updates into an existing tensor according to
Compat aliases for migration
See Migration guide for more details.
tf.raw_ops.TensorScatterUpdate( tensor, indices, updates, name=None )
This operation creates a new tensor by applying sparse
updates to the passed
This operation is very similar to
tf.scatter_nd, except that the updates are
scattered onto an existing tensor (as opposed to a zero-tensor). If the memory
for the existing tensor cannot be re-used, a copy is made and updated.
indices contains duplicates, then we pick the last update for the index.
If an out of bound index is found on CPU, an error is returned.
- If an out of bound index is found, the index is ignored.
- The order in which updates are applied is nondeterministic, so the output
will be nondeterministic if
indices is an integer tensor containing indices into a new tensor of shape
indicesmust have at least 2 axes:
- The last axis of
indicesis how deep to index into
tensorso this index depth must be less than the rank of
indices.shape[-1] <= tensor.ndim
indices.shape[-1] = tensor.rank this Op indexes and updates scalar elements.
indices.shape[-1] < tensor.rank it indexes and updates slices of the input
update has a rank of
tensor.rank - indices.shape[-1].
The overall shape of
indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
For usage examples see the python tf.tensor_scatter_nd_update function
||A name for the operation (optional).|