def scatter_add(ref, indices, updates, use_locking=False, name=None):
# pylint: disable=line-too-long
r"""Adds sparse updates to the variable referenced by `resource`.
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 updated value.
Duplicate entries are handled correctly: if multiple `indices` reference
the same location, their contributions add.
Requires `updates.shape = indices.shape + ref.shape[1:]`.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src='https://www.tensorflow.org/images/ScatterAdd.png' alt>
</div>
Args:
ref: A `Variable`.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A tensor of indices into the first dimension of `ref`.
updates: A `Tensor`. Must have the same type as `ref`.
A tensor of updated values to store in `ref`.
use_locking: An optional `bool`. Defaults to `False`.
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:
Same as `ref`. Returned as a convenience for operations that want
to use the updated values after the update is done.
"""
if ref.dtype._is_ref_dtype:
return gen_state_ops.scatter_add(ref,
indices,
updates,
use_locking=use_locking,
name=name)
return ref._lazy_read(
gen_resource_variable_ops.resource_scatter_add( # pylint: disable=protected-access
ref.handle,
indices,
ops.convert_to_tensor(updates, ref.dtype),
name=name))
def scatter_add(ref, indices, updates, use_locking=False, name=None):
# pylint: disable=line-too-long
r"""Adds sparse updates to the variable referenced by `resource`.
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 updated value.
Duplicate entries are handled correctly: if multiple `indices` reference
the same location, their contributions add.
Requires `updates.shape = indices.shape + ref.shape[1:]`.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src='https://www.tensorflow.org/images/ScatterAdd.png' alt>
</div>
Args:
ref: A `Variable`.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A tensor of indices into the first dimension of `ref`.
updates: A `Tensor`. Must have the same type as `ref`.
A tensor of updated values to store in `ref`.
use_locking: An optional `bool`. Defaults to `False`.
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:
Same as `ref`. Returned as a convenience for operations that want
to use the updated values after the update is done.
"""
if ref.dtype._is_ref_dtype:
return gen_state_ops.scatter_add(ref, indices, updates,
use_locking=use_locking, name=name)
return ref._lazy_read(gen_resource_variable_ops.resource_scatter_add( # pylint: disable=protected-access
ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
name=name))
