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lstm1d.py
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lstm1d.py
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# 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.contrib.rnn.python.ops import core_rnn_cell_impl
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 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 = core_rnn_cell_impl.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 = core_rnn_cell_impl.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 = core_rnn_cell_impl.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