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48 lines
1.6 KiB
Matlab
48 lines
1.6 KiB
Matlab
10 years ago
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function checkCostFunction(lambda)
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%CHECKCOSTFUNCTION Creates a collaborative filering problem
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%to check your cost function and gradients
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% CHECKCOSTFUNCTION(lambda) Creates a collaborative filering problem
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% to check your cost function and gradients, it will output the
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% analytical gradients produced by your code and the numerical gradients
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% (computed using computeNumericalGradient). These two gradient
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% computations should result in very similar values.
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% Set lambda
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if ~exist('lambda', 'var') || isempty(lambda)
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lambda = 0;
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end
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%% Create small problem
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X_t = rand(4, 3);
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Theta_t = rand(5, 3);
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% Zap out most entries
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Y = X_t * Theta_t';
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Y(rand(size(Y)) > 0.5) = 0;
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R = zeros(size(Y));
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R(Y ~= 0) = 1;
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%% Run Gradient Checking
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X = randn(size(X_t));
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Theta = randn(size(Theta_t));
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num_users = size(Y, 2);
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num_movies = size(Y, 1);
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num_features = size(Theta_t, 2);
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numgrad = computeNumericalGradient( ...
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@(t) cofiCostFunc(t, Y, R, num_users, num_movies, ...
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num_features, lambda), [X(:); Theta(:)]);
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[cost, grad] = cofiCostFunc([X(:); Theta(:)], Y, R, num_users, ...
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num_movies, num_features, lambda);
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disp([numgrad grad]);
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fprintf(['The above two columns you get should be very similar.\n' ...
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'(Left-Your Numerical Gradient, Right-Analytical Gradient)\n\n']);
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diff = norm(numgrad-grad)/norm(numgrad+grad);
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fprintf(['If your backpropagation implementation is correct, then \n' ...
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'the relative difference will be small (less than 1e-9). \n' ...
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'\nRelative Difference: %g\n'], diff);
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end
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