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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.
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 while sp_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 of sp_input.
sp_input: The input SparseTensor.new_shape: None or a vector representing the new shape for the returned
SparseTensor.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
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: - If new_shape has dimension sizes that are too small.