如何选择神经网络中隐藏层和节点的数量? [英] How to choose number of hidden layers and nodes in neural network?

问题描述

多层感知器神经网络中的隐藏层数会对神经网络的行为产生什么影响?对于隐藏层中的节点数有相同的问题吗?

What does number of hidden layers in a multilayer perceptron neural network do to the way neural network behaves? Same question for number of nodes in hidden layers?

比方说，我想使用神经网络进行手写字符识别.在这种情况下，我将像素颜色强度值作为输入节点，将字符类作为输出节点.

Let's say I want to use a neural network for hand written character recognition. In this case I put pixel colour intensity values as input nodes, and character classes as output nodes.

我将如何选择隐藏层和节点的数量来解决此类问题?

How would I choose number of hidden layers and nodes to solve such problem?

推荐答案

注意:此答案在提出时是正确的，但此后已经过时.

Note: this answer was correct at the time it was made, but has since become outdated.

在神经网络中很少有两个以上的隐藏层.层数通常不是您会非常担心的网络参数.

It is rare to have more than two hidden layers in a neural network. The number of layers will usually not be a parameter of your network you will worry much about.

尽管具有许多层的多层神经网络可以表示 深度回路，训练深度网络一直被视为 挑战.直到最近，经验研究经常发现 深度网络通常并没有更好，而且通常更糟， 而不是具有一两个隐藏层的神经网络.

Although multi-layer neural networks with many layers can represent deep circuits, training deep networks has always been seen as somewhat of a challenge. Until very recently, empirical studies often found that deep networks generally performed no better, and often worse, than neural networks with one or two hidden layers.

Bengio，Y. LeCun，Y.，2007年.将学习算法扩展到AI.大型内核机，(1)，第1-41页.

所引用的论文对于了解网络深度的影响，深度网络教学的最新进展以及一般的深度学习提供了很好的参考.

The cited paper is a good reference for learning about the effect of network depth, recent progress in teaching deep networks, and deep learning in general.

这篇关于如何选择神经网络中隐藏层和节点的数量?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！