def _maybe_reshape_input_tensor(tensor, column_name, output_rank):
"""Reshape the input tensor by the following rule.
1. If `output_rank > input_rank + 1`, raise a `ValueError`.
2. If `output_rank == input_rank + 1`, expand the tensor by one dimension.
3. If `output_rank == input_rank`, do nothing.
4. If `output_rank < input_rank`, flatten the inner dimensions of the tensor.
Args:
tensor: A Tensor or SparseTensor to be reshaped.
column_name: A string name of the feature column for the tensor.
output_rank: the desired rank of the tensor.
Returns:
A reshaped Tensor or SparseTensor.
Raises:
ValueError: if `output_rank > input_rank + 1` for the input tensor.
"""
input_rank = tensor.get_shape().ndims
if input_rank is None and isinstance(tensor,
sparse_tensor_py.SparseTensor):
# Try to get the rank of a sparse tensor by its dense_shape's shape.
input_rank = tensor.dense_shape.get_shape().as_list()[0]
if input_rank is None:
raise ValueError(
'Error while processing column {}. Rank of input Tensor '
'can not be None.'.format(column_name))
if output_rank > input_rank + 1:
raise ValueError(
'Error while processing column {}. Rank of input Tensor '
'({}) should be the same as output_rank ({}). For '
'example, sequence data should typically be 3 '
'dimensional (rank 3) while non-sequence data is '
'typically 2 dimensional (rank 2).'.format(column_name, input_rank,
output_rank))
elif output_rank == input_rank + 1:
# Expand the tensor's shape by 1 dimension.
if isinstance(tensor, sparse_tensor_py.SparseTensor):
output_shape = array_ops.concat([tensor.dense_shape, [1]], 0)
return sparse_ops.sparse_reshape(tensor, output_shape)
else:
reshaped = array_ops.expand_dims(tensor, -1)
# Try to calculate the new shape.
static_shape = tensor.get_shape()
if static_shape is not None and static_shape.dims is not None:
reshaped.set_shape(static_shape.as_list() + [1])
return reshaped
elif output_rank < input_rank:
return layers._inner_flatten(tensor, output_rank) # pylint: disable=protected-access
else:
return tensor
def _maybe_reshape_input_tensor(tensor, column_name, output_rank):
"""Reshape the input tensor by the following rule.
1. If `output_rank > input_rank + 1`, raise a `ValueError`.
2. If `output_rank == input_rank + 1`, expand the tensor by one dimension.
3. If `output_rank == input_rank`, do nothing.
4. If `output_rank < input_rank`, flatten the inner dimensions of the tensor.
Args:
tensor: A Tensor or SparseTensor to be reshaped.
column_name: A string name of the feature column for the tensor.
output_rank: the desired rank of the tensor.
Returns:
A reshaped Tensor or SparseTensor.
Raises:
ValueError: if `output_rank > input_rank + 1` for the input tensor.
"""
input_rank = tensor.get_shape().ndims
if input_rank is None and isinstance(tensor, sparse_tensor_py.SparseTensor):
# Try to get the rank of a sparse tensor by its dense_shape's shape.
input_rank = tensor.dense_shape.get_shape().as_list()[0]
if input_rank is None:
raise ValueError('Error while processing column {}. Rank of input Tensor '
'can not be None.'.format(column_name))
if output_rank > input_rank + 1:
raise ValueError('Error while processing column {}. Rank of input Tensor '
'({}) should be the same as output_rank ({}). For '
'example, sequence data should typically be 3 '
'dimensional (rank 3) while non-sequence data is '
'typically 2 dimensional (rank 2).'.format(
column_name, input_rank, output_rank))
elif output_rank == input_rank + 1:
# Expand the tensor's shape by 1 dimension.
if isinstance(tensor, sparse_tensor_py.SparseTensor):
output_shape = array_ops.concat([tensor.dense_shape, [1]], 0)
return sparse_ops.sparse_reshape(tensor, output_shape)
else:
reshaped = array_ops.expand_dims(tensor, -1)
# Try to calculate the new shape.
static_shape = tensor.get_shape()
if static_shape is not None and static_shape.dims is not None:
reshaped.set_shape(static_shape.as_list() + [1])
return reshaped
elif output_rank < input_rank:
return layers._inner_flatten(tensor, output_rank) # pylint: disable=protected-access
else:
return tensor
def _embeddings_from_arguments(column,
args,
weight_collections,
trainable,
output_rank=2):
"""Returns embeddings for a column based on the computed arguments.
Args:
column: the column name.
args: the _DeepEmbeddingLookupArguments for this column.
weight_collections: collections to store weights in.
trainable: whether these embeddings should be trainable.
output_rank: the desired rank of the returned `Tensor`. Inner dimensions will
be combined to produce the desired rank.
Returns:
the embeddings.
Raises:
ValueError: if not possible to create.
"""
# pylint: disable=protected-access
input_tensor = layers._inner_flatten(args.input_tensor, output_rank)
weight_tensor = None
if args.weight_tensor is not None:
weight_tensor = layers._inner_flatten(args.weight_tensor, output_rank)
# pylint: enable=protected-access
# This option is only enabled for scattered_embedding_column.
if args.hash_key:
embeddings = contrib_variables.model_variable(
name='weights',
shape=[args.vocab_size],
dtype=dtypes.float32,
initializer=args.initializer,
trainable=trainable,
collections=weight_collections)
return embedding_ops.scattered_embedding_lookup_sparse(
embeddings, input_tensor, args.dimension,
hash_key=args.hash_key,
combiner=args.combiner, name='lookup')
if args.shared_embedding_name is not None:
shared_embedding_collection_name = (
'SHARED_EMBEDDING_COLLECTION_' + args.shared_embedding_name.upper())
graph = ops.get_default_graph()
shared_embedding_collection = (
graph.get_collection_ref(shared_embedding_collection_name))
shape = [args.vocab_size, args.dimension]
if shared_embedding_collection:
if len(shared_embedding_collection) > 1:
raise ValueError('Collection %s can only contain one '
'(partitioned) variable.'
% shared_embedding_collection_name)
else:
embeddings = shared_embedding_collection[0]
if embeddings.get_shape() != shape:
raise ValueError('The embedding variable with name {} already '
'exists, but its shape does not match required '
'embedding shape here. Please make sure to use '
'different shared_embedding_name for different '
'shared embeddings.'.format(
args.shared_embedding_name))
else:
embeddings = contrib_variables.model_variable(
name=args.shared_embedding_name,
shape=shape,
dtype=dtypes.float32,
initializer=args.initializer,
trainable=trainable,
collections=weight_collections)
graph.add_to_collection(shared_embedding_collection_name, embeddings)
else:
embeddings = contrib_variables.model_variable(
name='weights',
shape=[args.vocab_size, args.dimension],
dtype=dtypes.float32,
initializer=args.initializer,
trainable=trainable,
collections=weight_collections)
if isinstance(embeddings, variables.Variable):
embeddings = [embeddings]
else:
embeddings = embeddings._get_variable_list() # pylint: disable=protected-access
# pylint: disable=protected-access
_maybe_restore_from_checkpoint(
column._checkpoint_path(), embeddings)
return embedding_ops.safe_embedding_lookup_sparse(
embeddings,
input_tensor,
sparse_weights=weight_tensor,
combiner=args.combiner,
name=column.name + 'weights',
max_norm=args.max_norm)
def _embeddings_from_arguments(column,
args,
weight_collections,
trainable,
output_rank=2):
"""Returns embeddings for a column based on the computed arguments.
Args:
column: the column name.
args: the _DeepEmbeddingLookupArguments for this column.
weight_collections: collections to store weights in.
trainable: whether these embeddings should be trainable.
output_rank: the desired rank of the returned `Tensor`. Inner dimensions will
be combined to produce the desired rank.
Returns:
the embeddings.
Raises:
ValueError: if not possible to create.
"""
# pylint: disable=protected-access
input_tensor = layers._inner_flatten(args.input_tensor, output_rank)
weight_tensor = None
if args.weight_tensor is not None:
weight_tensor = layers._inner_flatten(args.weight_tensor, output_rank)
# pylint: enable=protected-access
if args.hashed:
embeddings = contrib_variables.model_variable(
name='weights',
shape=[args.vocab_size],
dtype=dtypes.float32,
initializer=args.initializer,
trainable=trainable,
collections=weight_collections)
return embedding_ops.hashed_embedding_lookup_sparse(
embeddings, input_tensor, args.dimension,
combiner=args.combiner, name='lookup')
if args.shared_embedding_name is not None:
shared_embedding_collection_name = (
'SHARED_EMBEDDING_COLLECTION_' + args.shared_embedding_name.upper())
graph = ops.get_default_graph()
shared_embedding_collection = (
graph.get_collection_ref(shared_embedding_collection_name))
shape = [args.vocab_size, args.dimension]
if shared_embedding_collection:
if len(shared_embedding_collection) > 1:
raise ValueError('Collection %s can only contain one '
'(partitioned) variable.'
% shared_embedding_collection_name)
else:
embeddings = shared_embedding_collection[0]
if embeddings.get_shape() != shape:
raise ValueError('The embedding variable with name {} already '
'exists, but its shape does not match required '
'embedding shape here. Please make sure to use '
'different shared_embedding_name for different '
'shared embeddings.'.format(
args.shared_embedding_name))
else:
embeddings = contrib_variables.model_variable(
name=args.shared_embedding_name,
shape=shape,
dtype=dtypes.float32,
initializer=args.initializer,
trainable=trainable,
collections=weight_collections)
graph.add_to_collection(shared_embedding_collection_name, embeddings)
else:
embeddings = contrib_variables.model_variable(
name='weights',
shape=[args.vocab_size, args.dimension],
dtype=dtypes.float32,
initializer=args.initializer,
trainable=trainable,
collections=weight_collections)
if isinstance(embeddings, variables.Variable):
embeddings = [embeddings]
else:
embeddings = embeddings._get_variable_list() # pylint: disable=protected-access
# pylint: disable=protected-access
_maybe_restore_from_checkpoint(
column._checkpoint_path(), embeddings)
return embedding_ops.safe_embedding_lookup_sparse(
embeddings,
input_tensor,
sparse_weights=weight_tensor,
combiner=args.combiner,
name=column.name + 'weights',
max_norm=args.max_norm)
