Coursera吴恩达机器学习week5的ex4编程作业代码

Machine-learning-ex4

这是Coursera上 Week5 的ml-ex4的编程作业代码。经过测验，全部通过。

具体文件可以进入我的github

包括以下3个文件：

%     sigmoidGradient.m
%     randInitializeWeights.m
%     nnCostFunction.m

sigmoidGradient.m

function g = sigmoidGradient(z)
%SIGMOIDGRADIENT returns the gradient of the sigmoid function
%evaluated at z
%   g = SIGMOIDGRADIENT(z) computes the gradient of the sigmoid function
%   evaluated at z. This should work regardless if z is a matrix or a
%   vector. In particular, if z is a vector or matrix, you should return
%   the gradient for each element.

g = zeros(size(z));

% ====================== YOUR CODE HERE ======================
% Instructions: Compute the gradient of the sigmoid function evaluated at
%               each value of z (z can be a matrix, vector or scalar).

g=sigmoid(z) .* (1-sigmoid(z));
% =============================================================
end

randInitializeWeights.m

function W = randInitializeWeights(L_in, L_out)
%RANDINITIALIZEWEIGHTS Randomly initialize the weights of a layer with L_in
%incoming connections and L_out outgoing connections
%   W = RANDINITIALIZEWEIGHTS(L_in, L_out) randomly initializes the weights 
%   of a layer with L_in incoming connections and L_out outgoing 
%   connections. 
%
%   Note that W should be set to a matrix of size(L_out, 1 + L_in) as
%   the first column of W handles the "bias" terms
%

% You need to return the following variables correctly 
W = zeros(L_out, 1 + L_in);

% ====================== YOUR CODE HERE ======================
% Instructions: Initialize W randomly so that we break the symmetry while
%               training the neural network.
%
% Note: The first column of W corresponds to the parameters for the bias unit
%

epsilon init = 0.12; 
W = rand(L out, 1 + L in) * 2 * epsilon init − epsilon init;

% =========================================================================

end

nnCostFunction.m

function [J grad] = nnCostFunction(nn_params, ...
                                   input_layer_size, ...
                                   hidden_layer_size, ...
                                   num_labels, ...
                                   X, y, lambda)
%NNCOSTFUNCTION Implements the neural network cost function for a two layer
%neural network which performs classification
%   [J grad] = NNCOSTFUNCTON(nn_params, hidden_layer_size, num_labels, ...
%   X, y, lambda) computes the cost and gradient of the neural network. The
%   parameters for the neural network are "unrolled" into the vector
%   nn_params and need to be converted back into the weight matrices. 
% 
%   The returned parameter grad should be a "unrolled" vector of the
%   partial derivatives of the neural network.
%

% Reshape nn_params back into the parameters Theta1 and Theta2, the weight matrices
% for our 2 layer neural network
Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...
                 hidden_layer_size, (input_layer_size + 1));

Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ...
                 num_labels, (hidden_layer_size + 1));

% Setup some useful variables
m = size(X, 1);

% You need to return the following variables correctly 
J = 0;
Theta1_grad = zeros(size(Theta1));
Theta2_grad = zeros(size(Theta2));

% ====================== YOUR CODE HERE ======================
% Instructions: You should complete the code by working through the
%               following parts.
%
% Part 1: Feedforward the neural network and return the cost in the
%         variable J. After implementing Part 1, you can verify that your
%         cost function computation is correct by verifying the cost
%         computed in ex4.m
%
% Part 2: Implement the backpropagation algorithm to compute the gradients
%         Theta1_grad and Theta2_grad. You should return the partial derivatives of
%         the cost function with respect to Theta1 and Theta2 in Theta1_grad and
%         Theta2_grad, respectively. After implementing Part 2, you can check
%         that your implementation is correct by running checkNNGradients
%
%         Note: The vector y passed into the function is a vector of labels
%               containing values from 1..K. You need to map this vector into a 
%               binary vector of 1's and 0's to be used with the neural network
%               cost function.
%
%         Hint: We recommend implementing backpropagation using a for-loop
%               over the training examples if you are implementing it for the 
%               first time.
%
% Part 3: Implement regularization with the cost function and gradients.
%
%         Hint: You can implement this around the code for
%               backpropagation. That is, you can compute the gradients for
%               the regularization separately and then add them to Theta1_grad
%               and Theta2_grad from Part 2.
%
X=[ones(m,1),X];
a1=Theta1*X';
z1=[ones(m,1),sigmoid(a1)'];
a2=Theta2*z1';
h=sigmoid(a2);

yk=zeros(m,num_labels);
for i=1:m
    yk(i,y(i))=1;
end

J = (1/m)* sum(sum(((-yk) .* log(h') - (1 - yk) .* log(1 - h'))));

r=(lambda/2/m)*(sum(sum(Theta1(:,2:end) .^ 2))+sum(sum(Theta2(:,2:end) .^ 2)));
J=J+r;


for ex=1:m
    a1=X(ex,:);
    a1=a1';
    z2=Theta1*a1;
    a2=[1;sigmoid(z2)];
    z3=Theta2*a2;
    a3=sigmoid(z3);
    y=yk(ex,:);
    delta3=a3-y';
    delta2 = Theta2(:,2:end)' * delta3 .* sigmoidGradient(z2);  % delta2 is a 25x1 column vector
    Theta1_grad = Theta1_grad + delta2 * a1';
    Theta2_grad = Theta2_grad + delta3 * a2';
end

Theta1_grad=Theta1_grad ./ m;
Theta2_grad=Theta2_grad ./ m;

Theta1(:,1) = 0;
Theta2(:,1) = 0;
Theta1_grad = Theta1_grad + lambda / m * Theta1;
Theta2_grad = Theta2_grad + lambda / m * Theta2;

% -------------------------------------------------------------

% =========================================================================

% Unroll gradients
grad = [Theta1_grad(:) ; Theta2_grad(:)];


end