(self, src_net: "Network", beta: TfExpressionEx = 0.99, beta_nontrainable: TfExpressionEx = 0.0)
Construct a TensorFlow op that updates the variables of this network
        to be slightly closer to those of the given network.
Construct a TensorFlow op that updates the variables of this network
        to be slightly closer to those of the given network.
Construct a TensorFlow op that updates the variables of this network to be slightly closer to those of the given network .
def setup_as_moving_average_of(self, src_net: "Network", beta: TfExpressionEx = 0.99, beta_nontrainable: TfExpressionEx = 0.0) -> tf.Operation:
        """Construct a TensorFlow op that updates the variables of this network
        to be slightly closer to those of the given network."""
        with tfutil.absolute_name_scope(self.scope + "/_MovingAvg"):
            ops = []
            for name, var in self.vars.items():
                if name in src_net.vars:
                    cur_beta = beta if name in self.trainables else beta_nontrainable
                    new_value = tfutil.lerp(src_net.vars[name], var, cur_beta)
                    ops.append(var.assign(new_value))
            return tf.group(*ops)
def setup_as_moving_average_of ( self , src_net : "Network" , beta : TfExpressionEx = 0.99 , beta_nontrainable : TfExpressionEx = 0.0 ) -> tf . Operation : with tfutil . absolute_name_scope ( self . scope + "/_MovingAvg" ) : ops = [ ] for name , var in self . vars . items ( ) : if name in src_net . vars : cur_beta = beta if name in self . trainables else beta_nontrainable new_value = tfutil . lerp ( src_net . vars [ name ] , var , cur_beta ) ops . append ( var . assign ( new_value ) ) return tf . group ( * ops )