AttentionLayer
AttentionLayer is a conceptual component within certain artificial neural network architectures, particularly those designed for processing sequential data such as text or time series. Its primary function is to selectively focus on specific parts of the input sequence when generating an output or performing a computation. This selective focus is achieved through a mechanism that assigns weights to different elements of the input. Elements deemed more relevant to the current task receive higher weights, while less relevant elements receive lower weights.
The core idea behind an AttentionLayer is to mitigate the limitations of traditional recurrent neural networks,