Coursera Machine Learning 第五周week5 ex4Neural Networks Learning编程全套满分题目+注释

Coursera Machine Learning 第五周week5 ex4Neural Networks Learning编程全套满分题目+注释

function checkNNGradients（lambda）
%CHECKNNGRADIENTS Creates a small neural network to check the
%backpropagation gradients
%   CHECKNNGRADIENTS（lambda） Creates a small neural network to check the
%   backpropagation gradients it will output the analytical gradients
%   produced by your backprop code and the numerical gradients （computed
%   using computeNumericalGradient）. These two gradient computations should
%   result in very similar values.
%

if ~exist（‘lambda‘ ‘var‘） || isempty（lambda）
    lambda = 0;
end

input_layer_size = 3;
hidden_layer_size = 5;
num_labels = 3;
m = 5;

% We generate some ‘random‘ test data
Theta1 = debugInitializeWeights（hidden_layer_size input_layer_size）;
Theta2 = debugInitializeWeights（num_labels hidden_layer_size）;
% Reusing debugInitializeWeights to generate X
X  = debugInitializeWeights（m input_layer_size - 1）;
y  = 1 + mod（1:m num_labels）‘;

% Unroll parameters
nn_params = [Theta1（:） ; Theta2（:）];

% Short hand for cost function
costFunc = @（p） nnCostFunction（p input_layer_size hidden_layer_size ...
                               num_labels X y lambda）;

[cost grad] = costFunc（nn_params）;
numgrad = computeNumericalGradient（costFunc nn_params）;

% Visually examine the two gradient computations.  The two columns
% you get should be very similar. 
disp（[numgrad grad]）;
fprintf（[‘The above two columns you get should be very similar.\n‘ ...
         ‘（Left-Your Numerical Gradient Right-Analytical Gradient）\n\n‘]）;

% Evaluate the norm of the difference between two solutions.  
% If you have a correct implementation and assuming you used EPSILON = 0.0001 
% in computeNumericalGradient.m then diff below should be less than 1e-9
diff = norm（numgrad-grad）/norm（numgrad+grad）;

fprintf（[‘If your backpropagation implementation is correct then \n‘ ...
         ‘the relative difference will be small （less than 1e-9）. \n‘ ...
         ‘\nRelative Difference: %g\n‘] diff）;

end

 属性            大小     日期    时间   名称
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     文件       1950  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\checkNNGradients.m

     文件       1095  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\computeNumericalGradient.m

     文件        841  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\debugInitializeWeights.m

     文件       1502  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\displayData.m

     文件       8263  2017-12-10 10:20  machine-learning-ex4\machine-learning-ex4\ex4\ex4.m

     文件    7511764  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\ex4data1.mat

     文件      79592  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\ex4weights.mat

     文件       8749  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\fmincg.m

     文件       1624  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab\AUTHORS.txt

     文件       3862  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab\ChangeLog.txt

     文件        881  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab\jsonopt.m

     文件       1551  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab\LICENSE_BSD.txt

     文件      18732  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab\loadjson.m

     文件      15574  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab\loadubjson.m

     文件        771  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab\mergestruct.m

     文件      19369  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab\README.txt

     文件      17462  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab\savejson.m

     文件      16123  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab\saveubjson.m

     文件       1094  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab\varargin2struct.m

     文件       1195  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\makeValidFieldName.m

     文件       5562  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\submitWithConfiguration.m

     文件       5633  2017-12-10 09:53  machine-learning-ex4\machine-learning-ex4\ex4\nnCostFunction.m

     文件        585  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\predict.m

     文件        985  2017-12-09 10:05  machine-learning-ex4\machine-learning-ex4\ex4\randInitializeWeights.m

     文件        137  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\sigmoid.m

     文件        739  2017-12-09 09:58  machine-learning-ex4\machine-learning-ex4\ex4\sigmoidGradient.m

     文件       1635  2017-12-08 16:48  machine-learning-ex4\machine-learning-ex4\ex4\submit.m

     文件        262  2017-12-10 10:22  machine-learning-ex4\machine-learning-ex4\ex4\token.mat

     文件     356692  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4.pdf

     目录          0  2017-03-13 18:40  machine-learning-ex4\machine-learning-ex4\ex4\lib\jsonlab

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