def __init__(self,
num_layers,
num_units,
bidirectional=False,
residual_connections=False,
dropout=0.3,
reducer=ConcatReducer(),
cell_class=None,
**kwargs):
"""Initializes the parameters of the encoder.
Args:
num_layers: The number of layers.
num_units: The number of units in each layer.
bidirectional: Use a bidirectional RNN.
residual_connections: If ``True``, each layer input will be added to its
output.
reducer: A :class:`opennmt.layers.reducer.Reducer` instance to merge
bidirectional state and outputs.
dropout: The probability to drop units in each layer output.
cell_class: The inner cell class or a callable taking :obj:`num_units` as
argument and returning a cell. Defaults to a LSTM cell.
**kwargs: Additional layer arguments.
"""
super(RNNEncoderV2, self).__init__(**kwargs)
cell = rnn.make_rnn_cell(num_layers,
num_units,
dropout=dropout,
residual_connections=residual_connections,
cell_class=cell_class)
self.rnn = rnn.RNN(cell, bidirectional=bidirectional, reducer=reducer)
def __init__(self,
num_layers,
num_units,
reduction_factor=2,
cell_class=None,
dropout=0.3):
"""Initializes the parameters of the encoder.
Args:
num_layers: The number of layers.
num_units: The number of units in each layer.
reduction_factor: The time reduction factor.
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.
"""
super(PyramidalRNNEncoder, self).__init__()
self.reduction_factor = reduction_factor
self.state_reducer = JoinReducer()
self.layers = []
for _ in range(num_layers):
self.layers.append(
BidirectionalRNNEncoder(1,
num_units,
reducer=ConcatReducer(),
cell_class=cell_class,
dropout=dropout))
def __init__(self, num_layers, num_units, dropout=0.3):
"""Initializes the parameters of the encoder.
Args:
num_layers: The number of layers.
num_units: The number of units in each layer.
dropout: The probability to drop units in each layer output.
Raises:
ValueError: if :obj:`num_layers` < 2.
"""
super(GoogleRNNEncoder, self).__init__()
if num_layers < 2:
raise ValueError("GoogleRNNEncoder requires at least 2 layers")
self.bidirectional = BidirectionalRNNEncoder(
1,
num_units,
reducer=ConcatReducer(),
cell_class=tf.nn.rnn_cell.LSTMCell,
dropout=dropout)
self.unidirectional = UnidirectionalRNNEncoder(
num_layers - 1,
num_units,
cell_class=tf.nn.rnn_cell.LSTMCell,
dropout=dropout,
residual_connections=True)
def __init__(self,
num_layers=6,
num_units=1024,
cell_class=None,
dropout=0.3):
"""Initializes the parameters of the encoder.
Args:
num_layers: The number of layers.
num_units: The number of units in each RNN layer and the final output.
cell_class: The inner cell class or a callable taking :obj:`num_units` as
argument and returning a cell. Defaults to a layer normalized LSTM cell.
For efficiency, consider using the standard ``tf.nn.rnn_cell.LSTMCell``
instead.
dropout: The probability to drop units in each layer output.
"""
super(RNMTPlusEncoder, self).__init__()
if cell_class is None:
cell_class = tf.contrib.rnn.LayerNormBasicLSTMCell
self._num_units = num_units
self._dropout = dropout
self._layers = [
BidirectionalRNNEncoder(num_layers=1,
num_units=num_units * 2,
reducer=ConcatReducer(),
cell_class=cell_class,
dropout=0.0) for _ in range(num_layers)
]
def __init__(self,
num_layers,
num_units,
bidirectional=False,
residual_connections=False,
dropout=0.3,
reducer=ConcatReducer(),
**kwargs):
"""Initializes the parameters of the encoder.
Args:
num_layers: The number of layers.
num_units: The number of units in each layer output.
bidirectional: Make each LSTM layer bidirectional.
residual_connections: If ``True``, each layer input will be added to its
output.
dropout: The probability to drop units in each layer output.
reducer: A :class:`opennmt.layers.Reducer` instance to merge
bidirectional state and outputs.
**kwargs: Additional layer arguments.
"""
lstm_layer = rnn.LSTM(
num_layers,
num_units,
bidirectional=bidirectional,
reducer=reducer,
dropout=dropout,
residual_connections=residual_connections,
)
super().__init__(lstm_layer, **kwargs)
def __init__(self, inputters, reducer=ConcatReducer(), dropout=0.0):
"""Initializes a mixed inputter.
Args:
inputters: A list of :class:`opennmt.inputters.Inputter`.
reducer: A :class:`opennmt.layers.Reducer` to merge all inputs.
dropout: The probability to drop units in the merged inputs.
"""
super(MixedInputter, self).__init__(inputters, reducer=reducer)
self.dropout = dropout
def __init__(self, maximum_position=32, reducer=ConcatReducer()):
"""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__()
self.maximum_position = maximum_position
self.embedding = None
def __init__(self,
encoders,
outputs_reducer=ConcatReducer(axis=1),
states_reducer=JoinReducer(),
outputs_layer_fn=None,
combined_output_layer_fn=None,
share_parameters=False):
"""Initializes the parameters of the encoder.
Args:
encoders: A list of :class:`opennmt.encoders.encoder.Encoder` or a single
one, in which case the same encoder is applied to each input.
outputs_reducer: A :class:`opennmt.layers.reducer.Reducer` to merge all
outputs. If ``None``, defaults to
:class:`opennmt.layers.reducer.JoinReducer`.
states_reducer: A :class:`opennmt.layers.reducer.Reducer` to merge all
states. If ``None``, defaults to
:class:`opennmt.layers.reducer.JoinReducer`.
outputs_layer_fn: A callable or list of callables applied to the
encoders outputs If it is a single callable, it is on each encoder
output. Otherwise, the ``i`` th callable is applied on encoder ``i``
output.
combined_output_layer_fn: A callable to apply on the combined output
(i.e. the output of :obj:`outputs_reducer`).
share_parameters: If ``True``, share parameters between the parallel
encoders. For stateful encoders, simply pass a single encoder instance
to :obj:`encoders` for parameter sharing.
Raises:
ValueError: if :obj:`outputs_layer_fn` is a list with a size not equal
to the number of encoders.
"""
if (isinstance(encoders, list) and outputs_layer_fn is not None
and isinstance(outputs_layer_fn, list)
and len(outputs_layer_fn) != len(encoders)):
raise ValueError(
"The number of output layers must match the number of encoders; "
"expected %d layers but got %d." %
(len(encoders), len(outputs_layer_fn)))
super(ParallelEncoder, self).__init__()
self.encoders = encoders
self.outputs_reducer = outputs_reducer if outputs_reducer is not None else JoinReducer(
)
self.states_reducer = states_reducer if states_reducer is not None else JoinReducer(
)
self.outputs_layer_fn = outputs_layer_fn
self.combined_output_layer_fn = combined_output_layer_fn
self.share_parameters = share_parameters
def __init__(self,
encoders,
outputs_reducer=ConcatReducer(axis=1),
states_reducer=JoinReducer()):
"""Initializes the parameters of the encoder.
Args:
encoders: A list of :class:`opennmt.encoders.encoder.Encoder`.
outputs_reducer: A :class:`opennmt.layers.reducer.Reducer` to merge all
outputs.
states_reducer: A :class:`opennmt.layers.reducer.Reducer` to merge all
states.
"""
self.encoders = encoders
self.outputs_reducer = outputs_reducer
self.states_reducer = states_reducer
def __init__(self,
encoders,
outputs_reducer=ConcatReducer(axis=1),
states_reducer=JoinReducer(),
outputs_layer_fn=None,
combined_output_layer_fn=None):
"""Initializes the parameters of the encoder.
Args:
encoders: A list of :class:`opennmt.encoders.encoder.Encoder`.
outputs_reducer: A :class:`opennmt.layers.reducer.Reducer` to merge all
outputs.
states_reducer: A :class:`opennmt.layers.reducer.Reducer` to merge all
states.
outputs_layer_fn: A callable or list of callables applied to the
encoders outputs If it is a single callable, it is on each encoder
output. Otherwise, the ``i`` th callable is applied on encoder ``i``
output.
combined_output_layer_fn: A callable to apply on the combined output
(i.e. the output of :obj:`outputs_reducer`).
Raises:
ValueError: if :obj:`outputs_layer_fn` is a list with a size not equal
to the number of encoders.
"""
if (outputs_layer_fn is not None
and isinstance(outputs_layer_fn, list)
and len(outputs_layer_fn) != len(encoders)):
raise ValueError(
"The number of output layers must match the number of encoders; "
"expected %d layers but got %d." %
(len(encoders), len(outputs_layer_fn)))
self.encoders = encoders
self.outputs_reducer = outputs_reducer
self.states_reducer = states_reducer
self.outputs_layer_fn = outputs_layer_fn
self.combined_output_layer_fn = combined_output_layer_fn