GRU Parameters Calculator
Calculate the number of trainable parameters in a GRU layer.
Inputs
Total Parameters
1,182,720
Weight Parameters
1,179,648
Step by step
GRU has 3 gates: reset, update, new
gates = 3
= 3
Weight params: 3 × (input×hidden + hidden²)
3 × (256×512 + 512×512)
= 1,179,648
Total
1,179,648 + 3,072
= 1,182,720
How it works
A GRU has 3 gates (reset, update, new), each with input-to-hidden and hidden-to-hidden weight matrices. Total parameters = 3 × (input_size × hidden_size + hidden_size²) + biases. GRUs have 75% of LSTM parameters (3 gates vs 4).
Formula
GRU Parameters
params = 3 × (input_size × hidden_size + hidden_size^2) + 3 × 2 × hidden_size
- 3
- Number of gates (reset, update, new)
Frequently Asked Questions
How do GRU parameters compare to LSTM?
GRUs have 3 gates vs LSTMs' 4 gates, so GRUs have 75% the parameters of an equivalently-sized LSTM. GRUs often match LSTM performance with fewer parameters.
When should I use GRU vs LSTM?
GRUs are preferred when you want fewer parameters and faster training. LSTMs may perform slightly better on very long sequences due to their separate cell state mechanism.
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