# Source code for chainer.links.connection.lstm

```
import numpy
import six
import chainer
from chainer import cuda
from chainer.functions.activation import lstm
from chainer.functions.array import concat
from chainer.functions.array import split_axis
from chainer import initializers
from chainer import link
from chainer.links.connection import linear
from chainer import variable
class LSTMBase(link.Chain):
def __init__(self, in_size, out_size,
lateral_init=None, upward_init=None,
bias_init=0, forget_bias_init=0):
super(LSTMBase, self).__init__(
upward=linear.Linear(in_size, 4 * out_size, initialW=0),
lateral=linear.Linear(out_size, 4 * out_size,
initialW=0, nobias=True),
)
self.state_size = out_size
self.lateral_init = lateral_init
self.upward_init = upward_init
self.bias_init = bias_init
self.forget_bias_init = forget_bias_init
if in_size is not None:
self._initialize_params()
def _initialize_params(self):
for i in six.moves.range(0, 4 * self.state_size, self.state_size):
initializers.init_weight(
self.lateral.W.data[i:i + self.state_size, :],
self.lateral_init)
initializers.init_weight(
self.upward.W.data[i:i + self.state_size, :], self.upward_init)
a, i, f, o = lstm._extract_gates(
self.upward.b.data.reshape(1, 4 * self.state_size, 1))
initializers.init_weight(a, self.bias_init)
initializers.init_weight(i, self.bias_init)
initializers.init_weight(f, self.forget_bias_init)
initializers.init_weight(o, self.bias_init)
[docs]class StatelessLSTM(LSTMBase):
"""Stateless LSTM layer.
This is a fully-connected LSTM layer as a chain. Unlike the
:func:`~chainer.functions.lstm` function, this chain holds upward and
lateral connections as child links. This link doesn't keep cell and
hidden states.
Args:
in_size (int or None): Dimension of input vectors. If ``None``,
parameter initialization will be deferred until the first forward
data pass at which time the size will be determined.
out_size (int): Dimensionality of output vectors.
Attributes:
upward (chainer.links.Linear): Linear layer of upward connections.
lateral (chainer.links.Linear): Linear layer of lateral connections.
"""
[docs] def __call__(self, c, h, x):
"""Returns new cell state and updated output of LSTM.
Args:
c (~chainer.Variable): Cell states of LSTM units.
h (~chainer.Variable): Output at the previous time step.
x (~chainer.Variable): A new batch from the input sequence.
Returns:
tuple of ~chainer.Variable: Returns ``(c_new, h_new)``, where
``c_new`` represents new cell state, and ``h_new`` is updated
output of LSTM units.
"""
if self.upward.has_uninitialized_params:
in_size = x.size // x.shape[0]
with cuda.get_device_from_id(self._device_id):
self.upward._initialize_params(in_size)
self._initialize_params()
lstm_in = self.upward(x)
if h is not None:
lstm_in += self.lateral(h)
if c is None:
xp = self.xp
with cuda.get_device_from_id(self._device_id):
c = variable.Variable(
xp.zeros((x.shape[0], self.state_size), dtype=x.dtype),
volatile='auto')
return lstm.lstm(c, lstm_in)
[docs]class LSTM(LSTMBase):
"""Fully-connected LSTM layer.
This is a fully-connected LSTM layer as a chain. Unlike the
:func:`~chainer.functions.lstm` function, which is defined as a stateless
activation function, this chain holds upward and lateral connections as
child links.
It also maintains *states*, including the cell state and the output
at the previous time step. Therefore, it can be used as a *stateful LSTM*.
This link supports variable length inputs. The mini-batch size of the
current input must be equal to or smaller than that of the previous one.
The mini-batch size of ``c`` and ``h`` is determined as that of the first
input ``x``.
When mini-batch size of ``i``-th input is smaller than that of the previous
input, this link only updates ``c[0:len(x)]`` and ``h[0:len(x)]`` and
doesn't change the rest of ``c`` and ``h``.
So, please sort input sequences in descending order of lengths before
applying the function.
Args:
in_size (int): Dimension of input vectors. If ``None``, parameter
initialization will be deferred until the first forward data pass
at which time the size will be determined.
out_size (int): Dimensionality of output vectors.
lateral_init: A callable that takes ``numpy.ndarray`` or
``cupy.ndarray`` and edits its value.
It is used for initialization of the lateral connections.
Maybe be ``None`` to use default initialization.
upward_init: A callable that takes ``numpy.ndarray`` or
``cupy.ndarray`` and edits its value.
It is used for initialization of the upward connections.
Maybe be ``None`` to use default initialization.
bias_init: A callable that takes ``numpy.ndarray`` or
``cupy.ndarray`` and edits its value
It is used for initialization of the biases of cell input,
input gate and output gate.and gates of the upward connection.
Maybe a scalar, in that case, the bias is
initialized by this value.
Maybe be ``None`` to use default initialization.
forget_bias_init: A callable that takes ``numpy.ndarray`` or
``cupy.ndarray`` and edits its value
It is used for initialization of the biases of the forget gate of
the upward connection.
Maybe a scalar, in that case, the bias is
initialized by this value.
Maybe be ``None`` to use default initialization.
Attributes:
upward (~chainer.links.Linear): Linear layer of upward connections.
lateral (~chainer.links.Linear): Linear layer of lateral connections.
c (~chainer.Variable): Cell states of LSTM units.
h (~chainer.Variable): Output at the previous time step.
"""
def __init__(self, in_size, out_size, **kwargs):
super(LSTM, self).__init__(in_size, out_size, **kwargs)
self.reset_state()
def to_cpu(self):
super(LSTM, self).to_cpu()
if self.c is not None:
self.c.to_cpu()
if self.h is not None:
self.h.to_cpu()
def to_gpu(self, device=None):
super(LSTM, self).to_gpu(device)
if self.c is not None:
self.c.to_gpu(device)
if self.h is not None:
self.h.to_gpu(device)
[docs] def set_state(self, c, h):
"""Sets the internal state.
It sets the :attr:`c` and :attr:`h` attributes.
Args:
c (~chainer.Variable): A new cell states of LSTM units.
h (~chainer.Variable): A new output at the previous time step.
"""
assert isinstance(c, chainer.Variable)
assert isinstance(h, chainer.Variable)
c_ = c
h_ = h
if self.xp == numpy:
c_.to_cpu()
h_.to_cpu()
else:
c_.to_gpu(self._device_id)
h_.to_gpu(self._device_id)
self.c = c_
self.h = h_
[docs] def reset_state(self):
"""Resets the internal state.
It sets ``None`` to the :attr:`c` and :attr:`h` attributes.
"""
self.c = self.h = None
[docs] def __call__(self, x):
"""Updates the internal state and returns the LSTM outputs.
Args:
x (~chainer.Variable): A new batch from the input sequence.
Returns:
~chainer.Variable: Outputs of updated LSTM units.
"""
if self.upward.has_uninitialized_params:
with cuda.get_device_from_id(self._device_id):
in_size = x.size // x.shape[0]
self.upward._initialize_params(in_size)
self._initialize_params()
batch = x.shape[0]
lstm_in = self.upward(x)
h_rest = None
if self.h is not None:
h_size = self.h.shape[0]
if batch == 0:
h_rest = self.h
elif h_size < batch:
msg = ('The batch size of x must be equal to or less than'
'the size of the previous state h.')
raise TypeError(msg)
elif h_size > batch:
h_update, h_rest = split_axis.split_axis(
self.h, [batch], axis=0)
lstm_in += self.lateral(h_update)
else:
lstm_in += self.lateral(self.h)
if self.c is None:
xp = self.xp
with cuda.get_device_from_id(self._device_id):
self.c = variable.Variable(
xp.zeros((batch, self.state_size), dtype=x.dtype),
volatile='auto')
self.c, y = lstm.lstm(self.c, lstm_in)
if h_rest is None:
self.h = y
elif len(y.data) == 0:
self.h = h_rest
else:
self.h = concat.concat([y, h_rest], axis=0)
return y
```