Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Encoder + Decoder + Attention
The integration of encoder, decoder, and the attention mechanism, when applied
to our sequence-to-sequence problem, is depicted in the figure below (that’s the
same figure from the "Computing the Context Vector" section).
Figure 9.17 - Encoder + decoder + attention
Take time to visualize the flow of information:
• First, the data points in the source sequence (in red) feed the encoder (in blue)
and generate "keys" (K) and "values" (V) for the attention mechanism (in
black).
• Next, each input of the decoder (in green) generates one "query" (Q) at a time
to produce a context vector (in black).
• Finally, the context vector gets concatenated to the decoder’s current hidden
state (in green) and transformed to predicted coordinates (in green) by the
output layer (in green).
Our former EncoderDecoder class works seamlessly with an instance of
DecoderAttn:
730 | Chapter 9 — Part I: Sequence-to-Sequence