# Artificial Neural Network Classifier in Matlab

I am trying to build a neural network classifier. I have created a neural network with 1 hidden layer (25 neurons) and 1 output layer (1 neuron/binary classification).

The dataset I am using has the following dimensions:

size(X_Train): 125973 x 122
size(Y_Train): 125973 x 1
size(X_Test): 22543 x 122
size(Y_test): 22543 x 1


My overall goal is to compare different training functions. But, I would like first to get your feedback about my code and how I improve it.

% Neural Network Binary-classification

clear ; close all; clc

load('dataset.mat'); % training data stored in arrays X, y
X_training=X_training';
Y_training=Y_training';
X_testing=X_testing';
Y_testing=Y_testing';

%% Create the neural network
% 1, 2: ONE input, TWO layers (one hidden layer and one output layer)
% [1; 1]: both 1st and 2nd layer have a bias node
% [1; 0]: the input is a source for the 1st layer
% [0 0; 1 0]: the 1st layer is a source for the 2nd layer
% [0 1]: the 2nd layer is a source for your output
net = network(1, 2, [1; 1], [1; 0], [0 0; 1 0], [0 1]);
net.inputs{1}.size = 122; % input size
net.layers{1}.size = 25; % hidden layer size
net.layers{2}.size = 1; % output layer size

%% Transfer function in layers
net.layers{1}.transferFcn = 'logsig';
net.layers{2}.transferFcn = 'logsig';

net.layers{1}.initFcn = 'initnw';
net.layers{2}.initFcn = 'initnw';

net=init(net);

%% divide data into training and test
net.divideFcn= 'dividerand';
net.divideParam.trainRatio = 60/100; % 80% training
net.divideParam.valRatio = 20/100; % 20% validation set
net.divideParam.testRatio = 20/100; % 20% validation set

net.performFcn = 'crossentropy';

%% Training functions
net.trainFcn = 'trainscg'; %Scaled conjugate gradient backpropagation

%% Train the neural network
[net,tr] = train(net,X_training,Y_training); % return the network and training record

%% Test the Neural Network on the training set
outputs = net(X_training);
errors = gsubtract(Y_training,outputs);
performance = perform(net,Y_training,outputs);

%% Plots  (%training)
figure, plotperform(tr)
figure, plottrainstate(tr)

%% Test the Neural Network on the testing test
outputs1 = net(X_testing);
errors1 = gsubtract(Y_testing,outputs1);
performance1 = perform(net,Y_testing,outputs1);

figure, plotconfusion(Y_testing,outputs1)
figure, ploterrhist(errors1)


Below if the validation curve.

Confusion Matrix (Training set)

Confusion Matrix (Testing set)

Any remarks?

Edit:

I have used feature scaling or normalization:

net.performParam.normalization = 'standard';


which has improved the overall accuracy:

• Not knowing what your datasets might be and how sophisticated that may be, you may focus on 10^-3 to 10^-6 convergence range. It might increase the performance (e.g., confusion matrices) of your network.