# Long Short-Term Memory (LSTM) RNN Model

(Redirected from LSTM RNN)

## References

### 2018a

• (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/long_short-term_memory Retrieved:2018-3-27.
• Long short-term memory (LSTM) units (or blocks) are a building unit for layers of a recurrent neural network (RNN). A RNN composed of LSTM units is often called an LSTM network. A common LSTM unit is composed of 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.

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. An LSTM is well-suited to classify, process and predict time series given time lags of unknown size and duration between important events. 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 .

### 2015f

 Figure 2: A Long Short-Term Memory Cell. Figure 3: A LSTM network.

(...) Fig. 3 shows a LSTM sequence tagging model which employs aforementioned LSTM memory cells (dashed boxes with rounded corners).