tf.raw_ops.ResourceSparseApplyAdagradV2

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

tf.raw_ops.ResourceSparseApplyAdagradV2(
    var, accum, lr, epsilon, grad, indices, use_locking=False, update_slots=True,
    name=None
)

That is for rows we have grad for, we update var and accum as follows: accum += grad * grad var -= lr * grad * (1 / sqrt(accum))

Args:

  • var: A Tensor of type resource. Should be from a Variable().
  • accum: A Tensor of type resource. Should be from a Variable().
  • lr: A 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. Learning rate. Must be a scalar.
  • epsilon: A Tensor. Must have the same type as lr. Constant factor. Must be a scalar.
  • grad: A Tensor. Must have the same type as lr. 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.
  • 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).

Returns:

The created Operation.