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ApplyAdagradDa

public final class ApplyAdagradDa

Update '*var' according to the proximal adagrad scheme.

Nested Classes

class ApplyAdagradDa.Options Optional attributes for ApplyAdagradDa

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

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 ApplyAdagradDa <T> create ( Scope scope, Operand <T> var, Operand <T> gradientAccumulator, Operand <T> gradientSquaredAccumulator, Operand <T> grad, Operand <T> lr, Operand <T> l1, Operand <T> l2, Operand < TInt64 > globalStep, Options... options)

Factory method to create a class wrapping a new ApplyAdagradDa 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.
lr Scaling factor. 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 ApplyAdagradDa

public Output <T> out ()

Same as "var".

public static ApplyAdagradDa.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.