I am learning about neural networks. I found a course on Coursera about machine learning https://www.coursera.org/course/ml . What I am trying to implement is a neural network to calculate logical XOR.
I am using network with input layer, 1 hidden layer and output layer. They have 2, 2, 1 neurons respectively (also first 2 layers has bias neurons). I have written this small program in Octave. I am observing such results: firstly cost function
J has some value which is near some number (about 0.7). This continues for some random time, then suddenly cost function gives a rush to zero, and network gives right results.
As you can see I'm trying to implement backpropagation algorithm with gradient descent using formulas from that Andrew Ng's course.
The problem is that cost function doesn't decrease every step of gradient descent. By the way, examples with logical AND and logical OR worked great.
I tried to add more training data, as far as I can see, that doesn't change anything.
Question is: What can I change to make gradient descent converge fast (to make it not stuck)?
Slides from lecture about backpropagation: https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture9.pdf
m = 4; %number of examples X = [0 0; 1 0; 0 1; 1 1]; Y = [1; 0; 0; 1]; eps = 0.01; Theta1 = rand(2, 3) * 2 * eps - eps; Theta2 = rand(1, 3) * 2 * eps - eps; Theta1_grad = zeros(size(Theta1)); Theta2_grad = zeros(size(Theta2)); function [ans] = sigmoid(z) ans = 1.0 / (1.0 + exp(-z)); end alpha = 0.0001; while J > 0.001, J = 0; for i = 1:m %forward propagation a1 = [1 X(i, :)]'; z2 = Theta1 * a1; a2 = [1 sigmoid(z2)]'; z3 = Theta2 * a2; a3 = sigmoid(z3); %update cost function J += Y(i) * log(a3) + (1.0 - Y(i)) * log(1.0 - a3); %backward propagation delta3 = a3 - Y(i); delta2 = ((Theta2' * delta3) .* (a2 .* (1 - a2)))(2:end); Theta1_grad += delta2 * a1'; Theta2_grad += delta3 * a2'; end J /= -m; %Updating theta Theta1 -= alpha / m * Theta1_grad; Theta2 -= alpha / m * Theta2_grad; disp(J); disp(Theta1_grad); disp(Theta2_grad); end %output on training data for i = 1:m a1 = [1 X(i, :)]'; z2 = Theta1 * a1; a2 = [1 sigmoid(z2)]'; z3 = Theta2 * a2; a3 = sigmoid(z3); disp('a3 = '); disp(a3); end