tf.raw_ops.SparseApplyAdadelta

var: Should be from a Variable().

Compat aliases for migration

tf.compat.v1.raw_ops.SparseApplyAdadelta

tf.raw_ops.SparseApplyAdadelta(
    var, accum, accum_update, lr, rho, epsilon, grad, indices, use_locking=False,
    name=None
)

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`.
`accum`	A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable().
`accum_update`	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.
`rho`	A `Tensor`. Must have the same type as `var`. Decay factor. Must be a scalar.
`epsilon`	A `Tensor`. Must have the same type as `var`. Constant factor. 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