RNNs
  • Recurrent Neural Networks and LSTM
  • Forecasting
  • Sequence Data
  • Recurrent Neural Networks
    • Architecture of RNN
    • Training an RNN
    • Vanishing Gradient
  • Long Short Term Memory (LSTM)
    • Architecture of LSTM
    • Walk through of LSTM
  • Gated Recurrent Unit
    • GRU
    • Architecture of GRU
  • Activation Functions
    • Various Activation Functions
  • Regularization of Neural Networks
    • Various Methods in Regularization
  • Applications of RNNs
    • Various Applications of RNN
    • RNNs Movie
  • Forecasting Stock Trends using RNNs
    • Objective
    • Alpha Vantage API
    • Stock Trends Prediction using LSTM.
    • Bidirectional RNN
    • Stock Trends Prediction using Bidirectional RNNs
  • Conclusion
    • Conclusion
    • References
  • License
    • License
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  1. Gated Recurrent Unit

Architecture of GRU

PreviousGRUNextVarious Activation Functions

Last updated 5 years ago

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Update Gate: First, we have the Update gate. This gate decides what information should be thrown away or kept. Information from the previous hidden state and information from the current input is passed through the sigmoid function. Values come out between 0 and 1. The closer to 0 means to forget, and the closer to 1 means to keep.

Reset Gate : The reset gate is another gate is used to decide how much past information to forget.

Several similarities and differences between GRU networks and LSTM networks are outlined in .The study found that both models performed better than the other only in certain tasks.

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