tf.raw_ops.SparseApplyProximalAdagrad

Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.

Compat aliases for migration

tf.compat.v1.raw_ops.SparseApplyProximalAdagrad

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

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

$accum += grad * grad$

$prox_v = var$

$prox_v -= lr * grad * (1 / sqrt(accum))$

$var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}$

Args
`var`	A mutable `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `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`. Learning rate. 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.
`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, 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