tf.raw_ops.ApplyProximalAdagrad

Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.

tf.raw_ops.ApplyProximalAdagrad(
    var, accum, lr, l1, l2, grad, use_locking=False, name=None
)

accum += grad * grad prox_v = var - lr * grad * (1 / sqrt(accum)) var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*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().
  • accum: A mutable Tensor. Must have the same type as var. Should be from a Variable().
  • lr: 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.
  • 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 and accum 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.