tf.raw_ops.ApplyAdagradV2

Update '*var' according to the adagrad scheme.

tf.raw_ops.ApplyAdagradV2(
    var,
    accum,
    lr,
    epsilon,
    grad,
    use_locking=False,
    update_slots=True,
    name=None
)

accum += grad * grad var -= lr * grad * (1 / sqrt(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. Scaling 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.
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.
update_slots An optional bool. Defaults to True.
name A name for the operation (optional).

A mutable Tensor. Has the same type as var.