def sparse_tensor_to_dense(sp_input, default_value, name=None):
"""Converts a `SparseTensor` into a dense tensor.
This op is a convenience wrapper around `sparse_to_dense` for `SparseTensor`s.
For example, if `sp_input` has shape `[3, 5]` and non-empty string values:
[0, 1]: a
[0, 3]: b
[2, 0]: c
and `default_value` is `x`, then the output will be a dense `[3, 5]`
string tensor with values:
[[x a x b x]
[x x x x x]
[c x x x x]]
Args:
sp_input: The input `SparseTensor`.
default_value: Scalar value to set for indices not specified in
`sp_input`.
name: A name prefix for the returned tensors (optional).
Returns:
A dense tensor with shape `sp_input.shape` and values specified by
the non-empty values in `sp_input`. Indices not in `sp_input` are assigned
`default_value`.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
if not isinstance(sp_input, ops.SparseTensor):
raise TypeError("Input must be a SparseTensor")
return gen_sparse_ops.sparse_to_dense(
sp_input.indices,
sp_input.shape,
sp_input.values,
default_value,
name=name)
def sparse_tensor_to_dense(sp_input, default_value, name=None):
"""Converts a `SparseTensor` into a dense tensor.
This op is a convenience wrapper around `sparse_to_dense` for `SparseTensor`s.
For example, if `sp_input` has shape `[3, 5]` and non-empty string values:
[0, 1]: a
[0, 3]: b
[2, 0]: c
and `default_value` is `x`, then the output will be a dense `[3, 5]`
string tensor with values:
[[x a x b x]
[x x x x x]
[c x x x x]]
Args:
sp_input: The input `SparseTensor`.
default_value: Scalar value to set for indices not specified in
`sp_input`.
name: A name prefix for the returned tensors (optional).
Returns:
A dense tensor with shape `sp_input.shape` and values specified by
the non-empty values in `sp_input`. Indices not in `sp_input` are assigned
`default_value`.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
if not isinstance(sp_input, ops.SparseTensor):
raise TypeError("Input must be a SparseTensor")
return gen_sparse_ops.sparse_to_dense(
sp_input.indices,
sp_input.shape,
sp_input.values,
default_value,
name=name)
def sparse_fill_empty_rows(sp_input, default_value, name=None):
"""Fills empty rows in the input 2-D `SparseTensor` with a default value.
This op adds entries with the specified `default_value` at index
`[row, 0]` for any row in the input that does not already have a value.
For example, suppose `sp_input` has shape `[5, 6]` and non-empty values:
[0, 1]: a
[0, 3]: b
[2, 0]: c
[3, 1]: d
Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values:
[0, 1]: a
[0, 3]: b
[1, 0]: default_value
[2, 0]: c
[3, 1]: d
[4, 0]: default_value
Note that the input may have empty columns at the end, with no effect on
this op.
The output `SparseTensor` will be in row-major order and will have the
same shape as the input.
This op also returns an indicator vector such that
empty_row_indicator[i] = True iff row i was an empty row.
Args:
sp_input: A `SparseTensor` with shape `[N, M]`.
default_value: The value to fill for empty rows, with the same type as
`sp_input.`
name: A name prefix for the returned tensors (optional)
Returns:
sp_ordered_output: A `SparseTensor` with shape `[N, M]`, and with all empty
rows filled in with `default_value`.
empty_row_indicator: A bool vector of length `N` indicating whether each
input row was empty.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
if not isinstance(sp_input, ops.SparseTensor):
raise TypeError("Input must be a SparseTensor")
with ops.op_scope([sp_input], name, "SparseFillEmptyRows"):
default_value = ops.convert_to_tensor(default_value,
dtype=sp_input.values.dtype)
num_rows = math_ops.cast(sp_input.shape[0], types.int32)
all_row_indices = math_ops.cast(math_ops.range(0, num_rows, 1),
types.int64)
empty_row_indices, _ = array_ops.list_diff(all_row_indices,
sp_input.indices[:, 0])
empty_row_indicator = gen_sparse_ops.sparse_to_dense(
empty_row_indices, array_ops.expand_dims(sp_input.shape[0], -1),
True, False)
empty_row_indices_as_column = array_ops.reshape(
empty_row_indices, [-1, 1])
additional_indices = array_ops.concat(1, [
empty_row_indices_as_column,
array_ops.zeros_like(empty_row_indices_as_column)
])
additional_values = array_ops.fill(array_ops.shape(empty_row_indices),
default_value)
all_indices_unordered = array_ops.concat(
0, [sp_input.indices, additional_indices])
all_values_unordered = array_ops.concat(
0, [sp_input.values, additional_values])
sp_unordered_output = ops.SparseTensor(all_indices_unordered,
all_values_unordered,
sp_input.shape)
sp_ordered_output = sparse_reorder(sp_unordered_output)
return sp_ordered_output, empty_row_indicator
def sparse_fill_empty_rows(sp_input, default_value, name=None):
"""Fills empty rows in the input 2-D `SparseTensor` with a default value.
This op adds entries with the specified `default_value` at index
`[row, 0]` for any row in the input that does not already have a value.
For example, suppose `sp_input` has shape `[5, 6]` and non-empty values:
[0, 1]: a
[0, 3]: b
[2, 0]: c
[3, 1]: d
Rows 1 and 4 are empty, so the output will be of shape `[5, 6]` with values:
[0, 1]: a
[0, 3]: b
[1, 0]: default_value
[2, 0]: c
[3, 1]: d
[4, 0]: default_value
Note that the input may have empty columns at the end, with no effect on
this op.
The output `SparseTensor` will be in row-major order and will have the
same shape as the input.
This op also returns an indicator vector such that
empty_row_indicator[i] = True iff row i was an empty row.
Args:
sp_input: A `SparseTensor` with shape `[N, M]`.
default_value: The value to fill for empty rows, with the same type as
`sp_input.`
name: A name prefix for the returned tensors (optional)
Returns:
sp_ordered_output: A `SparseTensor` with shape `[N, M]`, and with all empty
rows filled in with `default_value`.
empty_row_indicator: A bool vector of length `N` indicating whether each
input row was empty.
Raises:
TypeError: If `sp_input` is not a `SparseTensor`.
"""
if not isinstance(sp_input, ops.SparseTensor):
raise TypeError("Input must be a SparseTensor")
with ops.op_scope([sp_input], name, "SparseFillEmptyRows"):
default_value = ops.convert_to_tensor(
default_value, dtype=sp_input.values.dtype)
num_rows = math_ops.cast(sp_input.shape[0], types.int32)
all_row_indices = math_ops.cast(
math_ops.range(0, num_rows, 1), types.int64)
empty_row_indices, _ = array_ops.list_diff(
all_row_indices, sp_input.indices[:, 0])
empty_row_indicator = gen_sparse_ops.sparse_to_dense(
empty_row_indices, array_ops.expand_dims(sp_input.shape[0], -1), True,
False)
empty_row_indices_as_column = array_ops.reshape(empty_row_indices, [-1, 1])
additional_indices = array_ops.concat(
1,
[empty_row_indices_as_column,
array_ops.zeros_like(empty_row_indices_as_column)])
additional_values = array_ops.fill(array_ops.shape(empty_row_indices),
default_value)
all_indices_unordered = array_ops.concat(
0, [sp_input.indices, additional_indices])
all_values_unordered = array_ops.concat(
0, [sp_input.values, additional_values])
sp_unordered_output = ops.SparseTensor(
all_indices_unordered, all_values_unordered, sp_input.shape)
sp_ordered_output = sparse_reorder(sp_unordered_output)
return sp_ordered_output, empty_row_indicator
