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An implementation of multiplicative LSTM in TensorFlow
# 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 <>.
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# Notice:
# This file is tested on TensorFlow v0.12.0 only.
import numpy as np
import tensorflow as tf
from tensorflow.python.ops.rnn_cell import RNNCell
# Thanks to '' of Project RNN Enhancement:
def orthogonal_initializer(scale=1.0):
def _initializer(shape, dtype=tf.float32):
flat_shape = (shape[0],[1:]))
a = np.random.normal(0.0, 1.0, flat_shape)
u, _, v = np.linalg.svd(a, full_matrices=False)
q = u if u.shape == flat_shape else v
q = q.reshape(shape)
return tf.constant(scale * q[:shape[0], :shape[1]], dtype=tf.float32)
return _initializer
class MultiplicativeLSTMCell(RNNCell):
"""Multiplicative LSTM.
Ben Krause, Liang Lu, Iain Murray, and Steve Renals,
"Multiplicative LSTM for sequence modelling, "
in Workshop Track of ICLA 2017,
def __init__(self, num_units,
"""Initialize the parameters for an LSTM cell.
num_units: int, The number of units in the LSTM cell.
use_peepholes: bool, set True to enable diagonal/peephole
cell_clip: (optional) A float value, if provided the cell state
is clipped by this value prior to the cell output activation.
initializer: (optional) The initializer to use for the weight
num_proj: (optional) int, The output dimensionality for
the projection matrices. If None, no projection is performed.
forget_bias: Biases of the forget gate are initialized by default
to 1 in order to reduce the scale of forgetting at the beginning of
the training.
activation: Activation function of the inner states.
self.num_units = num_units
self.use_peepholes = use_peepholes
self.cell_clip = cell_clip
self.num_proj = num_proj
self.proj_clip = proj_clip
self.initializer = initializer
self.forget_bias = forget_bias
self.state_is_tuple = state_is_tuple
self.activation = activation
if num_proj:
self._state_size = (
tf.nn.rnn_cell.LSTMStateTuple(num_units, num_proj)
if state_is_tuple else num_units + num_proj)
self._output_size = num_proj
self._state_size = (
tf.nn.rnn_cell.LSTMStateTuple(num_units, num_units)
if state_is_tuple else 2 * num_units)
self._output_size = num_units
def state_size(self):
return self._state_size
def output_size(self):
return self._output_size
def __call__(self, inputs, state, scope=None):
num_proj = self.num_units if self.num_proj is None else self.num_proj
if self.state_is_tuple:
(c_prev, h_prev) = state
c_prev = tf.slice(state, [0, 0], [-1, self.num_units])
h_prev = tf.slice(state, [0, self.num_units], [-1, num_proj])
dtype = inputs.dtype
input_size = inputs.get_shape().with_rank(2)[1]
with tf.variable_scope(scope or type(self).__name__):
if input_size.value is None:
raise ValueError(
"Could not infer input size from inputs.get_shape()[-1]")
with tf.variable_scope("Multipli_Weight"):
concat = _linear([inputs, h_prev], 2 * self.num_units, True)
Wx, Wh = tf.split(1, 2, concat)
m = Wx * Wh # equation (18)
with tf.variable_scope("LSTM_Weight"):
lstm_matrix = _linear([inputs, m], 4 * self.num_units, True)
i, j, f, o = tf.split(1, 4, lstm_matrix)
# Diagonal connections
if self.use_peepholes:
w_f_diag = tf.get_variable(
"W_F_diag", shape=[self.num_units], dtype=dtype)
w_i_diag = tf.get_variable(
"W_I_diag", shape=[self.num_units], dtype=dtype)
w_o_diag = tf.get_variable(
"W_O_diag", shape=[self.num_units], dtype=dtype)
if self.use_peepholes:
c = c_prev * tf.sigmoid(f + self.forget_bias +
w_f_diag * c_prev) + \
tf.sigmoid(i + w_i_diag * c_prev) * j
c = c_prev * tf.sigmoid(f + self.forget_bias) + \
tf.sigmoid(i) * j
if self.cell_clip is not None:
c = tf.clip_by_value(c, -self.cell_clip, self.cell_clip)
if self.use_peepholes:
h = tf.sigmoid(o + w_o_diag * c) * \
self.activation(c * (o + w_o_diag * c))
h = self.activation(c * o)
if self.num_proj is not None:
w_proj = tf.get_variable(
"W_P", [self.num_units, num_proj], dtype=dtype)
h = tf.matmul(h, w_proj)
if self.proj_clip is not None:
h = tf.clip_by_value(h, -self.proj_clip, self.proj_clip)
new_state = (tf.nn.rnn_cell.LSTMStateTuple(c, h)
if self.state_is_tuple else tf.concat(1, [c, h]))
return h, new_state
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: 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".
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.
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))
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)
res = tf.matmul(tf.concat(1, args), matrix)
if not bias:
return res
bias_term = tf.get_variable(
"Bias", [output_size],
return res + bias_term

If I may ask, did you test for performance? Did this implementation show the faster convergence reported in the paper?

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