TensorFlow 2 version View source on GitHub

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

Inputs are query tensor of shape [batch_size, Tq, dim], value tensor of shape [batch_size, Tv, dim] and key tensor of shape [batch_size, Tv, dim]. The calculation follows the steps:

  1. Calculate scores with shape [batch_size, Tq, Tv] as a query-key dot product: scores = tf.matmul(query, key, transpose_b=True).
  2. Use scores to calculate a distribution with shape [batch_size, Tq, Tv]: distribution = tf.nn.softmax(scores).
  3. Use distribution to create a linear combination of value with shape batch_size, Tq, dim]: return tf.matmul(distribution, value).

use_scale If True, will create a scalar variable to scale the attention scores.
causal 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.

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.

Output shape:

Attention outputs of shape [batch_size, Tq, dim].

The meaning of query, value and key depend on the application. In the case of text similarity, for example, query is the sequence embeddings of the first piece of text and value is the sequence embeddings of the second piece of text. key is usually the same tensor as value.

Here is a code example for using Attention in a CNN+Attention network:

# Variable-length int sequences.
query_input = tf.keras.Input(shape=(None,), dtype='int32')
value_input = tf.keras.Input(shape=(None,), dtype='int32')

# Embedding lookup.
token_embedding = tf.keras.layers.Embedding(max_tokens, dimension)
# Query embeddings of shape [batch_size, Tq, dimension].
query_embeddings = token_embedding(query_input)
# Value embeddings of shape [batch_size, Tv, dimension].
value_embeddings = token_embedding(query_input)

# CNN layer.
cnn_layer = tf.keras.layers.Conv1D(
    # Use 'same' padding so outputs have the same shape as inputs.
# Query encoding of shape [batch_size, Tq, filters].
query_seq_encoding = cnn_layer(query_embeddings)
# Value encoding of shape [batch_size, Tv, filters].
value_seq_encoding = cnn_layer(value_embeddings)

# Query-value attention of shape [batch_size, Tq, filters].
query_value_attention_seq = tf.keras.layers.Attention()(
    [query_seq_encoding, value_seq_encoding])

# Reduce over the sequence axis to produce encodings of shape
# [batch_size, filters].
query_encoding = tf.keras.layers.GlobalAveragePooling1D()(
query_value_attention = tf.keras.layers.GlobalAveragePooling1D()(

# Concatenate query and document encodings to produce a DNN input layer.
input_layer = tf.keras.layers.Concatenate()(
    [query_encoding, query_value_attention])

# Add DNN layers, and create Model.
# ...