本博客代码为本人选修《深度学习引论》课程所用，源代码属于Zhang Yi教授。
github地址

1.原理

大致就是这样一个简单的模型，参数已经写好，正向传过去就完了。其中完成的效果相当于：

2.code

fc_c.m

function a_next = fc_c(w, a)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% implement forward computation from layer l to layer l+1
% in component form
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % define the activation function
    f = @(s) s >= 0;
    
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Your code BELOW
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % 1. add external inputs with value 1
    a = [a; 1];
    % for each neuron located in layer l+1
    for i=1:size(w,1)
        % 2. calculate net input
        a_next(i) = w(i,:)*a;
        % 3. calculate activation
        a_next(i) = f( a_next(i) );
    end
    a_next = a_next';
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Your code ABOVE
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
end

fc_v.m

function a_next = fc_v(w, a)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% implement forward computation from layer l to layer l+1
% in vector form
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % define the activation function
    f = @(s) s >= 0;

    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Your code BELOW
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % 1. add external inputs with value 1
    a = [a; 1];
    % 2. calculate net input
    a_next = w * a;
    % 3. calculate activation
    a_next = f(a_next);

    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Your code ABOVE
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
end

main_c.m

% prepare the samples
data = [1 0 0 1
    0 1 0 1]; % samples
labels = [1 1 0 0]; % labels

m = size(data, 2); % number of samples

% assign connection weight
w1 = [ 2  2  -1
    -1 -1 1.5]; % connection from layer 1 to layer 2
w2 = [ 1  1  -1.5 ]; % connection from layer 2 to layer 3

% for each sample
for i = 1:m
    x = data(:, i); % retrieve the i-th column of data

    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Your code BELOW
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    % layer 1 - input layer
    a1 = x;

    % layer 2 - hidden layer
    a2 = [];
    % 1. calculate the activation a2, call function fc_c
    a2 = fc_c(w1, a1);

    % layer 3 - output layer
    a3 = [];
    % 2. calculate the activation a3, call function fc_c
    a3 = fc_c(w2, a2);

    % collect result
    y = a3;
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Your code ABOVE
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    % display the result
    fprintf('Sample [%i %i] is classified as %i.\n', x(1), x(2), y);
end