class AttentionDecoder(RNNDecoder):
"""An RNN Decoder that uses attention over an input sequence.
Args:
cell: An instance of ` tf.contrib.rnn.RNNCell`
helper: An instance of `tf.contrib.seq2seq.Helper` to assist decoding
initial_state: A tensor or tuple of tensors used as the initial cell
state.
vocab_size: Output vocabulary size, i.e. number of units
in the softmax layer
attention_keys: The sequence used to calculate attention scores.
A tensor of shape `[B, T, ...]`.
attention_values: The sequence to attend over.
A tensor of shape `[B, T, input_dim]`.
attention_values_length: Sequence length of the attention values.
An int32 Tensor of shape `[B]`.
attention_fn: The attention function to use. This function map from
`(state, inputs)` to `(attention_scores, attention_context)`.
For an example, see `seq2seq.decoder.attention.AttentionLayer`.
reverse_scores: Optional, an array of sequence length. If set,
reverse the attention scores in the output. This is used for when
a reversed source sequence is fed as an input but you want to
return the scores in non-reversed order.
"""
def __init__(self,
params,
mode,
vocab_size,
attention_keys,
attention_values,
attention_values_length,
attention_fn,
reverse_scores_lengths=None,
name="attention_decoder"):
super(AttentionDecoder, self).__init__(params, mode, name)
self.vocab_size = vocab_size
self.attention_keys = attention_keys
self.attention_values = attention_values
self.attention_values_length = attention_values_length
self.attention_fn = attention_fn
self.reverse_scores_lengths = reverse_scores_lengths
@property
def output_size(self):
return AttentionDecoderOutput(
logits=self.vocab_size,
predicted_ids=tf.TensorShape([]),
cell_output=self.cell.output_size,
attention_scores=tf.shape(self.attention_values)[1:-1],
attention_context=self.attention_values.get_shape()[-1])
@property
def output_dtype(self):
return AttentionDecoderOutput(logits=tf.float32,
predicted_ids=tf.int32,
cell_output=tf.float32,
attention_scores=tf.float32,
attention_context=tf.float32)
def initialize(self, name=None):
finished, first_inputs = self.helper.initialize()
# Concat empty attention context
attention_context = tf.zeros([
tf.shape(first_inputs)[0],
self.attention_values.get_shape().as_list()[-1]
])
print(first_inputs)
print(attention_context)
first_inputs = tf.concat([first_inputs, attention_context],
1,
name="first_inputs")
return finished, first_inputs, self.initial_state
def compute_output(self, cell_output):
"""Computes the decoder outputs."""
# Compute attention
att_scores, attention_context = self.attention_fn(
query=cell_output,
keys=self.attention_keys,
values=self.attention_values,
values_length=self.attention_values_length)
# TODO: Make this a parameter: We may or may not want this.
# Transform attention context.
# This makes the softmax smaller and allows us to synthesize information
# between decoder state and attention context
# see https://arxiv.org/abs/1508.04025v5
softmax_input = tf.contrib.layers.fully_connected(
inputs=tf.concat([cell_output, attention_context], 1),
num_outputs=self.cell.output_size,
activation_fn=tf.nn.tanh,
scope="attention_mix")
# Softmax computation
logits = tf.contrib.layers.fully_connected(inputs=softmax_input,
num_outputs=self.vocab_size,
activation_fn=None,
scope="logits")
return softmax_input, logits, att_scores, attention_context
def _setup(self, initial_state, helper):
self.initial_state = initial_state
def att_next_inputs(time, outputs, state, sample_ids, name=None):
"""Wraps the original decoder helper function to append the attention
context.
"""
finished, next_inputs, next_state = helper.next_inputs(
time=time,
outputs=outputs,
state=state,
sample_ids=sample_ids,
name=name)
next_inputs = tf.concat([next_inputs, outputs.attention_context],
1)
return (finished, next_inputs, next_state)
self.helper = CustomHelper(initialize_fn=helper.initialize,
sample_fn=helper.sample,
next_inputs_fn=att_next_inputs)
def step(self, time_, inputs, state, name=None):
cell_output, cell_state = self.cell(inputs, state)
cell_output_new, logits, attention_scores, attention_context = \
self.compute_output(cell_output)
if self.reverse_scores_lengths is not None:
attention_scores = tf.reverse_sequence(
input=attention_scores,
seq_lengths=self.reverse_scores_lengths,
seq_dim=1,
batch_dim=0)
sample_ids = self.helper.sample(time=time_,
outputs=logits,
state=cell_state)
outputs = AttentionDecoderOutput(logits=logits,
predicted_ids=sample_ids,
cell_output=cell_output_new,
attention_scores=attention_scores,
attention_context=attention_context)
finished, next_inputs, next_state = self.helper.next_inputs(
time=time_,
outputs=outputs,
state=cell_state,
sample_ids=sample_ids)
return (outputs, next_state, next_inputs, finished)
class CopyGenDecoder(RNNDecoder):
def __init__(self,
params,
mode,
vocab_size,
attention_keys,
attention_values,
attention_values_length,
attention_fn,
source_embedding=None,
reverse_scores_lengths=None,
name="CopyGenDecoder"):
super(CopyGenDecoder, self).__init__(params, mode, name)
self.vocab_size = vocab_size
self.source_embedding = source_embedding
self.attention_keys = attention_keys
self.attention_values = attention_values
self.attention_values_length = attention_values_length
self.attention_fn = attention_fn
self.reverse_scores_lengths = reverse_scores_lengths
@property
def output_size(self):
return CopyGenDecoderOutput(
logits=self.vocab_size,
predicted_ids=tf.TensorShape([]),
cell_output=self.cell.output_size,
attention_scores=tf.shape(self.attention_values)[1:-1],
attention_context=self.attention_values.get_shape()[-1],
pgens=tf.TensorShape([1]))
@property
def output_dtype(self):
return CopyGenDecoderOutput(logits=tf.float32,
predicted_ids=tf.int32,
cell_output=tf.float32,
attention_scores=tf.float32,
attention_context=tf.float32,
pgens=tf.float32)
def initialize(self, name=None):
finished, first_inputs = self.helper.initialize()
# Concat empty attention context
attention_context = tf.zeros([
tf.shape(first_inputs)[0],
self.attention_values.get_shape().as_list()[-1]
])
first_inputs = tf.concat([first_inputs, attention_context], 1)
return finished, first_inputs, self.initial_state
def cal_gen_probability(self, out_features):
#probability of generation
pgen = tf.contrib.layers.fully_connected(
inputs=out_features,
num_outputs=1,
activation_fn=tf.nn.tanh,
)
return pgen
def compute_output(self, cell_output):
"""Computes the decoder outputs."""
# Compute attention
att_scores, attention_context = self.attention_fn(
query=cell_output,
keys=self.attention_keys,
values=self.attention_values,
values_length=self.attention_values_length)
# TODO: Make this a parameter: We may or may not want this.
# Transform attention context.
# This makes the softmax smaller and allows us to synthesize information
# between decoder state and attention context
# see https://arxiv.org/abs/1508.04025v5
decode_out_features = tf.concat([cell_output, attention_context], 1)
softmax_input = tf.contrib.layers.fully_connected(
inputs=decode_out_features,
num_outputs=self.cell.output_size,
activation_fn=tf.nn.tanh,
scope="attention_mix")
# Softmax computation
logits = tf.contrib.layers.fully_connected(inputs=softmax_input,
num_outputs=self.vocab_size,
activation_fn=None,
scope="logits")
#generation probability
pgens = self.cal_gen_probability(decode_out_features)
return softmax_input, logits, att_scores, attention_context, pgens
def _setup(self, initial_state, helper):
self.initial_state = initial_state
self.W_u = 0
# wout_dim = 2 * self.params["decoder.params"]["rnn_cell"]["cell_params"]["num_units"] # dim(context_vector + hidden states)
# word_dim = self.source_embedding.shape[1].value
# with tf.variable_scope("copy_gen_project_wout"):
# self.wout_proj = tf.get_variable("project_wout", shape=[word_dim, word_dim], dtype=tf.float32, initializer=tf.truncated_normal_initializer)
# self.wout = tf.tanh(tf.matmul(self.source_embedding, self.wout_proj)) #[v,d] * [d,d] = [v,d]
def att_next_inputs(time, outputs, state, sample_ids, name=None):
"""Wraps the original decoder helper function to append the attention
context.
"""
finished, next_inputs, next_state = helper.next_inputs(
time=time,
outputs=outputs,
state=state,
sample_ids=sample_ids,
name=name)
next_inputs = tf.concat([next_inputs, outputs.attention_context],
1)
return (finished, next_inputs, next_state)
self.helper = CustomHelper(initialize_fn=helper.initialize,
sample_fn=helper.sample,
next_inputs_fn=att_next_inputs)
def step(self, time_, inputs, state, name=None):
cell_output, cell_state = self.cell(inputs, state)
cell_output_new, logits, attention_scores, attention_context, pgens = \
self.compute_output(cell_output)
if self.reverse_scores_lengths is not None:
attention_scores = tf.reverse_sequence(
input=attention_scores,
seq_lengths=self.reverse_scores_lengths,
seq_dim=1,
batch_dim=0)
sample_ids = self.helper.sample(time=time_,
outputs=logits,
state=cell_state)
outputs = CopyGenDecoderOutput(logits=logits,
predicted_ids=sample_ids,
cell_output=cell_output_new,
attention_scores=attention_scores,
attention_context=attention_context,
pgens=pgens)
finished, next_inputs, next_state = self.helper.next_inputs(
time=time_,
outputs=outputs,
state=cell_state,
sample_ids=sample_ids)
return (outputs, next_state, next_inputs, finished)
def _build(self, initial_state, helper):
if not self.initial_state:
self._setup(initial_state, helper)
scope = tf.get_variable_scope()
scope.set_initializer(
tf.random_uniform_initializer(-self.params["init_scale"],
self.params["init_scale"]))
maximum_iterations = None
if self.mode == tf.contrib.learn.ModeKeys.INFER:
maximum_iterations = self.params["max_decode_length"]
outputs, final_state = dynamic_decode(
decoder=self,
output_time_major=True,
impute_finished=False,
maximum_iterations=maximum_iterations)
return self.finalize(outputs, final_state)
class SchemaAttentionDecoder(RNNDecoder):
"""An RNN Decoder that uses attention over an input sequence and a schema.
Args:
cell: An instance of ` tf.contrib.rnn.RNNCell`
helper: An instance of `tf.contrib.seq2seq.Helper` to assist decoding
initial_state: A tensor or tuple of tensors used as the initial cell
state.
vocab_size: Output vocabulary size, i.e. number of units
in the softmax layer
attention_keys: The sequence used to calculate attention scores.
A tensor of shape `[B, T, ...]`.
attention_values: The sequence to attend over.
A tensor of shape `[B, T, input_dim]`.
attention_values_length: Sequence length of the attention values.
An int32 Tensor of shape `[B]`.
attention_fn: The attention function to use. This function map from
`(state, inputs)` to `(attention_scores, attention_context)`.
For an example, see `seq2seq.decoder.attention.AttentionLayer`.
reverse_scores: Optional, an array of sequence length. If set,
reverse the attention scores in the output. This is used for when
a reversed source sequence is fed as an input but you want to
return the scores in non-reversed order.
"""
# the definition of schema_attention_function is in models/schema_attention_seq2seq.py
def __init__(
self,
params,
mode,
vocab_size,
attention_keys,
attention_values,
attention_values_length,
attention_fn,
# 4 extra values
reverse_scores_lengths=None,
schema_attention_keys=None,
schema_attention_values=None,
schema_attention_values_length=None,
schema_attention_fn=None,
name="schema_attention_decoder"):
super(SchemaAttentionDecoder, self).__init__(params, mode, name)
self.vocab_size = vocab_size
self.attention_keys = attention_keys
self.attention_values = attention_values
self.attention_values_length = attention_values_length
self.attention_fn = attention_fn
self.reverse_scores_lengths = reverse_scores_lengths
self.schema_attention_keys = schema_attention_keys
self.schema_attention_values = schema_attention_values
self.schema_attention_values_length = schema_attention_values_length
if schema_attention_fn:
self.schema_attention_fn = schema_attention_fn
else:
self.schema_attention_fn = attention_fn
@property
def output_size(self):
return SchemaAttentionDecoderOutput(
logits=self.vocab_size,
predicted_ids=tf.TensorShape([]),
cell_output=self.cell.output_size,
attention_scores=tf.shape(self.attention_values)[1:-1],
attention_context=self.attention_values.get_shape()[-1],
schema_attention_scores=tf.shape(
self.schema_attention_values)[1:-1],
schema_attention_context=self.schema_attention_values.get_shape()
[-1])
@property
def output_dtype(self):
return SchemaAttentionDecoderOutput(
logits=tf.float32,
predicted_ids=tf.int32,
cell_output=tf.float32,
attention_scores=tf.float32,
attention_context=tf.float32,
schema_attention_scores=tf.float32,
schema_attention_context=tf.float32)
def initialize(self, name=None):
finished, first_inputs = self.helper.initialize()
# Concat empty attention context
attention_context = tf.zeros([
tf.shape(first_inputs)[0],
self.attention_values.get_shape().as_list()[-1]
])
schema_attention_context = tf.zeros([
tf.shape(first_inputs)[0],
self.schema_attention_values.get_shape().as_list()[-1]
])
first_inputs = tf.concat(
[first_inputs, attention_context, schema_attention_context], 1)
return finished, first_inputs, self.initial_state
def compute_output(self, cell_output, calculate_softmax=True):
"""Computes the decoder outputs."""
# Compute attention
att_scores, attention_context = self.attention_fn(
query=cell_output,
keys=self.attention_keys,
values=self.attention_values,
values_length=self.attention_values_length)
# there is a key and a schema attention value
# which is key? where to find the schema attention function?
schema_att_scores, schema_attention_context = self.schema_attention_fn(
query=cell_output,
keys=self.schema_attention_keys,
values=self.schema_attention_values,
values_length=self.schema_attention_values_length)
softmax_input = None
logits = None
if calculate_softmax:
softmax_input, logits = self._calculate_softmax(
[cell_output, attention_context, schema_attention_context])
return softmax_input, logits, att_scores, attention_context, schema_att_scores, schema_attention_context
def _calculate_softmax(self, list_of_contexts):
softmax_input = tf.contrib.layers.fully_connected(
inputs=tf.concat(list_of_contexts, 1),
num_outputs=self.cell.output_size,
activation_fn=tf.nn.tanh,
scope="attention_mix")
# Softmax computation
logits = tf.contrib.layers.fully_connected(inputs=softmax_input,
num_outputs=self.vocab_size,
activation_fn=None,
scope="logits")
return softmax_input, logits
def _setup(self, initial_state, helper):
self.initial_state = initial_state
def att_next_inputs(time, outputs, state, sample_ids, name=None):
"""Wraps the original decoder helper function to append the attention
context.
"""
finished, next_inputs, next_state = helper.next_inputs(
time=time,
outputs=outputs,
state=state,
sample_ids=sample_ids,
name=name)
next_inputs = tf.concat([
next_inputs, outputs.attention_context,
outputs.schema_attention_context
], 1)
return (finished, next_inputs, next_state)
self.helper = CustomHelper(initialize_fn=helper.initialize,
sample_fn=helper.sample,
next_inputs_fn=att_next_inputs)
def step(self, time_, inputs, state, name=None):
cell_output, cell_state = self.cell(inputs, state)
(cell_output_new, logits, attention_scores, attention_context,
schema_attention_scores, schema_attention_context) = \
self.compute_output(cell_output)
if self.reverse_scores_lengths is not None:
attention_scores = tf.reverse_sequence(
input=attention_scores,
seq_lengths=self.reverse_scores_lengths,
seq_dim=1,
batch_dim=0)
sample_ids = self.helper.sample(time=time_,
outputs=logits,
state=cell_state)
outputs = SchemaAttentionDecoderOutput(
logits=logits,
predicted_ids=sample_ids,
cell_output=cell_output_new,
attention_scores=attention_scores,
attention_context=attention_context,
schema_attention_scores=schema_attention_scores,
schema_attention_context=schema_attention_context)
finished, next_inputs, next_state = self.helper.next_inputs(
time=time_,
outputs=outputs,
state=cell_state,
sample_ids=sample_ids)
return (outputs, next_state, next_inputs, finished)
class AttentionDecoder(RNNDecoder):
"""An RNN Decoder that uses attention over an input sequence.
Args:
cell: An instance of ` tf.contrib.rnn.RNNCell`
helper: An instance of `tf.contrib.seq2seq.Helper` to assist decoding
initial_state: A tensor or tuple of tensors used as the initial cell
state.
vocab_size: Output vocabulary size, i.e. number of units
in the softmax layer
attention_keys: The sequence used to calculate attention scores.
A tensor of shape `[B, T, ...]`.
attention_values: The sequence to attend over.
A tensor of shape `[B, T, input_dim]`.
attention_values_length: Sequence length of the attention values.
An int32 Tensor of shape `[B]`.
attention_fn: The attention function to use. This function map from
`(state, inputs)` to `(attention_scores, attention_context)`.
For an example, see `seq2seq.decoder.attention.AttentionLayer`.
reverse_scores: Optional, an array of sequence length. If set,
reverse the attention scores in the output. This is used for when
a reversed source sequence is fed as an input but you want to
return the scores in non-reversed order.
"""
def __init__(self,
params,
mode,
vocab_size,
attention_keys,
attention_values,
attention_values_length,
attention_fn,
reverse_scores_lengths=None,
name="attention_decoder"):
super(AttentionDecoder, self).__init__(params, mode, name)
self.vocab_size = vocab_size
self.attention_keys = attention_keys
self.attention_values = attention_values
self.attention_values_length = attention_values_length
self.attention_fn = attention_fn
self.reverse_scores_lengths = reverse_scores_lengths
@property
def output_size(self):
return AttentionDecoderOutput(
logits=self.vocab_size,
predicted_ids=tf.TensorShape([]),
cell_output=self.cell.output_size,
attention_scores=tf.shape(self.attention_values)[1:-1],
attention_context=self.attention_values.get_shape()[-1])
@property
def output_dtype(self):
return AttentionDecoderOutput(
logits=tf.float32,
predicted_ids=tf.int32,
cell_output=tf.float32,
attention_scores=tf.float32,
attention_context=tf.float32)
def initialize(self, name=None):
finished, first_inputs = self.helper.initialize()
# Concat empty attention context
attention_context = tf.zeros([
tf.shape(first_inputs)[0],
self.attention_values.get_shape().as_list()[-1]
])
first_inputs = tf.concat([first_inputs, attention_context], 1)
return finished, first_inputs, self.initial_state
def compute_output(self, cell_output):
"""Computes the decoder outputs."""
# Compute attention
att_scores, attention_context = self.attention_fn(
query=cell_output,
keys=self.attention_keys,
values=self.attention_values,
values_length=self.attention_values_length)
# TODO: Make this a parameter: We may or may not want this.
# Transform attention context.
# This makes the softmax smaller and allows us to synthesize information
# between decoder state and attention context
# see https://arxiv.org/abs/1508.04025v5
softmax_input = tf.contrib.layers.fully_connected(
inputs=tf.concat([cell_output, attention_context], 1),
num_outputs=self.cell.output_size,
activation_fn=tf.nn.tanh,
scope="attention_mix")
# Softmax computation
logits = tf.contrib.layers.fully_connected(
inputs=softmax_input,
num_outputs=self.vocab_size,
activation_fn=None,
scope="logits")
return softmax_input, logits, att_scores, attention_context
def _setup(self, initial_state, helper):
self.initial_state = initial_state
def att_next_inputs(time, outputs, state, sample_ids, name=None):
"""Wraps the original decoder helper function to append the attention
context.
"""
finished, next_inputs, next_state = helper.next_inputs(
time=time,
outputs=outputs,
state=state,
sample_ids=sample_ids,
name=name)
next_inputs = tf.concat([next_inputs, outputs.attention_context], 1)
return (finished, next_inputs, next_state)
self.helper = CustomHelper(
initialize_fn=helper.initialize,
sample_fn=helper.sample,
next_inputs_fn=att_next_inputs)
def step(self, time_, inputs, state, name=None):
cell_output, cell_state = self.cell(inputs, state)
cell_output_new, logits, attention_scores, attention_context = \
self.compute_output(cell_output)
if self.reverse_scores_lengths is not None:
attention_scores = tf.reverse_sequence(
input=attention_scores,
seq_lengths=self.reverse_scores_lengths,
seq_dim=1,
batch_dim=0)
sample_ids = self.helper.sample(
time=time_, outputs=logits, state=cell_state)
outputs = AttentionDecoderOutput(
logits=logits,
predicted_ids=sample_ids,
cell_output=cell_output_new,
attention_scores=attention_scores,
attention_context=attention_context)
finished, next_inputs, next_state = self.helper.next_inputs(
time=time_, outputs=outputs, state=cell_state, sample_ids=sample_ids)
return (outputs, next_state, next_inputs, finished)
class SchemaAttentionCopyingDecoder(SchemaAttentionDecoder):
"""
The version of SchemaAttentionCopyingDecoder that uses
F(score_n, rowembedding_n, h, c, W) to generate a score for the
n-th field in the schema.
"""
def __init__(self,
params,
mode,
vocab_size,
attention_keys,
attention_values,
attention_values_length,
attention_fn,
reverse_scores_lengths=None,
schema_attention_keys=None,
schema_attention_values=None,
schema_attention_values_length=None,
schema_attention_fn=None,
name="schema_attention_copying_decoder"):
super(SchemaAttentionCopyingDecoder, self).__init__(
params, mode, vocab_size, attention_keys, attention_values,
attention_values_length, attention_fn, reverse_scores_lengths,
schema_attention_keys, schema_attention_values,
schema_attention_values_length, schema_attention_fn, name)
self.schema_embs = schema_attention_values
@property
def output_size(self):
return SchemaCopyingAttentionDecoderOutput(
logits=self.vocab_size,
predicted_ids=tf.TensorShape([]),
cell_output=self.cell.output_size,
attention_scores=tf.shape(self.attention_values)[1:-1],
attention_context=self.attention_values.get_shape()[-1],
schema_attention_scores=tf.shape(
self.schema_attention_values)[1:-1],
schema_attention_context=self.schema_attention_values.get_shape()
[-1],
schema_attention_copy_vals=tf.shape(
self.schema_attention_values)[1:-1])
@property
def output_dtype(self):
return SchemaCopyingAttentionDecoderOutput(
logits=tf.float32,
predicted_ids=tf.int32,
cell_output=tf.float32,
attention_scores=tf.float32,
attention_context=tf.float32,
schema_attention_scores=tf.float32,
schema_attention_context=tf.float32,
schema_attention_copy_vals=tf.float32)
def compute_output(self, cell_output):
(softmax_input, logits, att_scores, attention_context,
schema_att_scores,
schema_attention_context) = super(SchemaAttentionCopyingDecoder,
self).compute_output(cell_output)
schema_attention_copy_vals = schema_att_scores
weighted_schema_embs_size = self.cell.output_size + \
self.attention_values.get_shape().as_list()[-1]
weighted_schema_embs = tf.contrib.layers.fully_connected(
inputs=self.schema_embs,
num_outputs=weighted_schema_embs_size,
activation_fn=None,
scope="weighted_schema_embs")
concatenated = tf.expand_dims(tf.concat(
[cell_output, attention_context], 1),
axis=2)
schema_attention_copy_vals = schema_att_scores * tf.squeeze(
tf.matmul(weighted_schema_embs, concatenated), axis=2)
return softmax_input, logits, att_scores, attention_context, schema_att_scores, schema_attention_context, schema_attention_copy_vals
def _setup(self, initial_state, helper):
#TODO: Take advantage of inheritance rather than copy-paste
self.initial_state = initial_state
def att_next_inputs(time, outputs, state, sample_ids, name=None):
"""Wraps the original decoder helper function to append the attention
context.
"""
finished, next_inputs, next_state = helper.next_inputs(
time=time,
outputs=outputs,
state=state,
sample_ids=sample_ids,
name=name)
next_inputs = tf.concat([
next_inputs, outputs.attention_context,
outputs.schema_attention_context
], 1)
return (finished, next_inputs, next_state)
self.helper = CustomHelper(initialize_fn=helper.initialize,
sample_fn=helper.sample,
next_inputs_fn=att_next_inputs)
def step(self, time_, inputs, state, name=None):
cell_output, cell_state = self.cell(inputs, state)
(cell_output_new, logits, attention_scores, attention_context,
schema_attention_scores, schema_attention_context,
schema_attention_copy_vals) = \
self.compute_output(cell_output)
if self.reverse_scores_lengths is not None:
attention_scores = tf.reverse_sequence(
input=attention_scores,
seq_lengths=self.reverse_scores_lengths,
seq_dim=1,
batch_dim=0)
sample_ids = self.helper.sample(time=time_,
outputs=logits,
state=cell_state)
outputs = SchemaCopyingAttentionDecoderOutput(
logits=logits,
predicted_ids=sample_ids,
cell_output=cell_output_new,
attention_scores=attention_scores,
attention_context=attention_context,
schema_attention_scores=schema_attention_scores,
schema_attention_context=schema_attention_context,
schema_attention_copy_vals=schema_attention_copy_vals)
finished, next_inputs, next_state = self.helper.next_inputs(
time=time_,
outputs=outputs,
state=cell_state,
sample_ids=sample_ids)
return (outputs, next_state, next_inputs, finished)