如何找到每个隐藏层中的隐藏层和神经元的数量以进行回归? [英] How to find the number of hidden layers and neurons in each hidden layer for regression?

问题描述

我读过一些文章和文章，内容涉及为分类问题找到正确数量的隐藏层以及每个隐藏层中的神经元数量.但是，我找不到任何有关回归的信息吗?

I have read posts and articles that talk about finding the right number of hidden layer and number of neurons in each hidden layer for a classification problem. However, I couldn't find any relevant information for regression?

有人可以帮忙解释其中隐藏层和神经元的正确数量的计算吗?

Can someone help explain the calculation of right number of hidden layer and neurons in them?

推荐答案

@skillsmuggler 指出:

没有计算或公式，也没有找出层/神经元的数量，其踪迹以及带有参数自定义和优化的错误.

There is no calculation or formula or find out the number of layers/neurons, its sort of a trail and error with parameter customization and optimization.

我认为有一个参数可能会影响层/神经元的数量及其数据集的大小. 如果数据集的大小相对较小，则网络的大小不应该很大"(根据我的经验)，否则您的模块很快就会变得过拟合.

There is one parameters that I think could have an impact on the number of layers/neurons and its the size of the dataset. If the dataset size is relatively small the size of your network should not be "large" (based on my practice) or your module will get overfit pretty quickly.

您可以阅读'

You can read the 'How to prevent Overfitting in your Deep Learning Models' for more information.

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