tf.raw_ops.ResourceSparseApplyFtrlV2

Update relevant entries in '*var' according to the Ftrl-proximal scheme.

tf.raw_ops.ResourceSparseApplyFtrlV2(
    var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power,
    use_locking=False, name=None
)

That is for rows we have grad for, we update var, accum and linear as follows: grad_with_shrinkage = grad + 2 * l2_shrinkage * var accum_new = accum + grad_with_shrinkage * grad_with_shrinkage linear += grad_with_shrinkage + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new

Args:

  • var: A Tensor of type resource. Should be from a Variable().
  • accum: A Tensor of type resource. Should be from a Variable().
  • linear: A Tensor of type resource. Should be from a Variable().
  • grad: 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. 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.
  • lr: A Tensor. Must have the same type as grad. Scaling factor. Must be a scalar.
  • l1: A Tensor. Must have the same type as grad. L1 regularization. Must be a scalar.
  • l2: A Tensor. Must have the same type as grad. L2 shrinkage regularization. Must be a scalar.
  • l2_shrinkage: A Tensor. Must have the same type as grad.
  • lr_power: A Tensor. Must have the same type as grad. Scaling factor. Must be a scalar.
  • 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:

The created Operation.