def test_decode_train(self):
"""Tests decoding in training mode.
"""
seq_length = np.random.randint(self._max_time, size=[self._batch_size
]) + 1
encoder_values_length = tf.constant(seq_length)
hparams = {
"attention": {
"kwargs": {
"num_units": self._attention_dim,
# Note: to use sparsemax in TF-CPU, it looks
# `memory_sequence_length` must equal max_time.
# "probability_fn": "sparsemax"
}
}
}
decoder = AttentionRNNDecoder(
memory=self._encoder_output,
memory_sequence_length=encoder_values_length,
vocab_size=self._vocab_size,
hparams=hparams)
helper_train = get_helper(
decoder.hparams.helper_train.type,
inputs=self._inputs,
sequence_length=[self._max_time] * self._batch_size,
**decoder.hparams.helper_train.kwargs.todict())
outputs, final_state, sequence_lengths = decoder(helper=helper_train)
# 4+1 trainable variables: cell-kernel, cell-bias,
# fc-weight, fc-bias, and
# memory_layer: For LuongAttention, we only transform the memory layer;
# thus num_units *must* match the expected query depth.
self.assertEqual(len(decoder.trainable_variables), 5)
cell_dim = decoder.hparams.rnn_cell.kwargs.num_units
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs_, final_state_, sequence_lengths_ = sess.run(
[outputs, final_state, sequence_lengths],
feed_dict={context.global_mode(): tf.estimator.ModeKeys.TRAIN})
self.assertIsInstance(outputs_, AttentionRNNDecoderOutput)
self.assertEqual(
outputs_.logits.shape,
(self._batch_size, self._max_time, self._vocab_size))
self.assertEqual(outputs_.sample_id.shape,
(self._batch_size, self._max_time))
self.assertEqual(final_state_.cell_state[0].shape,
(self._batch_size, cell_dim))
np.testing.assert_array_equal(sequence_lengths_,
[self._max_time] * self._batch_size)
def test_decode_infer(self):
"""Tests decoding in inference mode.
"""
seq_length = np.random.randint(self._max_time, size=[self._batch_size
]) + 1
encoder_values_length = tf.constant(seq_length)
hparams = {
"attention": {
"kwargs": {
"num_units": 256,
}
}
}
decoder = AttentionRNNDecoder(
vocab_size=self._vocab_size,
memory=self._encoder_output,
memory_sequence_length=encoder_values_length,
hparams=hparams)
helper_infer = get_helper(
decoder.hparams.helper_infer.type,
embedding=self._embedding,
start_tokens=[1] * self._batch_size,
end_token=2,
**decoder.hparams.helper_train.kwargs.todict())
outputs, final_state, sequence_lengths = decoder(helper=helper_infer)
# 4+1 trainable variables: cell-kernel, cell-bias,
# fc-weight, fc-bias, and
# memory_layer: For LuongAttention, we only transform the memory layer;
# thus num_units *must* match the expected query depth.
self.assertEqual(len(decoder.trainable_variables), 5)
cell_dim = decoder.hparams.rnn_cell.kwargs.num_units
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs_, final_state_, sequence_lengths_ = sess.run(
[outputs, final_state, sequence_lengths],
feed_dict={
context.global_mode(): tf.estimator.ModeKeys.PREDICT
})
self.assertIsInstance(outputs_, AttentionRNNDecoderOutput)
max_length = max(sequence_lengths_)
self.assertEqual(outputs_.logits.shape,
(self._batch_size, max_length, self._vocab_size))
self.assertEqual(outputs_.sample_id.shape,
(self._batch_size, max_length))
self.assertEqual(final_state_.cell_state[0].shape,
(self._batch_size, cell_dim))
def test_beam_search_cell(self):
"""Tests :meth:`texar.tf.modules.AttentionRNNDecoder._get_beam_search_cell`
"""
seq_length = np.random.randint(self._max_time, size=[self._batch_size
]) + 1
encoder_values_length = tf.constant(seq_length)
hparams = {
"attention": {
"kwargs": {
"num_units": self._attention_dim,
"probability_fn": "sparsemax"
}
}
}
decoder = AttentionRNNDecoder(
memory=self._encoder_output,
memory_sequence_length=encoder_values_length,
vocab_size=self._vocab_size,
hparams=hparams)
helper_train = get_helper(
decoder.hparams.helper_train.type,
inputs=self._inputs,
sequence_length=[self._max_time] * self._batch_size,
**decoder.hparams.helper_train.kwargs.todict())
_, _, _ = decoder(helper=helper_train)
# 4+1 trainable variables: cell-kernel, cell-bias,
# fc-weight, fc-bias, and
# memory_layer: For LuongAttention, we only transform the memory layer;
# thus num_units *must* match the expected query depth.
self.assertEqual(len(decoder.trainable_variables), 5)
beam_width = 3
beam_cell = decoder._get_beam_search_cell(beam_width)
cell_input = tf.random_uniform(
[self._batch_size * beam_width, self._emb_dim])
cell_state = beam_cell.zero_state(self._batch_size * beam_width,
tf.float32)
_ = beam_cell(cell_input, cell_state)
# Test if beam_cell is sharing variables with decoder cell.
for tvar in beam_cell.trainable_variables:
self.assertTrue(tvar in decoder.trainable_variables)
def test_decode_train(self):
"""Tests decoding in training mode.
"""
output_layer = tf.layers.Dense(self._vocab_size)
decoder = BasicRNNDecoder(vocab_size=self._vocab_size,
output_layer=output_layer)
helper_train = get_helper(
decoder.hparams.helper_train.type,
inputs=self._inputs,
sequence_length=[self._max_time] * self._batch_size,
**decoder.hparams.helper_train.kwargs.todict())
outputs, final_state, sequence_lengths = decoder(helper=helper_train)
self._test_outputs(decoder, outputs, final_state, sequence_lengths)
outputs, final_state, sequence_lengths = decoder(
inputs=self._inputs,
sequence_length=[self._max_time] * self._batch_size)
self._test_outputs(decoder, outputs, final_state, sequence_lengths)
outputs, final_state, sequence_lengths = decoder(
decoding_strategy=None,
inputs=self._inputs,
sequence_length=[self._max_time] * self._batch_size)
self._test_outputs(decoder, outputs, final_state, sequence_lengths)
outputs, final_state, sequence_lengths = decoder(
decoding_strategy=None,
embedding=self._embedding,
start_tokens=[1] * self._batch_size,
end_token=2,
mode=tf.estimator.ModeKeys.EVAL)
self._test_outputs(decoder,
outputs,
final_state,
sequence_lengths,
test_mode=True)
def test_decode_infer(self):
"""Tests decoding in inferencee mode.
"""
output_layer = tf.layers.Dense(self._vocab_size)
decoder = BasicRNNDecoder(vocab_size=self._vocab_size,
output_layer=output_layer)
helper_infer = get_helper(
decoder.hparams.helper_infer.type,
embedding=self._embedding,
start_tokens=[self._vocab_size - 2] * self._batch_size,
end_token=self._vocab_size - 1,
**decoder.hparams.helper_train.kwargs.todict())
outputs, final_state, sequence_lengths = decoder(helper=helper_infer)
# 4 trainable variables: embedding, cell-kernel, cell-bias,
# fc-layer-weights, fc-layer-bias
self.assertEqual(len(decoder.trainable_variables), 4)
cell_dim = decoder.hparams.rnn_cell.kwargs.num_units
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs_, final_state_, sequence_lengths_ = sess.run(
[outputs, final_state, sequence_lengths],
feed_dict={
context.global_mode(): tf.estimator.ModeKeys.PREDICT
})
self.assertIsInstance(outputs_, BasicRNNDecoderOutput)
max_length = max(sequence_lengths_)
self.assertEqual(outputs_.logits.shape,
(self._batch_size, max_length, self._vocab_size))
self.assertEqual(outputs_.sample_id.shape,
(self._batch_size, max_length))
self.assertEqual(final_state_[0].shape,
(self._batch_size, cell_dim))
def test_decode_train_with_tf(self):
"""Compares decoding results with TF built-in decoder.
"""
_inputs_placeholder = tf.placeholder(
tf.int32, [self._batch_size, self._max_time], name="inputs")
_embedding_placeholder = tf.placeholder(
tf.float32, [self._vocab_size, self._emb_dim], name="emb")
inputs = tf.nn.embedding_lookup(_embedding_placeholder,
_inputs_placeholder)
output_layer = tf.layers.Dense(self._vocab_size)
decoder = BasicRNNDecoder(vocab_size=self._vocab_size,
output_layer=output_layer)
helper_train = get_helper(
decoder.hparams.helper_train.type,
inputs=inputs,
sequence_length=[self._max_time] * self._batch_size,
**decoder.hparams.helper_train.kwargs.todict())
outputs, final_state, sequence_lengths = decoder(helper=helper_train)
tf_helper = tf.contrib.seq2seq.TrainingHelper(
inputs, [self._max_time] * self._batch_size)
tf_decoder = tf.contrib.seq2seq.BasicDecoder(decoder.cell,
tf_helper,
decoder.cell.zero_state(
self._batch_size,
tf.float32),
output_layer=output_layer)
tf_outputs, tf_final_state, tf_sequence_lengths = \
tf.contrib.seq2seq.dynamic_decode(tf_decoder)
cell_dim = decoder.hparams.rnn_cell.kwargs.num_units
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
inputs_ = np.random.randint(self._vocab_size,
size=(self._batch_size,
self._max_time),
dtype=np.int32)
embedding_ = np.random.randn(self._vocab_size, self._emb_dim)
outputs_, final_state_, sequence_lengths_ = sess.run(
[outputs, final_state, sequence_lengths],
feed_dict={
context.global_mode(): tf.estimator.ModeKeys.TRAIN,
_inputs_placeholder: inputs_,
_embedding_placeholder: embedding_
})
self.assertEqual(final_state_[0].shape,
(self._batch_size, cell_dim))
tf_outputs_, tf_final_state_, tf_sequence_lengths_ = sess.run(
[tf_outputs, tf_final_state, tf_sequence_lengths],
feed_dict={
context.global_mode(): tf.estimator.ModeKeys.TRAIN,
_inputs_placeholder: inputs_,
_embedding_placeholder: embedding_
})
np.testing.assert_array_equal(outputs_.logits,
tf_outputs_.rnn_output)
np.testing.assert_array_equal(outputs_.sample_id,
tf_outputs_.sample_id)
np.testing.assert_array_equal(final_state_.c, tf_final_state_.c)
np.testing.assert_array_equal(final_state_.h, tf_final_state_.h)
np.testing.assert_array_equal(sequence_lengths_,
tf_sequence_lengths_)
def _build(self,
decoding_strategy="train_greedy",
initial_state=None,
inputs=None,
memory=None,
sequence_length=None,
embedding=None,
start_tokens=None,
end_token=None,
softmax_temperature=None,
max_decoding_length=None,
impute_finished=False,
output_time_major=False,
input_time_major=False,
helper=None,
mode=None,
**kwargs):
# Memory
for _mechanism in self._cell._attention_mechanisms:
_mechanism.initialize_memory(memory)
# Helper
if helper is not None:
pass
elif decoding_strategy is not None:
if decoding_strategy == "train_greedy":
helper = rnn_decoder_helpers._get_training_helper(
inputs, sequence_length, embedding, input_time_major)
elif decoding_strategy == "infer_greedy":
helper = tx_helper.GreedyEmbeddingHelper(
embedding, start_tokens, end_token)
elif decoding_strategy == "infer_sample":
helper = tx_helper.SampleEmbeddingHelper(
embedding, start_tokens, end_token, softmax_temperature)
else:
raise ValueError(
"Unknown decoding strategy: {}".format(decoding_strategy))
else:
if is_train_mode_py(mode):
kwargs_ = copy.copy(self._hparams.helper_train.kwargs.todict())
helper_type = self._hparams.helper_train.type
else:
kwargs_ = copy.copy(self._hparams.helper_infer.kwargs.todict())
helper_type = self._hparams.helper_infer.type
kwargs_.update({
"inputs": inputs,
"sequence_length": sequence_length,
"time_major": input_time_major,
"embedding": embedding,
"start_tokens": start_tokens,
"end_token": end_token,
"softmax_temperature": softmax_temperature})
kwargs_.update(kwargs)
helper = rnn_decoder_helpers.get_helper(helper_type, **kwargs_)
self._helper = helper
# Initial state
if initial_state is not None:
self._initial_state = initial_state
else:
self._initial_state = self.zero_state(
batch_size=self.batch_size, dtype=tf.float32)
# Maximum decoding length
max_l = max_decoding_length
if max_l is None:
max_l_train = self._hparams.max_decoding_length_train
if max_l_train is None:
max_l_train = utils.MAX_SEQ_LENGTH
max_l_infer = self._hparams.max_decoding_length_infer
if max_l_infer is None:
max_l_infer = utils.MAX_SEQ_LENGTH
max_l = tf.cond(is_train_mode(mode),
lambda: max_l_train, lambda: max_l_infer)
self.max_decoding_length = max_l
# Decode
outputs, final_state, sequence_lengths = dynamic_decode(
decoder=self, impute_finished=impute_finished,
maximum_iterations=max_l, output_time_major=output_time_major)
if not self._built:
self._add_internal_trainable_variables()
# Add trainable variables of `self._cell` which may be
# constructed externally.
self._add_trainable_variable(
layers.get_rnn_cell_trainable_variables(self._cell))
if isinstance(self._output_layer, tf.layers.Layer):
self._add_trainable_variable(
self._output_layer.trainable_variables)
# Add trainable variables of `self._beam_search_rnn_cell` which
# may already be constructed and used.
if self._beam_search_cell is not None:
self._add_trainable_variable(
self._beam_search_cell.trainable_variables)
self._built = True
return outputs, final_state, sequence_lengths
def _build(self,
decoding_strategy="train_greedy",
initial_state=None,
inputs=None,
sequence_length=None,
embedding=None,
start_tokens=None,
end_token=None,
softmax_temperature=None,
max_decoding_length=None,
impute_finished=False,
output_time_major=False,
input_time_major=False,
helper=None,
mode=None,
**kwargs):
"""Performs decoding. This is a shared interface for both
:class:`~texar.tf.modules.BasicRNNDecoder` and
:class:`~texar.tf.modules.AttentionRNNDecoder`.
The function provides **3 ways** to specify the
decoding method, with varying flexibility:
1. The :attr:`decoding_strategy` argument: A string taking value of:
- **"train_greedy"**: decoding in teacher-forcing fashion \
(i.e., feeding \
`ground truth` to decode the next step), and each sample is \
obtained by taking the `argmax` of the RNN output logits. \
Arguments :attr:`(inputs, sequence_length, input_time_major)` \
are required for this strategy, and argument :attr:`embedding` \
is optional.
- **"infer_greedy"**: decoding in inference fashion (i.e., feeding \
the `generated` sample to decode the next step), and each sample\
is obtained by taking the `argmax` of the RNN output logits.\
Arguments :attr:`(embedding, start_tokens, end_token)` are \
required for this strategy, and argument \
:attr:`max_decoding_length` is optional.
- **"infer_sample"**: decoding in inference fashion, and each
sample is obtained by `random sampling` from the RNN output
distribution. Arguments \
:attr:`(embedding, start_tokens, end_token)` are \
required for this strategy, and argument \
:attr:`max_decoding_length` is optional.
This argument is used only when argument :attr:`helper` is `None`.
Example:
.. code-block:: python
embedder = WordEmbedder(vocab_size=data.vocab.size)
decoder = BasicRNNDecoder(vocab_size=data.vocab.size)
# Teacher-forcing decoding
outputs_1, _, _ = decoder(
decoding_strategy='train_greedy',
inputs=embedder(data_batch['text_ids']),
sequence_length=data_batch['length']-1)
# Random sample decoding. Gets 100 sequence samples
outputs_2, _, sequence_length = decoder(
decoding_strategy='infer_sample',
start_tokens=[data.vocab.bos_token_id]*100,
end_token=data.vocab.eos.token_id,
embedding=embedder,
max_decoding_length=60)
2. The :attr:`helper` argument: An instance of subclass of \
:class:`texar.tf.modules.Helper`. This
provides a superset of decoding strategies than above, for example:
- :class:`~texar.tf.modules.TrainingHelper` corresponding to the \
"train_greedy" strategy.
- :class:`~texar.tf.modules.GreedyEmbeddingHelper` and \
:class:`~texar.tf.modules.SampleEmbeddingHelper` corresponding to \
the "infer_greedy" and "infer_sample", respectively.
- :class:`~texar.tf.modules.TopKSampleEmbeddingHelper` for Top-K \
sample decoding.
- :class:`ScheduledEmbeddingTrainingHelper` and \
:class:`ScheduledOutputTrainingHelper` for scheduled \
sampling.
- :class:`~texar.tf.modules.SoftmaxEmbeddingHelper` and \
:class:`~texar.tf.modules.GumbelSoftmaxEmbeddingHelper` for \
soft decoding and gradient backpropagation.
Helpers give the maximal flexibility of configuring the decoding\
strategy.
Example:
.. code-block:: python
embedder = WordEmbedder(vocab_size=data.vocab.size)
decoder = BasicRNNDecoder(vocab_size=data.vocab.size)
# Teacher-forcing decoding, same as above with
# `decoding_strategy='train_greedy'`
helper_1 = tx.modules.TrainingHelper(
inputs=embedders(data_batch['text_ids']),
sequence_length=data_batch['length']-1)
outputs_1, _, _ = decoder(helper=helper_1)
# Gumbel-softmax decoding
helper_2 = GumbelSoftmaxEmbeddingHelper(
embedding=embedder,
start_tokens=[data.vocab.bos_token_id]*100,
end_token=data.vocab.eos_token_id,
tau=0.1)
outputs_2, _, sequence_length = decoder(
max_decoding_length=60, helper=helper_2)
3. :attr:`hparams["helper_train"]` and :attr:`hparams["helper_infer"]`:\
Specifying the helper through hyperparameters. Train and infer \
strategy is toggled based on :attr:`mode`. Appriopriate arguments \
(e.g., :attr:`inputs`, :attr:`start_tokens`, etc) are selected to \
construct the helper. Additional arguments for helper constructor \
can be provided either through :attr:`**kwargs`, or through \
:attr:`hparams["helper_train/infer"]["kwargs"]`.
This means is used only when both :attr:`decoding_strategy` and \
:attr:`helper` are `None`.
Example:
.. code-block:: python
h = {
"helper_infer": {
"type": "GumbelSoftmaxEmbeddingHelper",
"kwargs": { "tau": 0.1 }
}
}
embedder = WordEmbedder(vocab_size=data.vocab.size)
decoder = BasicRNNDecoder(vocab_size=data.vocab.size, hparams=h)
# Gumbel-softmax decoding
output, _, _ = decoder(
decoding_strategy=None, # Sets to None explicit
embedding=embedder,
start_tokens=[data.vocab.bos_token_id]*100,
end_token=data.vocab.eos_token_id,
max_decoding_length=60,
mode=tf.estimator.ModeKeys.PREDICT)
# PREDICT mode also shuts down dropout
Args:
decoding_strategy (str): A string specifying the decoding
strategy. Different arguments are required based on the
strategy.
Ignored if :attr:`helper` is given.
initial_state (optional): Initial state of decoding.
If `None` (default), zero state is used.
inputs (optional): Input tensors for teacher forcing decoding.
Used when `decoding_strategy` is set to "train_greedy", or
when `hparams`-configured helper is used.
- If :attr:`embedding` is `None`, `inputs` is directly \
fed to the decoder. E.g., in `"train_greedy"` strategy, \
`inputs` must be a 3D Tensor of shape \
`[batch_size, max_time, emb_dim]` (or \
`[max_time, batch_size, emb_dim]` if `input_time_major`==True).
- If `embedding` is given, `inputs` is used as index \
to look up embeddings and feed in the decoder. \
E.g., if `embedding` is an instance of \
:class:`~texar.tf.modules.WordEmbedder`, \
then :attr:`inputs` is usually a 2D int Tensor \
`[batch_size, max_time]` (or \
`[max_time, batch_size]` if `input_time_major`==True) \
containing the token indexes.
sequence_length (optional): A 1D int Tensor containing the
sequence length of :attr:`inputs`.
Used when `decoding_strategy="train_greedy"` or
`hparams`-configured helper is used.
embedding (optional): Embedding used when:
- "infer_greedy" or "infer_sample" `decoding_strategy` is \
used. This can be a callable or the `params` argument for \
:tf_main:`embedding_lookup <nn/embedding_lookup>`. \
If a callable, it can take a vector tensor of token `ids`, \
or take two arguments (`ids`, `times`), where `ids` \
is a vector tensor of token ids, and `times` is a vector tensor\
of time steps (i.e., position ids). The latter case can be used\
when attr:`embedding` is a combination of word embedding and\
position embedding. `embedding` is required in this case.
- "train_greedy" `decoding_strategy` is used.\
This can be a callable or the `params` argument for \
:tf_main:`embedding_lookup <nn/embedding_lookup>`. \
If a callable, it can take :attr:`inputs` and returns \
the input embedding. `embedding` is optional in this case.
start_tokens (optional): A int Tensor of shape `[batch_size]`,
the start tokens. Used when `decoding_strategy="infer_greedy"`
or `"infer_sample"`, or when the helper specified in `hparams`
is used.
Example:
.. code-block:: python
data = tx.data.MonoTextData(hparams)
iterator = DataIterator(data)
batch = iterator.get_next()
bos_token_id = data.vocab.bos_token_id
start_tokens=tf.ones_like(batch['length'])*bos_token_id
end_token (optional): A int 0D Tensor, the token that marks end
of decoding.
Used when `decoding_strategy="infer_greedy"` or
`"infer_sample"`, or when the helper specified in `hparams`
is used.
softmax_temperature (optional): A float 0D Tensor, value to divide
the logits by before computing the softmax. Larger values
(above 1.0) result in more random samples. Must > 0. If `None`,
1.0 is used.
Used when `decoding_strategy="infer_sample"`.
max_decoding_length: A int scalar Tensor indicating the maximum
allowed number of decoding steps. If `None` (default), either
`hparams["max_decoding_length_train"]` or
`hparams["max_decoding_length_infer"]` is used
according to :attr:`mode`.
impute_finished (bool): If `True`, then states for batch
entries which are marked as finished get copied through and
the corresponding outputs get zeroed out. This causes some
slowdown at each time step, but ensures that the final state
and outputs have the correct values and that backprop ignores
time steps that were marked as finished.
output_time_major (bool): If `True`, outputs are returned as
time major tensors. If `False` (default), outputs are returned
as batch major tensors.
input_time_major (optional): Whether the :attr:`inputs` tensor is
time major.
Used when `decoding_strategy="train_greedy"` or
`hparams`-configured helper is used.
helper (optional): An instance of
:class:`texar.tf.modules.Helper`
that defines the decoding strategy. If given,
`decoding_strategy`
and helper configs in :attr:`hparams` are ignored.
mode (str, optional): A string taking value in
:tf_main:`tf.estimator.ModeKeys <estimator/ModeKeys>`. If
`TRAIN`, training related hyperparameters are used (e.g.,
`hparams['max_decoding_length_train']`), otherwise,
inference related hyperparameters are used (e.g.,
`hparams['max_decoding_length_infer']`).
If `None` (default), `TRAIN` mode is used.
**kwargs: Other keyword arguments for constructing helpers
defined by `hparams["helper_trainn"]` or
`hparams["helper_infer"]`.
Returns:
`(outputs, final_state, sequence_lengths)`, where
- **`outputs`**: an object containing the decoder output on all \
time steps.
- **`final_state`**: is the cell state of the final time step.
- **`sequence_lengths`**: is an int Tensor of shape `[batch_size]` \
containing the length of each sample.
"""
# Helper
if helper is not None:
pass
elif decoding_strategy is not None:
if decoding_strategy == "train_greedy":
helper = rnn_decoder_helpers._get_training_helper(
inputs, sequence_length, embedding, input_time_major)
elif decoding_strategy == "infer_greedy":
helper = tx_helper.GreedyEmbeddingHelper(
embedding, start_tokens, end_token)
elif decoding_strategy == "infer_sample":
helper = tx_helper.SampleEmbeddingHelper(
embedding, start_tokens, end_token, softmax_temperature)
else:
raise ValueError(
"Unknown decoding strategy: {}".format(decoding_strategy))
else:
if is_train_mode_py(mode):
kwargs_ = copy.copy(self._hparams.helper_train.kwargs.todict())
helper_type = self._hparams.helper_train.type
else:
kwargs_ = copy.copy(self._hparams.helper_infer.kwargs.todict())
helper_type = self._hparams.helper_infer.type
kwargs_.update({
"inputs": inputs,
"sequence_length": sequence_length,
"time_major": input_time_major,
"embedding": embedding,
"start_tokens": start_tokens,
"end_token": end_token,
"softmax_temperature": softmax_temperature
})
kwargs_.update(kwargs)
helper = rnn_decoder_helpers.get_helper(helper_type, **kwargs_)
self._helper = helper
# Initial state
if initial_state is not None:
self._initial_state = initial_state
else:
self._initial_state = self.zero_state(batch_size=self.batch_size,
dtype=tf.float32)
# Maximum decoding length
max_l = max_decoding_length
if max_l is None:
max_l_train = self._hparams.max_decoding_length_train
if max_l_train is None:
max_l_train = utils.MAX_SEQ_LENGTH
max_l_infer = self._hparams.max_decoding_length_infer
if max_l_infer is None:
max_l_infer = utils.MAX_SEQ_LENGTH
max_l = tf.cond(is_train_mode(mode), lambda: max_l_train,
lambda: max_l_infer)
self.max_decoding_length = max_l
# Decode
outputs, final_state, sequence_lengths = dynamic_decode(
decoder=self,
impute_finished=impute_finished,
maximum_iterations=max_l,
output_time_major=output_time_major)
if not self._built:
self._add_internal_trainable_variables()
# Add trainable variables of `self._cell` which may be
# constructed externally.
self._add_trainable_variable(
layers.get_rnn_cell_trainable_variables(self._cell))
if isinstance(self._output_layer, tf.layers.Layer):
self._add_trainable_variable(
self._output_layer.trainable_variables)
# Add trainable variables of `self._beam_search_rnn_cell` which
# may already be constructed and used.
if self._beam_search_cell is not None:
self._add_trainable_variable(
self._beam_search_cell.trainable_variables)
self._built = True
return outputs, final_state, sequence_lengths
