问题描述
我发布了一个问题昨天有关我的XOR运算符的反向传播神经网络遇到的问题.我做了更多的工作,意识到可能与没有偏向神经元有关.
I posted a question yesterday regarding issues that I was having with my backpropagating neural network for the XOR operator. I did a little more work and realized that it may have to do with not having a bias neuron.
我的问题是,一般而言,偏向神经元的作用是什么?它在识别XOR算子的反向传播神经网络中的作用是什么?是否可以创建一个没有偏见神经元的人?
My question is, what is the role of the bias neuron in general, and what is its role in a backpropagating neural network that recognizes the XOR operator? Is it possible to create one without a bias neuron?
推荐答案
可以创建一个没有偏向神经元的神经网络...它可以正常工作,但有关更多信息,我建议您查看此问题的答案.问题:
It's possible to create a neural network without a bias neuron... it would work just fine, but for more information I would recommend you see the answers to this question:
更新:试图模型异或的偏向神经元在神经网络中的作用是最大程度地减小神经网络的大小.通常,对于原始"(不确定这是否是正确的术语)逻辑功能,例如AND
,OR
,NAND
等,您尝试创建具有2个输入神经元,2个隐藏神经元的神经网络.和1个输出神经元.对于XOR
,这是无法完成的,因为对XOR
建模的最简单方法是使用两个NAND
:
Update:the role of the bias neuron in the neural net that attempts to model XOR is to minimize the size of the neural net. Usually, for "primitive" (not sure if this is the correct term) logic functions such as AND
, OR
, NAND
, etc, you are trying to create a neural network with 2 input neurons, 2 hidden neurons and 1 output neuron. This can't be done for XOR
because the simplest way you can model an XOR
is with two NAND
s:
您可以将A
和B
视为输入神经元,中间的门是您的偏置"神经元,后面的两个门是您的隐藏"神经元,最后您得到了输出神经元.您可以在没有偏向神经元的情况下求解XOR
,但是这需要将隐藏神经元的数量增加到最少3个隐藏神经元.在这种情况下,第三神经元本质上充当偏置神经元.这是另一个讨论关于XOR
的偏向神经元的问题:
You can consider A
and B
as your input neurons, the gate in the middle is your "bias" neuron, the two gates following are your "hidden" neurons and finally you have the output neuron. You can solve XOR
without having a bias neuron, but it would require that you increase the number of hidden neurons to a minimum of 3 hidden neurons. In this case, the 3rd neuron essentially acts as a bias neuron. Here is another question that discusses the bias neuron with regards to XOR
: XOR problem solvable with 2x2x1 neural network without bias?
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