多层神经网络不会预测负值 [英] Multi-layer neural network won't predict negative values

问题描述

我已经实现了多层感知器来预测输入向量的正弦.向量由随机选择的四个-1,0,1和一个设为1的偏差组成.网络应预测向量内容之和的正弦.

例如输入=< 0,1，-1,0,1>输出= Sin(0 + 1 +(-1)+ 0 + 1)

我遇到的问题是网络永远不会预测负值，并且许多向量的正弦值都是负值.它可以完美地预测所有正输出或零输出.我假设在更新权重时会出现问题，权重会在每个时期后更新.有没有人遇到过NN的问题?任何帮助都很好！！

注意:该网络有5个输入，1个隐藏层有6个隐藏单元和1个输出.我在激活隐藏层和输出层上使用了S型函数，并尝试了数吨的学习率(当前为0.1)；

解决方案

由于我研究多层感知器已经很长时间了，因此请带些盐.

我会将您的问题域重新调整为[0,1]域，而不是[-1,1].如果您看一下逻辑功能图:

它生成介于[0,1]之间的值.我不期望它会产生负面结果.我可能是错的，强硬.

编辑:

您实际上可以将后勤功能扩展到您的问题域.使用广义逻辑曲线将A和K参数设置为您域的边界.

另一种选择是双曲正切，它的取值范围为[-1，+ 1]，并且没有要设置的常数.

I have implemented a multilayer perceptron to predict the sin of input vectors. The vectors consist of four -1,0,1's chosen at random and a bias set to 1. The network should predict the sin of sum of the vectors contents.

eg Input = <0,1,-1,0,1> Output = Sin(0+1+(-1)+0+1)

The problem I am having is that the network will never predict a negative value and many of the vectors' sin values are negative. It predicts all positive or zero outputs perfectly. I am presuming that there is a problem with updating the weights, which are updated after every epoch. Has anyone encountered this problem with NN's before? Any help at all would be great!!

note: The network has 5inputs,6hidden units in 1 hidden layer and 1 output.I am using a sigmoid function on the activations hidden and output layers, and have tried tonnes of learning rates (currently 0.1);

解决方案

Being a long time since I looked into multilayer perceptrons hence take this with a grain of salt.

I'd rescale your problem domain to the [0,1] domain instead of [-1,1]. If you take a look at the logistic function graph:

It generates values between [0,1]. I do not expect it to produce negative results. I might be wrong, tough.

EDIT:

You can actually extend the logistic function to your problem domain. Use the generalized logistic curve setting A and K parameters to the boundaries of your domain.

Another option is the hyperbolic tangent, which goes from [-1,+1] and has no constants to set up.

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