Update '*var' according to the Adam algorithm.
$$\text{lr}_t := \mathrm{learning_rate} * \sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$ $$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$ $$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$ $$\text{variable} := \text{variable} - \text{lr}_t * m_t / (\sqrt{v_t} + \epsilon)$$
Nested Classes
class | ResourceApplyAdam.Options | Optional attributes for ResourceApplyAdam
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Constants
String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
static <T extends TType> ResourceApplyAdam | |
static ResourceApplyAdam.Options |
useLocking(Boolean useLocking)
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static ResourceApplyAdam.Options |
useNesterov(Boolean useNesterov)
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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 static ResourceApplyAdam create (Scope scope, Operand<?> var, Operand<?> m, Operand<?> v, Operand<T> beta1Power, Operand<T> beta2Power, 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 ResourceApplyAdam 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. |
beta2Power | 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 ResourceApplyAdam
public static ResourceApplyAdam.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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public static ResourceApplyAdam.Options useNesterov (Boolean useNesterov)
Parameters
useNesterov | If `True`, uses the nesterov update. |
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