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
)

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.