tf.raw_ops.ResourceApplyAdamWithAmsgrad

Update '*var' according to the Adam algorithm.

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.raw_ops.ResourceApplyAdamWithAmsgrad

lrt:=learningrate1βt2/(1βt1)

mt:=β1mt1+(1β1)g

vt:=β2vt1+(1β2)gg

ˆvt:=maxˆvt1,vt

variable:=variablelrtmt/(ˆvt+ϵ)

var A Tensor of type resource. Should be from a Variable().
m A Tensor of type resource. Should be from a Variable().
v A Tensor of type resource. Should be from a Variable().
vhat A Tensor of type resource. Should be from a Variable().
beta1_power A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, qint16, quint16, uint16, complex128, half, uint32, uint64. Must be a scalar.
beta2_power A Tensor. Must have the same type as beta1_power. Must be a scalar.
lr A Tensor. Must have the same type as beta1_power. Scaling factor. Must be a scalar.
beta1 A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.
beta2 A Tensor. Must have the same type as beta1_power. Momentum factor. Must be a scalar.
epsilon A Tensor. Must have the same type as beta1_power. Ridge term. Must be a scalar.
grad A Tensor. Must have the same type as beta1_power. The gradient.
use_locking An optional bool. Defaults to False. 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.
name A name for the operation (optional).

The created Operation.