Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al.[1] The GRU is like a long short-term memory (LSTM) with a forget gate,[2] but has fewer parameters than LSTM, as it lacks an output gate.[3] GRU's performance on certain tasks of polyphonic music modeling, speech signal modeling and natural language processing was found to be similar to that of LSTM.[4][5] GRUs showed that gating is indeed helpful in general, and Bengio's team came to no concrete conclusion on which of the two gating units was better.[6][7]


There are several variations on the full gated unit, with gating done using the previous hidden state and the bias in various combinations, and a simplified form called minimal gated unit.[8]

The operator denotes the Hadamard product in the following.

Fully gated unit

Gated Recurrent Unit, fully gated version

Initially, for , the output vector is .


Activation functions

Alternative activation functions are possible, provided that .

Type 1
Type 2
Type 3

Alternate forms can be created by changing and [9]

Minimal gated unit

The minimal gated unit (MGU) is similar to the fully gated unit, except the update and reset gate vector is merged into a forget gate. This also implies that the equation for the output vector must be changed:[10]


Light gated recurrent unit

The light gated recurrent unit (LiGRU)[4] removes the reset gate altogether, replaces tanh with the ReLU activation, and applies batch normalization (BN):

LiGRU has been studied from a Bayesian perspective.[11] This analysis yielded a variant called light Bayesian recurrent unit (LiBRU), which showed slight improvements over the LiGRU on speech recognition tasks.


  1. ^ Cho, Kyunghyun; van Merrienboer, Bart; Bahdanau, DZmitry; Bougares, Fethi; Schwenk, Holger; Bengio, Yoshua (2014). "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation". arXiv:1406.1078. ((cite journal)): Cite journal requires |journal= (help)
  2. ^ Felix Gers; Jürgen Schmidhuber; Fred Cummins (1999). "Learning to forget: Continual prediction with LSTM". 9th International Conference on Artificial Neural Networks: ICANN '99. Vol. 1999. pp. 850–855. doi:10.1049/cp:19991218. ISBN 0-85296-721-7.
  3. ^ "Recurrent Neural Network Tutorial, Part 4 – Implementing a GRU/LSTM RNN with Python and Theano – WildML". 2015-10-27. Archived from the original on 2021-11-10. Retrieved May 18, 2016.
  4. ^ a b Ravanelli, Mirco; Brakel, Philemon; Omologo, Maurizio; Bengio, Yoshua (2018). "Light Gated Recurrent Units for Speech Recognition". IEEE Transactions on Emerging Topics in Computational Intelligence. 2 (2): 92–102. arXiv:1803.10225. doi:10.1109/TETCI.2017.2762739. S2CID 4402991.
  5. ^ Su, Yuahang; Kuo, Jay (2019). "On extended long short-term memory and dependent bidirectional recurrent neural network". Neurocomputing. 356: 151–161. arXiv:1803.01686. doi:10.1016/j.neucom.2019.04.044. S2CID 3675055.
  6. ^ Chung, Junyoung; Gulcehre, Caglar; Cho, KyungHyun; Bengio, Yoshua (2014). "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling". arXiv:1412.3555 [cs.NE].
  7. ^ Gruber, N.; Jockisch, A. (2020), "Are GRU cells more specific and LSTM cells more sensitive in motive classification of text?", Frontiers in Artificial Intelligence, 3: 40, doi:10.3389/frai.2020.00040, PMC 7861254, PMID 33733157, S2CID 220252321
  8. ^ Chung, Junyoung; Gulcehre, Caglar; Cho, KyungHyun; Bengio, Yoshua (2014). "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling". arXiv:1412.3555 [cs.NE].
  9. ^ Dey, Rahul; Salem, Fathi M. (2017-01-20). "Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks". arXiv:1701.05923 [cs.NE].
  10. ^ Heck, Joel; Salem, Fathi M. (2017-01-12). "Simplified Minimal Gated Unit Variations for Recurrent Neural Networks". arXiv:1701.03452 [cs.NE].
  11. ^ Bittar, Alexandre; Garner, Philip N. (May 2021). "A Bayesian Interpretation of the Light Gated Recurrent Unit". ICASSP 2021. 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toronto, ON, Canada: IEEE. pp. 2965–2969. 10.1109/ICASSP39728.2021.9414259.