diff --git a/Neural graph module/df_test_a0.1_e50_b8.csv b/Neural graph module/df_test_a0.1_e50_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5a8e4587c84bc4ec2c3402bd91828eef8ca9719e
--- /dev/null
+++ b/Neural graph module/df_test_a0.1_e50_b8.csv	
@@ -0,0 +1,348 @@
+correct_pred,prediction,pred_probability,gs_pred
+dbo:associatedband,dbp:artist,0.8587912,dbp:artist
+dbp:canonizedby,dbp:canonizedby,0.7482736,dbp:canonizedby
+dbo:ingredient,dbo:ingredient,0.8449717,dbo:ingredient
+dbp:characters,dbo:creator,0.5272995,dbp:characters
+dbo:stadium,dbp:owner,0.2646646,dbp:league
+dbo:broadcastarea,dbo:country,0.40996015,dbp:area
+dbo:occupation,dbp:education,0.4218032,dbo:occupation
+dbp:starring,dbo:director,0.34308416,dbo:director
+dbp:spouse,dbo:director,0.23827481,dbo:occupation
+dbo:stylisticorigin,dbp:artist,0.49266008,dbp:region
+dbo:city,dbo:formerteam,0.13040659,dbp:city
+dbp:party,dbp:primeminister,0.352836,dbp:successor
+dbo:countyseat,dbp:owner,0.30809897,dbo:country
+dbp:children,dbo:commander,0.35775298,dbo:deathplace
+dbp:format,dbp:area,0.67764324,dbp:owner
+dbp:owner,dbp:owner,0.67684245,dbp:owner
+dbo:locationcity,dbp:education,0.35216463,dbo:birthplace
+dbp:placeofburial,dbp:education,0.45573953,None
+dbo:architect,dbp:owner,0.6472375,dbp:developer
+dbp:firstteam,dbo:formerteam,0.385956,dbo:manufacturer
+dbo:nonfictionsubject,dbo:author,0.4903412,dbo:author
+dbo:award,dbo:director,0.5511264,dbo:award
+dbo:award,dbo:award,0.64191943,dbo:award
+dbp:managerclubs,dbo:sport,0.49404874,dbp:team
+dbp:managerclubs,dbp:league,0.19773334,dbp:managerclubs
+dbo:anthem,dbo:country,0.32393286,dbo:anthem
+dbo:outflow,dbp:cities,0.95445126,dbp:cities
+dbp:position,dbo:formerteam,0.9943968,dbp:position
+dbp:firstdriver,dbo:poledriver,0.9388186,dbo:poledriver
+dbp:type,dbp:membership,0.5257862,dbp:owner
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+dbp:tenants,dbp:owner,0.6250052,dbo:formerteam
+dbo:deathplace,dbp:primeminister,0.4087746,dbo:deathplace
+dbo:deathplace,dbp:primeminister,0.48344192,dbo:deathplace
+dbp:hubs,dbp:owner,0.35399142,dbp:headquarters
+dbo:homestadium,dbp:league,0.848291,dbp:arena
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+dbp:draftteam,dbo:formerteam,0.81644773,dbp:owner
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+dbo:formerteam,dbo:formerteam,0.8747029,dbo:formerteam
+dbo:formerteam,dbo:formerteam,0.9618173,dbo:formerteam
+dbp:hometown,dbp:primeminister,0.2005012,dbp:birthplace
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+dbo:mayor,dbo:country,0.5598388,dbp:successor
+dbo:formerteam,dbo:formerteam,0.9767319,dbo:formerteam
+dbo:formerteam,dbo:formerteam,0.9810838,dbo:formerteam
+dbp:designer,dbo:author,0.3094792,dbo:parent
+dbo:award,dbo:almamater,0.15130459,dbo:almamater
+dbo:religion,dbp:education,0.23363972,dbp:affiliation
+dbo:ceremonialcounty,dbp:borough,0.48115367,dbo:country
+dbo:computingplatform,dbo:designer,0.9380132,dbp:developer
+dbp:order,dbp:primeminister,0.48653427,None
+dbp:place,dbp:artist,0.3118441,dbo:country
+dbp:debutteam,dbo:formerteam,0.9294321,dbo:deathplace
+dbp:debutteam,dbo:formerteam,0.81782186,dbp:youthclubs
+dbo:relation,dbo:deathplace,0.1408754,dbp:relatives
+dbp:battles,dbo:family,0.3307023,dbp:battles
+dbo:artist,dbp:artist,0.49907926,dbp:artist
+dbp:origin,dbo:currency,0.1703795,dbo:birthplace
+dbp:branch,dbo:author,0.42393935,dbo:occupation
+dbp:titles,dbp:canonizedby,0.25281325,dbp:title
+dbp:ground,dbo:sport,0.35002866,dbo:deathplace
+dbo:binomialauthority,dbo:family,0.2649683,dbo:binomialauthority
+dbp:league,dbp:owner,0.7936983,dbp:owner
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+dbo:affiliation,dbp:affiliation,0.5008779,dbp:affiliation
+dbo:creator,dbo:creator,0.9886594,dbo:creator
+dbo:creator,dbo:creator,0.7724437,dbo:creator
+dbp:successor,dbp:primeminister,0.6601572,dbp:primeminister
+dbp:religiousaffiliation,dbp:affiliation,0.7202069,dbo:country
+dbo:owner,dbp:owner,0.76570225,dbp:owner
+dbo:managerclub,dbp:youthclubs,0.41512367,dbp:youthclubs
+dbp:architecture,dbo:author,0.2760455,dbp:design
+dbp:origin,dbp:primeminister,0.29667193,dbp:label
+dbp:locationtown,dbp:owner,0.4339045,dbo:country
+dbo:recordedin,dbp:artist,0.28619757,dbo:birthplace
+dbp:branch,dbo:commander,0.595145,dbo:country
+dbo:almamater,dbo:almamater,0.663137,None
+dbo:deathplace,dbo:director,0.34890968,dbo:deathplace
+dbp:programminglanguage,dbo:author,0.4202631,dbo:language
+dbo:nationality,dbo:country,0.27813122,dbo:birthplace
+dbp:currentmembers,dbp:artist,0.95697474,dbp:label
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+dbo:child,dbo:commander,0.38021255,dbo:parent
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+dbp:teamname,dbo:sport,0.6737192,None
+dbo:tenant,dbp:owner,0.58352387,None
+dbo:operatingsystem,dbo:operatingsystem,0.84486604,dbp:operatingsystem
+dbo:deathplace,dbp:education,0.15388547,dbp:race
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+dbp:cities,dbp:cities,0.49662775,dbo:deathplace
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+dbp:birthplace,dbp:education,0.26528153,dbp:birthplace
+dbp:battles,dbp:primeminister,0.30193624,dbp:birthplace
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+dbo:manufacturer,dbo:manufacturer,0.6250248,dbo:manufacturer
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+dbo:languagefamily,dbp:region,0.9714327,dbo:language
+dbp:music,dbo:creator,0.39276507,dbp:artist
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+dbp:nationalorigin,dbo:manufacturer,0.6300164,dbo:manufacturer
+dbo:origin,dbo:manufacturer,0.5157337,dbo:manufacturer
+dbo:occupation,dbo:author,0.31411043,dbo:occupation
+dbo:occupation,dbo:director,0.30903333,None
+dbo:deathplace,dbo:country,0.26515704,dbo:deathplace
+dbo:country,dbo:deathplace,0.3256211,dbo:country
+dbp:cityserved,dbo:country,0.38397956,dbp:city
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+dbo:associatedband,dbp:education,0.40953046,dbp:birthplace
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+dbp:highschool,dbp:artist,0.17074777,dbp:birthplace
+dbp:employer,dbp:primeminister,0.44770652,None
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+dbp:genre,dbo:author,0.3297503,dbp:artist
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+dbp:characters,dbo:creator,0.45484456,dbp:characters
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+dbo:owningcompany,dbo:creator,0.17798197,dbp:owner
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+dbp:narrated,dbo:author,0.53588486,dbp:artist
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+dbp:placeofburial,dbo:director,0.24979384,dbo:restingplace
+dbp:developer,dbp:owner,0.31145024,dbp:developer
+dbo:stateoforigin,dbo:family,0.3199097,dbo:country
+dbp:line,dbo:sport,0.3737474,dbo:owner
+dbp:origin,dbo:country,0.6564525,dbp:manufacturer
+dbp:locationcountry,dbo:commander,0.6213783,dbo:country
+dbp:buildingtype,dbp:owner,0.30804455,dbo:origin
+dbo:computingplatform,dbo:operatingsystem,0.52414644,dbp:successor
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+dbp:owner,dbo:author,0.24875525,dbp:affiliation
+dbp:affiliation,dbo:sport,0.6721557,dbp:affiliation
+dbp:placeofburial,dbo:commander,0.45525888,dbo:restingplace
+dbo:commandstructure,dbo:manufacturer,0.28387704,dbp:battles
+dbo:commandstructure,dbo:manufacturer,0.25910485,dbp:role
+dbp:playedfor,dbo:formerteam,0.98449856,dbp:playedfor
+dbp:playedfor,dbo:formerteam,0.93921614,dbo:formerteam
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+dbp:meaning,dbo:country,0.26815808,dbp:name
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+dbp:distributor,dbo:director,0.4953371,dbo:employer
+dbo:cinematography,dbo:director,0.62271273,dbo:director
+dbo:type,dbo:ingredient,0.6278609,dbo:ingredient
+dbp:neighboringmunicipalities,dbo:country,0.32965204,dbo:birthplace
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+dbo:homestadium,dbp:league,0.6867591,dbp:league
+dbo:hubairport,dbo:country,0.46140116,dbp:headquarters
+dbo:largestcity,dbo:country,0.7728961,dbo:country
+dbo:birthplace,dbo:author,0.19189322,dbo:location
+dbp:office,dbo:institution,0.3323073,dbo:almamater
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+dbo:service,dbp:education,0.45851746,dbp:owner
+dbo:award,dbp:education,0.6269109,None
+dbo:birthplace,dbo:country,0.5105453,dbo:deathplace
+dbo:foundedby,dbo:author,0.48615476,dbp:area
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+dbp:publisher,dbp:label,0.2710806,dbp:developer
+dbp:successor,dbp:education,0.37509722,None
+dbp:label,dbp:artist,0.5370665,dbp:label
+dbp:assembly,dbp:owner,0.30264258,dbo:assembly
+dbo:product,dbo:author,0.42293534,dbo:parent
+dbo:series,dbo:author,0.35379595,dbo:author
+dbp:college,dbp:education,0.2636713,dbp:birthplace
+dbp:almamater,dbp:owner,0.5124444,None
+dbp:engine,dbo:manufacturer,0.48025262,dbo:manufacturer
+dbp:officialname,dbo:ingredient,0.4082971,dbp:country
+dbp:field,dbp:owner,0.44400397,dbp:venue
+dbo:voice,dbp:education,0.122421816,dbo:creator
+dbp:spouse,dbo:award,0.24524537,dbo:deathplace
+dbo:doctoralstudent,dbo:almamater,0.3320907,dbo:doctoralstudent
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+dbo:spouse,dbp:education,0.35489458,dbp:birthplace
+dbo:battle,dbo:commander,0.9383056,dbo:country
+dbo:narrator,dbp:artist,0.25610903,None
+dbp:name,dbp:owner,0.46909982,None
+dbo:manager,dbp:owner,0.5945927,None
+dbo:writer,dbo:director,0.52128595,dbo:director
+dbo:routeend,dbp:owner,0.22116773,dbp:owner
+dbo:occupation,dbo:author,0.18086943,None
+dbo:developer,dbo:author,0.64367694,dbp:developer
+dbp:cities,dbp:cities,0.8565855,dbp:cities
+dbp:sisterstations,dbp:area,0.9939499,dbp:area
+dbo:relative,dbo:almamater,0.24455544,dbo:creator
+dbp:notablecommanders,dbo:commander,0.39271572,dbo:commander
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+dbo:manufacturer,dbo:manufacturer,0.53463525,dbo:manufacturer
+dbp:awards,dbo:award,0.9916152,dbp:awards
+dbp:mascot,dbo:sport,0.29944655,dbp:affiliation
+dbp:race,dbp:race,0.99445456,dbp:race
+dbp:flagbearer,dbo:formerteam,0.38924843,dbp:rank
+dbp:album,dbp:artist,0.9355019,dbp:artist
+dbo:relative,dbo:veneratedin,0.5084705,dbo:veneratedin
+dbp:design,dbp:owner,0.20782979,dbp:design
+dbo:occupation,dbo:award,0.7352814,None
+dbp:inflow,dbo:country,0.2837661,dbo:country
+dbo:language,dbp:artist,0.34866583,dbo:language
+dbp:race,dbp:race,0.9733114,dbp:race
+dbo:race,dbp:race,0.9567068,dbp:race
+dbp:chancellor,dbo:almamater,0.26480666,dbo:deathplace
+dbp:governor,dbp:primeminister,0.42168948,dbp:education
+dbp:governor,dbp:primeminister,0.30021736,dbp:education
+dbo:manufacturer,dbo:author,0.64833236,dbp:manufacturer
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+dbo:almamater,dbo:almamater,0.36117932,dbo:almamater
+dbp:creator,dbp:artist,0.59512466,dbo:creator
+dbo:musicby,dbp:artist,0.48171565,dbp:music
+dbo:location,dbo:country,0.5594798,dbo:deathplace
+dbo:operator,dbo:ingredient,0.38670373,dbp:manufacturer
+dbo:nationality,dbo:country,0.2419293,dbo:deathplace
+dbp:deathplace,dbo:almamater,0.264792,dbo:deathplace
+dbp:houses,dbp:membership,0.20180264,dbp:houses
+dbp:awards,dbo:award,0.46273586,dbo:award
+dbo:occupation,dbp:affiliation,0.16648938,dbo:occupation
+dbo:occupation,dbo:author,0.19529742,dbo:deathplace
+dbo:wineregion,dbo:country,0.6050014,dbo:deathplace
+dbp:allegiance,dbp:primeminister,0.26402083,dbp:birthplace
+dbo:relative,dbo:institution,0.3494947,dbo:relative
+dbp:state,dbp:region,0.29992363,None
+dbp:region,dbp:region,0.29504302,dbp:region
+dbo:kingdom,dbo:family,0.26025087,None
+dbp:producer,dbo:director,0.3833194,dbp:artist
+dbo:narrator,dbo:author,0.34884647,dbo:director
+dbp:city,dbp:league,0.43675536,dbp:city
+dbp:city,dbp:city,0.9812688,dbp:owner
+dbo:ingredient,dbo:ingredient,0.7449214,dbo:ingredient
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+dbp:birthplace,dbo:award,0.9051804,dbo:birthplace
+dbp:battles,dbo:commander,0.8229087,dbp:primeminister
+dbo:placeofburial,dbo:commander,0.44814235,dbo:deathplace
+dbp:champion,dbo:formerteam,0.8267507,dbp:team
+dbo:almamater,dbp:education,0.5446769,None
+dbo:university,dbp:education,0.39831606,dbo:director
+dbo:almamater,dbo:commander,0.30878937,dbo:award
+dbp:area,dbo:country,0.69435686,dbo:country
+dbo:bandmember,dbp:artist,0.840266,dbp:label
+dbo:movement,dbo:author,0.67077446,dbo:occupation
+dbo:parentorganisation,dbo:author,0.5416305,dbp:owner
+dbp:creator,dbo:author,0.3563214,dbo:country
+dbo:leader,dbo:commander,0.2965191,dbp:successor
+dbo:knownfor,dbo:director,0.32777014,None
+dbo:service,dbp:label,0.17107868,dbp:products
+dbo:literarygenre,dbo:creator,0.28144664,dbo:occupation
+dbo:launchsite,dbp:label,0.13737953,dbo:manufacturer
+dbp:nationalteam,dbo:formerteam,0.3796916,dbp:managerclubs
+dbo:stateoforigin,dbo:award,0.18886441,dbo:country
+dbo:bandmember,dbp:artist,0.47742215,dbo:occupation
+dbp:party,dbp:primeminister,0.64967066,dbo:order
+dbp:party,dbp:primeminister,0.91161466,None
+dbo:institution,dbp:education,0.34502757,dbo:institution
+dbp:education,dbp:education,0.59138554,dbp:education
+dbp:crosses,dbp:owner,0.44284916,dbp:design
+dbo:owner,dbp:owner,0.74795777,dbp:owner
+dbo:developer,dbo:author,0.731216,dbo:designer
+dbp:lieutenant,dbp:education,0.7811978,None
+dbo:training,dbp:education,0.4911879,dbo:deathplace
+dbp:leadername,dbo:author,0.41278863,dbp:name
+dbo:parent,dbp:primeminister,0.2640215,dbo:parent
+dbp:poledriver,dbo:poledriver,0.98413694,dbo:poledriver
+dbp:poledriver,dbo:poledriver,0.9183406,dbo:poledriver
+dbp:governingbody,dbp:owner,0.37372443,dbp:governingbody
+dbp:doctoraladvisor,dbp:education,0.6538311,dbp:doctoraladvisor
+dbp:prizes,dbo:award,0.74548155,dbp:prizes
+dbp:line,dbp:owner,0.510768,dbo:owner
+dbp:manufacturer,dbp:manufacturer,0.5701846,dbp:manufacturer
+dbp:distributor,dbp:manufacturer,0.3140489,dbp:manufacturer
+dbo:neighboringmunicipality,dbo:country,0.3659376,dbp:borough
+dbo:incumbent,dbo:institution,0.19580252,dbp:birthplace
+dbo:location,dbp:owner,0.7814846,dbp:locationcountry
+dbo:location,dbp:owner,0.8248256,dbp:name
+dbp:homestadium,dbo:country,0.65057176,dbp:region
+dbp:region,dbp:artist,0.3882984,dbp:region
+dbp:region,dbo:country,0.3831907,dbp:region
+dbp:developer,dbo:author,0.37051037,dbp:developer
+dbo:developer,dbo:author,0.31179318,dbp:developer
+dbo:anthem,dbo:country,0.5321355,dbo:occupation
+dbp:president,dbp:education,0.60872215,dbo:almamater
+dbp:president,dbp:primeminister,0.35612887,dbp:education
+dbp:type,dbo:sport,0.33313105,dbo:type
+dbp:state,dbp:education,0.42487797,None
+dbp:presenter,dbo:creator,0.32375288,dbo:director
+dbp:locationcity,dbo:creator,0.14217848,dbp:owner
+dbp:training,dbp:education,0.4435892,dbp:birthplace
+dbo:majorshrine,dbp:owner,0.2990446,dbo:deathplace
+dbo:majorshrine,dbp:education,0.45483655,None
+dbo:chairman,dbp:league,0.14957495,dbp:owner
+dbp:placeofbirth,dbo:country,0.42834368,dbo:birthplace
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+dbo:party,dbo:otherparty,0.34296212,dbp:primeminister
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+dbo:lieutenant,dbo:commander,0.5802923,dbp:successor
+dbo:commander,dbo:commander,0.91531634,None
+dbo:team,dbo:formerteam,0.58800995,dbp:league
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+dbp:youthclubs,dbp:youthclubs,0.33674788,dbp:youthclubs
+dbo:almamater,dbo:almamater,0.28487456,dbo:almamater
+dbo:computingplatform,dbo:operatingsystem,0.33997145,dbo:operatingsystem
+dbp:title,dbo:award,0.5370789,dbp:birthplace
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+dbo:sisterstation,dbp:area,0.9820228,dbp:area
+dbo:editor,dbo:author,0.30355138,dbp:country
+dbo:service,dbp:label,0.24382246,dbp:owner
+dbo:cinematography,dbp:education,0.5455198,dbo:director
+dbo:discoverer,dbo:currency,0.3718936,dbp:successor
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+dbo:tenant,dbo:ingredient,0.48961118,dbp:affiliation
+dbp:employer,dbo:sport,0.35296363,None
diff --git a/Neural graph module/df_train_a0.1_e50_b8.csv b/Neural graph module/df_train_a0.1_e50_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..51faa64556d52415277c25e9438257e4b9c5c470
--- /dev/null
+++ b/Neural graph module/df_train_a0.1_e50_b8.csv	
@@ -0,0 +1,1119 @@
+correct_pred,prediction,pred_probability,gs_pred
+dbp:tenants,dbp:owner,0.69305116,None
+dbo:family,dbo:family,0.9978085,dbo:family
+dbo:wineregion,dbo:ingredient,0.38256827,dbp:region
+dbp:youthclubs,dbp:youthclubs,0.99336123,dbp:youthclubs
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+dbp:programminglanguage,dbo:operatingsystem,0.5776632,dbo:language
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+dbp:successor,dbp:primeminister,0.37165785,dbp:successor
+dbp:owner,dbp:owner,0.9735357,dbp:owner
+dbp:teamname,dbo:sport,0.5126013,dbp:city
+dbo:executiveproducer,dbo:author,0.22498485,dbo:creator
+dbp:constituency,dbp:primeminister,0.56750524,dbp:constituency
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+dbp:starring,dbo:creator,0.21253492,dbp:starring
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+dbp:combatant,dbo:family,0.3513528,dbo:creator
+dbp:distributor,dbp:manufacturer,0.3240742,dbp:manufacturer
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+dbp:origin,dbo:country,0.80819255,dbo:birthplace
+dbo:formerteam,dbo:formerteam,0.64527684,None
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+dbo:origin,dbo:manufacturer,0.51020825,dbp:manufacturer
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+dbo:bronzemedalist,dbo:sport,0.3235524,dbo:silvermedalist
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+dbo:maintainedby,dbo:country,0.80521804,dbo:maintainedby
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+dbo:veneratedin,dbo:veneratedin,0.9964893,dbo:veneratedin
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+dbo:literarygenre,dbo:author,0.3736775,dbo:literarygenre
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+dbo:debutteam,dbo:formerteam,0.8783077,dbp:team
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+dbo:notablework,dbp:artist,0.27622512,dbo:almamater
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+dbo:birthplace,dbp:education,0.30382985,None
+dbo:country,dbo:country,0.7222394,dbo:country
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+dbo:firstdriver,dbo:poledriver,0.75049794,dbo:poledriver
+dbo:relation,dbo:commander,0.24817456,dbo:deathplace
+dbp:affiliation,dbp:affiliation,0.6787062,None
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+dbo:editing,dbo:award,0.70079863,dbo:director
+dbo:commander,dbo:commander,0.91199684,None
+dbo:occupation,dbo:deathplace,0.23146218,None
+dbo:formerteam,dbo:formerteam,0.955186,dbo:formerteam
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+dbp:architect,dbp:owner,0.2741644,None
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+dbo:athletics,dbp:league,0.5136229,None
+dbp:owner,dbp:owner,0.6699934,None
+dbo:sport,dbo:sport,0.997389,None
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+dbo:location,dbo:manufacturer,0.32779294,dbo:deathplace
+dbp:athletics,dbp:affiliation,0.34198144,None
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+dbp:stadium,dbp:league,0.6464023,dbp:league
+dbo:tenant,dbp:owner,0.827644,None
+dbo:spouse,dbo:country,0.17297181,dbo:spouse
+dbo:parentcompany,dbp:owner,0.40789512,dbp:owner
+dbo:associatedmusicalartist,dbp:race,0.23468535,dbp:label
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+dbp:operator,dbo:country,0.26713535,dbp:owner
+dbo:hubairport,dbp:owner,0.23944645,dbo:targetairport
+dbo:partner,dbp:primeminister,0.55665076,dbp:birthplace
+dbp:headquarters,dbp:owner,0.60568964,dbp:owner
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+dbo:genre,dbp:artist,0.8196056,dbo:occupation
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+dbo:formerteam,dbo:formerteam,0.92794657,dbo:formerteam
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+dbp:race,dbp:race,0.9549854,dbp:race
+dbo:writer,dbp:artist,0.33503884,dbp:artist
+dbo:owner,dbp:owner,0.36390725,dbp:owner
+dbo:manufacturer,dbo:manufacturer,0.9927739,dbo:manufacturer
+dbp:music,dbo:creator,0.2986168,dbp:title
+dbo:territory,dbp:cities,0.1970172,dbo:commander
+dbp:label,dbp:label,0.97658175,dbp:label
+dbo:manufacturer,dbo:manufacturer,0.95798314,dbo:manufacturer
+dbo:editing,dbo:award,0.5324023,dbo:director
+dbo:executiveproducer,dbo:creator,0.62900037,dbo:creator
+dbp:citizenship,dbo:deathplace,0.45845813,dbo:location
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+dbo:commander,dbo:commander,0.92265373,dbo:commander
+dbp:genre,dbp:label,0.6099559,dbp:genre
+dbp:creators,dbo:creator,0.29371905,dbo:creator
+dbo:poledriver,dbo:poledriver,0.9622905,dbo:poledriver
+dbo:spouse,dbp:artist,0.7083778,dbp:birthplace
+dbp:locationcity,dbo:country,0.30100226,dbp:keypeople
+dbo:literarygenre,dbo:author,0.33088422,dbo:literarygenre
+dbo:birthplace,dbp:primeminister,0.6151001,dbp:birthplace
+dbp:allegiance,dbp:primeminister,0.6029178,dbp:primeminister
+dbp:animator,dbo:director,0.8784354,dbo:director
+dbo:relative,dbo:award,0.9598322,dbo:almamater
+dbo:institution,dbo:institution,0.8032339,dbo:institution
+dbp:membership,dbp:membership,0.7633639,dbo:deathplace
+dbp:owner,dbp:owner,0.98778206,None
+dbp:deputy,dbp:primeminister,0.4002969,dbp:successor
+dbo:operator,dbo:manufacturer,0.16669433,dbo:manufacturer
+dbp:affiliation,dbp:affiliation,0.852727,dbp:affiliation
+dbo:race,dbp:race,0.20919655,dbp:race
+dbo:owningcompany,dbp:owner,0.86608773,dbp:owner
+dbp:licensee,dbp:area,0.24548674,dbp:owner
+dbp:owner,dbp:firstteam,0.59055036,dbp:owner
+dbp:highschool,dbo:almamater,0.24922295,dbo:award
+dbp:owner,dbp:owner,0.7870287,None
+dbp:branch,dbo:country,0.21807347,dbo:country
+dbp:outflow,dbp:cities,0.62928975,dbo:deathplace
+dbo:license,dbo:operatingsystem,0.30649796,dbo:license
+dbp:languages,dbo:country,0.13960797,dbp:region
+dbo:writer,dbp:race,0.25300455,dbo:director
+dbo:order,dbo:order,0.9880717,dbo:order
+dbo:builder,dbo:country,0.20928487,dbp:name
+dbo:parent,dbo:award,0.4260999,dbo:parent
+dbp:designer,dbp:artist,0.2187833,dbo:manufacturer
+dbp:playedfor,dbp:league,0.4522306,dbo:formerteam
+dbo:regionserved,dbo:country,0.7114217,dbo:deathplace
+dbo:keyperson,dbp:owner,0.37046516,dbp:keypeople
+dbo:opponent,dbp:primeminister,0.19637212,dbo:deathplace
+dbp:residence,dbp:primeminister,0.40555593,dbp:successor
+dbp:international,dbo:author,0.3323406,dbo:veneratedin
+dbp:birthplace,dbo:poledriver,0.22137858,dbp:birthplace
+dbo:commander,dbo:commander,0.96551317,dbo:commander
+dbo:order,dbo:family,0.64418745,dbo:order
+dbo:mouthmountain,dbo:deathplace,0.27490586,dbo:deathplace
+dbp:deathplace,dbo:director,0.26094326,dbo:deathplace
+dbo:manufacturer,dbo:manufacturer,0.9686053,dbo:manufacturer
+dbp:style,dbo:almamater,0.50986695,dbp:design
+dbp:operatingsystem,dbo:author,0.37522814,dbp:genre
+dbp:relatives,dbo:author,0.24259335,dbp:relatives
+dbp:creator,dbo:author,0.42324665,dbo:author
+dbp:flagbearer,dbo:country,0.2437814,dbp:rank
+dbp:partner,dbo:commander,0.095232666,dbo:partner
+dbp:title,dbp:artist,0.14602414,dbp:title
+dbp:license,dbo:operatingsystem,0.4245845,dbo:license
+dbp:deathcause,dbo:author,0.14850214,dbo:commander
+dbo:author,dbo:author,0.8576771,dbo:author
+dbp:affiliations,dbp:owner,0.22336832,dbp:affiliation
+dbp:producer,dbo:author,0.35942578,dbo:director
+dbo:chairman,dbp:youthclubs,0.40339935,None
+dbp:nearestcity,dbp:artist,0.36326554,dbp:name
+dbo:nearestcity,dbo:country,0.2319538,dbo:deathplace
+dbp:poledriver,dbo:poledriver,0.89446646,dbo:poledriver
+dbp:publisher,dbo:author,0.37880835,dbo:creator
+dbo:primeminister,dbp:primeminister,0.36065614,dbp:primeminister
+dbp:manager,dbp:league,0.27661216,dbp:name
+dbp:country,dbp:affiliation,0.19932945,dbp:owner
+dbo:academicdiscipline,dbp:affiliation,0.5936467,dbo:academicdiscipline
+dbp:founded,dbp:affiliation,0.74809116,dbp:locationcountry
+dbo:commander,dbo:commander,0.8864373,dbo:commander
+dbo:rivermouth,dbp:owner,0.3022416,dbp:name
+dbo:associatedband,dbp:artist,0.65332484,dbp:artist
+dbo:academicadvisor,dbo:almamater,0.37973842,dbo:academicadvisor
+dbp:locationcountry,dbo:country,0.5060069,dbp:locationcountry
+dbp:college,dbo:institution,0.31374398,dbp:birthplace
+dbp:draftteam,dbo:formerteam,0.5908183,dbo:formerteam
+dbo:kingdom,dbo:family,0.94049436,None
+dbp:lyrics,dbo:creator,0.2122394,dbo:author
+dbo:formerteam,dbo:formerteam,0.77781117,dbo:formerteam
+dbp:recorded,dbp:label,0.4387848,dbp:label
+dbp:cities,dbp:cities,0.9279403,dbp:cities
+dbo:voice,dbo:creator,0.7520374,dbo:voice
+dbo:builder,dbo:author,0.26044273,dbo:builder
+dbp:recorded,dbp:label,0.25097844,dbp:label
+dbo:award,dbo:award,0.9854196,dbo:award
+dbo:team,dbo:formerteam,0.9149097,dbp:league
+dbo:ground,dbp:owner,0.3673656,dbp:league
+dbp:order,dbp:primeminister,0.31681782,None
+dbo:religion,dbo:veneratedin,0.92973804,None
+dbp:religion,dbo:otherparty,0.18130207,dbp:birthplace
+dbo:knownfor,dbo:formerteam,0.42315662,dbp:birthplace
+dbp:engine,dbo:manufacturer,0.26512986,dbp:manufacturer
+dbo:launchsite,dbp:artist,0.31920105,dbo:manufacturer
+dbp:domain,dbo:order,0.96220386,None
+dbo:otherparty,dbo:otherparty,0.9874691,dbo:otherparty
+dbp:locationcity,dbp:artist,0.16303156,dbo:country
+dbo:mountainrange,dbp:cities,0.5812342,dbo:mountainrange
+dbp:placeofdeath,dbo:country,0.48139194,dbo:deathplace
+dbo:foundedby,dbo:family,0.27304515,dbp:name
+dbo:commander,dbo:commander,0.9182979,dbo:commander
+dbo:owner,dbo:currency,0.27017522,dbp:owner
+dbp:role,dbo:commander,0.18596712,dbp:role
+dbo:ingredient,dbo:ingredient,0.996412,dbo:ingredient
+dbp:author,dbo:author,0.3890084,dbo:author
+dbo:owningcompany,dbo:country,0.17916934,dbp:owner
+dbo:developer,dbo:author,0.45550737,dbp:developer
+dbo:family,dbo:family,0.9866577,dbp:title
+dbp:firstdriver,dbo:poledriver,0.8015499,dbo:poledriver
+dbp:guests,dbp:label,0.5981701,dbp:guests
+dbo:capital,dbo:currency,0.23224172,dbp:region
+dbo:doctoraladvisor,dbp:education,0.68790805,dbp:successor
+dbp:almamater,dbp:education,0.8725239,dbo:almamater
+dbp:order,dbp:primeminister,0.46950844,dbo:deathplace
+dbp:architect,dbp:owner,0.21118456,None
+dbo:sport,dbo:sport,0.9055692,dbo:sport
+dbo:occupation,dbo:formerteam,0.26505268,dbo:deathplace
+dbo:author,dbo:author,0.6070558,dbo:author
+dbp:officialname,dbo:country,0.1505429,dbp:country
+dbp:relatives,dbo:author,0.34573665,dbp:artist
+dbo:almamater,dbp:education,0.9284541,dbo:almamater
+dbp:province,dbo:country,0.4685464,dbp:affiliation
+dbp:garrison,dbo:commander,0.7981055,dbo:deathplace
+dbp:maininterests,dbp:primeminister,0.29538217,dbp:maininterests
+dbo:award,dbo:award,0.8428567,dbo:award
+dbo:employer,dbp:primeminister,0.3330561,None
+dbp:club,dbp:race,0.12711674,dbp:team
+dbo:family,dbo:family,0.9955929,dbo:family
+dbo:service,dbo:country,0.21418262,dbp:name
+dbo:order,dbo:order,0.94200116,dbo:order
+dbp:narrated,dbo:director,0.4468106,dbp:country
+dbp:residence,dbo:deathplace,0.76025945,dbo:deathplace
+dbo:field,dbo:family,0.16338998,None
+dbp:playedfor,dbo:formerteam,0.96711797,dbo:formerteam
+dbp:youthclubs,dbp:youthclubs,0.86758226,dbp:youthclubs
+dbo:author,dbo:author,0.9779458,dbo:author
+dbp:hometown,dbo:country,0.5538854,dbo:deathplace
+dbp:album,dbp:artist,0.8685823,dbp:artist
+dbo:creator,dbo:author,0.37392575,None
+dbo:creator,dbo:creator,0.94350576,dbo:creator
+dbo:almamater,dbo:almamater,0.60404444,None
+dbo:gender,dbo:sport,0.333809,dbo:religion
+dbo:knownfor,dbo:formerteam,0.45210823,dbo:occupation
+dbo:country,dbo:country,0.7973266,dbo:location
+dbo:stadium,dbo:sport,0.30492893,dbp:region
+dbo:training,dbp:education,0.47608602,dbp:birthplace
+dbo:rivermouth,dbp:cities,0.34502557,dbp:name
+dbo:timezone,dbo:country,0.26178524,None
+dbp:type,dbo:commander,0.26011205,dbp:name
+dbo:college,dbo:almamater,0.22745691,dbp:birthplace
+dbo:operatingsystem,dbo:operatingsystem,0.99858534,dbp:operatingsystem
+dbp:membership,dbp:membership,0.91708946,dbp:membership
+dbo:routestart,dbo:country,0.37122425,dbo:routestart
+dbo:training,dbo:award,0.41288358,dbp:almamater
+dbp:coverartist,dbo:author,0.34754375,dbo:author
+dbo:birthplace,dbp:primeminister,0.6328183,dbp:birthplace
+dbo:computingplatform,dbo:author,0.67697203,dbp:design
+dbo:album,dbp:artist,0.6791781,dbp:artist
+dbp:distributor,dbp:label,0.6167848,dbp:label
+dbo:literarygenre,dbo:author,0.21226002,dbp:discipline
+dbo:designer,dbo:author,0.1661224,dbo:manufacturer
+dbo:nationality,dbo:almamater,0.29167047,dbp:nationality
+dbo:manufacturer,dbo:manufacturer,0.91672075,dbo:manufacturer
+dbo:license,dbo:author,0.74292326,dbo:author
+dbp:education,dbp:education,0.93192756,None
+dbo:servingrailwayline,dbp:cities,0.17752409,dbo:servingrailwayline
+dbo:almamater,dbo:almamater,0.71710765,dbo:almamater
+dbo:firstascentperson,dbp:education,0.23864605,dbo:firstascentperson
+dbp:race,dbp:race,0.68775046,dbp:race
+dbo:stateoforigin,dbo:country,0.3935069,None
+dbo:currency,dbo:currency,0.9913941,dbo:currency
+dbp:prizes,dbo:award,0.7963489,dbo:award
+dbo:ingredient,dbo:ingredient,0.9777103,dbo:ingredient
+dbo:party,dbp:primeminister,0.70348877,dbo:deathplace
+dbp:screenplay,dbp:label,0.28582487,dbo:director
+dbp:deathplace,dbp:primeminister,0.35947865,dbo:deathplace
+dbp:region,dbp:region,0.9969127,dbp:region
+dbp:employer,dbo:author,0.6028593,dbp:employer
+dbp:governingbody,dbp:owner,0.7809872,dbp:governingbody
+dbo:battle,dbo:commander,0.3860979,dbp:battles
+dbp:producer,dbp:label,0.5221879,dbp:label
+dbo:architecturalstyle,dbp:owner,0.5595432,dbp:name
+dbp:nationalorigin,dbo:manufacturer,0.16853891,dbo:manufacturer
+dbp:hometown,dbp:education,0.2887068,dbp:birthplace
+dbp:venue,dbo:author,0.21872512,dbp:country
+dbo:officiallanguage,dbp:region,0.42751348,dbo:currency
+dbp:agencyname,dbp:membership,0.53441375,dbp:headquarters
+dbp:headquarters,dbp:owner,0.2189325,dbp:headquarters
+dbo:militarybranch,dbo:commander,0.3129045,dbo:country
+dbp:creator,dbo:creator,0.5338518,dbo:creator
+dbo:creator,dbo:creator,0.9824175,dbo:creator
+dbp:associatedacts,dbp:artist,0.4222453,dbp:associatedacts
+dbp:borough,dbp:borough,0.8936305,dbp:borough
+dbo:commander,dbo:commander,0.5388146,dbo:commander
+dbp:language,dbo:country,0.53207815,dbo:origin
+dbo:ceremonialcounty,dbp:primeminister,0.24367915,dbp:owner
+dbo:voice,dbo:director,0.819628,dbo:creator
+dbp:creators,dbo:creator,0.8806639,dbp:artist
+dbo:ingredient,dbo:ingredient,0.5806019,dbo:ingredient
diff --git a/Neural graph module/df_valid_a0.1_e50_b8.csv b/Neural graph module/df_valid_a0.1_e50_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e07082c834cdf6840ff7260ae2a3ffd60badcc8d
--- /dev/null
+++ b/Neural graph module/df_valid_a0.1_e50_b8.csv	
@@ -0,0 +1,281 @@
+correct_pred,prediction,pred_probability,gs_pred
+dbp:music,dbp:label,0.59822667,dbo:author
+dbp:veneratedin,dbo:veneratedin,0.69384336,dbo:veneratedin
+dbo:profession,dbo:otherparty,0.13542876,None
+dbp:deathdate,dbo:commander,0.3506227,dbo:genre
+dbp:deathplace,dbo:commander,0.4597283,dbo:deathplace
+dbp:deathplace,dbo:ingredient,0.19669501,dbo:deathplace
+dbp:starring,dbp:artist,0.24744529,dbo:author
+dbo:party,dbp:primeminister,0.28738827,dbp:primeminister
+dbp:branch,dbo:country,0.5045579,dbp:owner
+dbp:purpose,dbo:sport,0.3440359,dbp:membership
+dbp:children,dbp:artist,0.16509587,dbo:deathplace
+dbp:poledriver,dbo:poledriver,0.62260616,dbo:poledriver
+dbo:academicadvisor,dbo:author,0.49002326,dbo:institution
+dbp:team,dbo:author,0.5061282,dbp:league
+dbo:academicdiscipline,dbp:affiliation,0.47375867,dbp:fields
+dbo:hometown,dbo:country,0.57204324,dbo:birthplace
+dbo:ingredient,dbo:ingredient,0.6849914,dbo:ingredient
+dbo:owner,dbo:country,0.6895489,dbp:owner
+dbo:almamater,dbp:education,0.22494937,dbp:education
+dbp:school,dbo:institution,0.21218944,dbp:birthplace
+dbp:battles,dbo:commander,0.87573797,dbp:battles
+dbp:allegiance,dbp:primeminister,0.23046702,dbp:name
+dbp:services,dbo:operatingsystem,0.5724356,dbp:products
+dbp:currency,dbo:currency,0.21054849,dbo:currency
+dbp:notablecommanders,dbo:commander,0.6764414,dbo:country
+dbp:currentclub,dbp:youthclubs,0.47731838,dbp:youthclubs
+dbo:campus,dbo:country,0.20041457,dbo:birthplace
+dbp:language,dbp:artist,0.29712865,dbp:artist
+dbo:subsequentwork,dbo:author,0.6380196,dbp:label
+dbo:manufacturer,dbo:manufacturer,0.96052337,dbo:manufacturer
+dbo:residence,dbp:owner,0.31802392,None
+dbo:narrator,dbp:label,0.15470907,dbo:executiveproducer
+dbp:nationality,dbo:country,0.23007101,dbp:primeminister
+dbo:militarybranch,dbo:country,0.38555276,dbo:country
+dbp:debutteam,dbp:artist,0.3861537,dbo:formerteam
+dbo:currency,dbo:currency,0.91589457,dbo:currency
+dbp:membership,dbp:membership,0.6193741,dbp:membership
+dbp:sisterstations,dbp:area,0.81082195,dbo:employer
+dbp:company,dbo:author,0.33190244,dbo:presenter
+dbp:maininterests,dbo:author,0.21777506,dbp:region
+dbp:predecessor,dbp:primeminister,0.9411546,dbp:successor
+dbo:occupation,dbp:owner,0.5777153,dbp:youthclubs
+dbp:purpose,dbo:award,0.2343887,dbo:occupation
+dbo:nearestcity,dbo:country,0.74885297,dbo:deathplace
+dbo:genre,dbo:manufacturer,0.2616701,dbo:type
+dbo:leader,dbp:membership,0.21779901,dbp:headquarters
+dbo:headquarter,dbo:country,0.76492065,dbo:deathplace
+dbo:deathplace,dbo:formerteam,0.36352408,dbo:deathplace
+dbp:president,dbp:primeminister,0.25269252,dbp:education
+dbp:currentclub,dbp:owner,0.48110425,dbp:team
+dbo:location,dbo:country,0.4636356,dbp:country
+dbp:debutteam,dbp:artist,0.6173337,dbp:birthplace
+dbo:child,dbp:education,0.35845524,dbp:successor
+dbo:voice,dbo:director,0.518792,dbo:creator
+dbo:ideology,dbo:otherparty,0.36927482,dbo:country
+dbp:nationalorigin,dbo:manufacturer,0.2759515,dbo:manufacturer
+dbo:citizenship,dbo:institution,0.12868126,dbo:country
+dbp:license,dbo:designer,0.83250886,dbp:developer
+dbp:coach,dbp:owner,0.20844027,dbp:name
+dbo:launchsite,dbp:cities,0.10133581,dbp:garrison
+dbo:denomination,dbo:country,0.66617316,None
+dbp:address,dbp:owner,0.44666332,dbp:owner
+dbo:president,dbo:country,0.6881121,dbp:affiliation
+dbp:publisher,dbo:author,0.88752824,dbp:publisher
+dbo:architecturalstyle,dbo:country,0.44470903,dbo:country
+dbo:managerclub,dbp:youthclubs,0.2772495,dbp:team
+dbp:appointer,dbp:primeminister,0.8596084,None
+dbo:associatedmusicalartist,dbp:artist,0.72703195,dbp:birthplace
+dbo:rivermouth,dbp:cities,0.43529877,dbp:name
+dbo:occupation,dbp:education,0.36205208,dbo:deathplace
+dbo:rivermouth,dbp:owner,0.19947647,dbp:name
+dbo:relation,dbp:primeminister,0.46157998,None
+dbp:writers,dbo:author,0.49359247,dbo:author
+dbo:cpu,dbo:manufacturer,0.46249124,dbo:computingplatform
+dbo:founder,dbp:owner,0.55019486,dbp:area
+dbo:formerteam,dbo:formerteam,0.9370466,dbo:formerteam
+dbo:publisher,dbo:author,0.5290379,dbo:author
+dbo:birthplace,dbp:cities,0.32074827,dbp:birthplace
+dbp:fields,dbp:education,0.37249988,dbo:institution
+dbp:guests,dbp:label,0.20478,None
+dbo:voice,dbo:creator,0.73536766,dbo:voice
+dbp:tenants,dbp:owner,0.33985338,dbo:formerteam
+dbo:employer,dbo:almamater,0.16743384,None
+dbp:place,dbp:cities,0.14228678,dbo:author
+dbp:coach,dbo:almamater,0.40342107,dbo:coach
+dbp:houses,dbp:primeminister,0.24517609,dbp:houses
+dbo:producer,dbo:director,0.3025882,dbo:creator
+dbp:publisher,dbo:author,0.75151604,dbp:owner
+dbo:manager,dbp:league,0.6185439,None
+dbp:almamater,dbo:almamater,0.70528555,dbo:almamater
+dbo:religion,dbp:primeminister,0.23468208,dbo:creator
+dbp:nearestcity,dbo:country,0.20578209,dbp:name
+dbo:formerpartner,dbp:primeminister,0.37052166,dbo:country
+dbp:engine,dbo:manufacturer,0.51007766,dbp:manufacturer
+dbp:license,dbo:operatingsystem,0.26540005,dbo:license
+dbo:birthplace,dbp:cities,0.25290322,dbo:deathplace
+dbp:team,dbp:artist,0.19089036,dbp:league
+dbo:mountainrange,dbp:cities,0.44713688,dbo:mountainrange
+dbo:destination,dbp:cities,0.15849686,dbp:headquarters
+dbp:coach,dbo:formerteam,0.6663993,dbp:name
+dbo:opponent,dbo:formerteam,0.27490243,dbo:opponent
+dbp:headquarters,dbo:country,0.41959625,dbo:deathplace
+dbo:stateoforigin,dbo:almamater,0.58684766,dbp:birthplace
+dbp:birthplace,dbo:country,0.32941815,dbo:deathplace
+dbo:relative,dbp:race,0.22501385,dbo:creator
+dbo:origin,dbo:currency,0.27092832,dbo:origin
+dbo:manager,dbp:owner,0.3071352,dbp:manager
+dbo:affiliation,dbp:affiliation,0.5073706,dbp:affiliation
+dbo:county,dbp:owner,0.33238932,dbp:city
+dbo:knownfor,dbo:ingredient,0.14990944,dbp:education
+dbp:title,dbo:formerteam,0.10674234,dbp:title
+dbp:editor,dbo:manufacturer,0.2124304,dbp:owner
+dbp:primeminister,dbo:commander,0.2908727,dbp:primeminister
+dbp:commander,dbo:commander,0.7772141,dbo:commander
+dbp:league,dbp:league,0.39119986,dbp:league
+dbp:subject,dbo:author,0.18706499,dbo:author
+dbp:foundation,dbp:education,0.1373954,dbo:country
+dbp:location,dbo:manufacturer,0.1546361,dbo:deathplace
+dbo:religion,dbo:creator,0.3320039,dbo:creator
+dbp:employer,dbp:affiliation,0.41590935,dbp:affiliation
+dbo:profession,dbo:veneratedin,0.6402867,dbo:occupation
+dbo:subsequentwork,dbp:artist,0.9561437,dbp:artist
+dbo:operator,dbo:author,0.53918827,dbp:owner
+dbp:meaning,dbo:ingredient,0.121695824,dbp:region
+dbo:regionserved,dbo:country,0.51277083,dbo:deathplace
+dbp:nationality,dbp:region,0.3215404,dbp:combatant
+dbp:playedfor,dbo:formerteam,0.34519723,dbo:formerteam
+dbp:author,dbo:almamater,0.2250532,None
+dbo:citizenship,dbo:country,0.8119868,dbo:country
+dbo:campus,dbp:artist,0.12579969,dbo:birthplace
+dbo:operator,dbo:designer,0.5234667,dbo:operator
+dbo:maintainedby,dbo:country,0.1531232,dbo:maintainedby
+dbo:relation,dbp:primeminister,0.247444,dbo:almamater
+dbp:education,dbp:education,0.64550894,None
+dbp:crosses,dbp:owner,0.596508,dbp:crosses
+dbo:highschool,dbo:formerteam,0.20723091,dbp:highschool
+dbp:label,dbp:label,0.9280205,dbp:label
+dbo:writer,dbp:artist,0.31386602,dbo:creator
+dbp:occupation,dbo:award,0.46783128,None
+dbo:deathplace,dbo:commander,0.52453554,dbo:country
+dbo:composer,dbo:director,0.1808789,dbp:music
+dbo:distributinglabel,dbp:label,0.8260029,dbp:name
+dbp:headquarters,dbp:membership,0.12428596,dbo:deathplace
+dbp:managerclubs,dbo:formerteam,0.27402416,dbp:managerclubs
+dbo:routeend,dbp:borough,0.9819021,dbp:owner
+dbp:education,dbp:education,0.9876721,dbp:education
+dbo:deathplace,dbo:director,0.49642068,dbo:deathplace
+dbo:language,dbp:region,0.66592014,dbo:country
+dbo:child,dbo:almamater,0.38229802,None
+dbo:owner,dbo:author,0.74914974,dbo:author
+dbo:party,dbo:country,0.5213949,dbp:membership
+dbo:author,dbp:label,0.35662442,dbo:author
+dbo:almamater,dbo:almamater,0.49542627,dbo:almamater
+dbo:award,dbo:award,0.44646883,None
+dbo:order,dbo:family,0.28036937,dbo:order
+dbo:capital,dbo:currency,0.90155715,dbo:deathplace
+dbp:deathplace,dbo:award,0.31831792,dbo:deathplace
+dbo:jurisdiction,dbo:commander,0.1387152,dbo:deathplace
+dbo:keyperson,dbo:author,0.25481373,dbp:owner
+dbo:mountainrange,dbo:country,0.14348824,dbp:name
+dbp:writer,dbo:creator,0.20982578,dbp:artist
+dbp:party,dbo:otherparty,0.8694174,dbp:affiliation
+dbo:type,dbp:cities,0.24898969,dbo:origin
+dbo:opponent,dbp:primeminister,0.53357625,dbo:almamater
+dbp:deathplace,dbp:artist,0.32303408,dbo:deathplace
+dbp:almamater,dbp:education,0.5907544,dbp:education
+dbo:birthplace,dbo:country,0.19751799,dbo:occupation
+dbp:outflow,dbp:owner,0.4991835,dbp:inflow
+dbo:field,dbp:education,0.1520919,dbo:occupation
+dbo:programmeformat,dbp:area,0.23589873,dbo:genre
+dbp:maininterests,dbo:author,0.3834387,dbp:maininterests
+dbo:team,dbo:formerteam,0.94861233,dbp:league
+dbp:relatives,dbo:institution,0.297323,dbp:artist
+dbp:recorded,dbp:artist,0.9357394,dbp:artist
+dbo:citizenship,dbp:primeminister,0.2739693,dbo:deathplace
+dbo:debutteam,dbo:formerteam,0.22655953,dbp:birthplace
+dbo:militaryunit,dbo:commander,0.34900606,dbo:militaryunit
+dbp:label,dbp:artist,0.46225545,dbp:label
+dbo:breeder,dbp:race,0.7188256,dbo:breeder
+dbo:spouse,dbp:primeminister,0.4967791,dbo:deathplace
+dbp:prizes,dbo:award,0.86133474,dbo:award
+dbp:deputy,dbp:education,0.7105832,dbp:successor
+dbp:beatifiedby,dbp:canonizedby,0.90452564,dbp:canonizedby
+dbo:institution,dbo:institution,0.67769146,dbo:institution
+dbp:birthplace,dbp:education,0.250201,dbo:deathplace
+dbp:residence,dbo:country,0.89376706,dbo:country
+dbo:opponent,dbp:primeminister,0.42540294,dbo:opponent
+dbo:currency,dbo:currency,0.7650599,dbo:currency
+dbo:trainer,dbp:owner,0.2687744,dbp:birthplace
+dbo:veneratedin,dbo:veneratedin,0.9742467,None
+dbp:writer,dbo:author,0.8986965,dbo:creator
+dbp:hubs,dbo:country,0.12636971,dbp:founded
+dbp:artist,dbp:artist,0.93785274,dbp:artist
+dbo:race,dbp:race,0.31641135,dbp:race
+dbp:flagbearer,dbo:country,0.45401868,dbp:rank
+dbo:almamater,dbo:award,0.9046519,dbo:award
+dbo:operatingsystem,dbo:manufacturer,0.27175993,dbo:manufacturer
+dbo:honours,dbo:commander,0.3532182,dbp:race
+dbp:flagbearer,dbp:league,0.41815993,dbp:rank
+dbo:federalstate,dbo:country,0.47863916,dbo:country
+dbp:starring,dbo:director,0.2916259,dbo:director
+dbp:prizes,dbo:award,0.87828875,dbp:prizes
+dbp:country,dbo:country,0.45067903,dbp:country
+dbo:sport,dbo:sport,0.47710973,dbo:sport
+dbp:narrated,dbo:director,0.557468,dbo:executiveproducer
+dbo:institution,dbo:almamater,0.69023734,dbp:education
+dbo:creator,dbo:creator,0.5268502,dbo:creator
+dbp:destinations,dbp:cities,0.7929118,dbo:deathplace
+dbo:city,dbo:currency,0.28278947,dbp:owner
+dbp:deathplace,dbo:country,0.18544665,dbo:location
+dbo:lyrics,dbp:artist,0.46373063,dbo:lyrics
+dbp:editor,dbo:author,0.85866505,dbo:editor
+dbp:nearestcity,dbo:country,0.3841269,dbp:area
+dbo:party,dbo:otherparty,0.21956955,dbp:primeminister
+dbo:campus,dbo:sport,0.6256267,dbp:area
+dbo:militarybranch,dbo:country,0.3803014,dbp:owner
+dbo:team,dbo:formerteam,0.9857414,dbp:league
+dbp:relatives,dbo:creator,0.11912443,dbp:relatives
+dbo:tenant,dbp:owner,0.38580674,dbo:formerteam
+dbo:ground,dbp:league,0.3705313,dbp:league
+dbp:cinematography,dbp:artist,0.2687349,dbo:director
+dbo:relation,dbo:almamater,0.38288945,dbo:commander
+dbo:child,dbp:primeminister,0.62193215,dbo:award
+dbo:child,dbo:creator,0.21109323,dbp:children
+dbp:highschool,dbp:owner,0.2524153,dbp:highschool
+dbo:city,dbp:education,0.2182418,dbo:deathplace
+dbp:hubs,dbo:ingredient,0.1450006,dbp:headquarters
+dbp:genre,dbo:author,0.2613797,dbp:artist
+dbp:prizes,dbo:award,0.9229887,dbo:award
+dbp:firstteam,dbp:firstteam,0.9966456,dbp:firstteam
+dbp:domain,dbo:order,0.34451637,None
+dbo:race,dbp:race,0.52629584,dbp:race
+dbp:homestadium,dbo:country,0.35884,dbp:region
+dbo:foundationplace,dbo:country,0.7522553,dbo:deathplace
+dbp:recorded,dbp:label,0.5152781,dbp:label
+dbp:label,dbp:label,0.88546157,dbp:label
+dbo:stateoforigin,dbo:country,0.9117704,dbo:deathplace
+dbp:nationalorigin,dbo:manufacturer,0.808024,dbo:origin
+dbp:birthname,dbp:primeminister,0.40070727,dbo:almamater
+dbp:spouse,dbo:award,0.98761344,dbp:birthplace
+dbp:locationcountry,dbo:country,0.74932843,dbp:locationcountry
+dbp:headquarters,dbo:country,0.6765156,dbo:deathplace
+dbp:firstdriver,dbo:poledriver,0.90374607,dbo:poledriver
+dbo:artist,dbo:author,0.44144607,dbp:artist
+dbo:bronzemedalist,dbo:formerteam,0.21367279,dbp:name
+dbp:club,dbp:owner,0.39704332,dbp:youthclubs
+dbp:district,dbp:education,0.23239976,dbo:almamater
+dbp:workinstitutions,dbp:affiliation,0.45961294,dbp:education
+dbo:programmeformat,dbp:area,0.22903259,dbp:area
+dbp:creator,dbp:artist,0.5265107,dbo:country
+dbo:officiallanguage,dbp:region,0.6658942,dbo:currency
+dbp:neighboringmunicipalities,dbo:country,0.25230616,dbo:deathplace
+dbp:education,dbo:author,0.41711557,dbp:education
+dbp:battles,dbo:commander,0.24350786,dbp:battles
+dbp:state,dbp:education,0.52167946,None
+dbo:locatedinarea,dbo:deathplace,0.1981868,dbo:deathplace
+dbo:region,dbo:operatingsystem,0.56860524,None
+dbo:series,dbo:creator,0.84051746,None
+dbp:race,dbp:race,0.93894744,dbp:race
+dbo:associatedband,dbp:artist,0.92986935,dbp:artist
+dbp:narrated,dbp:artist,0.47575092,dbo:creator
+dbp:almamater,dbo:almamater,0.64028245,None
+dbo:keyperson,dbo:author,0.23425278,dbp:successor
+dbo:voice,dbo:director,0.8270472,dbo:creator
+dbp:city,dbo:country,0.2109276,dbp:name
+dbo:stateoforigin,dbo:award,0.24712889,dbo:country
+dbo:director,dbo:director,0.35315794,dbo:director
+dbp:developer,dbo:country,0.20925985,dbp:developer
+dbp:place,dbo:currency,0.33693856,dbo:country
+dbo:narrator,dbp:label,0.39374676,dbo:director
+dbo:employer,dbp:label,0.4314736,None
+dbp:placeofburial,dbp:owner,0.44176173,dbp:owner
+dbp:currentmembers,dbo:author,0.29097646,dbp:label
+dbo:battle,dbo:sport,0.23830774,dbo:deathplace
+dbo:team,dbo:formerteam,0.9815871,dbp:birthplace
+dbo:predecessor,dbo:commander,0.16609128,dbp:locationcountry
+dbp:publisher,dbo:author,0.18220726,dbp:parent
+dbo:producer,dbp:artist,0.6608386,dbp:artist
+dbp:spouse,dbp:education,0.3250142,dbo:spouse
+dbo:college,dbp:education,0.742814,dbo:deathplace
diff --git a/Neural graph module/loss_a0.1_e50_b8 b/Neural graph module/loss_a0.1_e50_b8
new file mode 100644
index 0000000000000000000000000000000000000000..7a8a2092e76637380d66f1a54433db6dd62a37bd
--- /dev/null
+++ b/Neural graph module/loss_a0.1_e50_b8	
@@ -0,0 +1 @@
+[5.571724585124424, 5.555871830667768, 5.527821094649179, 5.508293989726475, 5.487793234416417, 5.474524024554661, 5.459395016942706, 5.456178917203631, 5.450260656220572, 5.441645990099226, 5.4305480820792065, 5.426060461997986, 5.415000339916774, 5.403450056484767, 5.398498776980809, 5.386524568285261, 5.380222102573939, 5.3738896812711445, 5.369027144568307, 5.362112028258188, 5.351012631825038, 5.349169928686959, 5.3459252289363315, 5.341955191748482, 5.335924700328282, 5.329608018057687, 5.325289372035435, 5.316059531484331, 5.316460456166949, 5.31416221005576, 5.313663227217538, 5.307156934056963, 5.30150477205004, 5.294568957601275, 5.296395346096584, 5.291880117143903, 5.286564421653748, 5.290026085717337, 5.287465947014945, 5.286537405422756, 5.284834323610578, 5.277639051846095, 5.277994441986084, 5.273067879676819, 5.266493163790021, 5.265891354424613, 5.2635238715580535, 5.2592038086482455, 5.254022972924369, 5.2520079476492745]
\ No newline at end of file
diff --git a/Neural graph module/ngm.ipynb b/Neural graph module/ngm.ipynb
index 0b03885395c7c3a0c08a106eff7bd49f9a197fbe..b84cd327e95012a6e4ef76dd4834dea35ac53b07 100644
--- a/Neural graph module/ngm.ipynb	
+++ b/Neural graph module/ngm.ipynb	
@@ -2,9 +2,18 @@
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": 41,
+      "execution_count": 1,
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "b:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+            "  from .autonotebook import tqdm as notebook_tqdm\n"
+          ]
+        }
+      ],
       "source": [
         "import datasets\n",
         "import torch\n",
@@ -21,7 +30,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 42,
+      "execution_count": 2,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -31,7 +40,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 43,
+      "execution_count": 3,
       "metadata": {},
       "outputs": [
         {
@@ -51,7 +60,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 44,
+      "execution_count": 4,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -82,7 +91,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 45,
+      "execution_count": 5,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -221,7 +230,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 46,
+      "execution_count": 6,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -245,7 +254,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 47,
+      "execution_count": 7,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -384,7 +393,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 48,
+      "execution_count": 8,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -396,7 +405,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 49,
+      "execution_count": 9,
       "metadata": {},
       "outputs": [
         {
@@ -410,7 +419,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 2052/2052 [00:00<00:00, 2215.97it/s]"
+            "100%|██████████| 2052/2052 [00:01<00:00, 2039.76it/s]\n"
           ]
         },
         {
@@ -418,24 +427,17 @@
           "output_type": "stream",
           "text": [
             "Finished with batches\n",
-            "features: ['[CLS] who are in the liang chow club? [SEP] [ obj ] [SEP] liang _ chow [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'] mask: tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0,\n",
-            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-            "         0, 0, 0, 0, 0, 0, 0, 0, 0]]) label_index tensor(105) valids ('http://dbpedia.org/ontology/wikipagewikilink http://dbpedia.org/ontology/wikipageredirects http://dbpedia.org/ontology/club http://dbpedia.org/property/headcoach http://dbpedia.org/property/formercoach http://dbpedia.org/property/club http://dbpedia.org/ontology/coach http://dbpedia.org/ontology/owner http://xmlns.com/foaf/0.1/primarytopic',)\n",
-            "valid features: tensor([[  101,  2073,  2003,  1996,  2697,  2284,  2046,  2029,  1996, 11093,\n",
-            "          2314,  6223,  1029,   102,  1031, 27885,  3501,  1033,   102, 11093,\n",
-            "          1035,  2314,   102,     0,     0,     0,     0,     0,     0,     0,\n",
+            "features: ['[CLS] give me the total number of architect of the buildings whose one of the architect was louis d. astorino? [SEP] [ obj ] [SEP] louis _ d _ astorino [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]'] mask: tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+            "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+            "         0, 0, 0, 0, 0, 0, 0, 0, 0]]) label_index tensor(6) valids ('',)\n",
+            "valid features: tensor([[  101,  2129,  2116,  2372,  2024,  2045,  1997,  1996,  3029,  9403,\n",
+            "          2012,  2572, 13473,  2140,  3726,  2368,  1029,   102,  1031, 27885,\n",
+            "          3501,  1033,   102,  2572, 13473,  2140,  3726,  2368,   102,     0,\n",
             "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
             "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
-            "             0,     0,     0,     0,     0,     0,     0]]) valid mask: tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
-            "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-            "         0, 0, 0, 0, 0, 0, 0, 0, 0]]) valid label_index tensor(439)\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "\n"
+            "             0,     0,     0,     0,     0,     0,     0]]) valid mask: tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+            "         1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+            "         0, 0, 0, 0, 0, 0, 0, 0, 0]]) valid label_index tensor(382)\n"
           ]
         }
       ],
@@ -480,19 +482,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 50,
+      "execution_count": 10,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']\n",
-            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "SPARQL_ENDPOINT = \"https://dbpedia.org/sparql\"\n",
         "\n",
@@ -503,15 +495,12 @@
         "headers = {\n",
         "    'Accept': 'application/sparql-results+json',\n",
         "    'Content-Type': 'application/x-www-form-urlencoded',\n",
-        "}\n",
-        "\n",
-        "# Initialize model\n",
-        "model = NgmOne(device, relations)"
+        "}"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 51,
+      "execution_count": 11,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -558,34 +547,91 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 52,
+      "execution_count": 12,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias']\n",
+            "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+            "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Initialize model\n",
+        "model = NgmOne(device, relations)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
       "metadata": {},
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "1 Train 1.9950409718922206 , Valid  6.086820043836322\n",
-            "2 Train 1.974610483646393 , Valid  6.082748562949044\n",
-            "3 Train 1.9063476758343834 , Valid  6.075958919525147\n",
-            "4 Train 1.868471566268376 , Valid  6.070137473515102\n",
-            "5 Train 1.8407497908387864 , Valid  6.063200610024588\n"
+            "1 Train 5.57195748261043 , Valid  5.578094809395926\n",
+            "2 Train 5.554840326309204 , Valid  5.575157519749233\n",
+            "3 Train 5.535530410494123 , Valid  5.556477464948382\n",
+            "4 Train 5.5098095859800065 , Valid  5.548334584917341\n",
+            "5 Train 5.489036996023995 , Valid  5.543145302363804\n",
+            "6 Train 5.469569032532828 , Valid  5.535323524475098\n",
+            "7 Train 5.4597343172345845 , Valid  5.533391925266811\n",
+            "8 Train 5.45037328515734 , Valid  5.532663140978132\n",
+            "9 Train 5.447723358018058 , Valid  5.530487305777413\n",
+            "10 Train 5.438918685913086 , Valid  5.524020862579346\n",
+            "11 Train 5.430657751219613 , Valid  5.52385219846453\n",
+            "12 Train 5.428742098808288 , Valid  5.524552549634661\n",
+            "13 Train 5.416880416870117 , Valid  5.52349694115775\n",
+            "14 Train 5.410125701768058 , Valid  5.524705042157854\n",
+            "15 Train 5.401101180485317 , Valid  5.513791574750628\n",
+            "16 Train 5.396071638379778 , Valid  5.509561429704939\n"
+          ]
+        },
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[1;32mc:\\Users\\Albin\\Documents\\TDDE19\\codebase\\Neural graph module\\ngm.ipynb Cell 13\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X14sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m valid_obj_relations \u001b[39m=\u001b[39m sample_batched_train[\u001b[39m4\u001b[39m]\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X14sZmlsZQ%3D%3D?line=26'>27</a>\u001b[0m \u001b[39m# Forward pass\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X14sZmlsZQ%3D%3D?line=27'>28</a>\u001b[0m output \u001b[39m=\u001b[39m model(train, train_mask)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X14sZmlsZQ%3D%3D?line=29'>30</a>\u001b[0m preds \u001b[39m=\u001b[39m [relations[np\u001b[39m.\u001b[39margmax(pred)\u001b[39m.\u001b[39mitem()]\u001b[39mfor\u001b[39;00m pred \u001b[39min\u001b[39;00m output\u001b[39m.\u001b[39mdetach()\u001b[39m.\u001b[39mcpu()\u001b[39m.\u001b[39mnumpy()]\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X14sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m relation_loss \u001b[39m=\u001b[39m []\n",
+            "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1129\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
+            "\u001b[1;32mc:\\Users\\Albin\\Documents\\TDDE19\\codebase\\Neural graph module\\ngm.ipynb Cell 13\u001b[0m in \u001b[0;36mNgmOne.forward\u001b[1;34m(self, tokenized_seq, tokenized_mask)\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X14sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m tokenized_mask \u001b[39m=\u001b[39m tokenized_mask\u001b[39m.\u001b[39mto(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X14sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X14sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m     x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbert\u001b[39m.\u001b[39;49mforward(tokenized_seq, attention_mask\u001b[39m=\u001b[39;49mtokenized_mask)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X14sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m x \u001b[39m=\u001b[39m x[\u001b[39m0\u001b[39m][:,\u001b[39m0\u001b[39m,:]\u001b[39m.\u001b[39mto(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Albin/Documents/TDDE19/codebase/Neural%20graph%20module/ngm.ipynb#X14sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlinear(x)\n",
+            "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:1014\u001b[0m, in \u001b[0;36mBertModel.forward\u001b[1;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[0;32m   1005\u001b[0m head_mask \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_head_mask(head_mask, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconfig\u001b[39m.\u001b[39mnum_hidden_layers)\n\u001b[0;32m   1007\u001b[0m embedding_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39membeddings(\n\u001b[0;32m   1008\u001b[0m     input_ids\u001b[39m=\u001b[39minput_ids,\n\u001b[0;32m   1009\u001b[0m     position_ids\u001b[39m=\u001b[39mposition_ids,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1012\u001b[0m     past_key_values_length\u001b[39m=\u001b[39mpast_key_values_length,\n\u001b[0;32m   1013\u001b[0m )\n\u001b[1;32m-> 1014\u001b[0m encoder_outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mencoder(\n\u001b[0;32m   1015\u001b[0m     embedding_output,\n\u001b[0;32m   1016\u001b[0m     attention_mask\u001b[39m=\u001b[39;49mextended_attention_mask,\n\u001b[0;32m   1017\u001b[0m     head_mask\u001b[39m=\u001b[39;49mhead_mask,\n\u001b[0;32m   1018\u001b[0m     encoder_hidden_states\u001b[39m=\u001b[39;49mencoder_hidden_states,\n\u001b[0;32m   1019\u001b[0m     encoder_attention_mask\u001b[39m=\u001b[39;49mencoder_extended_attention_mask,\n\u001b[0;32m   1020\u001b[0m     past_key_values\u001b[39m=\u001b[39;49mpast_key_values,\n\u001b[0;32m   1021\u001b[0m     use_cache\u001b[39m=\u001b[39;49muse_cache,\n\u001b[0;32m   1022\u001b[0m     output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[0;32m   1023\u001b[0m     output_hidden_states\u001b[39m=\u001b[39;49moutput_hidden_states,\n\u001b[0;32m   1024\u001b[0m     return_dict\u001b[39m=\u001b[39;49mreturn_dict,\n\u001b[0;32m   1025\u001b[0m )\n\u001b[0;32m   1026\u001b[0m sequence_output \u001b[39m=\u001b[39m encoder_outputs[\u001b[39m0\u001b[39m]\n\u001b[0;32m   1027\u001b[0m pooled_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpooler(sequence_output) \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpooler \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n",
+            "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1129\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
+            "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:603\u001b[0m, in \u001b[0;36mBertEncoder.forward\u001b[1;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[0;32m    594\u001b[0m     layer_outputs \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mutils\u001b[39m.\u001b[39mcheckpoint\u001b[39m.\u001b[39mcheckpoint(\n\u001b[0;32m    595\u001b[0m         create_custom_forward(layer_module),\n\u001b[0;32m    596\u001b[0m         hidden_states,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    600\u001b[0m         encoder_attention_mask,\n\u001b[0;32m    601\u001b[0m     )\n\u001b[0;32m    602\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 603\u001b[0m     layer_outputs \u001b[39m=\u001b[39m layer_module(\n\u001b[0;32m    604\u001b[0m         hidden_states,\n\u001b[0;32m    605\u001b[0m         attention_mask,\n\u001b[0;32m    606\u001b[0m         layer_head_mask,\n\u001b[0;32m    607\u001b[0m         encoder_hidden_states,\n\u001b[0;32m    608\u001b[0m         encoder_attention_mask,\n\u001b[0;32m    609\u001b[0m         past_key_value,\n\u001b[0;32m    610\u001b[0m         output_attentions,\n\u001b[0;32m    611\u001b[0m     )\n\u001b[0;32m    613\u001b[0m hidden_states \u001b[39m=\u001b[39m layer_outputs[\u001b[39m0\u001b[39m]\n\u001b[0;32m    614\u001b[0m \u001b[39mif\u001b[39;00m use_cache:\n",
+            "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1129\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
+            "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:489\u001b[0m, in \u001b[0;36mBertLayer.forward\u001b[1;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[0;32m    477\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\n\u001b[0;32m    478\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[0;32m    479\u001b[0m     hidden_states: torch\u001b[39m.\u001b[39mTensor,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    486\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tuple[torch\u001b[39m.\u001b[39mTensor]:\n\u001b[0;32m    487\u001b[0m     \u001b[39m# decoder uni-directional self-attention cached key/values tuple is at positions 1,2\u001b[39;00m\n\u001b[0;32m    488\u001b[0m     self_attn_past_key_value \u001b[39m=\u001b[39m past_key_value[:\u001b[39m2\u001b[39m] \u001b[39mif\u001b[39;00m past_key_value \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m--> 489\u001b[0m     self_attention_outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mattention(\n\u001b[0;32m    490\u001b[0m         hidden_states,\n\u001b[0;32m    491\u001b[0m         attention_mask,\n\u001b[0;32m    492\u001b[0m         head_mask,\n\u001b[0;32m    493\u001b[0m         output_attentions\u001b[39m=\u001b[39;49moutput_attentions,\n\u001b[0;32m    494\u001b[0m         past_key_value\u001b[39m=\u001b[39;49mself_attn_past_key_value,\n\u001b[0;32m    495\u001b[0m     )\n\u001b[0;32m    496\u001b[0m     attention_output \u001b[39m=\u001b[39m self_attention_outputs[\u001b[39m0\u001b[39m]\n\u001b[0;32m    498\u001b[0m     \u001b[39m# if decoder, the last output is tuple of self-attn cache\u001b[39;00m\n",
+            "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1129\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
+            "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:419\u001b[0m, in \u001b[0;36mBertAttention.forward\u001b[1;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[0;32m    409\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\n\u001b[0;32m    410\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[0;32m    411\u001b[0m     hidden_states: torch\u001b[39m.\u001b[39mTensor,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    417\u001b[0m     output_attentions: Optional[\u001b[39mbool\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m,\n\u001b[0;32m    418\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tuple[torch\u001b[39m.\u001b[39mTensor]:\n\u001b[1;32m--> 419\u001b[0m     self_outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mself(\n\u001b[0;32m    420\u001b[0m         hidden_states,\n\u001b[0;32m    421\u001b[0m         attention_mask,\n\u001b[0;32m    422\u001b[0m         head_mask,\n\u001b[0;32m    423\u001b[0m         encoder_hidden_states,\n\u001b[0;32m    424\u001b[0m         encoder_attention_mask,\n\u001b[0;32m    425\u001b[0m         past_key_value,\n\u001b[0;32m    426\u001b[0m         output_attentions,\n\u001b[0;32m    427\u001b[0m     )\n\u001b[0;32m    428\u001b[0m     attention_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput(self_outputs[\u001b[39m0\u001b[39m], hidden_states)\n\u001b[0;32m    429\u001b[0m     outputs \u001b[39m=\u001b[39m (attention_output,) \u001b[39m+\u001b[39m self_outputs[\u001b[39m1\u001b[39m:]  \u001b[39m# add attentions if we output them\u001b[39;00m\n",
+            "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1129\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m   1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
+            "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\transformers\\models\\bert\\modeling_bert.py:347\u001b[0m, in \u001b[0;36mBertSelfAttention.forward\u001b[1;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[0;32m    344\u001b[0m     attention_scores \u001b[39m=\u001b[39m attention_scores \u001b[39m+\u001b[39m attention_mask\n\u001b[0;32m    346\u001b[0m \u001b[39m# Normalize the attention scores to probabilities.\u001b[39;00m\n\u001b[1;32m--> 347\u001b[0m attention_probs \u001b[39m=\u001b[39m nn\u001b[39m.\u001b[39;49mfunctional\u001b[39m.\u001b[39;49msoftmax(attention_scores, dim\u001b[39m=\u001b[39;49m\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m)\n\u001b[0;32m    349\u001b[0m \u001b[39m# This is actually dropping out entire tokens to attend to, which might\u001b[39;00m\n\u001b[0;32m    350\u001b[0m \u001b[39m# seem a bit unusual, but is taken from the original Transformer paper.\u001b[39;00m\n\u001b[0;32m    351\u001b[0m attention_probs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdropout(attention_probs)\n",
+            "File \u001b[1;32mb:\\Programs\\Miniconda\\envs\\tdde19\\lib\\site-packages\\torch\\nn\\functional.py:1804\u001b[0m, in \u001b[0;36msoftmax\u001b[1;34m(input, dim, _stacklevel, dtype)\u001b[0m\n\u001b[0;32m   1800\u001b[0m         ret \u001b[39m=\u001b[39m (\u001b[39m-\u001b[39m\u001b[39minput\u001b[39m)\u001b[39m.\u001b[39msoftmax(dim, dtype\u001b[39m=\u001b[39mdtype)\n\u001b[0;32m   1801\u001b[0m     \u001b[39mreturn\u001b[39;00m ret\n\u001b[1;32m-> 1804\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msoftmax\u001b[39m(\u001b[39minput\u001b[39m: Tensor, dim: Optional[\u001b[39mint\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m, _stacklevel: \u001b[39mint\u001b[39m \u001b[39m=\u001b[39m \u001b[39m3\u001b[39m, dtype: Optional[DType] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[0;32m   1805\u001b[0m     \u001b[39mr\u001b[39m\u001b[39m\"\"\"Applies a softmax function.\u001b[39;00m\n\u001b[0;32m   1806\u001b[0m \n\u001b[0;32m   1807\u001b[0m \u001b[39m    Softmax is defined as:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1827\u001b[0m \n\u001b[0;32m   1828\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m   1829\u001b[0m     \u001b[39mif\u001b[39;00m has_torch_function_unary(\u001b[39minput\u001b[39m):\n",
+            "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
           ]
         }
       ],
       "source": [
-        "# Train with data loader.\n",
+        "# Train with data loader\n",
+        "\n",
         "criterion = nn.CrossEntropyLoss(reduction=\"none\")\n",
         "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
         "#optimizer = optim.SGD(model.parameters(), lr=0.0001, momentum=0.5)\n",
         "\n",
-        "epoch = 5\n",
+        "epoch = 50\n",
         "batch_size = 8\n",
-        "alpha = 0.8\n",
+        "alpha = 0.1\n",
         "train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
         "valid_dataloader = DataLoader(valid_data, batch_size=batch_size, shuffle=True)\n",
         "\n",
         "model.train()\n",
+        "loss_train_save = []\n",
+        "loss_valid_save = []\n",
         "for e in range(epoch):\n",
         "    train_loss_epoch = 0\n",
         "    valid_loss_epoch = 0\n",
@@ -621,23 +667,39 @@
         "        valid = sample_batched_valid[0]\n",
         "        valid_mask = sample_batched_valid[1]\n",
         "        label_index = sample_batched_valid[2].to(device)\n",
-        "        valid_obj_relations = sample_batched_train[4]\n",
+        "        valid_obj_relations = sample_batched_valid[4]\n",
         "        \n",
         "        # Forward pass\n",
         "        with torch.no_grad():\n",
         "            output = model(valid, valid_mask)\n",
         "            loss = criterion(output, label_index)\n",
         "\n",
-        "            \n",
+        "            preds = [relations[np.argmax(pred).item()]for pred in output.detach().cpu().numpy()]\n",
+        "            relation_loss = []\n",
+        "            for i in range(len(preds)):\n",
+        "                if prefixes_reverse[\"\".join(preds[i].split(\":\")[0]) + \":\"] + preds[i].split(\":\")[1] in valid_obj_relations[i]:\n",
+        "                    relation_loss.append(0)\n",
+        "                else:\n",
+        "                    relation_loss.append(1)\n",
+        "\n",
+        "            relation_loss = torch.FloatTensor(relation_loss).to(device)\n",
+        "            loss = criterion(output, label_index)\n",
+        "            loss = loss * (1-alpha) + (relation_loss) * alpha\n",
         "\n",
         "        valid_loss_epoch = valid_loss_epoch + loss.mean().item()\n",
         "\n",
-        "    print(e+1, \"Train\", train_loss_epoch/(i_train+1), \", Valid \", valid_loss_epoch/(i_valid+1))"
+        "            \n",
+        "\n",
+        "        #valid_loss_epoch = valid_loss_epoch + loss.mean().item()\n",
+        "\n",
+        "    print(e+1, \"Train\", train_loss_epoch/(i_train+1), \", Valid \", valid_loss_epoch/(i_valid+1))\n",
+        "    loss_train_save.append(train_loss_epoch/(i_train+1))\n",
+        "    loss_valid_save.append(valid_loss_epoch/(i_valid+1))"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 53,
+      "execution_count": 16,
       "metadata": {},
       "outputs": [
         {
@@ -651,7 +713,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 514/514 [00:00<00:00, 2225.04it/s]\n"
+            "100%|██████████| 514/514 [00:00<00:00, 1869.07it/s]\n"
           ]
         },
         {
@@ -659,7 +721,7 @@
           "output_type": "stream",
           "text": [
             "Finished with batches\n",
-            "test loss 6.051962852478027\n"
+            "test loss 6.012964725494385\n"
           ]
         }
       ],
@@ -672,25 +734,28 @@
         "test_data = MyDataset(test, test_mask, corr_rels_test, ents=ents_test, relations=relations, valid_obj_relations=valid_obj_relations_test)\n",
         "test_dataloader = DataLoader(test_data, batch_size=len(test_data), shuffle=False)\n",
         "train_dataloader = DataLoader(train_data, batch_size=len(train_data), shuffle=False)\n",
+        "valid_dataloader = DataLoader(valid_data, batch_size=len(valid_data), shuffle=False)\n",
         "\n",
         "test_batch, test_mask_batch, corr_rels_test_batch, sub_objs_test, valid_obj_relations_test = next(iter(test_dataloader))\n",
         "train_batch, train_mask_batch, corr_rels_train_batch, sub_objs_train, valid_obj_relations_train = next(iter(train_dataloader))\n",
+        "valid_batch, valid_mask_batch, corr_rels_valid_batch, sub_objs_valid, valid_obj_relations_valid = next(iter(valid_dataloader))\n",
         "corr_rels_test_batch = corr_rels_test_batch.to(device)\n",
         "with torch.no_grad():\n",
         "    output_train = model(train_batch, train_mask_batch)\n",
+        "    output_valid = model(valid_batch, valid_mask_batch)\n",
         "    output_test = model(test_batch, test_mask_batch)\n",
         "    loss = criterion(output_test, corr_rels_test_batch)\n",
         "    print(\"test loss\", loss.mean().item())\n",
         "    loss_gs = []\n",
         "\n",
         "output_train = output_train.detach().cpu().numpy()\n",
-        "#output_test = output_gs_test.detach().cpu().numpy()\n",
+        "output_valid = output_valid.detach().cpu().numpy()\n",
         "output_test = output_test.detach().cpu().numpy()\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 54,
+      "execution_count": 21,
       "metadata": {},
       "outputs": [
         {
@@ -698,36 +763,64 @@
           "output_type": "stream",
           "text": [
             "Final pred: None No prediction existed in dbpedia relations for (e, r?, e_s/o)\n",
-            "lowest confidence train 0.116354935\n",
-            "lowest confidence test 0.104292065\n",
-            "Accuracy train: 0.11717352415026834\n",
-            "Accuracy test: 0.06051873198847262\n",
-            "Accuracy test graphsearch: 0.1786743515850144\n"
+            "lowest confidence train 0.095232666\n",
+            "lowest confidence valid 0.095232666\n",
+            "lowest confidence test 0.08918257\n",
+            "Accuracy train: 0.2110912343470483\n",
+            "Accuracy train graphsearch: 0.29427549194991054\n",
+            "Accuracy valid: 0.075\n",
+            "Accuracy valid graphsearch: 0.175\n",
+            "Accuracy test: 0.10086455331412104\n",
+            "Accuracy test graphsearch: 0.20461095100864554\n"
           ]
         }
       ],
       "source": [
         "\n",
-        "\n",
-        "final_preds_test = []\n",
-        "for j, probs_pred in enumerate(output_test):\n",
-        "    pred = \"\"\n",
-        "    #Sort predictions based on the probability, highest first\n",
-        "    # List contains indices of the sorted predictions\n",
-        "    preds_decending = sorted(range(len(probs_pred)), key=lambda k: probs_pred[k], reverse=True)\n",
-        "    for index in preds_decending:\n",
-        "        if j < len(valid_obj_relations_test):\n",
-        "            if prefixes_reverse[\"\".join(relations[index].split(\":\")[0]) + \":\"] + relations[index].split(\":\")[1] in valid_obj_relations_test[j]:\n",
-        "                pred = relations[index]\n",
-        "                final_preds_test.append(pred)\n",
-        "                #print(\"Final pred:\", pred)\n",
-        "                break        \n",
+        "def predict_only_valid_rel(output, valid_obj_relations):\n",
+        "    final_preds = []\n",
+        "    for j, probs_pred in enumerate(output):\n",
+        "        pred = \"\"\n",
+        "        #Sort predictions based on the probability, highest first\n",
+        "        # List contains indices of the sorted predictions\n",
+        "        preds_decending = sorted(range(len(probs_pred)), key=lambda k: probs_pred[k], reverse=True)\n",
+        "        for index in preds_decending:\n",
+        "            if j < len(valid_obj_relations):\n",
+        "                if prefixes_reverse[\"\".join(relations[index].split(\":\")[0]) + \":\"] + relations[index].split(\":\")[1] in valid_obj_relations[j]:\n",
+        "                    pred = relations[index]\n",
+        "                    final_preds.append(pred)\n",
+        "                    #print(\"Final pred:\", pred)\n",
+        "                    break        \n",
+        "        \n",
+        "        if pred == \"\":\n",
+        "            final_preds.append(\"None\")\n",
+        "            #print(\"Final pred: None\")\n",
+        "    return final_preds\n",
         "    \n",
-        "    if pred == \"\":\n",
-        "        final_preds_test.append(\"None\")\n",
-        "        #print(\"Final pred: None\")\n",
+        "\n",
+        "# final_preds_test = []\n",
+        "# for j, probs_pred in enumerate(output_test):\n",
+        "#     pred = \"\"\n",
+        "#     #Sort predictions based on the probability, highest first\n",
+        "#     # List contains indices of the sorted predictions\n",
+        "#     preds_decending = sorted(range(len(probs_pred)), key=lambda k: probs_pred[k], reverse=True)\n",
+        "#     for index in preds_decending:\n",
+        "#         if j < len(valid_obj_relations_test):\n",
+        "#             if prefixes_reverse[\"\".join(relations[index].split(\":\")[0]) + \":\"] + relations[index].split(\":\")[1] in valid_obj_relations_test[j]:\n",
+        "#                 pred = relations[index]\n",
+        "#                 final_preds_test.append(pred)\n",
+        "#                 #print(\"Final pred:\", pred)\n",
+        "#                 break        \n",
         "    \n",
+        "#     if pred == \"\":\n",
+        "#         final_preds_test.append(\"None\")\n",
+        "#         #print(\"Final pred: None\")\n",
+        "test_gs_preds = predict_only_valid_rel(output_test, valid_obj_relations_test)\n",
+        "train_gs_preds = predict_only_valid_rel(output_train, valid_obj_relations_train)\n",
+        "valid_gs_preds = predict_only_valid_rel(output_valid, valid_obj_relations_valid)\n",
         "\n",
+        "\n",
+        "    \n",
         "print(\"Final pred:\", \"None\", \"No prediction existed in dbpedia relations for (e, r?, e_s/o)\")\n",
         "#print(\"Final preds:\", final_preds)\n",
         "\n",
@@ -736,12 +829,16 @@
         "probability_train = [pred[np.argmax(pred)] for pred in output_train]\n",
         "correct_pred_train = [relations[corr_rels_train_batch.numpy()[i]] for i in range(len(output_train))]\n",
         "\n",
+        "prediction_valid = [relations[np.argmax(pred).item()]for pred in output_valid]\n",
+        "probability_valid = [pred[np.argmax(pred)] for pred in output_valid]\n",
+        "correct_pred_valid = [relations[corr_rels_valid_batch.numpy()[i]] for i in range(len(output_valid))]\n",
         "\n",
         "prediction_test = [relations[np.argmax(pred).item()]for pred in output_test]\n",
         "probability_test = [pred[np.argmax(pred)] for pred in output_test]\n",
         "correct_pred_test = [relations[corr_rels_test_batch.cpu().detach().numpy()[i]] for i in range(len(output_test))]\n",
         "\n",
         "print(\"lowest confidence train\", min(probability_train))\n",
+        "print(\"lowest confidence valid\", min(probability_train))\n",
         "print(\"lowest confidence test\", min(probability_test))\n",
         "\n",
         "def accuracy_score(y_true, y_pred):\n",
@@ -755,8 +852,19 @@
         "    return corr_preds/(corr_preds+wrong_preds)\n",
         "\n",
         "print(\"Accuracy train:\", accuracy_score(correct_pred_train, prediction_train))\n",
+        "print(\"Accuracy train graphsearch:\", accuracy_score(correct_pred_train, train_gs_preds))\n",
+        "print(\"Accuracy valid:\", accuracy_score(correct_pred_valid, prediction_valid))\n",
+        "print(\"Accuracy valid graphsearch:\", accuracy_score(correct_pred_valid, valid_gs_preds))\n",
         "print(\"Accuracy test:\", accuracy_score(correct_pred_test, prediction_test))\n",
-        "print(\"Accuracy test graphsearch:\", accuracy_score(correct_pred_test, final_preds_test))"
+        "print(\"Accuracy test graphsearch:\", accuracy_score(correct_pred_test, test_gs_preds))\n",
+        "\n",
+        "test_df = pd.DataFrame({\"correct_pred\": correct_pred_test, \"prediction\": prediction_test, \"pred_probability\": probability_test, \"gs_pred\": test_gs_preds})\n",
+        "valid_df = pd.DataFrame({\"correct_pred\": correct_pred_valid, \"prediction\": prediction_valid, \"pred_probability\": probability_valid, \"gs_pred\": valid_gs_preds})\n",
+        "train_df = pd.DataFrame({\"correct_pred\": correct_pred_train, \"prediction\": prediction_train, \"pred_probability\": probability_train, \"gs_pred\": train_gs_preds})\n",
+        "\n",
+        "test_df.to_csv(f\"df_test_a{alpha}_e{epoch}_b{batch_size}.csv\", index=False)\n",
+        "valid_df.to_csv(f\"df_valid_a{alpha}_e{epoch}_b{batch_size}.csv\", index=False)\n",
+        "train_df.to_csv(f\"df_train_a{alpha}_e{epoch}_b{batch_size}.csv\", index=False)\n"
       ]
     },
     {
diff --git a/Neural graph module/stats.ipynb b/Neural graph module/stats.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ed7844b548eef53fa8bbb139f12ebea3fdcc9f0e
--- /dev/null
+++ b/Neural graph module/stats.ipynb	
@@ -0,0 +1,96 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train accuracy:  0.10086455331412104 gs: 0.20461095100864554\n",
+      "Valid accuracy:  0.075 gs: 0.175\n",
+      "Test accuracy:  0.2110912343470483 gs: 0.29427549194991054\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "def accuracy_score(y_true, y_pred):\n",
+    "    corr_preds=0\n",
+    "    wrong_preds=0\n",
+    "    for pred, correct in zip(y_pred, y_true):\n",
+    "        if pred == correct:\n",
+    "            corr_preds += 1\n",
+    "        else:\n",
+    "            wrong_preds += 1\n",
+    "    return corr_preds/(corr_preds+wrong_preds)\n",
+    "\n",
+    "# read dataframe from csv file\n",
+    "def read_test_valid_train_csv(path_train, path_valid, path_test):\n",
+    "    test_df = pd.read_csv(path_test)\n",
+    "    valid_df = pd.read_csv(path_valid)\n",
+    "    train_df = pd.read_csv(path_train)\n",
+    "\n",
+    "    return train_df, valid_df, test_df\n",
+    "\n",
+    "\n",
+    "def eval_dfs(train_df, valid_df, test_df):\n",
+    "    test_acc = accuracy_score(test_df[\"correct_pred\"], test_df[\"prediction\"])\n",
+    "    valid_acc = accuracy_score(valid_df[\"correct_pred\"], valid_df[\"prediction\"])\n",
+    "    train_acc = accuracy_score(train_df[\"correct_pred\"], train_df[\"prediction\"])\n",
+    "    test_acc_gs = accuracy_score(test_df[\"correct_pred\"], test_df[\"gs_pred\"])\n",
+    "    valid_acc_gs = accuracy_score(valid_df[\"correct_pred\"], valid_df[\"gs_pred\"])\n",
+    "    train_acc_gs = accuracy_score(train_df[\"correct_pred\"], train_df[\"gs_pred\"])\n",
+    "\n",
+    "\n",
+    "    return train_acc, valid_acc, test_acc, train_acc_gs, valid_acc_gs, test_acc_gs\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_df, valid_df, train_df = read_test_valid_train_csv(\"df_train_a0.1_e50_b8.csv\", \"df_valid_a0.1_e50_b8.csv\", \"df_test_a0.1_e50_b8.csv\")\n",
+    "train_acc, valid_acc, test_acc, train_acc_gs, valid_acc_gs, test_acc_gs = eval_dfs(train_df, valid_df, test_df)\n",
+    "\n",
+    "print(\"Train accuracy: \", train_acc, \"gs:\", train_acc_gs)\n",
+    "print(\"Valid accuracy: \", valid_acc, \"gs:\", valid_acc_gs)\n",
+    "print(\"Test accuracy: \", test_acc, \"gs:\", test_acc_gs)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.4 ('tdde19')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "8e4aa0e1a1e15de86146661edda0b2884b54582522f7ff2b916774ba6b8accb1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Neural graph module/test_predictions.csv b/Neural graph module/test_predictions.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5a8e4587c84bc4ec2c3402bd91828eef8ca9719e
--- /dev/null
+++ b/Neural graph module/test_predictions.csv	
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+dbo:knownfor,dbo:director,0.32777014,None
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