tensorflow::ops::ResourceSparseApplyAdagrad

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

Summary

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:

• scope: A Scope object
• var: Should be from a Variable().
• accum: Should be from a Variable().
• lr: Learning rate. Must be a scalar.
• grad: The gradient.
• indices: A vector of indices into the first dimension of var and accum.

Optional attributes (see Attrs):

• use_locking: 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.

Returns:

• the created Operation

Public attributes

Operation operation

Public functions

 ResourceSparseApplyAdagrad(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input lr,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices
)

 ResourceSparseApplyAdagrad(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input lr,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices,
  const ResourceSparseApplyAdagrad::Attrs & attrs
)

 operator::tensorflow::Operation() const 

Public static functions

Attrs UpdateSlots(
  bool x
)

Attrs UseLocking(
  bool x
)