def scatter_nd_update(ref, indices, updates, use_locking=True, name=None):
r"""Applies sparse `updates` to individual values or slices in a Variable.
`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 update 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:
```python
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])
update = tf.scatter_nd_update(ref, indices, updates)
with tf.Session() as sess:
print sess.run(update)
```
The resulting update to ref would look like this:
[1, 11, 3, 10, 9, 6, 7, 12]
See @{tf.scatter_nd} for more details about how to make updates to
slices.
Args:
ref: A Variable.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A tensor of indices into ref.
updates: A `Tensor`. Must have the same type as `ref`.
A Tensor. Must have the same type as ref. A tensor of updated
values to add to ref.
use_locking: An optional `bool`. Defaults to `True`.
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:
The value of the variable after the update.
"""
if ref.dtype._is_ref_dtype:
return gen_state_ops.scatter_nd_update(
ref, indices, updates, use_locking, name)
return ref._lazy_read(gen_state_ops.resource_scatter_nd_update( # pylint: disable=protected-access
ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
name=name))
def scatter_nd_update(ref, indices, updates, use_locking=True, name=None):
r"""Applies sparse `updates` to individual values or slices in a Variable.
`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 update 4 scattered elements to a rank-1 tensor to
8 elements. In Python, that update would look like this:
```python
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])
update = tf.scatter_nd_update(ref, indices, updates)
with tf.Session() as sess:
print sess.run(update)
```
The resulting update to ref would look like this:
[1, 11, 3, 10, 9, 6, 7, 12]
See `tf.scatter_nd` for more details about how to make updates to
slices.
Args:
ref: A Variable.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A tensor of indices into ref.
updates: A `Tensor`. Must have the same type as `ref`.
A Tensor. Must have the same type as ref. A tensor of updated
values to add to ref.
use_locking: An optional `bool`. Defaults to `True`.
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:
The value of the variable after the update.
"""
if ref.dtype._is_ref_dtype:
return gen_state_ops.scatter_nd_update(
ref, indices, updates, use_locking, name)
return ref._lazy_read(gen_state_ops.resource_scatter_nd_update( # pylint: disable=protected-access
ref.handle, indices, ops.convert_to_tensor(updates, ref.dtype),
name=name))
def testScatterNdUpdateAddOps(self):
with self.session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.float32, shape=[8])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([1] * 8, dtype=dtypes.float32)))
indices = constant_op.constant([[4], [3], [1], [7]], dtype=dtypes.int32)
updates = constant_op.constant([9, 10, 11, 12], dtype=dtypes.float32)
expected = np.array([1, 11, 1, 10, 9, 1, 1, 12])
sess.run(
gen_state_ops.resource_scatter_nd_update(handle, indices, updates))
read = resource_variable_ops.read_variable_op(
handle, dtype=dtypes.float32)
self.assertAllClose(expected, self.evaluate(read))
def testScatterNdUpdateAddOps(self):
with self.test_session() as sess, self.test_scope():
handle = resource_variable_ops.var_handle_op(
dtype=dtypes.float32, shape=[8])
sess.run(
resource_variable_ops.assign_variable_op(
handle, constant_op.constant([1] * 8, dtype=dtypes.float32)))
indices = constant_op.constant([[4], [3], [1], [7]], dtype=dtypes.int32)
updates = constant_op.constant([9, 10, 11, 12], dtype=dtypes.float32)
expected = np.array([1, 11, 1, 10, 9, 1, 1, 12])
sess.run(
gen_state_ops.resource_scatter_nd_update(handle, indices, updates))
read = resource_variable_ops.read_variable_op(
handle, dtype=dtypes.float32)
self.assertAllClose(expected, self.evaluate(read))
