SparseApplyAdagradDa

public final class SparseApplyAdagradDa

Update entries in '*var' and '*accum' according to the proximal adagrad scheme.

Nested Classes

class SparseApplyAdagradDa.Options Optional attributes for SparseApplyAdagradDa  

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> SparseApplyAdagradDa<T>
create(Scope scope, Operand<T> var, Operand<T> gradientAccumulator, Operand<T> gradientSquaredAccumulator, Operand<T> grad, Operand<? extends TNumber> indices, Operand<T> lr, Operand<T> l1, Operand<T> l2, Operand<TInt64> globalStep, Options... options)
Factory method to create a class wrapping a new SparseApplyAdagradDa operation.
Output<T>
out()
Same as "var".
static SparseApplyAdagradDa.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: "SparseApplyAdagradDA"

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 SparseApplyAdagradDa<T> create (Scope scope, Operand<T> var, Operand<T> gradientAccumulator, Operand<T> gradientSquaredAccumulator, Operand<T> grad, Operand<? extends TNumber> indices, Operand<T> lr, Operand<T> l1, Operand<T> l2, Operand<TInt64> globalStep, Options... options)

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

Parameters
scope current scope
var Should be from a Variable().
gradientAccumulator Should be from a Variable().
gradientSquaredAccumulator Should be from a Variable().
grad The gradient.
indices A vector of indices into the first dimension of var and accum.
lr Learning rate. Must be a scalar.
l1 L1 regularization. Must be a scalar.
l2 L2 regularization. Must be a scalar.
globalStep Training step number. Must be a scalar.
options carries optional attributes values
Returns
  • a new instance of SparseApplyAdagradDa

public Output<T> out ()

Same as "var".

public static SparseApplyAdagradDa.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.