line-loss-training · pygal · anyplot.ai0.20.20.40.40.60.60.80.8111.21.21.41.41.61.61.81.8222.22.22.42.45101520253035404550line-loss-training · pygal · anyplot.aiEpochCross-Entropy Loss2.41772514984.1230769230769246.769230769230492.227594186169.96295133437988239.476646456501382.079523423255.80282574568284389.5539072780351.94583319341.6427001569859525.05576907771591.771117048427.4825745682888702.139479058822251.64227574513.3224489795917832.72670695554051.559606916599.1623233908947916.51577158648481.433579755685.00219780219771044.2507191863341.307491152770.84207221350061172.04794064099361.234173611856.68194662480371246.3590031740964101.127688925942.52182103610661354.2866564596021.047917621028.36169544740961435.13893802534220.98847595031114.20156985871241495.38609799387680.87743388171200.04144427001571607.93289094252030.81848717311285.88131868131841667.67838272084150.78384750051371.72119309262171702.78745538866560.721395321457.56106750392451766.0859216410440.69860434341543.40094191522761789.1857373920730.62861923591629.24081632653061860.11920879276070.5764952211715.08069073783351912.9495530428132200.59524791551800.92056514913661893.9427419086820.52559663351886.76043956043921964.53786410160180.49839412941972.60031397174251992.10898775987060.43802244162058.44018838304562053.29876856982360.42745055362144.28006279434842064.0139155784714250.41454398232230.11993720565122077.09538304773560.3652929312315.9598116169542127.0138322765250.37366022132401.79968602825742118.53315736922920.33367119022487.63956043956022159.0640773878150.32096100822573.4794348508632171.946494271821300.29732393172659.3193092621662195.9038753422650.33030741482745.15918367346972162.4734351169340.27813322942830.9990580847722215.35462971629930.24353266752916.8389324960752250.4240617250270.26847605483002.67880690737772225.1426680899685350.21592003413088.5186813186812278.41087223334670.23372456483174.35855572998432260.3650733795460.18039382133260.19843014128672314.4184985997880.18382920013346.038304552592310.9365671781190.20584273473431.8781789638932288.624728524445400.20883997363517.7180533751962285.5868741680330.1902655133603.5579277864992304.4130361808650.17784874773689.3978021978022316.9980603223930.1679765143775.23767660910472327.0040720212580.13873886633861.07755102040762356.637917295058450.14866055293946.9174254317112346.5817825170050.14899657554032.75729984301372346.24120649744870.17487644794118.5971742543172320.01063755987340.15647510274204.437048665622338.66133829914270.11052829414290.2769230769232385.230769230769502.30422830584.12307692307692161.804063728048282.137971473169.96295133437988330.313831754603141.994036106255.80282574568284476.19965840084891.906652894341.6427001569859564.76699467011461.801712592427.4825745682888671.129336273127651.689146089513.3224489795917785.22122095262241.533864181599.1623233908947942.60734481966981.454504476685.00219780219771023.04244868661091.384899045770.84207221350061093.5910979368691.321412553856.68194662480371157.937892243103101.20055483942.52182103610661280.43335040722331.1373644351028.36169544740961344.48003498606951.0426156661114.20156985871241440.51273828651260.97732932861200.04144427001571506.68376687554660.97923259761285.88131868131841504.7547068020963150.94113072851371.72119309262171543.37289203909840.84754860371457.56106750392451638.22314247577630.83251024791543.40094191522761653.465282136020.76914678171629.24081632653061717.6873827377550.69781942441715.08069073783351789.981292710178200.68967048411800.92056514913661798.24065883533330.68921763031886.76043956043921798.69964924843770.60866673121972.60031397174251880.34208863449730.62559945462058.44018838304561863.17991096968540.47108771682144.28006279434842019.7854278348793250.55031630232230.11993720565121939.48322065037220.50856185372315.9598116169541981.80348116428760.48059260492401.79968602825742010.15173961857660.47882452192487.63956043956022011.9437817372990.40556373722573.4794348508632086.197318884264300.45010917762659.3193092621662041.04824592043840.46112871472745.15918367346972029.87938372641880.49051995092830.9990580847722000.08986870827770.42831822172916.8389324960752063.13448973620140.41908877073002.67880690737772072.489008522104350.42945300233088.5186813186812061.9843318214920.47471581393174.35855572998432016.10816665446550.4613599433260.19843014128672029.6450220703160.44126117043346.038304552592050.01615198769920.47954541763431.8781789638932011.2131172509617400.47531985763517.7180533751962015.49593755282790.51092891273603.5579277864991979.40434631714270.47141186543689.3978021978022019.45688669283940.49433366573775.23767660910471996.22447453942730.50512480453861.07755102040761985.2871056533425450.48669345433946.9174254317112003.9682179151880.55413490214032.75729984301371935.61287524560680.56863512884118.5971742543171920.91615678363450.57736989044204.437048665621912.06303097238010.58741636794290.2769230769231901.880414338463250Training LossValidation Loss