tf.raw_ops.SparseApplyFtrl

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

Compat aliases for migration

tf.compat.v1.raw_ops.SparseApplyFtrl

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

That is for rows we have grad for, we update var, accum and linear as follows:

$$accum_new = accum + grad * grad$$

$$linear += grad + (accum_{new}^{-lr_{power} } - accum^{-lr_{power} } / lr * var$$

$$quadratic = 1.0 / (accum_{new}^{lr_{power} } * lr) + 2 * l2$$

$$var = (sign(linear) * l1 - linear) / quadratic\ if\ |linear| > l1\ else\ 0.0$$

$$accum = accum_{new}$$

Args
`var`	A 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().
`linear`	A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable().
`grad`	A `Tensor`. Must have the same type as `var`. 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 `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.
`lr_power`	A `Tensor`. Must have the same type as `var`. 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
A mutable `Tensor`. Has the same type as `var`.

Args