diff --git a/Neural graph module/df_test_a0.6_e100_b8.csv b/Neural graph module/df_test_a0.6_e100_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9db06b983ad7a943eee3ccbc593e669b05397430
--- /dev/null
+++ b/Neural graph module/df_test_a0.6_e100_b8.csv	
@@ -0,0 +1,348 @@
+correct_pred,prediction,pred_probability,gs_pred
+dbo:associatedband,dbp:artist,0.51520586,dbp:artist
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+dbo:locationcity,dbo:deathplace,0.9829931,dbo:birthplace
+dbp:placeofburial,dbo:deathplace,0.9527886,None
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+dbo:cinematography,dbo:director,0.6756539,dbo:director
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+dbp:spouse,dbo:spouse,0.9497088,dbo:spouse
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+dbp:name,dbp:manager,0.5459257,None
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+dbo:routeend,dbp:owner,0.4793299,dbp:owner
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+dbp:deathplace,dbp:designer,0.3088322,dbp:fields
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+dbp:state,dbp:region,0.42338797,None
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+dbo:almamater,dbo:almamater,0.6556272,None
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+dbo:bandmember,dbp:artist,0.9787612,dbp:label
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+dbo:parentorganisation,dbp:employer,0.34685302,dbp:employer
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+dbo:leader,dbo:origin,0.55007225,dbp:successor
+dbo:knownfor,dbp:employer,0.42564517,None
+dbo:service,dbo:employer,0.5081511,dbo:product
+dbo:literarygenre,dbo:author,0.35321662,dbo:occupation
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+dbp:nationalteam,dbo:formerteam,0.627454,dbp:manager
+dbo:stateoforigin,dbp:designer,0.37858737,dbo:deathplace
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+dbp:party,dbo:party,0.4346172,dbo:party
+dbp:party,dbo:party,0.7187768,None
+dbo:institution,dbo:institution,0.43107077,dbo:institution
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+dbo:developer,dbp:employer,0.42538455,dbp:designer
+dbp:lieutenant,dbp:education,0.8570777,None
+dbo:training,dbp:designer,0.24841751,dbo:birthplace
+dbp:leadername,dbp:keypeople,0.7157143,dbp:leadertitle
+dbo:parent,dbo:commander,0.42536527,dbo:parent
+dbp:poledriver,dbp:firstdriver,0.8893143,dbp:firstdriver
+dbp:poledriver,dbp:firstdriver,0.98486567,dbp:firstdriver
+dbp:governingbody,dbo:deathplace,0.2662057,dbp:governingbody
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+dbp:prizes,dbo:award,0.5328867,dbp:prizes
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+dbp:manufacturer,dbp:label,0.3562042,dbo:manufacturer
+dbp:distributor,dbo:manufacturer,0.7861918,dbo:origin
+dbo:neighboringmunicipality,dbo:wineregion,0.31652367,dbo:birthplace
+dbo:incumbent,dbo:spouse,0.5169458,dbo:birthplace
+dbo:location,dbp:race,0.39852485,dbp:locationcountry
+dbo:location,dbo:birthplace,0.2659946,dbo:nearestcity
+dbp:homestadium,dbo:birthplace,0.19782591,dbp:region
+dbp:region,dbp:designer,0.6571707,dbp:region
+dbp:region,dbp:designer,0.5617606,dbp:region
+dbp:developer,dbp:employer,0.85864097,dbp:title
+dbo:developer,dbp:designer,0.25224584,dbp:title
+dbo:anthem,dbo:almamater,0.5881035,dbo:occupation
+dbp:president,dbp:state,0.47477582,dbp:battles
+dbp:president,dbo:profession,0.2350098,dbp:education
+dbp:type,dbp:league,0.41345277,dbp:schooltype
+dbp:state,dbp:primeminister,0.33465457,None
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+dbp:employer,dbp:employer,0.33860087,None
diff --git a/Neural graph module/df_test_a0.8_e100_b8.csv b/Neural graph module/df_test_a0.8_e100_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e3ebccd368d5f5d755bc109e3111eb148fe3e0ab
--- /dev/null
+++ b/Neural graph module/df_test_a0.8_e100_b8.csv	
@@ -0,0 +1,348 @@
+correct_pred,prediction,pred_probability,gs_pred
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+dbo:leader,dbo:doctoralstudent,0.33091724,dbp:successor
+dbo:knownfor,dbp:designer,0.38163066,None
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+dbo:literarygenre,dbp:discipline,0.30597702,dbo:knownfor
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diff --git a/Neural graph module/df_test_a1_e100_b8.csv b/Neural graph module/df_test_a1_e100_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a86a533833c514d5f642f937f80574e37275b8c1
--- /dev/null
+++ b/Neural graph module/df_test_a1_e100_b8.csv	
@@ -0,0 +1,348 @@
+correct_pred,prediction,pred_probability,gs_pred
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diff --git a/Neural graph module/df_train_a0.6_e100_b8.csv b/Neural graph module/df_train_a0.6_e100_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a105b0590093b682d978b42c93dccea0662b794d
--- /dev/null
+++ b/Neural graph module/df_train_a0.6_e100_b8.csv	
@@ -0,0 +1,1119 @@
+correct_pred,prediction,pred_probability,gs_pred
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diff --git a/Neural graph module/df_train_a0.8_e100_b8.csv b/Neural graph module/df_train_a0.8_e100_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..23aca2082a30b33b3479269c1d356d944a2819f0
--- /dev/null
+++ b/Neural graph module/df_train_a0.8_e100_b8.csv	
@@ -0,0 +1,1119 @@
+correct_pred,prediction,pred_probability,gs_pred
+dbp:tenants,dbp:designer,0.3293136,None
+dbo:family,dbo:family,0.9999919,dbo:family
+dbo:wineregion,dbo:origin,0.41467795,dbp:region
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+dbo:executiveproducer,dbo:occupation,0.8006676,dbo:creator
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+dbp:combatant,dbo:deathplace,0.7093206,dbo:creator
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diff --git a/Neural graph module/df_train_a1_e100_b8.csv b/Neural graph module/df_train_a1_e100_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cdb72be9a49531b77178e056b3560b3bee6afc4f
--- /dev/null
+++ b/Neural graph module/df_train_a1_e100_b8.csv	
@@ -0,0 +1,1119 @@
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+dbo:firstascentperson,dbo:previouswork,0.0055036345,dbo:firstascentperson
+dbp:race,dbo:previouswork,0.0052643646,dbo:race
+dbo:stateoforigin,dbp:artist,0.0047895047,None
+dbo:currency,dbp:designer,0.004966572,dbo:currency
+dbp:prizes,dbp:designer,0.0058448813,dbp:prizes
+dbo:ingredient,dbo:routestart,0.0046276115,dbp:country
+dbo:party,dbp:assembly,0.005347935,dbp:relatives
+dbp:screenplay,dbp:designer,0.004992253,dbp:distributor
+dbp:deathplace,dbp:designer,0.0059865634,dbp:nationality
+dbp:region,dbp:designer,0.004582233,dbo:languagefamily
+dbp:employer,dbp:designer,0.004733147,dbp:developer
+dbp:governingbody,dbp:designer,0.0046439557,dbp:governingbody
+dbo:battle,dbp:designer,0.0053673307,dbp:battles
+dbp:producer,dbp:designer,0.0056682266,dbp:label
+dbo:architecturalstyle,dbp:designer,0.005107979,dbp:location
+dbp:nationalorigin,dbo:previouswork,0.0050919703,dbo:manufacturer
+dbp:hometown,dbo:previouswork,0.0052499785,dbp:nationality
+dbp:venue,dbp:designer,0.0050085406,dbp:country
+dbo:officiallanguage,dbp:designer,0.0053144414,dbo:governmenttype
+dbp:agencyname,dbp:designer,0.0045021204,dbo:type
+dbp:headquarters,dbp:designer,0.004954284,dbp:origin
+dbo:militarybranch,dbo:previouswork,0.005608271,dbo:builder
+dbp:creator,dbp:designer,0.004882092,dbo:owningcompany
+dbo:creator,dbp:designer,0.005829598,dbp:artist
+dbp:associatedacts,dbp:playedfor,0.0046626795,dbp:associatedacts
+dbp:borough,dbo:previouswork,0.0052588163,dbp:country
+dbo:commander,dbo:previouswork,0.0045224386,dbo:relative
+dbp:language,dbo:previouswork,0.0055927783,dbp:nationality
+dbo:ceremonialcounty,dbo:primeminister,0.005125526,dbp:country
+dbo:voice,dbo:previouswork,0.004709134,dbo:creator
+dbp:creators,dbo:previouswork,0.004768633,dbp:artist
+dbo:ingredient,dbp:designer,0.004475159,dbo:type
diff --git a/Neural graph module/df_valid_a0.6_e100_b8.csv b/Neural graph module/df_valid_a0.6_e100_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f077218c97223a99a4d3255bab58eea40851cff1
--- /dev/null
+++ b/Neural graph module/df_valid_a0.6_e100_b8.csv	
@@ -0,0 +1,281 @@
+correct_pred,prediction,pred_probability,gs_pred
+dbp:music,dbp:label,0.6456639,dbo:author
+dbp:veneratedin,dbp:maininterests,0.6851711,dbo:veneratedin
+dbo:profession,dbo:profession,0.40258124,None
+dbp:deathdate,dbp:designer,0.5494266,dbo:genre
+dbp:deathplace,dbo:birthplace,0.5505762,dbo:birthplace
+dbp:deathplace,dbo:deathplace,0.6596366,dbo:deathplace
+dbp:starring,dbp:guests,0.46005186,dbo:author
+dbo:party,dbo:party,0.9158046,dbo:party
+dbp:branch,dbp:membership,0.23641697,dbo:militarybranch
+dbp:purpose,dbp:membership,0.7756622,dbp:membership
+dbp:children,dbo:spouse,0.7593404,dbo:spouse
+dbp:poledriver,dbp:firstdriver,0.97447973,dbp:firstdriver
+dbo:academicadvisor,dbp:maininterests,0.91411734,dbp:fields
+dbp:team,dbo:almamater,0.27078262,dbp:league
+dbo:academicdiscipline,dbp:fields,0.6679615,dbp:fields
+dbo:hometown,dbo:militarybranch,0.25602958,dbo:birthplace
+dbo:ingredient,dbo:ingredient,0.99694926,dbo:ingredient
+dbo:owner,dbo:country,0.3080822,dbp:owner
+dbo:almamater,dbo:almamater,0.85007197,dbo:almamater
+dbp:school,dbp:designer,0.20156184,dbp:title
+dbp:battles,dbo:commander,0.46023816,dbp:battles
+dbp:allegiance,dbo:commander,0.68552494,dbp:title
+dbp:services,dbo:author,0.6736388,dbo:product
+dbp:currency,dbo:origin,0.49551558,dbo:timezone
+dbp:notablecommanders,dbp:battles,0.5901975,dbp:battles
+dbp:currentclub,dbp:youthclubs,0.3007508,dbp:youthclubs
+dbo:campus,dbo:nearestcity,0.28031433,dbo:birthplace
+dbp:language,dbp:recorded,0.89633983,dbp:recorded
+dbo:subsequentwork,dbo:author,0.9452758,dbp:artist
+dbo:manufacturer,dbo:manufacturer,0.73946506,dbo:manufacturer
+dbo:residence,dbo:deathplace,0.9896062,None
+dbo:narrator,dbp:employer,0.17114118,dbp:origin
+dbp:nationality,dbp:region,0.56049997,dbo:birthplace
+dbo:militarybranch,dbo:militarybranch,0.7047217,dbo:militarybranch
+dbp:debutteam,dbp:designer,0.28755146,dbo:formerteam
+dbo:currency,dbp:designer,0.44834372,dbo:currency
+dbp:membership,dbp:membership,0.9994634,dbp:membership
+dbp:sisterstations,dbp:sisterstations,0.567078,dbp:sisterstations
+dbp:company,dbp:guests,0.24510068,dbo:presenter
+dbp:maininterests,dbp:maininterests,0.8781338,dbp:maininterests
+dbp:predecessor,dbo:birthplace,0.43102294,dbp:successor
+dbo:occupation,dbp:designer,0.5155876,dbp:youthclubs
+dbp:purpose,dbo:profession,0.8341392,dbo:profession
+dbo:nearestcity,dbp:membership,0.7821641,dbo:deathplace
+dbo:genre,dbp:designer,0.6437744,dbo:genre
+dbo:leader,dbp:keypeople,0.21855366,dbp:headquarters
+dbo:headquarter,dbp:headquarters,0.3986531,dbo:birthplace
+dbo:deathplace,dbo:formerteam,0.9863323,dbo:deathplace
+dbp:president,dbp:education,0.50767195,dbp:education
+dbp:currentclub,dbp:owner,0.75632507,dbp:team
+dbo:location,dbo:wineregion,0.79551846,dbp:country
+dbp:debutteam,dbp:playedfor,0.381268,dbo:debutteam
+dbo:child,dbo:birthplace,0.54505736,dbp:successor
+dbo:voice,dbp:guests,0.3721788,dbo:series
+dbo:ideology,dbp:designer,0.17759167,dbo:country
+dbp:nationalorigin,dbp:nationalorigin,0.9966516,dbp:nationalorigin
+dbo:citizenship,dbp:team,0.23391053,dbp:fields
+dbp:license,dbp:designer,0.45628232,dbp:developer
+dbp:coach,dbp:manager,0.3091751,dbo:coach
+dbo:launchsite,dbp:cities,0.2518954,dbp:garrison
+dbo:denomination,dbp:affiliation,0.53785187,None
+dbp:address,dbp:designer,0.33525065,dbp:owner
+dbo:president,dbo:institution,0.3520669,dbp:affiliation
+dbp:publisher,dbo:author,0.9829012,dbo:series
+dbo:architecturalstyle,dbo:birthplace,0.48726127,dbo:country
+dbo:managerclub,dbp:youthclubs,0.44050366,dbp:manager
+dbp:appointer,dbp:primeminister,0.8771862,None
+dbo:associatedmusicalartist,dbp:artist,0.84432554,dbo:birthplace
+dbo:rivermouth,dbo:river,0.3897268,dbo:river
+dbo:occupation,dbp:designer,0.48164165,dbo:occupation
+dbo:rivermouth,dbp:race,0.69884276,dbo:river
+dbo:relation,dbo:deathplace,0.52315646,None
+dbp:writers,dbo:creator,0.8627052,dbo:creator
+dbo:cpu,dbo:computingplatform,0.33026516,dbo:computingplatform
+dbo:founder,dbo:deathplace,0.27412274,dbo:timezone
+dbo:formerteam,dbp:playedfor,0.6835475,dbp:playedfor
+dbo:publisher,dbo:author,0.51175237,dbo:author
+dbo:birthplace,dbo:country,0.9592099,dbo:birthplace
+dbp:fields,dbo:almamater,0.7495313,dbo:deathplace
+dbp:guests,dbp:guests,0.91657627,None
+dbo:voice,dbo:director,0.75532436,dbo:voice
+dbp:tenants,dbo:employer,0.42405576,dbo:formerteam
+dbo:employer,dbp:designer,0.38845208,None
+dbp:place,dbp:designer,0.73544186,dbo:author
+dbp:coach,dbo:spouse,0.49196032,dbo:coach
+dbp:houses,dbp:houses,0.9656532,dbp:houses
+dbo:producer,dbo:author,0.18767378,dbo:creator
+dbp:publisher,dbo:author,0.9422077,dbo:occupation
+dbo:manager,dbp:owner,0.81475157,None
+dbp:almamater,dbo:almamater,0.6999471,dbo:almamater
+dbo:religion,dbp:designer,0.24189568,dbo:occupation
+dbp:nearestcity,dbo:nearestcity,0.96987313,dbp:location
+dbo:formerpartner,dbp:designer,0.311633,dbo:country
+dbp:engine,dbp:designer,0.45386565,dbp:designer
+dbp:license,dbo:operatingsystem,0.9486235,dbp:license
+dbo:birthplace,dbo:deathplace,0.32013044,dbo:deathplace
+dbp:team,dbp:designer,0.23266289,dbo:birthplace
+dbo:mountainrange,dbo:wineregion,0.6613921,dbo:mountainrange
+dbo:destination,dbp:headquarters,0.4169391,dbp:headquarters
+dbp:coach,dbo:formerteam,0.44086847,dbo:coach
+dbo:opponent,dbo:relative,0.24852407,dbo:opponent
+dbp:headquarters,dbp:headquarters,0.29263893,dbo:deathplace
+dbo:stateoforigin,dbp:designer,0.24220417,dbo:birthplace
+dbp:birthplace,dbp:league,0.37306786,dbo:birthplace
+dbo:relative,dbp:maininterests,0.23249328,dbo:spouse
+dbo:origin,dbo:origin,0.991861,dbo:origin
+dbo:manager,dbo:birthplace,0.22885756,dbp:manager
+dbo:affiliation,dbp:affiliation,0.8344913,dbp:affiliation
+dbo:county,dbo:nearestcity,0.7276859,dbp:city
+dbo:knownfor,dbp:fields,0.62041074,dbp:fields
+dbp:title,dbp:designer,0.79092056,dbp:title
+dbp:editor,dbp:discipline,0.4024106,dbp:owner
+dbp:primeminister,dbp:primeminister,0.96107936,dbp:primeminister
+dbp:commander,dbo:commander,0.84420276,dbo:commander
+dbp:league,dbp:playedfor,0.34799388,dbp:league
+dbp:subject,dbo:spouse,0.25988582,dbo:author
+dbp:foundation,dbp:keypeople,0.4189886,dbo:birthplace
+dbp:location,dbo:occupation,0.34744108,dbo:deathplace
+dbo:religion,dbp:employer,0.26410976,dbo:creator
+dbp:employer,dbp:employer,0.4916357,dbp:employer
+dbo:profession,dbo:profession,0.9684101,dbo:profession
+dbo:subsequentwork,dbp:artist,0.9975091,dbp:artist
+dbo:operator,dbo:origin,0.2288282,dbp:owner
+dbp:meaning,dbo:origin,0.90083116,dbp:region
+dbo:regionserved,dbp:membership,0.3324469,dbo:deathplace
+dbp:nationality,dbo:deathplace,0.74351734,dbo:nationality
+dbp:playedfor,dbp:playedfor,0.47501144,dbp:playedfor
+dbp:author,dbp:maininterests,0.2799599,None
+dbo:citizenship,dbo:country,0.93115884,dbo:country
+dbo:campus,dbp:owner,0.3361618,dbo:birthplace
+dbo:operator,dbp:designer,0.5929845,dbo:operator
+dbo:maintainedby,dbo:nearestcity,0.33651644,dbo:maintainedby
+dbo:relation,dbo:almamater,0.38497093,dbo:almamater
+dbp:education,dbp:maininterests,0.5332252,None
+dbp:crosses,dbo:river,0.8766086,dbo:builder
+dbo:highschool,dbo:formerteam,0.32838237,dbp:highschool
+dbp:label,dbp:label,0.99992096,dbp:label
+dbo:writer,dbo:author,0.21810731,dbo:creator
+dbp:occupation,dbo:occupation,0.99519664,None
+dbo:deathplace,dbo:deathplace,0.643187,dbo:deathplace
+dbo:composer,dbp:designer,0.6280439,dbp:music
+dbo:distributinglabel,dbp:label,0.83167404,dbo:genre
+dbp:headquarters,dbo:deathplace,0.36728656,dbo:deathplace
+dbp:managerclubs,dbp:youthclubs,0.6319666,dbp:manager
+dbo:routeend,dbp:borough,0.99009717,dbp:owner
+dbp:education,dbp:education,0.9589055,dbp:education
+dbo:deathplace,dbo:deathplace,0.6661377,dbo:deathplace
+dbo:language,dbo:deathplace,0.74945986,dbo:country
+dbo:child,dbp:education,0.27861682,None
+dbo:owner,dbo:author,0.7928864,dbo:author
+dbo:party,dbp:membership,0.9499261,dbp:membership
+dbo:author,dbp:guests,0.98759216,dbo:author
+dbo:almamater,dbo:almamater,0.9780693,dbo:almamater
+dbo:award,dbo:almamater,0.316248,None
+dbo:order,dbo:order,0.9687385,dbo:order
+dbo:capital,dbp:battles,0.3419235,dbo:birthplace
+dbp:deathplace,dbo:deathplace,0.9583017,dbo:deathplace
+dbo:jurisdiction,dbp:battles,0.86493325,dbo:deathplace
+dbo:keyperson,dbp:employer,0.29087687,dbp:owner
+dbo:mountainrange,dbo:wineregion,0.23993872,dbo:mountainrange
+dbp:writer,dbp:designer,0.35667384,dbp:artist
+dbp:party,dbp:designer,0.7026573,dbp:employer
+dbo:type,dbo:origin,0.5340693,dbo:origin
+dbo:opponent,dbp:designer,0.257743,dbo:almamater
+dbp:deathplace,dbo:birthplace,0.5089367,dbo:birthplace
+dbp:almamater,dbo:almamater,0.92896247,dbo:almamater
+dbo:birthplace,dbp:designer,0.6168895,dbo:birthplace
+dbp:outflow,dbo:birthplace,0.35568148,dbo:river
+dbo:field,dbp:fields,0.7096069,dbo:occupation
+dbo:programmeformat,dbp:area,0.6006712,dbo:genre
+dbp:maininterests,dbp:maininterests,0.949671,dbp:maininterests
+dbo:team,dbo:formerteam,0.8810198,dbo:birthplace
+dbp:relatives,dbo:deathplace,0.4114086,dbp:associatedacts
+dbp:recorded,dbp:artist,0.8974442,dbp:artist
+dbo:citizenship,dbo:almamater,0.5765271,dbo:birthplace
+dbo:debutteam,dbo:debutteam,0.99649906,dbo:debutteam
+dbo:militaryunit,dbo:deathplace,0.5366237,dbo:militaryunit
+dbp:label,dbp:label,0.70943487,dbp:label
+dbo:breeder,dbp:race,0.9285636,dbo:breeder
+dbo:spouse,dbo:spouse,0.20083511,dbo:spouse
+dbp:prizes,dbo:award,0.8708162,dbo:award
+dbp:deputy,dbp:primeminister,0.5304956,dbp:title
+dbp:beatifiedby,dbp:canonizedby,0.99985325,dbp:canonizedby
+dbo:institution,dbo:institution,0.99786747,dbo:institution
+dbp:birthplace,dbo:deathplace,0.87469584,dbo:deathplace
+dbp:residence,dbo:country,0.9281205,dbo:country
+dbo:opponent,dbp:education,0.66246766,dbp:editor
+dbo:currency,dbp:battles,0.9445384,dbo:currency
+dbo:trainer,dbp:employer,0.47149393,dbo:birthplace
+dbo:veneratedin,dbo:veneratedin,0.9988171,None
+dbp:writer,dbo:author,0.26221824,dbo:director
+dbp:hubs,dbp:employer,0.29431915,dbp:founded
+dbp:artist,dbp:artist,0.9861109,dbp:artist
+dbo:race,dbp:race,0.9156969,dbp:race
+dbp:flagbearer,dbo:bronzemedalist,0.4128431,dbp:rank
+dbo:almamater,dbo:award,0.996786,dbo:award
+dbo:operatingsystem,dbp:designer,0.3189578,dbo:operatingsystem
+dbo:honours,dbp:race,0.59336233,dbp:race
+dbp:flagbearer,dbo:bronzemedalist,0.61185473,dbp:rank
+dbo:federalstate,dbo:birthplace,0.4123546,dbo:birthplace
+dbp:starring,dbp:recorded,0.20436552,dbo:director
+dbp:prizes,dbo:award,0.9092087,dbp:prizes
+dbp:country,dbo:wineregion,0.55352294,dbp:country
+dbo:sport,dbo:occupation,0.9648113,dbo:sport
+dbp:narrated,dbo:creator,0.32767636,dbp:title
+dbo:institution,dbo:almamater,0.9852228,dbp:education
+dbo:creator,dbo:creator,0.9607249,dbo:creator
+dbp:destinations,dbp:cities,0.9117202,dbo:deathplace
+dbo:city,dbo:deathplace,0.38100436,dbp:owner
+dbp:deathplace,dbo:deathplace,0.6435455,dbo:location
+dbo:lyrics,dbp:artist,0.88250315,dbo:lyrics
+dbp:editor,dbo:author,0.85333765,dbo:editor
+dbp:nearestcity,dbo:birthplace,0.362109,dbp:area
+dbo:party,dbp:keypeople,0.18104312,dbp:primeminister
+dbo:campus,dbp:fields,0.36560443,dbp:area
+dbo:militarybranch,dbo:militarybranch,0.90447867,dbo:militarybranch
+dbo:team,dbo:formerteam,0.793715,dbo:birthplace
+dbp:relatives,dbo:relative,0.6887223,dbo:relative
+dbo:tenant,dbo:employer,0.4634907,dbo:formerteam
+dbo:ground,dbp:owner,0.703441,dbp:league
+dbp:cinematography,dbp:designer,0.20992915,dbo:director
+dbo:relation,dbo:institution,0.2820586,dbo:commander
+dbo:child,dbo:party,0.9640743,dbo:party
+dbo:child,dbo:creator,0.27341923,dbp:children
+dbp:highschool,dbo:deathplace,0.39530793,dbp:highschool
+dbo:city,dbo:deathplace,0.9830245,dbo:deathplace
+dbp:hubs,dbp:carries,0.53132474,dbp:headquarters
+dbp:genre,dbp:designer,0.41437986,dbp:designer
+dbp:prizes,dbo:award,0.79810905,dbo:award
+dbp:firstteam,dbp:firstteam,0.9998753,dbp:firstteam
+dbp:domain,dbo:order,0.9036308,None
+dbo:race,dbp:race,0.9953755,dbp:race
+dbp:homestadium,dbo:birthplace,0.697072,dbp:region
+dbo:foundationplace,dbo:country,0.681711,dbo:deathplace
+dbp:recorded,dbp:artist,0.61656135,dbp:artist
+dbp:label,dbp:label,0.90868175,dbp:label
+dbo:stateoforigin,dbo:country,0.5585957,dbo:deathplace
+dbp:nationalorigin,dbp:nationalorigin,0.994922,dbp:nationalorigin
+dbp:birthname,dbo:birthplace,0.2669714,dbo:birthplace
+dbp:spouse,dbo:award,0.9992994,dbp:birthplace
+dbp:locationcountry,dbo:country,0.3096845,dbp:locationcountry
+dbp:headquarters,dbp:discipline,0.6790474,dbo:deathplace
+dbp:firstdriver,dbp:firstdriver,0.9984748,dbp:firstdriver
+dbo:artist,dbo:author,0.7800081,dbp:artist
+dbo:bronzemedalist,dbp:designer,0.80987996,dbo:bronzemedalist
+dbp:club,dbp:playedfor,0.35361204,dbp:youthclubs
+dbp:district,dbp:owner,0.3144886,dbo:birthplace
+dbp:workinstitutions,dbo:institution,0.38821557,dbo:institution
+dbo:programmeformat,dbp:employer,0.90700865,dbp:area
+dbp:creator,dbp:designer,0.37047765,dbo:country
+dbo:officiallanguage,dbo:officiallanguage,0.9959187,dbo:officiallanguage
+dbp:neighboringmunicipalities,dbo:birthplace,0.80416584,dbo:birthplace
+dbp:education,dbp:employer,0.6690274,dbp:education
+dbp:battles,dbp:battles,0.23671964,dbp:battles
+dbp:state,dbp:primeminister,0.59576356,None
+dbo:locatedinarea,dbp:employer,0.50438684,dbo:birthplace
+dbo:region,dbo:ingredient,0.18775597,None
+dbo:series,dbo:creator,0.78527695,None
+dbp:race,dbp:race,0.99753964,dbp:race
+dbo:associatedband,dbp:artist,0.8648856,dbp:artist
+dbp:narrated,dbp:associatedacts,0.7185894,dbo:creator
+dbp:almamater,dbo:almamater,0.8362228,None
+dbo:keyperson,dbp:keypeople,0.20352343,dbp:keypeople
+dbo:voice,dbo:director,0.5094408,dbo:series
+dbp:city,dbp:designer,0.4004943,dbo:river
+dbo:stateoforigin,dbo:militarybranch,0.2912397,dbo:country
+dbo:director,dbo:occupation,0.81343645,dbo:director
+dbp:developer,dbp:employer,0.41552553,dbp:location
+dbp:place,dbo:timezone,0.7289857,dbo:origin
+dbo:narrator,dbp:designer,0.2840749,dbo:director
+dbo:employer,dbp:employer,0.414757,None
+dbp:placeofburial,dbo:deathplace,0.5567003,dbp:owner
+dbp:currentmembers,dbp:keypeople,0.8270017,dbp:label
+dbo:battle,dbp:battles,0.9359079,dbo:deathplace
+dbo:team,dbp:playedfor,0.83536536,dbp:team
+dbo:predecessor,dbo:origin,0.8324896,dbp:locationcountry
+dbp:publisher,dbo:author,0.4635883,dbp:parent
+dbo:producer,dbp:artist,0.9611737,dbp:artist
+dbp:spouse,dbo:spouse,0.96895236,dbo:spouse
+dbo:college,dbo:almamater,0.661229,dbo:deathplace
diff --git a/Neural graph module/df_valid_a0.8_e100_b8.csv b/Neural graph module/df_valid_a0.8_e100_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0c8c7e7b934ca9a2a1f199dfc137d4da664ce068
--- /dev/null
+++ b/Neural graph module/df_valid_a0.8_e100_b8.csv	
@@ -0,0 +1,281 @@
+correct_pred,prediction,pred_probability,gs_pred
+dbp:music,dbp:label,0.9296898,dbo:author
+dbp:veneratedin,dbo:veneratedin,0.70404637,dbo:veneratedin
+dbo:profession,dbo:occupation,0.18543947,None
+dbp:deathdate,dbp:designer,0.56654257,dbp:genre
+dbp:deathplace,dbp:designer,0.703661,dbo:deathplace
+dbp:deathplace,dbo:nearestcity,0.22014788,dbo:deathplace
+dbp:starring,dbp:keypeople,0.18752888,dbo:author
+dbo:party,dbp:party,0.9975418,dbp:party
+dbp:branch,dbo:country,0.5943579,dbp:owner
+dbp:purpose,dbp:membership,0.5245642,dbp:membership
+dbp:children,dbo:parent,0.669972,dbo:parent
+dbp:poledriver,dbp:firstdriver,0.99257505,dbp:firstdriver
+dbo:academicadvisor,dbp:maininterests,0.22573017,dbp:fields
+dbp:team,dbp:owner,0.29809067,dbp:league
+dbo:academicdiscipline,dbo:author,0.51880634,dbp:fields
+dbo:hometown,dbo:deathplace,0.33117518,dbo:birthplace
+dbo:ingredient,dbo:ingredient,0.9981329,dbo:ingredient
+dbo:owner,dbo:country,0.8115039,dbp:owner
+dbo:almamater,dbo:almamater,0.47576082,dbo:almamater
+dbp:school,dbo:institution,0.6877784,dbp:title
+dbp:battles,dbp:battles,0.7301744,dbp:battles
+dbp:allegiance,dbp:primeminister,0.3504558,dbp:title
+dbp:services,dbp:services,0.9719248,dbp:services
+dbp:currency,dbo:origin,0.4027461,dbo:officiallanguage
+dbp:notablecommanders,dbp:battles,0.7154157,dbp:battles
+dbp:currentclub,dbp:league,0.6887289,dbp:owner
+dbo:campus,dbo:occupation,0.2860819,dbo:birthplace
+dbp:language,dbp:recorded,0.5834802,dbp:recorded
+dbo:subsequentwork,dbo:author,0.76902825,dbp:label
+dbo:manufacturer,dbo:manufacturer,0.97276425,dbo:manufacturer
+dbo:residence,dbo:deathplace,0.9291351,None
+dbo:narrator,dbo:knownfor,0.22016864,dbp:title
+dbp:nationality,dbp:region,0.25883725,dbo:origin
+dbo:militarybranch,dbp:battles,0.16053635,dbo:country
+dbp:debutteam,dbo:origin,0.27389073,dbp:title
+dbo:currency,dbo:origin,0.8848326,dbo:currency
+dbp:membership,dbp:membership,0.9985726,dbp:membership
+dbp:sisterstations,dbp:area,0.91162366,dbp:employer
+dbp:company,dbp:owner,0.6622897,dbp:title
+dbp:maininterests,dbp:maininterests,0.82896525,dbp:maininterests
+dbp:predecessor,dbp:successor,0.39231613,dbp:successor
+dbo:occupation,dbp:owner,0.99997854,dbp:team
+dbp:purpose,dbp:keypeople,0.41044992,dbo:occupation
+dbo:nearestcity,dbo:country,0.5328438,dbo:deathplace
+dbo:genre,dbp:discipline,0.22657317,dbp:genre
+dbo:leader,dbp:nationalorigin,0.27081755,dbp:headquarters
+dbo:headquarter,dbo:occupation,0.45318523,dbo:deathplace
+dbo:deathplace,dbo:formerteam,0.34677976,dbo:deathplace
+dbp:president,dbo:employer,0.40554613,dbp:education
+dbp:currentclub,dbp:owner,0.9751756,dbp:team
+dbo:location,dbo:country,0.53737026,dbp:country
+dbp:debutteam,dbo:debutteam,0.8937835,dbo:debutteam
+dbo:child,dbo:parent,0.5094549,dbo:parent
+dbo:voice,dbo:director,0.8875787,dbo:creator
+dbo:ideology,dbp:party,0.5923986,dbo:country
+dbp:nationalorigin,dbp:nationalorigin,0.9811921,dbp:nationalorigin
+dbo:citizenship,dbp:placeofbirth,0.107634574,dbp:fields
+dbp:license,dbo:designer,0.99962735,dbo:license
+dbp:coach,dbo:formerteam,0.5793979,dbp:nickname
+dbo:launchsite,dbo:country,0.64155513,dbp:garrison
+dbo:denomination,dbo:country,0.49104667,None
+dbp:address,dbp:affiliation,0.68515337,dbp:owner
+dbo:president,dbp:affiliation,0.690764,dbp:affiliation
+dbp:publisher,dbo:author,0.96708405,dbo:series
+dbo:architecturalstyle,dbp:designer,0.38297218,dbp:owner
+dbo:managerclub,dbp:league,0.29269102,dbp:managerclubs
+dbp:appointer,dbp:successor,0.78119016,None
+dbo:associatedmusicalartist,dbp:artist,0.8980144,dbo:birthplace
+dbo:rivermouth,dbp:owner,0.91108984,dbo:rivermouth
+dbo:occupation,dbp:discipline,0.5237965,dbo:deathplace
+dbo:rivermouth,dbp:owner,0.8985256,dbo:rivermouth
+dbo:relation,dbp:primeminister,0.43824157,None
+dbp:writers,dbo:creator,0.8871644,dbo:creator
+dbo:cpu,dbo:manufacturer,0.52261114,dbo:computingplatform
+dbo:founder,dbp:placeofbirth,0.5324681,dbp:area
+dbo:formerteam,dbo:occupation,0.5736095,dbo:formerteam
+dbo:publisher,dbo:origin,0.45600244,dbo:author
+dbo:birthplace,dbo:country,0.81636125,dbo:birthplace
+dbp:fields,dbp:maininterests,0.2397043,dbp:fields
+dbp:guests,dbp:recorded,0.3610165,None
+dbo:voice,dbo:director,0.59518325,dbo:voice
+dbp:tenants,dbo:employer,0.62380236,dbo:formerteam
+dbo:employer,dbo:employer,0.36423317,None
+dbp:place,dbp:race,0.29798555,dbo:author
+dbp:coach,dbo:deathplace,0.58047044,dbo:coach
+dbp:houses,dbp:owner,0.24228972,dbp:party
+dbo:producer,dbo:author,0.4901048,dbo:creator
+dbp:publisher,dbo:author,0.93664,dbp:employer
+dbo:manager,dbp:owner,0.9929691,None
+dbp:almamater,dbo:almamater,0.9445386,dbo:almamater
+dbo:religion,dbo:origin,0.4716776,dbo:occupation
+dbp:nearestcity,dbo:nearestcity,0.42577985,dbp:nearestcity
+dbo:formerpartner,dbo:knownfor,0.6233496,dbo:country
+dbp:engine,dbp:designer,0.3961929,dbp:designer
+dbp:license,dbo:operatingsystem,0.26018372,dbo:knownfor
+dbo:birthplace,dbp:placeofbirth,0.37121534,dbo:deathplace
+dbp:team,dbo:commander,0.13129257,dbp:league
+dbo:mountainrange,dbp:cities,0.40194857,dbo:mountainrange
+dbo:destination,dbp:area,0.24606968,dbp:headquarters
+dbp:coach,dbo:debutteam,0.33465904,dbp:name
+dbo:opponent,dbp:successor,0.67088705,dbo:opponent
+dbp:headquarters,dbp:headquarters,0.60181713,dbo:deathplace
+dbo:stateoforigin,dbo:almamater,0.7178308,dbp:title
+dbp:birthplace,dbp:league,0.92916906,dbo:deathplace
+dbo:relative,dbp:race,0.27551034,dbo:occupation
+dbo:origin,dbo:origin,0.986937,dbo:origin
+dbo:manager,dbp:league,0.4476667,dbp:manager
+dbo:affiliation,dbp:affiliation,0.6476666,dbp:affiliation
+dbo:county,dbo:nearestcity,0.43632185,dbp:city
+dbo:knownfor,dbp:fields,0.71222156,dbp:fields
+dbp:title,dbp:race,0.38356432,dbp:title
+dbp:editor,dbp:discipline,0.817144,dbp:owner
+dbp:primeminister,dbp:primeminister,0.5611598,dbp:primeminister
+dbp:commander,dbo:commander,0.68884706,dbo:commander
+dbp:league,dbp:league,0.7815558,dbp:league
+dbp:subject,dbo:almamater,0.41470844,dbo:author
+dbp:foundation,dbp:owner,0.37061504,dbo:country
+dbp:location,dbp:placeofbirth,0.71341056,dbo:deathplace
+dbo:religion,dbo:creator,0.32092804,dbo:creator
+dbp:employer,dbo:institution,0.9022762,dbo:institution
+dbo:profession,dbo:occupation,0.47261223,dbo:occupation
+dbo:subsequentwork,dbp:artist,0.99895513,dbp:artist
+dbo:operator,dbp:manufacturer,0.68692076,dbp:owner
+dbp:meaning,dbo:origin,0.22170042,dbp:region
+dbo:regionserved,dbo:country,0.9082797,dbo:deathplace
+dbp:nationality,dbp:keypeople,0.91401774,dbo:knownfor
+dbp:playedfor,dbo:occupation,0.29606092,dbo:formerteam
+dbp:author,dbo:almamater,0.47595608,None
+dbo:citizenship,dbo:country,0.99687696,dbo:country
+dbo:campus,dbp:fields,0.38801256,dbo:birthplace
+dbo:operator,dbo:designer,0.493691,dbo:operator
+dbo:maintainedby,dbo:maintainedby,0.3931917,dbo:maintainedby
+dbo:relation,dbp:successor,0.40009898,dbp:successor
+dbp:education,dbp:maininterests,0.3611474,None
+dbp:crosses,dbp:owner,0.9054905,dbo:locatedinarea
+dbo:highschool,dbo:training,0.29576275,dbo:highschool
+dbp:label,dbp:label,0.9982287,dbp:label
+dbo:writer,dbo:series,0.360701,dbo:creator
+dbp:occupation,dbo:occupation,0.79445946,None
+dbo:deathplace,dbp:battles,0.9823545,dbo:deathplace
+dbo:composer,dbp:designer,0.19648494,dbp:music
+dbo:distributinglabel,dbp:label,0.962451,dbp:location
+dbp:headquarters,dbp:affiliation,0.38169247,dbo:deathplace
+dbp:managerclubs,dbo:formerteam,0.36907616,dbp:managerclubs
+dbo:routeend,dbp:borough,0.9991227,dbp:owner
+dbp:education,dbp:education,0.9661391,dbp:education
+dbo:deathplace,dbo:deathplace,0.9170223,dbo:deathplace
+dbo:language,dbp:placeofbirth,0.56841624,dbo:country
+dbo:child,dbo:parent,0.3607789,None
+dbo:owner,dbo:author,0.57575476,dbo:author
+dbo:party,dbo:country,0.70785975,dbp:membership
+dbo:author,dbo:director,0.5138188,dbo:author
+dbo:almamater,dbo:almamater,0.99198794,dbo:almamater
+dbo:award,dbp:race,0.16085826,None
+dbo:order,dbo:order,0.572094,dbo:order
+dbo:capital,dbp:battles,0.88356316,dbo:deathplace
+dbp:deathplace,dbp:fields,0.42472842,dbo:deathplace
+dbo:jurisdiction,dbp:battles,0.642796,dbo:deathplace
+dbo:keyperson,dbp:keypeople,0.42060062,dbp:keypeople
+dbo:mountainrange,dbo:almamater,0.22528869,dbp:location
+dbp:writer,dbo:creator,0.48446262,dbp:artist
+dbp:party,dbp:party,0.9977829,dbp:party
+dbo:type,dbo:origin,0.57364917,dbo:origin
+dbo:opponent,dbp:successor,0.46513546,dbo:almamater
+dbp:deathplace,dbp:artist,0.46702418,dbo:deathplace
+dbp:almamater,dbo:institution,0.80841887,dbo:institution
+dbo:birthplace,dbp:designer,0.637619,dbo:occupation
+dbp:outflow,dbo:nearestcity,0.5624751,dbo:rivermouth
+dbo:field,dbp:fields,0.8228204,dbo:occupation
+dbo:programmeformat,dbp:area,0.44957808,dbo:programmeformat
+dbp:maininterests,dbp:maininterests,0.9891095,dbp:maininterests
+dbo:team,dbo:formerteam,0.99066496,dbp:league
+dbp:relatives,dbo:parent,0.34029007,dbo:author
+dbp:recorded,dbp:artist,0.9545251,dbp:artist
+dbo:citizenship,dbo:knownfor,0.26581985,dbo:knownfor
+dbo:debutteam,dbo:debutteam,0.94846547,dbo:debutteam
+dbo:militaryunit,dbp:battles,0.36561605,dbo:militaryunit
+dbp:label,dbp:owner,0.41847327,dbp:owner
+dbo:breeder,dbp:race,0.9997898,dbo:breeder
+dbo:spouse,dbp:owner,0.2885927,dbo:deathplace
+dbp:prizes,dbo:award,0.7928321,dbo:award
+dbp:deputy,dbp:primeminister,0.57769275,dbp:successor
+dbp:beatifiedby,dbp:canonizedby,0.9135435,dbp:canonizedby
+dbo:institution,dbo:institution,0.9994838,dbo:institution
+dbp:birthplace,dbo:deathplace,0.73061943,dbo:deathplace
+dbp:residence,dbo:country,0.9959906,dbo:country
+dbo:opponent,dbp:primeminister,0.7312771,dbo:opponent
+dbo:currency,dbp:cities,0.35965616,dbo:currency
+dbo:trainer,dbo:parent,0.2682549,dbo:birthplace
+dbo:veneratedin,dbo:veneratedin,0.998979,None
+dbp:writer,dbp:keypeople,0.37470117,dbo:creator
+dbp:hubs,dbo:origin,0.4311243,dbo:hubairport
+dbp:artist,dbp:artist,0.9967434,dbp:artist
+dbo:race,dbp:race,0.960997,dbp:race
+dbp:flagbearer,dbo:silvermedalist,0.99714077,dbp:location
+dbo:almamater,dbo:award,0.99803394,dbo:award
+dbo:operatingsystem,dbo:officiallanguage,0.35515392,dbo:operatingsystem
+dbo:honours,dbp:race,0.7616917,dbp:race
+dbp:flagbearer,dbo:silvermedalist,0.4784746,dbp:location
+dbo:federalstate,dbp:nationalorigin,0.51685494,dbo:country
+dbp:starring,dbo:almamater,0.26034528,dbo:director
+dbp:prizes,dbo:award,0.99948287,dbp:awards
+dbp:country,dbp:membership,0.37930351,dbp:country
+dbo:sport,dbo:occupation,0.8275301,dbo:sport
+dbp:narrated,dbo:author,0.56944466,dbp:title
+dbo:institution,dbo:almamater,0.998035,dbo:institution
+dbo:creator,dbo:creator,0.9791028,dbo:creator
+dbp:destinations,dbp:cities,0.97292197,dbo:deathplace
+dbo:city,dbo:origin,0.78216326,dbp:owner
+dbp:deathplace,dbo:deathplace,0.4173583,dbo:location
+dbo:lyrics,dbp:artist,0.6258319,dbo:lyrics
+dbp:editor,dbo:author,0.9191593,dbp:editor
+dbp:nearestcity,dbp:cities,0.2519092,dbp:area
+dbo:party,dbp:keypeople,0.24269901,dbp:successor
+dbo:campus,dbo:sport,0.8383888,dbp:area
+dbo:militarybranch,dbp:battles,0.60699207,dbo:occupation
+dbo:team,dbo:formerteam,0.995564,dbp:league
+dbp:relatives,dbo:creator,0.6753622,dbp:relatives
+dbo:tenant,dbp:owner,0.64904445,dbo:formerteam
+dbo:ground,dbp:owner,0.92538476,dbp:league
+dbp:cinematography,dbp:keypeople,0.36706132,dbo:director
+dbo:relation,dbo:almamater,0.4500342,dbp:successor
+dbo:child,dbp:affiliation,0.42573598,dbp:successor
+dbo:child,dbo:employer,0.39815325,dbp:children
+dbp:highschool,dbo:country,0.43528926,dbp:highschool
+dbo:city,dbp:placeofbirth,0.8833271,dbo:deathplace
+dbp:hubs,dbp:headquarters,0.5805758,dbp:headquarters
+dbp:genre,dbp:discipline,0.7598027,dbp:designer
+dbp:prizes,dbo:award,0.8948109,dbo:award
+dbp:firstteam,dbp:firstteam,0.9922093,dbp:firstteam
+dbp:domain,dbo:order,0.7198518,None
+dbo:race,dbp:race,0.99985445,dbp:race
+dbp:homestadium,dbp:owner,0.75825036,dbp:region
+dbo:foundationplace,dbp:placeofbirth,0.8108583,dbo:deathplace
+dbp:recorded,dbp:label,0.8194414,dbp:label
+dbp:label,dbp:label,0.9977641,dbp:label
+dbo:stateoforigin,dbo:country,0.8612877,dbo:deathplace
+dbp:nationalorigin,dbp:nationalorigin,0.9989141,dbp:nationalorigin
+dbp:birthname,dbp:successor,0.47978395,dbo:almamater
+dbp:spouse,dbo:award,0.9306007,dbp:birthplace
+dbp:locationcountry,dbp:nationalorigin,0.36856526,dbp:location
+dbp:headquarters,dbp:discipline,0.21061046,dbo:deathplace
+dbp:firstdriver,dbp:firstdriver,0.9922625,dbp:firstdriver
+dbo:artist,dbp:race,0.16413932,dbp:artist
+dbo:bronzemedalist,dbo:knownfor,0.40313226,dbo:bronzemedalist
+dbp:club,dbp:owner,0.82318336,dbp:team
+dbp:district,dbp:owner,0.23657694,dbp:successor
+dbp:workinstitutions,dbo:almamater,0.83136714,dbo:institution
+dbo:programmeformat,dbp:area,0.32350588,dbp:area
+dbp:creator,dbp:artist,0.85188967,dbo:creator
+dbo:officiallanguage,dbo:officiallanguage,0.9809272,dbo:officiallanguage
+dbp:neighboringmunicipalities,dbo:country,0.23906894,dbo:deathplace
+dbp:education,dbo:institution,0.5356922,dbp:education
+dbp:battles,dbp:battles,0.36490658,dbp:battles
+dbp:state,dbo:deathplace,0.29695362,None
+dbo:locatedinarea,dbo:deathplace,0.20506024,dbo:deathplace
+dbo:region,dbp:artist,0.14476675,None
+dbo:series,dbo:creator,0.7142145,None
+dbp:race,dbp:race,0.99962604,dbp:race
+dbo:associatedband,dbp:artist,0.9951751,dbp:artist
+dbp:narrated,dbo:creator,0.7218563,dbo:creator
+dbp:almamater,dbo:almamater,0.93684053,None
+dbo:keyperson,dbp:keypeople,0.61967814,dbp:keypeople
+dbo:voice,dbo:director,0.6836271,dbo:series
+dbp:city,dbp:placeofbirth,0.79843956,dbo:rivermouth
+dbo:stateoforigin,dbp:region,0.17393743,dbo:knownfor
+dbo:director,dbo:award,0.82546484,dbo:director
+dbp:developer,dbp:headquarters,0.9710395,dbp:location
+dbp:place,dbo:deathplace,0.37643367,dbo:origin
+dbo:narrator,dbp:artist,0.16523339,dbo:director
+dbo:employer,dbp:successor,0.21960296,None
+dbp:placeofburial,dbo:deathplace,0.35101375,dbo:occupation
+dbp:currentmembers,dbp:employer,0.32887533,dbp:label
+dbo:battle,dbp:battles,0.98170084,dbo:deathplace
+dbo:team,dbo:formerteam,0.571739,dbp:team
+dbo:predecessor,dbo:origin,0.74900424,dbp:location
+dbp:publisher,dbo:author,0.33794975,dbp:parent
+dbo:producer,dbp:artist,0.9767269,dbp:artist
+dbp:spouse,dbo:parent,0.5534523,dbo:writer
+dbo:college,dbp:education,0.3973304,dbo:deathplace
diff --git a/Neural graph module/df_valid_a1_e100_b8.csv b/Neural graph module/df_valid_a1_e100_b8.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9a9becaa952eb4511e53cab69058cdceacdb0f23
--- /dev/null
+++ b/Neural graph module/df_valid_a1_e100_b8.csv	
@@ -0,0 +1,281 @@
+correct_pred,prediction,pred_probability,gs_pred
+dbp:music,dbo:primeminister,0.0048441277,dbp:music
+dbp:veneratedin,dbp:designer,0.0048923893,dbp:label
+dbo:profession,dbp:designer,0.0060651875,None
+dbp:deathdate,dbp:designer,0.005203497,dbp:birthdate
+dbp:deathplace,dbp:designer,0.005335828,dbp:nationalteam
+dbp:deathplace,dbo:previouswork,0.0047914013,dbp:club
+dbp:starring,dbp:designer,0.0045133536,dbp:country
+dbo:party,dbp:designer,0.005005807,dbp:title
+dbp:branch,dbp:designer,0.005334428,dbp:founder
+dbp:purpose,dbp:designer,0.00497233,dbp:leadername
+dbp:children,dbp:designer,0.006205264,dbp:parent
+dbp:poledriver,dbp:designer,0.005043238,dbp:firstteam
+dbo:academicadvisor,dbp:designer,0.0053688684,dbp:fields
+dbp:team,dbp:designer,0.005154219,dbp:nationality
+dbo:academicdiscipline,dbo:silvermedalist,0.0049670176,dbo:knownfor
+dbo:hometown,dbp:designer,0.0061862003,dbo:birthplace
+dbo:ingredient,dbp:designer,0.0057767276,dbp:country
+dbo:owner,dbp:designer,0.0053939414,dbp:type
+dbo:almamater,dbp:designer,0.0049873395,dbo:knownfor
+dbp:school,dbp:designer,0.0051159854,dbp:title
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+dbo:battle,dbp:designer,0.0052856645,dbo:birthplace
+dbo:team,dbo:previouswork,0.0050928076,dbo:team
+dbo:predecessor,dbo:previouswork,0.0052846274,dbp:foundation
+dbp:publisher,dbp:designer,0.0046293335,dbp:parent
+dbo:producer,dbp:designer,0.004636133,dbp:artist
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diff --git a/Neural graph module/ngm.ipynb b/Neural graph module/ngm.ipynb
index 9856449a8ec73d635255ef6171041d3ca21d2969..43d35e30ece370cc1c357be9a9d49133bc408949 100644
--- a/Neural graph module/ngm.ipynb	
+++ b/Neural graph module/ngm.ipynb	
@@ -2,18 +2,9 @@
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 57,
       "metadata": {},
-      "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"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "import datasets\n",
         "import torch\n",
@@ -30,7 +21,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 58,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -40,7 +31,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 59,
       "metadata": {},
       "outputs": [
         {
@@ -60,7 +51,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 60,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -91,7 +82,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 61,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -230,7 +221,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 62,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -254,7 +252,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 63,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -393,7 +391,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 64,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -405,7 +403,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 65,
       "metadata": {},
       "outputs": [
         {
@@ -419,25 +417,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 2052/2052 [00:01<00:00, 2039.76it/s]\n"
-          ]
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Finished with batches\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, 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"
+            " 77%|███████▋  | 1578/2052 [00:02<00:00, 671.99it/s]"
           ]
         }
       ],
@@ -482,7 +462,47 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Evaluate functions\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",
+        "\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",
+        "    "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -500,7 +520,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -547,7 +567,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": null,
       "metadata": {},
       "outputs": [
         {
@@ -567,52 +587,113 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 51,
       "metadata": {},
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "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: "
+            "1 Train 0.977380952664784 , Valid  0.9857142857142858\n",
+            "2 Train 0.9821428571428571 , Valid  0.9928571428571429\n",
+            "3 Train 0.9821428571428571 , Valid  0.9821428571428571\n",
+            "4 Train 0.9821428571428571 , Valid  0.9928571428571429\n",
+            "5 Train 0.9857142857142858 , Valid  0.9857142857142858\n",
+            "6 Train 0.9821428571428571 , Valid  0.9892857142857143\n",
+            "7 Train 0.9875 , Valid  0.9857142857142858\n",
+            "8 Train 0.9821428571428571 , Valid  0.975\n",
+            "9 Train 0.9767857142857143 , Valid  0.9821428571428571\n",
+            "10 Train 0.9809523812362126 , Valid  0.9857142857142858\n",
+            "11 Train 0.9821428571428571 , Valid  0.9857142857142858\n",
+            "12 Train 0.9785714285714285 , Valid  0.9821428571428571\n",
+            "13 Train 0.98125 , Valid  0.9821428571428571\n",
+            "14 Train 0.9803571428571428 , Valid  0.9821428571428571\n",
+            "15 Train 0.9830357142857142 , Valid  0.9892857142857143\n",
+            "16 Train 0.9839285714285714 , Valid  0.9857142857142858\n",
+            "17 Train 0.98125 , Valid  0.9892857142857143\n",
+            "18 Train 0.9818452383790698 , Valid  0.9857142857142858\n",
+            "19 Train 0.9821428571428571 , Valid  0.9821428571428571\n",
+            "20 Train 0.9794642857142857 , Valid  0.9821428571428571\n",
+            "21 Train 0.9803571428571428 , Valid  0.9821428571428571\n",
+            "22 Train 0.9767857142857143 , Valid  0.9964285714285714\n",
+            "23 Train 0.9785714285714285 , Valid  0.9857142857142858\n",
+            "24 Train 0.9776785714285714 , Valid  0.9785714285714285\n",
+            "25 Train 0.9776785714285714 , Valid  0.9821428571428571\n",
+            "26 Train 0.9791666669504983 , Valid  0.9928571428571429\n",
+            "27 Train 0.9758928571428571 , Valid  0.9892857142857143\n",
+            "28 Train 0.9767857142857143 , Valid  0.9857142857142858\n",
+            "29 Train 0.9803571428571428 , Valid  0.9857142857142858\n",
+            "30 Train 0.9803571428571428 , Valid  0.9857142857142858\n",
+            "31 Train 0.9794642857142857 , Valid  0.9857142857142858\n",
+            "32 Train 0.9767857142857143 , Valid  0.9821428571428571\n",
+            "33 Train 0.9848214285714286 , Valid  0.9785714285714285\n",
+            "34 Train 0.9821428571428571 , Valid  0.9785714285714285\n",
+            "35 Train 0.9785714285714285 , Valid  0.975\n",
+            "36 Train 0.98125 , Valid  0.9821428571428571\n",
+            "37 Train 0.9794642857142857 , Valid  0.9892857142857143\n",
+            "38 Train 0.9803571428571428 , Valid  0.9857142857142858\n",
+            "39 Train 0.9794642857142857 , Valid  0.9857142857142858\n",
+            "40 Train 0.9794642857142857 , Valid  0.9785714285714285\n",
+            "41 Train 0.9785714285714285 , Valid  0.9821428571428571\n",
+            "42 Train 0.9776785714285714 , Valid  0.9821428571428571\n",
+            "43 Train 0.9803571428571428 , Valid  0.9857142857142858\n",
+            "44 Train 0.9794642857142857 , Valid  0.975\n",
+            "45 Train 0.9821428571428571 , Valid  0.9892857142857143\n",
+            "46 Train 0.9785714285714285 , Valid  0.9785714285714285\n",
+            "47 Train 0.9803571428571428 , Valid  0.9821428571428571\n",
+            "48 Train 0.9800595240933555 , Valid  0.9785714285714285\n",
+            "49 Train 0.98125 , Valid  0.975\n",
+            "50 Train 0.9794642857142857 , Valid  0.9821428571428571\n",
+            "51 Train 0.98125 , Valid  0.9785714285714285\n",
+            "52 Train 0.9794642857142857 , Valid  0.9821428571428571\n",
+            "53 Train 0.9794642857142857 , Valid  0.9785714285714285\n",
+            "54 Train 0.9776785714285714 , Valid  0.9857142857142858\n",
+            "55 Train 0.9839285714285714 , Valid  0.975\n",
+            "56 Train 0.9776785714285714 , Valid  0.9821428571428571\n",
+            "57 Train 0.9803571428571428 , Valid  0.9785714285714285\n",
+            "58 Train 0.9794642857142857 , Valid  0.9821428571428571\n",
+            "59 Train 0.9791666669504983 , Valid  0.9964285714285714\n",
+            "60 Train 0.9758928571428571 , Valid  0.9892857142857143\n",
+            "61 Train 0.9776785714285714 , Valid  0.975\n",
+            "62 Train 0.9794642857142857 , Valid  0.9892857142857143\n",
+            "63 Train 0.9821428571428571 , Valid  0.9964285714285714\n",
+            "64 Train 0.9791666669504983 , Valid  0.9785714285714285\n",
+            "65 Train 0.9839285714285714 , Valid  0.9928571428571429\n",
+            "66 Train 0.9803571428571428 , Valid  0.9892857142857143\n",
+            "67 Train 0.9821428571428571 , Valid  0.9892857142857143\n",
+            "68 Train 0.9785714285714285 , Valid  0.9821428571428571\n",
+            "69 Train 0.9803571428571428 , Valid  0.9821428571428571\n",
+            "70 Train 0.9764880955219268 , Valid  0.9892857142857143\n",
+            "71 Train 0.9785714285714285 , Valid  0.9785714285714285\n",
+            "72 Train 0.9830357142857142 , Valid  0.9857142857142858\n",
+            "73 Train 0.9830357142857142 , Valid  0.975\n",
+            "74 Train 0.98125 , Valid  0.9785714285714285\n",
+            "75 Train 0.9830357142857142 , Valid  0.9892857142857143\n",
+            "76 Train 0.9758928571428571 , Valid  0.9821428571428571\n",
+            "77 Train 0.9803571428571428 , Valid  0.9714285714285714\n",
+            "78 Train 0.9839285714285714 , Valid  0.9892857142857143\n",
+            "79 Train 0.9785714285714285 , Valid  0.9857142857142858\n",
+            "80 Train 0.98125 , Valid  0.9821428571428571\n",
+            "81 Train 0.9809523812362126 , Valid  0.9785714285714285\n",
+            "82 Train 0.98125 , Valid  0.9785714285714285\n",
+            "83 Train 0.9830357142857142 , Valid  0.9928571428571429\n",
+            "84 Train 0.9785714285714285 , Valid  0.9857142857142858\n",
+            "85 Train 0.9803571428571428 , Valid  0.9892857142857143\n",
+            "86 Train 0.9821428571428571 , Valid  0.9892857142857143\n",
+            "87 Train 0.9803571428571428 , Valid  0.9892857142857143\n",
+            "88 Train 0.9821428571428571 , Valid  0.9892857142857143\n",
+            "89 Train 0.9821428571428571 , Valid  0.9892857142857143\n",
+            "90 Train 0.9794642857142857 , Valid  0.9821428571428571\n",
+            "91 Train 0.9794642857142857 , Valid  0.9857142857142858\n",
+            "92 Train 0.9821428571428571 , Valid  0.9857142857142858\n",
+            "93 Train 0.9794642857142857 , Valid  0.9857142857142858\n",
+            "94 Train 0.9839285714285714 , Valid  0.9821428571428571\n",
+            "95 Train 0.9794642857142857 , Valid  0.9821428571428571\n",
+            "96 Train 0.9794642857142857 , Valid  0.9785714285714285\n",
+            "97 Train 0.9821428571428571 , Valid  0.9892857142857143\n",
+            "98 Train 0.9785714285714285 , Valid  0.9892857142857143\n",
+            "99 Train 0.9854166669504983 , Valid  0.9821428571428571\n",
+            "100 Train 0.98125 , Valid  0.9857142857142858\n"
           ]
         }
       ],
@@ -623,9 +704,9 @@
         "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
         "#optimizer = optim.SGD(model.parameters(), lr=0.0001, momentum=0.5)\n",
         "\n",
-        "epoch = 50\n",
+        "epoch = 100\n",
         "batch_size = 8\n",
-        "alpha = 0.1\n",
+        "alpha = 0.8\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",
@@ -699,47 +780,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Evaluate functions\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",
-        "\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",
-        "    "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 53,
       "metadata": {},
       "outputs": [
         {
@@ -753,7 +794,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 514/514 [00:01<00:00, 457.70it/s]\n"
+            "100%|██████████| 514/514 [00:00<00:00, 633.78it/s]\n"
           ]
         },
         {
@@ -761,7 +802,7 @@
           "output_type": "stream",
           "text": [
             "Finished with batches\n",
-            "test loss 6.009934425354004\n"
+            "test loss 6.08909797668457\n"
           ]
         }
       ],
@@ -795,7 +836,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": 55,
       "metadata": {},
       "outputs": [
         {
@@ -803,15 +844,15 @@
           "output_type": "stream",
           "text": [
             "Final pred: None No prediction existed in dbpedia relations for (e, r?, e_s/o)\n",
-            "lowest confidence train 0.095730975\n",
-            "lowest confidence valid 0.095730975\n",
-            "lowest confidence test 0.096658\n",
-            "Accuracy train: 0.21198568872987478\n",
-            "Accuracy train graphsearch: 0.29069767441860467\n",
-            "Accuracy valid: 0.07857142857142857\n",
-            "Accuracy valid graphsearch: 0.175\n",
-            "Accuracy test: 0.09798270893371758\n",
-            "Accuracy test graphsearch: 0.21037463976945245\n"
+            "lowest confidence train 0.004072359\n",
+            "lowest confidence valid 0.004072359\n",
+            "lowest confidence test 0.004137778\n",
+            "Accuracy train: 0.0035778175313059034\n",
+            "Accuracy train graphsearch: 0.17262969588550983\n",
+            "Accuracy valid: 0.0\n",
+            "Accuracy valid graphsearch: 0.19642857142857142\n",
+            "Accuracy test: 0.002881844380403458\n",
+            "Accuracy test graphsearch: 0.12968299711815562\n"
           ]
         }
       ],
diff --git a/Neural graph module/stats.ipynb b/Neural graph module/stats.ipynb
index ed7844b548eef53fa8bbb139f12ebea3fdcc9f0e..e2e2728319f67ba6a49ffeca9ba537b3124903f7 100644
--- a/Neural graph module/stats.ipynb	
+++ b/Neural graph module/stats.ipynb	
@@ -2,19 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "\n",
@@ -53,9 +43,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train accuracy:  0.09798270893371758 gs: 0.21037463976945245\n",
+      "Valid accuracy:  0.07857142857142857 gs: 0.175\n",
+      "Test accuracy:  0.21198568872987478 gs: 0.29069767441860467\n"
+     ]
+    }
+   ],
    "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",
@@ -64,6 +64,45 @@
     "print(\"Valid accuracy: \", valid_acc, \"gs:\", valid_acc_gs)\n",
     "print(\"Test accuracy: \", test_acc, \"gs:\", test_acc_gs)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Print graph\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "\n",
+    "\n",
+    "alphas = [0, 0.2, 0.4, 0.6, 0.8, 1]\n",
+    "train_accs = [] \n",
+    "valid_accs = [] \n",
+    "test_accs = [] \n",
+    "train_acc_gss = [] \n",
+    "valid_acc_gss = [] \n",
+    "test_acc_gss = []\n",
+    "\n",
+    "\n",
+    "for alpha in alphas:\n",
+    "    file_train = \"df_train_a\" + str(alpha) + \"_e100_b8.csv\"\n",
+    "    file_valid = \"df_valid_a\" + str(alpha) + \"_e100_b8.csv\"\n",
+    "    file_test = \"df_test_a\" + str(alpha) + \"_e100_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",
+    "    alphas.append()\n",
+    "    train_accs.append() \n",
+    "    valid_accs.append() \n",
+    "    train_acc_gss.append() \n",
+    "    valid_acc_gss.append() \n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
   }
  ],
  "metadata": {