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
|
op {
graph_op_name: "ScatterDiv"
in_arg {
name: "ref"
description: <<END
Should be from a `Variable` node.
END
}
in_arg {
name: "indices"
description: <<END
A tensor of indices into the first dimension of `ref`.
END
}
in_arg {
name: "updates"
description: <<END
A tensor of values that `ref` is divided by.
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
If True, the operation will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
END
}
summary: "Divides a variable reference by sparse updates."
description: <<END
This operation computes
```python
# Scalar indices
ref[indices, ...] /= updates[...]
# Vector indices (for each i)
ref[indices[i], ...] /= updates[i, ...]
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...]
```
This operation outputs `ref` after the update is done.
This makes it easier to chain operations that need to use the reset value.
Duplicate entries are handled correctly: if multiple `indices` reference
the same location, their contributions divide.
Requires `updates.shape = indices.shape + ref.shape[1:]` or `updates.shape = []`.
END
}
|
