MATLAB Backpropagation Algorithm not functioning as expected - matlab

Question

I am attempting to write a Multi-Layer Perceptron Network inside MATLAB to help me better understand the calculus required for backpropagation.
The aim is so provide the network with XOR data (where upper-right and lower-left quadrant data is class 1 and the remaining quadrants class 0), train the network on this data, and then test it on new data.
My problem is that my loss curve looks very very strange:
It appears to bounce between very low error very high error and converge in the middle to a pretty poor error.
I was wondering if someone could check that I have correctly implemented the chain rule in MATLAB syntax.
The MLP network is structured as follows: Input-layer has 2 neurons, 1 hidden-layer with 2 neurons, and 1 output neuron.
Here is the MATLAB code:
%Create XOR Dataset
x1pos = rand(500,1);
x1neg = -rand(500,1);
x1 = [x1pos; x1neg];
p = randperm(length(x1));
x1 = x1(p);
x2pos = rand(500,1);
x2neg = -rand(500,1);
x2 = [x1pos; x1neg];
p = randperm(length(x2));
x2 = x2(p);
Data = [x1 x2];
TrainingData = Data(1:800,:);
TestData = Data(801:length(Data),:);
T = gt((Data(:,1).*Data(:,2)),0); %Create class label for data and assign to matrix T
%Neural Net
%Training
W1 = rand(2,2); %Initialize random weights
W2 = rand(1,2); %Initialize random weights
B1 = rand(2,1); %Initialize random biases
B2 = rand(1,1); %Initialize random biases
n = 0.05; %Set Learning Rate
for i = 1:800
%Fwd Pass
x1 = Data(i,1);
x2 = Data(i,2);
X = [x1; x2];
A1 = W1*X + B1;
H1 = sigmoid(A1);
A2 = W2*H1 + B2;
Y = sigmoid(A2);
%Loss
Loss = (Y-T(i))*(Y-T(i));
scatter(i, Loss)
hold on;
%Backpropagation
dEdY = 2*(Y-T(i)); %The partial derivative of the loss with respect to the output
dYdA2 = Y*(1-Y); %The partial derivative of the output with respect to the hidden layer output
dA2dH1 = W2.'; %The partial derivative of the hidden layer output with respect to the first layer activations
dH1dA1 = H1.*(1-H1); %The partial derivative of the first layer activations with respect to the first layer output
%Chain Rule
dEdW2 = dEdY.*dYdA2.*W2.';
dEdW1 = dEdY.*dYdA2.*dA2dH1.*dH1dA1.*W1.';
dEdB2 = dEdY.*dYdA2;
dEdB1 = dEdY.*dYdA2.*dA2dH1.*dH1dA1;
%Update Weights
W2 = (W2.' - n.*dEdW2).';
W1 = (W1.' - n.*dEdW1).';
%Update Biases
B2 = B2 - n.*dEdB2;
B1 = B1 - n.*dEdB1;
%Next training loop
end
%Testing
for i = 801:1000
x1 = Data(i,1);
x2 = Data(i,2);
X = [x1; x2];
A1 = W1*X + B1;
H1 = sigmoid(A1);
A2 = W2*H1 + B2;
Y = sigmoid(A2);
end
function o = sigmoid(input)
o = [];
for i = 1:length(input)
o = [o; 1/(1+exp(-input(i)))];
end
end