tf.sparse.reset_shape

Resets the shape of a SparseTensor with indices and values unchanged.

View aliases

Compat aliases for migration

tf.compat.v1.sparse.reset_shape, tf.compat.v1.sparse_reset_shape

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 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`.

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 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.

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 `sp_input`. Its shape is `new_shape` if that is set. Otherwise it is the tight bounding box of `sp_input`

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`	If `new_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.

tf.sparse.reset_shape

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Args

Returns

Raises