def apply_step(self, variables, deltas, loss_sampled):
        """
        Applies the given (and already calculated) step deltas to the variable values.

        Args:
            variables: List of variables.
            deltas: List of deltas of same length.
            loss_sampled : the sampled loss

        Returns:
            The step-applied operation. A tf.group of tf.assign_add ops.
        """
        update_stats_op = self.compute_and_apply_stats(
            loss_sampled, var_list=var_list)
        grads = [(a, b) for a, b in zip(deltas, varlist)]
        kfacOptim, _ = self.apply_gradients_kfac(grads)
        return kfacOptim
def apply_step(self, variables, deltas, loss_sampled):
        """
        Applies the given (and already calculated) step deltas to the variable values.

        Args:
            variables: List of variables.
            deltas: List of deltas of same length.
            loss_sampled : the sampled loss

        Returns:
            The step-applied operation. A tf.group of tf.assign_add ops.
        """
        update_stats_op = self.compute_and_apply_stats(
            loss_sampled, var_list=var_list)
        grads = [(a, b) for a, b in zip(deltas, varlist)]
        kfacOptim, _ = self.apply_gradients_kfac(grads)
        return kfacOptim
def apply_step ( self , variables , deltas , loss_sampled ) : update_stats_op = self . compute_and_apply_stats ( loss_sampled , var_list = var_list ) grads = [ ( a , b ) for a , b in zip ( deltas , varlist ) ] kfacOptim , _ = self . apply_gradients_kfac ( grads ) return kfacOptim
Applies the given (and already calculated) step deltas to the variable values.

        Args:
            variables: List of variables.
            deltas: List of deltas of same length.
            loss_sampled : the sampled loss

        Returns:
            The step-applied operation. A tf.group of tf.assign_add ops.
Applies the given ( and already calculated ) step deltas to the variable values .