Recent　years　have　witnessed　numerous　successful　applications　of　incorporating　attention　module　into　feed-forward　convolutional　neural　networks.　Along　this　line　of　research,　we　design　a　novel　lightweight　general-purpose　attention　module　by　simultaneously　taking　channel　attention　and　spatial　attention　into　consideration.　Specifically,　inspired　by　the　characteristics　of　channel　attention　and　spatial　attention,　a　nonlinear　hybrid　method　is　proposed　to　combine　such　two　types　of　attention　feature　maps,　which　is　highly　beneficial　to　better　network　fine-tuning.　Further,　the　parameters　of　each　attention　branch　can　be　adjustable　for　the　purpose　of　making　the　attention　module　more　flexible　and　adaptable.　From　another　point　of　view,　we　found　that　the　currently　popular　SE,　and　CBAM　modules　are　actually　two　particular　cases　of　our　proposed　attention　module.　We　also　explore　the　latest　attention　module　ADCM.　To　validate　the　module,　we　conduct　experiments　on　CIFAR10,　CIFAR100,　Fashion　MINIST　datasets.　Results　show　that,　after　integrating　with　our　attention　module,　existing　networks　tend　to　be　more　efficient　in　training　process　and　have　better　performance　as　compared　with　state-of-the-art　competitors.　Also,　it　is　worthy　to　stress　the　following　two　points:　(1)　our　attention　module　can　be　used　in　existing　state-of-the-art　deep　architectures　and　get　better　performance　at　a　small　computational　cost;　(2)　the　module　can　be　added　to　existing　deep　architectures　in　a　simple　way　through　stacking　the　integration　of　networks　block　and　our　module.