tf.raw_ops.ApplyAdaMax

Update '*var' according to the AdaMax algorithm.

tf.raw_ops.ApplyAdaMax(
    var,
    m,
    v,
    beta1_power,
    lr,
    beta1,
    beta2,
    epsilon,
    grad,
    use_locking=False,
    name=None
)

mt <- beta1 * m{t-1} + (1 - beta1) * g vt <- max(beta2 * v{t-1}, abs(g)) variable <- variable - learning_rate / (1 - beta1^t) * m_t / (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, qint16, quint16, 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.
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.
name A name for the operation (optional).

A mutable Tensor. Has the same type as var.