tf.raw_ops.SparseApplyMomentum

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

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:

\[accum = accum * momentum + grad\]

\[var -= lr * accum\]

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.
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).

A mutable Tensor. Has the same type as var.