吳恩達機器學習 - 神經網路

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				版權宣告：如果感覺寫的不錯，轉載標明出處連結哦~blog.csdn.net/wyg1997					https://blog.csdn.net/wyg1997/article/details/80739334				</div>
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						<p>題目連結：<a href="https://s3.amazonaws.com/spark-public/ml/exercises/on-demand/machine-learning-ex3.zip" rel="nofollow" target="_blank">點選開啟連結</a></p>
 

先貼出筆記：

本題模型：

程式碼

predict.m(其實要實現的就這一個)：

function p = predict(Theta1, Theta2, X)
%PREDICT Predict the label of an input given a trained neural network
%   p = PREDICT(Theta1, Theta2, X) outputs the predicted label of X given the
%   trained weights of a neural network (Theta1, Theta2)

% Useful values
m = size(X, 1
 
);
num_labels = size(Theta2, 1);

% You need to return the following variables correctly 
p = zeros(size(X, 1), 1);

% ====================== YOUR CODE HERE ======================
% Instructions: Complete the following code to make predictions using
%               your learned neural network. You should set
 
 p to a 
%               vector containing labels between 1 to num_labels.
%
% Hint: The max function might come in useful. In particular, the max
%       function can also return the index of the max element, for more
%       information see 'help max'. If your examples are in rows, then, you
%       can use max(A, [], 2) to obtain the max for each row.
%

X = [ones(m,1), X];
a2 = sigmoid(X*Theta1');

a2 = [ones(m,1), a2];
a3 = sigmoid(a2*Theta2');

[~, p] = max(a3, [], 2);

% =========================================================================


end

  

   
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