attention-seeking-in-pytorch

This repo contains implementation of various forms of attention:

• Location based attention
• Content based dot product
• Content based concatenation
• Content based general attention
• Pointer networks

and finally

• No attention

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Task to learn

Each of these sequence to sequence models is trained to learn how to sort a shuffled array of numbers from 1 to N. The code to generate this data is here.

There is a considerable improvement if an attention based model is used versus the no attention model.

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Organization of code

All the models and the data loader are defined in code/.

• Each model is defined in a separate file. The file containing a model also contains train and test functions which are self-explanatory.

• Output logs are stored under training_outputs/

• Attention weights can be visualized using the code in the notebook Visualizing attention.