Michael Brenndoerfer | Data & AI, Private Equity, Technology

Michael Brenndoerfer

GRU Architecture › GRU vs LSTM: The Key Differences

Michael BrenndoerferMay 14, 20254 min read

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Part 11 of 67Recurrent Neural Networks

Chapter 80GRU Architecture › GRU vs LSTM: The Key Differences

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Before diving into the mechanics, let's establish what makes GRUs different from LSTMs. The comparison illuminates the design philosophy behind both architectures.

Out[4]:

Visualization

LSTM architecture diagram with separate cell and hidden state bars, forget, input, and output gates, and a summary reading three gates plus two states. — The LSTM maintains separate cell and hidden states with three sigmoid gates. Its summary panel records three gates and two state vectors.

GRU architecture diagram with one combined hidden state bar, reset and update gates, and a summary reading two gates plus one state. — The GRU combines memory into one hidden state and uses reset and update gates. Its summary panel records two gates and one state vector, explaining its lower parameter count.

The key structural differences are:

State vectors: LSTMs maintain two state vectors (cell state $C_t$ and hidden state $h_t$ ), while GRUs use only one (hidden state $h_t$ ). The GRU's hidden state plays both roles.
Number of gates: LSTMs have three gates (forget, input, output), while GRUs have two (reset, update). The update gate combines the forget and input gate functions.
Parameter count: With fewer gates and one less state vector, GRUs have roughly 25% fewer parameters than LSTMs of equivalent hidden size.

Despite these simplifications, GRUs retain the essential capability that makes gated architectures powerful: the ability to selectively remember and forget information over long sequences. The question is whether the simplification sacrifices important expressiveness.