Cursera: machine learning ex3.

machine_learning/mlclass-ex3-008/mlclass-ex3/ex3_nn.m

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+%% 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 =============
+%  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;
+
+%% ================ 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
+    fprintf('Program paused. Press enter to continue.\n');
+    pause;
+end
+