Machine-learning-ex3

本博客记录吴恩达的《深度学习专项系列课程（Deep Learning Specialization）

编程作业：Multi-class Classi cation and Neural Networks

1 lrCostFunction函数

function [J, grad] = lrCostFunction(theta, X, y, lambda)
%LRCOSTFUNCTION Compute cost and gradient for logistic regression with
%regularization
%   J = LRCOSTFUNCTION(theta, X, y, lambda) computes the cost of using
%   theta as the parameter for regularized logistic regression and the
%   gradient of the cost w.r.t. to the parameters. % Initialize some useful values
m = length(y); % number of training examples% You need to return the following variables correctly
J = 0;
grad = zeros(size(theta));% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta.
%               You should set J to the cost.
%               Compute the partial derivatives and set grad to the partial
%               derivatives of the cost w.r.t. each parameter in theta
%
% Hint: The computation of the cost function and gradients can be
%       efficiently vectorized. For example, consider the computation
%
%           sigmoid(X * theta)
%
%       Each row of the resulting matrix will contain the value of the
%       prediction for that example. You can make use of this to vectorize
%       the cost function and gradient computations.
%
% Hint: When computing the gradient of the regularized cost function,
%       there're many possible vectorized solutions, but one solution
%       looks like:
%           grad = (unregularized gradient for logistic regression)
%           temp = theta;
%           temp(1) = 0;   % because we don't add anything for j = 0
%           grad = grad + YOUR_CODE_HERE (using the temp variable)
%value = X*theta;
J = (-(y' * log(sigmoid(value)) + (1 - y)' * log(1 - sigmoid(value)))+ ((lambda/2) * (theta'*theta - theta(1)^2)))/m;grad = (X'*(sigmoid(value) - y) + lambda*theta)/m;
grad(1) = ((sigmoid(value) - y)' * X(:,1))/m;% =============================================================grad = grad(:);end

2 oneVsAll函数

function [all_theta] = oneVsAll(X, y, num_labels, lambda)
%ONEVSALL trains multiple logistic regression classifiers and returns all
%the classifiers in a matrix all_theta, where the i-th row of all_theta
%corresponds to the classifier for label i
%   [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels
%   logistic regression classifiers and returns each of these classifiers
%   in a matrix all_theta, where the i-th row of all_theta corresponds
%   to the classifier for label i% Some useful variables
m = size(X, 1);
n = size(X, 2);% You need to return the following variables correctly
all_theta = zeros(num_labels, n + 1);% Add ones to the X data matrix
X = [ones(m, 1) X];% ====================== YOUR CODE HERE ======================
% Instructions: You should complete the following code to train num_labels
%               logistic regression classifiers with regularization
%               parameter lambda.
%
% Hint: theta(:) will return a column vector.
%
% Hint: You can use y == c to obtain a vector of 1's and 0's that tell you
%       whether the ground truth is true/false for this class.
%
% Note: For this assignment, we recommend using fmincg to optimize the cost
%       function. It is okay to use a for-loop (for c = 1:num_labels) to
%       loop over the different classes.
%
%       fmincg works similarly to fminunc, but is more efficient when we
%       are dealing with large number of parameters.
%
% Example Code for fmincg:
%
%     % Set Initial theta
%     initial_theta = zeros(n + 1, 1);
%
%     % Set options for fminunc
%     options = optimset('GradObj', 'on', 'MaxIter', 50);
%
%     % Run fmincg to obtain the optimal theta
%     % This function will return theta and the cost
%     [theta] = ...
%         fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ...
%                 initial_theta, options);
%for c = 1:num_labels% Set Initial thetainitial_theta = zeros(n + 1, 1);%  Set options for fminuncoptions = optimset('GradObj', 'on', 'MaxIter', 50);% Run fmincg to obtain the optimal theta% This function will return theta and the cost  [initial_theta] = fmincg(@(t)(lrCostFunction(t, X, (y == c), lambda)), initial_theta, options);all_theta(c,:) = initial_theta';
endfor% =========================================================================end

3 predictOneVsAll函数

function p = predictOneVsAll(all_theta, X)
%PREDICT Predict the label for a trained one-vs-all classifier. The labels
%are in the range 1..K, where K = size(all_theta, 1).
%  p = PREDICTONEVSALL(all_theta, X) will return a vector of predictions
%  for each example in the matrix X. Note that X contains the examples in
%  rows. all_theta is a matrix where the i-th row is a trained logistic
%  regression theta vector for the i-th class. You should set p to a vector
%  of values from 1..K (e.g., p = [1; 3; 1; 2] predicts classes 1, 3, 1, 2
%  for 4 examples) m = size(X, 1);
num_labels = size(all_theta, 1);% You need to return the following variables correctly
p = zeros(size(X, 1), 1);% Add ones to the X data matrix
X = [ones(m, 1) X];% ====================== YOUR CODE HERE ======================
% Instructions: Complete the following code to make predictions using
%               your learned logistic regression parameters (one-vs-all).
%               You should set p to a vector of predictions (from 1 to
%               num_labels).
%
% Hint: This code can be done all vectorized using the max function.
%       In particular, the max function can also return the index of the
%       max element, for more information see 'help max'. If your examples
%       are in rows, then, you can use max(A, [], 2) to obtain the max
%       for each row.
%       [p, p] = max(X * all_theta', [], 2);% =========================================================================end

4 predict函数

function p = predict(Theta1, Theta2, X)
%PREDICT Predict the label of an input given a trained neural network
%   p = PREDICT(Theta1, Theta2, X) outputs the predicted label of X given the
%   trained weights of a neural network (Theta1, Theta2)% Useful values
m = size(X, 1);
num_labels = size(Theta2, 1);% You need to return the following variables correctly
p = zeros(size(X, 1), 1);% ====================== YOUR CODE HERE ======================
% Instructions: Complete the following code to make predictions using
%               your learned neural network. You should set p to a
%               vector containing labels between 1 to num_labels.
%
% Hint: The max function might come in useful. In particular, the max
%       function can also return the index of the max element, for more
%       information see 'help max'. If your examples are in rows, then, you
%       can use max(A, [], 2) to obtain the max for each row.
%X = [ones(m, 1) X];
X = X * Theta1';
X = [ones(m, 1) sigmoid(X)];
X = X * Theta2';
[p, p] = max(X, [], 2);% =========================================================================end

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Machine-learning-ex3

1 lrCostFunction函数

2 oneVsAll函数

3 predictOneVsAll函数

4 predict函数

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