Nowadays,　machine　learning　models,　especially　neural　networks,　have　became　prevalent　in　many　real-world　applications.　These　models　are　trained　based　on　a　one-way　trip　from　user　data:　as　long　as　users　contribute　their　data,　there　is　no　way　to　withdraw.　To　this　end,　machine　unlearning　becomes　a　popular　research　topic,　which　allows　the　model　trainer　to　unlearn　unexpected　data　from　a　trained　machine　learning　model.　In　this　article,　we　propose　the　first　uniform　metric　called　forgetting　rate　to　measure　the　effectiveness　of　a　machine　unlearning　method.　It　is　based　on　the　concept　of　membership　inference　and　describes　the　transformation　rate　of　the　eliminated　data　from　＂memorized＂　to　＂unknown＂　after　conducting　unlearning.　We　also　propose　a　novel　unlearning　method　called　Forsaken.　It　is　superior　to　previous　work　in　either　utility　or　efficiency　(when　achieving　the　same　forgetting　rate).　We　benchmark　Forsaken　with　eight　standard　datasets　to　evaluate　its　performance.　The　experimental　results　show　that　it　can　achieve　more　than　90%　forgetting　rate　on　average　and　only　causeless　than　5%　accuracy　loss.