Home▸Machine Learning & Neural Networks▸GRU Cell: Reset & Update Gates

GRU Cell: Reset & Update Gates

Watch a Gated Recurrent Unit process a live time series step by step: reset and update gates open and close in 3D, the candidate state forms, and the hidden state blends old and new memory.

Machine Learning & Neural Networks3DAdvanced60 FPS📱 Mobile-adapted⇄ 2D version

A Gated Recurrent Unit (GRU) is the leaner cousin of the LSTM — the same idea of gating what a recurrent network remembers, done with two gates and no separate cell state. This simulator runs a real 8-unit GRU cell over a live input sequence you choose (sine, square, impulse, or noise), rendering the input, the reset gate, the update gate, the candidate hidden state, and the persisting hidden state as five rows of 3D bars that update every step. Two bias sliders let you push the reset and update gates toward always-open or always-closed so you can watch, directly, how a GRU decides what to forget and what to keep.

⚙ Under the hood

Watch a live 8-unit Gated Recurrent Unit process an input sequence step by step, with the reset gate, update gate, candidate state, and hidden memory rendered as animated 3D bars.

GRUrecurrent neural networkgatingdeep learningsequence modelingRNN

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install