tf.raw_ops.ApplyAdam

Update '*var' according to the Adam algorithm.

tf.raw_ops.ApplyAdam(
    var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad,
    use_locking=False, use_nesterov=False, name=None
)
$$lr_t := \text{learning\_rate} * \sqrt{1 - beta_2^t} / (1 - beta_1^t)$$
$$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$
$$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$
$$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$

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

A mutable Tensor. Has the same type as var.