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89 lines
2.6 KiB
Matlab
89 lines
2.6 KiB
Matlab
10 years ago
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%% Machine Learning Online Class - Exercise 3 | Part 2: Neural Networks
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% Instructions
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% ------------
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%
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% This file contains code that helps you get started on the
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% linear exercise. You will need to complete the following functions
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% in this exericse:
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%
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% lrCostFunction.m (logistic regression cost function)
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% oneVsAll.m
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% predictOneVsAll.m
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% predict.m
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%
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% For this exercise, you will not need to change any code in this file,
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% or any other files other than those mentioned above.
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%
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%% Initialization
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clear ; close all; clc
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%% Setup the parameters you will use for this exercise
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input_layer_size = 400; % 20x20 Input Images of Digits
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hidden_layer_size = 25; % 25 hidden units
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num_labels = 10; % 10 labels, from 1 to 10
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% (note that we have mapped "0" to label 10)
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%% =========== Part 1: Loading and Visualizing Data =============
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% We start the exercise by first loading and visualizing the dataset.
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% You will be working with a dataset that contains handwritten digits.
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%
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% Load Training Data
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fprintf('Loading and Visualizing Data ...\n')
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load('ex3data1.mat');
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m = size(X, 1);
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% Randomly select 100 data points to display
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sel = randperm(size(X, 1));
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sel = sel(1:100);
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displayData(X(sel, :));
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================ Part 2: Loading Pameters ================
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% In this part of the exercise, we load some pre-initialized
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% neural network parameters.
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fprintf('\nLoading Saved Neural Network Parameters ...\n')
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% Load the weights into variables Theta1 and Theta2
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load('ex3weights.mat');
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%% ================= Part 3: Implement Predict =================
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% After training the neural network, we would like to use it to predict
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% the labels. You will now implement the "predict" function to use the
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% neural network to predict the labels of the training set. This lets
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% you compute the training set accuracy.
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pred = predict(Theta1, Theta2, X);
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fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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% To give you an idea of the network's output, you can also run
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% through the examples one at the a time to see what it is predicting.
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% Randomly permute examples
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rp = randperm(m);
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for i = 1:m
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% Display
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fprintf('\nDisplaying Example Image\n');
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displayData(X(rp(i), :));
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pred = predict(Theta1, Theta2, X(rp(i),:));
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fprintf('\nNeural Network Prediction: %d (digit %d)\n', pred, mod(pred, 10));
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% Pause
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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end
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