ApplyAdaMax

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public final class ApplyAdaMax

Update '*var' according to the AdaMax algorithm.

m_t <- beta1 * m_{t-1} + (1 - beta1) * g v_t <- max(beta2 * v_{t-1}, abs(g)) variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon)

Nested Classes

class ApplyAdaMax.Options Optional attributes for ApplyAdaMax  

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> ApplyAdaMax<T>
create(Scope scope, Operand<T> var, Operand<T> m, Operand<T> v, Operand<T> beta1Power, Operand<T> lr, Operand<T> beta1, Operand<T> beta2, Operand<T> epsilon, Operand<T> grad, Options... options)
Factory method to create a class wrapping a new ApplyAdaMax operation.
Output<T>
out()
Same as "var".
static ApplyAdaMax.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: "ApplyAdaMax"

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 ApplyAdaMax<T> create (Scope scope, Operand<T> var, Operand<T> m, Operand<T> v, Operand<T> beta1Power, Operand<T> lr, Operand<T> beta1, Operand<T> beta2, Operand<T> epsilon, Operand<T> grad, Options... options)

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

Parameters
scope current scope
var Should be from a Variable().
m Should be from a Variable().
v Should be from a Variable().
beta1Power Must be a scalar.
lr Scaling factor. Must be a scalar.
beta1 Momentum factor. Must be a scalar.
beta2 Momentum factor. Must be a scalar.
epsilon Ridge term. Must be a scalar.
grad The gradient.
options carries optional attributes values
Returns
  • a new instance of ApplyAdaMax

public Output<T> out ()

Same as "var".

public static ApplyAdaMax.Options useLocking (Boolean useLocking)

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