我正在尝试在Matlab中编写上面的表达式。不幸的是，我似乎得到了10.441460而不是0.287629的成本，所以我超过了36倍！
至于每个符号：
m is the number of training examples. [a scalar number]
K is the number of output nodes. [a scalar number]
y is the vector of training outputs [an m by 1 vector]
y^{(i)}_{k} is the ith training output (target) for the kth output node.
[a scalar number]
x^{(i)} is the ith training input. [a column vector for all the input
nodes]
h_{\theta}(x^{(i)})_{k} is the value of the hypothesis at output k, with
weights theta, and training input i. [a scalar number]
note: h_{\theta}(x^{(i)}) will be a column vector with K rows.
我尝试使用成本函数：
Theta1 = [ones(1,size(Theta1,2));Theta1];
X = [ones(m,1) , X]; %Add a column of 1's to X
R=zeros(m,1);
for i = 1:m
a = y(i) == [10 1:9];
R(i) = -(a*(log(sigmoid(Theta2*(sigmoid(Theta1*X(i,:)'))))) + (1-a)*(log(1-sigmoid(Theta2*(sigmoid(Theta1*X(i,:)'))))))/m;
end
J = sum(R);
这可能对参考有用：
function [J grad] = nnCostFunction(nn_params, ...
input_layer_size, ...
hidden_layer_size, ...
num_labels, ...
X, y, lambda)
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);
% ====================== 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
%
% =========================================================================
end