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235 lines
7.9 KiB
Matlab
235 lines
7.9 KiB
Matlab
%% Machine Learning Online Class - Exercise 4 Neural Network Learning
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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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% sigmoidGradient.m
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% randInitializeWeights.m
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% nnCostFunction.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('ex4data1.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 Parameters ================
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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('ex4weights.mat');
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% Unroll parameters
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nn_params = [Theta1(:) ; Theta2(:)];
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%% ================ Part 3: Compute Cost (Feedforward) ================
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% To the neural network, you should first start by implementing the
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% feedforward part of the neural network that returns the cost only. You
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% should complete the code in nnCostFunction.m to return cost. After
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% implementing the feedforward to compute the cost, you can verify that
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% your implementation is correct by verifying that you get the same cost
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% as us for the fixed debugging parameters.
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%
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% We suggest implementing the feedforward cost *without* regularization
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% first so that it will be easier for you to debug. Later, in part 4, you
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% will get to implement the regularized cost.
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%
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fprintf('\nFeedforward Using Neural Network ...\n')
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% Weight regularization parameter (we set this to 0 here).
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lambda = 0;
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J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ...
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num_labels, X, y, lambda);
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fprintf(['Cost at parameters (loaded from ex4weights): %f '...
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'\n(this value should be about 0.287629)\n'], J);
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% =============== Part 4: Implement Regularization ===============
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% Once your cost function implementation is correct, you should now
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% continue to implement the regularization with the cost.
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%
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fprintf('\nChecking Cost Function (w/ Regularization) ... \n')
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% Weight regularization parameter (we set this to 1 here).
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lambda = 1;
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J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ...
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num_labels, X, y, lambda);
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fprintf(['Cost at parameters (loaded from ex4weights): %f '...
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'\n(this value should be about 0.383770)\n'], J);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================ Part 5: Sigmoid Gradient ================
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% Before you start implementing the neural network, you will first
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% implement the gradient for the sigmoid function. You should complete the
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% code in the sigmoidGradient.m file.
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%
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fprintf('\nEvaluating sigmoid gradient...\n')
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g = sigmoidGradient([1 -0.5 0 0.5 1]);
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fprintf('Sigmoid gradient evaluated at [1 -0.5 0 0.5 1]:\n ');
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fprintf('%f ', g);
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fprintf('\n\n');
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================ Part 6: Initializing Pameters ================
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% In this part of the exercise, you will be starting to implment a two
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% layer neural network that classifies digits. You will start by
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% implementing a function to initialize the weights of the neural network
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% (randInitializeWeights.m)
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fprintf('\nInitializing Neural Network Parameters ...\n')
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initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size);
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initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels);
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% Unroll parameters
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initial_nn_params = [initial_Theta1(:) ; initial_Theta2(:)];
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%% =============== Part 7: Implement Backpropagation ===============
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% Once your cost matches up with ours, you should proceed to implement the
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% backpropagation algorithm for the neural network. You should add to the
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% code you've written in nnCostFunction.m to return the partial
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% derivatives of the parameters.
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%
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fprintf('\nChecking Backpropagation... \n');
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% Check gradients by running checkNNGradients
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checkNNGradients;
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% =============== Part 8: Implement Regularization ===============
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% Once your backpropagation implementation is correct, you should now
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% continue to implement the regularization with the cost and gradient.
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%
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fprintf('\nChecking Backpropagation (w/ Regularization) ... \n')
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% Check gradients by running checkNNGradients
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lambda = 3;
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checkNNGradients(lambda);
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% Also output the costFunction debugging values
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debug_J = nnCostFunction(nn_params, input_layer_size, ...
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hidden_layer_size, num_labels, X, y, lambda);
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fprintf(['\n\nCost at (fixed) debugging parameters (w/ lambda = 10): %f ' ...
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'\n(this value should be about 0.576051)\n\n'], debug_J);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =================== Part 8: Training NN ===================
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% You have now implemented all the code necessary to train a neural
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% network. To train your neural network, we will now use "fmincg", which
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% is a function which works similarly to "fminunc". Recall that these
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% advanced optimizers are able to train our cost functions efficiently as
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% long as we provide them with the gradient computations.
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%
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fprintf('\nTraining Neural Network... \n')
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% After you have completed the assignment, change the MaxIter to a larger
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% value to see how more training helps.
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options = optimset('MaxIter', 50);
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% You should also try different values of lambda
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lambda = 1;
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% Create "short hand" for the cost function to be minimized
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costFunction = @(p) nnCostFunction(p, ...
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input_layer_size, ...
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hidden_layer_size, ...
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num_labels, X, y, lambda);
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% Now, costFunction is a function that takes in only one argument (the
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% neural network parameters)
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[nn_params, cost] = fmincg(costFunction, initial_nn_params, options);
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% Obtain Theta1 and Theta2 back from nn_params
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Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...
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hidden_layer_size, (input_layer_size + 1));
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Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ...
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num_labels, (hidden_layer_size + 1));
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================= Part 9: Visualize Weights =================
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% You can now "visualize" what the neural network is learning by
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% displaying the hidden units to see what features they are capturing in
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% the data.
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fprintf('\nVisualizing Neural Network... \n')
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displayData(Theta1(:, 2:end));
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ================= Part 10: 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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