Feed Forward ANN Training using Back Propagation

Find a new weights by using Matlab code, when shown below is presented the input pattern (1 2 3) and the target output is 1. Using learning rate of 0.2 and bipolar sigmoid activation function, the bias is set to1.

Find a new weights using back-propagation when the net shown below is presented the input patterns (1 -1 1) and the desired output is 1. Using learning rate of 0.3 and Unipolar continuous activation function .the bias is set to 1, 𝝀 = 𝟎. 𝟔.

Answer 1

[Shaik]

Hi Warren,

Can you check this code for your need once:

% Define the input and output patterns
P = [1 -1 1];
T = 1;

% Define the network architecture and parameters
nInputs = length(P);
nHidden = 1;
nOutputs = 1;
learningRate = 0.3;
bias = 1;
lambda = 0.6;

% Initialize the weights randomly
w1 = randn(nInputs,nHidden);
w2 = randn(nHidden,nOutputs);

% Define the activation functions
sigmoid = @(x) 1 ./ (1 + exp(-x));
dsigmoid = @(x) sigmoid(x) .* (1 - sigmoid(x));
unipolar = @(x) 0.5 * (1 + tanh(lambda * x/2));
dunipolar = @(x) lambda/2 * (1 - unipolar(x).^2);

% Initialize the delta arrays
delta2 = zeros(nOutputs,1);
delta1 = zeros(nHidden,1);

% Loop through the iterations of the backpropagation algorithm
for i = 1:100
    % Forward pass
    net1 = P * w1 + bias;
    y1 = unipolar(net1);
    net2 = y1 * w2 + bias;
    y2 = unipolar(net2);
    
    % Backward pass
    delta2 = dunipolar(net2) * (T - y2);
    delta1 = dunipolar(net1) .* (delta2 * w2');
    
    % Update the weights
    w2 = w2 + learningRate * y1' * delta2;
    w1 = w1 + learningRate * P' * delta1;
end

% Test the network with the input pattern (1 -1 1)
net1 = P * w1 + bias;
y1 = unipolar(net1);
net2 = y1 * w2 + bias;
y2 = unipolar(net2);

% Print the output
fprintf('The predicted output is: %f\n', y2);

The predicted output is: 0.757313

Hope it helps!

[John D'Errico]

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