SparseApplyAdadelta

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public final class SparseApplyAdadelta

var: Should be from a Variable().

Nested Classes

class SparseApplyAdadelta.Options Optional attributes for SparseApplyAdadelta  

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.
static <T extends TType> SparseApplyAdadelta<T>
create(Scope scope, Operand<T> var, Operand<T> accum, Operand<T> accumUpdate, Operand<T> lr, Operand<T> rho, Operand<T> epsilon, Operand<T> grad, Operand<? extends TNumber> indices, Options... options)
Factory method to create a class wrapping a new SparseApplyAdadelta operation.
Output<T>
out()
Same as "var".
static SparseApplyAdadelta.Options
useLocking(Boolean useLocking)

Inherited Methods

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "SparseApplyAdadelta"

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 SparseApplyAdadelta<T> create (Scope scope, Operand<T> var, Operand<T> accum, Operand<T> accumUpdate, Operand<T> lr, Operand<T> rho, Operand<T> epsilon, Operand<T> grad, Operand<? extends TNumber> indices, Options... options)

Factory method to create a class wrapping a new SparseApplyAdadelta operation.

Parameters
scope current scope
accum Should be from a Variable().
accumUpdate : Should be from a Variable().
lr Learning rate. Must be a scalar.
rho Decay factor. Must be a scalar.
epsilon Constant factor. Must be a scalar.
grad The gradient.
indices A vector of indices into the first dimension of var and accum.
options carries optional attributes values
Returns
  • a new instance of SparseApplyAdadelta

public Output<T> out ()

Same as "var".

public static SparseApplyAdadelta.Options useLocking (Boolean useLocking)

Parameters
useLocking If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.