Attention layer

[source]

Attention class

keras.layers.Attention(
    use_scale=False, score_mode="dot", dropout=0.0, seed=None, **kwargs
)

Dot-product attention layer, a.k.a. Luong-style attention.

Inputs are a list with 2 or 3 elements: 1. A query tensor of shape (batch_size, Tq, dim). 2. A value tensor of shape (batch_size, Tv, dim). 3. A optional key tensor of shape (batch_size, Tv, dim). If none supplied, value will be used as a key.

The calculation follows the steps: 1. Calculate attention scores using query and key with shape (batch_size, Tq, Tv). 2. Use scores to calculate a softmax distribution with shape (batch_size, Tq, Tv). 3. Use the softmax distribution to create a linear combination of value with shape (batch_size, Tq, dim).

Arguments

• use_scale: If True, will create a scalar variable to scale the attention scores.
• dropout: Float between 0 and 1. Fraction of the units to drop for the attention scores. Defaults to 0.0.
• seed: A Python integer to use as random seed in case of dropout.
• score_mode: Function to use to compute attention scores, one of {"dot", "concat"}. "dot" refers to the dot product between the query and key vectors. "concat" refers to the hyperbolic tangent of the concatenation of the query and key vectors.

Call arguments

• inputs: List of the following tensors:
  • query: Query tensor of shape (batch_size, Tq, dim).
  • value: Value tensor of shape (batch_size, Tv, dim).
  • key: Optional key tensor of shape (batch_size, Tv, dim). If not given, will use value for both key and value, which is the most common case.
• mask: List of the following tensors:
  • query_mask: A boolean mask tensor of shape (batch_size, Tq). If given, the output will be zero at the positions where mask==False.
  • value_mask: A boolean mask tensor of shape (batch_size, Tv). If given, will apply the mask such that values at positions where mask==False do not contribute to the result.
• return_attention_scores: bool, it True, returns the attention scores (after masking and softmax) as an additional output argument.
• training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (no dropout).
• use_causal_mask: Boolean. Set to True for decoder self-attention. Adds a mask such that position i cannot attend to positions j > i. This prevents the flow of information from the future towards the past. Defaults to False.

Output: Attention outputs of shape (batch_size, Tq, dim). (Optional) Attention scores after masking and softmax with shape (batch_size, Tq, Tv).