This is an implementation of multi-headed attention based on "Attention is all you Need". If query, key, value are the same, then this is self-attention. Each timestep in query attends to the corresponding sequence in key, and returns a fixed-width vector.

layer_multi_head_attention( inputs, num_heads, key_dim, value_dim = NULL, dropout = 0, use_bias = TRUE, output_shape = NULL, attention_axes = NULL, kernel_initializer = "glorot_uniform", bias_initializer = "zeros", kernel_regularizer = NULL, bias_regularizer = NULL, activity_regularizer = NULL, kernel_constraint = NULL, bias_constraint = NULL, ... )

inputs | a list of inputs first should be the query tensor, the second the value tensor |
---|---|

num_heads | Number of attention heads. |

key_dim | Size of each attention head for query and key. |

value_dim | Size of each attention head for value. |

dropout | Dropout probability. |

use_bias | Boolean, whether the dense layers use bias vectors/matrices. |

output_shape | The expected shape of an output tensor, besides the batch and sequence dims. If not specified, projects back to the key feature dim. |

attention_axes | axes over which the attention is applied. None means attention over all axes, but batch, heads, and features. |

kernel_initializer | Initializer for dense layer kernels. |

bias_initializer | Initializer for dense layer biases. |

kernel_regularizer | Regularizer for dense layer kernels. |

bias_regularizer | Regularizer for dense layer biases. |

activity_regularizer | Regularizer for dense layer activity. |

kernel_constraint | Constraint for dense layer kernels. |

bias_constraint | Constraint for dense layer kernels. |

... | Other arguments passed to the layer. Eg, |

attention_output: The result of the computation, of shape

`[B, T, E]`

, where T is for target sequence shapes and E is the query input last dimension if output_shape is None. Otherwise, the multi-head outputs are project to the shape specified by output_shape.attention_scores: (Optional) multi-head attention coeffients over attention axes.

This layer first projects query, key and value. These are (effectively) a list
of tensors of length num_attention_heads, where the corresponding shapes are
`[batch_size, , key_dim]`

, `[batch_size, , key_dim]`

, `[batch_size, , value_dim]`

.

Then, the query and key tensors are dot-producted and scaled. These are softmaxed to obtain attention probabilities. The value tensors are then interpolated by these probabilities, then concatenated back to a single tensor.

Finally, the result tensor with the last dimension as value_dim can take an linear projection and return.

query: Query Tensor of shape

`[B, T, dim]`

.value: Value Tensor of shape

`[B, S, dim]`

.key: Optional key Tensor of shape

`[B, S, dim]`

. If not given, will use value for both key and value, which is the most common case.attention_mask: a boolean mask of shape

`[B, T, S]`

, that prevents attention to certain positions.return_attention_scores: A boolean to indicate whether the output should be attention output if TRUE, or (attention_output, attention_scores) if FALSE. Defaults to FALSE.

training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (no dropout). Defaults to either using the training mode of the parent layer/model, or FALSE (inference) if there is no parent layer.