You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
36 lines
1.0 KiB
Matlab
36 lines
1.0 KiB
Matlab
10 years ago
|
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.
|
||
|
|
%
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
% =========================================================================
|
||
|
|
|
||
|
|
|
||
|
|
end
|