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# coding: utf-8
"""Definition of various recurrent neural network cells."""
__all__ = ['VariationalDropoutCell']
from ...rnn import BidirectionalCell, SequentialRNNCell, ModifierCell
from ...rnn.rnn_cell import _format_sequence, _get_begin_state
[docs]class VariationalDropoutCell(ModifierCell):
"""
Applies Variational Dropout on base cell.
(https://arxiv.org/pdf/1512.05287.pdf,
https://www.stat.berkeley.edu/~tsmoon/files/Conference/asru2015.pdf).
Variational dropout uses the same dropout mask across time-steps. It can be applied to RNN
inputs, outputs, and states. The masks for them are not shared.
The dropout mask is initialized when stepping forward for the first time and will remain
the same until .reset() is called. Thus, if using the cell and stepping manually without calling
.unroll(), the .reset() should be called after each sequence.
Parameters
----------
base_cell : RecurrentCell
The cell on which to perform variational dropout.
drop_inputs : float, default 0.
The dropout rate for inputs. Won't apply dropout if it equals 0.
drop_states : float, default 0.
The dropout rate for state inputs on the first state channel.
Won't apply dropout if it equals 0.
drop_outputs : float, default 0.
The dropout rate for outputs. Won't apply dropout if it equals 0.
"""
def __init__(self, base_cell, drop_inputs=0., drop_states=0., drop_outputs=0.):
assert not drop_states or not isinstance(base_cell, BidirectionalCell), \
"BidirectionalCell doesn't support variational state dropout. " \
"Please add VariationalDropoutCell to the cells underneath instead."
assert not drop_states \
or not isinstance(base_cell, SequentialRNNCell) or not base_cell._bidirectional, \
"Bidirectional SequentialRNNCell doesn't support variational state dropout. " \
"Please add VariationalDropoutCell to the cells underneath instead."
super(VariationalDropoutCell, self).__init__(base_cell)
self.drop_inputs = drop_inputs
self.drop_states = drop_states
self.drop_outputs = drop_outputs
self.drop_inputs_mask = None
self.drop_states_mask = None
self.drop_outputs_mask = None
def _alias(self):
return 'vardrop'
def reset(self):
super(VariationalDropoutCell, self).reset()
self.drop_inputs_mask = None
self.drop_states_mask = None
self.drop_outputs_mask = None
def _initialize_input_masks(self, F, inputs, states):
if self.drop_states and self.drop_states_mask is None:
self.drop_states_mask = F.Dropout(F.ones_like(states[0]),
p=self.drop_states)
if self.drop_inputs and self.drop_inputs_mask is None:
self.drop_inputs_mask = F.Dropout(F.ones_like(inputs),
p=self.drop_inputs)
def _initialize_output_mask(self, F, output):
if self.drop_outputs and self.drop_outputs_mask is None:
self.drop_outputs_mask = F.Dropout(F.ones_like(output),
p=self.drop_outputs)
def hybrid_forward(self, F, inputs, states):
cell = self.base_cell
self._initialize_input_masks(F, inputs, states)
if self.drop_states:
states = list(states)
# state dropout only needs to be applied on h, which is always the first state.
states[0] = states[0] * self.drop_states_mask
if self.drop_inputs:
inputs = inputs * self.drop_inputs_mask
next_output, next_states = cell(inputs, states)
self._initialize_output_mask(F, next_output)
if self.drop_outputs:
next_output = next_output * self.drop_outputs_mask
return next_output, next_states
def __repr__(self):
s = '{name}(p_out = {drop_outputs}, p_state = {drop_states})'
return s.format(name=self.__class__.__name__,
**self.__dict__)
[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()`.
"""
# Dropout on inputs and outputs can be performed on the whole sequence
# only when state dropout is not present.
if self.drop_states:
return super(VariationalDropoutCell, self).unroll(length, inputs, begin_state,
layout, merge_outputs)
self.reset()
inputs, axis, F, batch_size = _format_sequence(length, inputs, layout, True)
states = _get_begin_state(self, F, begin_state, inputs, batch_size)
if self.drop_inputs:
first_input = inputs.slice_axis(axis, 0, 1).split(1, axis=axis, squeeze_axis=True)
self._initialize_input_masks(F, first_input, states)
inputs = F.broadcast_mul(inputs, self.drop_inputs_mask.expand_dims(axis=axis))
outputs, states = self.base_cell.unroll(length, inputs, states, layout, merge_outputs=True)
if self.drop_outputs:
first_output = outputs.slice_axis(axis, 0, 1).split(1, axis=axis, squeeze_axis=True)
self._initialize_output_mask(F, first_output)
outputs = F.broadcast_mul(outputs, self.drop_outputs_mask.expand_dims(axis=axis))
outputs, _, _, _ = _format_sequence(length, outputs, layout, merge_outputs)
return outputs, states
