tf.raw_ops.ResourceSparseApplyProximalGradientDescent

Sparse update '*var' as FOBOS algorithm with fixed learning rate.

tf.raw_ops.ResourceSparseApplyProximalGradientDescent(
    var, alpha, l1, l2, grad, indices, use_locking=False, name=None
)

That is for rows we have grad for, we update var as follows: prox_v = var - alpha * grad var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}

Args:

  • var: A Tensor of type resource. Should be from a Variable().
  • alpha: 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. Scaling factor. Must be a scalar.
  • l1: A Tensor. Must have the same type as alpha. L1 regularization. Must be a scalar.
  • l2: A Tensor. Must have the same type as alpha. L2 regularization. Must be a scalar.
  • grad: A Tensor. Must have the same type as alpha. The gradient.
  • indices: A Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
  • 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:

The created Operation.