4-class perceptron classification

2018 2 8
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更新时间：2024 5 20
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Good evening, I hope everyone is well. I have a single-layer perceptron that is meant to take in two inputs and provide an output as one of four classifications. I then need to plot the inputs and the hyperplanes dividing the four classes. I'm getting errors when I try to run the code that I suspect are related to the number of outputs I'm trying to get. Here's the code I'm working with:
if true
% Initialize the Input Space (extended input matrix)
x = [1 1 1 2 2 -1 -2 -1 -2; 1 1 2 -1 0 2 1 -1 -2];
% Initialize the extended target vector
t = [0 1 1 2 2 3 3 4 4];
% Plot the input locations
figure
hold on
for i=1:length(x)
if (t(i)==1)
scatter(x(1,i), x(2,i),'k ', 'filled');
elseif (t(i)==2)
scatter(x(1,i), x(2,i),'r ', 'filled');
elseif (t(i)==3)
scatter(x(1,i), x(2,i),'b ', 'filled');
elseif (t(i)==4)
scatter(x(1,i), x(2,i),'g ', 'filled');
end
end
grid on
line([0 0], ylim, 'linewidth', 1); %y-axis
line(xlim, [0 0], 'linewidth', 1); %x-axis
legend('class_1', 'class_2', 'class_3', 'class_4')
net = perceptron;
net.trainparam.epochs = 100; % Set # of training epochs
net.trainparam.goal = 1e-2; % Set desired max error
net.trainparam.lr = 0.1; % Set desired learning rate
train(net, x, t); % Train the perceptron
predictions = net(x); % Get data predictions
net.IW{:}; % Learned weights
net.b{:}; % Learned biases
% Equation of a line: w1*x1 + w2*x2 + b = 0
% Plot the lines
plot([-net.b{1}/net.IW{:}(1,1),0],[0,-net.b{1}/net.IW{:}(1,2)])
plot([-net.b{2}/net.IW{:}(2,1),0],[0,-net.b{2}/net.IW{:}(2,2)])
hold off;
end
The trainparam epochs, goal and lr are initial conditions an can change but I don't think that's where the problem is.
I'd appreciate any help you're able to provide. Best regards.

4 个评论
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Greg Heath
Greg Heath 2018-2-13
编辑：Greg Heath 2018-2-13
use length(t) not length(x)
point t = 0 is not plotted
what are your error messages?
Bill Symolon
Bill Symolon 2018-2-13
Thanks, Greg. I figured out that I needed to use a second perceptron and two sets of targets to get the required output. The code is working now. Regards, Bill
Akash
Akash 2024-5-19
can you send the modified code
Bill Symolon
Bill Symolon 2024-5-20
Dang, asking me to go back 6 years .... it took me a minute to find this :)
% Initialize Variables for the Input Spaces (x)
p1 = [1; 1];
p2 = [1; 2];
p3 = [-2; -1];
p4 = [2; 0];
p5 = [-1; 2];
p6 = [-2; 1];
p7 = [-1; -1];
p8 = [-2; -2];
% Concatenate the input vectors into a single input matrix
x = [p1 p2 p3 p4 p5 p6 p7 p8];
% Initialize target vector
t1 = [0 0 0 0 1 1 1 1];
t2 = [0 0 1 1 0 0 1 1];
% Plot the input locations
figure
hold on;
for i=1:length(x)
if t1(i) == 0 && t2(i) == 0
scatter(x(1,i), x(2,i),'k ', 'filled');
elseif t1(i) == 0 && t2(i) == 1
scatter(x(1,i), x(2,i),'r ', 'filled');
elseif t1(i) == 1 && t2(i) == 0
scatter(x(1,i), x(2,i),'b ', 'filled');
else
scatter(x(1,i), x(2,i),'g ', 'filled');
end
end
grid on
line([0 0], ylim, 'linewidth', 1); %y-axis
line(xlim, [0 0], 'linewidth', 1); %x-axis
% Create two perceptrons
net1 = perceptron;
net2 = perceptron;
% Set training parameters
net1.trainparam.epochs = 1000; % Set # of training epochs
net2.trainparam.epochs = 1000;
net1.trainparam.goal = 1e-5; % Set desired max error
net2.trainparam.goal = 1e-5;
net1.trainparam.lr = 0.01; % Set desired learning rate
net2.trainparam.lr = 0.01;
% Train the perceptrons
net1 = train(net1, x, t1);
net2 = train(net2, x, t2);
% Get data predictions
pred1 = net1(x);
pred2 = net2(x);
w_net1 = net1.IW{:}; % net1 learned weights
b_net1 = net1.b{:}; % net1 learned biases
w_net2 = net2.IW{:}; % net2 learned weights
b_net2 = net2.b{:}; % net2 learned biases
% Plot the lines
plot([-b_net1/w_net1(1),0],[0,-b_net1/w_net1(2)], 'k-');
plot([-b_net2/w_net2(1),0],[0,-b_net2/w_net2(2)], 'k--');
hold off;

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