diff --git a/higher/res/log/Aug_mod(Data_augV5(Uniform-14TFx3-Mag)-MobileNetV2)-200 epochs (dataug:0)- 1 in_it.json b/higher/res/log/Aug_mod(Data_augV5(Uniform-14TFx3-Mag)-MobileNetV2)-200 epochs (dataug:0)- 1 in_it.json new file mode 100644 index 0000000..8b5247f --- /dev/null +++ b/higher/res/log/Aug_mod(Data_augV5(Uniform-14TFx3-Mag)-MobileNetV2)-200 epochs (dataug:0)- 1 in_it.json @@ -0,0 +1,13027 @@ +{ + "Accuracy": 68.63, + "Time": [ + 351.92671754065, + 3.3304700359747104, + 70618.151818803 + ], + "Device": "Graphics Device", + "Param_names": [ + "Identity", + "FlipUD", + "FlipLR", + "Rotate", + "TranslateX", + "TranslateY", + "ShearX", + "ShearY", + "Contrast", + "Color", + "Brightness", + "Sharpness", + "Posterize", + "Solarize" + ], + "Log": [ + { + "epoch": 1, + "train_loss": 1.0052108764648438, + "val_loss": 1.128347396850586, + "acc": 63.53, + "time": 349.1326721279911, + "param": [ + { + "p": 0.08234741538763046, + "m": 1.0 + }, + { + "p": 0.0004318627470638603, + "m": 1.0 + }, + { + "p": 0.04407211020588875, + "m": 1.0 + }, + { + "p": 0.012474559247493744, + "m": 0.9504178166389465 + }, + { + "p": 0.0, + "m": 0.9361149668693542 + }, + { + "p": 0.03242402523756027, + "m": 0.9508930444717407 + }, + { + "p": 0.00351925496943295, + "m": 1.0 + }, + { + "p": 0.06699219346046448, + "m": 0.9810126423835754 + }, + { + "p": 0.051975738257169724, + "m": 0.9022859930992126 + }, + { + "p": 0.35808879137039185, + "m": 0.9859144687652588 + }, + { + "p": 0.026360508054494858, + "m": 0.8507086634635925 + }, + { + "p": 0.14685335755348206, + "m": 0.9990066289901733 + }, + { + "p": 0.17131944000720978, + "m": 1.0 + }, + { + "p": 0.003140683751553297, + "m": 1.0 + } + ] + }, + { + "epoch": 2, + "train_loss": 0.9762787818908691, + "val_loss": 1.0899169445037842, + "acc": 63.59, + "time": 350.0168772699981, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8844788670539856 + }, + { + "p": 0.007329513318836689, + "m": 0.8842112421989441 + }, + { + "p": 0.0, + "m": 0.9018689393997192 + }, + { + "p": 0.12701673805713654, + "m": 0.9985126852989197 + }, + { + "p": 0.043070994317531586, + "m": 0.9226253032684326 + }, + { + "p": 0.06238861009478569, + "m": 0.7812468409538269 + }, + { + "p": 0.34910714626312256, + "m": 0.9258043169975281 + }, + { + "p": 0.09808184206485748, + "m": 0.7444273829460144 + }, + { + "p": 0.05718023329973221, + "m": 0.925621509552002 + }, + { + "p": 0.2546517848968506, + "m": 1.0 + }, + { + "p": 0.0011732494458556175, + "m": 1.0 + } + ] + }, + { + "epoch": 3, + "train_loss": 1.032353162765503, + "val_loss": 1.261133074760437, + "acc": 54.6, + "time": 351.7623607650021, + "param": [ + { + "p": 0.03443565219640732, + "m": 1.0 + }, + { + "p": 0.002689182525500655, + "m": 1.0 + }, + { + "p": 0.017930928617715836, + "m": 1.0 + }, + { + "p": 0.01648831181228161, + "m": 0.7768539190292358 + }, + { + "p": 0.042698152363300323, + "m": 0.92160964012146 + }, + { + "p": 0.0, + "m": 0.9534847140312195 + }, + { + "p": 0.022111596539616585, + "m": 0.8746533989906311 + }, + { + "p": 0.0, + "m": 0.8930509686470032 + }, + { + "p": 0.053646814078092575, + "m": 0.7590095400810242 + }, + { + "p": 0.48675715923309326, + "m": 1.0 + }, + { + "p": 0.13217245042324066, + "m": 0.8226022720336914 + }, + { + "p": 0.0, + "m": 0.9716771245002747 + }, + { + "p": 0.18858936429023743, + "m": 1.0 + }, + { + "p": 0.0024803830310702324, + "m": 1.0 + } + ] + }, + { + "epoch": 4, + "train_loss": 0.8105615377426147, + "val_loss": 1.2121365070343018, + "acc": 62.03, + "time": 350.4292698770005, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.005168926902115345, + "m": 1.0 + }, + { + "p": 0.02724279649555683, + "m": 1.0 + }, + { + "p": 0.0010494055459275842, + "m": 0.7194018363952637 + }, + { + "p": 0.002846039365977049, + "m": 0.8809906840324402 + }, + { + "p": 0.0, + "m": 0.8050956130027771 + }, + { + "p": 0.062163107097148895, + "m": 0.759458601474762 + }, + { + "p": 0.03643247112631798, + "m": 0.9931071996688843 + }, + { + "p": 0.06257501989603043, + "m": 0.7509932518005371 + }, + { + "p": 0.43629372119903564, + "m": 0.9737178087234497 + }, + { + "p": 0.1605670005083084, + "m": 0.7093345522880554 + }, + { + "p": 0.0, + "m": 0.8746848702430725 + }, + { + "p": 0.20566152036190033, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 5, + "train_loss": 0.7829158306121826, + "val_loss": 1.1070994138717651, + "acc": 63.14, + "time": 355.89855234199786, + "param": [ + { + "p": 0.003721296088770032, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.12228960543870926, + "m": 0.8819286823272705 + }, + { + "p": 0.0, + "m": 0.651027500629425 + }, + { + "p": 0.0, + "m": 0.6690056920051575 + }, + { + "p": 0.0767453983426094, + "m": 0.6828619837760925 + }, + { + "p": 0.003123936476185918, + "m": 1.0 + }, + { + "p": 0.004155511036515236, + "m": 0.7587857842445374 + }, + { + "p": 0.2908964157104492, + "m": 0.9405736327171326 + }, + { + "p": 0.15410485863685608, + "m": 0.5866462588310242 + }, + { + "p": 0.04800361767411232, + "m": 0.9597349166870117 + }, + { + "p": 0.29695940017700195, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 6, + "train_loss": 0.9088900089263916, + "val_loss": 1.0040154457092285, + "acc": 63.62, + "time": 351.8011273529992, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.07049809396266937, + "m": 1.0 + }, + { + "p": 0.11441096663475037, + "m": 0.9164626598358154 + }, + { + "p": 0.0, + "m": 0.5914590358734131 + }, + { + "p": 0.006732712499797344, + "m": 0.6827904582023621 + }, + { + "p": 0.029389385133981705, + "m": 0.721000611782074 + }, + { + "p": 0.0, + "m": 0.9783102869987488 + }, + { + "p": 0.0, + "m": 0.8173961639404297 + }, + { + "p": 0.24499580264091492, + "m": 0.9402304887771606 + }, + { + "p": 0.1394181251525879, + "m": 0.5190548896789551 + }, + { + "p": 0.11280491203069687, + "m": 0.9991357922554016 + }, + { + "p": 0.28174999356269836, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 7, + "train_loss": 0.7088822722434998, + "val_loss": 0.9900627732276917, + "acc": 64.0, + "time": 350.7838515869953, + "param": [ + { + "p": 0.0448637455701828, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.11669375002384186, + "m": 1.0 + }, + { + "p": 0.0625818595290184, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.6812732815742493 + }, + { + "p": 0.0, + "m": 0.6903979778289795 + }, + { + "p": 0.0, + "m": 0.7996267676353455 + }, + { + "p": 0.0, + "m": 0.9679665565490723 + }, + { + "p": 0.0, + "m": 0.8248274326324463 + }, + { + "p": 0.19264614582061768, + "m": 0.9869178533554077 + }, + { + "p": 0.12259384244680405, + "m": 0.38251566886901855 + }, + { + "p": 0.15784969925880432, + "m": 1.0 + }, + { + "p": 0.3027708828449249, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 8, + "train_loss": 1.059874415397644, + "val_loss": 1.2019768953323364, + "acc": 63.05, + "time": 351.02259334099654, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.14262624084949493, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9789015054702759 + }, + { + "p": 0.0, + "m": 0.69205641746521 + }, + { + "p": 0.0, + "m": 0.6709055304527283 + }, + { + "p": 0.003284105798229575, + "m": 0.849364161491394 + }, + { + "p": 0.0, + "m": 0.9309942722320557 + }, + { + "p": 0.01317575853317976, + "m": 0.856231153011322 + }, + { + "p": 0.2192910611629486, + "m": 1.0 + }, + { + "p": 0.2398974597454071, + "m": 0.3900165855884552 + }, + { + "p": 0.17421270906925201, + "m": 1.0 + }, + { + "p": 0.20751269161701202, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 9, + "train_loss": 1.1594561338424683, + "val_loss": 1.203176498413086, + "acc": 62.64, + "time": 349.8803763510077, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.016396986320614815, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9509888291358948 + }, + { + "p": 0.0, + "m": 0.7418984770774841 + }, + { + "p": 0.0, + "m": 0.5420684814453125 + }, + { + "p": 0.0, + "m": 0.8961572647094727 + }, + { + "p": 0.0, + "m": 0.8871431350708008 + }, + { + "p": 0.08591049909591675, + "m": 0.9078526496887207 + }, + { + "p": 0.11458401381969452, + "m": 1.0 + }, + { + "p": 0.1714761108160019, + "m": 0.0 + }, + { + "p": 0.309347540140152, + "m": 1.0 + }, + { + "p": 0.3022848665714264, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 10, + "train_loss": 1.094713568687439, + "val_loss": 1.0436511039733887, + "acc": 62.18, + "time": 355.04965348599944, + "param": [ + { + "p": 0.000949771492742002, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.03929372504353523, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9607307314872742 + }, + { + "p": 0.0, + "m": 0.6886497139930725 + }, + { + "p": 0.0, + "m": 0.5913254618644714 + }, + { + "p": 0.0, + "m": 0.9075612425804138 + }, + { + "p": 0.0, + "m": 0.953923761844635 + }, + { + "p": 0.0, + "m": 0.9173848032951355 + }, + { + "p": 0.0, + "m": 0.9509899020195007 + }, + { + "p": 0.10323746502399445, + "m": 0.0003981581539846957 + }, + { + "p": 0.5065800547599792, + "m": 0.9523518681526184 + }, + { + "p": 0.3499390482902527, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 11, + "train_loss": 0.8159509301185608, + "val_loss": 1.0278056859970093, + "acc": 63.25, + "time": 351.1906883789925, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9788485169410706 + }, + { + "p": 0.0, + "m": 0.6498849391937256 + }, + { + "p": 0.0, + "m": 0.5548929572105408 + }, + { + "p": 0.0, + "m": 0.8904518485069275 + }, + { + "p": 0.0, + "m": 0.9192264080047607 + }, + { + "p": 0.0, + "m": 0.8285799026489258 + }, + { + "p": 0.0, + "m": 0.8993571400642395 + }, + { + "p": 0.0, + "m": 0.06197613105177879 + }, + { + "p": 0.6636099815368652, + "m": 0.9796412587165833 + }, + { + "p": 0.3363899886608124, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 12, + "train_loss": 0.8970941305160522, + "val_loss": 1.0995864868164062, + "acc": 63.83, + "time": 352.20030412400956, + "param": [ + { + "p": 0.1505427360534668, + "m": 1.0 + }, + { + "p": 0.016466932371258736, + "m": 1.0 + }, + { + "p": 0.011303690262138844, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9939537048339844 + }, + { + "p": 0.01226914580911398, + "m": 0.5232779383659363 + }, + { + "p": 0.0, + "m": 0.6163504123687744 + }, + { + "p": 0.0, + "m": 0.9331618547439575 + }, + { + "p": 0.0, + "m": 0.9081336259841919 + }, + { + "p": 0.0, + "m": 0.7867230772972107 + }, + { + "p": 0.01850954256951809, + "m": 0.9213379621505737 + }, + { + "p": 0.0, + "m": 0.13344155251979828 + }, + { + "p": 0.5773165822029114, + "m": 0.9508364200592041 + }, + { + "p": 0.2097792774438858, + "m": 1.0 + }, + { + "p": 0.003812165465205908, + "m": 1.0 + } + ] + }, + { + "epoch": 13, + "train_loss": 0.7902970314025879, + "val_loss": 1.00189208984375, + "acc": 63.32, + "time": 352.45867782700225, + "param": [ + { + "p": 0.21363095939159393, + "m": 1.0 + }, + { + "p": 0.0009124809294007719, + "m": 1.0 + }, + { + "p": 0.0021739238873124123, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9575809836387634 + }, + { + "p": 0.0, + "m": 0.5467348694801331 + }, + { + "p": 0.0079704774543643, + "m": 0.6711128354072571 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.023404667153954506, + "m": 0.9556736350059509 + }, + { + "p": 0.012796741910278797, + "m": 0.8317908644676208 + }, + { + "p": 0.0, + "m": 0.9125058054924011 + }, + { + "p": 0.04421167075634003, + "m": 0.13883130252361298 + }, + { + "p": 0.4968228042125702, + "m": 0.985878586769104 + }, + { + "p": 0.19807623326778412, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 14, + "train_loss": 0.8730485439300537, + "val_loss": 1.072264313697815, + "acc": 63.22, + "time": 354.77753365100943, + "param": [ + { + "p": 0.15852361917495728, + "m": 1.0 + }, + { + "p": 0.001165405148640275, + "m": 1.0 + }, + { + "p": 0.07393556088209152, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9134358763694763 + }, + { + "p": 0.0, + "m": 0.5557371973991394 + }, + { + "p": 0.0, + "m": 0.447126179933548 + }, + { + "p": 0.003380032954737544, + "m": 0.996569812297821 + }, + { + "p": 0.0, + "m": 0.9216868281364441 + }, + { + "p": 0.06781625002622604, + "m": 0.8710727095603943 + }, + { + "p": 0.0452919602394104, + "m": 0.8771645426750183 + }, + { + "p": 0.0749630331993103, + "m": 0.035874638706445694 + }, + { + "p": 0.5060321092605591, + "m": 0.807984471321106 + }, + { + "p": 0.06889205425977707, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 15, + "train_loss": 0.9134953022003174, + "val_loss": 1.099703311920166, + "acc": 62.86, + "time": 348.2273779660027, + "param": [ + { + "p": 0.018147217109799385, + "m": 1.0 + }, + { + "p": 0.023485122248530388, + "m": 1.0 + }, + { + "p": 0.17323681712150574, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9634120464324951 + }, + { + "p": 0.0, + "m": 0.5514428615570068 + }, + { + "p": 0.0010717023396864533, + "m": 0.3120802938938141 + }, + { + "p": 0.07373072952032089, + "m": 0.995254635810852 + }, + { + "p": 0.0027376189827919006, + "m": 0.768951952457428 + }, + { + "p": 0.09828044474124908, + "m": 0.9850113987922668 + }, + { + "p": 0.009333640336990356, + "m": 0.8319339752197266 + }, + { + "p": 0.06021416187286377, + "m": 0.009864671155810356 + }, + { + "p": 0.5186936855316162, + "m": 0.8443876504898071 + }, + { + "p": 0.021068831905722618, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 16, + "train_loss": 0.9866026043891907, + "val_loss": 1.1049305200576782, + "acc": 63.87, + "time": 353.13774510299845, + "param": [ + { + "p": 0.031408898532390594, + "m": 1.0 + }, + { + "p": 0.05620709806680679, + "m": 1.0 + }, + { + "p": 0.10732106119394302, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9137057662010193 + }, + { + "p": 0.00856856070458889, + "m": 0.6498122215270996 + }, + { + "p": 0.0005732870777137578, + "m": 0.21983735263347626 + }, + { + "p": 0.11712038516998291, + "m": 0.9634524583816528 + }, + { + "p": 0.03168627247214317, + "m": 0.7956297397613525 + }, + { + "p": 0.11938581615686417, + "m": 0.8527504801750183 + }, + { + "p": 0.0, + "m": 0.7373632788658142 + }, + { + "p": 0.04037712141871452, + "m": 0.027087200433015823 + }, + { + "p": 0.4198623299598694, + "m": 0.8583220839500427 + }, + { + "p": 0.06748923659324646, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 17, + "train_loss": 0.877082347869873, + "val_loss": 1.0923690795898438, + "acc": 62.67, + "time": 356.861807897003, + "param": [ + { + "p": 0.0575861856341362, + "m": 1.0 + }, + { + "p": 0.01858399622142315, + "m": 1.0 + }, + { + "p": 0.08185343444347382, + "m": 1.0 + }, + { + "p": 0.13705472648143768, + "m": 0.9448909163475037 + }, + { + "p": 0.0, + "m": 0.6826411485671997 + }, + { + "p": 0.09382850676774979, + "m": 0.2907867431640625 + }, + { + "p": 0.08092430979013443, + "m": 0.9149721264839172 + }, + { + "p": 0.02476355992257595, + "m": 0.8153336048126221 + }, + { + "p": 0.09297696501016617, + "m": 0.7772626280784607 + }, + { + "p": 0.06389057636260986, + "m": 0.737411379814148 + }, + { + "p": 0.05224226042628288, + "m": 0.0 + }, + { + "p": 0.24833270907402039, + "m": 0.8597215414047241 + }, + { + "p": 0.04796270281076431, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 18, + "train_loss": 0.921488881111145, + "val_loss": 1.092433214187622, + "acc": 62.63, + "time": 348.5257026709878, + "param": [ + { + "p": 0.0012742476537823677, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.03875388577580452, + "m": 1.0 + }, + { + "p": 0.1693749725818634, + "m": 0.9128310680389404 + }, + { + "p": 0.0, + "m": 0.6729590892791748 + }, + { + "p": 0.0, + "m": 0.30873554944992065 + }, + { + "p": 0.0, + "m": 0.9160518050193787 + }, + { + "p": 0.0, + "m": 0.8037461042404175 + }, + { + "p": 0.04516099393367767, + "m": 0.8280601501464844 + }, + { + "p": 0.09169235080480576, + "m": 0.7167913913726807 + }, + { + "p": 0.0, + "m": 0.0 + }, + { + "p": 0.6255106329917908, + "m": 0.9118356108665466 + }, + { + "p": 0.02823297120630741, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 19, + "train_loss": 0.8137305974960327, + "val_loss": 1.3242361545562744, + "acc": 58.21, + "time": 352.8276632549969, + "param": [ + { + "p": 0.015334853902459145, + "m": 1.0 + }, + { + "p": 0.0008457225048914552, + "m": 1.0 + }, + { + "p": 0.0057504018768668175, + "m": 1.0 + }, + { + "p": 0.05192168802022934, + "m": 0.7936176657676697 + }, + { + "p": 0.0, + "m": 0.7071818709373474 + }, + { + "p": 0.009869911707937717, + "m": 0.2198810875415802 + }, + { + "p": 0.0, + "m": 0.9095132946968079 + }, + { + "p": 0.0, + "m": 0.8788821697235107 + }, + { + "p": 0.05207042768597603, + "m": 0.8415011763572693 + }, + { + "p": 0.0, + "m": 0.8227017521858215 + }, + { + "p": 0.09657398611307144, + "m": 0.0001880024210549891 + }, + { + "p": 0.7628644704818726, + "m": 0.9927769899368286 + }, + { + "p": 0.002757471054792404, + "m": 1.0 + }, + { + "p": 0.00201106583699584, + "m": 1.0 + } + ] + }, + { + "epoch": 20, + "train_loss": 1.07203209400177, + "val_loss": 1.203399658203125, + "acc": 62.32, + "time": 355.72835940800724, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8105767369270325 + }, + { + "p": 0.0, + "m": 0.6466808915138245 + }, + { + "p": 0.0, + "m": 0.3756966292858124 + }, + { + "p": 0.0, + "m": 0.9436954855918884 + }, + { + "p": 0.0, + "m": 0.9540258049964905 + }, + { + "p": 0.03943678364157677, + "m": 0.8604230284690857 + }, + { + "p": 0.004348804708570242, + "m": 0.7540082931518555 + }, + { + "p": 0.0, + "m": 0.03385306894779205 + }, + { + "p": 0.956214427947998, + "m": 0.7270808219909668 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 21, + "train_loss": 1.0913348197937012, + "val_loss": 1.0372028350830078, + "acc": 63.71, + "time": 348.72116640300374, + "param": [ + { + "p": 0.11074618995189667, + "m": 1.0 + }, + { + "p": 0.0186600424349308, + "m": 1.0 + }, + { + "p": 0.0004815021820832044, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9223507642745972 + }, + { + "p": 0.0, + "m": 0.7672094106674194 + }, + { + "p": 0.0006004244787618518, + "m": 0.41253018379211426 + }, + { + "p": 0.059560563415288925, + "m": 0.925604522228241 + }, + { + "p": 0.0, + "m": 0.9782103896141052 + }, + { + "p": 0.0002763225347734988, + "m": 0.9120616912841797 + }, + { + "p": 0.011920678429305553, + "m": 0.7606329321861267 + }, + { + "p": 0.0, + "m": 0.048530902713537216 + }, + { + "p": 0.7395774126052856, + "m": 0.9302625060081482 + }, + { + "p": 0.058176927268505096, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 22, + "train_loss": 1.040681004524231, + "val_loss": 1.0844416618347168, + "acc": 63.28, + "time": 351.4578822570038, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8795247077941895 + }, + { + "p": 0.0, + "m": 0.651556670665741 + }, + { + "p": 0.0, + "m": 0.37242624163627625 + }, + { + "p": 0.0, + "m": 0.8864831924438477 + }, + { + "p": 0.0030619169119745493, + "m": 0.9292011260986328 + }, + { + "p": 0.017655575647950172, + "m": 0.8986137509346008 + }, + { + "p": 0.0, + "m": 0.695194661617279 + }, + { + "p": 0.0011998803820461035, + "m": 0.0 + }, + { + "p": 0.9724180102348328, + "m": 0.9532650113105774 + }, + { + "p": 0.005664600525051355, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 23, + "train_loss": 1.075500726699829, + "val_loss": 1.0283244848251343, + "acc": 62.79, + "time": 352.1043943250115, + "param": [ + { + "p": 0.01038618478924036, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.07844619452953339, + "m": 1.0 + }, + { + "p": 0.0005194239201955497, + "m": 0.9793772101402283 + }, + { + "p": 0.0, + "m": 0.7782559990882874 + }, + { + "p": 0.0011449551675468683, + "m": 0.2745988667011261 + }, + { + "p": 0.04203036054968834, + "m": 0.9264103770256042 + }, + { + "p": 0.06296985596418381, + "m": 0.8371508717536926 + }, + { + "p": 0.00020629153004847467, + "m": 0.8557165861129761 + }, + { + "p": 0.10169711709022522, + "m": 0.6975941061973572 + }, + { + "p": 0.013075039722025394, + "m": 0.049993619322776794 + }, + { + "p": 0.590729832649231, + "m": 0.917391836643219 + }, + { + "p": 0.09879475086927414, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 24, + "train_loss": 0.7645032405853271, + "val_loss": 1.0255844593048096, + "acc": 62.83, + "time": 354.4770100500027, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.022939717397093773, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9973196983337402 + }, + { + "p": 0.0, + "m": 0.7600322365760803 + }, + { + "p": 0.026808366179466248, + "m": 0.21087287366390228 + }, + { + "p": 0.0, + "m": 0.97639000415802 + }, + { + "p": 0.0, + "m": 0.8157875537872314 + }, + { + "p": 0.0, + "m": 0.8352004289627075 + }, + { + "p": 0.06311964243650436, + "m": 0.6264415383338928 + }, + { + "p": 0.0, + "m": 0.005885164253413677 + }, + { + "p": 0.8231488466262817, + "m": 0.839684009552002 + }, + { + "p": 0.06398344784975052, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 25, + "train_loss": 1.2260884046554565, + "val_loss": 1.2276021242141724, + "acc": 61.54, + "time": 353.9131253759988, + "param": [ + { + "p": 0.001723577850498259, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0010417108424007893, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8263481855392456 + }, + { + "p": 0.0, + "m": 0.20922692120075226 + }, + { + "p": 0.011562923900783062, + "m": 1.0 + }, + { + "p": 0.0032000115606933832, + "m": 0.9489887952804565 + }, + { + "p": 0.012452877126634121, + "m": 0.66031414270401 + }, + { + "p": 0.012399363331496716, + "m": 0.7227699756622314 + }, + { + "p": 0.007981049828231335, + "m": 0.011227842420339584 + }, + { + "p": 0.9423237442970276, + "m": 0.7576417922973633 + }, + { + "p": 0.007314781658351421, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 26, + "train_loss": 1.036177635192871, + "val_loss": 1.0784268379211426, + "acc": 63.23, + "time": 351.78591587200935, + "param": [ + { + "p": 0.0246670451015234, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.026328857988119125, + "m": 1.0 + }, + { + "p": 0.02132454514503479, + "m": 0.9187753200531006 + }, + { + "p": 0.02371244877576828, + "m": 0.8817130327224731 + }, + { + "p": 0.0, + "m": 0.231641948223114 + }, + { + "p": 0.02169569954276085, + "m": 0.954390287399292 + }, + { + "p": 0.021938038989901543, + "m": 0.897864580154419 + }, + { + "p": 0.039651721715927124, + "m": 0.7300006747245789 + }, + { + "p": 0.06640473753213882, + "m": 0.73818039894104 + }, + { + "p": 0.0074897222220897675, + "m": 0.033098239451646805 + }, + { + "p": 0.7467871904373169, + "m": 0.7094181180000305 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 27, + "train_loss": 1.0019561052322388, + "val_loss": 1.0712121725082397, + "acc": 63.02, + "time": 356.35642597000697, + "param": [ + { + "p": 0.014931270852684975, + "m": 1.0 + }, + { + "p": 0.0009426103788428009, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.010153787210583687, + "m": 0.8187072277069092 + }, + { + "p": 0.005413471255451441, + "m": 0.8709405660629272 + }, + { + "p": 0.020098421722650528, + "m": 0.20741450786590576 + }, + { + "p": 0.0395231768488884, + "m": 0.9624444842338562 + }, + { + "p": 0.002137334318831563, + "m": 0.8793716430664062 + }, + { + "p": 0.02635865844786167, + "m": 0.7162165641784668 + }, + { + "p": 0.0404762327671051, + "m": 0.7733225226402283 + }, + { + "p": 0.01485639251768589, + "m": 0.047100797295570374 + }, + { + "p": 0.8251086473464966, + "m": 0.6245468854904175 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 28, + "train_loss": 1.0774078369140625, + "val_loss": 1.1548372507095337, + "acc": 63.22, + "time": 352.46115306401043, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.008020893670618534, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8322421312332153 + }, + { + "p": 0.0, + "m": 0.7850528359413147 + }, + { + "p": 0.0, + "m": 0.18410517275333405 + }, + { + "p": 0.0012263372773304582, + "m": 0.906805694103241 + }, + { + "p": 0.0007858466124162078, + "m": 0.8546329140663147 + }, + { + "p": 0.0, + "m": 0.6509681940078735 + }, + { + "p": 0.0, + "m": 0.8075469732284546 + }, + { + "p": 0.00046023790491744876, + "m": 0.0 + }, + { + "p": 0.989506721496582, + "m": 0.6156229972839355 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 29, + "train_loss": 1.1843886375427246, + "val_loss": 1.1496789455413818, + "acc": 63.05, + "time": 350.38700665999204, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8334067463874817 + }, + { + "p": 0.0, + "m": 0.8419780731201172 + }, + { + "p": 0.0, + "m": 0.30695757269859314 + }, + { + "p": 0.0, + "m": 0.970190703868866 + }, + { + "p": 0.0, + "m": 0.8818405866622925 + }, + { + "p": 0.0, + "m": 0.6017179489135742 + }, + { + "p": 0.0, + "m": 0.8164499998092651 + }, + { + "p": 0.0, + "m": 0.035248786211013794 + }, + { + "p": 1.0, + "m": 0.6304486989974976 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 30, + "train_loss": 0.9702933430671692, + "val_loss": 1.0302419662475586, + "acc": 63.39, + "time": 352.99649409101403, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.001634772284887731, + "m": 1.0 + }, + { + "p": 0.0015486134216189384, + "m": 0.8265712857246399 + }, + { + "p": 0.0, + "m": 0.8570484519004822 + }, + { + "p": 0.0, + "m": 0.3165823519229889 + }, + { + "p": 0.0, + "m": 0.9889117479324341 + }, + { + "p": 0.0, + "m": 0.9998685717582703 + }, + { + "p": 0.0, + "m": 0.45599114894866943 + }, + { + "p": 0.03309386223554611, + "m": 0.7622667551040649 + }, + { + "p": 0.0, + "m": 0.05979832634329796 + }, + { + "p": 0.9601673483848572, + "m": 0.7000172734260559 + }, + { + "p": 0.003555434290319681, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 31, + "train_loss": 0.9634566307067871, + "val_loss": 1.1090335845947266, + "acc": 63.86, + "time": 348.1700504319888, + "param": [ + { + "p": 0.0006170603446662426, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8337236046791077 + }, + { + "p": 0.0, + "m": 0.38859647512435913 + }, + { + "p": 0.0, + "m": 0.9805118441581726 + }, + { + "p": 0.02145782671868801, + "m": 0.9957575798034668 + }, + { + "p": 0.021979380398988724, + "m": 0.47900527715682983 + }, + { + "p": 0.0, + "m": 0.8990901708602905 + }, + { + "p": 0.027265194803476334, + "m": 0.06851962208747864 + }, + { + "p": 0.8078045845031738, + "m": 0.7968161106109619 + }, + { + "p": 0.12087593227624893, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 32, + "train_loss": 0.7846786975860596, + "val_loss": 1.1366190910339355, + "acc": 63.49, + "time": 356.31480439400184, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8558943867683411 + }, + { + "p": 0.0, + "m": 0.3160953223705292 + }, + { + "p": 0.0, + "m": 0.917755663394928 + }, + { + "p": 0.0, + "m": 0.9637845158576965 + }, + { + "p": 0.0, + "m": 0.4436893165111542 + }, + { + "p": 0.03984770551323891, + "m": 0.8458492159843445 + }, + { + "p": 0.02006465010344982, + "m": 0.01731654442846775 + }, + { + "p": 0.8264390230178833, + "m": 0.8548875451087952 + }, + { + "p": 0.11364863067865372, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 33, + "train_loss": 0.8797395825386047, + "val_loss": 1.1736149787902832, + "acc": 64.22, + "time": 360.03855365100026, + "param": [ + { + "p": 0.0007786003407090902, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.056706707924604416, + "m": 1.0 + }, + { + "p": 0.02707158960402012, + "m": 0.9715370535850525 + }, + { + "p": 0.0, + "m": 0.8303648829460144 + }, + { + "p": 0.0, + "m": 0.3225562572479248 + }, + { + "p": 0.08418095856904984, + "m": 0.9049980044364929 + }, + { + "p": 0.0, + "m": 0.9561870694160461 + }, + { + "p": 0.04128425940871239, + "m": 0.408970445394516 + }, + { + "p": 0.0, + "m": 0.7862632870674133 + }, + { + "p": 0.024441776797175407, + "m": 0.0 + }, + { + "p": 0.7651564478874207, + "m": 0.676425039768219 + }, + { + "p": 0.0003796500095631927, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 34, + "train_loss": 0.929000973701477, + "val_loss": 1.1345500946044922, + "acc": 63.96, + "time": 358.4979761359864, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8324518203735352 + }, + { + "p": 0.0, + "m": 0.3558428883552551 + }, + { + "p": 0.06694858521223068, + "m": 0.9848265051841736 + }, + { + "p": 0.0, + "m": 0.9640851020812988 + }, + { + "p": 0.009240029379725456, + "m": 0.4308766722679138 + }, + { + "p": 0.0, + "m": 0.7971755266189575 + }, + { + "p": 0.0, + "m": 0.04608044773340225 + }, + { + "p": 0.9238113760948181, + "m": 0.7666881084442139 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 35, + "train_loss": 0.9735637903213501, + "val_loss": 1.1033740043640137, + "acc": 62.63, + "time": 350.403019106001, + "param": [ + { + "p": 0.022757939994335175, + "m": 1.0 + }, + { + "p": 0.01766064390540123, + "m": 1.0 + }, + { + "p": 0.05925265699625015, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8731397390365601 + }, + { + "p": 0.0, + "m": 0.8761951327323914 + }, + { + "p": 0.010636643506586552, + "m": 0.3900314271450043 + }, + { + "p": 0.017853030934929848, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9812427163124084 + }, + { + "p": 0.11837571114301682, + "m": 0.5000932216644287 + }, + { + "p": 0.0, + "m": 0.7024911046028137 + }, + { + "p": 0.06566986441612244, + "m": 0.03748374432325363 + }, + { + "p": 0.687793493270874, + "m": 0.8685516715049744 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 36, + "train_loss": 0.7748291492462158, + "val_loss": 1.1267281770706177, + "acc": 63.69, + "time": 351.5572145699989, + "param": [ + { + "p": 0.10523419082164764, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0003482279716990888, + "m": 1.0 + }, + { + "p": 0.009590605273842812, + "m": 0.9851516485214233 + }, + { + "p": 0.0, + "m": 0.8428779244422913 + }, + { + "p": 0.004524657037109137, + "m": 0.40195804834365845 + }, + { + "p": 0.008828969672322273, + "m": 1.0 + }, + { + "p": 0.00012575452274177223, + "m": 0.9537868499755859 + }, + { + "p": 0.07424401491880417, + "m": 0.650028645992279 + }, + { + "p": 0.053993839770555496, + "m": 0.8502860069274902 + }, + { + "p": 0.0, + "m": 0.03315574303269386 + }, + { + "p": 0.7360842823982239, + "m": 0.9159091114997864 + }, + { + "p": 0.007025460246950388, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 37, + "train_loss": 1.0255664587020874, + "val_loss": 1.0370539426803589, + "acc": 63.55, + "time": 352.84692240499135, + "param": [ + { + "p": 0.11686161905527115, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.00191045633982867, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8619852066040039 + }, + { + "p": 0.0, + "m": 0.4206210970878601 + }, + { + "p": 0.0, + "m": 0.9731733202934265 + }, + { + "p": 0.00012398092076182365, + "m": 0.9601179957389832 + }, + { + "p": 0.0, + "m": 0.7199260592460632 + }, + { + "p": 0.0, + "m": 0.8693051934242249 + }, + { + "p": 0.01253662072122097, + "m": 0.022090455517172813 + }, + { + "p": 0.8685672879219055, + "m": 0.9137407541275024 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 38, + "train_loss": 0.8533076047897339, + "val_loss": 1.068311333656311, + "acc": 63.87, + "time": 347.8890530839999, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8114556670188904 + }, + { + "p": 0.0, + "m": 0.43293389678001404 + }, + { + "p": 0.0, + "m": 0.839898943901062 + }, + { + "p": 0.0, + "m": 0.901800811290741 + }, + { + "p": 0.0, + "m": 0.7373645305633545 + }, + { + "p": 0.0022328754421323538, + "m": 0.8220021724700928 + }, + { + "p": 0.0, + "m": 0.01687737926840782 + }, + { + "p": 0.9977670907974243, + "m": 0.7644631862640381 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 39, + "train_loss": 0.9012355208396912, + "val_loss": 1.0528512001037598, + "acc": 63.6, + "time": 354.64720004399715, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.019961638376116753, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9728114008903503 + }, + { + "p": 0.0, + "m": 0.8841508626937866 + }, + { + "p": 0.0, + "m": 0.5753611326217651 + }, + { + "p": 0.0, + "m": 0.9693646430969238 + }, + { + "p": 0.0, + "m": 0.8354195952415466 + }, + { + "p": 0.0, + "m": 0.8630024790763855 + }, + { + "p": 0.00018169861868955195, + "m": 0.844904363155365 + }, + { + "p": 0.0, + "m": 0.025408344343304634 + }, + { + "p": 0.9798566699028015, + "m": 0.9129040241241455 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 40, + "train_loss": 1.1199983358383179, + "val_loss": 1.108699083328247, + "acc": 63.56, + "time": 349.62157682699035, + "param": [ + { + "p": 0.00016062958457041532, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9650524854660034 + }, + { + "p": 0.0, + "m": 0.8374713063240051 + }, + { + "p": 0.0, + "m": 0.5705327987670898 + }, + { + "p": 0.0, + "m": 0.9175630807876587 + }, + { + "p": 0.0, + "m": 0.9314171671867371 + }, + { + "p": 0.0, + "m": 0.8129401206970215 + }, + { + "p": 0.0, + "m": 0.8748237490653992 + }, + { + "p": 0.0005687514203600585, + "m": 0.01762341894209385 + }, + { + "p": 0.9992706179618835, + "m": 0.9856683015823364 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 41, + "train_loss": 1.102338194847107, + "val_loss": 1.1025285720825195, + "acc": 64.05, + "time": 351.1440296350047, + "param": [ + { + "p": 0.06886414438486099, + "m": 1.0 + }, + { + "p": 0.0574222169816494, + "m": 1.0 + }, + { + "p": 0.17151792347431183, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.977167010307312 + }, + { + "p": 0.044712163507938385, + "m": 0.8341030478477478 + }, + { + "p": 0.0, + "m": 0.5391026735305786 + }, + { + "p": 0.018393665552139282, + "m": 0.9006132483482361 + }, + { + "p": 0.02336953394114971, + "m": 0.9472209215164185 + }, + { + "p": 0.03772468864917755, + "m": 0.9010568857192993 + }, + { + "p": 0.06610134989023209, + "m": 0.9128263592720032 + }, + { + "p": 0.0, + "m": 0.0 + }, + { + "p": 0.4901830554008484, + "m": 0.9114301800727844 + }, + { + "p": 0.021711256355047226, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 42, + "train_loss": 1.0569617748260498, + "val_loss": 1.0157277584075928, + "acc": 64.08, + "time": 349.7462631989911, + "param": [ + { + "p": 0.017995618283748627, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.1392887383699417, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9274241924285889 + }, + { + "p": 0.0, + "m": 0.8535759449005127 + }, + { + "p": 0.0, + "m": 0.5250709056854248 + }, + { + "p": 0.0, + "m": 0.9227752685546875 + }, + { + "p": 0.0, + "m": 0.9943519234657288 + }, + { + "p": 0.02297603338956833, + "m": 0.9235482811927795 + }, + { + "p": 0.0, + "m": 0.9249307513237 + }, + { + "p": 0.0, + "m": 0.0 + }, + { + "p": 0.8066467642784119, + "m": 0.9453851580619812 + }, + { + "p": 0.013092799112200737, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 43, + "train_loss": 0.8557010889053345, + "val_loss": 1.1057325601577759, + "acc": 62.92, + "time": 350.8235590090044, + "param": [ + { + "p": 0.00026467503630556166, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.005713607184588909, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8845852613449097 + }, + { + "p": 0.0, + "m": 0.9061451554298401 + }, + { + "p": 0.001908475998789072, + "m": 0.43978703022003174 + }, + { + "p": 0.0, + "m": 0.9317137002944946 + }, + { + "p": 0.0, + "m": 0.9814069867134094 + }, + { + "p": 0.007341801188886166, + "m": 0.9237125515937805 + }, + { + "p": 0.00020001109805889428, + "m": 0.9106390476226807 + }, + { + "p": 0.0, + "m": 0.016253264620900154 + }, + { + "p": 0.9845714569091797, + "m": 0.9757555723190308 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 44, + "train_loss": 0.8603641986846924, + "val_loss": 1.0000178813934326, + "acc": 63.62, + "time": 349.92116497400275, + "param": [ + { + "p": 0.029557596892118454, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.03211907297372818, + "m": 0.9987913370132446 + }, + { + "p": 0.0, + "m": 0.971540093421936 + }, + { + "p": 0.0, + "m": 0.41627630591392517 + }, + { + "p": 0.000720170559361577, + "m": 0.8921645283699036 + }, + { + "p": 0.05041126161813736, + "m": 0.9842787384986877 + }, + { + "p": 0.002458242466673255, + "m": 0.9115133881568909 + }, + { + "p": 0.0, + "m": 0.9098312258720398 + }, + { + "p": 0.0, + "m": 0.014755988493561745 + }, + { + "p": 0.8803912997245789, + "m": 0.9487367272377014 + }, + { + "p": 0.004342381376773119, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 45, + "train_loss": 1.2763550281524658, + "val_loss": 0.9886731505393982, + "acc": 64.83, + "time": 349.1619035090116, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.915800154209137 + }, + { + "p": 0.0, + "m": 0.948357880115509 + }, + { + "p": 0.0, + "m": 0.4519113600254059 + }, + { + "p": 0.017880961298942566, + "m": 0.871387779712677 + }, + { + "p": 0.0, + "m": 0.9099056720733643 + }, + { + "p": 0.0, + "m": 0.8527151346206665 + }, + { + "p": 0.0, + "m": 0.8628697395324707 + }, + { + "p": 0.0, + "m": 0.0 + }, + { + "p": 0.9821190237998962, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 46, + "train_loss": 0.7805993556976318, + "val_loss": 1.1365622282028198, + "acc": 64.67, + "time": 350.8723502859939, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9327475428581238 + }, + { + "p": 0.0, + "m": 0.9017266631126404 + }, + { + "p": 0.0, + "m": 0.3400098383426666 + }, + { + "p": 0.0, + "m": 0.7037637233734131 + }, + { + "p": 0.0, + "m": 0.8534829616546631 + }, + { + "p": 6.651318108197302e-05, + "m": 0.9054773449897766 + }, + { + "p": 0.0, + "m": 0.8089823722839355 + }, + { + "p": 0.0, + "m": 0.004610843490809202 + }, + { + "p": 0.9999334812164307, + "m": 0.9462671875953674 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 47, + "train_loss": 1.158050298690796, + "val_loss": 1.0577373504638672, + "acc": 63.71, + "time": 352.2987019310094, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.906537652015686 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.4488321542739868 + }, + { + "p": 0.0, + "m": 0.6844857931137085 + }, + { + "p": 0.0, + "m": 0.9087236523628235 + }, + { + "p": 0.0, + "m": 0.7026191353797913 + }, + { + "p": 0.0, + "m": 0.8346490859985352 + }, + { + "p": 0.0, + "m": 0.0137123242020607 + }, + { + "p": 0.994867205619812, + "m": 0.8009483814239502 + }, + { + "p": 0.005132800899446011, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 48, + "train_loss": 0.7900687456130981, + "val_loss": 0.9684740304946899, + "acc": 64.26, + "time": 359.15641380699526, + "param": [ + { + "p": 0.027545664459466934, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0262259803712368, + "m": 0.8068143129348755 + }, + { + "p": 0.0, + "m": 0.982988715171814 + }, + { + "p": 0.0, + "m": 0.4601646661758423 + }, + { + "p": 0.002914083655923605, + "m": 0.7866881489753723 + }, + { + "p": 0.024664387106895447, + "m": 0.797296404838562 + }, + { + "p": 0.0, + "m": 0.6746291518211365 + }, + { + "p": 0.0005437466315925121, + "m": 0.852715253829956 + }, + { + "p": 0.0009491953533142805, + "m": 0.06684520840644836 + }, + { + "p": 0.871856689453125, + "m": 0.9079437851905823 + }, + { + "p": 0.045300208032131195, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 49, + "train_loss": 1.193993091583252, + "val_loss": 0.9857878684997559, + "acc": 64.26, + "time": 349.213761291001, + "param": [ + { + "p": 0.001565406797453761, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.015447520650923252, + "m": 0.8088741898536682 + }, + { + "p": 0.0, + "m": 0.996365487575531 + }, + { + "p": 0.0, + "m": 0.40768519043922424 + }, + { + "p": 0.0, + "m": 0.7209989428520203 + }, + { + "p": 0.0, + "m": 0.7829514145851135 + }, + { + "p": 0.0274185873568058, + "m": 0.6623122692108154 + }, + { + "p": 0.0013106372207403183, + "m": 0.9191389083862305 + }, + { + "p": 0.006090865936130285, + "m": 0.046918995678424835 + }, + { + "p": 0.9479096531867981, + "m": 0.9420968890190125 + }, + { + "p": 0.0002573436649981886, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 50, + "train_loss": 0.994657039642334, + "val_loss": 1.0140962600708008, + "acc": 64.65, + "time": 349.6987836470071, + "param": [ + { + "p": 0.10136827081441879, + "m": 1.0 + }, + { + "p": 0.00027991898241452873, + "m": 1.0 + }, + { + "p": 0.05912567675113678, + "m": 1.0 + }, + { + "p": 0.022061778232455254, + "m": 0.9268168807029724 + }, + { + "p": 0.0, + "m": 0.9899091124534607 + }, + { + "p": 0.002540531801059842, + "m": 0.4186527729034424 + }, + { + "p": 0.004169309511780739, + "m": 0.809137225151062 + }, + { + "p": 0.010901176370680332, + "m": 0.8844860196113586 + }, + { + "p": 0.04012112691998482, + "m": 0.6738829612731934 + }, + { + "p": 0.009451216086745262, + "m": 0.9256322979927063 + }, + { + "p": 0.025433706119656563, + "m": 0.0664636567234993 + }, + { + "p": 0.718054473400116, + "m": 0.9936365485191345 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.006492794491350651, + "m": 1.0 + } + ] + }, + { + "epoch": 51, + "train_loss": 0.7590826749801636, + "val_loss": 0.9877604246139526, + "acc": 64.32, + "time": 350.5087580780091, + "param": [ + { + "p": 0.03750455006957054, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.25937211513519287, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8127186894416809 + }, + { + "p": 0.0, + "m": 0.9521741271018982 + }, + { + "p": 0.0, + "m": 0.5543040037155151 + }, + { + "p": 0.0, + "m": 0.8439129590988159 + }, + { + "p": 0.0, + "m": 0.887962281703949 + }, + { + "p": 0.017565567046403885, + "m": 0.6419814825057983 + }, + { + "p": 0.18875153362751007, + "m": 0.9273831844329834 + }, + { + "p": 0.0, + "m": 0.061020348221063614 + }, + { + "p": 0.4962984323501587, + "m": 0.9844931960105896 + }, + { + "p": 0.0005078038666397333, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 52, + "train_loss": 1.161385416984558, + "val_loss": 0.988013505935669, + "acc": 64.93, + "time": 360.81720794999273, + "param": [ + { + "p": 0.055401891469955444, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.27261391282081604, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8842494487762451 + }, + { + "p": 0.0, + "m": 0.991681694984436 + }, + { + "p": 0.0, + "m": 0.5579416751861572 + }, + { + "p": 0.009782315231859684, + "m": 0.896325409412384 + }, + { + "p": 0.004195882938802242, + "m": 0.9350582361221313 + }, + { + "p": 0.015896297991275787, + "m": 0.6690604090690613 + }, + { + "p": 0.11644172668457031, + "m": 0.8052463531494141 + }, + { + "p": 0.0, + "m": 0.031387630850076675 + }, + { + "p": 0.5256679654121399, + "m": 0.8911458253860474 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 53, + "train_loss": 0.8825932741165161, + "val_loss": 1.036559820175171, + "acc": 64.12, + "time": 352.2996823010035, + "param": [ + { + "p": 0.013389338739216328, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.21196454763412476, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9382664561271667 + }, + { + "p": 0.0, + "m": 0.9791553020477295 + }, + { + "p": 0.0, + "m": 0.5157202482223511 + }, + { + "p": 0.0, + "m": 0.9629039764404297 + }, + { + "p": 0.010528303682804108, + "m": 0.8884670734405518 + }, + { + "p": 0.0005539054400287569, + "m": 0.5453089475631714 + }, + { + "p": 0.052030835300683975, + "m": 0.9475317597389221 + }, + { + "p": 0.02629699930548668, + "m": 0.0463511124253273 + }, + { + "p": 0.6636149883270264, + "m": 0.8509145379066467 + }, + { + "p": 0.02162112109363079, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 54, + "train_loss": 0.8549038171768188, + "val_loss": 1.0147007703781128, + "acc": 64.19, + "time": 350.48205910698744, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.14027732610702515, + "m": 1.0 + }, + { + "p": 0.03017737716436386, + "m": 0.9942449927330017 + }, + { + "p": 0.0, + "m": 0.9278059005737305 + }, + { + "p": 0.0, + "m": 0.4502841830253601 + }, + { + "p": 0.0, + "m": 0.9764037132263184 + }, + { + "p": 0.0, + "m": 0.9495770931243896 + }, + { + "p": 0.0, + "m": 0.5204154849052429 + }, + { + "p": 0.03798278048634529, + "m": 0.9376096725463867 + }, + { + "p": 0.0, + "m": 0.04955717921257019 + }, + { + "p": 0.7915624976158142, + "m": 0.8823720812797546 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 55, + "train_loss": 0.9054079055786133, + "val_loss": 0.9691270589828491, + "acc": 63.0, + "time": 348.6239073360048, + "param": [ + { + "p": 0.006588664837181568, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.03345233201980591, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9273672699928284 + }, + { + "p": 0.0, + "m": 0.8348234295845032 + }, + { + "p": 0.0, + "m": 0.5832479000091553 + }, + { + "p": 0.0, + "m": 0.8612295389175415 + }, + { + "p": 0.002203618874773383, + "m": 0.9167532920837402 + }, + { + "p": 0.07180645316839218, + "m": 0.49037206172943115 + }, + { + "p": 0.0, + "m": 0.966549277305603 + }, + { + "p": 0.0, + "m": 0.024489441886544228 + }, + { + "p": 0.8433288335800171, + "m": 0.8460918068885803 + }, + { + "p": 0.04262011498212814, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 56, + "train_loss": 1.011401653289795, + "val_loss": 1.14211106300354, + "acc": 63.19, + "time": 349.9812982720032, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.06404918432235718, + "m": 1.0 + }, + { + "p": 0.0010353293037042022, + "m": 0.902987539768219 + }, + { + "p": 0.0, + "m": 0.8015164136886597 + }, + { + "p": 0.0, + "m": 0.6806066036224365 + }, + { + "p": 0.07623475044965744, + "m": 0.8034348487854004 + }, + { + "p": 0.0, + "m": 0.9077516198158264 + }, + { + "p": 0.15549838542938232, + "m": 0.4318588376045227 + }, + { + "p": 0.0036341503728181124, + "m": 0.9972190856933594 + }, + { + "p": 0.0, + "m": 0.0036826725117862225 + }, + { + "p": 0.6995481848716736, + "m": 0.7382138967514038 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 57, + "train_loss": 0.8970358371734619, + "val_loss": 1.0596507787704468, + "acc": 64.98, + "time": 351.3987357659935, + "param": [ + { + "p": 0.0425424799323082, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8622163534164429 + }, + { + "p": 0.0, + "m": 0.8643782734870911 + }, + { + "p": 0.0, + "m": 0.7192850112915039 + }, + { + "p": 0.0, + "m": 0.8877915143966675 + }, + { + "p": 0.046697746962308884, + "m": 0.8084424734115601 + }, + { + "p": 0.17459821701049805, + "m": 0.5250610113143921 + }, + { + "p": 0.12228573113679886, + "m": 0.9771355390548706 + }, + { + "p": 0.0003976763691753149, + "m": 0.06584317237138748 + }, + { + "p": 0.6134781241416931, + "m": 0.8950584530830383 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 58, + "train_loss": 0.8892992734909058, + "val_loss": 0.9977450966835022, + "acc": 65.1, + "time": 350.2400281540031, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8642621040344238 + }, + { + "p": 0.0, + "m": 0.8823109269142151 + }, + { + "p": 0.0, + "m": 0.7229664921760559 + }, + { + "p": 0.0, + "m": 0.9478060603141785 + }, + { + "p": 0.0, + "m": 0.7951192259788513 + }, + { + "p": 0.1735890954732895, + "m": 0.6704216599464417 + }, + { + "p": 0.06697579473257065, + "m": 0.9499987363815308 + }, + { + "p": 0.0, + "m": 0.023767726495862007 + }, + { + "p": 0.7594351172447205, + "m": 0.9775904417037964 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 59, + "train_loss": 0.8364126086235046, + "val_loss": 1.0450974702835083, + "acc": 63.8, + "time": 352.10347134999756, + "param": [ + { + "p": 0.02573953941464424, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.001868154969997704, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.7751397490501404 + }, + { + "p": 0.0, + "m": 0.8135461211204529 + }, + { + "p": 0.0, + "m": 0.8447313904762268 + }, + { + "p": 0.004922034218907356, + "m": 1.0 + }, + { + "p": 0.00208422658033669, + "m": 0.9428560137748718 + }, + { + "p": 0.11124508082866669, + "m": 0.5963609218597412 + }, + { + "p": 0.008867260068655014, + "m": 0.8452016711235046 + }, + { + "p": 0.01825045235455036, + "m": 0.03341467306017876 + }, + { + "p": 0.809475302696228, + "m": 0.9273601174354553 + }, + { + "p": 0.017547937110066414, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 60, + "train_loss": 0.9620381593704224, + "val_loss": 1.15511155128479, + "acc": 62.2, + "time": 350.6435313980037, + "param": [ + { + "p": 0.0043628341518342495, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.013053521513938904, + "m": 1.0 + }, + { + "p": 0.0035502139944583178, + "m": 0.8145883083343506 + }, + { + "p": 0.0, + "m": 0.8074387907981873 + }, + { + "p": 0.0, + "m": 0.850530743598938 + }, + { + "p": 0.006730012595653534, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9697037935256958 + }, + { + "p": 0.15033601224422455, + "m": 0.6210141777992249 + }, + { + "p": 0.023819487541913986, + "m": 0.8672773241996765 + }, + { + "p": 0.006209923420101404, + "m": 0.0 + }, + { + "p": 0.7919380068778992, + "m": 0.8901655077934265 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 61, + "train_loss": 0.9311283230781555, + "val_loss": 0.9564254879951477, + "acc": 64.79, + "time": 349.5751250299945, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.09904184192419052, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8413054347038269 + }, + { + "p": 0.0, + "m": 0.856540322303772 + }, + { + "p": 0.0, + "m": 0.924954891204834 + }, + { + "p": 0.0, + "m": 0.9167488217353821 + }, + { + "p": 0.0019284073496237397, + "m": 0.9007692337036133 + }, + { + "p": 0.05989080294966698, + "m": 0.6394032835960388 + }, + { + "p": 0.0, + "m": 0.9695469737052917 + }, + { + "p": 0.007023165002465248, + "m": 0.03176543489098549 + }, + { + "p": 0.8321157693862915, + "m": 0.7620615363121033 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 62, + "train_loss": 0.7809773087501526, + "val_loss": 0.9961250424385071, + "acc": 65.66, + "time": 360.85100768999837, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8440154194831848 + }, + { + "p": 0.0, + "m": 0.8735591173171997 + }, + { + "p": 0.0, + "m": 0.888859212398529 + }, + { + "p": 0.0, + "m": 0.8686597943305969 + }, + { + "p": 0.0, + "m": 0.8221317529678345 + }, + { + "p": 0.00013496589963324368, + "m": 0.6249219179153442 + }, + { + "p": 0.0009760718676261604, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.1152995154261589 + }, + { + "p": 0.9988889694213867, + "m": 0.8133741021156311 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 63, + "train_loss": 1.0127637386322021, + "val_loss": 0.9252499938011169, + "acc": 64.46, + "time": 358.9809421760001, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0005220414604991674, + "m": 1.0 + }, + { + "p": 0.02603844739496708, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9552057385444641 + }, + { + "p": 0.0, + "m": 0.8915398716926575 + }, + { + "p": 0.0, + "m": 0.9995695948600769 + }, + { + "p": 0.007500411942601204, + "m": 0.960343599319458 + }, + { + "p": 0.05555574968457222, + "m": 0.8351946473121643 + }, + { + "p": 0.0, + "m": 0.7141293883323669 + }, + { + "p": 0.0464952290058136, + "m": 0.9155389666557312 + }, + { + "p": 0.08518940210342407, + "m": 0.10776761174201965 + }, + { + "p": 0.7713733911514282, + "m": 0.8642568588256836 + }, + { + "p": 0.007325367536395788, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 64, + "train_loss": 0.8845893144607544, + "val_loss": 1.158828616142273, + "acc": 61.16, + "time": 348.7005564370047, + "param": [ + { + "p": 0.006501486990600824, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9833735227584839 + }, + { + "p": 0.0, + "m": 0.9884290099143982 + }, + { + "p": 0.0, + "m": 0.9053659439086914 + }, + { + "p": 0.0, + "m": 0.8998381495475769 + }, + { + "p": 0.03790273517370224, + "m": 0.8830457329750061 + }, + { + "p": 0.05036882683634758, + "m": 0.8643934726715088 + }, + { + "p": 0.0014097252860665321, + "m": 0.02524111606180668 + }, + { + "p": 0.8962380886077881, + "m": 0.9443137645721436 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.007579104974865913, + "m": 1.0 + } + ] + }, + { + "epoch": 65, + "train_loss": 0.8855991363525391, + "val_loss": 1.0834739208221436, + "acc": 65.01, + "time": 350.64087749499595, + "param": [ + { + "p": 0.11158869415521622, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0001428462128387764, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9035137891769409 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9998562932014465 + }, + { + "p": 0.10732382535934448, + "m": 0.9312648177146912 + }, + { + "p": 0.0, + "m": 0.8650227189064026 + }, + { + "p": 0.003234029747545719, + "m": 0.9151499271392822 + }, + { + "p": 0.0, + "m": 0.8796634674072266 + }, + { + "p": 0.0, + "m": 0.08908040076494217 + }, + { + "p": 0.7759395837783813, + "m": 0.8566134572029114 + }, + { + "p": 0.0017710301326587796, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 66, + "train_loss": 0.7328090071678162, + "val_loss": 1.0225735902786255, + "acc": 64.08, + "time": 352.2812321780075, + "param": [ + { + "p": 0.1343628317117691, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0010127662681043148, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9032609462738037 + }, + { + "p": 0.0, + "m": 0.9865428805351257 + }, + { + "p": 0.0, + "m": 0.9663934111595154 + }, + { + "p": 0.0, + "m": 0.9255501627922058 + }, + { + "p": 0.0, + "m": 0.854480504989624 + }, + { + "p": 0.0, + "m": 0.9472442269325256 + }, + { + "p": 0.0, + "m": 0.8910903930664062 + }, + { + "p": 0.0, + "m": 0.100997693836689 + }, + { + "p": 0.8646243810653687, + "m": 0.8953762054443359 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 67, + "train_loss": 0.9319552183151245, + "val_loss": 0.9569193124771118, + "acc": 65.14, + "time": 352.68800335099513, + "param": [ + { + "p": 0.047722525894641876, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0123992208391428, + "m": 1.0 + }, + { + "p": 0.013957211747765541, + "m": 0.8522305488586426 + }, + { + "p": 0.0, + "m": 0.9434149861335754 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9765748381614685 + }, + { + "p": 0.08822839707136154, + "m": 0.8105604648590088 + }, + { + "p": 0.06439217925071716, + "m": 0.9985319375991821 + }, + { + "p": 0.056351758539676666, + "m": 1.0 + }, + { + "p": 0.11301985383033752, + "m": 0.10491077601909637 + }, + { + "p": 0.5009828805923462, + "m": 0.8851521611213684 + }, + { + "p": 0.102945975959301, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 68, + "train_loss": 0.9311938285827637, + "val_loss": 1.0874125957489014, + "acc": 65.48, + "time": 351.34711591798987, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.02324712835252285, + "m": 0.9941964149475098 + }, + { + "p": 0.0, + "m": 0.9815410375595093 + }, + { + "p": 0.0, + "m": 0.9114174246788025 + }, + { + "p": 0.007845236919820309, + "m": 0.9643707871437073 + }, + { + "p": 0.0, + "m": 0.8141659498214722 + }, + { + "p": 0.0, + "m": 0.9917650818824768 + }, + { + "p": 0.0, + "m": 0.8595883250236511 + }, + { + "p": 0.21537472307682037, + "m": 0.0663931667804718 + }, + { + "p": 0.7535329461097717, + "m": 0.8856745362281799 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 69, + "train_loss": 1.0482503175735474, + "val_loss": 1.0078600645065308, + "acc": 64.29, + "time": 354.75989150100213, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.01723550632596016, + "m": 1.0 + }, + { + "p": 0.0034577108453959227, + "m": 1.0 + }, + { + "p": 0.0019270305056124926, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9082567691802979 + }, + { + "p": 0.0, + "m": 0.979668915271759 + }, + { + "p": 0.0, + "m": 0.9573721885681152 + }, + { + "p": 0.0, + "m": 0.8026936650276184 + }, + { + "p": 0.007714844774454832, + "m": 0.9467565417289734 + }, + { + "p": 0.029189489781856537, + "m": 0.842994749546051 + }, + { + "p": 0.14807254076004028, + "m": 0.03767943009734154 + }, + { + "p": 0.7819783687591553, + "m": 0.8776965141296387 + }, + { + "p": 0.010424531996250153, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 70, + "train_loss": 0.8331814408302307, + "val_loss": 1.0715950727462769, + "acc": 64.31, + "time": 351.5625358039979, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9819293022155762 + }, + { + "p": 0.0, + "m": 0.8989723920822144 + }, + { + "p": 0.0, + "m": 0.9981458187103271 + }, + { + "p": 0.0, + "m": 0.902209997177124 + }, + { + "p": 0.0, + "m": 0.9218694567680359 + }, + { + "p": 0.0, + "m": 0.9921329021453857 + }, + { + "p": 0.0, + "m": 0.8051231503486633 + }, + { + "p": 0.0, + "m": 0.03887840360403061 + }, + { + "p": 1.0, + "m": 0.9165027737617493 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 71, + "train_loss": 0.8802074193954468, + "val_loss": 1.0351439714431763, + "acc": 64.17, + "time": 347.4532545040129, + "param": [ + { + "p": 0.004072630312293768, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.010748449712991714, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.009061356075108051, + "m": 0.9389024376869202 + }, + { + "p": 0.0, + "m": 0.9874234795570374 + }, + { + "p": 0.01393672451376915, + "m": 0.9314316511154175 + }, + { + "p": 0.014033026993274689, + "m": 0.8533037900924683 + }, + { + "p": 0.0, + "m": 0.7917810678482056 + }, + { + "p": 0.017889196053147316, + "m": 0.8158379197120667 + }, + { + "p": 0.02208670601248741, + "m": 0.013213714584708214 + }, + { + "p": 0.9081718325614929, + "m": 0.8516765236854553 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 72, + "train_loss": 1.0632151365280151, + "val_loss": 0.9389412999153137, + "acc": 65.67, + "time": 348.57130135199986, + "param": [ + { + "p": 0.031140876933932304, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.111214779317379, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9992067813873291 + }, + { + "p": 0.0, + "m": 0.8198646903038025 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9368371963500977 + }, + { + "p": 0.0, + "m": 0.742242157459259 + }, + { + "p": 0.1332547515630722, + "m": 0.8208357691764832 + }, + { + "p": 0.0, + "m": 0.9642418622970581 + }, + { + "p": 0.0, + "m": 0.10586599260568619 + }, + { + "p": 0.6117609739303589, + "m": 0.8655710816383362 + }, + { + "p": 0.11262858659029007, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 73, + "train_loss": 0.8080184459686279, + "val_loss": 1.00523841381073, + "acc": 65.64, + "time": 350.61294105999696, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.07241974025964737, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9849813580513 + }, + { + "p": 0.0, + "m": 0.8067649602890015 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9316079020500183 + }, + { + "p": 4.560058005154133e-05, + "m": 0.8043686151504517 + }, + { + "p": 0.03884503245353699, + "m": 0.8171314597129822 + }, + { + "p": 6.0239453887334093e-05, + "m": 0.9823240041732788 + }, + { + "p": 0.07242723554372787, + "m": 0.07456137984991074 + }, + { + "p": 0.7901323437690735, + "m": 0.9427554607391357 + }, + { + "p": 0.026069743558764458, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 74, + "train_loss": 0.7774415612220764, + "val_loss": 1.0386730432510376, + "acc": 64.99, + "time": 353.06217102600203, + "param": [ + { + "p": 0.024213893339037895, + "m": 1.0 + }, + { + "p": 0.0036847081501036882, + "m": 1.0 + }, + { + "p": 0.05076874420046806, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9986646771430969 + }, + { + "p": 0.0, + "m": 0.8056511878967285 + }, + { + "p": 0.009153570979833603, + "m": 0.9408538937568665 + }, + { + "p": 0.0, + "m": 0.9885667562484741 + }, + { + "p": 0.0, + "m": 0.933976411819458 + }, + { + "p": 0.0019906966481357813, + "m": 0.8789709210395813 + }, + { + "p": 0.008647866547107697, + "m": 0.9970735311508179 + }, + { + "p": 0.04968082904815674, + "m": 0.057871654629707336 + }, + { + "p": 0.825006902217865, + "m": 0.9510544538497925 + }, + { + "p": 0.019823946058750153, + "m": 1.0 + }, + { + "p": 0.007028843741863966, + "m": 1.0 + } + ] + }, + { + "epoch": 75, + "train_loss": 1.0698903799057007, + "val_loss": 0.9777059555053711, + "acc": 63.4, + "time": 364.7930484649987, + "param": [ + { + "p": 0.023118333891034126, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.004000444430857897, + "m": 1.0 + }, + { + "p": 0.004389979876577854, + "m": 0.947387158870697 + }, + { + "p": 0.0, + "m": 0.8330758213996887 + }, + { + "p": 0.0, + "m": 0.9023625254631042 + }, + { + "p": 0.016846593469381332, + "m": 0.9723799824714661 + }, + { + "p": 0.00861397385597229, + "m": 0.95287024974823 + }, + { + "p": 0.005448938347399235, + "m": 0.8739855289459229 + }, + { + "p": 0.011341872625052929, + "m": 0.9777209758758545 + }, + { + "p": 0.0, + "m": 0.09641889482736588 + }, + { + "p": 0.8954229354858398, + "m": 1.0 + }, + { + "p": 0.030316926538944244, + "m": 1.0 + }, + { + "p": 0.0005000033415853977, + "m": 1.0 + } + ] + }, + { + "epoch": 76, + "train_loss": 0.973831057548523, + "val_loss": 1.0408601760864258, + "acc": 64.46, + "time": 351.1667573859886, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0014062563423067331, + "m": 1.0 + }, + { + "p": 0.0018535414710640907, + "m": 0.8652607798576355 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9806797504425049 + }, + { + "p": 0.0, + "m": 0.9886759519577026 + }, + { + "p": 0.0, + "m": 0.8561659455299377 + }, + { + "p": 0.0, + "m": 0.9222484230995178 + }, + { + "p": 0.0888294130563736, + "m": 1.0 + }, + { + "p": 0.03857084736227989, + "m": 0.1251092255115509 + }, + { + "p": 0.8693399429321289, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 77, + "train_loss": 0.7960330247879028, + "val_loss": 1.1026170253753662, + "acc": 61.8, + "time": 350.55551148099767, + "param": [ + { + "p": 0.004508250392973423, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.00021886579634156078, + "m": 1.0 + }, + { + "p": 0.012809673324227333, + "m": 0.9669201374053955 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.006173430941998959, + "m": 1.0 + }, + { + "p": 0.00244535063393414, + "m": 1.0 + }, + { + "p": 0.018935173749923706, + "m": 0.8772003054618835 + }, + { + "p": 0.0, + "m": 0.9725916385650635 + }, + { + "p": 0.0, + "m": 0.9185834527015686 + }, + { + "p": 0.03054083324968815, + "m": 0.06996463984251022 + }, + { + "p": 0.921650230884552, + "m": 0.8544962406158447 + }, + { + "p": 0.0027182043995708227, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 78, + "train_loss": 0.9575212001800537, + "val_loss": 1.177346110343933, + "acc": 60.12, + "time": 351.10750541799644, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.011987145058810711, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.012299912050366402, + "m": 0.9356601238250732 + }, + { + "p": 0.0, + "m": 0.9145728349685669 + }, + { + "p": 0.0, + "m": 0.9581261873245239 + }, + { + "p": 0.0222904235124588, + "m": 0.9765481352806091 + }, + { + "p": 0.0, + "m": 0.8752026557922363 + }, + { + "p": 0.004626336507499218, + "m": 0.8384753465652466 + }, + { + "p": 0.0018981609027832747, + "m": 0.9347787499427795 + }, + { + "p": 0.12034205347299576, + "m": 0.0 + }, + { + "p": 0.8265559673309326, + "m": 0.8682631850242615 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 79, + "train_loss": 0.91193687915802, + "val_loss": 1.0367850065231323, + "acc": 64.66, + "time": 351.867007814988, + "param": [ + { + "p": 0.0013949315762147307, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9908338785171509 + }, + { + "p": 0.0, + "m": 0.8470816612243652 + }, + { + "p": 0.0, + "m": 0.9966181516647339 + }, + { + "p": 0.0, + "m": 0.96385258436203 + }, + { + "p": 0.0, + "m": 0.9345499873161316 + }, + { + "p": 0.0, + "m": 0.884604275226593 + }, + { + "p": 0.0005856973584741354, + "m": 0.9101178646087646 + }, + { + "p": 0.07121425122022629, + "m": 0.04749934375286102 + }, + { + "p": 0.9268051385879517, + "m": 0.9549775123596191 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 80, + "train_loss": 0.9103509783744812, + "val_loss": 1.002493977546692, + "acc": 63.8, + "time": 350.010761507001, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9413113594055176 + }, + { + "p": 0.0, + "m": 0.8540084958076477 + }, + { + "p": 0.0, + "m": 0.914446234703064 + }, + { + "p": 0.0, + "m": 0.989450216293335 + }, + { + "p": 0.0, + "m": 0.9618663787841797 + }, + { + "p": 0.0, + "m": 0.8530836701393127 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.006831110920757055 + }, + { + "p": 1.0, + "m": 0.9878745079040527 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 81, + "train_loss": 0.8333619832992554, + "val_loss": 1.1963058710098267, + "acc": 65.06, + "time": 353.9630880210025, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8426083326339722 + }, + { + "p": 0.05419042333960533, + "m": 0.8830080628395081 + }, + { + "p": 0.0, + "m": 0.9979252815246582 + }, + { + "p": 0.08540678024291992, + "m": 0.9998964071273804 + }, + { + "p": 4.3634707253659144e-05, + "m": 0.9949780702590942 + }, + { + "p": 0.0, + "m": 0.7245885729789734 + }, + { + "p": 0.02423008531332016, + "m": 0.9182039499282837 + }, + { + "p": 0.0, + "m": 0.06611105054616928 + }, + { + "p": 0.8361290693283081, + "m": 0.9113134145736694 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 82, + "train_loss": 1.0080994367599487, + "val_loss": 1.0527764558792114, + "acc": 64.7, + "time": 357.23506897399784, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0024477432016283274, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8602921962738037 + }, + { + "p": 0.0, + "m": 0.9883842468261719 + }, + { + "p": 0.0, + "m": 0.960797905921936 + }, + { + "p": 0.0015850890194997191, + "m": 0.93308025598526 + }, + { + "p": 0.0, + "m": 0.8309540748596191 + }, + { + "p": 0.0, + "m": 0.7761585712432861 + }, + { + "p": 0.0, + "m": 0.986236572265625 + }, + { + "p": 0.0002744825615081936, + "m": 0.0892888531088829 + }, + { + "p": 0.9956926703453064, + "m": 0.8718437552452087 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 83, + "train_loss": 0.9110785722732544, + "val_loss": 0.9873633980751038, + "acc": 64.96, + "time": 349.4835398419964, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.037297412753105164, + "m": 1.0 + }, + { + "p": 0.0007215090445242822, + "m": 0.7964306473731995 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9593070149421692 + }, + { + "p": 0.0, + "m": 0.9297875761985779 + }, + { + "p": 0.0, + "m": 0.8012459874153137 + }, + { + "p": 0.03509272262454033, + "m": 0.8241265416145325 + }, + { + "p": 0.0, + "m": 0.9866469502449036 + }, + { + "p": 0.004314014222472906, + "m": 0.013875043019652367 + }, + { + "p": 0.8898864984512329, + "m": 0.8814289569854736 + }, + { + "p": 0.032687801867723465, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 84, + "train_loss": 0.8095586895942688, + "val_loss": 1.0352535247802734, + "acc": 64.77, + "time": 349.04281769999943, + "param": [ + { + "p": 0.0395323783159256, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0015246557304635644, + "m": 0.7088260650634766 + }, + { + "p": 0.0, + "m": 0.9906646609306335 + }, + { + "p": 0.0, + "m": 0.88450688123703 + }, + { + "p": 0.0, + "m": 0.9033310413360596 + }, + { + "p": 0.0029243514873087406, + "m": 0.8777615427970886 + }, + { + "p": 0.04549740254878998, + "m": 0.8657092452049255 + }, + { + "p": 0.04567674174904823, + "m": 0.9994337558746338 + }, + { + "p": 0.05846036225557327, + "m": 0.03947361558675766 + }, + { + "p": 0.8056086897850037, + "m": 0.9852219223976135 + }, + { + "p": 0.0007753892568871379, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 85, + "train_loss": 0.8404026031494141, + "val_loss": 1.0597643852233887, + "acc": 64.83, + "time": 348.52344586000254, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.7962689995765686 + }, + { + "p": 0.0, + "m": 0.9783124327659607 + }, + { + "p": 0.0, + "m": 0.8251438140869141 + }, + { + "p": 0.0, + "m": 0.9114162921905518 + }, + { + "p": 0.0, + "m": 0.9196234941482544 + }, + { + "p": 0.0, + "m": 0.8722212314605713 + }, + { + "p": 0.0, + "m": 0.9824167490005493 + }, + { + "p": 0.0, + "m": 0.07113592326641083 + }, + { + "p": 0.996246337890625, + "m": 0.9805688261985779 + }, + { + "p": 0.0037536141462624073, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 86, + "train_loss": 1.3194339275360107, + "val_loss": 1.0981229543685913, + "acc": 64.88, + "time": 360.3912014689995, + "param": [ + { + "p": 0.006251584738492966, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8455924391746521 + }, + { + "p": 0.0023247767239809036, + "m": 0.8946799039840698 + }, + { + "p": 0.0, + "m": 0.8603563904762268 + }, + { + "p": 0.024159083142876625, + "m": 0.9548937678337097 + }, + { + "p": 0.0008869178709574044, + "m": 0.9996291995048523 + }, + { + "p": 0.005787945818156004, + "m": 0.885267972946167 + }, + { + "p": 0.0, + "m": 0.9774549603462219 + }, + { + "p": 0.014198838733136654, + "m": 0.009524446912109852 + }, + { + "p": 0.8843657970428467, + "m": 0.9159607887268066 + }, + { + "p": 0.062025103718042374, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 87, + "train_loss": 0.9033305048942566, + "val_loss": 1.1189229488372803, + "acc": 64.59, + "time": 349.2728847309918, + "param": [ + { + "p": 0.14995382726192474, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.05523986369371414, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8405724167823792 + }, + { + "p": 0.0, + "m": 0.8362610936164856 + }, + { + "p": 0.005708646960556507, + "m": 0.816015899181366 + }, + { + "p": 0.0, + "m": 0.8378023505210876 + }, + { + "p": 0.05828222632408142, + "m": 0.8199902772903442 + }, + { + "p": 0.02490420825779438, + "m": 0.8614948391914368 + }, + { + "p": 0.0672307088971138, + "m": 0.9952079057693481 + }, + { + "p": 0.053487904369831085, + "m": 0.0830845832824707 + }, + { + "p": 0.5225257277488708, + "m": 0.967963457107544 + }, + { + "p": 0.008844543248414993, + "m": 1.0 + }, + { + "p": 0.05382237955927849, + "m": 1.0 + } + ] + }, + { + "epoch": 88, + "train_loss": 0.9501743316650391, + "val_loss": 0.9992170333862305, + "acc": 65.51, + "time": 348.06717377300083, + "param": [ + { + "p": 0.20220105350017548, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.05119890719652176, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9163252115249634 + }, + { + "p": 0.0, + "m": 0.9499033689498901 + }, + { + "p": 0.0, + "m": 0.8431797027587891 + }, + { + "p": 0.0, + "m": 0.8399964570999146 + }, + { + "p": 0.0, + "m": 0.7898011803627014 + }, + { + "p": 0.0, + "m": 0.8422114849090576 + }, + { + "p": 0.04381980374455452, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.09659349173307419 + }, + { + "p": 0.7027802467346191, + "m": 0.9650160074234009 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 89, + "train_loss": 0.8409700393676758, + "val_loss": 0.9430394172668457, + "acc": 65.64, + "time": 349.05359587300336, + "param": [ + { + "p": 0.08940403163433075, + "m": 1.0 + }, + { + "p": 0.022548938170075417, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9571424126625061 + }, + { + "p": 0.0, + "m": 0.8521296381950378 + }, + { + "p": 0.0, + "m": 0.8936713933944702 + }, + { + "p": 0.0056304773315787315, + "m": 0.8985217213630676 + }, + { + "p": 0.008563192561268806, + "m": 0.9119861721992493 + }, + { + "p": 0.02613252028822899, + "m": 0.8173421025276184 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.022263405844569206, + "m": 0.10618346184492111 + }, + { + "p": 0.8254574537277222, + "m": 0.9938117861747742 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 90, + "train_loss": 1.0112063884735107, + "val_loss": 0.9087930917739868, + "acc": 65.9, + "time": 360.1691498209984, + "param": [ + { + "p": 0.034152399748563766, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8416085243225098 + }, + { + "p": 0.0, + "m": 0.9118688702583313 + }, + { + "p": 0.0, + "m": 0.9630843997001648 + }, + { + "p": 0.0, + "m": 0.8722438812255859 + }, + { + "p": 0.0018920948496088386, + "m": 0.9422726035118103 + }, + { + "p": 0.012647286988794804, + "m": 0.8421730399131775 + }, + { + "p": 0.0, + "m": 0.8706175088882446 + }, + { + "p": 0.014350492507219315, + "m": 0.07984530925750732 + }, + { + "p": 0.9369577169418335, + "m": 0.8516952991485596 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 91, + "train_loss": 0.9193562269210815, + "val_loss": 1.0496699810028076, + "acc": 64.58, + "time": 349.4197021500004, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.012159053236246109, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9825924634933472 + }, + { + "p": 0.0, + "m": 0.7539999485015869 + }, + { + "p": 0.0, + "m": 0.9216527938842773 + }, + { + "p": 0.0, + "m": 0.8676486015319824 + }, + { + "p": 0.002056797267869115, + "m": 0.922163724899292 + }, + { + "p": 0.00845749955624342, + "m": 0.8229469060897827 + }, + { + "p": 0.0, + "m": 0.8764010667800903 + }, + { + "p": 0.0, + "m": 0.09085512161254883 + }, + { + "p": 0.9773266315460205, + "m": 0.8456946015357971 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 92, + "train_loss": 0.8338063359260559, + "val_loss": 1.055647373199463, + "acc": 66.41, + "time": 348.48368330000085, + "param": [ + { + "p": 0.04575391486287117, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.21189115941524506, + "m": 1.0 + }, + { + "p": 0.025295669212937355, + "m": 0.8777349591255188 + }, + { + "p": 0.0, + "m": 0.6391782164573669 + }, + { + "p": 0.0, + "m": 0.8484126925468445 + }, + { + "p": 0.0, + "m": 0.7934936881065369 + }, + { + "p": 0.1236853376030922, + "m": 0.8211784362792969 + }, + { + "p": 0.23906102776527405, + "m": 0.7738021016120911 + }, + { + "p": 0.06122748553752899, + "m": 0.9302817583084106 + }, + { + "p": 0.003741385182365775, + "m": 0.11973987519741058 + }, + { + "p": 0.28913813829421997, + "m": 0.8508028388023376 + }, + { + "p": 0.0002058778773061931, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 93, + "train_loss": 0.8409532308578491, + "val_loss": 1.030171275138855, + "acc": 65.93, + "time": 351.4606757110014, + "param": [ + { + "p": 0.0026376897003501654, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.26971909403800964, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8284460306167603 + }, + { + "p": 0.0, + "m": 0.7024937868118286 + }, + { + "p": 0.0, + "m": 0.8424895405769348 + }, + { + "p": 0.0, + "m": 0.8433985710144043 + }, + { + "p": 0.08532433956861496, + "m": 0.8449756503105164 + }, + { + "p": 0.2974701523780823, + "m": 0.8461617231369019 + }, + { + "p": 0.005588889587670565, + "m": 0.8867976069450378 + }, + { + "p": 0.00015306691057048738, + "m": 0.14166438579559326 + }, + { + "p": 0.3391067087650299, + "m": 0.8973148465156555 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 94, + "train_loss": 0.7473835945129395, + "val_loss": 0.974883496761322, + "acc": 66.57, + "time": 350.2035967099946, + "param": [ + { + "p": 0.05926116183400154, + "m": 1.0 + }, + { + "p": 0.016898849979043007, + "m": 1.0 + }, + { + "p": 0.32822176814079285, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.7929543852806091 + }, + { + "p": 0.0, + "m": 0.7831920385360718 + }, + { + "p": 0.0, + "m": 0.7994117140769958 + }, + { + "p": 0.0, + "m": 0.9065445065498352 + }, + { + "p": 0.0, + "m": 0.7723684310913086 + }, + { + "p": 0.2262912094593048, + "m": 0.7546712756156921 + }, + { + "p": 0.0018807423766702414, + "m": 0.9403680562973022 + }, + { + "p": 0.0, + "m": 0.11779827624559402 + }, + { + "p": 0.36744630336761475, + "m": 0.956752359867096 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 95, + "train_loss": 0.8503563404083252, + "val_loss": 0.9994708895683289, + "acc": 64.97, + "time": 353.2092727660056, + "param": [ + { + "p": 0.02494932897388935, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.2433719038963318, + "m": 1.0 + }, + { + "p": 0.01254972442984581, + "m": 0.7838732600212097 + }, + { + "p": 0.0, + "m": 0.8520004153251648 + }, + { + "p": 0.0, + "m": 0.7699780464172363 + }, + { + "p": 0.00720164505764842, + "m": 0.953841507434845 + }, + { + "p": 0.04048553854227066, + "m": 0.7969050407409668 + }, + { + "p": 0.21535047888755798, + "m": 0.6829193234443665 + }, + { + "p": 0.05113699287176132, + "m": 0.9027444124221802 + }, + { + "p": 0.1175495833158493, + "m": 0.10709863901138306 + }, + { + "p": 0.2723175585269928, + "m": 0.8981314301490784 + }, + { + "p": 0.010080351494252682, + "m": 1.0 + }, + { + "p": 0.005006792955100536, + "m": 1.0 + } + ] + }, + { + "epoch": 96, + "train_loss": 0.9727085828781128, + "val_loss": 0.9594752788543701, + "acc": 65.59, + "time": 357.57330805399397, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.18328243494033813, + "m": 1.0 + }, + { + "p": 0.05022900551557541, + "m": 0.8234572410583496 + }, + { + "p": 0.0, + "m": 0.9376571774482727 + }, + { + "p": 0.015351670794188976, + "m": 0.7950794100761414 + }, + { + "p": 0.06282193213701248, + "m": 1.0 + }, + { + "p": 6.591403507627547e-05, + "m": 0.8289007544517517 + }, + { + "p": 0.18180307745933533, + "m": 0.6359573006629944 + }, + { + "p": 0.0, + "m": 0.9576323628425598 + }, + { + "p": 0.26527026295661926, + "m": 0.0 + }, + { + "p": 0.24117574095726013, + "m": 0.8861528038978577 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 97, + "train_loss": 1.061455488204956, + "val_loss": 1.0764919519424438, + "acc": 66.83, + "time": 351.5533868850034, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.17884938418865204, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8072674870491028 + }, + { + "p": 0.0, + "m": 0.8853653073310852 + }, + { + "p": 0.0, + "m": 0.819833517074585 + }, + { + "p": 0.0, + "m": 0.9777705669403076 + }, + { + "p": 0.0, + "m": 0.8214160203933716 + }, + { + "p": 0.2568095624446869, + "m": 0.7780011296272278 + }, + { + "p": 0.0, + "m": 0.954846978187561 + }, + { + "p": 0.3862708508968353, + "m": 0.12228362262248993 + }, + { + "p": 0.17807020246982574, + "m": 0.9079675078392029 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 98, + "train_loss": 0.932767391204834, + "val_loss": 1.0761420726776123, + "acc": 66.97, + "time": 358.1132538200036, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.15793728828430176, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8450460433959961 + }, + { + "p": 0.0, + "m": 0.8468068838119507 + }, + { + "p": 0.0, + "m": 0.8582380414009094 + }, + { + "p": 0.0, + "m": 0.9931933879852295 + }, + { + "p": 0.0, + "m": 0.7931394577026367 + }, + { + "p": 0.31493592262268066, + "m": 0.8520820140838623 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.45411545038223267, + "m": 0.008427448570728302 + }, + { + "p": 0.07085373252630234, + "m": 0.9427413940429688 + }, + { + "p": 0.0021576129365712404, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 99, + "train_loss": 0.9778499007225037, + "val_loss": 1.0991250276565552, + "acc": 66.63, + "time": 355.03312290500617, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8945258259773254 + }, + { + "p": 0.0, + "m": 0.9094107747077942 + }, + { + "p": 0.0, + "m": 0.8927254676818848 + }, + { + "p": 0.0, + "m": 0.995523989200592 + }, + { + "p": 0.0, + "m": 0.8936157822608948 + }, + { + "p": 0.2770552933216095, + "m": 0.8606693744659424 + }, + { + "p": 0.0, + "m": 0.9704159498214722 + }, + { + "p": 0.7229446768760681, + "m": 0.06713377684354782 + }, + { + "p": 0.0, + "m": 0.9520936012268066 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 100, + "train_loss": 0.9353435635566711, + "val_loss": 1.0631221532821655, + "acc": 65.87, + "time": 350.92041604500264, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8922443389892578 + }, + { + "p": 0.0, + "m": 0.8181968331336975 + }, + { + "p": 0.0, + "m": 0.8795078992843628 + }, + { + "p": 0.0, + "m": 0.9974971413612366 + }, + { + "p": 0.0, + "m": 0.9224950671195984 + }, + { + "p": 0.23800994455814362, + "m": 0.8347540497779846 + }, + { + "p": 0.0, + "m": 0.9498761296272278 + }, + { + "p": 0.7619900703430176, + "m": 0.10198777914047241 + }, + { + "p": 0.0, + "m": 0.8732699751853943 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 101, + "train_loss": 1.1605968475341797, + "val_loss": 1.0677244663238525, + "acc": 65.31, + "time": 352.71033667901065, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9426605701446533 + }, + { + "p": 0.0, + "m": 0.8406925201416016 + }, + { + "p": 0.0, + "m": 0.8310679197311401 + }, + { + "p": 0.0, + "m": 0.9803369641304016 + }, + { + "p": 0.0, + "m": 0.9418635368347168 + }, + { + "p": 0.03593682497739792, + "m": 0.8768964409828186 + }, + { + "p": 0.0, + "m": 0.9634780287742615 + }, + { + "p": 0.9640631675720215, + "m": 0.10605001449584961 + }, + { + "p": 0.0, + "m": 0.9027390480041504 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 102, + "train_loss": 1.5246202945709229, + "val_loss": 1.0320791006088257, + "acc": 64.8, + "time": 349.5483688000095, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9517892003059387 + }, + { + "p": 0.0, + "m": 0.9998713135719299 + }, + { + "p": 0.0, + "m": 0.8976568579673767 + }, + { + "p": 0.0, + "m": 0.9340352416038513 + }, + { + "p": 0.0, + "m": 0.9497420787811279 + }, + { + "p": 0.0, + "m": 0.8806266188621521 + }, + { + "p": 0.0, + "m": 0.9719473123550415 + }, + { + "p": 1.0, + "m": 0.12763772904872894 + }, + { + "p": 0.0, + "m": 0.9718668460845947 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 103, + "train_loss": 1.1012084484100342, + "val_loss": 1.0807119607925415, + "acc": 64.9, + "time": 349.3814603339997, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9257650971412659 + }, + { + "p": 0.0, + "m": 0.9562956690788269 + }, + { + "p": 0.0, + "m": 0.7636436820030212 + }, + { + "p": 0.0, + "m": 0.8875564932823181 + }, + { + "p": 0.0, + "m": 0.9036235809326172 + }, + { + "p": 0.0, + "m": 0.9276662468910217 + }, + { + "p": 0.0, + "m": 0.8594430685043335 + }, + { + "p": 1.0, + "m": 0.11430323123931885 + }, + { + "p": 0.0, + "m": 0.9221058487892151 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 104, + "train_loss": 1.0139844417572021, + "val_loss": 1.033908724784851, + "acc": 64.84, + "time": 348.25608036399353, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8833078145980835 + }, + { + "p": 0.0, + "m": 0.9362544417381287 + }, + { + "p": 0.0, + "m": 0.699357807636261 + }, + { + "p": 0.0, + "m": 0.9318216443061829 + }, + { + "p": 0.0, + "m": 0.8384734988212585 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.832913875579834 + }, + { + "p": 1.0, + "m": 0.1118130013346672 + }, + { + "p": 0.0, + "m": 0.9328343868255615 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 105, + "train_loss": 0.9708904027938843, + "val_loss": 1.0295560359954834, + "acc": 65.16, + "time": 350.1762544110097, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8946872353553772 + }, + { + "p": 0.0, + "m": 0.829541027545929 + }, + { + "p": 0.0, + "m": 0.6825886368751526 + }, + { + "p": 0.0, + "m": 0.8367411494255066 + }, + { + "p": 0.0, + "m": 0.853822648525238 + }, + { + "p": 0.0, + "m": 0.9884824752807617 + }, + { + "p": 0.0, + "m": 0.8483586311340332 + }, + { + "p": 1.0, + "m": 0.014273793436586857 + }, + { + "p": 0.0, + "m": 0.8679446578025818 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 106, + "train_loss": 0.8661576509475708, + "val_loss": 0.9684035778045654, + "acc": 65.34, + "time": 356.47450597101124, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8199298977851868 + }, + { + "p": 0.0, + "m": 0.7987597584724426 + }, + { + "p": 0.0, + "m": 0.7213802933692932 + }, + { + "p": 0.0, + "m": 0.8291803002357483 + }, + { + "p": 0.0, + "m": 0.9143316149711609 + }, + { + "p": 0.0, + "m": 0.9940270781517029 + }, + { + "p": 0.0, + "m": 0.6995300054550171 + }, + { + "p": 1.0, + "m": 0.0927157923579216 + }, + { + "p": 0.0, + "m": 0.8588802814483643 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 107, + "train_loss": 0.9168267846107483, + "val_loss": 1.0563524961471558, + "acc": 65.38, + "time": 349.7760800639953, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.7694383263587952 + }, + { + "p": 0.0, + "m": 0.7920791506767273 + }, + { + "p": 0.0, + "m": 0.7753171324729919 + }, + { + "p": 0.0, + "m": 0.8710041046142578 + }, + { + "p": 0.0, + "m": 0.9719077944755554 + }, + { + "p": 0.0, + "m": 0.9952722787857056 + }, + { + "p": 0.0, + "m": 0.8073896169662476 + }, + { + "p": 1.0, + "m": 0.09098939597606659 + }, + { + "p": 0.0, + "m": 0.9154514670372009 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 108, + "train_loss": 0.802462100982666, + "val_loss": 1.05657958984375, + "acc": 65.69, + "time": 349.69777638099913, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.870011568069458 + }, + { + "p": 0.0, + "m": 0.7733046412467957 + }, + { + "p": 0.0, + "m": 0.741489827632904 + }, + { + "p": 0.0, + "m": 0.9143977761268616 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9583298563957214 + }, + { + "p": 0.0, + "m": 0.8741406798362732 + }, + { + "p": 1.0, + "m": 0.12698230147361755 + }, + { + "p": 0.0, + "m": 0.8921629190444946 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 109, + "train_loss": 0.9745259284973145, + "val_loss": 0.9607149362564087, + "acc": 64.86, + "time": 348.5296408300055, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8606276512145996 + }, + { + "p": 0.0, + "m": 0.8464143872261047 + }, + { + "p": 0.0, + "m": 0.757170557975769 + }, + { + "p": 0.0, + "m": 0.9429066777229309 + }, + { + "p": 0.0, + "m": 0.9976272583007812 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9446926116943359 + }, + { + "p": 1.0, + "m": 0.07760294526815414 + }, + { + "p": 0.0, + "m": 0.930457592010498 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 110, + "train_loss": 0.7541978359222412, + "val_loss": 1.0828866958618164, + "acc": 64.79, + "time": 348.73244414900546, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8819833993911743 + }, + { + "p": 0.0, + "m": 0.8877776861190796 + }, + { + "p": 0.0, + "m": 0.7591763138771057 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9602975249290466 + }, + { + "p": 0.0, + "m": 0.9563486576080322 + }, + { + "p": 0.0, + "m": 0.8886324763298035 + }, + { + "p": 1.0, + "m": 0.1115313172340393 + }, + { + "p": 0.0, + "m": 0.9297583103179932 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 111, + "train_loss": 0.9476534128189087, + "val_loss": 1.0833536386489868, + "acc": 64.91, + "time": 347.96089112199843, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8400319218635559 + }, + { + "p": 0.0, + "m": 0.9152807593345642 + }, + { + "p": 0.0, + "m": 0.8031553626060486 + }, + { + "p": 0.0, + "m": 0.9990929961204529 + }, + { + "p": 0.0, + "m": 0.9758163690567017 + }, + { + "p": 0.0, + "m": 0.960622251033783 + }, + { + "p": 0.0, + "m": 0.8888916969299316 + }, + { + "p": 1.0, + "m": 0.13773635029792786 + }, + { + "p": 0.0, + "m": 0.9636763334274292 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 112, + "train_loss": 0.7958709001541138, + "val_loss": 1.0293859243392944, + "acc": 64.6, + "time": 347.4425569800078, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8793304562568665 + }, + { + "p": 0.0, + "m": 0.917076587677002 + }, + { + "p": 0.0, + "m": 0.7224176526069641 + }, + { + "p": 0.0, + "m": 0.9864700436592102 + }, + { + "p": 0.0, + "m": 0.8703392148017883 + }, + { + "p": 0.0, + "m": 0.9980828762054443 + }, + { + "p": 0.0, + "m": 0.9454084634780884 + }, + { + "p": 1.0, + "m": 0.1560245156288147 + }, + { + "p": 0.0, + "m": 0.9846387505531311 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 113, + "train_loss": 0.8554370403289795, + "val_loss": 1.1304881572723389, + "acc": 64.07, + "time": 359.2885482929996, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9311556816101074 + }, + { + "p": 0.0, + "m": 0.827719509601593 + }, + { + "p": 0.0, + "m": 0.7027583122253418 + }, + { + "p": 0.0, + "m": 0.9679207801818848 + }, + { + "p": 0.0, + "m": 0.8332132697105408 + }, + { + "p": 0.0012046463089063764, + "m": 0.9998317360877991 + }, + { + "p": 0.0, + "m": 0.8900025486946106 + }, + { + "p": 0.9987953901290894, + "m": 0.13199765980243683 + }, + { + "p": 0.0, + "m": 0.9369401931762695 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 114, + "train_loss": 0.7473122477531433, + "val_loss": 1.0529159307479858, + "acc": 64.72, + "time": 355.83211647800636, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8659942746162415 + }, + { + "p": 0.0, + "m": 0.7944768071174622 + }, + { + "p": 0.0, + "m": 0.6093086004257202 + }, + { + "p": 0.0, + "m": 0.994209349155426 + }, + { + "p": 0.0, + "m": 0.8518351316452026 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9411993026733398 + }, + { + "p": 1.0, + "m": 0.16048647463321686 + }, + { + "p": 0.0, + "m": 0.9537240862846375 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 115, + "train_loss": 0.7440007925033569, + "val_loss": 1.100303053855896, + "acc": 64.7, + "time": 350.1471244189888, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8442316651344299 + }, + { + "p": 0.0, + "m": 0.7745749950408936 + }, + { + "p": 0.0, + "m": 0.5196072459220886 + }, + { + "p": 0.0, + "m": 0.95899498462677 + }, + { + "p": 0.0, + "m": 0.8881542086601257 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9011670351028442 + }, + { + "p": 1.0, + "m": 0.11156989634037018 + }, + { + "p": 0.0, + "m": 0.9470874071121216 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 116, + "train_loss": 0.8651993870735168, + "val_loss": 1.1408469676971436, + "acc": 61.83, + "time": 349.6468242999981, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.7858942151069641 + }, + { + "p": 0.0, + "m": 0.659950315952301 + }, + { + "p": 0.0, + "m": 0.5607444047927856 + }, + { + "p": 0.0, + "m": 0.9301915168762207 + }, + { + "p": 0.0, + "m": 0.8923908472061157 + }, + { + "p": 0.0, + "m": 0.9994635581970215 + }, + { + "p": 0.0, + "m": 0.8249080777168274 + }, + { + "p": 1.0, + "m": 0.07432951033115387 + }, + { + "p": 0.0, + "m": 0.9000647664070129 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 117, + "train_loss": 1.1257028579711914, + "val_loss": 1.0383577346801758, + "acc": 64.79, + "time": 351.78187860301114, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8783623576164246 + }, + { + "p": 0.0, + "m": 0.6596922874450684 + }, + { + "p": 0.0, + "m": 0.5147093534469604 + }, + { + "p": 0.0, + "m": 0.916191041469574 + }, + { + "p": 0.0, + "m": 0.9431420564651489 + }, + { + "p": 0.0, + "m": 0.9689106941223145 + }, + { + "p": 0.0, + "m": 0.9303717613220215 + }, + { + "p": 1.0, + "m": 0.1446196585893631 + }, + { + "p": 0.0, + "m": 0.9512720108032227 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 118, + "train_loss": 1.0711696147918701, + "val_loss": 1.167603850364685, + "acc": 64.86, + "time": 354.159202506009, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8354741930961609 + }, + { + "p": 0.0, + "m": 0.7159337401390076 + }, + { + "p": 0.0, + "m": 0.5109132528305054 + }, + { + "p": 0.0, + "m": 0.9692549705505371 + }, + { + "p": 0.0, + "m": 0.998089075088501 + }, + { + "p": 0.0, + "m": 0.9988403916358948 + }, + { + "p": 0.0, + "m": 0.9214417934417725 + }, + { + "p": 1.0, + "m": 0.12216751277446747 + }, + { + "p": 0.0, + "m": 0.9147505164146423 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 119, + "train_loss": 1.0818843841552734, + "val_loss": 1.0536274909973145, + "acc": 64.23, + "time": 351.17554884401034, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.7827908396720886 + }, + { + "p": 0.0, + "m": 0.5967153310775757 + }, + { + "p": 0.0, + "m": 0.44410207867622375 + }, + { + "p": 0.0, + "m": 0.8827019333839417 + }, + { + "p": 0.0, + "m": 0.8715797066688538 + }, + { + "p": 0.0, + "m": 0.9316810965538025 + }, + { + "p": 0.0, + "m": 0.9531346559524536 + }, + { + "p": 1.0, + "m": 0.062465932220220566 + }, + { + "p": 0.0, + "m": 0.9628332853317261 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 120, + "train_loss": 1.2186709642410278, + "val_loss": 1.0452905893325806, + "acc": 65.13, + "time": 349.48241754500486, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8446487188339233 + }, + { + "p": 0.0, + "m": 0.5759727954864502 + }, + { + "p": 0.0, + "m": 0.39453282952308655 + }, + { + "p": 0.0, + "m": 0.8683592081069946 + }, + { + "p": 0.0, + "m": 0.935368537902832 + }, + { + "p": 0.0, + "m": 0.9850068092346191 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 1.0, + "m": 0.09696494042873383 + }, + { + "p": 0.0, + "m": 0.9778807163238525 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 121, + "train_loss": 0.89054936170578, + "val_loss": 0.9470895528793335, + "acc": 65.65, + "time": 352.98763659299584, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8477270603179932 + }, + { + "p": 0.0, + "m": 0.6161484122276306 + }, + { + "p": 0.0, + "m": 0.42715221643447876 + }, + { + "p": 0.0, + "m": 0.906725287437439 + }, + { + "p": 0.0, + "m": 0.9328541159629822 + }, + { + "p": 0.0, + "m": 0.9984143376350403 + }, + { + "p": 0.0, + "m": 0.9498421549797058 + }, + { + "p": 1.0, + "m": 0.13873174786567688 + }, + { + "p": 0.0, + "m": 0.9240624904632568 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 122, + "train_loss": 1.4858382940292358, + "val_loss": 0.9891347885131836, + "acc": 65.62, + "time": 350.1231502199953, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8825296759605408 + }, + { + "p": 0.0, + "m": 0.7297717928886414 + }, + { + "p": 0.0, + "m": 0.4518848955631256 + }, + { + "p": 0.0, + "m": 0.9806254506111145 + }, + { + "p": 0.0, + "m": 0.9782614707946777 + }, + { + "p": 0.0, + "m": 0.9515109062194824 + }, + { + "p": 0.0, + "m": 0.9049190282821655 + }, + { + "p": 1.0, + "m": 0.14890512824058533 + }, + { + "p": 0.0, + "m": 0.9651309847831726 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 123, + "train_loss": 0.7495267987251282, + "val_loss": 1.0430221557617188, + "acc": 65.09, + "time": 352.7853941450012, + "param": [ + { + "p": 0.013623536564409733, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8421037793159485 + }, + { + "p": 0.0, + "m": 0.8476954102516174 + }, + { + "p": 0.0, + "m": 0.5381301641464233 + }, + { + "p": 0.0, + "m": 0.8902790546417236 + }, + { + "p": 0.0, + "m": 0.9035394787788391 + }, + { + "p": 0.0, + "m": 0.9298350214958191 + }, + { + "p": 0.0, + "m": 0.7578669786453247 + }, + { + "p": 0.9863764643669128, + "m": 0.09767145663499832 + }, + { + "p": 0.0, + "m": 0.8606792092323303 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 124, + "train_loss": 0.900298535823822, + "val_loss": 0.9803416728973389, + "acc": 64.49, + "time": 350.0084266079939, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0008583881426602602, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9297254681587219 + }, + { + "p": 0.0, + "m": 0.8008630275726318 + }, + { + "p": 0.0, + "m": 0.5906004309654236 + }, + { + "p": 0.0, + "m": 0.8273301720619202 + }, + { + "p": 0.0, + "m": 0.8783949613571167 + }, + { + "p": 0.0, + "m": 0.9668945074081421 + }, + { + "p": 0.0, + "m": 0.8393992185592651 + }, + { + "p": 0.9991416335105896, + "m": 0.18864676356315613 + }, + { + "p": 0.0, + "m": 0.940069854259491 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 125, + "train_loss": 1.1326425075531006, + "val_loss": 1.1721479892730713, + "acc": 60.46, + "time": 349.4511353919952, + "param": [ + { + "p": 0.006247566547244787, + "m": 1.0 + }, + { + "p": 0.0021660029888153076, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.00015719083603471518, + "m": 0.9165946245193481 + }, + { + "p": 0.0, + "m": 0.8820503354072571 + }, + { + "p": 0.0, + "m": 0.7305446267127991 + }, + { + "p": 0.0, + "m": 0.9834041595458984 + }, + { + "p": 0.0, + "m": 0.9303390383720398 + }, + { + "p": 0.004851392935961485, + "m": 0.9688795208930969 + }, + { + "p": 0.004358065780252218, + "m": 0.8963773846626282 + }, + { + "p": 0.9822198152542114, + "m": 0.1538710743188858 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 126, + "train_loss": 0.7456902265548706, + "val_loss": 0.9936262369155884, + "acc": 65.72, + "time": 349.15661438899406, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9141799211502075 + }, + { + "p": 0.0, + "m": 0.9021097421646118 + }, + { + "p": 0.0, + "m": 0.8519548177719116 + }, + { + "p": 0.0, + "m": 0.9236791729927063 + }, + { + "p": 0.0, + "m": 0.8522052764892578 + }, + { + "p": 0.0, + "m": 0.9845195412635803 + }, + { + "p": 0.0, + "m": 0.9647037982940674 + }, + { + "p": 1.0, + "m": 0.1310863345861435 + }, + { + "p": 0.0, + "m": 0.9654442071914673 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 127, + "train_loss": 0.7435653209686279, + "val_loss": 1.0906633138656616, + "acc": 64.84, + "time": 351.0451939409977, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9198945760726929 + }, + { + "p": 0.0, + "m": 0.8623435497283936 + }, + { + "p": 0.0, + "m": 0.8333909511566162 + }, + { + "p": 0.0, + "m": 0.8667205572128296 + }, + { + "p": 0.0, + "m": 0.8394814133644104 + }, + { + "p": 0.0, + "m": 0.9622649550437927 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 1.0, + "m": 0.11948808282613754 + }, + { + "p": 0.0, + "m": 0.9225057363510132 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 128, + "train_loss": 0.9929231405258179, + "val_loss": 1.0685428380966187, + "acc": 65.13, + "time": 351.5899579120014, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9573951959609985 + }, + { + "p": 0.0, + "m": 0.9282940626144409 + }, + { + "p": 0.0, + "m": 0.7685551643371582 + }, + { + "p": 0.0, + "m": 0.9267916083335876 + }, + { + "p": 0.0, + "m": 0.810503363609314 + }, + { + "p": 0.0, + "m": 0.9651771187782288 + }, + { + "p": 0.0, + "m": 0.9994354248046875 + }, + { + "p": 1.0, + "m": 0.1331678032875061 + }, + { + "p": 0.0, + "m": 0.9987555146217346 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 129, + "train_loss": 0.8380222916603088, + "val_loss": 1.1835262775421143, + "acc": 64.85, + "time": 349.1689876869932, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9976285696029663 + }, + { + "p": 0.0, + "m": 0.8638384342193604 + }, + { + "p": 0.0, + "m": 0.7995560765266418 + }, + { + "p": 0.0, + "m": 0.9140872955322266 + }, + { + "p": 0.0, + "m": 0.8344310522079468 + }, + { + "p": 0.0, + "m": 0.9472736120223999 + }, + { + "p": 0.0, + "m": 0.955012321472168 + }, + { + "p": 1.0, + "m": 0.13899457454681396 + }, + { + "p": 0.0, + "m": 0.9995312690734863 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 130, + "train_loss": 0.8821075558662415, + "val_loss": 1.009197473526001, + "acc": 65.61, + "time": 349.88114129200403, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9787303805351257 + }, + { + "p": 0.0, + "m": 0.8919808864593506 + }, + { + "p": 0.0, + "m": 0.7358888387680054 + }, + { + "p": 0.0, + "m": 0.8912910223007202 + }, + { + "p": 0.0, + "m": 0.8900801539421082 + }, + { + "p": 0.0, + "m": 0.9767082929611206 + }, + { + "p": 0.0, + "m": 0.9907114505767822 + }, + { + "p": 1.0, + "m": 0.18090060353279114 + }, + { + "p": 0.0, + "m": 0.9923566579818726 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 131, + "train_loss": 0.987838864326477, + "val_loss": 0.9580891728401184, + "acc": 65.28, + "time": 354.76489795600355, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.983731746673584 + }, + { + "p": 0.0, + "m": 0.9908266663551331 + }, + { + "p": 0.0, + "m": 0.8316361308097839 + }, + { + "p": 0.0, + "m": 0.988950252532959 + }, + { + "p": 0.0, + "m": 0.9789528846740723 + }, + { + "p": 0.0, + "m": 0.943534791469574 + }, + { + "p": 0.0, + "m": 0.9928524494171143 + }, + { + "p": 1.0, + "m": 0.2256125807762146 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 132, + "train_loss": 0.7490646839141846, + "val_loss": 1.0612680912017822, + "acc": 64.96, + "time": 353.0143214609998, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8747492432594299 + }, + { + "p": 0.0, + "m": 0.784278154373169 + }, + { + "p": 0.0, + "m": 0.7194169163703918 + }, + { + "p": 0.005862282123416662, + "m": 0.8355348110198975 + }, + { + "p": 0.0, + "m": 0.9999576210975647 + }, + { + "p": 0.00193820521235466, + "m": 0.9233331680297852 + }, + { + "p": 0.0, + "m": 0.8031511306762695 + }, + { + "p": 0.9921994805335999, + "m": 0.12467130273580551 + }, + { + "p": 0.0, + "m": 0.8839206695556641 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 133, + "train_loss": 1.198899745941162, + "val_loss": 1.0260467529296875, + "acc": 65.25, + "time": 362.7342641849973, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.001113436883315444, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8378899097442627 + }, + { + "p": 0.0, + "m": 0.8036078810691833 + }, + { + "p": 0.0, + "m": 0.639639675617218 + }, + { + "p": 0.0, + "m": 0.8045834898948669 + }, + { + "p": 0.0, + "m": 0.986542284488678 + }, + { + "p": 0.021845292299985886, + "m": 0.9383900165557861 + }, + { + "p": 0.0, + "m": 0.7899429202079773 + }, + { + "p": 0.9770413041114807, + "m": 0.1543504148721695 + }, + { + "p": 0.0, + "m": 0.8897976875305176 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 134, + "train_loss": 1.088026523590088, + "val_loss": 1.0097193717956543, + "acc": 65.71, + "time": 348.5155876570061, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8795498609542847 + }, + { + "p": 0.0, + "m": 0.8504377603530884 + }, + { + "p": 0.0, + "m": 0.6611717343330383 + }, + { + "p": 0.0, + "m": 0.8897542357444763 + }, + { + "p": 0.0, + "m": 0.9960525035858154 + }, + { + "p": 0.0, + "m": 0.9493858814239502 + }, + { + "p": 0.015383661724627018, + "m": 0.8801783919334412 + }, + { + "p": 0.9846163392066956, + "m": 0.20692358911037445 + }, + { + "p": 0.0, + "m": 0.980808436870575 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 135, + "train_loss": 0.89488285779953, + "val_loss": 1.012652039527893, + "acc": 65.59, + "time": 357.81619072800095, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.774149477481842 + }, + { + "p": 0.0, + "m": 0.7970216870307922 + }, + { + "p": 0.0, + "m": 0.6843963861465454 + }, + { + "p": 0.0, + "m": 0.9880890250205994 + }, + { + "p": 0.0, + "m": 0.9657889008522034 + }, + { + "p": 0.0, + "m": 0.9407699108123779 + }, + { + "p": 0.0, + "m": 0.9834775328636169 + }, + { + "p": 1.0, + "m": 0.2876397967338562 + }, + { + "p": 0.0, + "m": 0.9757402539253235 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 136, + "train_loss": 0.725060224533081, + "val_loss": 1.134408950805664, + "acc": 65.88, + "time": 353.79000286600785, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.7196853160858154 + }, + { + "p": 0.0, + "m": 0.9282577633857727 + }, + { + "p": 0.0, + "m": 0.6434202790260315 + }, + { + "p": 0.0, + "m": 0.9577339291572571 + }, + { + "p": 0.0, + "m": 0.957927942276001 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9191001057624817 + }, + { + "p": 1.0, + "m": 0.23889698088169098 + }, + { + "p": 0.0, + "m": 0.9298896193504333 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 137, + "train_loss": 0.9091039299964905, + "val_loss": 1.0359759330749512, + "acc": 64.8, + "time": 357.230180537008, + "param": [ + { + "p": 0.0011641758028417826, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.01701168343424797, + "m": 0.7514673471450806 + }, + { + "p": 0.0, + "m": 0.9811041355133057 + }, + { + "p": 0.0, + "m": 0.7229275703430176 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8631842732429504 + }, + { + "p": 0.006325725000351667, + "m": 0.9584108591079712 + }, + { + "p": 0.007090046536177397, + "m": 0.8767988085746765 + }, + { + "p": 0.9578765034675598, + "m": 0.2557269036769867 + }, + { + "p": 0.010531911626458168, + "m": 0.9860539436340332 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 138, + "train_loss": 0.7764231562614441, + "val_loss": 0.9761002659797668, + "acc": 65.39, + "time": 353.33818609999435, + "param": [ + { + "p": 0.00359495566226542, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.901618242263794 + }, + { + "p": 0.0, + "m": 0.9116842150688171 + }, + { + "p": 0.0, + "m": 0.6269455552101135 + }, + { + "p": 0.00045244948705658317, + "m": 0.9470548629760742 + }, + { + "p": 0.0, + "m": 0.846666693687439 + }, + { + "p": 0.0, + "m": 0.9913228750228882 + }, + { + "p": 0.0, + "m": 0.9617365598678589 + }, + { + "p": 0.9959526062011719, + "m": 0.2312987595796585 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 139, + "train_loss": 1.1653273105621338, + "val_loss": 1.084505558013916, + "acc": 65.41, + "time": 349.97060947099817, + "param": [ + { + "p": 0.004446270409971476, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9036425948143005 + }, + { + "p": 0.0, + "m": 0.7912015914916992 + }, + { + "p": 0.0, + "m": 0.6264380812644958 + }, + { + "p": 0.0, + "m": 0.9469086527824402 + }, + { + "p": 0.0, + "m": 0.9622432589530945 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8877614736557007 + }, + { + "p": 0.9955536723136902, + "m": 0.17282459139823914 + }, + { + "p": 0.0, + "m": 0.9679643511772156 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 140, + "train_loss": 0.891308069229126, + "val_loss": 0.9738665223121643, + "acc": 65.62, + "time": 352.9553157949995, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9713036417961121 + }, + { + "p": 0.0, + "m": 0.8485730886459351 + }, + { + "p": 0.0, + "m": 0.6918141841888428 + }, + { + "p": 0.0, + "m": 0.9959162473678589 + }, + { + "p": 0.0, + "m": 0.9501086473464966 + }, + { + "p": 0.0, + "m": 0.898727297782898 + }, + { + "p": 0.0, + "m": 0.9586641788482666 + }, + { + "p": 1.0, + "m": 0.13628248870372772 + }, + { + "p": 0.0, + "m": 0.9387639164924622 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 141, + "train_loss": 0.6921796798706055, + "val_loss": 0.9707247614860535, + "acc": 66.14, + "time": 349.20262070900935, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9568164944648743 + }, + { + "p": 0.0, + "m": 0.7977355718612671 + }, + { + "p": 0.0, + "m": 0.847521185874939 + }, + { + "p": 0.0, + "m": 0.9731346964836121 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9877133965492249 + }, + { + "p": 0.0, + "m": 0.9630600810050964 + }, + { + "p": 1.0, + "m": 0.1974509209394455 + }, + { + "p": 0.0, + "m": 0.9681603312492371 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 142, + "train_loss": 1.0488057136535645, + "val_loss": 1.0553405284881592, + "acc": 65.99, + "time": 352.9783206790016, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8668138384819031 + }, + { + "p": 0.0, + "m": 0.767405092716217 + }, + { + "p": 0.0, + "m": 0.8906003832817078 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9437016844749451 + }, + { + "p": 0.0, + "m": 0.9749094843864441 + }, + { + "p": 0.0, + "m": 0.9560184478759766 + }, + { + "p": 1.0, + "m": 0.170387402176857 + }, + { + "p": 0.0, + "m": 0.9919072389602661 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 143, + "train_loss": 0.9455082416534424, + "val_loss": 0.998805820941925, + "acc": 65.27, + "time": 350.36262300600356, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0036031529307365417, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9048542380332947 + }, + { + "p": 0.0, + "m": 0.7037850022315979 + }, + { + "p": 0.0, + "m": 0.873847246170044 + }, + { + "p": 0.0, + "m": 0.9679130911827087 + }, + { + "p": 0.0, + "m": 0.9874335527420044 + }, + { + "p": 0.0, + "m": 0.9938939213752747 + }, + { + "p": 0.0, + "m": 0.9955000877380371 + }, + { + "p": 0.9963968396186829, + "m": 0.22218111157417297 + }, + { + "p": 0.0, + "m": 0.8737579584121704 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 144, + "train_loss": 1.0339947938919067, + "val_loss": 1.0261121988296509, + "acc": 65.51, + "time": 350.3989864280011, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0005472734337672591, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9563734531402588 + }, + { + "p": 0.0, + "m": 0.650905966758728 + }, + { + "p": 0.0, + "m": 0.8535121083259583 + }, + { + "p": 0.0, + "m": 0.9341457486152649 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9970901608467102 + }, + { + "p": 0.0, + "m": 0.9825594425201416 + }, + { + "p": 0.9994527101516724, + "m": 0.2728336453437805 + }, + { + "p": 0.0, + "m": 0.9537482857704163 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 145, + "train_loss": 1.4364053010940552, + "val_loss": 1.0193122625350952, + "acc": 65.44, + "time": 348.6067005349905, + "param": [ + { + "p": 0.02665545605123043, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.888043999671936 + }, + { + "p": 0.0, + "m": 0.7078874111175537 + }, + { + "p": 0.0, + "m": 0.8908773064613342 + }, + { + "p": 0.0, + "m": 0.9528944492340088 + }, + { + "p": 0.0, + "m": 0.9955559968948364 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.848646879196167 + }, + { + "p": 0.9683907628059387, + "m": 0.2817479074001312 + }, + { + "p": 0.0, + "m": 0.9525023102760315 + }, + { + "p": 0.004953789059072733, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 146, + "train_loss": 1.1180236339569092, + "val_loss": 0.9652467370033264, + "acc": 65.76, + "time": 349.92360579498927, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0005210775998421013, + "m": 0.8658440113067627 + }, + { + "p": 0.0, + "m": 0.8424810171127319 + }, + { + "p": 0.0, + "m": 0.9021568894386292 + }, + { + "p": 0.003846186213195324, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.956831693649292 + }, + { + "p": 0.039347048848867416, + "m": 0.9993383288383484 + }, + { + "p": 0.0, + "m": 0.9017767310142517 + }, + { + "p": 0.9378927946090698, + "m": 0.14614355564117432 + }, + { + "p": 0.0, + "m": 0.9807295203208923 + }, + { + "p": 0.018392885103821754, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 147, + "train_loss": 0.9271669983863831, + "val_loss": 1.0961748361587524, + "acc": 66.09, + "time": 349.9055666320055, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9118149280548096 + }, + { + "p": 0.0, + "m": 0.9211058616638184 + }, + { + "p": 0.0, + "m": 0.9503365159034729 + }, + { + "p": 0.0, + "m": 0.9998140335083008 + }, + { + "p": 0.0, + "m": 0.8919000029563904 + }, + { + "p": 0.0, + "m": 0.9921974539756775 + }, + { + "p": 0.0, + "m": 0.8737715482711792 + }, + { + "p": 1.0, + "m": 0.17371074855327606 + }, + { + "p": 0.0, + "m": 0.9868430495262146 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 148, + "train_loss": 0.9775367975234985, + "val_loss": 1.0562589168548584, + "acc": 65.69, + "time": 353.95745402800094, + "param": [ + { + "p": 0.0035973284393548965, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.00011888878361787647, + "m": 0.9019823670387268 + }, + { + "p": 0.0, + "m": 0.9289629459381104 + }, + { + "p": 0.0, + "m": 0.8388054966926575 + }, + { + "p": 0.0, + "m": 0.9102784395217896 + }, + { + "p": 0.0028051743283867836, + "m": 0.8320090174674988 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0014579606940969825, + "m": 0.9185927510261536 + }, + { + "p": 0.9845499396324158, + "m": 0.1660812348127365 + }, + { + "p": 0.0054955435916781425, + "m": 0.9272724390029907 + }, + { + "p": 0.001975158927962184, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 149, + "train_loss": 0.8620166778564453, + "val_loss": 1.1042656898498535, + "acc": 65.48, + "time": 348.5377781330026, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9794649481773376 + }, + { + "p": 0.0, + "m": 0.9429430365562439 + }, + { + "p": 0.0, + "m": 0.7444606423377991 + }, + { + "p": 0.0, + "m": 0.9556963443756104 + }, + { + "p": 0.0019224926363676786, + "m": 0.8874272108078003 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9996184706687927 + }, + { + "p": 0.9963075518608093, + "m": 0.10064667463302612 + }, + { + "p": 0.0017699796007946134, + "m": 0.9247850775718689 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 150, + "train_loss": 1.1038908958435059, + "val_loss": 1.2013710737228394, + "acc": 64.98, + "time": 354.4266439609928, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8856819868087769 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8743973970413208 + }, + { + "p": 0.0, + "m": 0.926158607006073 + }, + { + "p": 0.0, + "m": 0.8334729075431824 + }, + { + "p": 0.0, + "m": 0.9846050143241882 + }, + { + "p": 0.0, + "m": 0.9556090831756592 + }, + { + "p": 1.0, + "m": 0.14980435371398926 + }, + { + "p": 0.0, + "m": 0.9982033967971802 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 151, + "train_loss": 0.7529006004333496, + "val_loss": 1.0633236169815063, + "acc": 65.16, + "time": 355.946386266005, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.00952508207410574, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9025133848190308 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9910635352134705 + }, + { + "p": 0.0, + "m": 0.8547878265380859 + }, + { + "p": 0.0, + "m": 0.9265225529670715 + }, + { + "p": 0.0, + "m": 0.9820605516433716 + }, + { + "p": 0.014817502349615097, + "m": 0.9438484907150269 + }, + { + "p": 0.9729088544845581, + "m": 0.208675816655159 + }, + { + "p": 0.0, + "m": 0.9754385948181152 + }, + { + "p": 0.0027485238388180733, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 152, + "train_loss": 1.4124001264572144, + "val_loss": 1.0119946002960205, + "acc": 65.88, + "time": 350.1336843439931, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0006179952179081738, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.984199583530426 + }, + { + "p": 0.0, + "m": 0.9560975432395935 + }, + { + "p": 0.0, + "m": 0.920512318611145 + }, + { + "p": 0.0, + "m": 0.8877029418945312 + }, + { + "p": 0.0, + "m": 0.91930091381073 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.9931557178497314, + "m": 0.2578067183494568 + }, + { + "p": 0.0, + "m": 0.9998424053192139 + }, + { + "p": 0.006226271856576204, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 153, + "train_loss": 0.9272465705871582, + "val_loss": 1.0196490287780762, + "acc": 64.58, + "time": 349.97478206398955, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9177510142326355 + }, + { + "p": 0.0, + "m": 0.9497907161712646 + }, + { + "p": 0.0, + "m": 0.9678187966346741 + }, + { + "p": 0.0, + "m": 0.8947629928588867 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9751031398773193 + }, + { + "p": 0.0, + "m": 0.9481963515281677 + }, + { + "p": 1.0, + "m": 0.22079624235630035 + }, + { + "p": 0.0, + "m": 0.9543145298957825 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 154, + "train_loss": 1.05708646774292, + "val_loss": 1.0277882814407349, + "acc": 65.58, + "time": 352.4411338009959, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9253188967704773 + }, + { + "p": 0.0, + "m": 0.8327301740646362 + }, + { + "p": 0.0, + "m": 0.9920327067375183 + }, + { + "p": 0.0, + "m": 0.970059871673584 + }, + { + "p": 0.0, + "m": 0.9857988953590393 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 1.0, + "m": 0.20820453763008118 + }, + { + "p": 0.0, + "m": 0.95340496301651 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 155, + "train_loss": 0.8845869302749634, + "val_loss": 0.9682626724243164, + "acc": 65.95, + "time": 350.98094481098815, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9410094022750854 + }, + { + "p": 0.0, + "m": 0.8699637055397034 + }, + { + "p": 0.0, + "m": 0.8786792755126953 + }, + { + "p": 0.0, + "m": 0.9628304839134216 + }, + { + "p": 0.0, + "m": 0.9524347186088562 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9679986834526062 + }, + { + "p": 1.0, + "m": 0.19286204874515533 + }, + { + "p": 0.0, + "m": 0.9125667810440063 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 156, + "train_loss": 1.156070351600647, + "val_loss": 1.0805858373641968, + "acc": 65.69, + "time": 351.42756841100345, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9508041739463806 + }, + { + "p": 0.0, + "m": 0.9488236904144287 + }, + { + "p": 0.0, + "m": 0.9763563275337219 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9823289513587952 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.9985145926475525, + "m": 0.20656953752040863 + }, + { + "p": 0.0, + "m": 0.9434439539909363 + }, + { + "p": 0.0014854377368465066, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 157, + "train_loss": 0.6264710426330566, + "val_loss": 1.0546964406967163, + "acc": 65.58, + "time": 349.96001054700173, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9812330007553101 + }, + { + "p": 0.0, + "m": 0.8459004163742065 + }, + { + "p": 0.0, + "m": 0.9778004884719849 + }, + { + "p": 0.0, + "m": 0.9710743427276611 + }, + { + "p": 0.0, + "m": 0.9049421548843384 + }, + { + "p": 0.00023556007363367826, + "m": 0.9742097854614258 + }, + { + "p": 0.0, + "m": 0.9227747917175293 + }, + { + "p": 0.9997644424438477, + "m": 0.23120762407779694 + }, + { + "p": 0.0, + "m": 0.9472414255142212 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 158, + "train_loss": 1.114215612411499, + "val_loss": 1.1014440059661865, + "acc": 65.23, + "time": 349.24252490299114, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9730181097984314 + }, + { + "p": 0.0, + "m": 0.887607991695404 + }, + { + "p": 0.0, + "m": 0.9545785784721375 + }, + { + "p": 0.0, + "m": 0.9740008115768433 + }, + { + "p": 0.0, + "m": 0.8900002837181091 + }, + { + "p": 0.0, + "m": 0.9567728638648987 + }, + { + "p": 0.0, + "m": 0.874732494354248 + }, + { + "p": 1.0, + "m": 0.1814108043909073 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 159, + "train_loss": 0.8513250350952148, + "val_loss": 1.0141143798828125, + "acc": 65.36, + "time": 347.84003659199516, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.947521984577179 + }, + { + "p": 0.0, + "m": 0.9729265570640564 + }, + { + "p": 0.0, + "m": 0.8887569904327393 + }, + { + "p": 0.0, + "m": 0.8826475739479065 + }, + { + "p": 0.0, + "m": 0.8792535662651062 + }, + { + "p": 0.0, + "m": 0.9212142825126648 + }, + { + "p": 0.0, + "m": 0.9874593019485474 + }, + { + "p": 1.0, + "m": 0.24581776559352875 + }, + { + "p": 0.0, + "m": 0.9667279124259949 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 160, + "train_loss": 0.921899139881134, + "val_loss": 1.06788170337677, + "acc": 65.17, + "time": 349.7949606320035, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0006331297336146235, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.817232072353363 + }, + { + "p": 0.0, + "m": 0.9815996289253235 + }, + { + "p": 0.0, + "m": 0.801529049873352 + }, + { + "p": 0.0, + "m": 0.975380539894104 + }, + { + "p": 0.0, + "m": 0.9812551736831665 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.9993668794631958, + "m": 0.23828798532485962 + }, + { + "p": 0.0, + "m": 0.9713152050971985 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 161, + "train_loss": 0.8326489925384521, + "val_loss": 0.9758449196815491, + "acc": 65.63, + "time": 360.01699641899904, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8728840947151184 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8429461121559143 + }, + { + "p": 0.0, + "m": 0.860893726348877 + }, + { + "p": 0.0, + "m": 0.9307040572166443 + }, + { + "p": 0.0, + "m": 0.999407947063446 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 1.0, + "m": 0.27002808451652527 + }, + { + "p": 0.0, + "m": 0.9445174932479858 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 162, + "train_loss": 1.1015915870666504, + "val_loss": 0.9613732099533081, + "acc": 65.94, + "time": 354.5033389300079, + "param": [ + { + "p": 0.012096293270587921, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9640445113182068 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8207394480705261 + }, + { + "p": 0.0, + "m": 0.8173890709877014 + }, + { + "p": 0.0, + "m": 0.867359459400177 + }, + { + "p": 0.0, + "m": 0.9064288139343262 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.9879037141799927, + "m": 0.2650674283504486 + }, + { + "p": 0.0, + "m": 0.9908247590065002 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 163, + "train_loss": 0.8271887302398682, + "val_loss": 0.9824612140655518, + "acc": 65.0, + "time": 349.6557988989953, + "param": [ + { + "p": 0.005391126498579979, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9035053849220276 + }, + { + "p": 0.0, + "m": 0.9732785820960999 + }, + { + "p": 0.0, + "m": 0.8818340301513672 + }, + { + "p": 0.0, + "m": 0.9110231995582581 + }, + { + "p": 0.0, + "m": 0.9211841225624084 + }, + { + "p": 0.0003663287789095193, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9385194182395935 + }, + { + "p": 0.9822255969047546, + "m": 0.3430977165699005 + }, + { + "p": 0.01201694831252098, + "m": 0.9147419929504395 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 164, + "train_loss": 1.0415174961090088, + "val_loss": 0.945896327495575, + "acc": 64.96, + "time": 349.00061555400316, + "param": [ + { + "p": 0.05398690700531006, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9586102962493896 + }, + { + "p": 0.0, + "m": 0.9829937815666199 + }, + { + "p": 0.0, + "m": 0.8324639797210693 + }, + { + "p": 0.0, + "m": 0.8926396369934082 + }, + { + "p": 0.009653700515627861, + "m": 0.8884184956550598 + }, + { + "p": 0.003678226377815008, + "m": 0.8952092528343201 + }, + { + "p": 0.04906945675611496, + "m": 0.8774563074111938 + }, + { + "p": 0.8537909388542175, + "m": 0.25548121333122253 + }, + { + "p": 0.0, + "m": 0.9140892624855042 + }, + { + "p": 0.029820751398801804, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 165, + "train_loss": 0.7649456262588501, + "val_loss": 0.973224401473999, + "acc": 66.21, + "time": 352.93512711900985, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9979166984558105 + }, + { + "p": 0.0, + "m": 0.8818772435188293 + }, + { + "p": 0.0, + "m": 0.8389298319816589 + }, + { + "p": 0.0, + "m": 0.8506726622581482 + }, + { + "p": 0.0, + "m": 0.9873746633529663 + }, + { + "p": 0.0, + "m": 0.908693253993988 + }, + { + "p": 0.0, + "m": 0.9354681372642517 + }, + { + "p": 1.0, + "m": 0.3069395422935486 + }, + { + "p": 0.0, + "m": 0.9651272892951965 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 166, + "train_loss": 0.9280370473861694, + "val_loss": 1.061629056930542, + "acc": 66.07, + "time": 349.9617935290007, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9792888164520264 + }, + { + "p": 0.0, + "m": 0.8407569527626038 + }, + { + "p": 0.0, + "m": 0.9323557019233704 + }, + { + "p": 2.1897808437643107e-06, + "m": 0.9966567754745483 + }, + { + "p": 0.0, + "m": 0.9541796445846558 + }, + { + "p": 0.0, + "m": 0.996833324432373 + }, + { + "p": 0.0, + "m": 0.9796203374862671 + }, + { + "p": 0.9999977946281433, + "m": 0.36703115701675415 + }, + { + "p": 0.0, + "m": 0.9590557813644409 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 167, + "train_loss": 0.685837984085083, + "val_loss": 0.9778453707695007, + "acc": 65.67, + "time": 347.8491906840063, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.66657954454422 + }, + { + "p": 0.0, + "m": 0.870646059513092 + }, + { + "p": 0.0019194085616618395, + "m": 0.9698532223701477 + }, + { + "p": 0.0, + "m": 0.9247902631759644 + }, + { + "p": 0.0, + "m": 0.9255308508872986 + }, + { + "p": 0.007061212323606014, + "m": 0.9593043923377991 + }, + { + "p": 0.9841082692146301, + "m": 0.3726041316986084 + }, + { + "p": 0.006911070551723242, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 168, + "train_loss": 0.9127511978149414, + "val_loss": 1.0652623176574707, + "acc": 65.74, + "time": 350.5374764120061, + "param": [ + { + "p": 0.0006572242127731442, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.005899235140532255, + "m": 0.9977657198905945 + }, + { + "p": 0.0, + "m": 0.6473227739334106 + }, + { + "p": 0.0, + "m": 0.8979516625404358 + }, + { + "p": 0.0, + "m": 0.9662899374961853 + }, + { + "p": 0.004348836373537779, + "m": 0.9581362009048462 + }, + { + "p": 0.0334109365940094, + "m": 0.888931393623352 + }, + { + "p": 0.009173444472253323, + "m": 0.9324641227722168 + }, + { + "p": 0.946510374546051, + "m": 0.36838915944099426 + }, + { + "p": 0.0, + "m": 0.9956223368644714 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 169, + "train_loss": 1.0025930404663086, + "val_loss": 1.1226210594177246, + "acc": 65.92, + "time": 355.83308200699685, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9736753106117249 + }, + { + "p": 0.0, + "m": 0.7596516609191895 + }, + { + "p": 0.0, + "m": 0.9174155592918396 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.002782121766358614, + "m": 0.9565474987030029 + }, + { + "p": 0.0, + "m": 0.9567195177078247 + }, + { + "p": 0.0, + "m": 0.960940957069397 + }, + { + "p": 0.9972178339958191, + "m": 0.4391520619392395 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 170, + "train_loss": 1.0151171684265137, + "val_loss": 0.9760214686393738, + "acc": 66.07, + "time": 352.0427127689909, + "param": [ + { + "p": 0.0278985146433115, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9177547097206116 + }, + { + "p": 0.0, + "m": 0.7722155451774597 + }, + { + "p": 0.0, + "m": 0.9532207250595093 + }, + { + "p": 0.01234503649175167, + "m": 0.9489313960075378 + }, + { + "p": 0.0, + "m": 0.9134966135025024 + }, + { + "p": 0.0025385546032339334, + "m": 0.9513339400291443 + }, + { + "p": 0.0, + "m": 0.8984655141830444 + }, + { + "p": 0.9571706652641296, + "m": 0.37945136427879333 + }, + { + "p": 4.7289700887631625e-05, + "m": 0.9488560557365417 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 171, + "train_loss": 0.704319953918457, + "val_loss": 0.912255585193634, + "acc": 66.73, + "time": 348.52507405600045, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8726407885551453 + }, + { + "p": 0.0, + "m": 0.7378756999969482 + }, + { + "p": 0.0, + "m": 0.8536967635154724 + }, + { + "p": 0.0, + "m": 0.995424211025238 + }, + { + "p": 0.0, + "m": 0.8965680003166199 + }, + { + "p": 0.002143298042938113, + "m": 1.0 + }, + { + "p": 0.011754196137189865, + "m": 0.9147876501083374 + }, + { + "p": 0.9813923835754395, + "m": 0.4030878245830536 + }, + { + "p": 0.0032610278576612473, + "m": 0.927318274974823 + }, + { + "p": 0.0014490751782432199, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 172, + "train_loss": 0.6466277241706848, + "val_loss": 0.9165001511573792, + "acc": 66.68, + "time": 354.40299776999746, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.028459517285227776, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9088626503944397 + }, + { + "p": 0.0, + "m": 0.523591935634613 + }, + { + "p": 0.0, + "m": 0.750659704208374 + }, + { + "p": 0.0, + "m": 0.9961528778076172 + }, + { + "p": 0.0, + "m": 0.9046663045883179 + }, + { + "p": 0.0, + "m": 0.9479154944419861 + }, + { + "p": 0.02832910791039467, + "m": 1.0 + }, + { + "p": 0.9432113766670227, + "m": 0.37806418538093567 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 173, + "train_loss": 0.8836069703102112, + "val_loss": 1.0314477682113647, + "acc": 67.24, + "time": 351.4680122819991, + "param": [ + { + "p": 0.00475815124809742, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.5127174258232117 + }, + { + "p": 0.0, + "m": 0.7688086628913879 + }, + { + "p": 0.0, + "m": 0.9079436659812927 + }, + { + "p": 0.0, + "m": 0.9239621758460999 + }, + { + "p": 0.003945551812648773, + "m": 0.9757905602455139 + }, + { + "p": 0.03478240966796875, + "m": 1.0 + }, + { + "p": 0.9565138220787048, + "m": 0.4067247807979584 + }, + { + "p": 0.0, + "m": 0.9825880527496338 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 174, + "train_loss": 0.6275290250778198, + "val_loss": 0.9917266964912415, + "acc": 66.91, + "time": 349.7408012759988, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0007563678664155304, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9885156154632568 + }, + { + "p": 0.0, + "m": 0.5726102590560913 + }, + { + "p": 0.0, + "m": 0.9093862175941467 + }, + { + "p": 0.013447052799165249, + "m": 0.8211304545402527 + }, + { + "p": 0.028278028592467308, + "m": 0.9477972388267517 + }, + { + "p": 0.03268889710307121, + "m": 1.0 + }, + { + "p": 0.016206296160817146, + "m": 0.9917542338371277 + }, + { + "p": 0.9086233973503113, + "m": 0.3821534514427185 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 175, + "train_loss": 0.6875118017196655, + "val_loss": 1.00272798538208, + "acc": 66.94, + "time": 356.37716577098763, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.000352883042069152, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.003101082518696785, + "m": 0.5953138470649719 + }, + { + "p": 0.0, + "m": 0.9423335790634155 + }, + { + "p": 0.0, + "m": 0.9607581496238708 + }, + { + "p": 0.0, + "m": 0.9997569918632507 + }, + { + "p": 0.08897271752357483, + "m": 1.0 + }, + { + "p": 0.0021203598007559776, + "m": 0.946679949760437 + }, + { + "p": 0.8752805590629578, + "m": 0.3933261036872864 + }, + { + "p": 0.0007281972793862224, + "m": 0.9643486142158508 + }, + { + "p": 0.02944415807723999, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 176, + "train_loss": 0.830325722694397, + "val_loss": 0.976344645023346, + "acc": 66.55, + "time": 350.71472902499954, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.5038725137710571 + }, + { + "p": 0.0, + "m": 0.992518424987793 + }, + { + "p": 0.0, + "m": 0.9229275584220886 + }, + { + "p": 0.0, + "m": 0.9946931004524231 + }, + { + "p": 0.0, + "m": 0.9030969142913818 + }, + { + "p": 0.0, + "m": 0.9990718364715576 + }, + { + "p": 1.0, + "m": 0.40832197666168213 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 177, + "train_loss": 0.9163617491722107, + "val_loss": 0.945801854133606, + "acc": 66.6, + "time": 348.0250058740057, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0009222933440469205, + "m": 0.8443523049354553 + }, + { + "p": 0.0, + "m": 0.4953317940235138 + }, + { + "p": 0.0, + "m": 0.9480120539665222 + }, + { + "p": 0.0002766222460195422, + "m": 0.9656386375427246 + }, + { + "p": 0.011964913457632065, + "m": 0.8171668648719788 + }, + { + "p": 0.004155043046921492, + "m": 0.9428068995475769 + }, + { + "p": 0.014342593960464, + "m": 0.9100260138511658 + }, + { + "p": 0.9683384895324707, + "m": 0.42214739322662354 + }, + { + "p": 0.0, + "m": 0.9818283319473267 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 178, + "train_loss": 0.7520712018013, + "val_loss": 1.0113016366958618, + "acc": 66.9, + "time": 356.14720307198877, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8836724758148193 + }, + { + "p": 0.0, + "m": 0.5880455374717712 + }, + { + "p": 0.0, + "m": 0.9074724912643433 + }, + { + "p": 0.0, + "m": 0.999625563621521 + }, + { + "p": 0.0, + "m": 0.9100646376609802 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.006837640888988972, + "m": 0.9436371326446533 + }, + { + "p": 0.9931623339653015, + "m": 0.4583768844604492 + }, + { + "p": 0.0, + "m": 0.9976795315742493 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 179, + "train_loss": 0.9010050892829895, + "val_loss": 0.9523979425430298, + "acc": 66.79, + "time": 350.6995970400021, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.977615475654602 + }, + { + "p": 0.0, + "m": 0.5164128541946411 + }, + { + "p": 0.0, + "m": 0.8894430994987488 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8710920214653015 + }, + { + "p": 0.0, + "m": 0.9997336268424988 + }, + { + "p": 0.0, + "m": 0.9944502711296082 + }, + { + "p": 0.9940759539604187, + "m": 0.5016496777534485 + }, + { + "p": 0.0, + "m": 0.9721684455871582 + }, + { + "p": 0.005924070253968239, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 180, + "train_loss": 0.7519791722297668, + "val_loss": 0.9182561039924622, + "acc": 66.67, + "time": 350.159498944995, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.001414826954714954, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9301642775535583 + }, + { + "p": 0.0, + "m": 0.44320425391197205 + }, + { + "p": 0.0, + "m": 0.9435281157493591 + }, + { + "p": 0.0, + "m": 0.9059385657310486 + }, + { + "p": 0.0, + "m": 0.9182944297790527 + }, + { + "p": 0.0391046516597271, + "m": 0.7540237307548523 + }, + { + "p": 0.025421254336833954, + "m": 0.8851344585418701 + }, + { + "p": 0.9340592622756958, + "m": 0.5970514416694641 + }, + { + "p": 0.0, + "m": 0.923018753528595 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 181, + "train_loss": 1.0187900066375732, + "val_loss": 0.9884350895881653, + "acc": 66.85, + "time": 351.1400024790055, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.00448724115267396, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.36829328536987305 + }, + { + "p": 0.0, + "m": 0.9273127913475037 + }, + { + "p": 0.0037842788733541965, + "m": 0.9287416934967041 + }, + { + "p": 0.0, + "m": 0.9449560046195984 + }, + { + "p": 0.010433828458189964, + "m": 0.8266562819480896 + }, + { + "p": 0.0, + "m": 0.9999691843986511 + }, + { + "p": 0.9782907366752625, + "m": 0.6917887330055237 + }, + { + "p": 0.003003988415002823, + "m": 0.9481704235076904 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 182, + "train_loss": 1.0299736261367798, + "val_loss": 1.083288550376892, + "acc": 66.6, + "time": 360.742216093, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.04583330452442169, + "m": 1.0 + }, + { + "p": 0.013506684452295303, + "m": 1.0 + }, + { + "p": 0.026800019666552544, + "m": 0.5692886710166931 + }, + { + "p": 0.0, + "m": 0.8073465824127197 + }, + { + "p": 0.0, + "m": 0.8996891975402832 + }, + { + "p": 0.0, + "m": 0.9643684029579163 + }, + { + "p": 0.0030966969206929207, + "m": 0.8866458535194397 + }, + { + "p": 0.04882609471678734, + "m": 1.0 + }, + { + "p": 0.834577202796936, + "m": 0.7883520126342773 + }, + { + "p": 0.00015225476818159223, + "m": 0.976662278175354 + }, + { + "p": 0.0272077564150095, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 183, + "train_loss": 0.7220801115036011, + "val_loss": 1.0523252487182617, + "acc": 67.54, + "time": 357.9834233970032, + "param": [ + { + "p": 0.006639439146965742, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.009075462818145752, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8424704670906067 + }, + { + "p": 0.0, + "m": 0.560758650302887 + }, + { + "p": 0.0, + "m": 0.7996054291725159 + }, + { + "p": 0.0, + "m": 0.9159884452819824 + }, + { + "p": 0.0, + "m": 0.9697225689888 + }, + { + "p": 0.0, + "m": 0.8967770934104919 + }, + { + "p": 0.0, + "m": 0.9018368721008301 + }, + { + "p": 0.9842851161956787, + "m": 0.8324344158172607 + }, + { + "p": 0.0, + "m": 0.8681449294090271 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 184, + "train_loss": 0.8447906970977783, + "val_loss": 0.9327510595321655, + "acc": 66.94, + "time": 350.3811515550042, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8396484851837158 + }, + { + "p": 0.0, + "m": 0.6781979203224182 + }, + { + "p": 0.0, + "m": 0.8004588484764099 + }, + { + "p": 0.0, + "m": 0.8184089660644531 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.013347038067877293, + "m": 0.982352614402771 + }, + { + "p": 0.03261171281337738, + "m": 0.9929338693618774 + }, + { + "p": 0.9540412425994873, + "m": 0.7451016902923584 + }, + { + "p": 0.0, + "m": 0.9579471945762634 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 185, + "train_loss": 0.9128320217132568, + "val_loss": 0.9066159725189209, + "acc": 67.29, + "time": 352.01997729198774, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9476720690727234 + }, + { + "p": 0.0, + "m": 0.693662703037262 + }, + { + "p": 0.0, + "m": 0.8191081881523132 + }, + { + "p": 0.0, + "m": 0.800645649433136 + }, + { + "p": 0.0, + "m": 0.9671154022216797 + }, + { + "p": 0.10788021236658096, + "m": 0.7553505897521973 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.8921197652816772, + "m": 0.6747942566871643 + }, + { + "p": 0.0, + "m": 0.9035367369651794 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 186, + "train_loss": 0.610500156879425, + "val_loss": 0.9579245448112488, + "acc": 67.81, + "time": 349.88644395797746, + "param": [ + { + "p": 0.0009730386082082987, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0017869232688099146, + "m": 0.9969682693481445 + }, + { + "p": 0.0, + "m": 0.7505832314491272 + }, + { + "p": 0.0, + "m": 0.8103500604629517 + }, + { + "p": 0.0, + "m": 0.8026546239852905 + }, + { + "p": 0.0, + "m": 0.9642780423164368 + }, + { + "p": 0.10147234797477722, + "m": 0.8598146438598633 + }, + { + "p": 0.021712416782975197, + "m": 1.0 + }, + { + "p": 0.8740552663803101, + "m": 0.7410706281661987 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 187, + "train_loss": 0.9698807597160339, + "val_loss": 0.9592393040657043, + "acc": 67.49, + "time": 351.1166015799972, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8569085597991943 + }, + { + "p": 0.0, + "m": 0.764346718788147 + }, + { + "p": 0.0, + "m": 0.8583520650863647 + }, + { + "p": 0.0, + "m": 0.8233702778816223 + }, + { + "p": 0.0, + "m": 0.990715503692627 + }, + { + "p": 0.0, + "m": 0.9377344846725464 + }, + { + "p": 0.0, + "m": 0.9969844818115234 + }, + { + "p": 0.9977551102638245, + "m": 0.8052107095718384 + }, + { + "p": 0.002244846662506461, + "m": 0.963729739189148 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 188, + "train_loss": 0.8334795832633972, + "val_loss": 0.9292126893997192, + "acc": 65.99, + "time": 350.2643813689938, + "param": [ + { + "p": 0.005797572433948517, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.04362163692712784, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8262969255447388 + }, + { + "p": 0.0, + "m": 0.9607605934143066 + }, + { + "p": 0.007350051775574684, + "m": 0.9424358606338501 + }, + { + "p": 0.0, + "m": 0.9261049628257751 + }, + { + "p": 0.027737697586417198, + "m": 0.9877888560295105 + }, + { + "p": 0.0, + "m": 0.9550193548202515 + }, + { + "p": 0.9088085889816284, + "m": 0.8318244814872742 + }, + { + "p": 0.006684441119432449, + "m": 0.946098804473877 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 189, + "train_loss": 0.8921919465065002, + "val_loss": 1.040013074874878, + "acc": 67.49, + "time": 350.6800950820034, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9353659749031067 + }, + { + "p": 0.0, + "m": 0.8333529233932495 + }, + { + "p": 0.0, + "m": 0.7761299014091492 + }, + { + "p": 0.0, + "m": 0.9933372139930725 + }, + { + "p": 0.0, + "m": 0.986411988735199 + }, + { + "p": 0.0, + "m": 0.915416419506073 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.9970992803573608, + "m": 0.8985147476196289 + }, + { + "p": 0.0, + "m": 0.9992964267730713 + }, + { + "p": 0.0029007650446146727, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 190, + "train_loss": 0.6248729825019836, + "val_loss": 0.8147443532943726, + "acc": 67.29, + "time": 349.35050324798794, + "param": [ + { + "p": 0.007149883080273867, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8557021617889404 + }, + { + "p": 0.0, + "m": 0.6952973008155823 + }, + { + "p": 0.0, + "m": 0.9813786149024963 + }, + { + "p": 0.0, + "m": 0.9616351127624512 + }, + { + "p": 0.0005833911709487438, + "m": 0.9565670490264893 + }, + { + "p": 0.0, + "m": 0.9976902604103088 + }, + { + "p": 0.9773619771003723, + "m": 0.8259230852127075 + }, + { + "p": 0.0, + "m": 0.9644985795021057 + }, + { + "p": 0.014904727227985859, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 191, + "train_loss": 0.8008970022201538, + "val_loss": 0.9113245606422424, + "acc": 67.84, + "time": 352.9062490659999, + "param": [ + { + "p": 0.000299356208415702, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9416027069091797 + }, + { + "p": 0.0, + "m": 0.9177805781364441 + }, + { + "p": 0.0, + "m": 0.6888850927352905 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9974547624588013 + }, + { + "p": 0.0, + "m": 0.9989689588546753 + }, + { + "p": 0.975938618183136, + "m": 0.8931012153625488 + }, + { + "p": 0.0, + "m": 0.968241274356842 + }, + { + "p": 0.023762013763189316, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 192, + "train_loss": 0.7863667011260986, + "val_loss": 1.0486462116241455, + "acc": 67.12, + "time": 348.47078572699684, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.004197028465569019, + "m": 1.0 + }, + { + "p": 0.004378775134682655, + "m": 0.938311755657196 + }, + { + "p": 0.0, + "m": 0.941575288772583 + }, + { + "p": 0.0, + "m": 0.733376145362854 + }, + { + "p": 0.0, + "m": 0.9259237051010132 + }, + { + "p": 0.00042855096398852766, + "m": 0.9984211921691895 + }, + { + "p": 0.0, + "m": 0.9667032957077026 + }, + { + "p": 0.0, + "m": 0.993665874004364 + }, + { + "p": 0.9893345236778259, + "m": 0.8953465223312378 + }, + { + "p": 0.0016611102037131786, + "m": 0.9966086745262146 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 193, + "train_loss": 0.6712859869003296, + "val_loss": 0.9560391306877136, + "acc": 67.7, + "time": 348.62161626300076, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.00042931665666401386, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9411839246749878 + }, + { + "p": 0.0, + "m": 0.9374734163284302 + }, + { + "p": 0.0, + "m": 0.7773698568344116 + }, + { + "p": 0.0, + "m": 0.9296632409095764 + }, + { + "p": 0.0, + "m": 0.9430310726165771 + }, + { + "p": 0.04554126039147377, + "m": 0.9226675033569336 + }, + { + "p": 0.0004982610116712749, + "m": 1.0 + }, + { + "p": 0.9367700219154358, + "m": 0.8466227650642395 + }, + { + "p": 0.0, + "m": 0.9964168667793274 + }, + { + "p": 0.016761187463998795, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 194, + "train_loss": 1.010571002960205, + "val_loss": 0.9586449265480042, + "acc": 67.62, + "time": 349.6865455340012, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.005559447221457958, + "m": 0.901840329170227 + }, + { + "p": 0.0, + "m": 0.9709093570709229 + }, + { + "p": 0.0, + "m": 0.8088489770889282 + }, + { + "p": 0.0, + "m": 0.9181555509567261 + }, + { + "p": 0.0, + "m": 0.9859821796417236 + }, + { + "p": 0.039362501353025436, + "m": 0.7779850363731384 + }, + { + "p": 0.0, + "m": 0.9585095047950745 + }, + { + "p": 0.9499511122703552, + "m": 0.8263463973999023 + }, + { + "p": 0.0, + "m": 0.9544174075126648 + }, + { + "p": 0.005126993637531996, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 195, + "train_loss": 0.8270485401153564, + "val_loss": 1.0313221216201782, + "acc": 67.32, + "time": 347.27522184400004, + "param": [ + { + "p": 0.0007355239940807223, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9819119572639465 + }, + { + "p": 0.0, + "m": 0.9697327017784119 + }, + { + "p": 0.0, + "m": 0.854897677898407 + }, + { + "p": 0.0004308822099119425, + "m": 0.9880890846252441 + }, + { + "p": 0.0002997691626660526, + "m": 0.9198796153068542 + }, + { + "p": 0.13208819925785065, + "m": 0.8953080177307129 + }, + { + "p": 0.0, + "m": 0.9988037347793579 + }, + { + "p": 0.8664456009864807, + "m": 0.7614929676055908 + }, + { + "p": 0.0, + "m": 0.9507588744163513 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 196, + "train_loss": 0.6830359697341919, + "val_loss": 0.9906006455421448, + "acc": 66.86, + "time": 356.9095091850031, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0038682010490447283, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9972213506698608 + }, + { + "p": 0.0, + "m": 0.9111630916595459 + }, + { + "p": 0.0, + "m": 0.9006194472312927 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.9579883217811584 + }, + { + "p": 0.07345842570066452, + "m": 0.8722522854804993 + }, + { + "p": 0.0, + "m": 0.9949038624763489 + }, + { + "p": 0.9226733446121216, + "m": 0.8086898326873779 + }, + { + "p": 0.0, + "m": 0.9651023745536804 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 197, + "train_loss": 0.7339588403701782, + "val_loss": 0.9834372997283936, + "acc": 67.92, + "time": 349.0224158260098, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8741854429244995 + }, + { + "p": 0.0, + "m": 0.7519800066947937 + }, + { + "p": 0.0, + "m": 0.9635024666786194 + }, + { + "p": 0.0, + "m": 0.9215209484100342 + }, + { + "p": 0.0, + "m": 0.9304232597351074 + }, + { + "p": 0.0, + "m": 0.8317615389823914 + }, + { + "p": 0.0, + "m": 0.998441219329834 + }, + { + "p": 1.0, + "m": 0.9183470606803894 + }, + { + "p": 0.0, + "m": 0.9633950591087341 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 198, + "train_loss": 1.0041497945785522, + "val_loss": 0.9337962865829468, + "acc": 68.24, + "time": 354.48014193799463, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8819610476493835 + }, + { + "p": 0.0, + "m": 0.7853930592536926 + }, + { + "p": 0.0, + "m": 0.9730435609817505 + }, + { + "p": 0.0, + "m": 0.8474541306495667 + }, + { + "p": 0.0, + "m": 0.8852977752685547 + }, + { + "p": 0.0012849566992372274, + "m": 0.844380795955658 + }, + { + "p": 0.0, + "m": 0.9605855941772461 + }, + { + "p": 0.9987150430679321, + "m": 0.8380168080329895 + }, + { + "p": 0.0, + "m": 0.9528668522834778 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 199, + "train_loss": 0.7571397423744202, + "val_loss": 0.9003289937973022, + "acc": 67.45, + "time": 350.0930762079952, + "param": [ + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.8629175424575806 + }, + { + "p": 0.0, + "m": 0.8392125964164734 + }, + { + "p": 0.0, + "m": 0.9415297508239746 + }, + { + "p": 0.0, + "m": 0.9707351326942444 + }, + { + "p": 0.0, + "m": 0.8579850196838379 + }, + { + "p": 0.0, + "m": 0.9424062967300415 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 1.0, + "m": 0.9397035837173462 + }, + { + "p": 0.0, + "m": 0.9802203178405762 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + }, + { + "epoch": 200, + "train_loss": 1.0510199069976807, + "val_loss": 0.9292319416999817, + "acc": 68.63, + "time": 351.89166626601946, + "param": [ + { + "p": 0.004005872644484043, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.05766316130757332, + "m": 1.0 + }, + { + "p": 0.0, + "m": 0.874803364276886 + }, + { + "p": 0.0, + "m": 0.8122043013572693 + }, + { + "p": 0.0, + "m": 0.994288444519043 + }, + { + "p": 0.009112370200455189, + "m": 0.9426521062850952 + }, + { + "p": 0.02242703177034855, + "m": 0.7338101267814636 + }, + { + "p": 0.07805930078029633, + "m": 0.8863920569419861 + }, + { + "p": 0.009542860090732574, + "m": 0.9319488406181335 + }, + { + "p": 0.8146741390228271, + "m": 0.959484875202179 + }, + { + "p": 0.0, + "m": 1.0 + }, + { + "p": 0.004515284672379494, + "m": 1.0 + }, + { + "p": 0.0, + "m": 1.0 + } + ] + } + ] +} \ No newline at end of file diff --git a/higher/res/log/Aug_mod(RandAug(14TFx2-Mag1)-MobileNetV2)-200 epochs (dataug:0)- 1 in_it.json b/higher/res/log/Aug_mod(RandAug(14TFx2-Mag1)-MobileNetV2)-200 epochs (dataug:0)- 1 in_it.json new file mode 100644 index 0000000..fce2500 --- /dev/null +++ b/higher/res/log/Aug_mod(RandAug(14TFx2-Mag1)-MobileNetV2)-200 epochs (dataug:0)- 1 in_it.json @@ -0,0 +1,13027 @@ +{ + "Accuracy": 64.7, + "Time": [ + 328.50648838121, + 3.8849018828380215, + 65939.00515182901 + ], + "Device": "Graphics Device", + "Param_names": [ + "Identity", + "FlipUD", + "FlipLR", + "Rotate", + "TranslateX", + "TranslateY", + "ShearX", + "ShearY", + "Contrast", + "Color", + "Brightness", + "Sharpness", + "Posterize", + "Solarize" + ], + "Log": [ + { + "epoch": 1, + "train_loss": 2.1605660915374756, + "val_loss": 2.0908026695251465, + "acc": 22.5, + "time": 327.54313821100004, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 2, + "train_loss": 2.0863418579101562, + "val_loss": 1.8005388975143433, + "acc": 32.33, + "time": 326.738800808, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 3, + "train_loss": 1.8738940954208374, + "val_loss": 1.6385836601257324, + "acc": 39.99, + "time": 324.130651941, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 4, + "train_loss": 1.8960946798324585, + "val_loss": 1.655354619026184, + "acc": 41.81, + "time": 335.59277711100003, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 5, + "train_loss": 1.9413517713546753, + "val_loss": 1.5779296159744263, + "acc": 42.61, + "time": 327.83270627699994, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 6, + "train_loss": 1.632403016090393, + "val_loss": 1.5945156812667847, + "acc": 44.45, + "time": 329.90467142800003, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 7, + "train_loss": 1.661102294921875, + "val_loss": 1.5905389785766602, + "acc": 46.01, + "time": 326.895761711, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 8, + "train_loss": 1.725764513015747, + "val_loss": 1.5912178754806519, + "acc": 46.22, + "time": 328.41825980800013, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 9, + "train_loss": 1.6860371828079224, + "val_loss": 1.6003594398498535, + "acc": 46.91, + "time": 328.900006846, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 10, + "train_loss": 1.4485887289047241, + "val_loss": 1.4340641498565674, + "acc": 46.79, + "time": 325.12490209, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 11, + "train_loss": 1.7375695705413818, + "val_loss": 1.4323976039886475, + "acc": 49.91, + "time": 329.96220456100036, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 12, + "train_loss": 1.4723776578903198, + "val_loss": 1.4863096475601196, + "acc": 48.34, + "time": 328.6292437940001, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 13, + "train_loss": 1.66366708278656, + "val_loss": 1.3886080980300903, + "acc": 50.62, + "time": 336.13389199699986, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 14, + "train_loss": 1.5438792705535889, + "val_loss": 1.500520944595337, + "acc": 43.98, + "time": 329.37133093100056, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 15, + "train_loss": 1.4693797826766968, + "val_loss": 1.4468894004821777, + "acc": 50.47, + "time": 326.99491073599984, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 16, + "train_loss": 1.4916006326675415, + "val_loss": 1.5008386373519897, + "acc": 50.45, + "time": 330.0061660749998, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 17, + "train_loss": 1.587764859199524, + "val_loss": 1.4069267511367798, + "acc": 51.69, + "time": 327.7060299149998, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 18, + "train_loss": 1.3860106468200684, + "val_loss": 1.3097182512283325, + "acc": 51.99, + "time": 324.5873633049996, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 19, + "train_loss": 1.315377950668335, + "val_loss": 1.3324623107910156, + "acc": 52.76, + "time": 330.33135422899977, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 20, + "train_loss": 1.4037600755691528, + "val_loss": 1.346764087677002, + "acc": 53.1, + "time": 327.23624969699995, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 21, + "train_loss": 1.289520502090454, + "val_loss": 1.348233938217163, + "acc": 52.61, + "time": 327.7733956709999, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 22, + "train_loss": 1.4605226516723633, + "val_loss": 1.2516907453536987, + "acc": 52.85, + "time": 327.0519693590004, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 23, + "train_loss": 1.3587477207183838, + "val_loss": 1.3835750818252563, + "acc": 54.04, + "time": 325.0718763730001, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 24, + "train_loss": 1.207484245300293, + "val_loss": 1.3379106521606445, + "acc": 54.81, + "time": 328.2252022679995, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 25, + "train_loss": 1.448989748954773, + "val_loss": 1.459587574005127, + "acc": 53.62, + "time": 325.8128038749992, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 26, + "train_loss": 1.2800248861312866, + "val_loss": 1.3092739582061768, + "acc": 54.12, + "time": 328.8798149970007, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 27, + "train_loss": 1.2440526485443115, + "val_loss": 1.3332312107086182, + "acc": 53.26, + "time": 324.44791356399946, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 28, + "train_loss": 1.2498626708984375, + "val_loss": 1.2817291021347046, + "acc": 55.03, + "time": 329.9223157310007, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 29, + "train_loss": 1.3368630409240723, + "val_loss": 1.2988530397415161, + "acc": 54.73, + "time": 328.4627980749992, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 30, + "train_loss": 1.2821359634399414, + "val_loss": 1.350172758102417, + "acc": 54.22, + "time": 327.9539070619994, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 31, + "train_loss": 1.1373648643493652, + "val_loss": 1.2770923376083374, + "acc": 53.15, + "time": 327.55974387599963, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 32, + "train_loss": 1.1613913774490356, + "val_loss": 1.3531378507614136, + "acc": 53.21, + "time": 326.9317487090011, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 33, + "train_loss": 1.199904441833496, + "val_loss": 1.3560826778411865, + "acc": 54.67, + "time": 329.56129924700144, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 34, + "train_loss": 1.113755702972412, + "val_loss": 1.3168400526046753, + "acc": 54.01, + "time": 331.2491397349986, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 35, + "train_loss": 1.2225736379623413, + "val_loss": 1.3518950939178467, + "acc": 54.46, + "time": 338.9124596310012, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 36, + "train_loss": 1.3000739812850952, + "val_loss": 1.2826594114303589, + "acc": 55.52, + "time": 337.9177408840005, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 37, + "train_loss": 1.2222378253936768, + "val_loss": 1.3207976818084717, + "acc": 54.76, + "time": 327.473589483001, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 38, + "train_loss": 1.1848640441894531, + "val_loss": 1.3520840406417847, + "acc": 54.13, + "time": 337.4954506989998, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 39, + "train_loss": 1.1297775506973267, + "val_loss": 1.385704755783081, + "acc": 54.93, + "time": 327.2285476240013, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 40, + "train_loss": 1.1577649116516113, + "val_loss": 1.2828705310821533, + "acc": 54.99, + "time": 329.29353077799897, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 41, + "train_loss": 1.1142334938049316, + "val_loss": 1.3944318294525146, + "acc": 55.71, + "time": 330.784749597, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 42, + "train_loss": 1.2722597122192383, + "val_loss": 1.2696136236190796, + "acc": 56.3, + "time": 327.00040673699914, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 43, + "train_loss": 1.1527506113052368, + "val_loss": 1.2710145711898804, + "acc": 56.1, + "time": 327.34729718000017, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 44, + "train_loss": 1.0189549922943115, + "val_loss": 1.1679898500442505, + "acc": 55.49, + "time": 325.236595503, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 45, + "train_loss": 0.9858098030090332, + "val_loss": 1.2826380729675293, + "acc": 56.27, + "time": 327.57157320499937, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 46, + "train_loss": 1.039755940437317, + "val_loss": 1.2959702014923096, + "acc": 54.77, + "time": 330.53730243099926, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 47, + "train_loss": 0.9982386827468872, + "val_loss": 1.22615385055542, + "acc": 56.62, + "time": 340.18704979700124, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 48, + "train_loss": 1.0106613636016846, + "val_loss": 1.3643922805786133, + "acc": 56.84, + "time": 338.4561849460006, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 49, + "train_loss": 1.0268144607543945, + "val_loss": 1.3010021448135376, + "acc": 56.11, + "time": 327.4064399079998, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 50, + "train_loss": 0.9989897012710571, + "val_loss": 1.242766261100769, + "acc": 55.39, + "time": 325.27060617700226, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 51, + "train_loss": 1.048647165298462, + "val_loss": 1.328948736190796, + "acc": 56.49, + "time": 329.88501188900045, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 52, + "train_loss": 1.0447413921356201, + "val_loss": 1.2260189056396484, + "acc": 57.01, + "time": 333.1939668909981, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 53, + "train_loss": 1.1568272113800049, + "val_loss": 1.2852314710617065, + "acc": 57.81, + "time": 330.73923411899887, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 54, + "train_loss": 1.1548278331756592, + "val_loss": 1.2612714767456055, + "acc": 56.06, + "time": 328.7314609540008, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 55, + "train_loss": 1.0098786354064941, + "val_loss": 1.263266682624817, + "acc": 57.56, + "time": 328.4338283629986, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 56, + "train_loss": 1.0889136791229248, + "val_loss": 1.3348058462142944, + "acc": 57.52, + "time": 328.7215751909971, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 57, + "train_loss": 1.0030834674835205, + "val_loss": 1.2665817737579346, + "acc": 56.99, + "time": 324.43350832099895, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 58, + "train_loss": 1.092786431312561, + "val_loss": 1.139597773551941, + "acc": 57.66, + "time": 326.26640913699885, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 59, + "train_loss": 0.9376211166381836, + "val_loss": 1.2189905643463135, + "acc": 57.05, + "time": 328.18833179899957, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 60, + "train_loss": 0.9396794438362122, + "val_loss": 1.2766376733779907, + "acc": 57.09, + "time": 331.11691632699876, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 61, + "train_loss": 0.8964793086051941, + "val_loss": 1.2524635791778564, + "acc": 56.94, + "time": 326.85803442999895, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 62, + "train_loss": 0.9941749572753906, + "val_loss": 1.3157291412353516, + "acc": 57.89, + "time": 329.04555512599836, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 63, + "train_loss": 1.0906386375427246, + "val_loss": 1.2301567792892456, + "acc": 58.1, + "time": 340.77401647900115, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 64, + "train_loss": 1.120635986328125, + "val_loss": 1.1767842769622803, + "acc": 58.43, + "time": 327.6372278130002, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 65, + "train_loss": 0.9877512454986572, + "val_loss": 1.0958484411239624, + "acc": 58.75, + "time": 325.9885613369988, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 66, + "train_loss": 1.0788336992263794, + "val_loss": 1.269873857498169, + "acc": 57.51, + "time": 326.7021351639996, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 67, + "train_loss": 0.9524862766265869, + "val_loss": 1.2803163528442383, + "acc": 57.95, + "time": 326.0696568610001, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 68, + "train_loss": 0.938431978225708, + "val_loss": 1.2206318378448486, + "acc": 57.6, + "time": 327.7635417349993, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 69, + "train_loss": 0.9699055552482605, + "val_loss": 1.157589316368103, + "acc": 58.6, + "time": 327.4193251739998, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 70, + "train_loss": 0.9798736572265625, + "val_loss": 1.267886996269226, + "acc": 58.06, + "time": 328.5502017660001, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 71, + "train_loss": 0.8733229637145996, + "val_loss": 1.2521255016326904, + "acc": 58.31, + "time": 331.9274948130005, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 72, + "train_loss": 0.9236745834350586, + "val_loss": 1.2017830610275269, + "acc": 57.89, + "time": 327.8872478150006, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 73, + "train_loss": 0.9752246141433716, + "val_loss": 1.2135027647018433, + "acc": 58.83, + "time": 330.429690039, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 74, + "train_loss": 1.0682045221328735, + "val_loss": 1.2048929929733276, + "acc": 59.07, + "time": 326.70809863400063, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 75, + "train_loss": 0.9331793189048767, + "val_loss": 1.232511043548584, + "acc": 58.93, + "time": 326.54373703999954, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 76, + "train_loss": 1.0415458679199219, + "val_loss": 1.2008339166641235, + "acc": 58.39, + "time": 326.6501678530003, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 77, + "train_loss": 0.9624651670455933, + "val_loss": 1.2789406776428223, + "acc": 58.97, + "time": 334.32713766699817, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 78, + "train_loss": 1.063321828842163, + "val_loss": 1.0991851091384888, + "acc": 59.47, + "time": 332.32249451700045, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 79, + "train_loss": 0.9210697412490845, + "val_loss": 1.1965128183364868, + "acc": 59.14, + "time": 328.42731822099813, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 80, + "train_loss": 1.1494228839874268, + "val_loss": 1.1561839580535889, + "acc": 59.16, + "time": 341.4050483239989, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 81, + "train_loss": 0.8988142013549805, + "val_loss": 1.2378394603729248, + "acc": 59.47, + "time": 325.30504704600025, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 82, + "train_loss": 1.098860740661621, + "val_loss": 1.1615720987319946, + "acc": 59.92, + "time": 339.8855297579976, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 83, + "train_loss": 1.0270111560821533, + "val_loss": 1.1863383054733276, + "acc": 58.82, + "time": 327.6578483029989, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 84, + "train_loss": 0.937251091003418, + "val_loss": 1.1688398122787476, + "acc": 59.24, + "time": 333.5864222240016, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 85, + "train_loss": 0.9028213620185852, + "val_loss": 1.1239500045776367, + "acc": 59.75, + "time": 327.128969096997, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 86, + "train_loss": 0.9716721773147583, + "val_loss": 1.238286018371582, + "acc": 57.97, + "time": 332.47698641900206, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 87, + "train_loss": 0.9638515710830688, + "val_loss": 1.2760862112045288, + "acc": 59.8, + "time": 328.6064031040005, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 88, + "train_loss": 0.914188027381897, + "val_loss": 1.209351658821106, + "acc": 59.83, + "time": 327.4812875989992, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 89, + "train_loss": 0.9449980854988098, + "val_loss": 1.2062479257583618, + "acc": 58.99, + "time": 326.8523608249998, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 90, + "train_loss": 0.9137877225875854, + "val_loss": 1.1390202045440674, + "acc": 58.84, + "time": 325.7157838059975, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 91, + "train_loss": 1.1031324863433838, + "val_loss": 1.0778656005859375, + "acc": 59.34, + "time": 330.10350169300364, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 92, + "train_loss": 0.9779165983200073, + "val_loss": 1.1737560033798218, + "acc": 60.01, + "time": 326.37689292899813, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 93, + "train_loss": 0.9133524894714355, + "val_loss": 1.1902060508728027, + "acc": 60.48, + "time": 324.97384197000065, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 94, + "train_loss": 0.8902379274368286, + "val_loss": 1.2185808420181274, + "acc": 59.16, + "time": 325.9945247040014, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 95, + "train_loss": 0.9281233549118042, + "val_loss": 1.1343649625778198, + "acc": 60.11, + "time": 326.09381213800225, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 96, + "train_loss": 0.9917454719543457, + "val_loss": 1.1067160367965698, + "acc": 60.23, + "time": 332.99156672000026, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 97, + "train_loss": 0.8502895832061768, + "val_loss": 1.2230134010314941, + "acc": 60.14, + "time": 322.5212112850022, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 98, + "train_loss": 0.9186282157897949, + "val_loss": 1.1025912761688232, + "acc": 59.94, + "time": 325.39108780499737, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 99, + "train_loss": 0.9065180420875549, + "val_loss": 1.1746193170547485, + "acc": 59.04, + "time": 330.2809620959997, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 100, + "train_loss": 0.9643510580062866, + "val_loss": 1.1451886892318726, + "acc": 60.17, + "time": 329.1106960140023, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 101, + "train_loss": 0.998581051826477, + "val_loss": 1.1262061595916748, + "acc": 60.3, + "time": 330.20337134700094, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 102, + "train_loss": 1.0041255950927734, + "val_loss": 1.1601802110671997, + "acc": 60.0, + "time": 334.20970428099827, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 103, + "train_loss": 1.0320748090744019, + "val_loss": 1.0713900327682495, + "acc": 60.62, + "time": 323.2067678940002, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 104, + "train_loss": 1.075124740600586, + "val_loss": 1.1401277780532837, + "acc": 58.62, + "time": 326.58248635700147, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 105, + "train_loss": 0.908841609954834, + "val_loss": 1.2139790058135986, + "acc": 61.09, + "time": 330.18350587599707, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 106, + "train_loss": 0.947121262550354, + "val_loss": 1.0649502277374268, + "acc": 60.4, + "time": 341.29368040199915, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 107, + "train_loss": 0.9392441511154175, + "val_loss": 1.2559306621551514, + "acc": 61.35, + "time": 332.0401086380007, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 108, + "train_loss": 0.8644181489944458, + "val_loss": 1.1925079822540283, + "acc": 61.04, + "time": 328.3071150370015, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 109, + "train_loss": 0.9758145809173584, + "val_loss": 1.0788178443908691, + "acc": 60.17, + "time": 340.60526933099754, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 110, + "train_loss": 0.8563110828399658, + "val_loss": 1.1516449451446533, + "acc": 60.84, + "time": 326.12231192000036, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 111, + "train_loss": 1.029859185218811, + "val_loss": 1.10728120803833, + "acc": 60.75, + "time": 325.1207628510019, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 112, + "train_loss": 0.8909634351730347, + "val_loss": 1.073420524597168, + "acc": 61.53, + "time": 326.14174995199573, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 113, + "train_loss": 0.9177039861679077, + "val_loss": 1.1227787733078003, + "acc": 60.9, + "time": 327.5459357840009, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 114, + "train_loss": 0.9076026082038879, + "val_loss": 1.0991928577423096, + "acc": 61.17, + "time": 329.1498078809964, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 115, + "train_loss": 0.8650639057159424, + "val_loss": 1.1086242198944092, + "acc": 60.5, + "time": 325.38163267100026, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 116, + "train_loss": 0.8257584571838379, + "val_loss": 1.1205204725265503, + "acc": 61.08, + "time": 326.0485978200013, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 117, + "train_loss": 0.8532935976982117, + "val_loss": 1.1296547651290894, + "acc": 60.38, + "time": 326.2673735430071, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 118, + "train_loss": 0.9023960828781128, + "val_loss": 1.1078778505325317, + "acc": 61.64, + "time": 323.16324741399876, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 119, + "train_loss": 0.9374569654464722, + "val_loss": 1.1601495742797852, + "acc": 60.96, + "time": 327.48784476199944, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 120, + "train_loss": 0.8451606631278992, + "val_loss": 1.0216219425201416, + "acc": 61.29, + "time": 334.2178597039965, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 121, + "train_loss": 0.9562646150588989, + "val_loss": 1.1792141199111938, + "acc": 61.25, + "time": 336.7907381920013, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 122, + "train_loss": 0.8950706124305725, + "val_loss": 1.1451303958892822, + "acc": 60.67, + "time": 329.86577037200186, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 123, + "train_loss": 0.993013858795166, + "val_loss": 1.2026963233947754, + "acc": 62.28, + "time": 327.1434861079979, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 124, + "train_loss": 0.9620710015296936, + "val_loss": 1.092362403869629, + "acc": 61.18, + "time": 322.8832247340033, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 125, + "train_loss": 0.8632125854492188, + "val_loss": 1.1099891662597656, + "acc": 60.86, + "time": 331.21128851899994, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 126, + "train_loss": 0.9216932058334351, + "val_loss": 1.1082093715667725, + "acc": 61.0, + "time": 340.1253235499971, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 127, + "train_loss": 0.8991696238517761, + "val_loss": 1.1700199842453003, + "acc": 60.66, + "time": 326.6205450399939, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 128, + "train_loss": 0.8524705767631531, + "val_loss": 1.157011866569519, + "acc": 59.84, + "time": 328.84330101499654, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 129, + "train_loss": 0.7903519868850708, + "val_loss": 1.0728199481964111, + "acc": 61.61, + "time": 330.3482290309985, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 130, + "train_loss": 0.8309649229049683, + "val_loss": 1.0919190645217896, + "acc": 61.64, + "time": 325.6877404449988, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 131, + "train_loss": 0.9092158675193787, + "val_loss": 1.09305739402771, + "acc": 62.14, + "time": 328.13996836599836, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 132, + "train_loss": 0.9380415678024292, + "val_loss": 1.05559241771698, + "acc": 62.29, + "time": 327.90571276300034, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 133, + "train_loss": 0.8731304407119751, + "val_loss": 1.234864354133606, + "acc": 61.57, + "time": 329.5325377350018, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 134, + "train_loss": 0.8447148203849792, + "val_loss": 1.1422866582870483, + "acc": 62.52, + "time": 324.6222348099982, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 135, + "train_loss": 0.908302903175354, + "val_loss": 1.127378225326538, + "acc": 62.06, + "time": 326.03144800200243, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 136, + "train_loss": 0.9359060525894165, + "val_loss": 1.0788462162017822, + "acc": 62.19, + "time": 326.12303053699725, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 137, + "train_loss": 1.057660460472107, + "val_loss": 1.2130472660064697, + "acc": 62.19, + "time": 327.5017990560009, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 138, + "train_loss": 0.9275438785552979, + "val_loss": 1.1311368942260742, + "acc": 62.59, + "time": 328.43947647700406, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 139, + "train_loss": 0.8885986804962158, + "val_loss": 1.1369558572769165, + "acc": 62.61, + "time": 328.01460093600326, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 140, + "train_loss": 0.8589940071105957, + "val_loss": 1.150256872177124, + "acc": 62.66, + "time": 327.31412415100203, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 141, + "train_loss": 0.856147050857544, + "val_loss": 1.1416652202606201, + "acc": 62.13, + "time": 327.09484992700163, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 142, + "train_loss": 0.8783614635467529, + "val_loss": 1.1436588764190674, + "acc": 62.1, + "time": 324.0850301579994, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 143, + "train_loss": 0.9449865221977234, + "val_loss": 1.0884971618652344, + "acc": 62.51, + "time": 333.5100120060015, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 144, + "train_loss": 1.0088245868682861, + "val_loss": 1.120575189590454, + "acc": 61.69, + "time": 329.76049020700157, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 145, + "train_loss": 0.8043997883796692, + "val_loss": 1.0623193979263306, + "acc": 62.29, + "time": 332.9677294139983, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 146, + "train_loss": 0.8792779445648193, + "val_loss": 1.1447253227233887, + "acc": 63.29, + "time": 328.37286786899494, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 147, + "train_loss": 0.8717527389526367, + "val_loss": 1.058564305305481, + "acc": 62.58, + "time": 331.13031147499714, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 148, + "train_loss": 0.8465150594711304, + "val_loss": 1.079736590385437, + "acc": 60.41, + "time": 327.3391813279959, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 149, + "train_loss": 0.8099269866943359, + "val_loss": 1.1300904750823975, + "acc": 62.72, + "time": 324.9014891639963, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 150, + "train_loss": 0.7839435935020447, + "val_loss": 1.0047632455825806, + "acc": 63.14, + "time": 325.0411795809996, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 151, + "train_loss": 0.8357760906219482, + "val_loss": 0.999203085899353, + "acc": 63.49, + "time": 326.6311077799983, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 152, + "train_loss": 0.8840800523757935, + "val_loss": 1.0445393323898315, + "acc": 62.83, + "time": 326.3118821349999, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 153, + "train_loss": 0.8597032427787781, + "val_loss": 1.2279020547866821, + "acc": 60.96, + "time": 336.1546423980035, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 154, + "train_loss": 0.8906964063644409, + "val_loss": 1.0875533819198608, + "acc": 62.81, + "time": 323.4531402720022, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 155, + "train_loss": 0.7327426075935364, + "val_loss": 1.0237292051315308, + "acc": 63.48, + "time": 330.93248956299794, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 156, + "train_loss": 0.9664064645767212, + "val_loss": 1.0393065214157104, + "acc": 63.32, + "time": 331.9771522219962, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 157, + "train_loss": 0.7852044105529785, + "val_loss": 1.0330737829208374, + "acc": 62.63, + "time": 328.5957084190013, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 158, + "train_loss": 0.9012215733528137, + "val_loss": 1.0762873888015747, + "acc": 61.31, + "time": 324.1109750100004, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 159, + "train_loss": 0.8004071712493896, + "val_loss": 1.1263465881347656, + "acc": 62.49, + "time": 326.907569185998, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 160, + "train_loss": 0.8331993222236633, + "val_loss": 1.021199107170105, + "acc": 63.46, + "time": 330.50611161399866, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 161, + "train_loss": 0.801805853843689, + "val_loss": 1.0271532535552979, + "acc": 62.64, + "time": 328.14547416999994, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 162, + "train_loss": 0.7769726514816284, + "val_loss": 1.0691393613815308, + "acc": 61.98, + "time": 326.26646127000276, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 163, + "train_loss": 0.8981873989105225, + "val_loss": 1.0396149158477783, + "acc": 62.42, + "time": 323.9883381730033, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 164, + "train_loss": 0.9289634227752686, + "val_loss": 1.0294703245162964, + "acc": 63.27, + "time": 321.54990916800307, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 165, + "train_loss": 0.7532284259796143, + "val_loss": 1.1004996299743652, + "acc": 62.52, + "time": 331.08365266700275, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 166, + "train_loss": 0.787761926651001, + "val_loss": 1.0639350414276123, + "acc": 63.29, + "time": 326.61115110899846, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 167, + "train_loss": 0.8505532741546631, + "val_loss": 1.1089205741882324, + "acc": 63.58, + "time": 325.3087633949981, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 168, + "train_loss": 0.7925806045532227, + "val_loss": 1.0315561294555664, + "acc": 63.63, + "time": 327.0840771450021, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 169, + "train_loss": 0.7764450311660767, + "val_loss": 1.0172317028045654, + "acc": 63.83, + "time": 323.29573299199546, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 170, + "train_loss": 0.78692626953125, + "val_loss": 1.007474422454834, + "acc": 63.49, + "time": 323.25396015899605, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 171, + "train_loss": 0.9282815456390381, + "val_loss": 1.1044458150863647, + "acc": 62.52, + "time": 328.7210468569974, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 172, + "train_loss": 0.7836191058158875, + "val_loss": 1.022830843925476, + "acc": 63.79, + "time": 324.1297140800016, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 173, + "train_loss": 0.8213129639625549, + "val_loss": 1.0548332929611206, + "acc": 63.24, + "time": 327.45634791900375, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 174, + "train_loss": 0.7890695333480835, + "val_loss": 1.0976709127426147, + "acc": 62.82, + "time": 323.65336360999936, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 175, + "train_loss": 0.9023985266685486, + "val_loss": 1.111925721168518, + "acc": 63.87, + "time": 327.56161503700423, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 176, + "train_loss": 0.7891160249710083, + "val_loss": 1.121705412864685, + "acc": 63.36, + "time": 326.66592256900185, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 177, + "train_loss": 0.8520789742469788, + "val_loss": 1.0197802782058716, + "acc": 64.14, + "time": 328.524481971006, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 178, + "train_loss": 0.7569721937179565, + "val_loss": 1.1503263711929321, + "acc": 63.99, + "time": 329.9043449180026, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 179, + "train_loss": 0.8329329490661621, + "val_loss": 1.0100518465042114, + "acc": 64.13, + "time": 323.69183806899673, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 180, + "train_loss": 0.7980643510818481, + "val_loss": 1.0620183944702148, + "acc": 64.27, + "time": 327.5493022849987, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 181, + "train_loss": 0.7755078077316284, + "val_loss": 1.045976161956787, + "acc": 63.43, + "time": 329.93863437200343, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 182, + "train_loss": 0.8200274705886841, + "val_loss": 0.9823777079582214, + "acc": 64.55, + "time": 328.6928837279993, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 183, + "train_loss": 0.8503549098968506, + "val_loss": 1.0856164693832397, + "acc": 63.95, + "time": 324.5139272400047, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 184, + "train_loss": 0.8212573528289795, + "val_loss": 1.1259204149246216, + "acc": 62.11, + "time": 326.85849687099835, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 185, + "train_loss": 0.683657705783844, + "val_loss": 1.0068539381027222, + "acc": 63.1, + "time": 324.0294616309984, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 186, + "train_loss": 0.744641900062561, + "val_loss": 1.0002107620239258, + "acc": 63.67, + "time": 323.7911839699955, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 187, + "train_loss": 0.7485579252243042, + "val_loss": 1.0518968105316162, + "acc": 63.61, + "time": 324.12603727199894, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 188, + "train_loss": 0.7427394390106201, + "val_loss": 0.9762161374092102, + "acc": 63.67, + "time": 326.46524613300426, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 189, + "train_loss": 0.8682398796081543, + "val_loss": 1.063512921333313, + "acc": 63.59, + "time": 326.216765293997, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 190, + "train_loss": 0.9195802211761475, + "val_loss": 1.0842386484146118, + "acc": 60.41, + "time": 327.06373690700275, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 191, + "train_loss": 0.6830422878265381, + "val_loss": 1.0165984630584717, + "acc": 63.57, + "time": 324.5155932769994, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 192, + "train_loss": 0.7855727672576904, + "val_loss": 1.1937222480773926, + "acc": 58.83, + "time": 327.95718058600323, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 193, + "train_loss": 0.7295229434967041, + "val_loss": 0.9437354803085327, + "acc": 63.97, + "time": 324.2247311070023, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 194, + "train_loss": 0.818834662437439, + "val_loss": 0.939775824546814, + "acc": 63.42, + "time": 323.42295168800047, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 195, + "train_loss": 0.8329800367355347, + "val_loss": 1.0300216674804688, + "acc": 64.48, + "time": 333.4774213089986, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 196, + "train_loss": 0.8160727620124817, + "val_loss": 1.1370943784713745, + "acc": 63.29, + "time": 330.72782527000527, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 197, + "train_loss": 0.8175878524780273, + "val_loss": 0.9991466403007507, + "acc": 62.86, + "time": 331.95961999899737, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 198, + "train_loss": 0.9180650115013123, + "val_loss": 0.958290696144104, + "acc": 63.83, + "time": 330.34958633399947, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 199, + "train_loss": 0.9907680749893188, + "val_loss": 0.9765826463699341, + "acc": 64.62, + "time": 326.6320036370089, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + }, + { + "epoch": 200, + "train_loss": 0.7615428566932678, + "val_loss": 0.9350480437278748, + "acc": 64.7, + "time": 324.9357150950091, + "param": [ + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + }, + { + "p": 0.0714285746216774, + "m": 1.0 + } + ] + } + ] +} \ No newline at end of file diff --git a/salvador/cams.py b/salvador/cams.py new file mode 100644 index 0000000..3e615f1 --- /dev/null +++ b/salvador/cams.py @@ -0,0 +1,98 @@ +import torch +import numpy as np +import torchvision +from PIL import Image +from torch import topk +import torch.nn.functional as F +from torch import topk +import cv2 +from torchvision import transforms +import os + +class SaveFeatures(): + features=None + def __init__(self, m): self.hook = m.register_forward_hook(self.hook_fn) + def hook_fn(self, module, input, output): self.features = ((output.cpu()).data).numpy() + def remove(self): self.hook.remove() + +def getCAM(feature_conv, weight_fc, class_idx): + _, nc, h, w = feature_conv.shape + cam = weight_fc[class_idx].dot(feature_conv.reshape((nc, h*w))) + cam = cam.reshape(h, w) + cam = cam - np.min(cam) + cam_img = cam / np.max(cam) + # cam_img = np.uint8(255 * cam_img) + return cam_img + +def main(cam): + device = 'cuda:0' + model_name = 'resnet50' + root = 'NEW_SS' + + os.makedirs(os.path.join(root + '_CAM', 'OK'), exist_ok=True) + os.makedirs(os.path.join(root + '_CAM', 'NOK'), exist_ok=True) + + train_transform = transforms.Compose([ + transforms.ToTensor(), + ]) + + dataset = torchvision.datasets.ImageFolder( + root=root, transform=train_transform, + ) + + loader = torch.utils.data.DataLoader(dataset, batch_size=1) + + model = torchvision.models.__dict__[model_name](pretrained=False) + model.fc = torch.nn.Linear(model.fc.in_features, 2) + + model.load_state_dict(torch.load('checkpoint.pt', map_location=lambda storage, loc: storage)) + model = model.to(device) + model.eval() + + weight_softmax_params = list(model._modules.get('fc').parameters()) + weight_softmax = np.squeeze(weight_softmax_params[0].cpu().data.numpy()) + + final_layer = model._modules.get('layer4') + + activated_features = SaveFeatures(final_layer) + + for i, (img, target ) in enumerate(loader): + img = img.to(device) + prediction = model(img) + pred_probabilities = F.softmax(prediction, dim=1).data.squeeze() + class_idx = topk(pred_probabilities,1)[1].int() + # if target.item() != class_idx: + # print(dataset.imgs[i][0]) + + if cam: + overlay = getCAM(activated_features.features, weight_softmax, class_idx ) + + import ipdb; ipdb.set_trace() + import PIL + from torchvision.transforms import ToPILImage + + img = ToPILImage()(overlay).resize(size=(1280, 1024), resample=PIL.Image.BILINEAR) + img.save('heat-pil.jpg') + + + img = cv2.imread(dataset.imgs[i][0]) + height, width, _ = img.shape + overlay = cv2.resize(overlay, (width, height)) + heatmap = cv2.applyColorMap(overlay, cv2.COLORMAP_JET) + cv2.imwrite('heat-cv2.jpg', heatmap) + + img = cv2.imread(dataset.imgs[i][0]) + height, width, _ = img.shape + overlay = cv2.resize(overlay, (width, height)) + heatmap = cv2.applyColorMap(overlay, cv2.COLORMAP_JET) + result = heatmap * 0.3 + img * 0.5 + + clss = dataset.imgs[i][0].split(os.sep)[1] + name = dataset.imgs[i][0].split(os.sep)[2].split('.')[0] + cv2.imwrite(os.path.join(root+"_CAM", clss, name + '.jpg'), result) + print(f'{os.path.join(root+"_CAM", clss, name + ".jpg")} saved') + + activated_features.remove() + +if __name__ == "__main__": + main(cam=True) diff --git a/salvador/checkpoint.pt b/salvador/checkpoint.pt new file mode 100644 index 0000000..02f739f Binary files /dev/null and b/salvador/checkpoint.pt differ diff --git a/salvador/data/NOK/nok054503377.tif b/salvador/data/NOK/nok054503377.tif new file mode 100644 index 0000000..dcea382 Binary files /dev/null and b/salvador/data/NOK/nok054503377.tif differ diff --git a/salvador/data/NOK/nok054503736.tif b/salvador/data/NOK/nok054503736.tif new file mode 100644 index 0000000..5dcabba Binary files /dev/null and b/salvador/data/NOK/nok054503736.tif differ diff --git a/salvador/data/NOK/nok054504079.tif b/salvador/data/NOK/nok054504079.tif new file mode 100644 index 0000000..7244a09 Binary files /dev/null and b/salvador/data/NOK/nok054504079.tif differ diff --git a/salvador/data/NOK/nok054506185.tif b/salvador/data/NOK/nok054506185.tif new file mode 100644 index 0000000..f6cd120 Binary files /dev/null and b/salvador/data/NOK/nok054506185.tif differ diff --git a/salvador/data/NOK/nok054507230.tif b/salvador/data/NOK/nok054507230.tif new file mode 100644 index 0000000..8f4efd1 Binary files /dev/null and b/salvador/data/NOK/nok054507230.tif differ diff --git a/salvador/data/NOK/nok054507589.tif b/salvador/data/NOK/nok054507589.tif new file mode 100644 index 0000000..f9de6cb Binary files /dev/null and b/salvador/data/NOK/nok054507589.tif differ diff --git a/salvador/data/NOK/nok054507932.tif b/salvador/data/NOK/nok054507932.tif new file mode 100644 index 0000000..0792a5c Binary files /dev/null and b/salvador/data/NOK/nok054507932.tif differ diff --git a/salvador/data/NOK/nok054508634.tif b/salvador/data/NOK/nok054508634.tif new file mode 100644 index 0000000..06f6b86 Binary files /dev/null and b/salvador/data/NOK/nok054508634.tif differ diff --git a/salvador/data/NOK/nok054510382.tif b/salvador/data/NOK/nok054510382.tif new file mode 100644 index 0000000..fbb9904 Binary files /dev/null and b/salvador/data/NOK/nok054510382.tif differ diff --git a/salvador/data/NOK/nok054510740.tif b/salvador/data/NOK/nok054510740.tif new file mode 100644 index 0000000..d670997 Binary files /dev/null and b/salvador/data/NOK/nok054510740.tif differ diff --git a/salvador/data/NOK/nok054513533.tif b/salvador/data/NOK/nok054513533.tif new file mode 100644 index 0000000..811c9ce Binary files /dev/null and b/salvador/data/NOK/nok054513533.tif differ diff --git a/salvador/data/NOK/nok054513892.tif b/salvador/data/NOK/nok054513892.tif new file mode 100644 index 0000000..bc77cd9 Binary files /dev/null and b/salvador/data/NOK/nok054513892.tif differ diff --git a/salvador/data/NOK/nok054519508.tif b/salvador/data/NOK/nok054519508.tif new file mode 100644 index 0000000..9cd485e Binary files /dev/null and b/salvador/data/NOK/nok054519508.tif differ diff --git a/salvador/data/NOK/nok054521957.tif b/salvador/data/NOK/nok054521957.tif new file mode 100644 index 0000000..e6e974b Binary files /dev/null and b/salvador/data/NOK/nok054521957.tif differ diff --git a/salvador/data/NOK/nok054522659.tif b/salvador/data/NOK/nok054522659.tif new file mode 100644 index 0000000..d3a9dbe Binary files /dev/null and b/salvador/data/NOK/nok054522659.tif differ diff --git a/salvador/data/NOK/nok054527916.tif b/salvador/data/NOK/nok054527916.tif new file mode 100644 index 0000000..0402fb3 Binary files /dev/null and b/salvador/data/NOK/nok054527916.tif differ diff --git a/salvador/data/NOK/nok054531083.tif b/salvador/data/NOK/nok054531083.tif new file mode 100644 index 0000000..fe05173 Binary files /dev/null and b/salvador/data/NOK/nok054531083.tif differ diff --git a/salvador/data/NOK/nok054532846.tif b/salvador/data/NOK/nok054532846.tif new file mode 100644 index 0000000..c533d4a Binary files /dev/null and b/salvador/data/NOK/nok054532846.tif differ diff --git a/salvador/data/NOK/nok054533891.tif b/salvador/data/NOK/nok054533891.tif new file mode 100644 index 0000000..4dd9da7 Binary files /dev/null and b/salvador/data/NOK/nok054533891.tif differ diff --git a/salvador/data/NOK/nok054538118.tif b/salvador/data/NOK/nok054538118.tif new file mode 100644 index 0000000..72d4d2a Binary files /dev/null and b/salvador/data/NOK/nok054538118.tif differ diff --git a/salvador/data/NOK/nok_054501630.tif b/salvador/data/NOK/nok_054501630.tif new file mode 100644 index 0000000..0a9f1a0 Binary files /dev/null and b/salvador/data/NOK/nok_054501630.tif differ diff --git a/salvador/data/NOK/nok_054502332.tif b/salvador/data/NOK/nok_054502332.tif new file mode 100644 index 0000000..bb958dc Binary files /dev/null and b/salvador/data/NOK/nok_054502332.tif differ diff --git a/salvador/data/NOK/nok_054509336.tif b/salvador/data/NOK/nok_054509336.tif new file mode 100644 index 0000000..db4c3d7 Binary files /dev/null and b/salvador/data/NOK/nok_054509336.tif differ diff --git a/salvador/data/OK/054501989.tif b/salvador/data/OK/054501989.tif new file mode 100644 index 0000000..c9bdef4 Binary files /dev/null and b/salvador/data/OK/054501989.tif differ diff --git a/salvador/data/OK/054502675.tif b/salvador/data/OK/054502675.tif new file mode 100644 index 0000000..2ec5054 Binary files /dev/null and b/salvador/data/OK/054502675.tif differ diff --git a/salvador/data/OK/054503034.tif b/salvador/data/OK/054503034.tif new file mode 100644 index 0000000..ba41634 Binary files /dev/null and b/salvador/data/OK/054503034.tif differ diff --git a/salvador/data/OK/054504438.tif b/salvador/data/OK/054504438.tif new file mode 100644 index 0000000..05f18d9 Binary files /dev/null and b/salvador/data/OK/054504438.tif differ diff --git a/salvador/data/OK/054504781.tif b/salvador/data/OK/054504781.tif new file mode 100644 index 0000000..c28d949 Binary files /dev/null and b/salvador/data/OK/054504781.tif differ diff --git a/salvador/data/OK/054505124.tif b/salvador/data/OK/054505124.tif new file mode 100644 index 0000000..99dfc28 Binary files /dev/null and b/salvador/data/OK/054505124.tif differ diff --git a/salvador/data/OK/054505483.tif b/salvador/data/OK/054505483.tif new file mode 100644 index 0000000..19faebc Binary files /dev/null and b/salvador/data/OK/054505483.tif differ diff --git a/salvador/data/OK/054505826.tif b/salvador/data/OK/054505826.tif new file mode 100644 index 0000000..1d71a0a Binary files /dev/null and b/salvador/data/OK/054505826.tif differ diff --git a/salvador/data/OK/054506528.tif b/salvador/data/OK/054506528.tif new file mode 100644 index 0000000..6f7f2d6 Binary files /dev/null and b/salvador/data/OK/054506528.tif differ diff --git a/salvador/data/OK/054506887.tif b/salvador/data/OK/054506887.tif new file mode 100644 index 0000000..6d8483f Binary files /dev/null and b/salvador/data/OK/054506887.tif differ diff --git a/salvador/data/OK/054508276.tif b/salvador/data/OK/054508276.tif new file mode 100644 index 0000000..defefb4 Binary files /dev/null and b/salvador/data/OK/054508276.tif differ diff --git a/salvador/data/OK/054508978.tif b/salvador/data/OK/054508978.tif new file mode 100644 index 0000000..edd9849 Binary files /dev/null and b/salvador/data/OK/054508978.tif differ diff --git a/salvador/data/OK/054509680.tif b/salvador/data/OK/054509680.tif new file mode 100644 index 0000000..4446b80 Binary files /dev/null and b/salvador/data/OK/054509680.tif differ diff --git a/salvador/data/OK/054510038.tif b/salvador/data/OK/054510038.tif new file mode 100644 index 0000000..9a768e7 Binary files /dev/null and b/salvador/data/OK/054510038.tif differ diff --git a/salvador/data/OK/054511084.tif b/salvador/data/OK/054511084.tif new file mode 100644 index 0000000..3713765 Binary files /dev/null and b/salvador/data/OK/054511084.tif differ diff --git a/salvador/data/OK/054511427.tif b/salvador/data/OK/054511427.tif new file mode 100644 index 0000000..f7f8cca Binary files /dev/null and b/salvador/data/OK/054511427.tif differ diff --git a/salvador/data/OK/054511786.tif b/salvador/data/OK/054511786.tif new file mode 100644 index 0000000..4769ba0 Binary files /dev/null and b/salvador/data/OK/054511786.tif differ diff --git a/salvador/data/OK/054512129.tif b/salvador/data/OK/054512129.tif new file mode 100644 index 0000000..55d0b43 Binary files /dev/null and b/salvador/data/OK/054512129.tif differ diff --git a/salvador/data/OK/054512488.tif b/salvador/data/OK/054512488.tif new file mode 100644 index 0000000..8c0e90d Binary files /dev/null and b/salvador/data/OK/054512488.tif differ diff --git a/salvador/data/OK/054512831.tif b/salvador/data/OK/054512831.tif new file mode 100644 index 0000000..ceb56df Binary files /dev/null and b/salvador/data/OK/054512831.tif differ diff --git a/salvador/data/OK/054513190.tif b/salvador/data/OK/054513190.tif new file mode 100644 index 0000000..09f25dd Binary files /dev/null and b/salvador/data/OK/054513190.tif differ diff --git a/salvador/data/OK/054514235.tif b/salvador/data/OK/054514235.tif new file mode 100644 index 0000000..af548f3 Binary files /dev/null and b/salvador/data/OK/054514235.tif differ diff --git a/salvador/data/OK/054514578.tif b/salvador/data/OK/054514578.tif new file mode 100644 index 0000000..dfa53da Binary files /dev/null and b/salvador/data/OK/054514578.tif differ diff --git a/salvador/dataug.py b/salvador/dataug.py new file mode 100755 index 0000000..6f246df --- /dev/null +++ b/salvador/dataug.py @@ -0,0 +1,1136 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.distributions import * + +#import kornia +#import random +import numpy as np +import copy + +import transformations as TF + +class Data_aug(nn.Module): #Rotation parametree + def __init__(self): + super(Data_aug, self).__init__() + self._data_augmentation = True + self._params = nn.ParameterDict({ + "prob": nn.Parameter(torch.tensor(0.5)), + "mag": nn.Parameter(torch.tensor(1.0)) + }) + + #self.params["mag"].register_hook(print) + + def forward(self, x): + + if self._data_augmentation and random.random() < self._params["prob"]: + #print('Aug') + batch_size = x.shape[0] + # create transformation (rotation) + alpha = self._params["mag"]*180 # in degrees + angle = torch.ones(batch_size, device=x.device) * alpha + + # define the rotation center + center = torch.ones(batch_size, 2, device=x.device) + center[..., 0] = x.shape[3] / 2 # x + center[..., 1] = x.shape[2] / 2 # y + + #print(x.shape, center) + # define the scale factor + scale = torch.ones(batch_size, device=x.device) + + # compute the transformation matrix + M = kornia.get_rotation_matrix2d(center, angle, scale) + + # apply the transformation to original image + x = kornia.warp_affine(x, M, dsize=(x.shape[2], x.shape[3])) #dsize=(h, w) + + return x + + def eval(self): + self.augment(mode=False) + nn.Module.eval(self) + + def augment(self, mode=True): + self._data_augmentation=mode + + def __getitem__(self, key): + return self._params[key] + + def __str__(self): + return "Data_aug(Mag-1 TF)" + +class Data_augV2(nn.Module): #Methode exacte + def __init__(self): + super(Data_augV2, self).__init__() + self._data_augmentation = True + + self._fixed_transf=[0.0, 45.0, 180.0] #Degree rotation + #self._fixed_transf=[0.0] + self._nb_tf= len(self._fixed_transf) + + self._params = nn.ParameterDict({ + "prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme + #"prob2": nn.Parameter(torch.ones(len(self._fixed_transf)).softmax(dim=0)) + }) + + #print(self._params["prob"], self._params["prob2"]) + + self.transf_idx=0 + + def forward(self, x): + + if self._data_augmentation: + #print('Aug',self._fixed_transf[self.transf_idx]) + device = x.device + batch_size = x.shape[0] + + # create transformation (rotation) + #alpha = 180 # in degrees + alpha = self._fixed_transf[self.transf_idx] + angle = torch.ones(batch_size, device=device) * alpha + + x = self.rotate(x,angle) + + return x + + def rotate(self, x, angle): + + device = x.device + batch_size = x.shape[0] + # define the rotation center + center = torch.ones(batch_size, 2, device=device) + center[..., 0] = x.shape[3] / 2 # x + center[..., 1] = x.shape[2] / 2 # y + + #print(x.shape, center) + # define the scale factor + scale = torch.ones(batch_size, device=device) + + # compute the transformation matrix + M = kornia.get_rotation_matrix2d(center, angle, scale) + + # apply the transformation to original image + return kornia.warp_affine(x, M, dsize=(x.shape[2], x.shape[3])) #dsize=(h, w) + + + def adjust_param(self): #Detach from gradient ? + self._params['prob'].data = self._params['prob'].clamp(min=0.0,max=1.0) + #print('proba',self._params['prob']) + self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1 + #print('Sum p', sum(self._params['prob'])) + + def eval(self): + self.augment(mode=False) + nn.Module.eval(self) + + def augment(self, mode=True): + self._data_augmentation=mode + + def __getitem__(self, key): + return self._params[key] + + def __str__(self): + return "Data_augV2(Exact-%d TF)" % self._nb_tf + +class Data_augV3(nn.Module): #Echantillonage uniforme/Mixte + def __init__(self, mix_dist=0.0): + super(Data_augV3, self).__init__() + self._data_augmentation = True + + #self._fixed_transf=[0.0, 45.0, 180.0] #Degree rotation + self._fixed_transf=[0.0, 1.0, -1.0] #Flips (Identity,Horizontal,Vertical) + #self._fixed_transf=[0.0] + self._nb_tf= len(self._fixed_transf) + + self._params = nn.ParameterDict({ + "prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme + #"prob2": nn.Parameter(torch.ones(len(self._fixed_transf)).softmax(dim=0)) + }) + + #print(self._params["prob"], self._params["prob2"]) + self._sample = [] + + self._mix_dist = False + if mix_dist != 0.0: + self._mix_dist = True + self._mix_factor = max(min(mix_dist, 1.0), 0.0) + + def forward(self, x): + + if self._data_augmentation: + device = x.device + batch_size = x.shape[0] + + + #good_distrib = Uniform(low=torch.zeros(batch_size,1, device=device),high=torch.new_full((batch_size,1),self._params["prob"], device=device)) + #bad_distrib = Uniform(low=torch.zeros(batch_size,1, device=device),high=torch.new_full((batch_size,1), 1-self._params["prob"], device=device)) + + #transform_dist = Categorical(probs=torch.tensor([self._params["prob"], 1-self._params["prob"]], device=device)) + #self._sample = transform_dist._sample(sample_shape=torch.Size([batch_size,1])) + + uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=0) + + if not self._mix_dist: + distrib = uniforme_dist + else: + distrib = (self._mix_factor*self._params["prob"]+(1-self._mix_factor)*uniforme_dist).softmax(dim=0) #Mix distrib reel / uniforme avec mix_factor + + cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*distrib) + self._sample = cat_distrib.sample() + + TF_param = torch.tensor([self._fixed_transf[x] for x in self._sample], device=device) #Approche de marco peut-etre plus rapide + + #x = self.rotate(x,angle=TF_param) + x = self.flip(x,flip_mat=TF_param) + + return x + + def rotate(self, x, angle): + + device = x.device + batch_size = x.shape[0] + # define the rotation center + center = torch.ones(batch_size, 2, device=device) + center[..., 0] = x.shape[3] / 2 # x + center[..., 1] = x.shape[2] / 2 # y + + #print(x.shape, center) + # define the scale factor + scale = torch.ones(batch_size, device=device) + + # compute the transformation matrix + M = kornia.get_rotation_matrix2d(center, angle, scale) + + # apply the transformation to original image + return kornia.warp_affine(x, M, dsize=(x.shape[2], x.shape[3])) #dsize=(h, w) + + def flip(self, x, flip_mat): + + #print(flip_mat) + device = x.device + batch_size = x.shape[0] + + h, w = x.shape[2], x.shape[3] # destination size + #points_src = torch.ones(batch_size, 4, 2, device=device) + #points_dst = torch.ones(batch_size, 4, 2, device=device) + + #Identity + iM=torch.tensor(np.eye(3)) + + #Horizontal flip + # the source points are the region to crop corners + #points_src = torch.FloatTensor([[ + # [w - 1, 0], [0, 0], [0, h - 1], [w - 1, h - 1], + #]]) + # the destination points are the image vertexes + #points_dst = torch.FloatTensor([[ + # [0, 0], [w - 1, 0], [w - 1, h - 1], [0, h - 1], + #]]) + # compute perspective transform + #hM = kornia.get_perspective_transform(points_src, points_dst) + hM =torch.tensor( [[[-1., 0., w-1], + [ 0., 1., 0.], + [ 0., 0., 1.]]]) + + #Vertical flip + # the source points are the region to crop corners + #points_src = torch.FloatTensor([[ + # [0, h - 1], [w - 1, h - 1], [w - 1, 0], [0, 0], + #]]) + # the destination points are the image vertexes + #points_dst = torch.FloatTensor([[ + # [0, 0], [w - 1, 0], [w - 1, h - 1], [0, h - 1], + #]]) + # compute perspective transform + #vM = kornia.get_perspective_transform(points_src, points_dst) + vM =torch.tensor( [[[ 1., 0., 0.], + [ 0., -1., h-1], + [ 0., 0., 1.]]]) + #print(vM) + + M=torch.ones(batch_size, 3, 3, device=device) + + for i in range(batch_size): # A optimiser + if flip_mat[i]==0.0: + M[i,]=iM + elif flip_mat[i]==1.0: + M[i,]=hM + elif flip_mat[i]==-1.0: + M[i,]=vM + + # warp the original image by the found transform + return kornia.warp_perspective(x, M, dsize=(h, w)) + + def adjust_param(self, soft=False): #Detach from gradient ? + + if soft : + self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible + else: + #self._params['prob'].clamp(min=0.0,max=1.0) + self._params['prob'].data = F.relu(self._params['prob'].data) + #self._params['prob'].data = self._params['prob'].clamp(min=0.0,max=1.0) + #print('proba',self._params['prob']) + self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1 + #print('Sum p', sum(self._params['prob'])) + + def loss_weight(self): + #w_loss = [self._params["prob"][x] for x in self._sample] + #print(self._sample.view(-1,1).shape) + #print(self._sample[:10]) + + w_loss = torch.zeros((self._sample.shape[0],self._nb_tf), device=self._sample.device) + w_loss.scatter_(1, self._sample.view(-1,1), 1) + #print(w_loss.shape) + #print(w_loss[:10,:]) + w_loss = w_loss * self._params["prob"] + #print(w_loss.shape) + #print(w_loss[:10,:]) + w_loss = torch.sum(w_loss,dim=1) + #print(w_loss.shape) + #print(w_loss[:10]) + return w_loss + + def train(self, mode=None): + if mode is None : + mode=self._data_augmentation + self.augment(mode=mode) #Inutile si mode=None + super(Data_augV3, self).train(mode) + + def eval(self): + self.train(mode=False) + #super(Augmented_model, self).eval() + + def augment(self, mode=True): + self._data_augmentation=mode + + def __getitem__(self, key): + return self._params[key] + + def __str__(self): + if not self._mix_dist: + return "Data_augV3(Uniform-%d TF)" % self._nb_tf + else: + return "Data_augV3(Mix %.1f-%d TF)" % (self._mix_factor, self._nb_tf) + +class Data_augV4(nn.Module): #Transformations avec mask + def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0): + super(Data_augV4, self).__init__() + assert len(TF_dict)>0 + + self._data_augmentation = True + + #self._TF_matrix={} + #self._input_info={'h':0, 'w':0, 'device':None} #Input associe a TF_matrix + #self._mag_fct = TF_dict + self._TF_dict = TF_dict + self._TF= list(self._TF_dict.keys()) + self._nb_tf= len(self._TF) + + self._N_seqTF = N_TF + + self._fixed_mag=5 #[0, PARAMETER_MAX] + self._params = nn.ParameterDict({ + "prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme + }) + + self._samples = [] + + self._mix_dist = False + if mix_dist != 0.0: + self._mix_dist = True + self._mix_factor = max(min(mix_dist, 1.0), 0.0) + + def forward(self, x): + if self._data_augmentation: + device = x.device + batch_size, h, w = x.shape[0], x.shape[2], x.shape[3] + + x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles) + self._samples = [] + + for _ in range(self._N_seqTF): + ## Echantillonage ## + uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=1) + + if not self._mix_dist: + self._distrib = uniforme_dist + else: + self._distrib = (self._mix_factor*self._params["prob"]+(1-self._mix_factor)*uniforme_dist).softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor + + cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*self._distrib) + sample = cat_distrib.sample() + self._samples.append(sample) + + ## Transformations ## + x = self.apply_TF(x, sample) + return x + ''' + def compute_TF_matrix(self, magnitude=None, sample_info= None): + print('Computing TF_matrix...') + if not magnitude : + magnitude=self._fixed_mag + + if sample_info: + self._input_info['h']= sample_info['h'] + self._input_info['w']= sample_info['w'] + self._input_info['device'] = sample_info['device'] + h, w, device= self._input_info['h'], self._input_info['w'], self._input_info['device'] + + self._TF_matrix={} + for tf in self._TF : + if tf=='Id': + self._TF_matrix[tf]=torch.tensor([[[ 1., 0., 0.], + [ 0., 1., 0.], + [ 0., 0., 1.]]], device=device) + elif tf=='Rot': + center = torch.ones(1, 2, device=device) + center[0, 0] = w / 2 # x + center[0, 1] = h / 2 # y + scale = torch.ones(1, device=device) + angle = self._mag_fct[tf](magnitude) * torch.ones(1, device=device) + R = kornia.get_rotation_matrix2d(center, angle, scale) #Rotation matrix (1,2,3) + self._TF_matrix[tf]=torch.cat((R,torch.tensor([[[ 0., 0., 1.]]], device=device)), dim=1) #TF matrix (1,3,3) + elif tf=='FlipLR': + self._TF_matrix[tf]=torch.tensor([[[-1., 0., w-1], + [ 0., 1., 0.], + [ 0., 0., 1.]]], device=device) + elif tf=='FlipUD': + self._TF_matrix[tf]=torch.tensor([[[ 1., 0., 0.], + [ 0., -1., h-1], + [ 0., 0., 1.]]], device=device) + else: + raise Exception("Invalid TF requested") + ''' + def apply_TF(self, x, sampled_TF): + device = x.device + smps_x=[] + masks=[] + for tf_idx in range(self._nb_tf): + mask = sampled_TF==tf_idx #Create selection mask + smp_x = x[mask] #torch.masked_select() ? + + if smp_x.shape[0]!=0: #if there's data to TF + magnitude=self._fixed_mag + tf=self._TF[tf_idx] + + ''' + ## Geometric TF ## + if tf=='Identity': + pass + elif tf=='FlipLR': + smp_x = TF.flipLR(smp_x) + elif tf=='FlipUD': + smp_x = TF.flipUD(smp_x) + elif tf=='Rotate': + smp_x = TF.rotate(smp_x, angle=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device)) + elif tf=='TranslateX' or tf=='TranslateY': + smp_x = TF.translate(smp_x, translation=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device)) + elif tf=='ShearX' or tf=='ShearY' : + smp_x = TF.shear(smp_x, shear=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device)) + + ## Color TF (Expect image in the range of [0, 1]) ## + elif tf=='Contrast': + smp_x = TF.contrast(smp_x, contrast_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device)) + elif tf=='Color': + smp_x = TF.color(smp_x, color_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device)) + elif tf=='Brightness': + smp_x = TF.brightness(smp_x, brightness_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device)) + elif tf=='Sharpness': + smp_x = TF.sharpeness(smp_x, sharpness_factor=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device)) + elif tf=='Posterize': + smp_x = TF.posterize(smp_x, bits=torch.tensor([1 for _ in smp_x], device=device)) + elif tf=='Solarize': + smp_x = TF.solarize(smp_x, thresholds=torch.tensor([self._mag_fct[tf](magnitude) for _ in smp_x], device=device)) + elif tf=='Equalize': + smp_x = TF.equalize(smp_x) + elif tf=='Auto_Contrast': + smp_x = TF.auto_contrast(smp_x) + else: + raise Exception("Invalid TF requested : ", tf) + + x[mask]=smp_x # Refusionner eviter x[mask] : in place + ''' + x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude) # Refusionner eviter x[mask] : in place + + #idx= mask.nonzero() + #print('-'*8) + #print(idx[0], tf_idx) + #print(smp_x[0,]) + #x=x.view(-1,3*32*32) + #x=x.scatter(dim=0, index=idx, src=smp_x.view(-1,3*32*32)) #Changement des Tensor mais pas visible sur la visualisation... + #x=x.view(-1,3,32,32) + #print(x[0,]) + + ''' + if len(self._TF_matrix)==0 or self._input_info['h']!=h or self._input_info['w']!=w or self._input_info['device']!=device: #Device different:Pas necessaire de tout recalculer + self.compute_TF_matrix(sample_info={'h': x.shape[2], + 'w': x.shape[3], + 'device': x.device}) + + TF_matrix = torch.zeros(batch_size, 3, 3, device=device) #All geom TF + + for tf_idx in range(self._nb_tf): + mask = self._sample==tf_idx #Create selection mask + TF_matrix[mask,]=self._TF_matrix[self._TF[tf_idx]] + + x=kornia.warp_perspective(x, TF_matrix, dsize=(h, w)) + ''' + return x + + def adjust_param(self, soft=False): #Detach from gradient ? + + if soft : + self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible + else: + #self._params['prob'].clamp(min=0.0,max=1.0) + self._params['prob'].data = F.relu(self._params['prob'].data) + #self._params['prob'].data = self._params['prob'].clamp(min=0.0,max=1.0) + + self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1 + + def loss_weight(self): + # 1 seule TF + #self._sample = self._samples[-1] + #w_loss = torch.zeros((self._sample.shape[0],self._nb_tf), device=self._sample.device) + #w_loss.scatter_(dim=1, index=self._sample.view(-1,1), value=1) + #w_loss = w_loss * self._params["prob"]/self._distrib #Ponderation par les proba (divisee par la distrib pour pas diminuer la loss) + #w_loss = torch.sum(w_loss,dim=1) + + #Plusieurs TF sequentielles + w_loss = torch.zeros((self._samples[0].shape[0],self._nb_tf), device=self._samples[0].device) + for sample in self._samples: + tmp_w = torch.zeros(w_loss.size(),device=w_loss.device) + tmp_w.scatter_(dim=1, index=sample.view(-1,1), value=1/self._N_seqTF) + w_loss += tmp_w + + w_loss = w_loss * self._params["prob"]/self._distrib #Ponderation par les proba (divisee par la distrib pour pas diminuer la loss) + w_loss = torch.sum(w_loss,dim=1) + return w_loss + + + def train(self, mode=None): + if mode is None : + mode=self._data_augmentation + self.augment(mode=mode) #Inutile si mode=None + super(Data_augV4, self).train(mode) + + def eval(self): + self.train(mode=False) + + def augment(self, mode=True): + self._data_augmentation=mode + + def __getitem__(self, key): + return self._params[key] + + def __str__(self): + if not self._mix_dist: + return "Data_augV4(Uniform-%d TF x %d)" % (self._nb_tf, self._N_seqTF) + else: + return "Data_augV4(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_seqTF) + +class Data_augV5(nn.Module): #Optimisation jointe (mag, proba) + def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True): + super(Data_augV5, self).__init__() + assert len(TF_dict)>0 + + self._data_augmentation = True + + self._TF_dict = TF_dict + self._TF= list(self._TF_dict.keys()) + self._nb_tf= len(self._TF) + + self._N_seqTF = N_TF + self._shared_mag = shared_mag + self._fixed_mag = fixed_mag + + #self._fixed_mag=5 #[0, PARAMETER_MAX] + self._params = nn.ParameterDict({ + "prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme + "mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX)) if self._shared_mag + else torch.tensor(float(TF.PARAMETER_MAX)).expand(self._nb_tf)), #[0, PARAMETER_MAX] + }) + + #for t in TF.TF_no_mag: self._params['mag'][self._TF.index(t)].data-=self._params['mag'][self._TF.index(t)].data #Mag inutile pour les TF ignore_mag + + #Distribution + self._fixed_prob=fixed_prob + self._samples = [] + self._mix_dist = False + if mix_dist != 0.0: + self._mix_dist = True + self._mix_factor = max(min(mix_dist, 1.0), 0.0) + + #Mag regularisation + if not self._fixed_mag: + if self._shared_mag : + self._reg_tgt = torch.tensor(TF.PARAMETER_MAX, dtype=torch.float) #Encourage amplitude max + else: + self._reg_mask=[self._TF.index(t) for t in self._TF if t not in TF.TF_ignore_mag] + self._reg_tgt=torch.full(size=(len(self._reg_mask),), fill_value=TF.PARAMETER_MAX) #Encourage amplitude max + + def forward(self, x): + self._samples = [] + if self._data_augmentation:# and TF.random.random() < 0.5: + device = x.device + batch_size, h, w = x.shape[0], x.shape[2], x.shape[3] + + x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles) + + for _ in range(self._N_seqTF): + ## Echantillonage ## + uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=1) + + if not self._mix_dist: + self._distrib = uniforme_dist + else: + prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"] + self._distrib = (self._mix_factor*prob+(1-self._mix_factor)*uniforme_dist).softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor + + cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*self._distrib) + sample = cat_distrib.sample() + self._samples.append(sample) + + ## Transformations ## + x = self.apply_TF(x, sample) + return x + + def apply_TF(self, x, sampled_TF): + device = x.device + batch_size, channels, h, w = x.shape + smps_x=[] + + for tf_idx in range(self._nb_tf): + mask = sampled_TF==tf_idx #Create selection mask + smp_x = x[mask] #torch.masked_select() ? (NEcessite d'expand le mask au meme dim) + + if smp_x.shape[0]!=0: #if there's data to TF + magnitude=self._params["mag"] if self._shared_mag else self._params["mag"][tf_idx] + if self._fixed_mag: magnitude=magnitude.detach() #Fmodel tente systematiquement de tracker les gradient de tout les param + + tf=self._TF[tf_idx] + #print(magnitude) + + #In place + #x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude) + + #Out of place + smp_x = self._TF_dict[tf](x=smp_x, mag=magnitude) + idx= mask.nonzero() + idx= idx.expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ... + x=x.scatter(dim=0, index=idx, src=smp_x) + + return x + + def adjust_param(self, soft=False): #Detach from gradient ? + if not self._fixed_prob: + if soft : + self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible + else: + self._params['prob'].data = F.relu(self._params['prob'].data) + #self._params['prob'].data = self._params['prob'].clamp(min=0.0,max=1.0) + self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1 + + if not self._fixed_mag: + #self._params['mag'].data = self._params['mag'].data.clamp(min=0.0,max=TF.PARAMETER_MAX) #Bloque une fois au extreme + self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX) + + def loss_weight(self): + if len(self._samples)==0 : return 1 #Pas d'echantillon = pas de ponderation + + prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"] + # 1 seule TF + #self._sample = self._samples[-1] + #w_loss = torch.zeros((self._sample.shape[0],self._nb_tf), device=self._sample.device) + #w_loss.scatter_(dim=1, index=self._sample.view(-1,1), value=1) + #w_loss = w_loss * self._params["prob"]/self._distrib #Ponderation par les proba (divisee par la distrib pour pas diminuer la loss) + #w_loss = torch.sum(w_loss,dim=1) + + #Plusieurs TF sequentielles (Attention ne prend pas en compte ordre !) + w_loss = torch.zeros((self._samples[0].shape[0],self._nb_tf), device=self._samples[0].device) + for sample in self._samples: + tmp_w = torch.zeros(w_loss.size(),device=w_loss.device) + tmp_w.scatter_(dim=1, index=sample.view(-1,1), value=1/self._N_seqTF) + w_loss += tmp_w + + w_loss = w_loss * prob/self._distrib #Ponderation par les proba (divisee par la distrib pour pas diminuer la loss) + w_loss = torch.sum(w_loss,dim=1) + return w_loss + + def reg_loss(self, reg_factor=0.005): + if self._fixed_mag: # or self._fixed_prob: #Pas de regularisation si trop peu de DOF + return torch.tensor(0) + else: + #return reg_factor * F.l1_loss(self._params['mag'][self._reg_mask], target=self._reg_tgt, reduction='mean') + params = self._params['mag'] if self._params['mag'].shape==torch.Size([]) else self._params['mag'][self._reg_mask] + return reg_factor * F.mse_loss(params, target=self._reg_tgt.to(params.device), reduction='mean') + + def train(self, mode=None): + if mode is None : + mode=self._data_augmentation + self.augment(mode=mode) #Inutile si mode=None + super(Data_augV5, self).train(mode) + + def eval(self): + self.train(mode=False) + + def augment(self, mode=True): + self._data_augmentation=mode + + def __getitem__(self, key): + return self._params[key] + + def __str__(self): + dist_param='' + if self._fixed_prob: dist_param+='Fx' + mag_param='Mag' + if self._fixed_mag: mag_param+= 'Fx' + if self._shared_mag: mag_param+= 'Sh' + if not self._mix_dist: + return "Data_augV5(Uniform%s-%dTFx%d-%s)" % (dist_param, self._nb_tf, self._N_seqTF, mag_param) + else: + return "Data_augV5(Mix%.1f%s-%dTFx%d-%s)" % (self._mix_factor,dist_param, self._nb_tf, self._N_seqTF, mag_param) + +class Data_augV6(nn.Module): #Optimisation sequentielle + def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=None, fixed_mag=True, shared_mag=True): + super(Data_augV6, self).__init__() + assert len(TF_dict)>0 + + self._data_augmentation = True + + self._TF_dict = TF_dict + self._TF= list(self._TF_dict.keys()) + self._nb_tf= len(self._TF) + + self._N_seqTF = N_TF + self._shared_mag = shared_mag + self._fixed_mag = fixed_mag + + self._TF_set_size = prob_set_size if prob_set_size else self._nb_tf + + self._fixed_TF=[0] #Identite + assert self._TF_set_size>=len(self._fixed_TF) + + if self._TF_set_size>self._nb_tf: + print("Warning : TF sets size higher than number of TF. Reducing set size to %d"%self._nb_tf) + self._TF_set_size=self._nb_tf + + ## Genenerate TF sets ## + if self._TF_set_size==len(self._fixed_TF): + print("Warning : using only fixed set of TF : ", self._fixed_TF) + self._TF_sets=torch.tensor([self._fixed_TF]) + else: + def generate_TF_sets(n_TF, set_size, idx_prefix=[]): + TF_sets=[] + if len(idx_prefix)!=0: + if set_size>2: + for i in range(idx_prefix[-1]+1, n_TF): + TF_sets += generate_TF_sets(n_TF=n_TF, set_size=set_size-1, idx_prefix=idx_prefix+[i]) + else: + #if i not in idx_prefix: + TF_sets+=[torch.tensor(idx_prefix+[i]) for i in range(idx_prefix[-1]+1, n_TF)] + elif set_size>1: + for i in range(0, n_TF): + TF_sets += generate_TF_sets(n_TF=n_TF, set_size=set_size, idx_prefix=[i]) + else: + TF_sets+=[torch.tensor([i]) for i in range(0, n_TF)] + return TF_sets + + self._TF_sets=generate_TF_sets(self._nb_tf, self._TF_set_size, self._fixed_TF) + + ## Plan TF learning schedule ## + self._TF_schedule = [list(range(len(self._TF_sets))) for _ in range(self._N_seqTF)] + for n_tf in range(self._N_seqTF) : + TF.random.shuffle(self._TF_schedule[n_tf]) + + self._current_TF_idx=0 #random.randint + self._start_prob = 1/self._TF_set_size + + + self._params = nn.ParameterDict({ + "prob": nn.Parameter(torch.tensor(self._start_prob).expand(self._nb_tf)), #Proba independantes + "mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX)) if self._shared_mag + else torch.tensor(float(TF.PARAMETER_MAX)).expand(self._nb_tf)), #[0, PARAMETER_MAX] + }) + + #for t in TF.TF_no_mag: self._params['mag'][self._TF.index(t)].data-=self._params['mag'][self._TF.index(t)].data #Mag inutile pour les TF ignore_mag + + #Distribution + self._fixed_prob=fixed_prob + self._samples = [] + self._mix_dist = False + if mix_dist != 0.0: + self._mix_dist = True + self._mix_factor = max(min(mix_dist, 1.0), 0.0) + + #Mag regularisation + if not self._fixed_mag: + if self._shared_mag : + self._reg_tgt = torch.tensor(TF.PARAMETER_MAX, dtype=torch.float) #Encourage amplitude max + else: + self._reg_mask=[self._TF.index(t) for t in self._TF if t not in TF.TF_ignore_mag] + self._reg_tgt=torch.full(size=(len(self._reg_mask),), fill_value=TF.PARAMETER_MAX) #Encourage amplitude max + + def forward(self, x): + self._samples = [] + if self._data_augmentation:# and TF.random.random() < 0.5: + device = x.device + batch_size, h, w = x.shape[0], x.shape[2], x.shape[3] + + x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles) + + for n_tf in range(self._N_seqTF): + + tf_set = self._TF_sets[self._TF_schedule[n_tf][self._current_TF_idx]].to(device) + #print(n_tf, tf_set) + ## Echantillonage ## + uniforme_dist = torch.ones(1,len(tf_set),device=device).softmax(dim=1) + + if not self._mix_dist: + self._distrib = uniforme_dist + else: + prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"] + curr_prob = torch.index_select(prob, 0, tf_set) + curr_prob = curr_prob /sum(curr_prob) #Contrainte sum(p)=1 + self._distrib = (self._mix_factor*curr_prob+(1-self._mix_factor)*uniforme_dist).softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor + + cat_distrib= Categorical(probs=torch.ones((batch_size, len(tf_set)), device=device)*self._distrib) + sample = cat_distrib.sample() + self._samples.append(sample) + + ## Transformations ## + x = self.apply_TF(x, sample) + return x + + def apply_TF(self, x, sampled_TF): + device = x.device + batch_size, channels, h, w = x.shape + smps_x=[] + + for sel_idx, tf_idx in enumerate(self._TF_sets[self._current_TF_idx]): + mask = sampled_TF==sel_idx #Create selection mask + smp_x = x[mask] #torch.masked_select() ? (NEcessite d'expand le mask au meme dim) + + if smp_x.shape[0]!=0: #if there's data to TF + magnitude=self._params["mag"] if self._shared_mag else self._params["mag"][tf_idx] + if self._fixed_mag: magnitude=magnitude.detach() #Fmodel tente systematiquement de tracker les gradient de tout les param + + tf=self._TF[tf_idx] + #print(magnitude) + + #In place + #x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude) + + #Out of place + smp_x = self._TF_dict[tf](x=smp_x, mag=magnitude) + idx= mask.nonzero() + idx= idx.expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ... + x=x.scatter(dim=0, index=idx, src=smp_x) + + return x + + def adjust_param(self, soft=False): #Detach from gradient ? + if not self._fixed_prob: + if soft : + self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible + else: + self._params['prob'].data = F.relu(self._params['prob'].data) + #self._params['prob'].data = self._params['prob'].clamp(min=0.0,max=1.0) + #self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1 + + self._params['prob'].data[0]=self._start_prob #Fixe p identite + + if not self._fixed_mag: + #self._params['mag'].data = self._params['mag'].data.clamp(min=0.0,max=TF.PARAMETER_MAX) #Bloque une fois au extreme + self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX) + + def loss_weight(self): #A verifier + if len(self._samples)==0 : return 1 #Pas d'echantillon = pas de ponderation + + prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"] + + #Plusieurs TF sequentielles (Attention ne prend pas en compte ordre !) + w_loss = torch.zeros((self._samples[0].shape[0],self._TF_set_size), device=self._samples[0].device) + for n_tf in range(self._N_seqTF): + tmp_w = torch.zeros(w_loss.size(),device=w_loss.device) + tmp_w.scatter_(dim=1, index=self._samples[n_tf].view(-1,1), value=1/self._N_seqTF) + + tf_set = self._TF_sets[self._TF_schedule[n_tf][self._current_TF_idx]].to(prob.device) + curr_prob = torch.index_select(prob, 0, tf_set) + curr_prob = curr_prob /sum(curr_prob) #Contrainte sum(p)=1 + + #ATTENTION DISTRIB DIFFERENTE AVEC MIX + assert not self._mix_dist + w_loss += tmp_w * curr_prob /self._distrib #Ponderation par les proba (divisee par la distrib pour pas diminuer la loss) + + w_loss = torch.sum(w_loss,dim=1) + return w_loss + + def reg_loss(self, reg_factor=0.005): + if self._fixed_mag: # or self._fixed_prob: #Pas de regularisation si trop peu de DOF + return torch.tensor(0) + else: + #return reg_factor * F.l1_loss(self._params['mag'][self._reg_mask], target=self._reg_tgt, reduction='mean') + params = self._params['mag'] if self._params['mag'].shape==torch.Size([]) else self._params['mag'][self._reg_mask] + return reg_factor * F.mse_loss(params, target=self._reg_tgt.to(params.device), reduction='mean') + + def next_TF_set(self, idx=None): + if idx: + self._current_TF_idx=idx + else: + self._current_TF_idx+=1 + + if self._current_TF_idx>=len(self._TF_schedule[0]): + self._current_TF_idx=0 + for n_tf in range(self._N_seqTF) : + TF.random.shuffle(self._TF_schedule[n_tf]) + + def train(self, mode=None): + if mode is None : + mode=self._data_augmentation + self.augment(mode=mode) #Inutile si mode=None + super(Data_augV6, self).train(mode) + + def eval(self): + self.train(mode=False) + + def augment(self, mode=True): + self._data_augmentation=mode + + def __getitem__(self, key): + return self._params[key] + + def __str__(self): + dist_param='' + if self._fixed_prob: dist_param+='Fx' + mag_param='Mag' + if self._fixed_mag: mag_param+= 'Fx' + if self._shared_mag: mag_param+= 'Sh' + if not self._mix_dist: + return "Data_augV6(Uniform%s-%dTF(%d)x%d-%s)" % (dist_param, self._nb_tf, self._TF_set_size, self._N_seqTF, mag_param) + else: + return "Data_augV6(Mix%.1f%s-%dTF(%d)x%d-%s)" % (self._mix_factor,dist_param, self._nb_tf, self._TF_set_size, self._N_seqTF, mag_param) + + +class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh + rapide + def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mag=TF.PARAMETER_MAX): + super(RandAug, self).__init__() + + self._data_augmentation = True + + self._TF_dict = TF_dict + self._TF= list(self._TF_dict.keys()) + self._nb_tf= len(self._TF) + self._N_seqTF = N_TF + + self.mag=nn.Parameter(torch.tensor(float(mag))) + self._params = nn.ParameterDict({ + "prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #pas utilise + "mag" : nn.Parameter(torch.tensor(float(mag))), + }) + self._shared_mag = True + self._fixed_mag = True + + def forward(self, x): + if self._data_augmentation:# and TF.random.random() < 0.5: + device = x.device + batch_size, h, w = x.shape[0], x.shape[2], x.shape[3] + + x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles) + + for _ in range(self._N_seqTF): + ## Echantillonage ## == sampled_ops = np.random.choice(transforms, N) + uniforme_dist = torch.ones(1,self._nb_tf,device=device).softmax(dim=1) + cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*uniforme_dist) + sample = cat_distrib.sample() + + ## Transformations ## + x = self.apply_TF(x, sample) + return x + + def apply_TF(self, x, sampled_TF): + smps_x=[] + + for tf_idx in range(self._nb_tf): + mask = sampled_TF==tf_idx #Create selection mask + smp_x = x[mask] #torch.masked_select() ? (NEcessite d'expand le mask au meme dim) + + if smp_x.shape[0]!=0: #if there's data to TF + magnitude=self._params["mag"].detach() + + tf=self._TF[tf_idx] + #print(magnitude) + + #In place + x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude) + + return x + + def adjust_param(self, soft=False): + pass #Pas de parametre a opti + + def loss_weight(self): + return 1 #Pas d'echantillon = pas de ponderation + + def reg_loss(self, reg_factor=0.005): + return torch.tensor(0) #Pas de regularisation + + def train(self, mode=None): + if mode is None : + mode=self._data_augmentation + self.augment(mode=mode) #Inutile si mode=None + super(RandAug, self).train(mode) + + def eval(self): + self.train(mode=False) + + def augment(self, mode=True): + self._data_augmentation=mode + + def __getitem__(self, key): + return self._params[key] + + def __str__(self): + return "RandAug(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag) + +class RandAugUDA(nn.Module): #RandAugment from UDA (for DA during training) + def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mag=TF.PARAMETER_MAX): + super(RandAugUDA, self).__init__() + + self._data_augmentation = True + + self._TF_dict = TF_dict + self._TF= list(self._TF_dict.keys()) + self._nb_tf= len(self._TF) + self._N_seqTF = N_TF + + self.mag=nn.Parameter(torch.tensor(float(mag))) + self._params = nn.ParameterDict({ + "prob": nn.Parameter(torch.tensor(0.5).unsqueeze(dim=0)), + "mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX))), + }) + self._shared_mag = True + self._fixed_mag = True + + self._op_list =[] + for tf in self._TF: + for mag in range(1, int(self._params['mag']*10), 1): + self._op_list+=[(tf, self._params['prob'].item(), mag/10)] + self._nb_op = len(self._op_list) + + def forward(self, x): + if self._data_augmentation:# and TF.random.random() < 0.5: + device = x.device + batch_size, h, w = x.shape[0], x.shape[2], x.shape[3] + + x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles) + + for _ in range(self._N_seqTF): + ## Echantillonage ## == sampled_ops = np.random.choice(transforms, N) + uniforme_dist = torch.ones(1, self._nb_op, device=device).softmax(dim=1) + cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_op), device=device)*uniforme_dist) + sample = cat_distrib.sample() + + ## Transformations ## + x = self.apply_TF(x, sample) + return x + + def apply_TF(self, x, sampled_TF): + smps_x=[] + + for op_idx in range(self._nb_op): + mask = sampled_TF==op_idx #Create selection mask + smp_x = x[mask] #torch.masked_select() ? (Necessite d'expand le mask au meme dim) + + if smp_x.shape[0]!=0: #if there's data to TF + if TF.random.random() < self._op_list[op_idx][1]: + magnitude=self._op_list[op_idx][2] + tf=self._op_list[op_idx][0] + + #In place + x[mask]=self._TF_dict[tf](x=smp_x, mag=torch.tensor(magnitude, device=x.device)) + + return x + + def adjust_param(self, soft=False): + pass #Pas de parametre a opti + + def loss_weight(self): + return 1 #Pas d'echantillon = pas de ponderation + + def reg_loss(self, reg_factor=0.005): + return torch.tensor(0) #Pas de regularisation + + def train(self, mode=None): + if mode is None : + mode=self._data_augmentation + self.augment(mode=mode) #Inutile si mode=None + super(RandAugUDA, self).train(mode) + + def eval(self): + self.train(mode=False) + + def augment(self, mode=True): + self._data_augmentation=mode + + def __getitem__(self, key): + return self._params[key] + + def __str__(self): + return "RandAugUDA(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag) + +class Augmented_model(nn.Module): + def __init__(self, data_augmenter, model): + super(Augmented_model, self).__init__() + + self._mods = nn.ModuleDict({ + 'data_aug': data_augmenter, + 'model': model + }) + + self.augment(mode=True) + + def initialize(self): + self._mods['model'].initialize() + + def forward(self, x): + return self._mods['model'](self._mods['data_aug'](x)) + + def augment(self, mode=True): + self._data_augmentation=mode + self._mods['data_aug'].augment(mode) + + def train(self, mode=None): + if mode is None : + mode=self._data_augmentation + self._mods['data_aug'].augment(mode) + super(Augmented_model, self).train(mode) + return self + + def eval(self): + return self.train(mode=False) + #super(Augmented_model, self).eval() + + def items(self): + """Return an iterable of the ModuleDict key/value pairs. + """ + return self._mods.items() + + def update(self, modules): + self._mods.update(modules) + + def is_augmenting(self): + return self._data_augmentation + + def TF_names(self): + try: + return self._mods['data_aug']._TF + except: + return None + + def __getitem__(self, key): + return self._mods[key] + + def __str__(self): + return "Aug_mod("+str(self._mods['data_aug'])+"-"+str(self._mods['model'])+")" \ No newline at end of file diff --git a/salvador/dataug_utils.py b/salvador/dataug_utils.py new file mode 100644 index 0000000..ea81ea3 --- /dev/null +++ b/salvador/dataug_utils.py @@ -0,0 +1,314 @@ +import numpy as np +import json, math, time, os +import matplotlib.pyplot as plt +import copy +import gc + +from torchviz import make_dot + +import torch +import torch.nn.functional as F + +import time + +class timer(): + def __init__(self): + self._start_time=time.time() + def exec_time(self): + end = time.time() + res = end-self._start_time + self._start_time=end + return res + +def print_graph(PyTorch_obj, fig_name='graph'): + graph=make_dot(PyTorch_obj) #Loss give the whole graph + graph.format = 'svg' #https://graphviz.readthedocs.io/en/stable/manual.html#formats + graph.render(fig_name) + +def plot_res(log, fig_name='res', param_names=None): + + epochs = [x["epoch"] for x in log] + + fig, ax = plt.subplots(ncols=3, figsize=(15, 3)) + + ax[0].set_title('Loss') + ax[0].plot(epochs,[x["train_loss"] for x in log], label='Train') + ax[0].plot(epochs,[x["val_loss"] for x in log], label='Val') + ax[0].legend() + + ax[1].set_title('Acc') + ax[1].plot(epochs,[x["acc"] for x in log]) + + if log[0]["param"]!= None: + if isinstance(log[0]["param"],float): + ax[2].set_title('Mag') + ax[2].plot(epochs,[x["param"] for x in log], label='Mag') + ax[2].legend() + else : + ax[2].set_title('Prob') + #for idx, _ in enumerate(log[0]["param"]): + #ax[2].plot(epochs,[x["param"][idx] for x in log], label='P'+str(idx)) + if not param_names : param_names = ['P'+str(idx) for idx, _ in enumerate(log[0]["param"])] + proba=[[x["param"][idx] for x in log] for idx, _ in enumerate(log[0]["param"])] + ax[2].stackplot(epochs, proba, labels=param_names) + ax[2].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5)) + + + fig_name = fig_name.replace('.',',') + plt.savefig(fig_name) + plt.close() + +def plot_resV2(log, fig_name='res', param_names=None): + + epochs = [x["epoch"] for x in log] + + fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(30, 15)) + + ax[0, 0].set_title('Loss') + ax[0, 0].plot(epochs,[x["train_loss"] for x in log], label='Train') + ax[0, 0].plot(epochs,[x["val_loss"] for x in log], label='Val') + ax[0, 0].legend() + + ax[1, 0].set_title('Acc') + ax[1, 0].plot(epochs,[x["acc"] for x in log]) + + if log[0]["param"]!= None: + if not param_names : param_names = ['P'+str(idx) for idx, _ in enumerate(log[0]["param"])] + #proba=[[x["param"][idx] for x in log] for idx, _ in enumerate(log[0]["param"])] + proba=[[x["param"][idx]['p'] for x in log] for idx, _ in enumerate(log[0]["param"])] + mag=[[x["param"][idx]['m'] for x in log] for idx, _ in enumerate(log[0]["param"])] + + ax[0, 1].set_title('Prob =f(epoch)') + ax[0, 1].stackplot(epochs, proba, labels=param_names) + #ax[0, 1].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5)) + + ax[1, 1].set_title('Prob =f(TF)') + mean = np.mean(proba, axis=1) + std = np.std(proba, axis=1) + ax[1, 1].bar(param_names, mean, yerr=std) + plt.sca(ax[1, 1]), plt.xticks(rotation=90) + + ax[0, 2].set_title('Mag =f(epoch)') + ax[0, 2].stackplot(epochs, mag, labels=param_names) + ax[0, 2].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5)) + + ax[1, 2].set_title('Mag =f(TF)') + mean = np.mean(mag, axis=1) + std = np.std(mag, axis=1) + ax[1, 2].bar(param_names, mean, yerr=std) + plt.sca(ax[1, 2]), plt.xticks(rotation=90) + + + fig_name = fig_name.replace('.',',') + plt.savefig(fig_name, bbox_inches='tight') + plt.close() + +def plot_compare(filenames, fig_name='res'): + + all_data=[] + legend="" + for idx, file in enumerate(filenames): + legend+=str(idx)+'-'+file+'\n' + with open(file) as json_file: + data = json.load(json_file) + all_data.append(data) + + fig, ax = plt.subplots(ncols=3, figsize=(30, 8)) + + for data_idx, log in enumerate(all_data): + log=log['Log'] + epochs = [x["epoch"] for x in log] + + ax[0].plot(epochs,[x["train_loss"] for x in log], label=str(data_idx)+'-Train') + ax[0].plot(epochs,[x["val_loss"] for x in log], label=str(data_idx)+'-Val') + + ax[1].plot(epochs,[x["acc"] for x in log], label=str(data_idx)) + #ax[1].text(x=0.5,y=0,s=str(data_idx)+'-'+filenames[data_idx], transform=ax[1].transAxes) + + if log[0]["param"]!= None: + if isinstance(log[0]["param"],float): + ax[2].plot(epochs,[x["param"] for x in log], label=str(data_idx)+'-Mag') + + else : + for idx, _ in enumerate(log[0]["param"]): + ax[2].plot(epochs,[x["param"][idx] for x in log], label=str(data_idx)+'-P'+str(idx)) + + fig.suptitle(legend) + ax[0].set_title('Loss') + ax[1].set_title('Acc') + ax[2].set_title('Param') + for a in ax: a.legend() + + fig_name = fig_name.replace('.',',') + plt.savefig(fig_name, bbox_inches='tight') + plt.close() + +def plot_res_compare(filenames, fig_name='res'): + + all_data=[] + #legend="" + for idx, file in enumerate(filenames): + #legend+=str(idx)+'-'+file+'\n' + with open(file) as json_file: + data = json.load(json_file) + all_data.append(data) + + n_tf = [len(x["Param_names"]) for x in all_data] + acc = [x["Accuracy"] for x in all_data] + time = [x["Time"][0] for x in all_data] + + fig, ax = plt.subplots(ncols=3, figsize=(30, 8)) + + ax[0].plot(n_tf, acc) + ax[1].plot(n_tf, time) + + ax[0].set_title('Acc') + ax[1].set_title('Time') + #for a in ax: a.legend() + + fig_name = fig_name.replace('.',',') + plt.savefig(fig_name, bbox_inches='tight') + plt.close() + +def plot_TF_res(log, tf_names, fig_name='res'): + + mean = np.mean([x["param"] for x in log], axis=0) + std = np.std([x["param"] for x in log], axis=0) + + fig, ax = plt.subplots(1, 1, figsize=(30, 8), sharey=True) + ax.bar(tf_names, mean, yerr=std) + #ax.bar(tf_names, log[-1]["param"]) + + fig_name = fig_name.replace('.',',') + plt.savefig(fig_name, bbox_inches='tight') + plt.close() + +def viz_sample_data(imgs, labels, fig_name='data_sample'): + + sample = imgs[0:25,].permute(0, 2, 3, 1).squeeze().cpu() + + plt.figure(figsize=(10,10)) + for i in range(25): + plt.subplot(5,5,i+1) + plt.xticks([]) + plt.yticks([]) + plt.grid(False) + plt.imshow(sample[i,].detach().numpy(), cmap=plt.cm.binary) + plt.xlabel(labels[i].item()) + + plt.savefig(fig_name) + print("Sample saved :", fig_name) + plt.close() + +def model_copy(src,dst, patch_copy=True, copy_grad=True): + #model=copy.deepcopy(fmodel) #Pas approprie, on ne souhaite que les poids/grad (pas tout fmodel et ses etats) + + dst.load_state_dict(src.state_dict()) #Do not copy gradient ! + + if patch_copy: + dst['model'].load_state_dict(src['model'].state_dict()) #Copie donnee manquante ? + dst['data_aug'].load_state_dict(src['data_aug'].state_dict()) + + #Copie des gradients + if copy_grad: + for paramName, paramValue, in src.named_parameters(): + for netCopyName, netCopyValue, in dst.named_parameters(): + if paramName == netCopyName: + netCopyValue.grad = paramValue.grad + #netCopyValue=copy.deepcopy(paramValue) + + try: #Data_augV4 + dst['data_aug']._input_info = src['data_aug']._input_info + dst['data_aug']._TF_matrix = src['data_aug']._TF_matrix + except: + pass + +def optim_copy(dopt, opt): + + #inner_opt.load_state_dict(diffopt.state_dict()) #Besoin sauver etat otpim (momentum, etc.) => Ne copie pas le state... + #opt_param=higher.optim.get_trainable_opt_params(diffopt) + + for group_idx, group in enumerate(opt.param_groups): + # print('gp idx',group_idx) + for p_idx, p in enumerate(group['params']): + opt.state[p]=dopt.state[group_idx][p_idx] + +def print_torch_mem(add_info=''): + + nb=0 + max_size=0 + for obj in gc.get_objects(): + #print(type(obj)) + try: + if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): # and len(obj.size())>1: + #print(i, type(obj), obj.size()) + size = np.sum(obj.size()) + if(size>max_size): max_size=size + nb+=1 + except: + pass + print(add_info, "-Pytroch tensor nb:",nb," / Max dim:", max_size) + + #print(add_info, "-Garbage size :",len(gc.garbage)) + + """Simple GPU memory report.""" + + mega_bytes = 1024.0 * 1024.0 + string = add_info + ' memory (MB)' + string += ' | allocated: {}'.format( + torch.cuda.memory_allocated() / mega_bytes) + string += ' | max allocated: {}'.format( + torch.cuda.max_memory_allocated() / mega_bytes) + string += ' | cached: {}'.format(torch.cuda.memory_cached() / mega_bytes) + string += ' | max cached: {}'.format( + torch.cuda.max_memory_cached()/ mega_bytes) + print(string) + +def plot_TF_influence(log, fig_name='TF_influence', param_names=None): + proba=[[x["param"][idx]['p'] for x in log] for idx, _ in enumerate(log[0]["param"])] + mag=[[x["param"][idx]['m'] for x in log] for idx, _ in enumerate(log[0]["param"])] + + plt.figure() + + mean = np.mean(proba, axis=1)*np.mean(mag, axis=1) #Pourrait etre interessant de multiplier avant le mean + std = np.std(proba, axis=1)*np.std(mag, axis=1) + plt.bar(param_names, mean, yerr=std) + + plt.xticks(rotation=90) + fig_name = fig_name.replace('.',',') + plt.savefig(fig_name, bbox_inches='tight') + plt.close() + +class loss_monitor(): #Voir https://github.com/pytorch/ignite + def __init__(self, patience, end_train=1): + self.patience = patience + self.end_train = end_train + self.counter = 0 + self.best_score = None + self.reached_limit = 0 + + def register(self, loss): + if self.best_score is None: + self.best_score = loss + elif loss > self.best_score: + self.counter += 1 + #if not self.reached_limit: + print("loss no improve counter", self.counter, self.reached_limit) + else: + self.best_score = loss + self.counter = 0 + def limit_reached(self): + if self.counter >= self.patience: + self.counter = 0 + self.reached_limit +=1 + self.best_score = None + return self.reached_limit + + def end_training(self): + if self.limit_reached() >= self.end_train: + return True + else: + return False + + def reset(self): + self.__init__(self.patience, self.end_train) \ No newline at end of file diff --git a/salvador/grad_cam.py b/salvador/grad_cam.py new file mode 100644 index 0000000..2aeada5 --- /dev/null +++ b/salvador/grad_cam.py @@ -0,0 +1,102 @@ +import torch +import numpy as np +import torchvision +from PIL import Image +from torch import topk +from torch import nn +import torch.nn.functional as F +from torch import topk +import cv2 +from torchvision import transforms +import os + +class Lambda(nn.Module): + "Create a layer that simply calls `func` with `x`" + def __init__(self, func): + super().__init__() + self.func=func + def forward(self, x): return self.func(x) + +class SaveFeatures(): + activations, gradients = None, None + def __init__(self, m): + self.forward = m.register_forward_hook(self.forward_hook_fn) + self.backward = m.register_backward_hook(self.backward_hook_fn) + + def forward_hook_fn(self, module, input, output): + self.activations = output.cpu().detach() + + def backward_hook_fn(self, module, grad_input, grad_output): + self.gradients = grad_output[0].cpu().detach() + + def remove(self): + self.forward.remove() + self.backward.remove() + +def main(cam): + device = 'cuda:0' + model_name = 'resnet50' + root = '/mnt/md0/data/cifar10/tmp/cifar/train' + _root = 'cifar' + + os.makedirs(os.path.join(_root + '_CAM'), exist_ok=True) + os.makedirs(os.path.join(_root + '_CAM'), exist_ok=True) + + train_transform = transforms.Compose([ + transforms.ToTensor(), + ]) + + dataset = torchvision.datasets.ImageFolder( + root=root, transform=train_transform, + ) + + loader = torch.utils.data.DataLoader(dataset, batch_size=1) + model = torchvision.models.__dict__[model_name](pretrained=True) + flat = list(model.children()) + body, head = nn.Sequential(*flat[:-2]), nn.Sequential(flat[-2], Lambda(func=lambda x: torch.flatten(x, 1)), nn.Linear(flat[-1].in_features, len(loader.dataset.classes))) + model = nn.Sequential(body, head) + + model.load_state_dict(torch.load('checkpoint.pt', map_location=lambda storage, loc: storage)) + model = model.to(device) + model.eval() + + activated_features = SaveFeatures(model[0]) + + for i, (img, target ) in enumerate(loader): + img = img.to(device) + pred = model(img) + import ipdb; ipdb.set_trace() + # get the gradient of the output with respect to the parameters of the model + pred[:, target.item()].backward() + + # import ipdb; ipdb.set_trace() + # pull the gradients out of the model + gradients = activated_features.gradients[0] + + pooled_gradients = gradients.mean(1).mean(1) + + # get the activations of the last convolutional layer + activations = activated_features.activations[0] + + heatmap = F.relu(((activations*pooled_gradients[...,None,None])).sum(0)) + heatmap /= torch.max(heatmap) + + heatmap = heatmap.numpy() + + + image = cv2.imread(dataset.imgs[i][0]) + heatmap = cv2.resize(heatmap, (image.shape[1], image.shape[0])) + heatmap = np.uint8(255 * heatmap) + heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) + # superimposed_img = heatmap * 0.3 + image * 0.5 + superimposed_img = heatmap + + clss = dataset.imgs[i][0].split(os.sep)[1] + name = dataset.imgs[i][0].split(os.sep)[2].split('.')[0] + cv2.imwrite(os.path.join(_root+"_CAM", name + '.jpg'), superimposed_img) + print(f'{os.path.join(_root+"_CAM", name + ".jpg")} saved') + + activated_features.remove() + +if __name__ == "__main__": + main(cam=True) diff --git a/salvador/train.py b/salvador/train.py new file mode 100644 index 0000000..a5cb4a5 --- /dev/null +++ b/salvador/train.py @@ -0,0 +1,375 @@ +import datetime +import os +import time +import sys + +import torch +import torch.utils.data +from torch import nn +import torchvision +from torchvision import transforms +from PIL import ImageEnhance +import random + +import utils +from fastprogress import master_bar, progress_bar +import numpy as np + +## DATA AUG ## +import higher +from dataug import * +from dataug_utils import * +tf_names = [ + ## Geometric TF ## + 'Identity', + 'FlipUD', + 'FlipLR', + 'Rotate', + 'TranslateX', + 'TranslateY', + 'ShearX', + 'ShearY', + + ## Color TF (Expect image in the range of [0, 1]) ## + 'Contrast', + 'Color', + 'Brightness', + 'Sharpness', + 'Posterize', + 'Solarize', #=>Image entre [0,1] #Pas opti pour des batch +] + +class Lambda(nn.Module): + "Create a layer that simply calls `func` with `x`" + def __init__(self, func): + super().__init__() + self.func=func + def forward(self, x): return self.func(x) + +class SubsetSampler(torch.utils.data.SubsetRandomSampler): + def __init__(self, indices): + super().__init__(indices) + + def __iter__(self): + return (self.indices[i] for i in range(len(self.indices))) + + def __len__(self): + return len(self.indices) + +def sharpness(img, factor): + sharpness_factor = random.uniform(1, factor) + sharp = ImageEnhance.Sharpness(img) + sharped = sharp.enhance(sharpness_factor) + return sharped + +def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, master_bar, Kldiv=False): + model.train() + metric_logger = utils.MetricLogger(delimiter=" ") + confmat = utils.ConfusionMatrix(num_classes=len(data_loader.dataset.classes)) + header = 'Epoch: {}'.format(epoch) + for _, (image, target) in metric_logger.log_every(data_loader, header=header, parent=master_bar): + + image, target = image.to(device), target.to(device) + + if not Kldiv : + output = model(image) + #output = F.log_softmax(output, dim=1) + loss = criterion(output, target) #Pas de softmax ? + + else : #Consume x2 memory + model.augment(mode=False) + output = model(image) + model.augment(mode=True) + log_sup=F.log_softmax(output, dim=1) + sup_loss = F.cross_entropy(log_sup, target) + + aug_output = model(image) + log_aug=F.log_softmax(aug_output, dim=1) + aug_loss=F.cross_entropy(log_aug, target) + + #KL div w/ logits - Similarite predictions (distributions) + KL_loss = F.softmax(output, dim=1)*(log_sup-log_aug) + KL_loss = KL_loss.sum(dim=-1) + #KL_loss = F.kl_div(aug_logits, sup_logits, reduction='none') + KL_loss = KL_loss.mean() + + unsupp_coeff = 1 + loss = sup_loss + (aug_loss + KL_loss) * unsupp_coeff + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + acc1 = utils.accuracy(output, target)[0] + batch_size = image.shape[0] + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + metric_logger.update(loss=loss.item()) + + confmat.update(target.flatten(), output.argmax(1).flatten()) + + + return metric_logger.loss.global_avg, confmat + + +def evaluate(model, criterion, data_loader, device): + model.eval() + metric_logger = utils.MetricLogger(delimiter=" ") + confmat = utils.ConfusionMatrix(num_classes=len(data_loader.dataset.classes)) + header = 'Test:' + missed = [] + with torch.no_grad(): + for i, (image, target) in metric_logger.log_every(data_loader, leave=False, header=header, parent=None): + image, target = image.to(device), target.to(device) + output = model(image) + loss = criterion(output, target) + if target.item() != output.topk(1)[1].item(): + missed.append(data_loader.dataset.imgs[data_loader.sampler.indices[i]]) + + confmat.update(target.flatten(), output.argmax(1).flatten()) + + acc1 = utils.accuracy(output, target)[0] + batch_size = image.shape[0] + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + metric_logger.update(loss=loss.item()) + + + return metric_logger.loss.global_avg, missed, confmat + +def get_train_valid_loader(args, augment, random_seed, valid_size=0.1, shuffle=True, num_workers=4, pin_memory=True): + """ + Utility function for loading and returning train and valid + multi-process iterators over the CIFAR-10 dataset. A sample + 9x9 grid of the images can be optionally displayed. + If using CUDA, num_workers should be set to 1 and pin_memory to True. + Params + ------ + - data_dir: path directory to the dataset. + - batch_size: how many samples per batch to load. + - augment: whether to apply the data augmentation scheme + mentioned in the paper. Only applied on the train split. + - random_seed: fix seed for reproducibility. + - valid_size: percentage split of the training set used for + the validation set. Should be a float in the range [0, 1]. + - shuffle: whether to shuffle the train/validation indices. + - show_sample: plot 9x9 sample grid of the dataset. + - num_workers: number of subprocesses to use when loading the dataset. + - pin_memory: whether to copy tensors into CUDA pinned memory. Set it to + True if using GPU. + Returns + ------- + - train_loader: training set iterator. + - valid_loader: validation set iterator. + """ + error_msg = "[!] valid_size should be in the range [0, 1]." + assert ((valid_size >= 0) and (valid_size <= 1)), error_msg + + # normalize = transforms.Normalize( + # mean=[0.4914, 0.4822, 0.4465], + # std=[0.2023, 0.1994, 0.2010], + # ) + + # define transforms + if augment: + train_transform = transforms.Compose([ + # transforms.ColorJitter(brightness=0.3), + # transforms.Lambda(lambda img: sharpness(img, 5)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + # normalize, + ]) + + valid_transform = transforms.Compose([ + # transforms.ColorJitter(brightness=0.3), + # transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + # normalize, + ]) + else: + train_transform = transforms.Compose([ + transforms.ToTensor(), + # normalize, + ]) + + valid_transform = transforms.Compose([ + transforms.ToTensor(), + # normalize, + ]) + + + # load the dataset + train_dataset = torchvision.datasets.ImageFolder( + root=args.data_path, transform=train_transform + ) + + valid_dataset = torchvision.datasets.ImageFolder( + root=args.data_path, transform=valid_transform + ) + + num_train = len(train_dataset) + indices = list(range(num_train)) + split = int(np.floor(valid_size * num_train)) + + if shuffle: + #np.random.seed(random_seed) + np.random.shuffle(indices) + + train_idx, valid_idx = indices[split:], indices[:split] + train_sampler = torch.utils.data.SubsetRandomSampler(train_idx) if not args.test_only else SubsetSampler(train_idx) + valid_sampler = SubsetSampler(valid_idx) + + train_loader = torch.utils.data.DataLoader( + train_dataset, batch_size=args.batch_size if not args.test_only else 1, sampler=train_sampler, + num_workers=num_workers, pin_memory=pin_memory, + ) + valid_loader = torch.utils.data.DataLoader( + valid_dataset, batch_size=1, sampler=valid_sampler, + num_workers=num_workers, pin_memory=pin_memory, + ) + + imgs = np.asarray(train_dataset.imgs) + + # print('Train') + # print(imgs[train_idx]) + #print('Valid') + #print(imgs[valid_idx]) + + tgt = [0,0] + for _, targets in train_loader: + for target in targets: + tgt[target]+=1 + print("Train targets :", tgt) + + tgt = [0,0] + for _, targets in valid_loader: + for target in targets: + tgt[target]+=1 + print("Valid targets :", tgt) + + return (train_loader, valid_loader) + +def main(args): + print(args) + + device = torch.device(args.device) + + torch.backends.cudnn.benchmark = True + + + #augment = True if not args.test_only else False + + if not args.test_only and args.augment=='flip' : augment = True + else : augment = False + + print("Augment", augment) + data_loader, data_loader_test = get_train_valid_loader(args=args, pin_memory=True, augment=augment, + num_workers=args.workers, valid_size=0.3, random_seed=999) + + print("Creating model") + model = torchvision.models.__dict__[args.model](pretrained=True) + flat = list(model.children()) + + body, head = nn.Sequential(*flat[:-2]), nn.Sequential(flat[-2], Lambda(func=lambda x: torch.flatten(x, 1)), nn.Linear(flat[-1].in_features, len(data_loader.dataset.classes))) + model = nn.Sequential(body, head) + + Kldiv=False + if not args.test_only and (args.augment=='Rand' or args.augment=='RandKL'): + tf_dict = {k: TF.TF_dict[k] for k in tf_names} + model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device) + + if args.augment=='RandKL': Kldiv=True + print("Augmodel") + + # model.fc = nn.Linear(model.fc.in_features, 2) + # import ipdb; ipdb.set_trace() + + criterion = nn.CrossEntropyLoss().to(device) + + # optimizer = torch.optim.SGD( + # model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) + + optimizer = torch.optim.Adam( + model.parameters(), lr=args.lr, weight_decay=args.weight_decay) + + lr_scheduler = torch.optim.lr_scheduler.LambdaLR( + optimizer, + lambda x: (1 - x / (len(data_loader) * args.epochs)) ** 0.9) + + es = utils.EarlyStopping() + + if args.test_only: + model.load_state_dict(torch.load('checkpoint.pt', map_location=lambda storage, loc: storage)) + model = model.to(device) + print('TEST') + _, missed, _ = evaluate(model, criterion, data_loader_test, device=device) + print(missed) + print('TRAIN') + _, missed, _ = evaluate(model, criterion, data_loader, device=device) + print(missed) + return + + model = model.to(device) + + print("Start training") + start_time = time.time() + mb = master_bar(range(args.epochs)) + + for epoch in mb: + _, train_confmat = train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, mb, Kldiv) + lr_scheduler.step( (epoch+1)*len(data_loader) ) + val_loss, _, valid_confmat = evaluate(model, criterion, data_loader_test, device=device) + es(val_loss, model) + + # print('Valid Missed') + # print(valid_missed) + + # print('Train') + # print(train_confmat) + print('Valid') + print(valid_confmat) + + # if es.early_stop: + # break + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + +def parse_args(): + import argparse + parser = argparse.ArgumentParser(description='PyTorch Classification Training') + + parser.add_argument('--data-path', default='/Salvador', help='dataset') + parser.add_argument('--model', default='resnet18', help='model') #'resnet18' + parser.add_argument('--device', default='cuda:1', help='device') + parser.add_argument('-b', '--batch-size', default=8, type=int) + parser.add_argument('--epochs', default=3, type=int, metavar='N', + help='number of total epochs to run') + parser.add_argument('-j', '--workers', default=0, type=int, metavar='N', + help='number of data loading workers (default: 16)') + parser.add_argument('--lr', default=0.001, type=float, help='initial learning rate') + parser.add_argument('--momentum', default=0.9, type=float, metavar='M', + help='momentum') + parser.add_argument('--wd', '--weight-decay', default=4e-5, type=float, + metavar='W', help='weight decay (default: 1e-4)', + dest='weight_decay') + + parser.add_argument( + "--test-only", + dest="test_only", + help="Only test the model", + action="store_true", + ) + + parser.add_argument('-a', '--augment', default='None', type=str, + metavar='N', help='Data augment', + dest='augment') + + args = parser.parse_args() + + return args + + +if __name__ == "__main__": + args = parse_args() + main(args) \ No newline at end of file diff --git a/salvador/train_dataug.py b/salvador/train_dataug.py new file mode 100644 index 0000000..c8a7dde --- /dev/null +++ b/salvador/train_dataug.py @@ -0,0 +1,585 @@ +import datetime +import os +import time +import sys + +import torch +import torch.utils.data +from torch import nn +import torchvision +from torchvision import transforms +from PIL import ImageEnhance +import random + +import utils +from fastprogress import master_bar, progress_bar +import numpy as np + + +## DATA AUG ## +import higher +from dataug import * +from dataug_utils import * +tf_names = [ + ## Geometric TF ## + 'Identity', + 'FlipUD', + 'FlipLR', + 'Rotate', + 'TranslateX', + 'TranslateY', + 'ShearX', + 'ShearY', + + ## Color TF (Expect image in the range of [0, 1]) ## + 'Contrast', + 'Color', + 'Brightness', + 'Sharpness', + 'Posterize', + 'Solarize', #=>Image entre [0,1] #Pas opti pour des batch +] + +def compute_vaLoss(model, dl_it, dl): + device = next(model.parameters()).device + try: + xs, ys = next(dl_it) + except StopIteration: #Fin epoch val + dl_it = iter(dl) + xs, ys = next(dl_it) + xs, ys = xs.to(device), ys.to(device) + + model.eval() #Validation sans transfornations ! + + return F.cross_entropy(model(xs), ys) + +def model_copy(src,dst, patch_copy=True, copy_grad=True): + #model=copy.deepcopy(fmodel) #Pas approprie, on ne souhaite que les poids/grad (pas tout fmodel et ses etats) + + dst.load_state_dict(src.state_dict()) #Do not copy gradient ! + + if patch_copy: + dst['model'].load_state_dict(src['model'].state_dict()) #Copie donnee manquante ? + dst['data_aug'].load_state_dict(src['data_aug'].state_dict()) + + #Copie des gradients + if copy_grad: + for paramName, paramValue, in src.named_parameters(): + for netCopyName, netCopyValue, in dst.named_parameters(): + if paramName == netCopyName: + netCopyValue.grad = paramValue.grad + #netCopyValue=copy.deepcopy(paramValue) + + try: #Data_augV4 + dst['data_aug']._input_info = src['data_aug']._input_info + dst['data_aug']._TF_matrix = src['data_aug']._TF_matrix + except: + pass + +def optim_copy(dopt, opt): + + #inner_opt.load_state_dict(diffopt.state_dict()) #Besoin sauver etat otpim (momentum, etc.) => Ne copie pas le state... + #opt_param=higher.optim.get_trainable_opt_params(diffopt) + + for group_idx, group in enumerate(opt.param_groups): + # print('gp idx',group_idx) + for p_idx, p in enumerate(group['params']): + opt.state[p]=dopt.state[group_idx][p_idx] + + +############# + +class Lambda(nn.Module): + "Create a layer that simply calls `func` with `x`" + def __init__(self, func): + super().__init__() + self.func=func + def forward(self, x): return self.func(x) + +class SubsetSampler(torch.utils.data.SubsetRandomSampler): + def __init__(self, indices): + super().__init__(indices) + + def __iter__(self): + return (self.indices[i] for i in range(len(self.indices))) + + def __len__(self): + return len(self.indices) + +def sharpness(img, factor): + sharpness_factor = random.uniform(1, factor) + sharp = ImageEnhance.Sharpness(img) + sharped = sharp.enhance(sharpness_factor) + return sharped + +def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, master_bar): + model.train() + metric_logger = utils.MetricLogger(delimiter=" ") + confmat = utils.ConfusionMatrix(num_classes=len(data_loader.dataset.classes)) + header = 'Epoch: {}'.format(epoch) + for _, (image, target) in metric_logger.log_every(data_loader, header=header, parent=master_bar): + + image, target = image.to(device), target.to(device) + output = model(image) + loss = criterion(output, target) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + acc1 = utils.accuracy(output, target)[0] + batch_size = image.shape[0] + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + metric_logger.update(loss=loss.item()) + + confmat.update(target.flatten(), output.argmax(1).flatten()) + + + return metric_logger.loss.global_avg, confmat + + +def evaluate(model, criterion, data_loader, device): + model.eval() + metric_logger = utils.MetricLogger(delimiter=" ") + confmat = utils.ConfusionMatrix(num_classes=len(data_loader.dataset.classes)) + header = 'Test:' + missed = [] + with torch.no_grad(): + for i, (image, target) in metric_logger.log_every(data_loader, leave=False, header=header, parent=None): + image, target = image.to(device), target.to(device) + output = model(image) + loss = criterion(output, target) + if target.item() != output.topk(1)[1].item(): + missed.append(data_loader.dataset.imgs[data_loader.sampler.indices[i]]) + + confmat.update(target.flatten(), output.argmax(1).flatten()) + + acc1 = utils.accuracy(output, target)[0] + batch_size = image.shape[0] + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + metric_logger.update(loss=loss.item()) + + + return metric_logger.loss.global_avg, missed, confmat + +def get_train_valid_loader(args, augment, random_seed, train_size=0.5, test_size=0.1, shuffle=True, num_workers=4, pin_memory=True): + """ + Utility function for loading and returning train and valid + multi-process iterators over the CIFAR-10 dataset. A sample + 9x9 grid of the images can be optionally displayed. + If using CUDA, num_workers should be set to 1 and pin_memory to True. + Params + ------ + - data_dir: path directory to the dataset. + - batch_size: how many samples per batch to load. + - augment: whether to apply the data augmentation scheme + mentioned in the paper. Only applied on the train split. + - random_seed: fix seed for reproducibility. + - valid_size: percentage split of the training set used for + the validation set. Should be a float in the range [0, 1]. + - shuffle: whether to shuffle the train/validation indices. + - show_sample: plot 9x9 sample grid of the dataset. + - num_workers: number of subprocesses to use when loading the dataset. + - pin_memory: whether to copy tensors into CUDA pinned memory. Set it to + True if using GPU. + Returns + ------- + - train_loader: training set iterator. + - valid_loader: validation set iterator. + """ + error_msg = "[!] test_size should be in the range [0, 1]." + assert ((test_size >= 0) and (test_size <= 1)), error_msg + + # normalize = transforms.Normalize( + # mean=[0.4914, 0.4822, 0.4465], + # std=[0.2023, 0.1994, 0.2010], + # ) + + # define transforms + if augment: + train_transform = transforms.Compose([ + # transforms.ColorJitter(brightness=0.3), + # transforms.Lambda(lambda img: sharpness(img, 5)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + # normalize, + ]) + + valid_transform = transforms.Compose([ + # transforms.ColorJitter(brightness=0.3), + # transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + # normalize, + ]) + else: + train_transform = transforms.Compose([ + transforms.ToTensor(), + # normalize, + ]) + + valid_transform = transforms.Compose([ + transforms.ToTensor(), + # normalize, + ]) + + + # load the dataset + train_dataset = torchvision.datasets.ImageFolder( + root=args.data_path, transform=train_transform + ) + + test_dataset = torchvision.datasets.ImageFolder( + root=args.data_path, transform=valid_transform + ) + + num_train = len(train_dataset) + indices = list(range(num_train)) + split = int(np.floor(test_size * num_train)) + + if shuffle: + np.random.seed(random_seed) + np.random.shuffle(indices) + + train_idx, test_idx = indices[split:], indices[:split] + train_idx, valid_idx = train_idx[:int(len(train_idx)*train_size)], train_idx[int(len(train_idx)*train_size):] + print("\nTrain", len(train_idx), "\nValid", len(valid_idx), "\nTest", len(test_idx)) + train_sampler = torch.utils.data.SubsetRandomSampler(train_idx) if not args.test_only else SubsetSampler(train_idx) + valid_sampler = torch.utils.data.SubsetRandomSampler(valid_idx) if not args.test_only else SubsetSampler(valid_idx) + test_sampler = SubsetSampler(test_idx) + + train_loader = torch.utils.data.DataLoader( + train_dataset, batch_size=args.batch_size if not args.test_only else 1, sampler=train_sampler, + num_workers=num_workers, pin_memory=pin_memory, + ) + valid_loader = torch.utils.data.DataLoader( + train_dataset, batch_size=args.batch_size if not args.test_only else 1, sampler=valid_sampler, + num_workers=num_workers, pin_memory=pin_memory, + ) + test_loader = torch.utils.data.DataLoader( + test_dataset, batch_size=1, sampler=test_sampler, + num_workers=num_workers, pin_memory=pin_memory, + ) + + imgs = np.asarray(train_dataset.imgs) + + # print('Train') + # print(imgs[train_idx]) + #print('Valid') + #print(imgs[valid_idx]) + + return (train_loader, valid_loader, test_loader) + +def main(args): + print(args) + + device = torch.device(args.device) + + torch.backends.cudnn.benchmark = True + + #augment = True if not args.test_only else False + augment = False + + data_loader, dl_val, data_loader_test = get_train_valid_loader(args=args, pin_memory=True, augment=augment, + num_workers=args.workers, train_size=0.99, test_size=0.2, random_seed=999) + + print("Creating model") + model = torchvision.models.__dict__[args.model](pretrained=True) + flat = list(model.children()) + + body, head = nn.Sequential(*flat[:-2]), nn.Sequential(flat[-2], Lambda(func=lambda x: torch.flatten(x, 1)), nn.Linear(flat[-1].in_features, len(data_loader.dataset.classes))) + model = nn.Sequential(body, head) + + # model.fc = nn.Linear(model.fc.in_features, 2) + # import ipdb; ipdb.set_trace() + + criterion = nn.CrossEntropyLoss().to(device) + + # optimizer = torch.optim.SGD( + # model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) + ''' + optimizer = torch.optim.Adam( + model.parameters(), lr=args.lr, weight_decay=args.weight_decay) + + lr_scheduler = torch.optim.lr_scheduler.LambdaLR( + optimizer, + lambda x: (1 - x / (len(data_loader) * args.epochs)) ** 0.9) + ''' + es = utils.EarlyStopping() + + if args.test_only: + model.load_state_dict(torch.load('checkpoint.pt', map_location=lambda storage, loc: storage)) + model = model.to(device) + print('TEST') + _, missed, _ = evaluate(model, criterion, data_loader_test, device=device) + print(missed) + print('TRAIN') + _, missed, _ = evaluate(model, criterion, data_loader, device=device) + print(missed) + return + + model = model.to(device) + + print("Start training") + start_time = time.time() + mb = master_bar(range(args.epochs)) + """ + for epoch in mb: + _, train_confmat = train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, mb) + lr_scheduler.step( (epoch+1)*len(data_loader) ) + val_loss, _, valid_confmat = evaluate(model, criterion, data_loader_test, device=device) + es(val_loss, model) + + # print('Valid Missed') + # print(valid_missed) + + + # print('Train') + # print(train_confmat) + print('Valid') + print(valid_confmat) + + # if es.early_stop: + # break + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + """ + + ####### + + inner_it = args.inner_it + dataug_epoch_start=0 + print_freq=1 + KLdiv=False + + tf_dict = {k: TF.TF_dict[k] for k in tf_names} + model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device) + #model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device) + + val_loss=torch.tensor(0) #Necessaire si pas de metastep sur une epoch + dl_val_it = iter(dl_val) + countcopy=0 + + #if inner_it!=0: + meta_opt = torch.optim.Adam(model['data_aug'].parameters(), lr=args.lr) #lr=1e-2 + #inner_opt = torch.optim.SGD(model['model'].parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) #lr=1e-2 / momentum=0.9 + inner_opt = torch.optim.Adam(model['model'].parameters(), lr=args.lr, weight_decay=args.weight_decay) + + lr_scheduler = torch.optim.lr_scheduler.LambdaLR( + inner_opt, + lambda x: (1 - x / (len(data_loader) * args.epochs)) ** 0.9) + + high_grad_track = True + if inner_it == 0: + high_grad_track=False + + model.train() + model.augment(mode=False) + + fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True) + diffopt = higher.optim.get_diff_optim(inner_opt, model.parameters(),fmodel=fmodel,track_higher_grads=high_grad_track) + + i=0 + + for epoch in mb: + + metric_logger = utils.MetricLogger(delimiter=" ") + confmat = utils.ConfusionMatrix(num_classes=len(data_loader.dataset.classes)) + header = 'Epoch: {}'.format(epoch) + + t0 = time.process_time() + for _, (image, target) in metric_logger.log_every(data_loader, header=header, parent=mb): + #for i, (xs, ys) in enumerate(dl_train): + #print_torch_mem("it"+str(i)) + i+=1 + image, target = image.to(device), target.to(device) + + if(not KLdiv): + #Methode uniforme + logits = fmodel(image) # modified `params` can also be passed as a kwarg + output = F.log_softmax(logits, dim=1) + loss = F.cross_entropy(output, target, reduction='none') # no need to call loss.backwards() + + if fmodel._data_augmentation: #Weight loss + w_loss = fmodel['data_aug'].loss_weight()#.to(device) + loss = loss * w_loss + loss = loss.mean() + + else: + #Methode KL div + fmodel.augment(mode=False) + sup_logits = fmodel(xs) + log_sup=F.log_softmax(sup_logits, dim=1) + fmodel.augment(mode=True) + loss = F.cross_entropy(log_sup, ys) + + if fmodel._data_augmentation: + aug_logits = fmodel(xs) + log_aug=F.log_softmax(aug_logits, dim=1) + aug_loss=0 + if epoch>50: #debut differe ? + #KL div w/ logits - Similarite predictions (distributions) + aug_loss = F.softmax(sup_logits, dim=1)*(log_sup-log_aug) + aug_loss=aug_loss.sum(dim=-1) + #aug_loss = F.kl_div(aug_logits, sup_logits, reduction='none') + w_loss = fmodel['data_aug'].loss_weight() #Weight loss + aug_loss = (w_loss * aug_loss).mean() + + aug_loss += (F.cross_entropy(log_aug, ys , reduction='none') * w_loss).mean() + #print(aug_loss) + unsupp_coeff = 1 + loss += aug_loss * unsupp_coeff + + diffopt.step(loss) #(opt.zero_grad, loss.backward, opt.step) + + if(high_grad_track and i%inner_it==0): #Perform Meta step + #print("meta") + #Peu utile si high_grad_track = False + val_loss = compute_vaLoss(model=fmodel, dl_it=dl_val_it, dl=dl_val) + fmodel['data_aug'].reg_loss() + #print_graph(val_loss) + + val_loss.backward() + + countcopy+=1 + model_copy(src=fmodel, dst=model) + optim_copy(dopt=diffopt, opt=inner_opt) + + #if epoch>50: + meta_opt.step() + model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1 + #model['data_aug'].next_TF_set() + + fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True) + diffopt = higher.optim.get_diff_optim(inner_opt, model.parameters(),fmodel=fmodel, track_higher_grads=high_grad_track) + + + acc1 = utils.accuracy(output, target)[0] + batch_size = image.shape[0] + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + metric_logger.update(loss=loss.item()) + + confmat.update(target.flatten(), output.argmax(1).flatten()) + + if(not high_grad_track and (torch.cuda.memory_cached()/1024.0**2)>20000): + countcopy+=1 + print_torch_mem("copy") + model_copy(src=fmodel, dst=model) + optim_copy(dopt=diffopt, opt=inner_opt) + val_loss = compute_vaLoss(model=fmodel, dl_it=dl_val_it, dl=dl_val) + + #Necessaire pour reset higher (Accumule les fast_param meme avec track_higher_grads = False) + fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True) + diffopt = higher.optim.get_diff_optim(inner_opt, model.parameters(),fmodel=fmodel, track_higher_grads=high_grad_track) + print_torch_mem("copy") + + if(not high_grad_track): + countcopy+=1 + print_torch_mem("end copy") + model_copy(src=fmodel, dst=model) + optim_copy(dopt=diffopt, opt=inner_opt) + val_loss = compute_vaLoss(model=fmodel, dl_it=dl_val_it, dl=dl_val) + + #Necessaire pour reset higher (Accumule les fast_param meme avec track_higher_grads = False) + fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True) + diffopt = higher.optim.get_diff_optim(inner_opt, model.parameters(),fmodel=fmodel, track_higher_grads=high_grad_track) + print_torch_mem("end copy") + + + tf = time.process_time() + + + #### Print #### + if(print_freq and epoch%print_freq==0): + print('-'*9) + print('Epoch : %d'%(epoch)) + print('Time : %.00f'%(tf - t0)) + print('Train loss :',loss.item(), '/ val loss', val_loss.item()) + print('Data Augmention : {} (Epoch {})'.format(model._data_augmentation, dataug_epoch_start)) + print('TF Proba :', model['data_aug']['prob'].data) + #print('proba grad',model['data_aug']['prob'].grad) + print('TF Mag :', model['data_aug']['mag'].data) + #print('Mag grad',model['data_aug']['mag'].grad) + #print('Reg loss:', model['data_aug'].reg_loss().item()) + #print('Aug loss', aug_loss.item()) + ############# + #### Log #### + #print(type(model['data_aug']) is dataug.Data_augV5) + ''' + param = [{'p': p.item(), 'm':model['data_aug']['mag'].item()} for p in model['data_aug']['prob']] if model['data_aug']._shared_mag else [{'p': p.item(), 'm': m.item()} for p, m in zip(model['data_aug']['prob'], model['data_aug']['mag'])] + data={ + "epoch": epoch, + "train_loss": loss.item(), + "val_loss": val_loss.item(), + "acc": accuracy, + "time": tf - t0, + + "param": param #if isinstance(model['data_aug'], Data_augV5) + #else [p.item() for p in model['data_aug']['prob']], + } + log.append(data) + ''' + ############# + + train_confmat=confmat + lr_scheduler.step( (epoch+1)*len(data_loader) ) + + test_loss, _, test_confmat = evaluate(model, criterion, data_loader_test, device=device) + es(test_loss, model) + + # print('Valid Missed') + # print(valid_missed) + + + # print('Train') + # print(train_confmat) + print('Test') + print(test_confmat) + + # if es.early_stop: + # break + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + +def parse_args(): + import argparse + parser = argparse.ArgumentParser(description='PyTorch Classification Training') + + parser.add_argument('--data-path', default='/Salvador', help='dataset') + parser.add_argument('--model', default='resnet50', help='model') + parser.add_argument('--device', default='cuda:1', help='device') + parser.add_argument('-b', '--batch-size', default=4, type=int) + parser.add_argument('--epochs', default=3, type=int, metavar='N', + help='number of total epochs to run') + parser.add_argument('-j', '--workers', default=0, type=int, metavar='N', + help='number of data loading workers (default: 16)') + parser.add_argument('--lr', default=0.001, type=float, help='initial learning rate') + parser.add_argument('--momentum', default=0.9, type=float, metavar='M', + help='momentum') + parser.add_argument('--wd', '--weight-decay', default=4e-5, type=float, + metavar='W', help='weight decay (default: 1e-4)', + dest='weight_decay') + + parser.add_argument( + "--test-only", + dest="test_only", + help="Only test the model", + action="store_true", + ) + + parser.add_argument('--in_it', '--inner_it', default=0, type=int, + metavar='N', help='higher inner_it', + dest='inner_it') + + args = parser.parse_args() + + return args + + +if __name__ == "__main__": + args = parse_args() + main(args) \ No newline at end of file diff --git a/salvador/transformations.py b/salvador/transformations.py new file mode 100755 index 0000000..82a8d9e --- /dev/null +++ b/salvador/transformations.py @@ -0,0 +1,346 @@ +import torch +import kornia +import random + +### Available TF for Dataug ### +''' +TF_dict={ #Dataugv4 + ## Geometric TF ## + 'Identity' : (lambda x, mag: x), + 'FlipUD' : (lambda x, mag: flipUD(x)), + 'FlipLR' : (lambda x, mag: flipLR(x)), + 'Rotate': (lambda x, mag: rotate(x, angle=torch.tensor([rand_int(mag, maxval=30)for _ in x], device=x.device))), + 'TranslateX': (lambda x, mag: translate(x, translation=torch.tensor([[rand_int(mag, maxval=20), 0] for _ in x], device=x.device))), + 'TranslateY': (lambda x, mag: translate(x, translation=torch.tensor([[0, rand_int(mag, maxval=20)] for _ in x], device=x.device))), + 'ShearX': (lambda x, mag: shear(x, shear=torch.tensor([[rand_float(mag, maxval=0.3), 0] for _ in x], device=x.device))), + 'ShearY': (lambda x, mag: shear(x, shear=torch.tensor([[0, rand_float(mag, maxval=0.3)] for _ in x], device=x.device))), + + ## Color TF (Expect image in the range of [0, 1]) ## + 'Contrast': (lambda x, mag: contrast(x, contrast_factor=torch.tensor([rand_float(mag, minval=0.1, maxval=1.9) for _ in x], device=x.device))), + 'Color':(lambda x, mag: color(x, color_factor=torch.tensor([rand_float(mag, minval=0.1, maxval=1.9) for _ in x], device=x.device))), + 'Brightness':(lambda x, mag: brightness(x, brightness_factor=torch.tensor([rand_float(mag, minval=0.1, maxval=1.9) for _ in x], device=x.device))), + 'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=torch.tensor([rand_float(mag, minval=0.1, maxval=1.9) for _ in x], device=x.device))), + 'Posterize': (lambda x, mag: posterize(x, bits=torch.tensor([rand_int(mag, minval=4, maxval=8) for _ in x], device=x.device))), + 'Solarize': (lambda x, mag: solarize(x, thresholds=torch.tensor([rand_int(mag,minval=1, maxval=256)/256. for _ in x], device=x.device))) , #=>Image entre [0,1] #Pas opti pour des batch + + #Non fonctionnel + #'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent) + #'Equalize': (lambda mag: None), +} +''' +''' +TF_dict={ #Dataugv5 #AutoAugment + ## Geometric TF ## + 'Identity' : (lambda x, mag: x), + 'FlipUD' : (lambda x, mag: flipUD(x)), + 'FlipLR' : (lambda x, mag: flipLR(x)), + 'Rotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30))), + 'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))), + 'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))), + 'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))), + 'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))), + + ## Color TF (Expect image in the range of [0, 1]) ## + 'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), + 'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), + 'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), + 'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), + 'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient + 'Solarize': (lambda x, mag: solarize(x, thresholds=rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1] + + #Non fonctionnel + #'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent) + #'Equalize': (lambda mag: None), +} +''' +TF_dict={ #Dataugv5 + ## Geometric TF ## + 'Identity' : (lambda x, mag: x), + 'FlipUD' : (lambda x, mag: flipUD(x)), + 'FlipLR' : (lambda x, mag: flipLR(x)), + 'Rotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30))), + 'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))), + 'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))), + 'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))), + 'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))), + + ## Color TF (Expect image in the range of [0, 1]) ## + 'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), + 'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), + 'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), + 'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))), + 'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient + 'Solarize': (lambda x, mag: solarize(x, thresholds=rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1] + + #Color TF (Common mag scale) + '+Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))), + '+Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))), + '+Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))), + '+Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))), + '-Contrast': (lambda x, mag: contrast(x, contrast_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))), + '-Color':(lambda x, mag: color(x, color_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))), + '-Brightness':(lambda x, mag: brightness(x, brightness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))), + '-Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))), + '=Posterize': (lambda x, mag: posterize(x, bits=invScale_rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient + '=Solarize': (lambda x, mag: solarize(x, thresholds=invScale_rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1] + + + 'BRotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30*3))), + 'BTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20*3), zero_pos=0))), + 'BTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20*3), zero_pos=1))), + 'BShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3*3), zero_pos=0))), + 'BShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3*3), zero_pos=1))), + + 'BadTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=20*2, maxval=20*3), zero_pos=0))), + 'BadTranslateX_neg': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-20*3, maxval=-20*2), zero_pos=0))), + 'BadTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=20*2, maxval=20*3), zero_pos=1))), + 'BadTranslateY_neg': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-20*3, maxval=-20*2), zero_pos=1))), + + 'BadColor':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))), + 'BadSharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))), + 'BadContrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))), + 'BadBrightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*2))), + + #Non fonctionnel + #'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent) + #'Equalize': (lambda mag: None), +} + +TF_no_mag={'Identity', 'FlipUD', 'FlipLR'} +TF_ignore_mag= TF_no_mag | {'Solarize', 'Posterize'} + +def int_image(float_image): #ATTENTION : legere perte d'info (granularite : 1/256 = 0.0039) + return (float_image*255.).type(torch.uint8) + +def float_image(int_image): + return int_image.type(torch.float)/255. + +#def rand_inverse(value): +# return value if random.random() < 0.5 else -value + +#def rand_int(mag, maxval, minval=None): #[(-maxval,minval), maxval] +# real_max = int_parameter(mag, maxval=maxval) +# if not minval : minval = -real_max +# return random.randint(minval, real_max) + +#def rand_float(mag, maxval, minval=None): #[(-maxval,minval), maxval] +# real_max = float_parameter(mag, maxval=maxval) +# if not minval : minval = -real_max +# return random.uniform(minval, real_max) + +def rand_floats(size, mag, maxval, minval=None): #[(-maxval,minval), maxval] + real_mag = float_parameter(mag, maxval=maxval) + if not minval : minval = -real_mag + #return random.uniform(minval, real_max) + return minval + (real_mag-minval) * torch.rand(size, device=mag.device) #[min_val, real_mag] + +def invScale_rand_floats(size, mag, maxval, minval): + #Mag=[0,PARAMETER_MAX] => [PARAMETER_MAX, 0] = [maxval, minval] + real_mag = float_parameter(float(PARAMETER_MAX) - mag, maxval=maxval-minval)+minval + return real_mag + (maxval-real_mag) * torch.rand(size, device=mag.device) #[real_mag, max_val] + +def zero_stack(tensor, zero_pos): + if zero_pos==0: + return torch.stack((tensor, torch.zeros((tensor.shape[0],), device=tensor.device)), dim=1) + if zero_pos==1: + return torch.stack((torch.zeros((tensor.shape[0],), device=tensor.device), tensor), dim=1) + else: + raise Exception("Invalid zero_pos : ", zero_pos) + +#https://github.com/tensorflow/models/blob/fc2056bce6ab17eabdc139061fef8f4f2ee763ec/research/autoaugment/augmentation_transforms.py#L137 +PARAMETER_MAX = 1 # What is the max 'level' a transform could be predicted +def float_parameter(level, maxval): + """Helper function to scale `val` between 0 and maxval . + Args: + level: Level of the operation that will be between [0, `PARAMETER_MAX`]. + maxval: Maximum value that the operation can have. This will be scaled + to level/PARAMETER_MAX. + Returns: + A float that results from scaling `maxval` according to `level`. + """ + + #return float(level) * maxval / PARAMETER_MAX + return (level * maxval / PARAMETER_MAX)#.to(torch.float) + +#def int_parameter(level, maxval): #Perte de gradient + """Helper function to scale `val` between 0 and maxval . + Args: + level: Level of the operation that will be between [0, `PARAMETER_MAX`]. + maxval: Maximum value that the operation can have. This will be scaled + to level/PARAMETER_MAX. + Returns: + An int that results from scaling `maxval` according to `level`. + """ + #return int(level * maxval / PARAMETER_MAX) +# return (level * maxval / PARAMETER_MAX) + +def flipLR(x): + device = x.device + (batch_size, channels, h, w) = x.shape + + M =torch.tensor( [[[-1., 0., w-1], + [ 0., 1., 0.], + [ 0., 0., 1.]]], device=device).expand(batch_size,-1,-1) + + # warp the original image by the found transform + return kornia.warp_perspective(x, M, dsize=(h, w)) + +def flipUD(x): + device = x.device + (batch_size, channels, h, w) = x.shape + + M =torch.tensor( [[[ 1., 0., 0.], + [ 0., -1., h-1], + [ 0., 0., 1.]]], device=device).expand(batch_size,-1,-1) + + # warp the original image by the found transform + return kornia.warp_perspective(x, M, dsize=(h, w)) + +def rotate(x, angle): + return kornia.rotate(x, angle=angle.type(torch.float)) #Kornia ne supporte pas les int + +def translate(x, translation): + #print(translation) + return kornia.translate(x, translation=translation.type(torch.float)) #Kornia ne supporte pas les int + +def shear(x, shear): + return kornia.shear(x, shear=shear) + +def contrast(x, contrast_factor): + return kornia.adjust_contrast(x, contrast_factor=contrast_factor) #Expect image in the range of [0, 1] + +#https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageEnhance.py +def color(x, color_factor): + (batch_size, channels, h, w) = x.shape + + gray_x = kornia.rgb_to_grayscale(x) + gray_x = gray_x.repeat_interleave(channels, dim=1) + return blend(gray_x, x, color_factor).clamp(min=0.0,max=1.0) #Expect image in the range of [0, 1] + +def brightness(x, brightness_factor): + device = x.device + + return blend(torch.zeros(x.size(), device=device), x, brightness_factor).clamp(min=0.0,max=1.0) #Expect image in the range of [0, 1] + +def sharpeness(x, sharpness_factor): + device = x.device + (batch_size, channels, h, w) = x.shape + + k = torch.tensor([[[ 1., 1., 1.], + [ 1., 5., 1.], + [ 1., 1., 1.]]], device=device) #Smooth Filter : https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageFilter.py + smooth_x = kornia.filter2D(x, kernel=k, border_type='reflect', normalized=True) #Peut etre necessaire de s'occuper du channel Alhpa differement + + return blend(smooth_x, x, sharpness_factor).clamp(min=0.0,max=1.0) #Expect image in the range of [0, 1] + +#https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py +def posterize(x, bits): + bits = bits.type(torch.uint8) #Perte du gradient + x = int_image(x) #Expect image in the range of [0, 1] + + mask = ~(2 ** (8 - bits) - 1).type(torch.uint8) + + (batch_size, channels, h, w) = x.shape + mask = mask.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ... + + return float_image(x & mask) + +def auto_contrast(x): #PAS OPTIMISE POUR DES BATCH #EXTRA LENT + # Optimisation : Application de LUT efficace / Calcul d'histogramme par batch/channel + print("Warning : Pas encore check !") + (batch_size, channels, h, w) = x.shape + x = int_image(x) #Expect image in the range of [0, 1] + #print('Start',x[0]) + for im_idx, img in enumerate(x.chunk(batch_size, dim=0)): #Operation par image + #print(img.shape) + for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel + #print(chan.shape) + hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE + + # find lowest/highest samples after preprocessing + for lo in range(256): + if hist[lo]: + break + for hi in range(255, -1, -1): + if hist[hi]: + break + if hi <= lo: + # don't bother + pass + else: + scale = 255.0 / (hi - lo) + offset = -lo * scale + for ix in range(256): + n_ix = int(ix * scale + offset) + if n_ix < 0: n_ix = 0 + elif n_ix > 255: n_ix = 255 + + chan[chan==ix]=n_ix + x[im_idx, chan_idx]=chan + + #print('End',x[0]) + return float_image(x) + +def equalize(x): #PAS OPTIMISE POUR DES BATCH + raise Exception(self, "not implemented") + # Optimisation : Application de LUT efficace / Calcul d'histogramme par batch/channel + (batch_size, channels, h, w) = x.shape + x = int_image(x) #Expect image in the range of [0, 1] + #print('Start',x[0]) + for im_idx, img in enumerate(x.chunk(batch_size, dim=0)): #Operation par image + #print(img.shape) + for chan_idx, chan in enumerate(img.chunk(channels, dim=1)): # Operation par channel + #print(chan.shape) + hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE + + return float_image(x) + +def solarize(x, thresholds): + batch_size, channels, h, w = x.shape + #imgs=[] + #for idx, t in enumerate(thresholds): #Operation par image + # mask = x[idx] > t #Perte du gradient + #In place + # inv_x = 1-x[idx][mask] + # x[idx][mask]=inv_x + # + + #Out of place + # im = x[idx] + # inv_x = 1-im[mask] + + # imgs.append(im.masked_scatter(mask,inv_x)) + + #idxs=torch.tensor(range(x.shape[0]), device=x.device) + #idxs=idxs.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ... + #x=x.scatter(dim=0, index=idxs, src=torch.stack(imgs)) + # + + thresholds = thresholds.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ... + #print(thresholds.grad_fn) + x=torch.where(x>thresholds,1-x, x) + #print(mask.grad_fn) + + #x=x.min(thresholds) + #inv_x = 1-x[mask] + #x=x.where(x= 0) & (a < n) + inds = n * a[k].to(torch.int64) + b[k] + self.mat += torch.bincount(inds, minlength=n**2).reshape(n, n) + + def reset(self): + self.mat.zero_() + + def compute(self): + h = self.mat.float() + acc_global = torch.diag(h).sum() / h.sum() + acc = torch.diag(h) / h.sum(1) + return acc_global, acc + + + def __str__(self): + acc_global, acc = self.compute() + return ( + 'global correct: {:.1f}\n' + 'average row correct: {}').format( + acc_global.item() * 100, + ['{:.1f}'.format(i) for i in (acc * 100).tolist()]) + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format( + type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append( + "{}: {}".format(name, str(meter)) + ) + return self.delimiter.join(loss_str) + + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, parent, header=None, **kwargs): + if not header: + header = '' + log_msg = self.delimiter.join([ + '{meters}' + ]) + + progrss = progress_bar(iterable, parent=parent, **kwargs) + + for idx, obj in enumerate(progrss): + yield idx, obj + progrss.comment = log_msg.format( + meters=str(self)) + + print('{header} {meters}'.format(header=header, meters=str(self))) + +def accuracy(output, target, topk=(1,)): + """Computes the accuracy over the k top predictions for the specified values of k""" + with torch.no_grad(): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target[None]) + + res = [] + for k in topk: + correct_k = correct[:k].flatten().sum(dtype=torch.float32) + res.append(correct_k * (100.0 / batch_size)) + return res + +class EarlyStopping: + """Early stops the training if validation loss doesn't improve after a given patience.""" + def __init__(self, patience=7, verbose=False, delta=0): + """ + Args: + patience (int): How long to wait after last time validation loss improved. + Default: 7 + verbose (bool): If True, prints a message for each validation loss improvement. + Default: False + delta (float): Minimum change in the monitored quantity to qualify as an improvement. + Default: 0 + """ + self.patience = patience + self.verbose = verbose + self.counter = 0 + self.best_score = None + self.early_stop = False + self.val_loss_min = np.Inf + self.delta = delta + + def __call__(self, val_loss, model): + + score = -val_loss + + if self.best_score is None: + self.best_score = score + self.save_checkpoint(val_loss, model) + elif score < self.best_score - self.delta: + self.counter += 1 + # print(f'EarlyStopping counter: {self.counter} out of {self.patience}') + # if self.counter >= self.patience: + # self.early_stop = True + else: + self.best_score = score + self.save_checkpoint(val_loss, model) + self.counter = 0 + + def save_checkpoint(self, val_loss, model): + '''Saves model when validation loss decrease.''' + if self.verbose: + print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...') + torch.save(model.state_dict(), 'checkpoint.pt') + self.val_loss_min = val_loss \ No newline at end of file