def __init__(self, reducer=SumReducer(), **kwargs):
"""Initializes the position encoder.
Args:
reducer: A :class:`opennmt.layers.Reducer` to merge inputs and position
encodings.
**kwargs: Additional layer keyword arguments.
"""
super(PositionEncoder, self).__init__(**kwargs)
self.reducer = reducer
def __init__(self, maximum_position=128, reducer=SumReducer()):
"""Initializes the position encoder.
Args:
maximum_position: The maximum position to embed. Positions greater
than this value will be set to :obj:`maximum_position`.
reducer: A :class:`opennmt.layers.reducer.Reducer` to merge inputs and
position encodings.
"""
super(PositionEmbedder, self).__init__(reducer=reducer)
self.maximum_position = maximum_position
def __init__(self, reducer=None, **kwargs):
"""Initializes the position encoder.
Args:
reducer: A :class:`opennmt.layers.Reducer` to merge inputs and position
encodings. Defaults to :class:`opennmt.layers.SumReducer`.
**kwargs: Additional layer keyword arguments.
"""
super(PositionEncoder, self).__init__(**kwargs)
if reducer is None:
reducer = SumReducer(dtype=kwargs.get("dtype"))
self.reducer = reducer
def __init__(self,
num_layers,
num_units,
reducer=SumReducer(),
cell_class=None,
dropout=0.3,
residual_connections=False):
"""Initializes the parameters of the encoder.
Args:
num_layers: The number of layers.
num_units: The number of units in each layer.
reducer: A :class:`opennmt.layers.reducer.Reducer` instance to merge
bidirectional state and outputs.
cell_class: The inner cell class or a callable taking :obj:`num_units` as
argument and returning a cell. Defaults to a LSTM cell.
dropout: The probability to drop units in each layer output.
residual_connections: If ``True``, each layer input will be added to its
output.
Raises:
ValueError: when using :class:`opennmt.layers.reducer.ConcatReducer` and
:obj:`num_units` is not divisible by 2.
"""
if isinstance(reducer, ConcatReducer):
if num_units % 2 != 0:
raise ValueError(
"num_units must be divisible by 2 to use the ConcatReducer."
)
num_units /= 2
self.reducer = reducer
super(BidirectionalRNNEncoder,
self).__init__(num_layers,
num_units,
cell_class=cell_class,
dropout=dropout,
residual_connections=residual_connections)
def __init__(self, reducer=SumReducer()):
self.reducer = reducer
def __init__(self, reducer=SumReducer()):
super(PositionEncoder, self).__init__()
self.reducer = reducer