Week5_神经网络实现

%% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all

%  Instructions
%  ------------
%
%  This file contains code that helps you get started on the
%  linear exercise. You will need to complete the following functions
%  in this exericse:
%
%     lrCostFunction.m (logistic regression cost function)
%     oneVsAll.m
%     predictOneVsAll.m
%     predict.m
%
%  For this exercise, you will not need to change any code in this file,
%  or any other files other than those mentioned above.
%

%% Initialization
clear ; close all; clc

%% Setup the parameters you will use for this part of the exercise
input_layer_size  = 400;  % 20x20 Input Images of Digits
num_labels = 10;          % 10 labels, from 1 to 10
                          % (note that we have mapped "0" to label 10)

%% =========== Part 1: Loading and Visualizing Data =============
%  We start the exercise by first loading and visualizing the dataset.
%  You will be working with a dataset that contains handwritten digits.
%

% Load Training Data
fprintf(‘Loading and Visualizing Data ...\n‘)

load(‘ex3data1.mat‘); % training data stored in arrays X, y
m = size(X, 1);

% Randomly select 100 data points to display
rand_indices = randperm(m);
sel = X(rand_indices(1:100), :);

displayData(sel);

fprintf(‘Program paused. Press enter to continue.\n‘);
pause;

%% ============ Part 2: Vectorize Logistic Regression ============
%  In this part of the exercise, you will reuse your logistic regression
%  code from the last exercise. You task here is to make sure that your
%  regularized logistic regression implementation is vectorized. After
%  that, you will implement one-vs-all classification for the handwritten
%  digit dataset.
%

fprintf(‘\nTraining One-vs-All Logistic Regression...\n‘)

lambda = 0.1;
[all_theta] = oneVsAll(X, y, num_labels, lambda);

fprintf(‘Program paused. Press enter to continue.\n‘);
pause;

%% ================ Part 3: Predict for One-Vs-All ================
%  After ...
pred = predictOneVsAll(all_theta, X);

fprintf(‘\nTraining Set Accuracy: %f\n‘, mean(double(pred == y)) * 100);

%% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all

% Instructions

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

%

% This file contains code that helps you get started on the

% linear exercise. You will need to complete the following functions

% in this exericse:

%

% lrCostFunction.m (logistic regression cost function)

% oneVsAll.m

% predictOneVsAll.m

% predict.m

%

% For this exercise, you will not need to change any code in this file,

% or any other files other than those mentioned above.

%

%% Initialization

clear ; close all; clc

%% Setup the parameters you will use for this part of the exercise

input_layer_size = 400; % 20x20 Input Images of Digits

num_labels = 10; % 10 labels, from 1 to 10

% (note that we have mapped "0" to label 10)

%% =========== Part 1: Loading and Visualizing Data =============

% We start the exercise by first loading and visualizing the dataset.

% You will be working with a dataset that contains handwritten digits.

%

% Load Training Data

fprintf(‘Loading and Visualizing Data ...\n‘)

load(‘ex3data1.mat‘); % training data stored in arrays X, y

m = size(X, 1);

% Randomly select 100 data points to display

rand_indices = randperm(m);

sel = X(rand_indices(1:100), :);

displayData(sel);

fprintf(‘Program paused. Press enter to continue.\n‘);

pause;

%% ============ Part 2: Vectorize Logistic Regression ============

% In this part of the exercise, you will reuse your logistic regression

% code from the last exercise. You task here is to make sure that your

% regularized logistic regression implementation is vectorized. After

% that, you will implement one-vs-all classification for the handwritten

% digit dataset.

%

fprintf(‘\nTraining One-vs-All Logistic Regression...\n‘)

lambda = 0.1;

[all_theta] = oneVsAll(X, y, num_labels, lambda);

fprintf(‘Program paused. Press enter to continue.\n‘);

pause;

%% ================ Part 3: Predict for One-Vs-All ================

% After ...

pred = predictOneVsAll(all_theta, X);

fprintf(‘\nTraining Set Accuracy: %f\n‘, mean(double(pred == y)) * 100);