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
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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.
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static <T extends TType> ApplyAdaMax<T> | |
Output<T> |
out()
Same as "var".
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static ApplyAdaMax.Options |
useLocking(Boolean useLocking)
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Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
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 |
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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 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. |
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