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 shown that gating is indeed helpful in general and Bengio's team concluding that 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
Gated Recurrent Unit, fully gated version

Initially, for , the output vector is .


Activation functions

Alternative activation functions are possible, provided that .

Type 1
Type 1
Type 2
Type 2
Type 3
Type 3

Alternate forms can be created by changing and [9]

Minimal gated unit

The minimal gated unit 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]


Learning Algorithm Recommendation Framework

A Learning Algorithm Recommendation Framework may help guiding the selection of learning algorithm and scientific discipline (e.g. RNN, GAN, RL, CNN,...). The framework has the advantage of having been generated from an extensive analysis of the literature and dedicated to recurrent neural networks and their variations. [11]


  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". Proc. ICANN'99, IEE, London. 1999: 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. ^ 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. ^ Feltus, Christophe (2022). "Learning Algorithm Recommendation Framework for IS and CPS Security: Analysis of the RNN, LSTM, and GRU Contributions". IGI International Journal of Systems and Software Security and Protection (IJSSSP). 13 (1). doi:10.4018/IJSSSP.293236.