1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
|
op {
graph_op_name: "ScatterNdAdd"
in_arg {
name: "ref"
description: <<END
A mutable Tensor. Should be from a Variable node.
END
}
in_arg {
name: "indices"
description: <<END
A Tensor. Must be one of the following types: int32, int64.
A tensor of indices into ref.
END
}
in_arg {
name: "updates"
description: <<END
A Tensor. Must have the same type as ref. A tensor of updated values
to add to ref.
END
}
out_arg {
name: "output_ref"
description: <<END
Same as ref. Returned as a convenience for operations that want
to use the updated values after the update is done.
END
}
attr {
name: "use_locking"
description: <<END
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.
END
}
summary: "Applies sparse addition between `updates` and individual values or slices"
description: <<END
within a given variable according to `indices`.
`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 `K`th
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 add 4 scattered elements to a rank-1 tensor to 8
elements. In Python, that addition 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])
add = tf.scatter_nd_add(ref, indices, updates)
with tf.Session() as sess:
print sess.run(add)
The resulting update to ref would look like this:
[1, 13, 3, 14, 14, 6, 7, 20]
See @{tf.scatter_nd} for more details about how to make updates to
slices.
END
}
|
