Understanding Scaled-Dot Product Attention with TensorFlow
Attention mechanisms have revolutionized the field of deep learning, enabling models to effectively focus on relevant information in a sequence or set of data. One such attention mechanism is Scaled-Dot Product Attention, which has gained significant popularity in various natural language processing (NLP) tasks. In this blog post, we will dive deep into the concept of Scaled-Dot Product Attention and demonstrate how to implement it using TensorFlow.
What is Scaled-Dot Product Attention?
Scaled-Dot Product Attention is a key component of the Transformer model architecture, originally introduced by Vaswani et al. in the context of machine translation. It allows the model to assign different weights, or attention scores, to different parts of the input sequence.
The attention mechanism calculates these attention scores by taking the dot product of two vectors: the query vector (Q) and the key vector (K). By scaling the dot product by the square root of the dimension of the key vector, we ensure that the gradients are not too large, providing more stable training.
Next, the attention scores are passed through a softmax function, which normalizes them to sum up to 1. These normalized scores represent the importance of each key vector with respect to the query vector. The…
