Gastric　cancer　is　the　third　most　common　cause　of　cancer-related　death　in　the　world.　Human　epidermal　growth　factor　receptor　2　(HER2)　positive　is　an　important　subtype　of　gastric　cancer,　which　can　provide　significant　diagnostic　information　for　gastric　cancer　pathologists.　However,　pathologists　usually　use　a　semi-quantitative　assessment　method　to　assign　HER2　scores　for　gastric　cancer　by　repeatedly　comparing　hematoxylin　and　eosin　(H&E)　whole　slide　images　(WSIs)　with　their　HER2　immunohistochemical　WSIs　one　by　one　under　the　microscope.　It　is　a　repetitive,　tedious,　and　highly　subjective　process.　Additionally,　WSIs　have　billions　of　pixels　in　an　image,　which　poses　computational　challenges　to　Computer-Aided　Diagnosis　(CAD)　systems.　This　study　proposed　a　deep　learning　algorithm　for　HER2　quantification　evaluation　of　gastric　cancer.　Different　from　other　studies　that　use　convolutional　neural　networks　for　extracting　feature　maps　or　pre-processing　on　WSIs,　we　proposed　a　novel　automatic　HER2　scoring　framework　in　this　study.　In　order　to　accelerate　the　computational　process,　we　proposed　to　use　the　re-parameterization　scheme　to　separate　the　training　model　from　the　deployment　model,　which　significantly　speedup　the　inference　process.　To　the　best　of　our　knowledge,　this　is　the　first　study　to　provide　a　deep　learning　quantification　algorithm　for　HER2　scoring　of　gastric　cancer　to　assist　the　pathologist＇s　diagnosis.　Experiment　results　have　demonstrated　the　effectiveness　of　our　proposed　method　with　an　accuracy　of　0.94　for　the　HER2　scoring　prediction.