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A TensorFlow implementation of Simple Recurrent Unit (SRU).
# -*- coding: utf-8 -*-
# Copyright (C) 2017 by Akira TAMAMORI
# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# this program. If not, see <http://www.gnu.org/licenses/>.
import tensorflow as tf
from tensorflow.python.ops.rnn_cell import RNNCell
class SRUCell(RNNCell):
"""Simple Recurrent Unit (SRU).
This implementation is based on:
Tao Lei and Yu Zhang,
"Training RNNs as Fast as CNNs,"
https://arxiv.org/abs/1709.02755
"""
def __init__(self, num_units, activation=None, reuse=None):
self._num_units = num_units
self._activation = activation or tf.tanh
@property
def output_size(self):
return self._num_units
@property
def state_size(self):
return self._num_units
def __call__(self, inputs, state, scope=None):
"""Run one step of SRU."""
with tf.variable_scope(scope or type(self).__name__): # "SRUCell"
with tf.variable_scope("Inputs"):
x = linear([inputs], self._num_units, False)
with tf.variable_scope("Gate"):
concat = tf.sigmoid(
linear([inputs], 2 * self._num_units, True))
if tf.__version__ == "0.12.1":
f, r = tf.split(1, 2, concat)
else:
f, r = tf.split(axis=1, num_or_size_splits=2, value=concat)
c = f * state + (1 - f) * x
# highway connection
h = r * self._activation(c) + (1 - r) * inputs
return h, c
def linear(args, output_size, bias, bias_start=0.0, scope=None):
"""Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
Args:
args: a 2D Tensor or a list of 2D, batch x n, Tensors.
output_size: int, second dimension of W[i].
bias: boolean, whether to add a bias term or not.
bias_start: starting value to initialize the bias; 0 by default.
scope: VariableScope for the created subgraph; defaults to "Linear".
Returns:
A 2D Tensor with shape [batch x output_size] equal to
sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
Raises:
ValueError: if some of the arguments has unspecified or wrong shape.
"""
if args is None or (isinstance(args, (list, tuple)) and not args):
raise ValueError("`args` must be specified")
if not isinstance(args, (list, tuple)):
args = [args]
# Calculate the total size of arguments on dimension 1.
total_arg_size = 0
shapes = [a.get_shape().as_list() for a in args]
for shape in shapes:
if len(shape) != 2:
raise ValueError(
"Linear is expecting 2D arguments: %s" % str(shapes))
if not shape[1]:
raise ValueError(
"Linear expects shape[1] of arguments: %s" % str(shapes))
else:
total_arg_size += shape[1]
# Now the computation.
with tf.variable_scope(scope or "Linear"):
matrix = tf.get_variable("Matrix", [total_arg_size, output_size])
if len(args) == 1:
res = tf.matmul(args[0], matrix)
else:
res = tf.matmul(tf.concat(1, args), matrix)
if not bias:
return res
bias_term = tf.get_variable(
"Bias", [output_size],
initializer=tf.constant_initializer(bias_start))
return res + bias_term
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