def get_sequence_dense_tensor(self, transformation_cache, state_manager):
"""Returns a `TensorSequenceLengthPair`.
Args:
transformation_cache: A `FeatureTransformationCache` object to access
features.
state_manager: A `StateManager` to create / access resources such as
lookup tables.
"""
sp_tensor = transformation_cache.get(self, state_manager)
dense_tensor = sparse_ops.sparse_tensor_to_dense(
sp_tensor, default_value=self.default_value)
# Reshape into [batch_size, T, variable_shape].
dense_shape = array_ops.concat(
[array_ops.shape(dense_tensor)[:1], [-1], self.variable_shape],
axis=0)
dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape)
# Get the number of timesteps per example
# For the 2D case, the raw values are grouped according to num_elements;
# for the 3D case, the grouping happens in the third dimension, and
# sequence length is not affected.
num_elements = (self.variable_shape.num_elements()
if sp_tensor.shape.ndims == 2 else 1)
seq_length = fc_old._sequence_length_from_sparse_tensor(
sp_tensor, num_elements=num_elements)
return fc.SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=dense_tensor, sequence_length=seq_length)
def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
# Do nothing with weight_collections and trainable since no variables are
# created in this function.
del weight_collections
del trainable
sp_tensor = inputs.get(self)
dense_tensor = sparse_ops.sparse_tensor_to_dense(
sp_tensor, default_value=self.default_value)
# Reshape into [batch_size, T, variable_shape].
dense_shape = array_ops.concat(
[array_ops.shape(dense_tensor)[:1], [-1], self._variable_shape],
axis=0)
dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape)
# Get the number of timesteps per example
# For the 2D case, the raw values are grouped according to num_elements;
# for the 3D case, the grouping happens in the third dimension, and
# sequence length is not affected.
num_elements = (self._variable_shape.num_elements()
if sp_tensor.shape.ndims == 2 else 1)
seq_length = fc._sequence_length_from_sparse_tensor(
sp_tensor, num_elements=num_elements)
return fc._SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=dense_tensor, sequence_length=seq_length)
def get_sequence_dense_tensor(self, transformation_cache, state_manager):
"""Returns a `TensorSequenceLengthPair`.
Args:
transformation_cache: A `FeatureTransformationCache` object to access
features.
state_manager: A `StateManager` to create / access resources such as
lookup tables.
"""
sp_tensor = transformation_cache.get(self, state_manager)
dense_tensor = sparse_ops.sparse_tensor_to_dense(
sp_tensor, default_value=self.default_value)
# Reshape into [batch_size, T, variable_shape].
dense_shape = array_ops.concat(
[array_ops.shape(dense_tensor)[:1], [-1], self.variable_shape],
axis=0)
dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape)
# Get the number of timesteps per example
# For the 2D case, the raw values are grouped according to num_elements;
# for the 3D case, the grouping happens in the third dimension, and
# sequence length is not affected.
num_elements = (self.variable_shape.num_elements()
if sp_tensor.shape.ndims == 2 else 1)
seq_length = fc_old._sequence_length_from_sparse_tensor(
sp_tensor, num_elements=num_elements)
return fc.SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=dense_tensor, sequence_length=seq_length)
def _get_sequence_dense_tensor(self,
inputs,
weight_collections=None,
trainable=None):
# Do nothing with weight_collections and trainable since no variables are
# created in this function.
del weight_collections
del trainable
sp_tensor = inputs.get(self)
dense_tensor = sparse_ops.sparse_tensor_to_dense(
sp_tensor, default_value=self.default_value)
# Reshape into [batch_size, T, variable_shape].
dense_shape = array_ops.concat(
[array_ops.shape(dense_tensor)[:1], [-1], self._variable_shape],
axis=0)
dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape)
# Get the number of timesteps per example
# For the 2D case, the raw values are grouped according to num_elements;
# for the 3D case, the grouping happens in the third dimension, and
# sequence length is not affected.
num_elements = (self._variable_shape.num_elements()
if sp_tensor.shape.ndims == 2 else 1)
seq_length = fc._sequence_length_from_sparse_tensor(
sp_tensor, num_elements=num_elements)
return fc._SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=dense_tensor, sequence_length=seq_length)
def _get_sequence_dense_tensor(self,
inputs,
weight_collections=None,
trainable=None):
del weight_collections, trainable
if self.signature != 'sequence':
raise ValueError(
'Column {} could not be used as sequence feature column. '
'Use sequence_text_embedding_column instead'.format(self.name))
sparse_keys = inputs.get(self)
sparse_keys.shape.with_rank_at_least(2)
sparse_keys.shape.with_rank_at_most(3)
batch_size, max_length = sparse_keys.dense_shape[
0], sparse_keys.dense_shape[1]
sparse_keys = tf.sparse_reshape(sparse_keys,
shape=tf.stack(
[batch_size, max_length, -1]))
dense_tensor = self._hub_module(sparse_keys, signature=self.signature)
sequence_length = feature_column._sequence_length_from_sparse_tensor(
inputs.get(self))
return feature_column._SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=dense_tensor, sequence_length=sequence_length)
def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
# Do nothing with weight_collections and trainable since no variables are
# created in this function.
del weight_collections
del trainable
sp_tensor = inputs.get(self)
dense_tensor = sparse_ops.sparse_tensor_to_dense(
sp_tensor, default_value=self.default_value)
# Reshape into [batch_size, T, variable_shape].
dense_shape = array_ops.concat(
[array_ops.shape(dense_tensor)[:1], [-1], self._variable_shape],
axis=0)
dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape)
sequence_length = fc._sequence_length_from_sparse_tensor(
sp_tensor, num_elements=self._variable_shape.num_elements())
return fc._SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=dense_tensor, sequence_length=sequence_length)
def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None):
# Do nothing with weight_collections and trainable since no variables are
# created in this function.
del weight_collections
del trainable
sp_tensor = inputs.get(self)
dense_tensor = sparse_ops.sparse_tensor_to_dense(
sp_tensor, default_value=self.default_value)
# Reshape into [batch_size, T, variable_shape].
dense_shape = array_ops.concat(
[array_ops.shape(dense_tensor)[:1], [-1], self._variable_shape],
axis=0)
dense_tensor = array_ops.reshape(dense_tensor, shape=dense_shape)
sequence_length = fc._sequence_length_from_sparse_tensor(
sp_tensor, num_elements=self._variable_shape.num_elements())
return fc._SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=dense_tensor, sequence_length=sequence_length)