Nsryjdtyk

I am trying to build a neural network classifier. I have created a neural network with 1 hidden layer (25 hidden 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



%% =========== Part 1: Loading Data =============



%% Load Training Data

fprintf('Loading Data ...n');



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 improved the overall accuracy:

For more information, I have added the error histogram: