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An implementation of multiplicative LSTM in TensorFlow
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# 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/>. | |
# 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, | |
# 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. | |
# 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 'initializers_enhanced.py' of Project RNN Enhancement: | |
# https://github.com/nicolas-ivanov/Seq2Seq_Upgrade_TensorFlow/blob/master/rnn_enhancement/initializers_enhanced.py | |
def orthogonal_initializer(scale=1.0): | |
def _initializer(shape, dtype=tf.float32): | |
flat_shape = (shape[0], np.prod(shape[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, | |
https://openreview.net/forum?id=SJCS5rXFl¬eId=SJCS5rXFl | |
""" | |
def __init__(self, num_units, | |
use_peepholes=False, | |
cell_clip=None, | |
initializer=orthogonal_initializer(), | |
num_proj=None, | |
proj_clip=None, | |
forget_bias=1.0, | |
state_is_tuple=True, | |
activation=tf.tanh): | |
"""Initialize the parameters for an LSTM cell. | |
Args: | |
num_units: int, The number of units in the LSTM cell. | |
use_peepholes: bool, set True to enable diagonal/peephole | |
connections. | |
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 | |
matrices. | |
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 | |
else: | |
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 | |
@property | |
def state_size(self): | |
return self._state_size | |
@property | |
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 | |
else: | |
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 | |
else: | |
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)) | |
else: | |
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: | |
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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If I may ask, did you test for performance? Did this implementation show the faster convergence reported in the paper?