Divides a variable reference by sparse updates.
This operation computes
# 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, ...]
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 = []`.
Nested Classes
class | ScatterDiv.Options | Optional attributes for ScatterDiv
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Constants
String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
Output<T> |
asOutput()
Returns the symbolic handle of the tensor.
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static <T extends TType> ScatterDiv<T> | |
Output<T> |
outputRef()
= Same as `ref`.
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static ScatterDiv.Options |
useLocking(Boolean useLocking)
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Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Public Methods
public Output<T> asOutput ()
Returns the symbolic handle of the tensor.
Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.
public static ScatterDiv<T> create (Scope scope, Operand<T> ref, Operand<? extends TNumber> indices, Operand<T> updates, Options... options)
Factory method to create a class wrapping a new ScatterDiv operation.
Parameters
scope | current scope |
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ref | Should be from a `Variable` node. |
indices | A tensor of indices into the first dimension of `ref`. |
updates | A tensor of values that `ref` is divided by. |
options | carries optional attributes values |
Returns
- a new instance of ScatterDiv
public Output<T> outputRef ()
= Same as `ref`. Returned as a convenience for operations that want to use the updated values after the update is done.
public static ScatterDiv.Options useLocking (Boolean useLocking)
Parameters
useLocking | If True, the operation will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. |
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