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53 lines
1.9 KiB
Matlab
53 lines
1.9 KiB
Matlab
10 years ago
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function checkNNGradients(lambda)
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%CHECKNNGRADIENTS Creates a small neural network to check the
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%backpropagation gradients
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% CHECKNNGRADIENTS(lambda) Creates a small neural network to check the
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% backpropagation gradients, it will output the analytical gradients
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% produced by your backprop code and the numerical gradients (computed
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% using computeNumericalGradient). These two gradient computations should
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% result in very similar values.
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%
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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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input_layer_size = 3;
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hidden_layer_size = 5;
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num_labels = 3;
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m = 5;
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% We generate some 'random' test data
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Theta1 = debugInitializeWeights(hidden_layer_size, input_layer_size);
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Theta2 = debugInitializeWeights(num_labels, hidden_layer_size);
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% Reusing debugInitializeWeights to generate X
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X = debugInitializeWeights(m, input_layer_size - 1);
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y = 1 + mod(1:m, num_labels)';
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% Unroll parameters
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nn_params = [Theta1(:) ; Theta2(:)];
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% Short hand for cost function
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costFunc = @(p) nnCostFunction(p, input_layer_size, hidden_layer_size, ...
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num_labels, X, y, lambda);
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[cost, grad] = costFunc(nn_params);
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numgrad = computeNumericalGradient(costFunc, nn_params);
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% Visually examine the two gradient computations. The two columns
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% you get should be very similar.
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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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% Evaluate the norm of the difference between two solutions.
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% If you have a correct implementation, and assuming you used EPSILON = 0.0001
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% in computeNumericalGradient.m, then diff below should be less than 1e-9
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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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