Long short-term memory
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Long short-term memory (LSTM) block or network is a simple recurrent neural network which can be used as a building component or block (of hidden layers) for an eventually bigger recurrent neural network. The LSTM block is itself a recurrent network because it contains recurrent connections similar to connections in a conventional recurrent neural network.
An LSTM block is composed of four main components: a cell, an input gate, an output gate and a forget gate. The cell is responsible for "remembering" values over arbitrary time intervals; hence the word "memory" in LSTM. Each of the three gates can be thought of as a "conventional" artificial neuron, as in a multi-layer (or feedforward) neural network: that is, they compute an activation (using an activation function) of a weighted sum. Intuitively, they can be thought as regulators of the flow of values that goes through the connections of the LSTM; hence the denotation "gate". There are connections between these gates and the cell. Some of the connections are recurrent, some of them are not.
The expression long short-term refers to the fact that LSTM is a model for the short-term memory which can last for a long period of time. There are different types of LSTMs, which differ among them in the components or connections that they have.
LSTMs were developed to deal with the exploding and vanishing gradient problem when training traditional RNNs. Relative insensitivity to gap length gives an advantage to LSTM over alternative RNNs, hidden Markov models and other sequence learning methods in numerous applications.
Among other successes, LSTM achieved record results in natural language text compression, unsegmented connected handwriting recognition and won the ICDAR handwriting competition (2009). LSTM networks were a major component of a network that achieved a record 17.7% phoneme error rate on the classic TIMIT natural speech dataset (2013).
As of 2016, major technology companies including Google, Apple, and Microsoft were using LSTM as fundamental components in new products. For example, Google used LSTM for speech recognition on the smartphone, for the smart assistant Allo and for Google Translate. Apple uses LSTM for the "Quicktype" function on the iPhone and for Siri. Amazon uses LSTM for Amazon Alexa.
In 2017 Microsoft reported reaching 95.1% recognition accuracy on the Switchboard corpus, incorporating a vocabulary of 165,000 words. The approach used "dialog session-based long-short-term memory".
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An LSTM network contains a (memory) cell. An LSTM cell "remembers" a value for either long or short time periods. The key to this ability is that it uses the identity or no activation function within its recurrent connection. In other words, the remembered value of the cell is not iteratively modified because there's the identity or no activation function through which the value flows. This is the key for the gradient not to tend to vanish when an LSTM network is trained with backpropagation through time.
A "standard" LSTM block contains three gates that control or regulate information flow: an input gate, an output gate and a forget gate. These gates compute an activation often using the logistic function. These gates can be thought as conventional artificial neurons. Thus each of the gates has its own parameters (i.e. weights and biases from possibly other units outside the LSTM block). Their output is multiplied with the output of the cell or the input to the LSTM to partially allow or deny information to flow into or out of the memory. More specifically, the input gate controls the extent to which a new value flows into the memory, the forget gate controls the extent to which a value remains in memory and the output gate controls the extent to which the value in memory is used to compute the output activation of the LSTM block.
In some implementations, the input and forget gates are merged into a single gate. The motivation for combining them is that the time to forget is when a new value worth remembering becomes available.
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In the equations below, each variable in lowercase italics represents a vector.
The weights in an LSTM block, grouped in the matrices and (i.e. the weights of the recurrent connections), are used to direct the operation of the gates. These weights are applied to the values that feed into the block (including the input vector and the output from the previous time at step ) at each of the gates. Thus, the LSTM block determines how to maintain its memory as a function of those values, and training its weights causes the block to learn the function that minimizes loss[further explanation needed].
LSTM with a forget gateEdit
where the initial values are and and the operator denotes the Hadamard product (entry-wise product). The subscripts refer to the time step.
- : input vector to the LSTM block
- : forget gate's activation vector
- : input gate's activation vector
- : output gate's activation vector
- : output vector of the LSTM block
- : cell state vector
- , and : weight matrices and bias vector parameters which need to be learned during training
- : sigmoid function.
- : hyperbolic tangent function.
- : hyperbolic tangent function or, as the peephole LSTM paper[which?] suggests, .
The figure on the right is a graphical representation of a LSTM (block) with peephole connections (i.e. a peephole LSTM). Peephole connections allow the gates to access the constant error carousel (CEC), whose activation is the cell state. is not used, is used instead in most places.
To minimize LSTM's total error on a set of training sequences, iterative gradient descent such as backpropagation through time can be used to change each weight in proportion to its derivative with respect to the error. A problem with using gradient descent for standard RNNs is that error gradients vanish exponentially quickly with the size of the time lag between important events. This is due to if the spectral radius of is smaller than 1. With LSTM blocks, however, when error values are back-propagated from the output, the error remains in the block's memory. This "error carousel" continuously feeds error back to each of the gates until they learn to cut off the value. Thus, regular backpropagation is effective at training an LSTM block to remember values for long durations.
LSTM can also be trained by a combination of artificial evolution for weights to the hidden units, and pseudo-inverse or support vector machines for weights to the output units. In reinforcement learning applications LSTM can be trained by policy gradient methods, evolution strategies or genetic algorithms.
CTC score functionEdit
Many applications use stacks of LSTM RNNs and train them by connectionist temporal classification (CTC) to find an RNN weight matrix that maximizes the probability of the label sequences in a training set, given the corresponding input sequences. CTC achieves both alignment and recognition.
Backpropagation in a LSTMEdit
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Applications of LSTM include:
- Robot control
- Time series prediction
- Speech recognition
- Rhythm learning
- Music composition
- Grammar learning
- Handwriting recognition
- Human action recognition
- Protein Homology Detection
- Predicting subcellular localization of proteins
LSTM has Turing completeness in the sense that given enough network units it can compute any result that a conventional computer can compute, provided it has the proper weight matrix, which may be viewed as its program[further explanation needed].
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- Recurrent Neural Networks with over 30 LSTM papers by Jürgen Schmidhuber's group at IDSIA
- Gers PhD thesis on LSTM networks.
- Fraud detection paper with two chapters devoted to explaining recurrent neural networks, especially LSTM.
- Paper on a high-performing extension of LSTM that has been simplified to a single node type and can train arbitrary architectures.
- Tutorial: How to implement LSTM in Python with Theano
- A Beginner’s Guide to Recurrent Networks and LSTMs