【转】Understanding LSTM

2024-05-09 07:58
文章标签 lstm understanding

本文主要是介绍【转】Understanding LSTM,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

Recurrent Neural Networks

Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence.

Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones.

Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.

Recurrent Neural Networks have loops.

In the above diagram, a chunk of neural network, AA, looks at some input xtxt and outputs a value htht. A loop allows information to be passed from one step of the network to the next.

These loops make recurrent neural networks seem kind of mysterious. However, if you think a bit more, it turns out that they aren’t all that different than a normal neural network. A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor. Consider what happens if we unroll the loop:

An unrolled recurrent neural network.
An unrolled recurrent neural network.

This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists. They’re the natural architecture of neural network to use for such data.

And they certainly are used! In the last few years, there have been incredible success applying RNNs to a variety of problems: speech recognition, language modeling, translation, image captioning… The list goes on. I’ll leave discussion of the amazing feats one can achieve with RNNs to Andrej Karpathy’s excellent blog post, The Unreasonable Effectiveness of Recurrent Neural Networks. But they really are pretty amazing.

Essential to these successes is the use of “LSTMs,” a very special kind of recurrent neural network which works, for many tasks, much much better than the standard version. Almost all exciting results based on recurrent neural networks are achieved with them. It’s these LSTMs that this essay will explore.

The Problem of Long-Term Dependencies

One of the appeals of RNNs is the idea that they might be able to connect previous information to the present task, such as using previous video frames might inform the understanding of the present frame. If RNNs could do this, they’d be extremely useful. But can they? It depends.

Sometimes, we only need to look at recent information to perform the present task. For example, consider a language model trying to predict the next word based on the previous ones. If we are trying to predict the last word in “the clouds are in the sky,” we don’t need any further context – it’s pretty obvious the next word is going to be sky. In such cases, where the gap between the relevant information and the place that it’s needed is small, RNNs can learn to use the past information.

But there are also cases where we need more context. Consider trying to predict the last word in the text “I grew up in France… I speak fluent French.” Recent information suggests that the next word is probably the name of a language, but if we want to narrow down which language, we need the context of France, from further back. It’s entirely possible for the gap between the relevant information and the point where it is needed to become very large.

Unfortunately, as that gap grows, RNNs become unable to learn to connect the information.

Neural networks struggle with long term dependencies.

In theory, RNNs are absolutely capable of handling such “long-term dependencies.” A human could carefully pick parameters for them to solve toy problems of this form. Sadly, in practice, RNNs don’t seem to be able to learn them. The problem was explored in depth by Hochreiter (1991) [German] and Bengio, et al. (1994), who found some pretty fundamental reasons why it might be difficult.

Thankfully, LSTMs don’t have this problem!

LSTM Networks

Long Short Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. They were introduced by Hochreiter & Schmidhuber (1997), and were refined and popularized by many people in following work.1 They work tremendously well on a large variety of problems, and are now widely used.

LSTMs are explicitly designed to avoid the long-term dependency problem. Remembering information for long periods of time is practically their default behavior, not something they struggle to learn!

All recurrent neural networks have the form of a chain of repeating modules of neural network. In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer.

The repeating module in a standard RNN contains a single layer.

LSTMs also have this chain like structure, but the repeating module has a different structure. Instead of having a single neural network layer, there are four, interacting in a very special way.

A LSTM neural network.
The repeating module in an LSTM contains four interacting layers.

Don’t worry about the details of what’s going on. We’ll walk through the LSTM diagram step by step later. For now, let’s just try to get comfortable with the notation we’ll be using.

In the above diagram, each line carries an entire vector, from the output of one node to the inputs of others. The pink circles represent pointwise operations, like vector addition, while the yellow boxes are learned neural network layers. Lines merging denote concatenation, while a line forking denote its content being copied and the copies going to different locations.

The Core Idea Behind LSTMs

The key to LSTMs is the cell state, the horizontal line running through the top of the diagram.

The cell state is kind of like a conveyor belt. It runs straight down the entire chain, with only some minor linear interactions. It’s very easy for information to just flow along it unchanged.

The LSTM does have the ability to remove or add information to the cell state, carefully regulated by structures called gates.

Gates are a way to optionally let information through. They are composed out of a sigmoid neural net layer and a pointwise multiplication operation.

The sigmoid layer outputs numbers between zero and one, describing how much of each component should be let through. A value of zero means “let nothing through,” while a value of one means “let everything through!”

An LSTM has three of these gates, to protect and control the cell state.

Step-by-Step LSTM Walk Through

The first step in our LSTM is to decide what information we’re going to throw away from the cell state. This decision is made by a sigmoid layer called the “forget gate layer.” It looks at ht−1ht−1 and xtxt, and outputs a number between 00 and 11 for each number in the cell state Ct−1Ct−1. A 11 represents “completely keep this” while a 00 represents “completely get rid of this.”

Let’s go back to our example of a language model trying to predict the next word based on all the previous ones. In such a problem, the cell state might include the gender of the present subject, so that the correct pronouns can be used. When we see a new subject, we want to forget the gender of the old subject.

The next step is to decide what new information we’re going to store in the cell state. This has two parts. First, a sigmoid layer called the “input gate layer” decides which values we’ll update. Next, a tanh layer creates a vector of new candidate values, C~tC~t, that could be added to the state. In the next step, we’ll combine these two to create an update to the state.

In the example of our language model, we’d want to add the gender of the new subject to the cell state, to replace the old one we’re forgetting.

It’s now time to update the old cell state, Ct−1Ct−1, into the new cell state CtCt. The previous steps already decided what to do, we just need to actually do it.

We multiply the old state by ftft, forgetting the things we decided to forget earlier. Then we add it∗C~tit∗C~t. This is the new candidate values, scaled by how much we decided to update each state value.

In the case of the language model, this is where we’d actually drop the information about the old subject’s gender and add the new information, as we decided in the previous steps.

Finally, we need to decide what we’re going to output. This output will be based on our cell state, but will be a filtered version. First, we run a sigmoid layer which decides what parts of the cell state we’re going to output. Then, we put the cell state through tanhtanh (to push the values to be between −1−1 and 11) and multiply it by the output of the sigmoid gate, so that we only output the parts we decided to.

For the language model example, since it just saw a subject, it might want to output information relevant to a verb, in case that’s what is coming next. For example, it might output whether the subject is singular or plural, so that we know what form a verb should be conjugated into if that’s what follows next.

Variants on Long Short Term Memory

What I’ve described so far is a pretty normal LSTM. But not all LSTMs are the same as the above. In fact, it seems like almost every paper involving LSTMs uses a slightly different version. The differences are minor, but it’s worth mentioning some of them.

One popular LSTM variant, introduced by Gers & Schmidhuber (2000), is adding “peephole connections.” This means that we let the gate layers look at the cell state.

The above diagram adds peepholes to all the gates, but many papers will give some peepholes and not others.

Another variation is to use coupled forget and input gates. Instead of separately deciding what to forget and what we should add new information to, we make those decisions together. We only forget when we’re going to input something in its place. We only input new values to the state when we forget something older.

A slightly more dramatic variation on the LSTM is the Gated Recurrent Unit, or GRU, introduced by Cho, et al. (2014). It combines the forget and input gates into a single “update gate.” It also merges the cell state and hidden state, and makes some other changes. The resulting model is simpler than standard LSTM models, and has been growing increasingly popular.

A gated recurrent unit neural network.

These are only a few of the most notable LSTM variants. There are lots of others, like Depth Gated RNNs by Yao, et al. (2015). There’s also some completely different approach to tackling long-term dependencies, like Clockwork RNNs by Koutnik, et al. (2014).

Which of these variants is best? Do the differences matter? Greff, et al. (2015) do a nice comparison of popular variants, finding that they’re all about the same. Jozefowicz, et al. (2015)tested more than ten thousand RNN architectures, finding some that worked better than LSTMs on certain tasks.

Conclusion

Earlier, I mentioned the remarkable results people are achieving with RNNs. Essentially all of these are achieved using LSTMs. They really work a lot better for most tasks!

Written down as a set of equations, LSTMs look pretty intimidating. Hopefully, walking through them step by step in this essay has made them a bit more approachable.

LSTMs were a big step in what we can accomplish with RNNs. It’s natural to wonder: is there another big step? A common opinion among researchers is: “Yes! There is a next step and it’s attention!” The idea is to let every step of an RNN pick information to look at from some larger collection of information. For example, if you are using an RNN to create a caption describing an image, it might pick a part of the image to look at for every word it outputs. In fact, Xu, et al.(2015) do exactly this – it might be a fun starting point if you want to explore attention! There’s been a number of really exciting results using attention, and it seems like a lot more are around the corner…

Attention isn’t the only exciting thread in RNN research. For example, Grid LSTMs byKalchbrenner, et al. (2015) seem extremely promising. Work using RNNs in generative models – such as Gregor, et al. (2015), Chung, et al. (2015), or Bayer & Osendorfer (2015) – also seems very interesting. The last few years have been an exciting time for recurrent neural networks, and the coming ones promise to only be more so!

Acknowledgments

I’m grateful to a number of people for helping me better understand LSTMs, commenting on the visualizations, and providing feedback on this post.

I’m very grateful to my colleagues at Google for their helpful feedback, especially Oriol Vinyals,Greg Corrado, Jon Shlens, Luke Vilnis, and Ilya Sutskever. I’m also thankful to many other friends and colleagues for taking the time to help me, including Dario Amodei, and Jacob Steinhardt. I’m especially thankful to Kyunghyun Cho for extremely thoughtful correspondence about my diagrams.

Before this post, I practiced explaining LSTMs during two seminar series I taught on neural networks. Thanks to everyone who participated in those for their patience with me, and for their feedback.

这篇关于【转】Understanding LSTM的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/972833

相关文章

自然语言处理系列六十三》神经网络算法》LSTM长短期记忆神经网络算法

注:此文章内容均节选自充电了么创始人,CEO兼CTO陈敬雷老师的新书《自然语言处理原理与实战》(人工智能科学与技术丛书)【陈敬雷编著】【清华大学出版社】 文章目录 自然语言处理系列六十三神经网络算法》LSTM长短期记忆神经网络算法Seq2Seq端到端神经网络算法 总结 自然语言处理系列六十三 神经网络算法》LSTM长短期记忆神经网络算法 长短期记忆网络(LSTM,Long S

Tensorflow lstm实现的小说撰写预测

最近,在研究深度学习方面的知识,结合Tensorflow,完成了基于lstm的小说预测程序demo。 lstm是改进的RNN,具有长期记忆功能,相对于RNN,增加了多个门来控制输入与输出。原理方面的知识网上很多,在此,我只是将我短暂学习的tensorflow写一个预测小说的demo,如果有错误,还望大家指出。 1、将小说进行分词,去除空格,建立词汇表与id的字典,生成初始输入模型的x与y d

Understanding the GitHub Flow

这里看下Github的入门介绍    --链接 GitHub Flow is a lightweight, branch-based workflow that supports teams and projects where deployments are made regularly. This guide explains how and why GitHub Flow works

CNN-LSTM模型中应用贝叶斯推断进行时间序列预测

这篇论文的标题是《在混合CNN-LSTM模型中应用贝叶斯推断进行时间序列预测》,作者是Thi-Lich Nghiem, Viet-Duc Le, Thi-Lan Le, Pierre Maréchal, Daniel Delahaye, Andrija Vidosavljevic。论文发表在2022年10月于越南富国岛举行的国际多媒体分析与模式识别会议(MAPR)上。 摘要部分提到,卷积

回归预测 | MATLAB实现PSO-LSTM(粒子群优化长短期记忆神经网络)多输入单输出

回归预测 | MATLAB实现PSO-LSTM(粒子群优化长短期记忆神经网络)多输入单输出 目录 回归预测 | MATLAB实现PSO-LSTM(粒子群优化长短期记忆神经网络)多输入单输出预测效果基本介绍模型介绍PSO模型LSTM模型PSO-LSTM模型 程序设计参考资料致谢 预测效果 Matlab实现PSO-LSTM多变量回归预测 1.input和outpu

RNN发展(RNN/LSTM/GRU/GNMT/transformer/RWKV)

RNN到GRU参考: https://blog.csdn.net/weixin_36378508/article/details/115101779 tRANSFORMERS参考: seq2seq到attention到transformer理解 GNMT 2016年9月 谷歌,基于神经网络的翻译系统(GNMT),并宣称GNMT在多个主要语言对的翻译中将翻译误差降低了55%-85%以上, G

REMEMBERING HISTORY WITH CONVOLUTIONAL LSTM FOR ANOMALY DETECTION——利用卷积LSTM记忆历史进行异常检测

上海科技大学的文章,上海科技大学有个组一直在做这方面的工作,好文章挺多的还有数据集。 ABSTRACT 本文解决了视频中的异常检测问题,由于异常是无界的,所以异常检测是一项极具挑战性的任务。我们通过利用卷积神经网络(CNN或ConvNet)对每一帧进行外观编码,并利用卷积长期记忆(ConvLSTM)来记忆与运动信息相对应的所有过去的帧来完成这项任务。然后将ConvNet和ConvLSTM与

【深度学习】LSTM模型,GRU模型计算公式及其优缺点介绍

一.LSTM介绍 LSTM(Long Short-Term Memory)也称长短时记忆结构, 它是传统RNN的变体, 与经典RNN相比能够有效捕捉长序列之间的语义关联, 缓解梯度消失或爆炸现象. 同时LSTM的结构更复杂, 它的核心结构可以分为四个部分去解析: 遗忘门输入门细胞状态输出门  1LSTM的内部结构图  1.1 LSTM结构分析 结构解释图:   遗忘门部分结构图与计算

CNN-LSTM用于时间序列预测,发二区5分+没问题!

为了进一步提高时序预测的性能,研究者们组合了CNN和LSTM的特点,提出了CNN-LSTM混合架构。 这种架构因为独特的结构设计,能同时处理时空数据、提取丰富的特征、并有效解决过拟合问题,实现对时间序列数据的高效、准确预测,远超传统方法。 因此,它已经成为我们应对时序预测任务离不开的模型,有关CNN-LSTM的研究也成了当下热门主题之一,高质量论文频发。 为了方便大家了解CNN-LSTM的最

【ShuQiHere】从 LSTM 到 GRU:简化结构中的高效之道

【ShuQiHere】 引言 在自然语言处理中,情感分析是一项关键任务,它通过分析文本的情感倾向(如积极、消极或中立)帮助我们理解文本背后的情感💬。这种任务需要捕捉文本中前后单词之间的依赖关系,因此循环神经网络(RNN)和长短期记忆网络(LSTM)通常被用来处理🔄。然而,尽管 LSTM 在应对长期依赖问题上表现出色,其复杂的门结构也带来了计算资源的高消耗和训练时间的延长⌛。为了克服这些挑战