%{
This MATLAB code generates sample graphs for a deep neural network.
The graphs show the architecture of the neural network with multiple hidden layers.

Author: [Your Name]
Date: [Date]
%}

% Define the number of input, hidden, and output layers
num_input_layers = 784; % Example: 28x28 pixel image
num_hidden_layers = [512, 256, 128]; % Example: 3 hidden layers with 512, 256, and 128 neurons
num_output_layers = 10; % Example: 10 classes for classification

% Create a figure for the neural network architecture
figure;
hold on;

% Plot input layer
rectangle('Position', [0, 0, 1, num_input_layers], 'FaceColor', 'b');
text(0.5, num_input_layers/2, 'Input Layer', 'HorizontalAlignment', 'center');

% Plot hidden layers
x_pos = 2;
for i = 1:length(num_hidden_layers)
rectangle('Position', [x_pos, 0, 1, num_hidden_layers(i)], 'FaceColor', 'g');
text(x_pos+0.5, num_hidden_layers(i)/2, ['Hidden Layer ', num2str(i)], 'HorizontalAlignment', 'center');
x_pos = x_pos + 1;
end

% Plot output layer
rectangle('Position', [x_pos, 0, 1, num_output_layers], 'FaceColor', 'r');
text(x_pos+0.5, num_output_layers/2, 'Output Layer', 'HorizontalAlignment', 'center');

% Set axis properties
axis equal;
axis off;
title('Deep Neural Network Architecture');

hold off;