tensorflow::ops::SparseApplyProximalAdagrad

Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.

Summary

That is for rows we have grad for, we update var and accum as follows: $$accum += grad * grad$$ $$prox_v = var$$ $$prox_v -= lr * grad * (1 / sqrt(accum))$$ $$var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}$$

Arguments:

• scope: A Scope object
• var: Should be from a Variable().
• accum: Should be from a Variable().
• lr: Learning rate. Must be a scalar.
• l1: L1 regularization. Must be a scalar.
• l2: L2 regularization. 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:

• Output: Same as "var".

Public attributes

Operation operation

::tensorflow::Output out

 SparseApplyProximalAdagrad(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input lr,
  ::tensorflow::Input l1,
  ::tensorflow::Input l2,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices
)

 SparseApplyProximalAdagrad(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input lr,
  ::tensorflow::Input l1,
  ::tensorflow::Input l2,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices,
  const SparseApplyProximalAdagrad::Attrs & attrs
)

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const 

Attrs UseLocking(
  bool x
)