An attention layer is a type of layer that is commonly used in neural networks to selectively focus on specific parts of the input.
Attention layers are widely used in natural language processing (NLP) tasks, such as machine translation and text classification, where the input consists of a sequence of tokens.
How to calculate
- The basic idea behind the attention layer is to compute a set of attention scores that indicate how much each input token should be attended to, based on its relevance to the current task.
- The attention scores are then used to weight the input tokens and compute a weighted sum, which represents the output of the attention layer.
Here is the equation for the attention layer output:
$$ \text{Attention}(Q, K, V) = \text{softmax}(\frac{QK^T}{\sqrt{d_k}})V $$
where $Q$, $K$, and $V$ are the query, key, and value matrices, respectively, and $d_k$ is the dimension of the key vectors.
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The dot product between the query and key matrices is normalized by the square root of $d_k$ to control the magnitude of the attention scores.
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The softmax function is applied to the resulting attention scores to ensure that they sum to 1,
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and the weighted sum of the value matrix is computed using the attention scores.
The attention layer can be thought of as a way of learning to attend to the most informative parts of the input sequence, and it has been shown to be highly effective in a wide range of NLP tasks.
The attention layer is typically used in conjunction with other layers, such as convolutional or recurrent layers, to form a complete neural network model.