Machine Learning Online Class - Exercise 3 | Part 2: Neural Networks
% 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 exercise
input_layer_size = 400; % 20x20 Input Images of Digits
hidden_layer_size = 25; % 25 hidden units
num_labels = 10; % 10 labels, from 1 to 10
% (note that we have mapped "0" to label 10)
相当于有5000副图像，每幅图像是20x20的，作为数据，如何理解“样本”的概念？
这里的理解一幅图为一个样本，各像素都算是特征。
=========== 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');
m = size(X, 1);
% Randomly select 100 data points to display
% X,y均来自ex3data1.mat
sel = randperm(size(X, 1));
sel = sel(1:100);
displayData(X(sel, :));
================ Part 2: Loading Pameters ================
In this part of the exercise, we load some pre-initialized neural network parameters.
fprintf('\nLoading Saved Neural Network Parameters ...\n')
% Load the weights into variables Theta1 and Theta2
load('ex3weights.mat');
% theta1、theta2均来自该文件
================= Part 3: Implement Predict =================
After training the neural network, we would like to use it to predict
the labels. You will now implement the "predict" function to use the
neural network to predict the labels of the training set. This lets
you compute the training set accuracy.
pred = predict(Theta1, Theta2, X);
fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
这部分内容，使用实时脚本效果一般，无法实现.m文件那样的运行一次可以显示一张图片的效果，不能实现图片刷新的效果
% To give you an idea of the network's output, you can also run
% through the examples one at the a time to see what it is predicting.
% Randomly permute examples，乱序处理1~m
rp = randperm(m);
for i = 1:m
% Display
fprintf('\nDisplaying Example Image\n');
displayData(X(rp(i), :));
pred = predict(Theta1, Theta2, X(rp(i),:));
fprintf('\nNeural Network Prediction: %d (digit %d)\n', pred, mod(pred, 10));
% Pause with quit option
s = input('Paused - press enter to continue, q to exit:','s');
if s == 'q'
break
end
end