反向传播算法的实现 [英] Backpropagation Algorithm Implementation

问题描述

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我正在尝试实现一个使用反向传播的神经网络.到目前为止，我进入了一个阶段，每个神经元都从上一层中的所有神经元接收加权输入，根据它们的总和计算出S型函数，并将其分布在下一层中.最后，整个网络产生一个结果O.A然后将误差计算为E = 1/2(D-O)^ 2，其中D是所需的值.此时，网络中所有神经元都有各自的输出以及网络的整体误差，我该如何反向传播以调整权重?

I am trying to implement a neural network which uses backpropagation. So far I got to the stage where each neuron receives weighted inputs from all neurons in the previous layer, calculates the sigmoid function based on their sum and distributes it across the following layer. Finally, the entire network produces a result O. A then calculate the error as E = 1/2(D-O)^2 where D is the desired value. At this point, having all neurons across the network their individual output and the overall error of the net, how can I backpropagate it to adjusts the weights?

干杯:)

推荐答案

我强烈建议您浏览此网站，这是我过去使用的方式:

I would highly suggest looking at this website, this is what I've used in the past:

http://www.codeproject. com/Articles/14342/神经网络库的设计与实现

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