预览加载中，请您耐心等待几秒...

基于TensorFlow手写数字识别模型改进.docx

预览
1/3
2/3
3/3

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

基于TensorFlow手写数字识别模型改进 Title:EnhancingHandwrittenDigitRecognitionModelusingTensorFlow Abstract: HandwrittenDigitRecognitionisafundamentaltaskinthefieldofcomputervisionandpatternrecognition.Inrecentyears,deeplearningmodelshavegreatlyimprovedtheperformanceofthistask.ThispaperaimstoenhancetheaccuracyandefficiencyofaHandwrittenDigitRecognitionmodelusingTensorFlow,apopularandpowerfulopen-sourcelibraryformachinelearning. Introduction: HandwrittenDigitRecognitionplaysavitalroleinvariousapplications,suchasopticalcharacterrecognition,automatedzipcodereading,bankcheckprocessing,andautomaticformfilling.Traditionally,thistaskwasachievedusingclassicalmachinelearningmethodslikeSupportVectorMachines(SVM)orRandomForests.However,thesemethodsoftenrequiredhandcraftedfeatureengineeringandlackedtheabilitytocapturecomplexspatialdependencies. Withtheadventofdeeplearning,ConvolutionalNeuralNetworks(CNNs)havebecomethestate-of-the-artapproachforHandwrittenDigitRecognition.CNNsexcelatlearningmeaningfulfeaturesfromrawdataandcanautomaticallycapturebothlocalandglobalspatialdependencies.TensorFlow,apopulardeeplearningframework,providesacomprehensiveecosystemforbuilding,training,andevaluatingsuchmodels. Methodology: Inthispaper,weproposeanenhancedHandwrittenDigitRecognitionmodelbasedonTensorFlow.Themodelconsistsofthreemaincomponents:datapreprocessing,networkarchitecture,andtrainingandevaluation. Preprocessing: Toimprovethemodel'sperformance,wepreprocesstheinputdatainthefollowingsteps: 1.Conversiontograyscale:Converttheimagestograyscaletoreducethecomplexityandcomputationalrequirementsofthemodel. 2.Imageresizing:Resizetheimagestoastandardsizewhilepreservingtheaspectratio,reducingdatadimensionalityandenablingfastertraining. 3.Normalization:Normalizethepixelvaluesbetween0and1toimprovemodelstabilityandconvergence. NetworkArchitecture: OurproposedmodelutilizesadeepCNNarchitectureinspiredbywidelysuccessfulmodelssuchasLeNet-5andAlexNet.Thearchitectureconsistsofthefollowinglayers: 1.ConvolutionalLayers:Mul