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1、DepartmentofComputerScience6King'sCollegeRd,TorontoUniversityofTorontoM5S3G4,Canadahttp://learning.cs.toronto.edufax:+14169781455CopyrightcGeo reyHinton2010.August2,2010UTMLTR2010{003APracticalGuidetoTrainingRestrictedBoltzmannMachinesVersion1Geo reyHintonDepartmentofComputerScience,U
2、niversityofTorontoAPracticalGuidetoTrainingRestrictedBoltzmannMachinesVersion1Geo reyHintonDepartmentofComputerScience,UniversityofTorontoContents1Introduction32AnoverviewofRestrictedBoltzmannMachinesandContrastiveDivergence33HowtocollectstatisticswhenusingContrastiveDivergence53.1Upd
3、atingthehiddenstates.................................53.2Updatingthevisiblestates.................................63.3Collectingthestatisticsneededforlearning........................63.4ArecipeforgettingthelearningsignalforCD1......................64Thesizeofamini-batch64.1Arecipeford
4、ividingthetrainingsetintomini-batches..................75Monitoringtheprogressoflearning75.1Arecipeforusingthereconstructionerror.........................76Monitoringtheover tting86.1Arecipeformonitoringtheover tting...........................87Thelearningrate87.1Arecipeforsettingthel
5、earningratesforweightsandbiases...............88Theinitialvaluesoftheweightsandbiases98.1Arecipeforsettingtheinitialvaluesoftheweightsandbiases.............99Momentum919.1Arecipeforusingmomentum................................1010Weight-decay1010.1Arecipeforusingweight-decay..........
6、.....................1111Encouragingsparsehiddenactivities1111.1Arecipeforsparsity.....................................1212Thenumberofhiddenunits1212.1Arecipeforchoosingthenumberofhiddenunits.....................1213Di erenttypesofunit1313.1Softmaxandmultinomialunits..................
7、.............1313.2Gaussianvisibleunits....................................1313.3Gaussianvisibleandhiddenunits..............................1413.4Binomialunits........................................1413.5Recti edlinearunits.....................................1414Varietiesofcontrasti
8、vediv
