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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.
# ==============================================================================
"""Seq2seq loss operations for use in neural networks.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import rnn_cell
from tensorflow.python.util import nest
__all__ = ["simple_decoder_fn_train",
"simple_decoder_fn_inference"]
def simple_decoder_fn_train(encoder_state, name=None):
""" Simple decoder function for a sequence-to-sequence model used in the
`dynamic_rnn_decoder`.
The `simple_decoder_fn_train` is a simple training function for a
sequence-to-sequence model. It should be used when `dynamic_rnn_decoder` is
in the training mode.
The `simple_decoder_fn_train` is called with a set of the user arguments and
returns the `decoder_fn`, which can be passed to the `dynamic_rnn_decoder`,
such that
```
dynamic_fn_train = simple_decoder_fn_train(encoder_state)
outputs_train, state_train = dynamic_rnn_decoder(
decoder_fn=dynamic_fn_train, ...)
```
Further usage can be found in the `kernel_tests/seq2seq_test.py`.
Args:
encoder_state: The encoded state to initialize the `dynamic_rnn_decoder`.
name: (default: `None`) NameScope for the decoder function;
defaults to "simple_decoder_fn_train"
Returns:
A decoder function with the required interface of `dynamic_rnn_decoder`
intended for training.
"""
with ops.name_scope(name, "simple_decoder_fn_train", [encoder_state]):
pass
def decoder_fn(time, cell_state, cell_input, cell_output, context_state):
""" Decoder function used in the `dynamic_rnn_decoder` with the purpose of
training.
Args:
time: positive integer constant reflecting the current timestep.
cell_state: state of RNNCell.
cell_input: input provided by `dynamic_rnn_decoder`.
cell_output: output of RNNCell.
context_state: context state provided by `dynamic_rnn_decoder`.
Returns:
A tuple (done, next state, next input, emit output, next context state)
where:
done: `None`, which is used by the `dynamic_rnn_decoder` to indicate
that `sequence_lengths` in `dynamic_rnn_decoder` should be used.
next state: `cell_state`, this decoder function does not modify the
given state.
next input: `cell_input`, this decoder function does not modify the
given input. The input could be modified when applying e.g. attention.
emit output: `cell_output`, this decoder function does not modify the
given output.
next context state: `context_state`, this decoder function does not
modify the given context state. The context state could be modified when
applying e.g. beam search.
"""
with ops.name_scope(name, "simple_decoder_fn_train",
[time, cell_state, cell_input, cell_output,
context_state]):
if cell_state is None: # first call, return encoder_state
return (None, encoder_state, cell_input, cell_output, context_state)
else:
return (None, cell_state, cell_input, cell_output, context_state)
return decoder_fn
def simple_decoder_fn_inference(output_fn, encoder_state, embeddings,
start_of_sequence_id, end_of_sequence_id,
maximum_length, num_decoder_symbols,
dtype=dtypes.int32, name=None):
""" Simple decoder function for a sequence-to-sequence model used in the
`dynamic_rnn_decoder`.
The `simple_decoder_fn_inference` is a simple inference function for a
sequence-to-sequence model. It should be used when `dynamic_rnn_decoder` is
in the inference mode.
The `simple_decoder_fn_inference` is called with a set of the user arguments
and returns the `decoder_fn`, which can be passed to the
`dynamic_rnn_decoder`, such that
```
dynamic_fn_inference = simple_decoder_fn_inference(...)
outputs_inference, state_inference = dynamic_rnn_decoder(
decoder_fn=dynamic_fn_inference, ...)
```
Further usage can be found in the `kernel_tests/seq2seq_test.py`.
Args:
output_fn: An output function to project your `cell_output` onto class
logits.
An example of an output function;
```
tf.variable_scope("decoder") as varscope
output_fn = lambda x: layers.linear(x, num_decoder_symbols,
scope=varscope)
outputs_train, state_train = seq2seq.dynamic_rnn_decoder(...)
logits_train = output_fn(outputs_train)
varscope.reuse_variables()
logits_inference, state_inference = seq2seq.dynamic_rnn_decoder(
output_fn=output_fn, ...)
```
If `None` is supplied it will act as an identity function, which
might be wanted when using the RNNCell `OutputProjectionWrapper`.
encoder_state: The encoded state to initialize the `dynamic_rnn_decoder`.
embeddings: The embeddings matrix used for the decoder sized
`[num_decoder_symbols, embedding_size]`.
start_of_sequence_id: The start of sequence ID in the decoder embeddings.
end_of_sequence_id: The end of sequence ID in the decoder embeddings.
maximum_length: The maximum allowed of time steps to decode.
num_decoder_symbols: The number of classes to decode at each time step.
dtype: (default: `dtypes.int32`) The default data type to use when
handling integer objects.
name: (default: `None`) NameScope for the decoder function;
defaults to "simple_decoder_fn_inference"
Returns:
A decoder function with the required interface of `dynamic_rnn_decoder`
intended for inference.
"""
with ops.name_scope(name, "simple_decoder_fn_inference",
[output_fn, encoder_state, embeddings,
start_of_sequence_id, end_of_sequence_id,
maximum_length, num_decoder_symbols, dtype]):
start_of_sequence_id = ops.convert_to_tensor(start_of_sequence_id, dtype)
end_of_sequence_id = ops.convert_to_tensor(end_of_sequence_id, dtype)
maximum_length = ops.convert_to_tensor(maximum_length, dtype)
num_decoder_symbols = ops.convert_to_tensor(num_decoder_symbols, dtype)
encoder_info = nest.flatten(encoder_state)[0]
batch_size = encoder_info.get_shape()[0].value
if output_fn is None:
output_fn = lambda x: x
if batch_size is None:
batch_size = array_ops.shape(encoder_info)[0]
def decoder_fn(time, cell_state, cell_input, cell_output, context_state):
""" Decoder function used in the `dynamic_rnn_decoder` with the purpose of
inference.
The main difference between this decoder function and the `decoder_fn` in
`simple_decoder_fn_train` is how `next_cell_input` is calculated. In this
decoder function we calculate the next input by applying an argmax across
the feature dimension of the output from the decoder. This is a
greedy-search approach. (Bahdanau et al., 2014) & (Sutskever et al., 2014)
use beam-search instead.
Args:
time: positive integer constant reflecting the current timestep.
cell_state: state of RNNCell.
cell_input: input provided by `dynamic_rnn_decoder`.
cell_output: output of RNNCell.
context_state: context state provided by `dynamic_rnn_decoder`.
Returns:
A tuple (done, next state, next input, emit output, next context state)
where:
done: A boolean vector to indicate which sentences has reached a
`end_of_sequence_id`. This is used for early stopping by the
`dynamic_rnn_decoder`. When `time>=maximum_length` a boolean vector with
all elements as `true` is returned.
next state: `cell_state`, this decoder function does not modify the
given state.
next input: The embedding from argmax of the `cell_output` is used as
`next_input`.
emit output: If `output_fn is None` the supplied `cell_output` is
returned, else the `output_fn` is used to update the `cell_output`
before calculating `next_input` and returning `cell_output`.
next context state: `context_state`, this decoder function does not
modify the given context state. The context state could be modified when
applying e.g. beam search.
"""
with ops.name_scope(name, "simple_decoder_fn_inference",
[time, cell_state, cell_input, cell_output,
context_state]):
if cell_input is not None:
raise ValueError("Expected cell_input to be None, but saw: %s" %
cell_input)
if cell_output is None:
# invariant that this is time == 0
next_input_id = array_ops.ones([batch_size,], dtype=dtype) * (
start_of_sequence_id)
done = array_ops.zeros([batch_size,], dtype=dtypes.bool)
cell_state = encoder_state
cell_output = array_ops.zeros([num_decoder_symbols],
dtype=dtypes.float32)
else:
cell_output = output_fn(cell_output)
next_input_id = math_ops.cast(
math_ops.argmax(cell_output, 1), dtype=dtype)
done = math_ops.equal(next_input_id, end_of_sequence_id)
next_input = array_ops.gather(embeddings, next_input_id)
# if time > maxlen, return all true vector
done = control_flow_ops.cond(math_ops.greater(time, maximum_length),
lambda: array_ops.ones([batch_size,], dtype=dtypes.bool),
lambda: done)
return (done, cell_state, next_input, cell_output, context_state)
return decoder_fn
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