TensorFlow 1 version | View source on GitHub |
Resets the shape of a SparseTensor
with indices and values unchanged.
tf.sparse.reset_shape(
sp_input, new_shape=None
)
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]:
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 input SparseTensor .
|
new_shape
|
None or a vector representing the new shape for the returned
SparseTensor .
|
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
|
If sp_input is not a SparseTensor .
|
ValueError
|
If new_shape represents a tensor with a different rank from
that of sp_input (if shapes are known when graph is constructed).
|
ValueError
|
If new_shape is determined during graph build to have
dimension sizes that are too small.
|
OpError
|
|