tf.raw_ops.SparseApplyAdagradDA

Update entries in 'var' and 'accum' according to the proximal adagrad scheme.

tf.raw_ops.SparseApplyAdagradDA(
    var,
    gradient_accumulator,
    gradient_squared_accumulator,
    grad,
    indices,
    lr,
    l1,
    l2,
    global_step,
    use_locking=False,
    name=None
)

varA 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().
gradient_accumulatorA mutable Tensor. Must have the same type as var. Should be from a Variable().
gradient_squared_accumulatorA mutable Tensor. Must have the same type as var. Should be from a Variable().
gradA Tensor. Must have the same type as var. The gradient.
indicesA Tensor. Must be one of the following types: int32, int64. A vector of indices into the first dimension of var and accum.
lrA Tensor. Must have the same type as var. Learning rate. Must be a scalar.
l1A Tensor. Must have the same type as var. L1 regularization. Must be a scalar.
l2A Tensor. Must have the same type as var. L2 regularization. Must be a scalar.
global_stepA Tensor of type int64. Training step number. Must be a scalar.
use_lockingAn 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.
nameA name for the operation (optional).

A mutable Tensor. Has the same type as var.