神经网络如何学习具有可变数量输入的函数? [英] How can neural networks learn functions with a variable number of inputs?
问题描述
一个简单的例子:给定一个输入序列，我希望神经网络输出该序列的中值.问题是，如果神经网络学会了计算n个输入的中位数，那么它如何计算更多输入的中位数?我知道循环神经网络可以学习序列中的max和parity之类的功能，但是计算这些功能仅需要恒定的内存.如果内存需求随着输入大小的增长而增加，例如计算中位数怎么办?
这是关于的后续问题，当输入数量可变时如何使用神经网络?. /p>
如果神经网络学会了计算n个输入的中位数，那么它如何计算更多输入的中位数?
首先，您应该了解神经网络的用法.我们通常将神经网络用于无法解决数学问题的问题.在此问题中，NN的使用并不重要/不建议使用.
还有诸如此类的其他问题，例如预测，随着时间的流逝，连续的数据会到达.
解决此问题的一种方法是隐马尔可夫模型(HMM).但是同样，这种模型取决于一段时间内输入之间的相关性.因此，对于输入完全随机的问题，该模型效率不高.
因此，如果输入完全是随机的，并且内存需求增加了
对此您无能为力，一种可能的解决方案是增加内存大小.
只需记住一件事，NN和类似的机器学习模型旨在从数据中提取有意义的信息.如果数据只是一些随机值，那么所有模型都会生成一些随机输出.
A simple example: Given an input sequence, I want the neural network to output the median of the sequence. The problem is, if a neural network learnt to compute the median of n inputs, how can it compute the median of even more inputs? I know that recurrent neural networks can learn functions like max and parity over a sequence, but computing these functions only requires constant memory. What if the memory requirement grows with the input size like computing the median?
This is a follow up question on How are neural networks used when the number of inputs could be variable?.
If a neural network learnt to compute the median of n inputs, how can it compute the median of even more inputs?
First of all, you should understand the use of a neural network. We, generally use the neural network in problems where a mathematical solution is not possible. In this problem, use of NN is not significant/ unadvisable.
There are other problems of such nature, like forecasting, in which continuous data arrives over time.
One solution to such problem can be Hidden Markov Model (HMM). But again, such models depends on the correlation between input over a period of time. So This model is not efficient for problems where the input is completely random.
So, If input is completely random and memory requirement grows
There is nothing much you can do about it, one possible solution could be growing your memory size.
Just remember one thing, NN and similar models of machine learning aims to extract meaningful information from the data. if data is just some random values then all models will generate some random output.
这篇关于神经网络如何学习具有可变数量输入的函数?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！
