Aliases:
tf.sparse.reset_shape
tf.sparse_reset_shape
tf.sparse.reset_shape(
sp_input,
new_shape=None
)
Defined in tensorflow/python/ops/sparse_ops.py
.
Resets the shape of a SparseTensor
with indices and values unchanged.
If new_shape
is None, returns a copy of sp_input
with its shape reset
to the tight bounding box of sp_input
. This will be a shape consisting of
all zeros if sp_input has no values.
If new_shape
is provided, then it must be larger or equal in all dimensions
compared to the shape of sp_input
. When this condition is met, the returned
SparseTensor will have its shape reset to new_shape
and its indices and
values unchanged from that of sp_input.
For example:
Consider a sp_input
with shape [2, 3, 5]:
[0, 0, 1]: a
[0, 1, 0]: b
[0, 2, 2]: c
[1, 0, 3]: d
It is an error to set
new_shape
as [3, 7] since this represents a rank-2 tensor whilesp_input
is rank-3. This is either a ValueError during graph construction (if both shapes are known) or an OpError during run time.Setting
new_shape
as [2, 3, 6] will be fine as this shape is larger or equal in every dimension compared to the original shape [2, 3, 5].On the other hand, setting new_shape as [2, 3, 4] is also an error: The third dimension is smaller than the original shape 2, 3, 5.
If
new_shape
is None, the returned SparseTensor will have a shape [2, 3, 4], which is the tight bounding box ofsp_input
.
Args:
sp_input
: The inputSparseTensor
.new_shape
: None or a vector representing the new shape for the returnedSparseTensor
.
Returns:
A SparseTensor
indices and values unchanged from input_sp
. Its shape is
new_shape
if that is set. Otherwise it is the tight bounding box of
input_sp
Raises:
TypeError
: Ifsp_input
is not aSparseTensor
.ValueError
: Ifnew_shape
represents a tensor with a different rank from that ofsp_input
(if shapes are known when graph is constructed).ValueError
: Ifnew_shape
is determined during graph build to have dimension sizes that are too small.OpError
: - Ifnew_shape
has dimension sizes that are too small.- If shapes are not known during graph construction time, and during run time it is found out that the ranks do not match.