opt2.py

 def _create_slots(self, var_list):
     # Create slots for allocation and later management of additional 
     # variables associated with the variables to train.
     # for example: the first and second moments.
     '''
     for v in var_list:
     self._zeros_slot(v, "m", self._name)
     self._zeros_slot(v, "v", self._name)
     '''
 def _apply_dense(self, grad, var):
     #define your favourite variable update
     # for example:
     '''
     # Here we apply gradient descents by substracting the variables 
     # with the gradient times the learning_rate (defined in __init__)
     var_update = state_ops.assign_sub(var, self.learning_rate * grad) 
     '''
     #The trick is now to pass the Ops in the control_flow_ops and 
     # eventually groups any particular computation of the slots your 
     # wish to keep track of:
     # for example:    
     '''
     m_t = ...m... #do something with m and grad
     v_t = ...v... # do something with v and grad
     '''
  return control_flow_ops.group(*[var_update, m_t, v_t])