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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.
# ==============================================================================
"""TensorFlow Ops for Sequence to Sequence models."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib import rnn
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import variable_scope as vs
def sequence_classifier(decoding, labels, sampling_decoding=None, name=None):
"""Returns predictions and loss for sequence of predictions.
Args:
decoding: List of Tensors with predictions.
labels: List of Tensors with labels.
sampling_decoding: Optional, List of Tensor with predictions to be used
in sampling. E.g. they shouldn't have dependncy on outputs.
If not provided, decoding is used.
name: Operation name.
Returns:
Predictions and losses tensors.
"""
with ops.name_scope(name, "sequence_classifier", [decoding, labels]):
predictions, xent_list = [], []
for i, pred in enumerate(decoding):
xent_list.append(nn.softmax_cross_entropy_with_logits(
labels=labels[i], logits=pred,
name="sequence_loss/xent_raw{0}".format(i)))
if sampling_decoding:
predictions.append(nn.softmax(sampling_decoding[i]))
else:
predictions.append(nn.softmax(pred))
xent = math_ops.add_n(xent_list, name="sequence_loss/xent")
loss = math_ops.reduce_sum(xent, name="sequence_loss")
return array_ops.stack(predictions, axis=1), loss
def seq2seq_inputs(x, y, input_length, output_length, sentinel=None, name=None):
"""Processes inputs for Sequence to Sequence models.
Args:
x: Input Tensor [batch_size, input_length, embed_dim].
y: Output Tensor [batch_size, output_length, embed_dim].
input_length: length of input x.
output_length: length of output y.
sentinel: optional first input to decoder and final output expected.
If sentinel is not provided, zeros are used. Due to fact that y is not
available in sampling time, shape of sentinel will be inferred from x.
name: Operation name.
Returns:
Encoder input from x, and decoder inputs and outputs from y.
"""
with ops.name_scope(name, "seq2seq_inputs", [x, y]):
in_x = array_ops.unstack(x, axis=1)
y = array_ops.unstack(y, axis=1)
if not sentinel:
# Set to zeros of shape of y[0], using x for batch size.
sentinel_shape = array_ops.stack(
[array_ops.shape(x)[0], y[0].get_shape()[1]])
sentinel = array_ops.zeros(sentinel_shape)
sentinel.set_shape(y[0].get_shape())
in_y = [sentinel] + y
out_y = y + [sentinel]
return in_x, in_y, out_y
def rnn_decoder(decoder_inputs, initial_state, cell, scope=None):
"""RNN Decoder that creates training and sampling sub-graphs.
Args:
decoder_inputs: Inputs for decoder, list of tensors.
This is used only in training sub-graph.
initial_state: Initial state for the decoder.
cell: RNN cell to use for decoder.
scope: Scope to use, if None new will be produced.
Returns:
List of tensors for outputs and states for training and sampling sub-graphs.
"""
with vs.variable_scope(scope or "dnn_decoder"):
states, sampling_states = [initial_state], [initial_state]
outputs, sampling_outputs = [], []
with ops.name_scope("training", values=[decoder_inputs, initial_state]):
for i, inp in enumerate(decoder_inputs):
if i > 0:
vs.get_variable_scope().reuse_variables()
output, new_state = cell(inp, states[-1])
outputs.append(output)
states.append(new_state)
with ops.name_scope("sampling", values=[initial_state]):
for i, _ in enumerate(decoder_inputs):
if i == 0:
sampling_outputs.append(outputs[i])
sampling_states.append(states[i])
else:
sampling_output, sampling_state = cell(sampling_outputs[-1],
sampling_states[-1])
sampling_outputs.append(sampling_output)
sampling_states.append(sampling_state)
return outputs, states, sampling_outputs, sampling_states
def rnn_seq2seq(encoder_inputs,
decoder_inputs,
encoder_cell,
decoder_cell=None,
dtype=dtypes.float32,
scope=None):
"""RNN Sequence to Sequence model.
Args:
encoder_inputs: List of tensors, inputs for encoder.
decoder_inputs: List of tensors, inputs for decoder.
encoder_cell: RNN cell to use for encoder.
decoder_cell: RNN cell to use for decoder, if None encoder_cell is used.
dtype: Type to initialize encoder state with.
scope: Scope to use, if None new will be produced.
Returns:
List of tensors for outputs and states for training and sampling sub-graphs.
"""
with vs.variable_scope(scope or "rnn_seq2seq"):
_, last_enc_state = rnn.static_rnn(
encoder_cell, encoder_inputs, dtype=dtype)
return rnn_decoder(decoder_inputs, last_enc_state, decoder_cell or
encoder_cell)
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