def __init__(self,
name: str,
encoder: Attendable,
dropout_keep_prob: float = 1.0,
state_size: int = None,
max_fertility: int = 5,
reuse: ModelPart = None,
save_checkpoint: str = None,
load_checkpoint: str = None,
initializers: InitializerSpecs = None) -> None:
check_argument_types()
Attention.__init__(self, name, encoder, dropout_keep_prob, state_size,
reuse, save_checkpoint, load_checkpoint,
initializers)
self.max_fertility = max_fertility
def build_attention(config: Dict,
encoder: RecurrentEncoder) -> Tuple[Attention, str]:
attention = Attention(name=ATTENTION_NAME, encoder=encoder)
attention_ini = ATTENTION_TEMPLATE.format(ATTENTION_NAME)
return attention, attention_ini
def __init__(self,
name: str,
encoder: Union[TemporalStateful, SpatialStateful],
dropout_keep_prob: float = 1.0,
state_size: int = None,
max_fertility: int = 5,
save_checkpoint: str = None,
load_checkpoint: str = None) -> None:
check_argument_types()
Attention.__init__(self, name, encoder, dropout_keep_prob, state_size,
save_checkpoint, load_checkpoint)
self.max_fertility = max_fertility
self.coverage_weights = tf.get_variable("coverage_matrix",
[1, 1, 1, self.state_size])
self.fertility_weights = tf.get_variable(
"fertility_matrix", [1, 1, self.context_vector_size])
self.fertility = 1e-8 + self.max_fertility * tf.sigmoid(
tf.reduce_sum(self.fertility_weights * self.attention_states, [2]))
def __init__(self,
name: str,
encoder: Attendable,
dropout_keep_prob: float = 1.0,
state_size: int = None,
max_fertility: int = 5,
reuse: ModelPart = None,
save_checkpoint: str = None,
load_checkpoint: str = None,
initializers: InitializerSpecs = None) -> None:
check_argument_types()
Attention.__init__(self, name, encoder, dropout_keep_prob, state_size,
reuse, save_checkpoint, load_checkpoint,
initializers)
self.max_fertility = max_fertility
self.coverage_weights = tf.get_variable("coverage_matrix",
[1, 1, 1, self.state_size])
self.fertility_weights = tf.get_variable(
"fertility_matrix", [1, 1, self.context_vector_size])
self.fertility = 1e-8 + self.max_fertility * tf.sigmoid(
tf.reduce_sum(self.fertility_weights * self.attention_states, [2]))