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# to you under the Apache License, Version 2.0 (the
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#
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#
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# KIND, either express or implied. See the License for the
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# under the License.
# coding: utf-8
# pylint: disable=no-member, invalid-name, protected-access, no-self-use
# pylint: disable=too-many-branches, too-many-arguments, no-self-use
# pylint: disable=too-many-lines, arguments-differ
"""Definition of various recurrent neural network cells."""
__all__ = ['RecurrentCell', 'HybridRecurrentCell',
'RNNCell', 'LSTMCell', 'GRUCell',
'SequentialRNNCell', 'DropoutCell',
'ModifierCell', 'ZoneoutCell', 'ResidualCell',
'BidirectionalCell']
from ... import symbol, ndarray
from ...base import string_types, numeric_types, _as_list
from ..block import Block, HybridBlock
from ..utils import _indent
from .. import tensor_types
from ..nn import LeakyReLU
def _cells_state_info(cells, batch_size):
return sum([c.state_info(batch_size) for c in cells], [])
def _cells_begin_state(cells, **kwargs):
return sum([c.begin_state(**kwargs) for c in cells], [])
def _get_begin_state(cell, F, begin_state, inputs, batch_size):
if begin_state is None:
if F is ndarray:
ctx = inputs.context if isinstance(inputs, tensor_types) else inputs[0].context
with ctx:
begin_state = cell.begin_state(func=F.zeros, batch_size=batch_size)
else:
begin_state = cell.begin_state(func=F.zeros, batch_size=batch_size)
return begin_state
def _format_sequence(length, inputs, layout, merge, in_layout=None):
assert inputs is not None, \
"unroll(inputs=None) has been deprecated. " \
"Please create input variables outside unroll."
axis = layout.find('T')
batch_axis = layout.find('N')
batch_size = 0
in_axis = in_layout.find('T') if in_layout is not None else axis
if isinstance(inputs, symbol.Symbol):
F = symbol
if merge is False:
assert len(inputs.list_outputs()) == 1, \
"unroll doesn't allow grouped symbol as input. Please convert " \
"to list with list(inputs) first or let unroll handle splitting."
inputs = list(symbol.split(inputs, axis=in_axis, num_outputs=length,
squeeze_axis=1))
elif isinstance(inputs, ndarray.NDArray):
F = ndarray
batch_size = inputs.shape[batch_axis]
if merge is False:
assert length is None or length == inputs.shape[in_axis]
inputs = _as_list(ndarray.split(inputs, axis=in_axis,
num_outputs=inputs.shape[in_axis],
squeeze_axis=1))
else:
assert length is None or len(inputs) == length
if isinstance(inputs[0], symbol.Symbol):
F = symbol
else:
F = ndarray
batch_size = inputs[0].shape[batch_axis]
if merge is True:
inputs = [F.expand_dims(i, axis=axis) for i in inputs]
inputs = F.concat(*inputs, dim=axis)
in_axis = axis
if isinstance(inputs, tensor_types) and axis != in_axis:
inputs = F.swapaxes(inputs, dim1=axis, dim2=in_axis)
return inputs, axis, F, batch_size
[docs]class RecurrentCell(Block):
"""Abstract base class for RNN cells
Parameters
----------
prefix : str, optional
Prefix for names of `Block`s
(this prefix is also used for names of weights if `params` is `None`
i.e. if `params` are being created and not reused)
params : Parameter or None, optional
Container for weight sharing between cells.
A new Parameter container is created if `params` is `None`.
"""
def __init__(self, prefix=None, params=None):
super(RecurrentCell, self).__init__(prefix=prefix, params=params)
self._modified = False
self.reset()
[docs] def reset(self):
"""Reset before re-using the cell for another graph."""
self._init_counter = -1
self._counter = -1
for cell in self._children:
cell.reset()
[docs] def state_info(self, batch_size=0):
"""shape and layout information of states"""
raise NotImplementedError()
[docs] def begin_state(self, batch_size=0, func=ndarray.zeros, **kwargs):
"""Initial state for this cell.
Parameters
----------
func : callable, default symbol.zeros
Function for creating initial state.
For Symbol API, func can be `symbol.zeros`, `symbol.uniform`,
`symbol.var etc`. Use `symbol.var` if you want to directly
feed input as states.
For NDArray API, func can be `ndarray.zeros`, `ndarray.ones`, etc.
batch_size: int, default 0
Only required for NDArray API. Size of the batch ('N' in layout)
dimension of input.
**kwargs :
Additional keyword arguments passed to func. For example
`mean`, `std`, `dtype`, etc.
Returns
-------
states : nested list of Symbol
Starting states for the first RNN step.
"""
assert not self._modified, \
"After applying modifier cells (e.g. ZoneoutCell) the base " \
"cell cannot be called directly. Call the modifier cell instead."
states = []
for info in self.state_info(batch_size):
self._init_counter += 1
if info is not None:
info.update(kwargs)
else:
info = kwargs
state = func(name='%sbegin_state_%d'%(self._prefix, self._init_counter),
**info)
states.append(state)
return states
[docs] def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None):
"""Unrolls an RNN cell across time steps.
Parameters
----------
length : int
Number of steps to unroll.
inputs : Symbol, list of Symbol, or None
If `inputs` is a single Symbol (usually the output
of Embedding symbol), it should have shape
(batch_size, length, ...) if `layout` is 'NTC',
or (length, batch_size, ...) if `layout` is 'TNC'.
If `inputs` is a list of symbols (usually output of
previous unroll), they should all have shape
(batch_size, ...).
begin_state : nested list of Symbol, optional
Input states created by `begin_state()`
or output state of another cell.
Created from `begin_state()` if `None`.
layout : str, optional
`layout` of input symbol. Only used if inputs
is a single Symbol.
merge_outputs : bool, optional
If `False`, returns outputs as a list of Symbols.
If `True`, concatenates output across time steps
and returns a single symbol with shape
(batch_size, length, ...) if layout is 'NTC',
or (length, batch_size, ...) if layout is 'TNC'.
If `None`, output whatever is faster.
Returns
-------
outputs : list of Symbol or Symbol
Symbol (if `merge_outputs` is True) or list of Symbols
(if `merge_outputs` is False) corresponding to the output from
the RNN from this unrolling.
states : list of Symbol
The new state of this RNN after this unrolling.
The type of this symbol is same as the output of `begin_state()`.
"""
self.reset()
inputs, _, F, batch_size = _format_sequence(length, inputs, layout, False)
begin_state = _get_begin_state(self, F, begin_state, inputs, batch_size)
states = begin_state
outputs = []
for i in range(length):
output, states = self(inputs[i], states)
outputs.append(output)
outputs, _, _, _ = _format_sequence(length, outputs, layout, merge_outputs)
return outputs, states
#pylint: disable=no-self-use
def _get_activation(self, F, inputs, activation, **kwargs):
"""Get activation function. Convert if is string"""
if isinstance(activation, string_types):
return F.Activation(inputs, act_type=activation, **kwargs)
elif isinstance(activation, LeakyReLU):
return F.LeakyReLU(inputs, act_type='leaky', slope=activation._alpha, **kwargs)
else:
return activation(inputs, **kwargs)
[docs] def forward(self, inputs, states):
"""Unrolls the recurrent cell for one time step.
Parameters
----------
inputs : sym.Variable
Input symbol, 2D, of shape (batch_size * num_units).
states : list of sym.Variable
RNN state from previous step or the output of begin_state().
Returns
-------
output : Symbol
Symbol corresponding to the output from the RNN when unrolling
for a single time step.
states : list of Symbol
The new state of this RNN after this unrolling.
The type of this symbol is same as the output of `begin_state()`.
This can be used as an input state to the next time step
of this RNN.
See Also
--------
begin_state: This function can provide the states for the first time step.
unroll: This function unrolls an RNN for a given number of (>=1) time steps.
"""
# pylint: disable= arguments-differ
self._counter += 1
return super(RecurrentCell, self).forward(inputs, states)
[docs]class HybridRecurrentCell(RecurrentCell, HybridBlock):
"""HybridRecurrentCell supports hybridize."""
def __init__(self, prefix=None, params=None):
super(HybridRecurrentCell, self).__init__(prefix=prefix, params=params)
def hybrid_forward(self, F, x, *args, **kwargs):
raise NotImplementedError
[docs]class RNNCell(HybridRecurrentCell):
r"""Elman RNN recurrent neural network cell.
Each call computes the following function:
.. math::
h_t = \tanh(w_{ih} * x_t + b_{ih} + w_{hh} * h_{(t-1)} + b_{hh})
where :math:`h_t` is the hidden state at time `t`, and :math:`x_t` is the hidden
state of the previous layer at time `t` or :math:`input_t` for the first layer.
If nonlinearity='relu', then `ReLU` is used instead of `tanh`.
Parameters
----------
hidden_size : int
Number of units in output symbol
activation : str or Symbol, default 'tanh'
Type of activation function.
i2h_weight_initializer : str or Initializer
Initializer for the input weights matrix, used for the linear
transformation of the inputs.
h2h_weight_initializer : str or Initializer
Initializer for the recurrent weights matrix, used for the linear
transformation of the recurrent state.
i2h_bias_initializer : str or Initializer
Initializer for the bias vector.
h2h_bias_initializer : str or Initializer
Initializer for the bias vector.
prefix : str, default 'rnn_'
Prefix for name of `Block`s
(and name of weight if params is `None`).
params : Parameter or None
Container for weight sharing between cells.
Created if `None`.
Inputs:
- **data**: input tensor with shape `(batch_size, input_size)`.
- **states**: a list of one initial recurrent state tensor with shape
`(batch_size, num_hidden)`.
Outputs:
- **out**: output tensor with shape `(batch_size, num_hidden)`.
- **next_states**: a list of one output recurrent state tensor with the
same shape as `states`.
"""
def __init__(self, hidden_size, activation='tanh',
i2h_weight_initializer=None, h2h_weight_initializer=None,
i2h_bias_initializer='zeros', h2h_bias_initializer='zeros',
input_size=0, prefix=None, params=None):
super(RNNCell, self).__init__(prefix=prefix, params=params)
self._hidden_size = hidden_size
self._activation = activation
self._input_size = input_size
self.i2h_weight = self.params.get('i2h_weight', shape=(hidden_size, input_size),
init=i2h_weight_initializer,
allow_deferred_init=True)
self.h2h_weight = self.params.get('h2h_weight', shape=(hidden_size, hidden_size),
init=h2h_weight_initializer,
allow_deferred_init=True)
self.i2h_bias = self.params.get('i2h_bias', shape=(hidden_size,),
init=i2h_bias_initializer,
allow_deferred_init=True)
self.h2h_bias = self.params.get('h2h_bias', shape=(hidden_size,),
init=h2h_bias_initializer,
allow_deferred_init=True)
def state_info(self, batch_size=0):
return [{'shape': (batch_size, self._hidden_size), '__layout__': 'NC'}]
def _alias(self):
return 'rnn'
def __repr__(self):
s = '{name}({mapping}'
if hasattr(self, '_activation'):
s += ', {_activation}'
s += ')'
shape = self.i2h_weight.shape
mapping = '{0} -> {1}'.format(shape[1] if shape[1] else None, shape[0])
return s.format(name=self.__class__.__name__,
mapping=mapping,
**self.__dict__)
def hybrid_forward(self, F, inputs, states, i2h_weight,
h2h_weight, i2h_bias, h2h_bias):
prefix = 't%d_'%self._counter
i2h = F.FullyConnected(data=inputs, weight=i2h_weight, bias=i2h_bias,
num_hidden=self._hidden_size,
name=prefix+'i2h')
h2h = F.FullyConnected(data=states[0], weight=h2h_weight, bias=h2h_bias,
num_hidden=self._hidden_size,
name=prefix+'h2h')
output = self._get_activation(F, i2h + h2h, self._activation,
name=prefix+'out')
return output, [output]
[docs]class LSTMCell(HybridRecurrentCell):
r"""Long-Short Term Memory (LSTM) network cell.
Each call computes the following function:
.. math::
\begin{array}{ll}
i_t = sigmoid(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\
f_t = sigmoid(W_{if} x_t + b_{if} + W_{hf} h_{(t-1)} + b_{hf}) \\
g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hc} h_{(t-1)} + b_{hg}) \\
o_t = sigmoid(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\
c_t = f_t * c_{(t-1)} + i_t * g_t \\
h_t = o_t * \tanh(c_t)
\end{array}
where :math:`h_t` is the hidden state at time `t`, :math:`c_t` is the
cell state at time `t`, :math:`x_t` is the hidden state of the previous
layer at time `t` or :math:`input_t` for the first layer, and :math:`i_t`,
:math:`f_t`, :math:`g_t`, :math:`o_t` are the input, forget, cell, and
out gates, respectively.
Parameters
----------
hidden_size : int
Number of units in output symbol.
i2h_weight_initializer : str or Initializer
Initializer for the input weights matrix, used for the linear
transformation of the inputs.
h2h_weight_initializer : str or Initializer
Initializer for the recurrent weights matrix, used for the linear
transformation of the recurrent state.
i2h_bias_initializer : str or Initializer, default 'lstmbias'
Initializer for the bias vector. By default, bias for the forget
gate is initialized to 1 while all other biases are initialized
to zero.
h2h_bias_initializer : str or Initializer
Initializer for the bias vector.
prefix : str, default 'lstm_'
Prefix for name of `Block`s
(and name of weight if params is `None`).
params : Parameter or None
Container for weight sharing between cells.
Created if `None`.
Inputs:
- **data**: input tensor with shape `(batch_size, input_size)`.
- **states**: a list of two initial recurrent state tensors. Each has shape
`(batch_size, num_hidden)`.
Outputs:
- **out**: output tensor with shape `(batch_size, num_hidden)`.
- **next_states**: a list of two output recurrent state tensors. Each has
the same shape as `states`.
"""
def __init__(self, hidden_size,
i2h_weight_initializer=None, h2h_weight_initializer=None,
i2h_bias_initializer='zeros', h2h_bias_initializer='zeros',
input_size=0, prefix=None, params=None):
super(LSTMCell, self).__init__(prefix=prefix, params=params)
self._hidden_size = hidden_size
self._input_size = input_size
self.i2h_weight = self.params.get('i2h_weight', shape=(4*hidden_size, input_size),
init=i2h_weight_initializer,
allow_deferred_init=True)
self.h2h_weight = self.params.get('h2h_weight', shape=(4*hidden_size, hidden_size),
init=h2h_weight_initializer,
allow_deferred_init=True)
self.i2h_bias = self.params.get('i2h_bias', shape=(4*hidden_size,),
init=i2h_bias_initializer,
allow_deferred_init=True)
self.h2h_bias = self.params.get('h2h_bias', shape=(4*hidden_size,),
init=h2h_bias_initializer,
allow_deferred_init=True)
def state_info(self, batch_size=0):
return [{'shape': (batch_size, self._hidden_size), '__layout__': 'NC'},
{'shape': (batch_size, self._hidden_size), '__layout__': 'NC'}]
def _alias(self):
return 'lstm'
def __repr__(self):
s = '{name}({mapping})'
shape = self.i2h_weight.shape
mapping = '{0} -> {1}'.format(shape[1] if shape[1] else None, shape[0])
return s.format(name=self.__class__.__name__,
mapping=mapping,
**self.__dict__)
def hybrid_forward(self, F, inputs, states, i2h_weight,
h2h_weight, i2h_bias, h2h_bias):
prefix = 't%d_'%self._counter
i2h = F.FullyConnected(data=inputs, weight=i2h_weight, bias=i2h_bias,
num_hidden=self._hidden_size*4, name=prefix+'i2h')
h2h = F.FullyConnected(data=states[0], weight=h2h_weight, bias=h2h_bias,
num_hidden=self._hidden_size*4, name=prefix+'h2h')
gates = i2h + h2h
slice_gates = F.SliceChannel(gates, num_outputs=4, name=prefix+'slice')
in_gate = F.Activation(slice_gates[0], act_type="sigmoid", name=prefix+'i')
forget_gate = F.Activation(slice_gates[1], act_type="sigmoid", name=prefix+'f')
in_transform = F.Activation(slice_gates[2], act_type="tanh", name=prefix+'c')
out_gate = F.Activation(slice_gates[3], act_type="sigmoid", name=prefix+'o')
next_c = F._internal._plus(forget_gate * states[1], in_gate * in_transform,
name=prefix+'state')
next_h = F._internal._mul(out_gate, F.Activation(next_c, act_type="tanh"),
name=prefix+'out')
return next_h, [next_h, next_c]
[docs]class GRUCell(HybridRecurrentCell):
r"""Gated Rectified Unit (GRU) network cell.
Note: this is an implementation of the cuDNN version of GRUs
(slight modification compared to Cho et al. 2014).
Each call computes the following function:
.. math::
\begin{array}{ll}
r_t = sigmoid(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
i_t = sigmoid(W_{ii} x_t + b_{ii} + W_hi h_{(t-1)} + b_{hi}) \\
n_t = \tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)}+ b_{hn})) \\
h_t = (1 - i_t) * n_t + i_t * h_{(t-1)} \\
\end{array}
where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the hidden
state of the previous layer at time `t` or :math:`input_t` for the first layer,
and :math:`r_t`, :math:`i_t`, :math:`n_t` are the reset, input, and new gates, respectively.
Parameters
----------
hidden_size : int
Number of units in output symbol.
i2h_weight_initializer : str or Initializer
Initializer for the input weights matrix, used for the linear
transformation of the inputs.
h2h_weight_initializer : str or Initializer
Initializer for the recurrent weights matrix, used for the linear
transformation of the recurrent state.
i2h_bias_initializer : str or Initializer
Initializer for the bias vector.
h2h_bias_initializer : str or Initializer
Initializer for the bias vector.
prefix : str, default 'gru_'
prefix for name of `Block`s
(and name of weight if params is `None`).
params : Parameter or None
Container for weight sharing between cells.
Created if `None`.
Inputs:
- **data**: input tensor with shape `(batch_size, input_size)`.
- **states**: a list of one initial recurrent state tensor with shape
`(batch_size, num_hidden)`.
Outputs:
- **out**: output tensor with shape `(batch_size, num_hidden)`.
- **next_states**: a list of one output recurrent state tensor with the
same shape as `states`.
"""
def __init__(self, hidden_size,
i2h_weight_initializer=None, h2h_weight_initializer=None,
i2h_bias_initializer='zeros', h2h_bias_initializer='zeros',
input_size=0, prefix=None, params=None):
super(GRUCell, self).__init__(prefix=prefix, params=params)
self._hidden_size = hidden_size
self._input_size = input_size
self.i2h_weight = self.params.get('i2h_weight', shape=(3*hidden_size, input_size),
init=i2h_weight_initializer,
allow_deferred_init=True)
self.h2h_weight = self.params.get('h2h_weight', shape=(3*hidden_size, hidden_size),
init=h2h_weight_initializer,
allow_deferred_init=True)
self.i2h_bias = self.params.get('i2h_bias', shape=(3*hidden_size,),
init=i2h_bias_initializer,
allow_deferred_init=True)
self.h2h_bias = self.params.get('h2h_bias', shape=(3*hidden_size,),
init=h2h_bias_initializer,
allow_deferred_init=True)
def state_info(self, batch_size=0):
return [{'shape': (batch_size, self._hidden_size), '__layout__': 'NC'}]
def _alias(self):
return 'gru'
def __repr__(self):
s = '{name}({mapping})'
shape = self.i2h_weight.shape
mapping = '{0} -> {1}'.format(shape[1] if shape[1] else None, shape[0])
return s.format(name=self.__class__.__name__,
mapping=mapping,
**self.__dict__)
def hybrid_forward(self, F, inputs, states, i2h_weight,
h2h_weight, i2h_bias, h2h_bias):
# pylint: disable=too-many-locals
prefix = 't%d_'%self._counter
prev_state_h = states[0]
i2h = F.FullyConnected(data=inputs,
weight=i2h_weight,
bias=i2h_bias,
num_hidden=self._hidden_size * 3,
name=prefix+'i2h')
h2h = F.FullyConnected(data=prev_state_h,
weight=h2h_weight,
bias=h2h_bias,
num_hidden=self._hidden_size * 3,
name=prefix+'h2h')
i2h_r, i2h_z, i2h = F.SliceChannel(i2h, num_outputs=3,
name=prefix+'i2h_slice')
h2h_r, h2h_z, h2h = F.SliceChannel(h2h, num_outputs=3,
name=prefix+'h2h_slice')
reset_gate = F.Activation(i2h_r + h2h_r, act_type="sigmoid",
name=prefix+'r_act')
update_gate = F.Activation(i2h_z + h2h_z, act_type="sigmoid",
name=prefix+'z_act')
next_h_tmp = F.Activation(i2h + reset_gate * h2h, act_type="tanh",
name=prefix+'h_act')
next_h = F._internal._plus((1. - update_gate) * next_h_tmp, update_gate * prev_state_h,
name=prefix+'out')
return next_h, [next_h]
[docs]class SequentialRNNCell(RecurrentCell):
"""Sequentially stacking multiple RNN cells."""
def __init__(self, prefix=None, params=None):
super(SequentialRNNCell, self).__init__(prefix=prefix, params=params)
def __repr__(self):
s = '{name}(\n{modstr}\n)'
return s.format(name=self.__class__.__name__,
modstr='\n'.join(['({i}): {m}'.format(i=i, m=_indent(m.__repr__(), 2))
for i, m in enumerate(self._children)]))
[docs] def add(self, cell):
"""Appends a cell into the stack.
Parameters
----------
cell : RecurrentCell
The cell to add.
"""
self.register_child(cell)
def state_info(self, batch_size=0):
return _cells_state_info(self._children, batch_size)
def begin_state(self, **kwargs):
assert not self._modified, \
"After applying modifier cells (e.g. ZoneoutCell) the base " \
"cell cannot be called directly. Call the modifier cell instead."
return _cells_begin_state(self._children, **kwargs)
def __call__(self, inputs, states):
self._counter += 1
next_states = []
p = 0
for cell in self._children:
assert not isinstance(cell, BidirectionalCell)
n = len(cell.state_info())
state = states[p:p+n]
p += n
inputs, state = cell(inputs, state)
next_states.append(state)
return inputs, sum(next_states, [])
def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None):
self.reset()
inputs, _, F, batch_size = _format_sequence(length, inputs, layout, None)
num_cells = len(self._children)
begin_state = _get_begin_state(self, F, begin_state, inputs, batch_size)
p = 0
next_states = []
for i, cell in enumerate(self._children):
n = len(cell.state_info())
states = begin_state[p:p+n]
p += n
inputs, states = cell.unroll(length, inputs=inputs, begin_state=states, layout=layout,
merge_outputs=None if i < num_cells-1 else merge_outputs)
next_states.extend(states)
return inputs, next_states
def __getitem__(self, i):
return self._children[i]
def __len__(self):
return len(self._children)
def hybrid_forward(self, *args, **kwargs):
raise NotImplementedError
[docs]class DropoutCell(HybridRecurrentCell):
"""Applies dropout on input.
Parameters
----------
rate : float
Percentage of elements to drop out, which
is 1 - percentage to retain.
Inputs:
- **data**: input tensor with shape `(batch_size, size)`.
- **states**: a list of recurrent state tensors.
Outputs:
- **out**: output tensor with shape `(batch_size, size)`.
- **next_states**: returns input `states` directly.
"""
def __init__(self, rate, prefix=None, params=None):
super(DropoutCell, self).__init__(prefix, params)
assert isinstance(rate, numeric_types), "rate must be a number"
self.rate = rate
def __repr__(self):
s = '{name}(rate = {rate})'
return s.format(name=self.__class__.__name__,
**self.__dict__)
def state_info(self, batch_size=0):
return []
def _alias(self):
return 'dropout'
def hybrid_forward(self, F, inputs, states):
if self.rate > 0:
inputs = F.Dropout(data=inputs, p=self.rate, name='t%d_fwd'%self._counter)
return inputs, states
def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None):
self.reset()
inputs, _, F, _ = _format_sequence(length, inputs, layout, merge_outputs)
if isinstance(inputs, tensor_types):
return self.hybrid_forward(F, inputs, begin_state if begin_state else [])
else:
return super(DropoutCell, self).unroll(
length, inputs, begin_state=begin_state, layout=layout,
merge_outputs=merge_outputs)
[docs]class ModifierCell(HybridRecurrentCell):
"""Base class for modifier cells. A modifier
cell takes a base cell, apply modifications
on it (e.g. Zoneout), and returns a new cell.
After applying modifiers the base cell should
no longer be called directly. The modifier cell
should be used instead.
"""
def __init__(self, base_cell):
assert not base_cell._modified, \
"Cell %s is already modified. One cell cannot be modified twice"%base_cell.name
base_cell._modified = True
super(ModifierCell, self).__init__(prefix=base_cell.prefix+self._alias(),
params=None)
self.base_cell = base_cell
@property
def params(self):
return self.base_cell.params
def state_info(self, batch_size=0):
return self.base_cell.state_info(batch_size)
def begin_state(self, func=symbol.zeros, **kwargs):
assert not self._modified, \
"After applying modifier cells (e.g. DropoutCell) the base " \
"cell cannot be called directly. Call the modifier cell instead."
self.base_cell._modified = False
begin = self.base_cell.begin_state(func=func, **kwargs)
self.base_cell._modified = True
return begin
def hybrid_forward(self, F, inputs, states):
raise NotImplementedError
def __repr__(self):
s = '{name}({base_cell})'
return s.format(name=self.__class__.__name__,
**self.__dict__)
[docs]class ZoneoutCell(ModifierCell):
"""Applies Zoneout on base cell."""
def __init__(self, base_cell, zoneout_outputs=0., zoneout_states=0.):
assert not isinstance(base_cell, BidirectionalCell), \
"BidirectionalCell doesn't support zoneout since it doesn't support step. " \
"Please add ZoneoutCell to the cells underneath instead."
assert not isinstance(base_cell, SequentialRNNCell) or not base_cell._bidirectional, \
"Bidirectional SequentialRNNCell doesn't support zoneout. " \
"Please add ZoneoutCell to the cells underneath instead."
super(ZoneoutCell, self).__init__(base_cell)
self.zoneout_outputs = zoneout_outputs
self.zoneout_states = zoneout_states
self._prev_output = None
def __repr__(self):
s = '{name}(p_out={zoneout_outputs}, p_state={zoneout_states}, {base_cell})'
return s.format(name=self.__class__.__name__,
**self.__dict__)
def _alias(self):
return 'zoneout'
def reset(self):
super(ZoneoutCell, self).reset()
self._prev_output = None
def hybrid_forward(self, F, inputs, states):
cell, p_outputs, p_states = self.base_cell, self.zoneout_outputs, self.zoneout_states
next_output, next_states = cell(inputs, states)
mask = (lambda p, like: F.Dropout(F.ones_like(like), p=p))
prev_output = self._prev_output
if prev_output is None:
prev_output = F.zeros_like(next_output)
output = (F.where(mask(p_outputs, next_output), next_output, prev_output)
if p_outputs != 0. else next_output)
states = ([F.where(mask(p_states, new_s), new_s, old_s) for new_s, old_s in
zip(next_states, states)] if p_states != 0. else next_states)
self._prev_output = output
return output, states
[docs]class ResidualCell(ModifierCell):
"""
Adds residual connection as described in Wu et al, 2016
(https://arxiv.org/abs/1609.08144).
Output of the cell is output of the base cell plus input.
"""
def __init__(self, base_cell):
super(ResidualCell, self).__init__(base_cell)
def hybrid_forward(self, F, inputs, states):
output, states = self.base_cell(inputs, states)
output = F.elemwise_add(output, inputs, name='t%d_fwd'%self._counter)
return output, states
def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None):
self.reset()
self.base_cell._modified = False
outputs, states = self.base_cell.unroll(length, inputs=inputs, begin_state=begin_state,
layout=layout, merge_outputs=merge_outputs)
self.base_cell._modified = True
merge_outputs = isinstance(outputs, tensor_types) if merge_outputs is None else \
merge_outputs
inputs, _, F, _ = _format_sequence(length, inputs, layout, merge_outputs)
if merge_outputs:
outputs = F.elemwise_add(outputs, inputs)
else:
outputs = [F.elemwise_add(i, j) for i, j in zip(outputs, inputs)]
return outputs, states
[docs]class BidirectionalCell(HybridRecurrentCell):
"""Bidirectional RNN cell.
Parameters
----------
l_cell : RecurrentCell
Cell for forward unrolling
r_cell : RecurrentCell
Cell for backward unrolling
"""
def __init__(self, l_cell, r_cell, output_prefix='bi_'):
super(BidirectionalCell, self).__init__(prefix='', params=None)
self.register_child(l_cell)
self.register_child(r_cell)
self._output_prefix = output_prefix
def __call__(self, inputs, states):
raise NotImplementedError("Bidirectional cannot be stepped. Please use unroll")
def __repr__(self):
s = '{name}(forward={l_cell}, backward={r_cell})'
return s.format(name=self.__class__.__name__,
l_cell=self._children[0],
r_cell=self._children[1])
def state_info(self, batch_size=0):
return _cells_state_info(self._children, batch_size)
def begin_state(self, **kwargs):
assert not self._modified, \
"After applying modifier cells (e.g. DropoutCell) the base " \
"cell cannot be called directly. Call the modifier cell instead."
return _cells_begin_state(self._children, **kwargs)
def unroll(self, length, inputs, begin_state=None, layout='NTC', merge_outputs=None):
self.reset()
inputs, axis, F, batch_size = _format_sequence(length, inputs, layout, False)
begin_state = _get_begin_state(self, F, begin_state, inputs, batch_size)
states = begin_state
l_cell, r_cell = self._children
l_outputs, l_states = l_cell.unroll(length, inputs=inputs,
begin_state=states[:len(l_cell.state_info(batch_size))],
layout=layout, merge_outputs=merge_outputs)
r_outputs, r_states = r_cell.unroll(length,
inputs=list(reversed(inputs)),
begin_state=states[len(l_cell.state_info(batch_size)):],
layout=layout, merge_outputs=merge_outputs)
if merge_outputs is None:
merge_outputs = (isinstance(l_outputs, tensor_types)
and isinstance(r_outputs, tensor_types))
l_outputs, _, _, _ = _format_sequence(None, l_outputs, layout, merge_outputs)
r_outputs, _, _, _ = _format_sequence(None, r_outputs, layout, merge_outputs)
if merge_outputs:
r_outputs = F.reverse(r_outputs, axis=axis)
outputs = F.concat(l_outputs, r_outputs, dim=2, name='%sout'%self._output_prefix)
else:
outputs = [F.concat(l_o, r_o, dim=1, name='%st%d'%(self._output_prefix, i))
for i, (l_o, r_o) in enumerate(zip(l_outputs, reversed(r_outputs)))]
states = l_states + r_states
return outputs, states
