LSTM原理，与GRU区别

LSTM 网络

LSTM Networks

长短期记忆网络——通常被称为 LSTM，是一种特殊的 RNN，能够学习长期依赖性。由 Hochreiter 和 Schmidhuber（1997）提出的，并且在接下来的工作中被许多人改进和推广。LSTM 在各种各样的问题上表现非常出色，现在被广泛使用。

LSTM 被明确设计用来避免长期依赖性问题。长时间记住信息实际上是 LSTM 的默认行为，而不是需要努力学习的东西！

所有递归神经网络都具有神经网络的链式重复模块。在标准的 RNN 中，这个重复模块具有非常简单的结构，例如只有单个 tanh 层。

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.

LSTM 也具有这种类似的链式结构，但重复模块具有不同的结构。不是一个单独的神经网络层，而是四个，并且以非常特殊的方式进行交互。

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.

不要担心细节。稍后我们将逐步浏览 LSTM 的图解。现在，让我们试着去熟悉我们将使用的符号。

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.

在上面的图中，每行包含一个完整的向量，从一个节点的输出到其他节点的输入。粉色圆圈表示逐点运算，如向量加法；而黄色框表示学习的神经网络层。行合并表示串联，而分支表示其内容正在被复制，并且副本将转到不同的位置。

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.

LSTM 的核心想法

The Core Idea Behind LSTMs

LSTM 的关键是细胞状态，即图中上方的水平线。

细胞状态有点像传送带。它贯穿整个链条，只有一些次要的线***互作用。信息很容易以不变的方式流过。

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.

LSTM 可以通过所谓“门”的精细结构向细胞状态添加或移除信息。

门可以选择性地以让信息通过。它们由 S 形神经网络层和逐点乘法运算组成。

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.

S 形网络的输出值介于 0 和 1 之间，表示有多大比例的信息通过。0 值表示“没有信息通过”，1 值表示“所有信息通过”。

一个 LSTM 有三种这样的门用来保持和控制细胞状态。

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.

逐步解析 LSTM 的流程

Step-by-Step LSTM Walk Through

LSTM 的第一步要决定从细胞状态中舍弃哪些信息。这一决定由所谓“遗忘门层”的 S 形网络层做出。它接收 ht−1ht−1 和 xtxt，并且对细胞状态 Ct−1Ct−1 中的每一个数来说输出值都介于 0 和 1 之间。1 表示“完全接受这个”，0 表示“完全忽略这个”。

让我们回到语言模型的例子，试图用先前的词汇预测下一个。在这个问题中，细胞状态可能包括当前主语的词性，因此可以使用正确的代词。当我们看到一个新的主语时，我们需要忘记先前主语的词性。

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.

下一步就是要确定需要在细胞状态中保存哪些新信息。这里分成两部分。第一部分，一个所谓“输入门层”的 S 形网络层确定哪些信息需要更新。第二部分，一个 tanh 形网络层创建一个新的备选值向量—— C~tC~t，可以用来添加到细胞状态。在下一步中我们将上面的两部分结合起来，产生对状态的更新。

在我们的语言模型中，我们要把新主语的词性加入状态，取代需要遗忘的旧主语。

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.

现在更新旧的细胞状态 Ct−1Ct−1 更新到 CtCt。先前的步骤已经决定要做什么，我们只需要照做就好。

我们对旧的状态乘以 ftft，用来忘记我们决定忘记的事。然后我们加上 it∗C~tit∗C~t，这是新的候选值，根据我们对每个状态决定的更新值按比例进行缩放。

语言模型的例子中，就是在这里我们根据先前的步骤舍弃旧主语的词性，添加新主语的词性。

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.

最后，我们需要确定输出值。输出依赖于我们的细胞状态，但会是一个“过滤的”版本。首先我们运行 S 形网络层，用来确定细胞状态中的哪些部分可以输出。然后，我们把细胞状态输入 tanhtanh（把数值调整到 −1−1 和 11 之间）再和 S 形网络层的输出值相乘，这样我们就可以输出想要输出的部分。

以语言模型为例子，一旦出现一个主语，主语的信息会影响到随后出现的动词。例如，知道主语是单数还是复数，就可以知道随后动词的形式。

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.