Octave：函数没有 return 预期值？

Question

此代码是吴恩达机器学习课程的编程作业。

该函数需要一个行向量 [J grad]。代码计算了 J（尽管是错误的，但这不是这里的问题），我为 grad 输入了一个虚拟值（因为我还没有编写代码来计算它）。当我运行代码时，它仅输出 ans 作为值为 J 的标量。 grad 去哪儿了？

function [J grad] = nnCostFunction(nn_params, ...
                               input_layer_size, ...
                               hidden_layer_size, ...
                               num_labels, ...
                               X, y, lambda)
%NNCOSTFUNCTION Implements the neural network cost function for a two layer
%neural network which performs classification
%   [J grad] = NNCOSTFUNCTON(nn_params, hidden_layer_size, num_labels, ...
%   X, y, lambda) computes the cost and gradient of the neural network. The
%   parameters for the neural network are "unrolled" into the vector
%   nn_params and need to be converted back into the weight matrices. 
% 
%   The returned parameter grad should be a "unrolled" vector of the
%   partial derivatives of the neural network.
%

% Reshape nn_params back into the parameters Theta1 and Theta2, the weight matrices
% for our 2 layer neural network
Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...
                 hidden_layer_size, (input_layer_size + 1));

Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ...
                 num_labels, (hidden_layer_size + 1));

% Setup some useful variables
m = size(X, 1);

% You need to return the following variables correctly 
J = 0;
Theta1_grad = zeros(size(Theta1));
Theta2_grad = zeros(size(Theta2));

% ====================== YOUR CODE HERE ======================
% Instructions: You should complete the code by working through the
%               following parts.
%
% Part 1: Feedforward the neural network and return the cost in the
%         variable J. After implementing Part 1, you can verify that your
%         cost function computation is correct by verifying the cost
%         computed in ex4.m
%
% Part 2: Implement the backpropagation algorithm to compute the gradients
%         Theta1_grad and Theta2_grad. You should return the partial derivatives of
%         the cost function with respect to Theta1 and Theta2 in Theta1_grad and
%         Theta2_grad, respectively. After implementing Part 2, you can check
%         that your implementation is correct by running checkNNGradients
%
%         Note: The vector y passed into the function is a vector of labels
%               containing values from 1..K. You need to map this vector into a 
%               binary vector of 1's and 0's to be used with the neural network
%               cost function.
%
%         Hint: We recommend implementing backpropagation using a for-loop
%               over the training examples if you are implementing it for the 
%               first time.
%
% Part 3: Implement regularization with the cost function and gradients.
%
%         Hint: You can implement this around the code for
%               backpropagation. That is, you can compute the gradients for
%               the regularization separately and then add them to Theta1_grad
%               and Theta2_grad from Part 2.
%

% PART 1

a1 = [ones(m,1) X]; % set a1 to equal X and add column of 1's

z2 = a1 * Theta1'; % matrix times matrix [5000*401 * 401*25 = 5000*25]
a2 = [ones(m,1),sigmoid(z2)]; % sigmoid function on matrix [5000*26]
z3 = a2 * Theta2'; % matrix times matrix [5000*26 * 26*10 = 5000 * 10]
hox = sigmoid(z3); % sigmoid function on matrix [5000*10]

for k = 1:num_labels

    yk = y == k; % using the correct column vector y each loop
    J = J + sum(-yk.*log(hox(:,k)) - (1-yk).*log(1-hox(:,k)));

end

J = 1/m * J;   

% -------------------------------------------------------------

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

% Unroll gradients
% grad = [Theta1_grad(:) ; Theta2_grad(:)];
grad = 6.6735;

end

Answer 1

您在函数声明中指定函数可以同时return多个输出值：

function [J grad] = nnCostFunction(nn_params, ...   % etc

如果您 'request' 通过分配给变量矩阵而不是单个变量来捕获两个输出：

[a, b] = nnCostFunction(input1, input2, etc)

如果你不这样做，你基本上 'requesting' 只是第一个 returned 变量：

a = nnCostFunction(input1, input2, etc)  % output 'b' is discarded.

如果您根本没有指定要分配给的变量，octave 默认分配给 'default' 变量 ans。所以它本质上等同于做

ans = nnCostFunction(input1, input2, etc)  % output 'b' is discarded.

请参阅 find 函数的文档（即在您的 Octave 终端中键入 help find）以查看此类函数的示例。

PS。如果你只想要 second 输出而不想 'waste' 第一个输出的变量名，你可以通过指定 ~ 作为第一个输出来做到这一点，例如：

[~, b] = nnCostFunction(input1, input2, etc)  % output 'a' is discarded

Octave：函数没有 return 预期值？

Octave: function doesn't return expected value?

syntax

octave