First 11 Epochs

[anderdad]

[Running] python -u "c:\Users\xxxxxxxx\xxxxxxxxxx\xxxxxxxxx\build_model.py"
No checkpoint found, initialized model base models ## No pretrained weights
Batch 0, Loss: 1.9156744480133057
Batch 10, Loss: 1.8626548051834106
Batch 20, Loss: 1.8565564155578613
Batch 30, Loss: 1.7862080335617065
Batch 40, Loss: 1.756113052368164
Batch 50, Loss: 1.6761970520019531
Batch 60, Loss: 1.9034875631332397
Batch 70, Loss: 1.9226258993148804
Batch 80, Loss: 1.688514232635498
Batch 90, Loss: 1.7509899139404297
Batch 100, Loss: 1.7324481010437012
Batch 110, Loss: 1.6220530271530151
Batch 120, Loss: 1.699600100517273
Batch 130, Loss: 1.7837945222854614
Batch 140, Loss: 1.5903843641281128
Batch 150, Loss: 1.763441562652588
Batch 160, Loss: 1.5288532972335815
Batch 170, Loss: 1.7576886415481567
Batch 180, Loss: 1.5279961824417114
Batch 190, Loss: 1.5802521705627441
Batch 200, Loss: 1.6948915719985962
Batch 210, Loss: 1.6916648149490356
Batch 220, Loss: 1.7030630111694336
Batch 230, Loss: 1.4639184474945068
Batch 240, Loss: 1.6928105354309082
Epoch 1/30, Loss: 1.7035062216161712
Checkpoint saved after training at epoch 1
Validation Loss: 1.6697299364136486, Accuracy: 35.601427115188585%
Checkpoint saved after validation at epoch 1
Batch 0, Loss: 1.6133414506912231
Batch 10, Loss: 1.5999548435211182
Batch 20, Loss: 1.6158747673034668
Batch 30, Loss: 1.4360835552215576
Batch 40, Loss: 1.8596746921539307
Batch 50, Loss: 1.3530008792877197
Batch 60, Loss: 1.7578260898590088
Batch 70, Loss: 1.607901692390442
Batch 80, Loss: 1.6088035106658936
Batch 90, Loss: 1.95505952835083
Batch 100, Loss: 1.6872538328170776
Batch 110, Loss: 1.548459529876709
Batch 120, Loss: 1.5038926601409912
Batch 130, Loss: 1.7046668529510498
Batch 140, Loss: 1.751694917678833
Batch 150, Loss: 1.4068156480789185
Batch 160, Loss: 1.5790209770202637
Batch 170, Loss: 1.432234525680542
Batch 180, Loss: 1.358766794204712
Batch 190, Loss: 1.3298461437225342
Batch 200, Loss: 1.8042343854904175
Batch 210, Loss: 1.4480602741241455
Batch 220, Loss: 1.634069800376892
Batch 230, Loss: 1.583912968635559
Batch 240, Loss: 1.455590009689331
Epoch 2/30, Loss: 1.5814120415749588
Checkpoint saved after training at epoch 2
Validation Loss: 1.5929197097212318, Accuracy: 41.25891946992864%
Checkpoint saved after validation at epoch 2
Batch 0, Loss: 1.5070292949676514
Batch 10, Loss: 1.5424340963363647
Batch 20, Loss: 1.453255295753479
Batch 30, Loss: 1.2382844686508179
Batch 40, Loss: 1.4153954982757568
Batch 50, Loss: 1.6224733591079712
Batch 60, Loss: 1.637902021408081
Batch 70, Loss: 1.156886339187622
Batch 80, Loss: 1.5556095838546753
Batch 90, Loss: 1.220657229423523
Batch 100, Loss: 1.3585582971572876
Batch 110, Loss: 1.5565907955169678
Batch 120, Loss: 1.3364578485488892
Batch 130, Loss: 1.3160583972930908
Batch 140, Loss: 1.2658745050430298
Batch 150, Loss: 1.4228127002716064
Batch 160, Loss: 1.4040803909301758
Batch 170, Loss: 1.4991264343261719
Batch 180, Loss: 1.2616233825683594
Batch 190, Loss: 1.361121416091919
Batch 200, Loss: 1.4586145877838135
Batch 210, Loss: 1.6240479946136475
Batch 220, Loss: 1.673764944076538
Batch 230, Loss: 1.2337945699691772
Batch 240, Loss: 1.4312593936920166
Epoch 3/30, Loss: 1.4768021911140379
Checkpoint saved after training at epoch 3
Validation Loss: 1.5412254391646967, Accuracy: 42.023445463812436%
Checkpoint saved after validation at epoch 3
Batch 0, Loss: 1.4433237314224243
Batch 10, Loss: 1.3442912101745605
Batch 20, Loss: 1.2225573062896729
Batch 30, Loss: 1.3247028589248657
Batch 40, Loss: 1.644689917564392
Batch 50, Loss: 1.2446593046188354
Batch 60, Loss: 1.5934934616088867
Batch 70, Loss: 1.318016767501831
Batch 80, Loss: 1.5058857202529907
Batch 90, Loss: 1.625394582748413
Batch 100, Loss: 1.331189513206482
Batch 110, Loss: 1.329154133796692
Batch 120, Loss: 1.1320215463638306
Batch 130, Loss: 1.2157723903656006
Batch 140, Loss: 1.4792433977127075
Batch 150, Loss: 1.433963656425476
Batch 160, Loss: 1.7915716171264648
Batch 170, Loss: 1.2077722549438477
Batch 180, Loss: 1.464971899986267
Batch 190, Loss: 1.1818430423736572
Batch 200, Loss: 1.2897958755493164
Batch 210, Loss: 1.3854953050613403
Batch 220, Loss: 0.9753021001815796
Batch 230, Loss: 1.6043521165847778
Batch 240, Loss: 0.9408539533615112
Epoch 4/30, Loss: 1.3418220567509411
Checkpoint saved after training at epoch 4
Validation Loss: 1.3619045101530183, Accuracy: 50.815494393476044%
Checkpoint saved after validation at epoch 4
Batch 0, Loss: 0.9928689002990723
Batch 10, Loss: 1.1908626556396484
Batch 20, Loss: 1.1277207136154175
Batch 30, Loss: 1.224690556526184
Batch 40, Loss: 1.3470462560653687
Batch 50, Loss: 1.0914629697799683
Batch 60, Loss: 0.9922074675559998
Batch 70, Loss: 1.259286642074585
Batch 80, Loss: 1.3383785486221313
Batch 90, Loss: 1.3343713283538818
Batch 100, Loss: 1.193608045578003
Batch 110, Loss: 0.9781741499900818
Batch 120, Loss: 1.6231099367141724
Batch 130, Loss: 1.0238127708435059
Batch 140, Loss: 0.9863274097442627
Batch 150, Loss: 1.4472482204437256
Batch 160, Loss: 0.9341237545013428
Batch 170, Loss: 0.9773645401000977
Batch 180, Loss: 1.156739592552185
Batch 190, Loss: 1.4230375289916992
Batch 200, Loss: 1.254294753074646
Batch 210, Loss: 0.886806845664978
Batch 220, Loss: 1.4000340700149536
Batch 230, Loss: 0.9686505198478699
Batch 240, Loss: 1.3307408094406128
Epoch 5/30, Loss: 1.196234073580765
Checkpoint saved after training at epoch 5
Validation Loss: 1.504090525755068, Accuracy: 45.69317023445464%
Checkpoint saved after validation at epoch 5
Batch 0, Loss: 0.9646356105804443
Batch 10, Loss: 1.069895625114441
Batch 20, Loss: 0.9800617694854736
Batch 30, Loss: 0.832708477973938
Batch 40, Loss: 0.8484237194061279
Batch 50, Loss: 1.05167818069458
Batch 60, Loss: 1.0394054651260376
Batch 70, Loss: 0.8484078049659729
Batch 80, Loss: 1.0619289875030518
Batch 90, Loss: 0.8489055037498474
Batch 100, Loss: 0.9609018564224243
Batch 110, Loss: 1.0775376558303833
Batch 120, Loss: 0.8700604438781738
Batch 130, Loss: 1.0073051452636719
Batch 140, Loss: 0.7354527711868286
Batch 150, Loss: 0.7949116826057434
Batch 160, Loss: 1.2638434171676636
Batch 170, Loss: 0.9351342916488647
Batch 180, Loss: 0.9091689586639404
Batch 190, Loss: 0.978523313999176
Batch 200, Loss: 0.7309710383415222
Batch 210, Loss: 1.3641188144683838
Batch 220, Loss: 0.8211721777915955
Batch 230, Loss: 0.9168440699577332
Batch 240, Loss: 0.9996311664581299
Epoch 6/30, Loss: 0.9892512610772761
Checkpoint saved after training at epoch 6
Validation Loss: 1.1413476016463302, Accuracy: 59.505606523955144%
Checkpoint saved after validation at epoch 6
Batch 0, Loss: 0.9633015394210815
Batch 10, Loss: 1.0169670581817627
Batch 20, Loss: 1.2054667472839355
Batch 30, Loss: 1.053060531616211
Batch 40, Loss: 0.8382384777069092
Batch 50, Loss: 0.6235209703445435
Batch 60, Loss: 0.9567490220069885
Batch 70, Loss: 0.9684983491897583
Batch 80, Loss: 0.939497709274292
Batch 90, Loss: 1.1582162380218506
Batch 100, Loss: 0.783268928527832
Batch 110, Loss: 0.8964506983757019
Batch 120, Loss: 0.7895589470863342
Batch 130, Loss: 1.0099689960479736
Batch 140, Loss: 1.000286340713501
Batch 150, Loss: 0.9230799078941345
Batch 160, Loss: 0.9614745378494263
Batch 170, Loss: 0.9803738594055176
Batch 180, Loss: 0.870471715927124
Batch 190, Loss: 0.8937320709228516
Batch 200, Loss: 1.0657416582107544
Batch 210, Loss: 0.8077185153961182
Batch 220, Loss: 0.9982946515083313
Batch 230, Loss: 0.7511869072914124
Batch 240, Loss: 0.8695698380470276
Epoch 7/30, Loss: 0.9257013034529802
Checkpoint saved after training at epoch 7
Validation Loss: 1.1340901056925456, Accuracy: 59.734964322120284%
Checkpoint saved after validation at epoch 7
Batch 0, Loss: 0.8942134976387024
Batch 10, Loss: 0.7508456707000732
Batch 20, Loss: 0.9622920155525208
Batch 30, Loss: 0.6686944961547852
Batch 40, Loss: 0.9433330297470093
Batch 50, Loss: 0.8622176647186279
Batch 60, Loss: 0.8230944871902466
Batch 70, Loss: 1.0735145807266235
Batch 80, Loss: 0.6659101843833923
Batch 90, Loss: 1.0533816814422607
Batch 100, Loss: 0.8526415824890137
Batch 110, Loss: 1.1522321701049805
Batch 120, Loss: 0.7088894248008728
Batch 130, Loss: 0.9097428917884827
Batch 140, Loss: 1.1404072046279907
Batch 150, Loss: 0.7508501410484314
Batch 160, Loss: 0.7884302735328674
Batch 170, Loss: 0.863334596157074
Batch 180, Loss: 1.0178215503692627
Batch 190, Loss: 0.8221424221992493
Batch 200, Loss: 0.8644483089447021
Batch 210, Loss: 1.0664187669754028
Batch 220, Loss: 0.8205272555351257
Batch 230, Loss: 0.8510411381721497
Batch 240, Loss: 1.0188981294631958
Epoch 8/30, Loss: 0.886499035406888
Checkpoint saved after training at epoch 8
Validation Loss: 1.1142425323889507, Accuracy: 59.811416921508666%
Checkpoint saved after validation at epoch 8
Batch 0, Loss: 0.7757568955421448
Batch 10, Loss: 0.9134659767150879
Batch 20, Loss: 0.7748557925224304
Batch 30, Loss: 0.9728901982307434
Batch 40, Loss: 0.7981626987457275
Batch 50, Loss: 0.8775278925895691
Batch 60, Loss: 0.7633670568466187
Batch 70, Loss: 0.9133449196815491
Batch 80, Loss: 0.7584460377693176
Batch 90, Loss: 0.8789072632789612
Batch 100, Loss: 0.9383776187896729
Batch 110, Loss: 0.8461180329322815
Batch 120, Loss: 1.0134092569351196
Batch 130, Loss: 0.8232463598251343
Batch 140, Loss: 0.9079266786575317
Batch 150, Loss: 0.9122776389122009
Batch 160, Loss: 0.8992060422897339
Batch 170, Loss: 0.8743405938148499
Batch 180, Loss: 0.744807243347168
Batch 190, Loss: 0.791610062122345
Batch 200, Loss: 0.8190324306488037
Batch 210, Loss: 1.0264064073562622
Batch 220, Loss: 0.7398329973220825
Batch 230, Loss: 0.9348060488700867
Batch 240, Loss: 0.8339088559150696
Epoch 9/30, Loss: 0.8562042856119513
Checkpoint saved after training at epoch 9
Validation Loss: 1.0956406474598055, Accuracy: 61.34046890927625%
Checkpoint saved after validation at epoch 9
Batch 0, Loss: 0.8304377794265747
Batch 10, Loss: 0.8835525512695312
Batch 20, Loss: 0.9710465669631958
Batch 30, Loss: 0.9593148827552795
Batch 40, Loss: 0.6672557592391968
Batch 50, Loss: 0.7892225980758667
Batch 60, Loss: 0.5049821138381958
Batch 70, Loss: 1.245028018951416
Batch 80, Loss: 0.8591259717941284
Batch 90, Loss: 1.0755175352096558
Batch 100, Loss: 0.8041462302207947
Batch 110, Loss: 0.888481616973877
Batch 120, Loss: 0.8654052019119263
Batch 130, Loss: 0.7939372658729553
Batch 140, Loss: 0.7892494201660156
Batch 150, Loss: 1.0469911098480225
Batch 160, Loss: 0.7524814009666443
Batch 170, Loss: 0.8445149660110474
Batch 180, Loss: 0.6852868795394897
Batch 190, Loss: 0.8074037432670593
Batch 200, Loss: 0.7345415353775024
Batch 210, Loss: 0.8010282516479492
Batch 220, Loss: 0.6815871596336365
Batch 230, Loss: 0.6268032193183899
Batch 240, Loss: 0.8586410880088806
Epoch 10/30, Loss: 0.823944639384262
Checkpoint saved after training at epoch 10
Validation Loss: 1.1015526647490215, Accuracy: 60.67787971457696%
Checkpoint saved after validation at epoch 10
Batch 0, Loss: 0.7843832969665527
Batch 10, Loss: 0.7088426351547241
Batch 20, Loss: 0.5975248217582703
Batch 30, Loss: 0.6702471375465393
Batch 40, Loss: 0.7955651879310608
Batch 50, Loss: 0.9211598634719849
Batch 60, Loss: 0.7646224498748779
Batch 70, Loss: 0.8884650468826294
Batch 80, Loss: 0.7156628966331482
Batch 90, Loss: 0.9466649293899536
Batch 100, Loss: 0.796012282371521
Batch 110, Loss: 0.7313655614852905
Batch 120, Loss: 0.6248000264167786
Batch 130, Loss: 0.6745001077651978
Batch 140, Loss: 1.0070054531097412
Batch 150, Loss: 0.8221409916877747
Batch 160, Loss: 0.7815259099006653
Batch 170, Loss: 0.6426569223403931
Batch 180, Loss: 0.9406752586364746
Batch 190, Loss: 0.8704614043235779
Batch 200, Loss: 0.9878426790237427
Batch 210, Loss: 1.0229030847549438
Batch 220, Loss: 0.7145501375198364
Batch 230, Loss: 0.8713546395301819
Batch 240, Loss: 0.8976824879646301
Epoch 11/30, Loss: 0.7896523332692743
Checkpoint saved after training at epoch 11
Validation Loss: 1.0878337832485758, Accuracy: 61.59531090723751%
Checkpoint saved after validation at epoch 11