ex3-nn 神经网络

%% 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)

Part 1: Loading and Visualizing Data

%% =========== 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
sel = randperm(size(X, 1));
sel = sel(1:100);displayData(X(sel, :));fprintf('Program paused. Press enter to continue.\n');
pause;

Function Definition

displayData

function [h, display_array] = displayData(X, example_width)
%DISPLAYDATA Display 2D data in a nice grid
%   [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data
%   stored in X in a nice grid. It returns the figure handle h and the
%   displayed array if requested.% Set example_width automatically if not passed in
if ~exist('example_width', 'var') || isempty(example_width) example_width = round(sqrt(size(X, 2)));
end% Gray Image
colormap(gray);% Compute rows, cols
[m n] = size(X);
example_height = (n / example_width);% Compute number of items to display
display_rows = floor(sqrt(m));
display_cols = ceil(m / display_rows);% Between images padding
pad = 1;% Setup blank display
display_array = - ones(pad + display_rows * (example_height + pad), ...pad + display_cols * (example_width + pad));% Copy each example into a patch on the display array
curr_ex = 1;
for j = 1:display_rowsfor i = 1:display_colsif curr_ex > m break; end% Copy the patch% Get the max value of the patchmax_val = max(abs(X(curr_ex, :)));display_array(pad + (j - 1) * (example_height + pad) + (1:example_height), ...pad + (i - 1) * (example_width + pad) + (1:example_width)) = ...reshape(X(curr_ex, :), example_height, example_width) / max_val;curr_ex = curr_ex + 1;endif curr_ex > mbreak; end
end% Display Image
h = imagesc(display_array, [-1 1]);% Do not show axis
axis image offdrawnow;end

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');

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);fprintf('Program paused. Press enter to continue.\n');
pause;%  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
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 options = input('Paused - press enter to continue, q to exit:','s');if s == 'q'breakend
end

Function Definition

predict

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.
%a1 = [ones(m, 1) X];
z2 = a1*Theta1';
a2 = [ones(size(z2, 1), 1) sigmoid(z2)];
z3 = a2*Theta2';
a3 = sigmoid(z3);[p_max, p] = max(a3, [], 2);% =========================================================================end

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ex3_nn神经网络

ex3-nn 神经网络

Part 1: Loading and Visualizing Data

Function Definition

displayData

Part 2: Loading Pameters

Part 3: Implement Predict

Function Definition

predict

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