def scatter_update(ref, indices, updates, use_locking=True, name=None):
# pylint: disable=line-too-long
r"""Applies sparse updates to a variable reference.
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.
If values in `ref` is to be updated more than once, because there are
duplicate entries in `indices`, the order at which the updates happen
for each value is undefined.
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/ScatterUpdate.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 `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:
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_update(ref,
indices,
updates,
use_locking=use_locking,
name=name)
with ops.control_dependencies([
gen_resource_variable_ops.resource_scatter_update(
ref.handle,
indices,
ops.convert_to_tensor(updates, ref.dtype),
name=name)
]):
return ref.read_value()
def scatter_update(ref, indices, updates, use_locking=True, name=None):
# pylint: disable=line-too-long
r"""Applies sparse updates to a variable reference.
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.
If values in `ref` is to be updated more than once, because there are
duplicate entries in `indices`, the order at which the updates happen
for each value is undefined.
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/ScatterUpdate.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 `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:
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_update(ref, indices, updates,
use_locking=use_locking, name=name)
with ops.control_dependencies(
[gen_resource_variable_ops.resource_scatter_update(
ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
name=name)]):
return ref.read_value()
