tf.raw_ops.SparseApplyMomentum

Update relevant entries in 'var' and 'accum' according to the momentum scheme.

Compat aliases for migration

tf.compat.v1.raw_ops.SparseApplyMomentum

tf.raw_ops.SparseApplyMomentum(
    var, accum, lr, grad, indices, momentum, use_locking=False, use_nesterov=False,
    name=None
)

Set use_nesterov = True if you want to use Nesterov momentum.

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

$$accum = accum * momentum + grad$$

$$var -= lr * accum$$

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().
`lr`	A `Tensor`. Must have the same type as `var`. Learning rate. 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.
`momentum`	A `Tensor`. Must have the same type as `var`. Momentum. 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.
`use_nesterov`	An optional `bool`. Defaults to `False`. If `True`, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum.
`name`	A name for the operation (optional).

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

Args