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))