Gated Recurrent Unit

What is a Gated Recurrent Unit?

A gated recurrent unit (GRU) is a gating mechanism in recurrent neural networks (RNN) similar to a long short-term memory (LSTM) unit but without an output gate. GRU’s try to solve the vanishing gradient problem that can come with standard recurrent neural networks. A GRU can be considered a variation of the long short-term memory (LSTM) unit because both have a similar design and produce equal results in some cases. GRU’s are able to solve the vanishing gradient problem by using an update gate and a reset gate. The update gate controls information that flows into memory, and the reset gate controls the information that flows out of memory. The update gate and reset gate are two vectors that decide which information will get passed on to the output. They can be trained to keep information from the past or remove information that is irrelevant to the prediction. 

Why is this Useful?

A GRU is a very useful mechanism for fixing the vanishing gradient problem in recurrent neural networks. The vanishing gradient problem occurs in machine learning when the gradient becomes vanishingly small, which prevents the weight from changing its value. They also have better performance than LSTM when dealing with smaller datasets.

Applications of a Gated Recurrent Unit

  • Polyphonic music modeling
  • Speech signal modeling
  • Handwriting recognition