dvc: commit experiment 33080a73fb18a526cec30caded9b87a0730f3350618b12…

…c11c9986cb2eb91f1f

dvc.lock

@@ -22,12 +22,12 @@ stages:
     outs:
     - path: evaluation
       hash: md5
-      md5: 59ffedce50481428ec9ea1ff05f9f9b1.dir
-      size: 2636
+      md5: 2637c04674e2dbe87b4b4332c88bb3eb.dir
+      size: 2622
       nfiles: 5
     - path: model
       hash: md5
-      md5: 4f9a1662bb288d228b5b764763b39c96.dir
+      md5: d370a96849f4adbb75dbd25c49137408.dir
       size: 2784925
       nfiles: 3
   inference:
@@ -42,17 +42,17 @@ stages:
       size: 2243
     - path: model
       hash: md5
-      md5: 4f9a1662bb288d228b5b764763b39c96.dir
+      md5: d370a96849f4adbb75dbd25c49137408.dir
       size: 2784925
       nfiles: 3
     outs:
     - path: mispredicted
       hash: md5
-      md5: c8e80ee42e7315ba8aa8fc83df8a8d0b.dir
-      size: 237782
+      md5: c05ad78c969271fa1254022453795e76.dir
+      size: 252205
       nfiles: 16
     - path: predictions
       hash: md5
-      md5: 18a02bbb86e0cfffb339b2db82082ad3.dir
-      size: 3098083
+      md5: cc0d9d5f720c967f512f20f6b0f6d3cc.dir
+      size: 3098092
       nfiles: 9708

evaluation/metrics.json

@@ -1,17 +1,17 @@
 {
-    "loss_0": 5.765158176422119,
-    "acc_0": 0.270812451839447,
+    "loss_0": 20.57746124267578,
+    "acc_0": 0.24974924325942993,
     "train": {
-        "loss": 0.8741952180862427,
-        "accuracy": 0.5920355916023254
+        "loss": 0.8842516541481018,
+        "accuracy": 0.5837343335151672
     },
     "eval": {
-        "loss": 0.8811243772506714,
-        "accuracy": 0.5897693037986755
+        "loss": 0.8593325614929199,
+        "accuracy": 0.6048144698143005
     },
     "step": 24,
-    "best_loss": 0.8517884612083435,
-    "best_acc": 0.6359077095985413,
-    "best_test_loss": 0.8401126861572266,
-    "best_test_acc": 0.6265664100646973
+    "best_loss": 0.8594814538955688,
+    "best_acc": 0.605817437171936,
+    "best_test_loss": 0.870830774307251,
+    "best_test_acc": 0.5889724493026733
 }

evaluation/plots/metrics/eval/accuracy.tsv

@@ -1,26 +1,26 @@
 step	accuracy
-0	0.42728185653686523
-1	0.41524574160575867
-2	0.505516529083252
-3	0.46840521693229675
-4	0.44734203815460205
-5	0.4874624013900757
-6	0.5616850256919861
-7	0.5456368923187256
-8	0.5606820583343506
-9	0.541624903678894
-10	0.5717151165008545
-11	0.5606820583343506
-12	0.4964894652366638
-13	0.573721170425415
-14	0.5406218767166138
-15	0.5817452073097229
-16	0.5897693037986755
-17	0.6158475279808044
-18	0.6068204641342163
-19	0.6359077095985413
-20	0.6168505549430847
-21	0.5827482342720032
-22	0.5787361860275269
-23	0.5095285773277283
-24	0.5897693037986755
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+2	0.5275827646255493
+3	0.429287850856781
+4	0.5145436525344849
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+6	0.5436308979988098
+7	0.5065195560455322
+8	0.5035105347633362
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+12	0.5346038341522217
+13	0.5867602825164795
+14	0.46439316868782043
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+16	0.599799394607544
+17	0.5656970739364624
+18	0.5797392129898071
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+21	0.5817452073097229
+22	0.605817437171936
+23	0.5386158227920532
+24	0.6048144698143005

evaluation/plots/metrics/eval/loss.tsv

@@ -1,26 +1,26 @@
 step	loss
-0	1.2657780647277832
-1	1.2188971042633057
-2	1.0592710971832275
-3	1.0585206747055054
-4	1.1309819221496582
-5	1.0509027242660522
-6	0.9744257926940918
-7	0.9881301522254944
-8	0.9796417355537415
-9	1.0359435081481934
-10	0.956732451915741
-11	0.9164311289787292
-12	1.0879404544830322
-13	0.950259268283844
-14	1.0209009647369385
-15	0.9111430644989014
-16	0.8983808755874634
-17	0.8813354969024658
-18	0.8827047348022461
-19	0.8517886996269226
-20	0.8648388981819153
-21	0.8656489253044128
-22	0.917814314365387
-23	1.0794671773910522
-24	0.8811243772506714
+0	2.497079372406006
+1	1.0534677505493164
+2	1.009337306022644
+3	1.199346661567688
+4	1.0148886442184448
+5	0.9904754161834717
+6	0.972639262676239
+7	1.0699676275253296
+8	1.0469926595687866
+9	1.069810390472412
+10	0.924776554107666
+11	0.9369533061981201
+12	0.9302251935005188
+13	0.9098711013793945
+14	1.8076422214508057
+15	0.9117749333381653
+16	0.895557701587677
+17	0.9049301743507385
+18	0.8871572017669678
+19	0.8835505247116089
+20	0.9280213117599487
+21	0.8716855049133301
+22	0.859481692314148
+23	0.9532277584075928
+24	0.8593325614929199

evaluation/plots/metrics/train/accuracy.tsv

@@ -1,26 +1,26 @@
 step	accuracy
-0	0.4032723903656006
-1	0.43876323103904724
-2	0.4730510115623474
-3	0.48111164569854736
-4	0.5014436841011047
-5	0.49891722202301025
-6	0.5162415504455566
-7	0.5327237844467163
-8	0.5256255865097046
-9	0.5406640768051147
-10	0.5463185906410217
-11	0.5494465827941895
-12	0.5464388728141785
-13	0.5583493709564209
-14	0.5581087470054626
-15	0.5714629292488098
-16	0.5700192451477051
-17	0.5751925110816956
-18	0.582771897315979
-19	0.5792829394340515
-20	0.5893888473510742
-21	0.591193437576294
-22	0.5963667035102844
-23	0.585418701171875
-24	0.5920355916023254
+0	0.40134745836257935
+1	0.4293792247772217
+2	0.47100576758384705
+3	0.479066401720047
+4	0.5001202821731567
+5	0.5004812479019165
+6	0.5135948061943054
+7	0.5205726623535156
+8	0.5274302363395691
+9	0.5303176045417786
+10	0.5316410064697266
+11	0.537536084651947
+12	0.5369345545768738
+13	0.5437921285629272
+14	0.5538979768753052
+15	0.5644850730895996
+16	0.5648459792137146
+17	0.5625601410865784
+18	0.5686958432197571
+19	0.5732675790786743
+20	0.5815688371658325
+21	0.5822906494140625
+22	0.5850577354431152
+23	0.5872232913970947
+24	0.5837343335151672

evaluation/plots/metrics/train/loss.tsv

@@ -1,26 +1,26 @@
 step	loss
-0	1.2714720964431763
-1	1.1343671083450317
-2	1.088356852531433
-3	1.066900372505188
-4	1.0359727144241333
-5	1.0330005884170532
-6	1.0147085189819336
-7	0.9916261434555054
-8	0.9845442175865173
-9	0.9711766242980957
-10	0.9513503909111023
-11	0.9510231018066406
-12	0.9487426280975342
-13	0.9395878911018372
-14	0.9364833235740662
-15	0.913422703742981
-16	0.9179505705833435
-17	0.9045124650001526
-18	0.8887510299682617
-19	0.9001860618591309
-20	0.8890814781188965
-21	0.8817993998527527
-22	0.8789404034614563
-23	0.8781567811965942
-24	0.8741952180862427
+0	1.2991658449172974
+1	1.1491724252700806
+2	1.0985190868377686
+3	1.0688788890838623
+4	1.0434705018997192
+5	1.0236742496490479
+6	1.015897274017334
+7	0.997305154800415
+8	0.9931501746177673
+9	0.9819074869155884
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+11	0.9628596901893616
+12	0.958746075630188
+13	0.9544571042060852
+14	0.9408972263336182
+15	0.9272348880767822
+16	0.9211111068725586
+17	0.922583818435669
+18	0.9061336517333984
+19	0.9071345329284668
+20	0.900648295879364
+21	0.896971583366394
+22	0.8937544822692871
+23	0.890902042388916
+24	0.8842516541481018