tf.sparse.reduce

TensorFlow 1 version

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

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Compat aliases for migration

See Migration guide for more details.

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.

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`	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.

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tf.sparse / reduce_sum

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Args

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