Fig. 5
The Encoder-Decoder GRU Model. The Encoder-Decoder GRU (Gated Recurrent Unit) model is a type of neural network architecture commonly used for sequence-to-sequence tasks. The input sequence, is passed one element at a time through the encoder’s GRU layer (Blue Blocks). The GRU layer computes the hidden states at each time step and updates the memory of the model. These hidden states effectively “encode” the information in the input sequence. After processing the entire input sequence, the final hidden state of the encoder is often used as a context vector (Grey Block), which summarizes the information in the input sequence. This context vector is passed to the decoder. The decoder receives the context vector (or context vectors) from the encoder and generates the output sequence. At each time step, the decoder generates a new output through the decoder’s GRU layer (Yellow Blocks) based on its previous output and the encoder’s context.
