tf.raw_ops.ApplyProximalGradientDescent

Update '*var' as FOBOS algorithm with fixed learning rate.

tf.raw_ops.ApplyProximalGradientDescent(
    var, alpha, l1, l2, delta, use_locking=False, name=None
)

prox_v = var - alpha * delta var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}

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().
  • alpha: A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.
  • l1: A Tensor. Must have the same type as var. L1 regularization. Must be a scalar.
  • l2: A Tensor. Must have the same type as var. L2 regularization. Must be a scalar.
  • delta: A Tensor. Must have the same type as var. The change.
  • use_locking: An optional bool. Defaults to False. If True, the subtraction 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.