Hello Ahmed,
I understand you want to implement neural networks using stochastic gradient descent with momentum.
For which you have to :
- Initialize the values for weights, biases, learning rate, momentum coefficient, and other hyperparameters.
- Loop through the entire dataset or till convergence and perform the following operations iteratively:
- Forward pass, calculate the node values.
- Calculate the loss function and perform back propagation and update the gradient and momentum.
- Update the weights and biases.
Here is an example code :
% Initialize network parameters
learning_rate = 0.01;
momentum = 0.9;
num_epochs = 100;
% Initialize weights and biases
weights = randn(2, 1); % Example: 2 input neurons
biases = randn(1);
% Initialize momentum terms
prev_delta_weights = zeros(size(weights));
prev_delta_biases = zeros(size(biases));
% Iterate through the training data
for epoch = 1:num_epochs
% Perform forward pass and backpropagation for each training sample
% Sample input and target output
input = randn(2, 1);
target_output = 0.5;
% Forward pass
output = weights' * input + biases;
% Calculate error
error = output - target_output;
% Backpropagation
gradient_weights = input * error;
gradient_biases = error;
% Update gradients with momentum
delta_weights = learning_rate * gradient_weights + momentum * prev_delta_weights;
delta_biases = learning_rate * gradient_biases + momentum * prev_delta_biases;
% Update weights and biases
weights = weights - delta_weights;
biases = biases - delta_biases;
% Update momentum terms
prev_delta_weights = delta_weights;
prev_delta_biases = delta_biases;
end
