tf.raw_ops.ApplyAdadelta

Update '*var' according to the adadelta scheme.

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

accum = rho() * accum + (1 - rho()) * grad.square(); update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; update_accum = rho() * update_accum + (1 - rho()) * update.square(); var -= update;

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().
  • 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. Scaling factor. 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.
  • use_locking: An optional bool. Defaults to False. If True, updating of the var, accum and update_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.