SparseApplyAdagradV2

public final class SparseApplyAdagradV2

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

That is for rows we have grad for, we update var and accum as follows:

$$accum += grad * grad$$

$$var -= lr * grad * (1 / sqrt(accum))$$

Nested Classes

class SparseApplyAdagradV2.Options Optional attributes for SparseApplyAdagradV2

Public Methods

Output <T>	asOutput () Returns the symbolic handle of a tensor.
static <T, U extends Number> SparseApplyAdagradV2 <T>	create ( Scope scope, Operand <T> var, Operand <T> accum, Operand <T> lr, Operand <T> epsilon, Operand <T> grad, Operand <U> indices, Options... options) Factory method to create a class wrapping a new SparseApplyAdagradV2 operation.
Output <T>	out () Same as "var".
static SparseApplyAdagradV2.Options	updateSlots (Boolean updateSlots)
static SparseApplyAdagradV2.Options	useLocking (Boolean useLocking)

Inherited Methods

From class org.tensorflow.op.PrimitiveOp

final boolean	equals (Object obj)
final int	hashCode ()
Operation	op () Returns the underlying `Operation`
final String	toString ()

From class java.lang.Object

boolean	equals (Object arg0)
final Class<?>	getClass ()
int	hashCode ()
final void	notify ()
final void	notifyAll ()
String	toString ()
final void	wait (long arg0, int arg1)
final void	wait (long arg0)
final void	wait ()

From interface org.tensorflow.Operand

abstract Output <T>

asOutput ()

Returns the symbolic handle of a tensor.

Public Methods

public Output <T> asOutput ()

Returns the symbolic handle of a 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 SparseApplyAdagradV2 <T> create ( Scope scope, Operand <T> var, Operand <T> accum, Operand <T> lr, Operand <T> epsilon, Operand <T> grad, Operand <U> indices, Options... options)

Factory method to create a class wrapping a new SparseApplyAdagradV2 operation.

Parameters

scope	current scope
var	Should be from a Variable().
accum	Should be from a Variable().
lr	Learning rate. 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 SparseApplyAdagradV2

public Output <T> out ()

Same as "var".

public static SparseApplyAdagradV2.Options updateSlots (Boolean updateSlots)

public static SparseApplyAdagradV2.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.