1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
|
# Copyright 2016 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.
# ==============================================================================
"""LSTM layers for sequences."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.contrib.framework.python.ops import variables
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import rnn
from tensorflow.python.ops import rnn_cell
from tensorflow.python.ops import variable_scope
def _shape(tensor):
return tensor.get_shape().as_list()
def ndlstm_base_unrolled(inputs, noutput, scope=None, reverse=False):
"""Run an LSTM, either forward or backward.
This is a 1D LSTM implementation using unrolling and the TensorFlow
LSTM op.
Args:
inputs: input sequence (length, batch_size, ninput)
noutput: depth of output
scope: optional scope name
reverse: run LSTM in reverse
Returns:
Output sequence (length, batch_size, noutput)
"""
with variable_scope.variable_scope(scope, "SeqLstmUnrolled", [inputs]):
length, batch_size, _ = _shape(inputs)
lstm_cell = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False)
state = array_ops.zeros([batch_size, lstm_cell.state_size])
output_u = []
inputs_u = array_ops.unstack(inputs)
if reverse:
inputs_u = list(reversed(inputs_u))
for i in xrange(length):
if i > 0:
variable_scope.get_variable_scope().reuse_variables()
output, state = lstm_cell(inputs_u[i], state)
output_u += [output]
if reverse:
output_u = list(reversed(output_u))
outputs = array_ops.stack(output_u)
return outputs
def ndlstm_base_dynamic(inputs, noutput, scope=None, reverse=False):
"""Run an LSTM, either forward or backward.
This is a 1D LSTM implementation using dynamic_rnn and
the TensorFlow LSTM op.
Args:
inputs: input sequence (length, batch_size, ninput)
noutput: depth of output
scope: optional scope name
reverse: run LSTM in reverse
Returns:
Output sequence (length, batch_size, noutput)
"""
with variable_scope.variable_scope(scope, "SeqLstm", [inputs]):
# TODO(tmb) make batch size, sequence_length dynamic
# example: sequence_length = tf.shape(inputs)[0]
_, batch_size, _ = _shape(inputs)
lstm_cell = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False)
state = array_ops.zeros([batch_size, lstm_cell.state_size])
sequence_length = int(inputs.get_shape()[0])
sequence_lengths = math_ops.to_int64(
array_ops.fill([batch_size], sequence_length))
if reverse:
inputs = array_ops.reverse_v2(inputs, [0])
outputs, _ = rnn.dynamic_rnn(
lstm_cell, inputs, sequence_lengths, state, time_major=True)
if reverse:
outputs = array_ops.reverse_v2(outputs, [0])
return outputs
def ndlstm_base(inputs, noutput, scope=None, reverse=False, dynamic=True):
"""Implements a 1D LSTM, either forward or backward.
This is a base case for multidimensional LSTM implementations, which
tend to be used differently from sequence-to-sequence
implementations. For general 1D sequence to sequence
transformations, you may want to consider another implementation
from TF slim.
Args:
inputs: input sequence (length, batch_size, ninput)
noutput: depth of output
scope: optional scope name
reverse: run LSTM in reverse
dynamic: use dynamic_rnn
Returns:
Output sequence (length, batch_size, noutput)
"""
# TODO(tmb) maybe add option for other LSTM implementations, like
# slim.rnn.basic_lstm_cell
if dynamic:
return ndlstm_base_dynamic(inputs, noutput, scope=scope, reverse=reverse)
else:
return ndlstm_base_unrolled(inputs, noutput, scope=scope, reverse=reverse)
def sequence_to_final(inputs, noutput, scope=None, name=None, reverse=False):
"""Run an LSTM across all steps and returns only the final state.
Args:
inputs: (length, batch_size, depth) tensor
noutput: size of output vector
scope: optional scope name
name: optional name for output tensor
reverse: run in reverse
Returns:
Batch of size (batch_size, noutput).
"""
with variable_scope.variable_scope(scope, "SequenceToFinal", [inputs]):
length, batch_size, _ = _shape(inputs)
lstm = rnn_cell.BasicLSTMCell(noutput, state_is_tuple=False)
state = array_ops.zeros([batch_size, lstm.state_size])
inputs_u = array_ops.unstack(inputs)
if reverse:
inputs_u = list(reversed(inputs_u))
for i in xrange(length):
if i > 0:
variable_scope.get_variable_scope().reuse_variables()
output, state = lstm(inputs_u[i], state)
outputs = array_ops.reshape(output, [batch_size, noutput], name=name)
return outputs
def sequence_softmax(inputs, noutput, scope=None, name=None, linear_name=None):
"""Run a softmax layer over all the time steps of an input sequence.
Args:
inputs: (length, batch_size, depth) tensor
noutput: output depth
scope: optional scope name
name: optional name for output tensor
linear_name: name for linear (pre-softmax) output
Returns:
A tensor of size (length, batch_size, noutput).
"""
length, _, ninputs = _shape(inputs)
inputs_u = array_ops.unstack(inputs)
output_u = []
with variable_scope.variable_scope(scope, "SequenceSoftmax", [inputs]):
initial_w = random_ops.truncated_normal([0 + ninputs, noutput], stddev=0.1)
initial_b = constant_op.constant(0.1, shape=[noutput])
w = variables.model_variable("weights", initializer=initial_w)
b = variables.model_variable("biases", initializer=initial_b)
for i in xrange(length):
with variable_scope.variable_scope(scope, "SequenceSoftmaxStep",
[inputs_u[i]]):
# TODO(tmb) consider using slim.fully_connected(...,
# activation_fn=tf.nn.softmax)
linear = nn_ops.xw_plus_b(inputs_u[i], w, b, name=linear_name)
output = nn_ops.softmax(linear)
output_u += [output]
outputs = array_ops.stack(output_u, name=name)
return outputs
|
