def call(self, features, training=None):
"""Returns sequence input corresponding to the `feature_columns`.
Args:
features: A dict mapping keys to tensors.
training: Python boolean or None, indicating whether to the layer is being
run in training mode. This argument is passed to the call method of any
`FeatureColumn` that takes a `training` argument. For example, if a
`FeatureColumn` performed dropout, the column could expose a `training`
argument to control whether the dropout should be applied. If `None`,
defaults to `tf.keras.backend.learning_phase()`.
Returns:
An `(input_layer, sequence_length)` tuple where:
- input_layer: A float `Tensor` of shape `[batch_size, T, D]`.
`T` is the maximum sequence length for this batch, which could differ
from batch to batch. `D` is the sum of `num_elements` for all
`feature_columns`.
- sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence
length for each example.
Raises:
ValueError: If features are not a dictionary.
"""
if not isinstance(features, dict):
raise ValueError('We expected a dictionary here. Instead we got: ',
features)
if training is None:
training = backend.learning_phase()
transformation_cache = fc.FeatureTransformationCache(features)
output_tensors = []
sequence_lengths = []
for column in self._feature_columns:
with backend.name_scope(column.name):
try:
dense_tensor, sequence_length = column.get_sequence_dense_tensor(
transformation_cache,
self._state_manager,
training=training)
except TypeError:
dense_tensor, sequence_length = column.get_sequence_dense_tensor(
transformation_cache, self._state_manager)
# Flattens the final dimension to produce a 3D Tensor.
output_tensors.append(
self._process_dense_tensor(column, dense_tensor))
sequence_lengths.append(sequence_length)
# Check and process sequence lengths.
fc._verify_static_batch_size_equality(sequence_lengths,
self._feature_columns)
sequence_length = _assert_all_equal_and_return(sequence_lengths)
return self._verify_and_concat_tensors(output_tensors), sequence_length
def call(self, features):
"""Returns sequence input corresponding to the `feature_columns`.
Args:
features: A dict mapping keys to tensors.
Returns:
An `(input_layer, sequence_length)` tuple where:
- input_layer: A float `Tensor` of shape `[batch_size, T, D]`.
`T` is the maximum sequence length for this batch, which could differ
from batch to batch. `D` is the sum of `num_elements` for all
`feature_columns`.
- sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence
length for each example.
Raises:
ValueError: If features are not a dictionary.
"""
if not isinstance(features, dict):
raise ValueError('We expected a dictionary here. Instead we got: ',
features)
transformation_cache = fc.FeatureTransformationCache(features)
output_tensors = []
sequence_lengths = []
for column in self._feature_columns:
with ops.name_scope(column.name):
dense_tensor, sequence_length = column.get_sequence_dense_tensor(
transformation_cache, self._state_manager)
# Flattens the final dimension to produce a 3D Tensor.
output_tensors.append(
self._process_dense_tensor(column, dense_tensor))
sequence_lengths.append(sequence_length)
# Check and process sequence lengths.
fc._verify_static_batch_size_equality(sequence_lengths,
self._feature_columns)
sequence_length = _assert_all_equal_and_return(sequence_lengths)
return self._verify_and_concat_tensors(output_tensors), sequence_length
def call(self, features):
"""Returns sequence input corresponding to the `feature_columns`.
Args:
features: A dict mapping keys to tensors.
Returns:
An `(input_layer, sequence_length)` tuple where:
- input_layer: A float `Tensor` of shape `[batch_size, T, D]`.
`T` is the maximum sequence length for this batch, which could differ
from batch to batch. `D` is the sum of `num_elements` for all
`feature_columns`.
- sequence_length: An int `Tensor` of shape `[batch_size]`. The sequence
length for each example.
Raises:
ValueError: If features are not a dictionary.
"""
if not isinstance(features, dict):
raise ValueError('We expected a dictionary here. Instead we got: ',
features)
transformation_cache = fc.FeatureTransformationCache(features)
output_tensors = []
sequence_lengths = []
for column in self._feature_columns:
with ops.name_scope(column.name):
dense_tensor, sequence_length = column.get_sequence_dense_tensor(
transformation_cache, self._state_manager)
# Flattens the final dimension to produce a 3D Tensor.
output_tensors.append(self._process_dense_tensor(column, dense_tensor))
sequence_lengths.append(sequence_length)
# Check and process sequence lengths.
fc._verify_static_batch_size_equality(sequence_lengths,
self._feature_columns)
sequence_length = _assert_all_equal_and_return(sequence_lengths)
return self._verify_and_concat_tensors(output_tensors), sequence_length
def _verify_and_concat_tensors(self, output_tensors):
"""Verifies and concatenates the dense output of several columns."""
feature_column_v2._verify_static_batch_size_equality( # pylint: disable=protected-access
output_tensors, self._feature_columns)
return array_ops.concat(output_tensors, -1)
def _verify_and_concat_tensors(self, output_tensors):
"""Verifies and concatenates the dense output of several columns."""
fc._verify_static_batch_size_equality(output_tensors,
self._feature_columns)
return array_ops.concat(output_tensors, -1)
