at regression here, where neural networks are a good fit.
can SVM be used in forecasting /prediction or it is used only for classification I haven't read anywhere about using SVM to forecast , and the results for SVM are also not good what can be the possible reason?
SVM has regression "clone" called SVR which can be used for any task NN (as a regressor) can be used. There are of course some typical characteristics of both (like SVR being non parametric estimator etc.). For the task at hand - both approaches (as well as any another regressor, there are dozens of them!) is fine.