The `GRU` (Gated Recurrent Unit) layer in `tensorflow.keras.layers` is a type of recurrent neural network layer used for sequence modeling tasks. It is similar to the LSTM (Long Short-Term Memory) layer but has a simpler architecture with fewer gates.
GRU layers are well-suited for handling temporal dependencies in sequential data, such as natural language processing and time series analysis. They are particularly useful when dealing with long sequences and can effectively capture long-term dependencies while mitigating the vanishing gradient problem.
The GRU layer implements a mechanism for selectively updating and forgetting information over time, allowing the model to retain important information from previous timesteps. This makes it a powerful choice for tasks involving sequential data where maintaining context and information from past inputs is crucial.
Overall, the `GRU` layer in `tensorflow.keras.layers` provides a flexible and efficient way to incorporate recurrent neural networks into deep learning models, enabling effective sequence modeling and capturing of long-term dependencies.
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