tf.raw_ops.SparseApplyAdagradDA

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

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.raw_ops.SparseApplyAdagradDA

tf.raw_ops.SparseApplyAdagradDA(
    var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1,
    l2, global_step, 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. Should be from a Variable().
gradient_accumulator	A mutable Tensor. Must have the same type as var. Should be from a Variable().
gradient_squared_accumulator	A mutable Tensor. Must have the same type as var. Should be from a Variable().
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.
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.
global_step	A Tensor of type int64. Training step number. 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.
name	A name for the operation (optional).

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