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tf.raw_ops.SparseApplyFtrlV2

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

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 * grad 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

`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 shrinkage regularization. Must be a scalar.
`l2_shrinkage` A `Tensor`. Must have the same type as `var`.
`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.
`multiply_linear_by_lr` An optional `bool`. Defaults to `False`.
`name` A name for the operation (optional).

A mutable `Tensor`. Has the same type as `var`.

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