Matlab Multilayer Perceptron (MLP) with back propagation algorithm to solve a 3-class classification problem.

Specification:

Number of layers: 2 (hidden + output) 

Number of input units: 13 (+ 1 for bias)

Number of hidden units: 8 (+1 for bias) 

Number of output units: 3 

Activation functions: sigmoid for hidden units; softmax for output units 

Initial weights: uniform random numbers between 0 and 1

Dataset:t http://archive.ics.uci.edu/ml/datasets/Wine

Code:

Output:

folowing steps are followed

1: forward pass to compute an,zn,bn and yn

2: compute the error signal.

3: Pass error backward to compter error for hidden layer.

4: Compute gradient

5: Update weight

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Total number of instance = 178 

learning rate = 0.500000 

Total number of iteration = 5000 

cost befor training 1.971846

Total number of correctly classified instance before traing = 71 

Start Training

Total number of correctly classified instance after training = 178 

Cost after Training = 0.020612 

Total time = 22.546547 sec

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