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# Copyright 2017 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Variational Dropout Wrapper."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class VariationalDropoutWrapper(tf.contrib.rnn.RNNCell):
"""Add variational dropout to a RNN cell."""
def __init__(self, cell, batch_size, input_size, recurrent_keep_prob,
input_keep_prob):
self._cell = cell
self._recurrent_keep_prob = recurrent_keep_prob
self._input_keep_prob = input_keep_prob
def make_mask(keep_prob, units):
random_tensor = keep_prob
# 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
random_tensor += tf.random_uniform(tf.stack([batch_size, units]))
return tf.floor(random_tensor) / keep_prob
self._recurrent_mask = make_mask(recurrent_keep_prob,
self._cell.state_size[0])
self._input_mask = self._recurrent_mask
@property
def state_size(self):
return self._cell.state_size
@property
def output_size(self):
return self._cell.output_size
def __call__(self, inputs, state, scope=None):
dropped_inputs = inputs * self._input_mask
dropped_state = (state[0], state[1] * self._recurrent_mask)
new_h, new_state = self._cell(dropped_inputs, dropped_state, scope)
return new_h, new_state
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