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from chainer.functions.connection import n_step_lstm as rnn
from chainer.links.connection import n_step_rnn
class NStepLSTMBase(n_step_rnn.NStepRNNBase):
"""Base link class for Stacked LSTM/BiLSTM links.
This link is base link class for :func:`chainer.links.NStepLSTM` and
:func:`chainer.links.NStepBiLSTM`.
This link's behavior depends on argument, ``use_bi_direction``.
Args:
n_layers (int): Number of layers.
in_size (int): Dimensionality of input vectors.
out_size (int): Dimensionality of hidden states and output vectors.
dropout (float): Dropout ratio.
use_bi_direction (bool): if ``True``, use Bi-directional LSTM.
.. seealso::
:func:`chainer.functions.n_step_lstm`
:func:`chainer.functions.n_step_bilstm`
"""
n_weights = 8
def forward(self, hx, cx, xs, **kwargs):
"""forward(self, hx, cx, xs)
Calculate all hidden states and cell states.
.. warning::
``train`` argument is not supported anymore since v2.
Instead, use ``chainer.using_config('train', train)``.
See :func:`chainer.using_config`.
Args:
hx (~chainer.Variable or None): Initial hidden states. If ``None``
is specified zero-vector is used. Its shape is ``(S, B, N)``
for uni-directional LSTM and ``(2S, B, N)`` for
bi-directional LSTM where ``S`` is the number of layers
and is equal to ``n_layers``, ``B`` is the mini-batch size,
and ``N`` is the dimension of the hidden units.
cx (~chainer.Variable or None): Initial cell states. If ``None``
is specified zero-vector is used.
It has the same shape as ``hx``.
xs (list of ~chainer.Variable): List of input sequences.
Each element ``xs[i]`` is a :class:`chainer.Variable` holding
a sequence. Its shape is ``(L_t, I)``, where ``L_t`` is the
length of a sequence for time ``t``, and ``I`` is the size of
the input and is equal to ``in_size``.
Returns:
tuple: This function returns a tuple containing three elements,
``hy``, ``cy`` and ``ys``.
- ``hy`` is an updated hidden states whose shape is the same as
``hx``.
- ``cy`` is an updated cell states whose shape is the same as
``cx``.
- ``ys`` is a list of :class:`~chainer.Variable` . Each element
``ys[t]`` holds hidden states of the last layer corresponding
to an input ``xs[t]``. Its shape is ``(L_t, N)`` for
uni-directional LSTM and ``(L_t, 2N)`` for bi-directional LSTM
where ``L_t`` is the length of a sequence for time ``t``,
and ``N`` is size of hidden units.
"""
(hy, cy), ys = self._call([hx, cx], xs, **kwargs)
return hy, cy, ys
class NStepLSTM(NStepLSTMBase):
"""__init__(self, n_layers, in_size, out_size, dropout)
Stacked Uni-directional LSTM for sequences.
This link is stacked version of Uni-directional LSTM for sequences.
It calculates hidden and cell states of all layer at end-of-string,
and all hidden states of the last layer for each time.
Unlike :func:`chainer.functions.n_step_lstm`, this function automatically
sort inputs in descending order by length, and transpose the sequence.
Users just need to call the link with a list of :class:`chainer.Variable`
holding sequences.
.. warning::
``use_cudnn`` argument is not supported anymore since v2.
Instead, use ``chainer.using_config('use_cudnn', use_cudnn)``.
See :func:`chainer.using_config`.
Args:
n_layers (int): Number of layers.
in_size (int): Dimensionality of input vectors.
out_size (int): Dimensionality of hidden states and output vectors.
dropout (float): Dropout ratio.
.. seealso::
:func:`chainer.functions.n_step_lstm`
"""
use_bi_direction = False
def rnn(self, *args):
return rnn.n_step_lstm(*args)
@property
def n_cells(self):
return 2
class NStepBiLSTM(NStepLSTMBase):
"""__init__(self, n_layers, in_size, out_size, dropout)
Stacked Bi-directional LSTM for sequences.
This link is stacked version of Bi-directional LSTM for sequences.
It calculates hidden and cell states of all layer at end-of-string,
and all hidden states of the last layer for each time.
Unlike :func:`chainer.functions.n_step_bilstm`, this function automatically
sort inputs in descending order by length, and transpose the sequence.
Users just need to call the link with a list of :class:`chainer.Variable`
holding sequences.
.. warning::
``use_cudnn`` argument is not supported anymore since v2.
Instead, use ``chainer.using_config('use_cudnn', use_cudnn)``.
See :func:`chainer.using_config`.
Args:
n_layers (int): Number of layers.
in_size (int): Dimensionality of input vectors.
out_size (int): Dimensionality of hidden states and output vectors.
dropout (float): Dropout ratio.
.. seealso::
:func:`chainer.functions.n_step_bilstm`
"""
use_bi_direction = True
def rnn(self, *args):
return rnn.n_step_bilstm(*args)
@property
def n_cells(self):
return 2