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Applies sparse updates to individual values or slices in a Variable.
tf.compat.v1.scatter_nd_update(
ref, indices, updates, use_locking=True, name=None
)
ref is a Tensor with rank P and indices is a Tensor of rank Q.
indices must be integer tensor, containing indices into ref.
It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P.
The innermost dimension of indices (with length K) corresponds to
indices into elements (if K = P) or slices (if K < P) along the Kth
dimension of ref.
updates is Tensor of rank Q-1+P-K with shape:
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
For example, say we want to update 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
update = tf.compat.v1.scatter_nd_update(ref, indices, updates)
with tf.compat.v1.Session() as sess:
print sess.run(update)
The resulting update to ref would look like this:
[1, 11, 3, 10, 9, 6, 7, 12]
See tf.scatter_nd for more details about how to make updates to
slices.
Args | |
|---|---|
ref
|
A Variable. |
indices
|
A Tensor. Must be one of the following types: int32, int64.
A tensor of indices into ref.
|
updates
|
A Tensor. Must have the same type as ref.
A Tensor. Must have the same type as ref. A tensor of updated
values to add to ref.
|
use_locking
|
An optional bool. Defaults to True.
An optional bool. Defaults to True. If True, the assignment will
be protected by a lock; otherwise the behavior is undefined,
but may exhibit less contention.
|
name
|
A name for the operation (optional). |
Returns | |
|---|---|
| The value of the variable after the update. |