tf.raw_ops.ResourceApplyAdaMax

Update '*var' according to the AdaMax algorithm.

tf.raw_ops.ResourceApplyAdaMax(
    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 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().
  • beta1_power: A 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. 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).

Returns:

The created Operation.