tf.raw_ops.ApplyAdaMax

Update '*var' according to the AdaMax algorithm.

Compat aliases for migration

tf.compat.v1.raw_ops.ApplyAdaMax

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)

Args
`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.
`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).

Returns
A mutable `Tensor`. Has the same type as `var`.

Args