吴恩达的机器学习编程作业11：nnCostFunction 求解神经网络的代价函数（含BP算法）

最新推荐文章于 2024-02-27 19:10:21 发布

原创

最新推荐文章于 2024-02-27 19:10:21 发布 · 2.4k 阅读

2 ·

CC 4.0 BY-SA版权

这段代码实现了吴恩达机器学习课程中，针对两层神经网络的代价函数nnCostFunction。该函数通过展开的参数nn_params重构权重矩阵Theta1和Theta2，然后进行前向传播计算成本，并使用反向传播算法求得梯度。代码中包括了正则化的成本计算，以及对Theta1和Theta2的梯度更新。

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 *