-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathae_slurm-10761012.out
3753 lines (3751 loc) · 136 KB
/
ae_slurm-10761012.out
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
trytonp/compiler/gcc/13.2.0 load complete.
trytonp/python3/3.11.7 load complete.
Loading trytonp/python3/3.11.7
Loading requirement: trytonp/compiler/gcc/13.2.0
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: certifi==2024.12.14 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (2024.12.14)
Requirement already satisfied: charset-normalizer==3.4.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 2)) (3.4.0)
Requirement already satisfied: colorama==0.4.6 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 3)) (0.4.6)
Requirement already satisfied: contourpy==1.3.1 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 4)) (1.3.1)
Requirement already satisfied: cycler==0.12.1 in /apl/trytonp/python/3.11.7/lib/python3.11/site-packages (from -r requirements.txt (line 5)) (0.12.1)
Requirement already satisfied: filelock==3.16.1 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 6)) (3.16.1)
Requirement already satisfied: fonttools==4.55.3 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 7)) (4.55.3)
Requirement already satisfied: fsspec==2024.12.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 8)) (2024.12.0)
Requirement already satisfied: idna==3.10 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 9)) (3.10)
Requirement already satisfied: imbalanced-learn==0.13.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 10)) (0.13.0)
Requirement already satisfied: imblearn==0.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 11)) (0.0)
Requirement already satisfied: Jinja2==3.1.5 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 12)) (3.1.5)
Requirement already satisfied: joblib==1.4.2 in /apl/trytonp/python/3.11.7/lib/python3.11/site-packages (from -r requirements.txt (line 13)) (1.4.2)
Requirement already satisfied: kiwisolver==1.4.7 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 14)) (1.4.7)
Requirement already satisfied: MarkupSafe==3.0.2 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 15)) (3.0.2)
Requirement already satisfied: matplotlib==3.10.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 16)) (3.10.0)
Requirement already satisfied: mpmath==1.3.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 17)) (1.3.0)
Requirement already satisfied: networkx==3.4.2 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 18)) (3.4.2)
Requirement already satisfied: numpy==2.2.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 19)) (2.2.0)
Requirement already satisfied: opencv-python==4.10.0.84 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 20)) (4.10.0.84)
Requirement already satisfied: packaging==24.2 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 21)) (24.2)
Requirement already satisfied: pandas==2.2.3 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 22)) (2.2.3)
Requirement already satisfied: pillow==11.0.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 23)) (11.0.0)
Requirement already satisfied: psutil==6.1.1 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 24)) (6.1.1)
Requirement already satisfied: py-cpuinfo==9.0.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 25)) (9.0.0)
Requirement already satisfied: pyparsing==3.2.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 26)) (3.2.0)
Requirement already satisfied: python-dateutil==2.9.0.post0 in /apl/trytonp/python/3.11.7/lib/python3.11/site-packages (from -r requirements.txt (line 27)) (2.9.0.post0)
Requirement already satisfied: pytz==2024.2 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 28)) (2024.2)
Requirement already satisfied: PyYAML==6.0.2 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 29)) (6.0.2)
Requirement already satisfied: requests==2.32.3 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 30)) (2.32.3)
Requirement already satisfied: scikit-learn==1.6.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 31)) (1.6.0)
Requirement already satisfied: scipy==1.14.1 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 32)) (1.14.1)
Requirement already satisfied: seaborn==0.13.2 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 33)) (0.13.2)
Requirement already satisfied: six==1.17.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 34)) (1.17.0)
Requirement already satisfied: sklearn-compat==0.1.3 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 35)) (0.1.3)
Requirement already satisfied: sympy==1.13.1 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 36)) (1.13.1)
Requirement already satisfied: threadpoolctl==3.5.0 in /apl/trytonp/python/3.11.7/lib/python3.11/site-packages (from -r requirements.txt (line 37)) (3.5.0)
Requirement already satisfied: torch==2.5.1 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 38)) (2.5.1)
Requirement already satisfied: torchvision==0.20.1 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 39)) (0.20.1)
Requirement already satisfied: tqdm==4.67.1 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 40)) (4.67.1)
Requirement already satisfied: typing_extensions==4.12.2 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 41)) (4.12.2)
Requirement already satisfied: tzdata==2024.2 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 42)) (2024.2)
Requirement already satisfied: ultralytics==8.3.52 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 43)) (8.3.52)
Requirement already satisfied: ultralytics-thop==2.0.13 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 44)) (2.0.13)
Requirement already satisfied: urllib3==2.2.3 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from -r requirements.txt (line 45)) (2.2.3)
Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (12.4.127)
Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (12.4.127)
Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (12.4.127)
Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (9.1.0.70)
Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (12.4.5.8)
Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (11.2.1.3)
Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (10.3.5.147)
Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (11.6.1.9)
Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (12.3.1.170)
Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (2.21.5)
Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (12.4.127)
Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (12.4.127)
Requirement already satisfied: triton==3.1.0 in /users/kdm/juliuszl/.local/lib/python3.11/site-packages (from torch==2.5.1->-r requirements.txt (line 38)) (3.1.0)
[notice] A new release of pip is available: 23.2.1 -> 24.3.1
[notice] To update, run: pip3 install --upgrade pip
Size of image: (128, 128, 3)
Batch size: 64
Number of epochs: 100
Latent dimension: 100
Learning rate: 0.0002
Beta: 0.5
==========AE==========
N: 499, Autoencoder(
(encoder): Encoder(
(conv2d_01): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_01): ReLU()
(conv2d_02): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_02): ReLU()
(conv2d_03): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_03): ReLU()
(flatten_04): Flatten(start_dim=1, end_dim=-1)
(linear_04): Linear(in_features=65536, out_features=1024, bias=True)
(relu_04): ReLU()
)
(decoder): Decoder(
(linear_01): Linear(in_features=1024, out_features=65536, bias=True)
(relu_01): ReLU()
(unflatten_01): Unflatten(dim=1, unflattened_size=(256, 16, 16))
(conv_transpose2d_02): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_02): ReLU()
(conv_transpose2d_03): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_03): ReLU()
(conv_transpose2d_04): ConvTranspose2d(64, 3, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(tanh_04): Tanh()
)
)
Epoch [1/100], Loss: 0.6018
Epoch [2/100], Loss: 0.3780
Epoch [3/100], Loss: 0.2249
Epoch [4/100], Loss: 0.1553
Epoch [5/100], Loss: 0.1224
Epoch [6/100], Loss: 0.1023
Epoch [7/100], Loss: 0.0873
Epoch [8/100], Loss: 0.0755
Epoch [9/100], Loss: 0.0653
Epoch [10/100], Loss: 0.0568
Epoch [11/100], Loss: 0.0490
Epoch [12/100], Loss: 0.0433
Epoch [13/100], Loss: 0.0384
Epoch [14/100], Loss: 0.0344
Epoch [15/100], Loss: 0.0309
Epoch [16/100], Loss: 0.0282
Epoch [17/100], Loss: 0.0254
Epoch [18/100], Loss: 0.0236
Epoch [19/100], Loss: 0.0219
Epoch [20/100], Loss: 0.0204
Epoch [21/100], Loss: 0.0189
Epoch [22/100], Loss: 0.0174
Epoch [23/100], Loss: 0.0161
Epoch [24/100], Loss: 0.0153
Epoch [25/100], Loss: 0.0143
Epoch [26/100], Loss: 0.0135
Epoch [27/100], Loss: 0.0131
Epoch [28/100], Loss: 0.0126
Epoch [29/100], Loss: 0.0124
Epoch [30/100], Loss: 0.0124
Epoch [31/100], Loss: 0.0118
Epoch [32/100], Loss: 0.0111
Epoch [33/100], Loss: 0.0107
Epoch [34/100], Loss: 0.0101
Epoch [35/100], Loss: 0.0097
Epoch [36/100], Loss: 0.0092
Epoch [37/100], Loss: 0.0088
Epoch [38/100], Loss: 0.0084
Epoch [39/100], Loss: 0.0081
Epoch [40/100], Loss: 0.0078
Epoch [41/100], Loss: 0.0076
Epoch [42/100], Loss: 0.0074
Epoch [43/100], Loss: 0.0073
Epoch [44/100], Loss: 0.0071
Epoch [45/100], Loss: 0.0069
Epoch [46/100], Loss: 0.0066
Epoch [47/100], Loss: 0.0062
Epoch [48/100], Loss: 0.0059
Epoch [49/100], Loss: 0.0057
Epoch [50/100], Loss: 0.0056
Epoch [51/100], Loss: 0.0055
Epoch [52/100], Loss: 0.0055
Epoch [53/100], Loss: 0.0056
Epoch [54/100], Loss: 0.0057
Epoch [55/100], Loss: 0.0055
Epoch [56/100], Loss: 0.0053
Epoch [57/100], Loss: 0.0052
Epoch [58/100], Loss: 0.0052
Epoch [59/100], Loss: 0.0051
Epoch [60/100], Loss: 0.0049
Epoch [61/100], Loss: 0.0047
Epoch [62/100], Loss: 0.0047
Epoch [63/100], Loss: 0.0046
Epoch [64/100], Loss: 0.0048
Epoch [65/100], Loss: 0.0048
Epoch [66/100], Loss: 0.0048
Epoch [67/100], Loss: 0.0046
Epoch [68/100], Loss: 0.0046
Epoch [69/100], Loss: 0.0045
Epoch [70/100], Loss: 0.0043
Epoch [71/100], Loss: 0.0042
Epoch [72/100], Loss: 0.0040
Epoch [73/100], Loss: 0.0039
Epoch [74/100], Loss: 0.0038
Epoch [75/100], Loss: 0.0038
Epoch [76/100], Loss: 0.0038
Epoch [77/100], Loss: 0.0038
Epoch [78/100], Loss: 0.0036
Epoch [79/100], Loss: 0.0036
Epoch [80/100], Loss: 0.0037
Epoch [81/100], Loss: 0.0037
Epoch [82/100], Loss: 0.0038
Epoch [83/100], Loss: 0.0038
Epoch [84/100], Loss: 0.0038
Epoch [85/100], Loss: 0.0037
Epoch [86/100], Loss: 0.0036
Epoch [87/100], Loss: 0.0036
Epoch [88/100], Loss: 0.0035
Epoch [89/100], Loss: 0.0035
Epoch [90/100], Loss: 0.0034
Epoch [91/100], Loss: 0.0032
Epoch [92/100], Loss: 0.0030
Epoch [93/100], Loss: 0.0030
Epoch [94/100], Loss: 0.0029
Epoch [95/100], Loss: 0.0029
Epoch [96/100], Loss: 0.0028
Epoch [97/100], Loss: 0.0027
Epoch [98/100], Loss: 0.0027
Epoch [99/100], Loss: 0.0027
Epoch [100/100], Loss: 0.0026
B: 500, Autoencoder(
(encoder): Encoder(
(conv2d_01): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_01): ReLU()
(conv2d_02): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_02): ReLU()
(conv2d_03): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_03): ReLU()
(flatten_04): Flatten(start_dim=1, end_dim=-1)
(linear_04): Linear(in_features=65536, out_features=1024, bias=True)
(relu_04): ReLU()
)
(decoder): Decoder(
(linear_01): Linear(in_features=1024, out_features=65536, bias=True)
(relu_01): ReLU()
(unflatten_01): Unflatten(dim=1, unflattened_size=(256, 16, 16))
(conv_transpose2d_02): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_02): ReLU()
(conv_transpose2d_03): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_03): ReLU()
(conv_transpose2d_04): ConvTranspose2d(64, 3, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(tanh_04): Tanh()
)
)
Epoch [1/100], Loss: 0.5280
Epoch [2/100], Loss: 0.4373
Epoch [3/100], Loss: 0.3142
Epoch [4/100], Loss: 0.1980
Epoch [5/100], Loss: 0.1517
Epoch [6/100], Loss: 0.1175
Epoch [7/100], Loss: 0.0962
Epoch [8/100], Loss: 0.0829
Epoch [9/100], Loss: 0.0707
Epoch [10/100], Loss: 0.0613
Epoch [11/100], Loss: 0.0536
Epoch [12/100], Loss: 0.0474
Epoch [13/100], Loss: 0.0413
Epoch [14/100], Loss: 0.0365
Epoch [15/100], Loss: 0.0323
Epoch [16/100], Loss: 0.0294
Epoch [17/100], Loss: 0.0271
Epoch [18/100], Loss: 0.0249
Epoch [19/100], Loss: 0.0228
Epoch [20/100], Loss: 0.0209
Epoch [21/100], Loss: 0.0194
Epoch [22/100], Loss: 0.0181
Epoch [23/100], Loss: 0.0167
Epoch [24/100], Loss: 0.0160
Epoch [25/100], Loss: 0.0154
Epoch [26/100], Loss: 0.0147
Epoch [27/100], Loss: 0.0141
Epoch [28/100], Loss: 0.0130
Epoch [29/100], Loss: 0.0122
Epoch [30/100], Loss: 0.0114
Epoch [31/100], Loss: 0.0106
Epoch [32/100], Loss: 0.0102
Epoch [33/100], Loss: 0.0104
Epoch [34/100], Loss: 0.0103
Epoch [35/100], Loss: 0.0098
Epoch [36/100], Loss: 0.0097
Epoch [37/100], Loss: 0.0097
Epoch [38/100], Loss: 0.0091
Epoch [39/100], Loss: 0.0088
Epoch [40/100], Loss: 0.0081
Epoch [41/100], Loss: 0.0078
Epoch [42/100], Loss: 0.0074
Epoch [43/100], Loss: 0.0070
Epoch [44/100], Loss: 0.0068
Epoch [45/100], Loss: 0.0066
Epoch [46/100], Loss: 0.0062
Epoch [47/100], Loss: 0.0059
Epoch [48/100], Loss: 0.0058
Epoch [49/100], Loss: 0.0056
Epoch [50/100], Loss: 0.0054
Epoch [51/100], Loss: 0.0053
Epoch [52/100], Loss: 0.0053
Epoch [53/100], Loss: 0.0053
Epoch [54/100], Loss: 0.0054
Epoch [55/100], Loss: 0.0053
Epoch [56/100], Loss: 0.0054
Epoch [57/100], Loss: 0.0054
Epoch [58/100], Loss: 0.0055
Epoch [59/100], Loss: 0.0054
Epoch [60/100], Loss: 0.0053
Epoch [61/100], Loss: 0.0051
Epoch [62/100], Loss: 0.0052
Epoch [63/100], Loss: 0.0053
Epoch [64/100], Loss: 0.0050
Epoch [65/100], Loss: 0.0049
Epoch [66/100], Loss: 0.0048
Epoch [67/100], Loss: 0.0046
Epoch [68/100], Loss: 0.0044
Epoch [69/100], Loss: 0.0042
Epoch [70/100], Loss: 0.0042
Epoch [71/100], Loss: 0.0041
Epoch [72/100], Loss: 0.0040
Epoch [73/100], Loss: 0.0038
Epoch [74/100], Loss: 0.0038
Epoch [75/100], Loss: 0.0038
Epoch [76/100], Loss: 0.0040
Epoch [77/100], Loss: 0.0041
Epoch [78/100], Loss: 0.0041
Epoch [79/100], Loss: 0.0040
Epoch [80/100], Loss: 0.0040
Epoch [81/100], Loss: 0.0039
Epoch [82/100], Loss: 0.0041
Epoch [83/100], Loss: 0.0039
Epoch [84/100], Loss: 0.0041
Epoch [85/100], Loss: 0.0042
Epoch [86/100], Loss: 0.0042
Epoch [87/100], Loss: 0.0041
Epoch [88/100], Loss: 0.0042
Epoch [89/100], Loss: 0.0040
Epoch [90/100], Loss: 0.0041
Epoch [91/100], Loss: 0.0041
Epoch [92/100], Loss: 0.0038
Epoch [93/100], Loss: 0.0036
Epoch [94/100], Loss: 0.0034
Epoch [95/100], Loss: 0.0033
Epoch [96/100], Loss: 0.0031
Epoch [97/100], Loss: 0.0030
Epoch [98/100], Loss: 0.0029
Epoch [99/100], Loss: 0.0028
Epoch [100/100], Loss: 0.0028
P: 500, Autoencoder(
(encoder): Encoder(
(conv2d_01): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_01): ReLU()
(conv2d_02): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_02): ReLU()
(conv2d_03): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_03): ReLU()
(flatten_04): Flatten(start_dim=1, end_dim=-1)
(linear_04): Linear(in_features=65536, out_features=1024, bias=True)
(relu_04): ReLU()
)
(decoder): Decoder(
(linear_01): Linear(in_features=1024, out_features=65536, bias=True)
(relu_01): ReLU()
(unflatten_01): Unflatten(dim=1, unflattened_size=(256, 16, 16))
(conv_transpose2d_02): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_02): ReLU()
(conv_transpose2d_03): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_03): ReLU()
(conv_transpose2d_04): ConvTranspose2d(64, 3, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(tanh_04): Tanh()
)
)
Epoch [1/100], Loss: 0.4917
Epoch [2/100], Loss: 0.3386
Epoch [3/100], Loss: 0.2912
Epoch [4/100], Loss: 0.2437
Epoch [5/100], Loss: 0.1887
Epoch [6/100], Loss: 0.1459
Epoch [7/100], Loss: 0.1117
Epoch [8/100], Loss: 0.0890
Epoch [9/100], Loss: 0.0730
Epoch [10/100], Loss: 0.0603
Epoch [11/100], Loss: 0.0499
Epoch [12/100], Loss: 0.0428
Epoch [13/100], Loss: 0.0378
Epoch [14/100], Loss: 0.0344
Epoch [15/100], Loss: 0.0325
Epoch [16/100], Loss: 0.0292
Epoch [17/100], Loss: 0.0269
Epoch [18/100], Loss: 0.0245
Epoch [19/100], Loss: 0.0222
Epoch [20/100], Loss: 0.0201
Epoch [21/100], Loss: 0.0185
Epoch [22/100], Loss: 0.0170
Epoch [23/100], Loss: 0.0159
Epoch [24/100], Loss: 0.0149
Epoch [25/100], Loss: 0.0142
Epoch [26/100], Loss: 0.0132
Epoch [27/100], Loss: 0.0126
Epoch [28/100], Loss: 0.0119
Epoch [29/100], Loss: 0.0115
Epoch [30/100], Loss: 0.0110
Epoch [31/100], Loss: 0.0109
Epoch [32/100], Loss: 0.0108
Epoch [33/100], Loss: 0.0103
Epoch [34/100], Loss: 0.0097
Epoch [35/100], Loss: 0.0092
Epoch [36/100], Loss: 0.0089
Epoch [37/100], Loss: 0.0084
Epoch [38/100], Loss: 0.0079
Epoch [39/100], Loss: 0.0076
Epoch [40/100], Loss: 0.0073
Epoch [41/100], Loss: 0.0070
Epoch [42/100], Loss: 0.0069
Epoch [43/100], Loss: 0.0068
Epoch [44/100], Loss: 0.0067
Epoch [45/100], Loss: 0.0065
Epoch [46/100], Loss: 0.0064
Epoch [47/100], Loss: 0.0062
Epoch [48/100], Loss: 0.0061
Epoch [49/100], Loss: 0.0060
Epoch [50/100], Loss: 0.0059
Epoch [51/100], Loss: 0.0058
Epoch [52/100], Loss: 0.0057
Epoch [53/100], Loss: 0.0056
Epoch [54/100], Loss: 0.0055
Epoch [55/100], Loss: 0.0053
Epoch [56/100], Loss: 0.0052
Epoch [57/100], Loss: 0.0052
Epoch [58/100], Loss: 0.0053
Epoch [59/100], Loss: 0.0055
Epoch [60/100], Loss: 0.0052
Epoch [61/100], Loss: 0.0049
Epoch [62/100], Loss: 0.0048
Epoch [63/100], Loss: 0.0046
Epoch [64/100], Loss: 0.0045
Epoch [65/100], Loss: 0.0043
Epoch [66/100], Loss: 0.0042
Epoch [67/100], Loss: 0.0040
Epoch [68/100], Loss: 0.0039
Epoch [69/100], Loss: 0.0038
Epoch [70/100], Loss: 0.0036
Epoch [71/100], Loss: 0.0035
Epoch [72/100], Loss: 0.0035
Epoch [73/100], Loss: 0.0034
Epoch [74/100], Loss: 0.0034
Epoch [75/100], Loss: 0.0033
Epoch [76/100], Loss: 0.0032
Epoch [77/100], Loss: 0.0032
Epoch [78/100], Loss: 0.0032
Epoch [79/100], Loss: 0.0032
Epoch [80/100], Loss: 0.0032
Epoch [81/100], Loss: 0.0032
Epoch [82/100], Loss: 0.0032
Epoch [83/100], Loss: 0.0031
Epoch [84/100], Loss: 0.0031
Epoch [85/100], Loss: 0.0030
Epoch [86/100], Loss: 0.0029
Epoch [87/100], Loss: 0.0029
Epoch [88/100], Loss: 0.0029
Epoch [89/100], Loss: 0.0029
Epoch [90/100], Loss: 0.0029
Epoch [91/100], Loss: 0.0029
Epoch [92/100], Loss: 0.0029
Epoch [93/100], Loss: 0.0029
Epoch [94/100], Loss: 0.0029
Epoch [95/100], Loss: 0.0028
Epoch [96/100], Loss: 0.0028
Epoch [97/100], Loss: 0.0027
Epoch [98/100], Loss: 0.0026
Epoch [99/100], Loss: 0.0026
Epoch [100/100], Loss: 0.0025
W: 499, Autoencoder(
(encoder): Encoder(
(conv2d_01): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_01): ReLU()
(conv2d_02): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_02): ReLU()
(conv2d_03): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_03): ReLU()
(flatten_04): Flatten(start_dim=1, end_dim=-1)
(linear_04): Linear(in_features=65536, out_features=1024, bias=True)
(relu_04): ReLU()
)
(decoder): Decoder(
(linear_01): Linear(in_features=1024, out_features=65536, bias=True)
(relu_01): ReLU()
(unflatten_01): Unflatten(dim=1, unflattened_size=(256, 16, 16))
(conv_transpose2d_02): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_02): ReLU()
(conv_transpose2d_03): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_03): ReLU()
(conv_transpose2d_04): ConvTranspose2d(64, 3, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(tanh_04): Tanh()
)
)
Epoch [1/100], Loss: 0.5702
Epoch [2/100], Loss: 0.4383
Epoch [3/100], Loss: 0.3680
Epoch [4/100], Loss: 0.2947
Epoch [5/100], Loss: 0.2232
Epoch [6/100], Loss: 0.1677
Epoch [7/100], Loss: 0.1286
Epoch [8/100], Loss: 0.0993
Epoch [9/100], Loss: 0.0803
Epoch [10/100], Loss: 0.0669
Epoch [11/100], Loss: 0.0573
Epoch [12/100], Loss: 0.0506
Epoch [13/100], Loss: 0.0448
Epoch [14/100], Loss: 0.0402
Epoch [15/100], Loss: 0.0362
Epoch [16/100], Loss: 0.0333
Epoch [17/100], Loss: 0.0304
Epoch [18/100], Loss: 0.0280
Epoch [19/100], Loss: 0.0260
Epoch [20/100], Loss: 0.0234
Epoch [21/100], Loss: 0.0218
Epoch [22/100], Loss: 0.0198
Epoch [23/100], Loss: 0.0184
Epoch [24/100], Loss: 0.0171
Epoch [25/100], Loss: 0.0159
Epoch [26/100], Loss: 0.0149
Epoch [27/100], Loss: 0.0140
Epoch [28/100], Loss: 0.0133
Epoch [29/100], Loss: 0.0125
Epoch [30/100], Loss: 0.0118
Epoch [31/100], Loss: 0.0112
Epoch [32/100], Loss: 0.0107
Epoch [33/100], Loss: 0.0102
Epoch [34/100], Loss: 0.0098
Epoch [35/100], Loss: 0.0095
Epoch [36/100], Loss: 0.0093
Epoch [37/100], Loss: 0.0089
Epoch [38/100], Loss: 0.0085
Epoch [39/100], Loss: 0.0082
Epoch [40/100], Loss: 0.0080
Epoch [41/100], Loss: 0.0077
Epoch [42/100], Loss: 0.0075
Epoch [43/100], Loss: 0.0073
Epoch [44/100], Loss: 0.0070
Epoch [45/100], Loss: 0.0067
Epoch [46/100], Loss: 0.0065
Epoch [47/100], Loss: 0.0061
Epoch [48/100], Loss: 0.0059
Epoch [49/100], Loss: 0.0058
Epoch [50/100], Loss: 0.0056
Epoch [51/100], Loss: 0.0053
Epoch [52/100], Loss: 0.0052
Epoch [53/100], Loss: 0.0051
Epoch [54/100], Loss: 0.0050
Epoch [55/100], Loss: 0.0049
Epoch [56/100], Loss: 0.0048
Epoch [57/100], Loss: 0.0047
Epoch [58/100], Loss: 0.0048
Epoch [59/100], Loss: 0.0051
Epoch [60/100], Loss: 0.0052
Epoch [61/100], Loss: 0.0050
Epoch [62/100], Loss: 0.0049
Epoch [63/100], Loss: 0.0050
Epoch [64/100], Loss: 0.0048
Epoch [65/100], Loss: 0.0045
Epoch [66/100], Loss: 0.0043
Epoch [67/100], Loss: 0.0041
Epoch [68/100], Loss: 0.0039
Epoch [69/100], Loss: 0.0037
Epoch [70/100], Loss: 0.0036
Epoch [71/100], Loss: 0.0036
Epoch [72/100], Loss: 0.0035
Epoch [73/100], Loss: 0.0034
Epoch [74/100], Loss: 0.0033
Epoch [75/100], Loss: 0.0033
Epoch [76/100], Loss: 0.0033
Epoch [77/100], Loss: 0.0033
Epoch [78/100], Loss: 0.0033
Epoch [79/100], Loss: 0.0032
Epoch [80/100], Loss: 0.0031
Epoch [81/100], Loss: 0.0030
Epoch [82/100], Loss: 0.0030
Epoch [83/100], Loss: 0.0029
Epoch [84/100], Loss: 0.0029
Epoch [85/100], Loss: 0.0028
Epoch [86/100], Loss: 0.0028
Epoch [87/100], Loss: 0.0028
Epoch [88/100], Loss: 0.0028
Epoch [89/100], Loss: 0.0028
Epoch [90/100], Loss: 0.0027
Epoch [91/100], Loss: 0.0027
Epoch [92/100], Loss: 0.0027
Epoch [93/100], Loss: 0.0027
Epoch [94/100], Loss: 0.0027
Epoch [95/100], Loss: 0.0027
Epoch [96/100], Loss: 0.0026
Epoch [97/100], Loss: 0.0027
Epoch [98/100], Loss: 0.0027
Epoch [99/100], Loss: 0.0026
Epoch [100/100], Loss: 0.0027
X: 500, Autoencoder(
(encoder): Encoder(
(conv2d_01): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_01): ReLU()
(conv2d_02): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_02): ReLU()
(conv2d_03): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_03): ReLU()
(flatten_04): Flatten(start_dim=1, end_dim=-1)
(linear_04): Linear(in_features=65536, out_features=1024, bias=True)
(relu_04): ReLU()
)
(decoder): Decoder(
(linear_01): Linear(in_features=1024, out_features=65536, bias=True)
(relu_01): ReLU()
(unflatten_01): Unflatten(dim=1, unflattened_size=(256, 16, 16))
(conv_transpose2d_02): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_02): ReLU()
(conv_transpose2d_03): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_03): ReLU()
(conv_transpose2d_04): ConvTranspose2d(64, 3, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(tanh_04): Tanh()
)
)
Epoch [1/100], Loss: 0.6048
Epoch [2/100], Loss: 0.4440
Epoch [3/100], Loss: 0.2932
Epoch [4/100], Loss: 0.1947
Epoch [5/100], Loss: 0.1364
Epoch [6/100], Loss: 0.1072
Epoch [7/100], Loss: 0.0880
Epoch [8/100], Loss: 0.0747
Epoch [9/100], Loss: 0.0635
Epoch [10/100], Loss: 0.0551
Epoch [11/100], Loss: 0.0482
Epoch [12/100], Loss: 0.0428
Epoch [13/100], Loss: 0.0385
Epoch [14/100], Loss: 0.0347
Epoch [15/100], Loss: 0.0315
Epoch [16/100], Loss: 0.0285
Epoch [17/100], Loss: 0.0265
Epoch [18/100], Loss: 0.0239
Epoch [19/100], Loss: 0.0218
Epoch [20/100], Loss: 0.0203
Epoch [21/100], Loss: 0.0188
Epoch [22/100], Loss: 0.0177
Epoch [23/100], Loss: 0.0167
Epoch [24/100], Loss: 0.0159
Epoch [25/100], Loss: 0.0150
Epoch [26/100], Loss: 0.0142
Epoch [27/100], Loss: 0.0133
Epoch [28/100], Loss: 0.0124
Epoch [29/100], Loss: 0.0119
Epoch [30/100], Loss: 0.0113
Epoch [31/100], Loss: 0.0107
Epoch [32/100], Loss: 0.0103
Epoch [33/100], Loss: 0.0097
Epoch [34/100], Loss: 0.0093
Epoch [35/100], Loss: 0.0091
Epoch [36/100], Loss: 0.0088
Epoch [37/100], Loss: 0.0085
Epoch [38/100], Loss: 0.0083
Epoch [39/100], Loss: 0.0081
Epoch [40/100], Loss: 0.0077
Epoch [41/100], Loss: 0.0076
Epoch [42/100], Loss: 0.0073
Epoch [43/100], Loss: 0.0072
Epoch [44/100], Loss: 0.0070
Epoch [45/100], Loss: 0.0067
Epoch [46/100], Loss: 0.0065
Epoch [47/100], Loss: 0.0063
Epoch [48/100], Loss: 0.0061
Epoch [49/100], Loss: 0.0059
Epoch [50/100], Loss: 0.0057
Epoch [51/100], Loss: 0.0055
Epoch [52/100], Loss: 0.0055
Epoch [53/100], Loss: 0.0053
Epoch [54/100], Loss: 0.0052
Epoch [55/100], Loss: 0.0051
Epoch [56/100], Loss: 0.0052
Epoch [57/100], Loss: 0.0050
Epoch [58/100], Loss: 0.0048
Epoch [59/100], Loss: 0.0047
Epoch [60/100], Loss: 0.0046
Epoch [61/100], Loss: 0.0045
Epoch [62/100], Loss: 0.0044
Epoch [63/100], Loss: 0.0044
Epoch [64/100], Loss: 0.0042
Epoch [65/100], Loss: 0.0041
Epoch [66/100], Loss: 0.0041
Epoch [67/100], Loss: 0.0040
Epoch [68/100], Loss: 0.0041
Epoch [69/100], Loss: 0.0040
Epoch [70/100], Loss: 0.0040
Epoch [71/100], Loss: 0.0040
Epoch [72/100], Loss: 0.0039
Epoch [73/100], Loss: 0.0037
Epoch [74/100], Loss: 0.0037
Epoch [75/100], Loss: 0.0036
Epoch [76/100], Loss: 0.0035
Epoch [77/100], Loss: 0.0034
Epoch [78/100], Loss: 0.0034
Epoch [79/100], Loss: 0.0033
Epoch [80/100], Loss: 0.0033
Epoch [81/100], Loss: 0.0033
Epoch [82/100], Loss: 0.0032
Epoch [83/100], Loss: 0.0033
Epoch [84/100], Loss: 0.0033
Epoch [85/100], Loss: 0.0034
Epoch [86/100], Loss: 0.0034
Epoch [87/100], Loss: 0.0034
Epoch [88/100], Loss: 0.0039
Epoch [89/100], Loss: 0.0036
Epoch [90/100], Loss: 0.0033
Epoch [91/100], Loss: 0.0032
Epoch [92/100], Loss: 0.0031
Epoch [93/100], Loss: 0.0030
Epoch [94/100], Loss: 0.0029
Epoch [95/100], Loss: 0.0028
Epoch [96/100], Loss: 0.0027
Epoch [97/100], Loss: 0.0026
Epoch [98/100], Loss: 0.0025
Epoch [99/100], Loss: 0.0025
Epoch [100/100], Loss: 0.0024
C: 500, Autoencoder(
(encoder): Encoder(
(conv2d_01): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_01): ReLU()
(conv2d_02): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_02): ReLU()
(conv2d_03): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_03): ReLU()
(flatten_04): Flatten(start_dim=1, end_dim=-1)
(linear_04): Linear(in_features=65536, out_features=1024, bias=True)
(relu_04): ReLU()
)
(decoder): Decoder(
(linear_01): Linear(in_features=1024, out_features=65536, bias=True)
(relu_01): ReLU()
(unflatten_01): Unflatten(dim=1, unflattened_size=(256, 16, 16))
(conv_transpose2d_02): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_02): ReLU()
(conv_transpose2d_03): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_03): ReLU()
(conv_transpose2d_04): ConvTranspose2d(64, 3, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(tanh_04): Tanh()
)
)
Epoch [1/100], Loss: 0.5579
Epoch [2/100], Loss: 0.4553
Epoch [3/100], Loss: 0.3463
Epoch [4/100], Loss: 0.2267
Epoch [5/100], Loss: 0.1774
Epoch [6/100], Loss: 0.1503
Epoch [7/100], Loss: 0.1296
Epoch [8/100], Loss: 0.1132
Epoch [9/100], Loss: 0.0973
Epoch [10/100], Loss: 0.0832
Epoch [11/100], Loss: 0.0710
Epoch [12/100], Loss: 0.0609
Epoch [13/100], Loss: 0.0524
Epoch [14/100], Loss: 0.0456
Epoch [15/100], Loss: 0.0407
Epoch [16/100], Loss: 0.0382
Epoch [17/100], Loss: 0.0364
Epoch [18/100], Loss: 0.0330
Epoch [19/100], Loss: 0.0308
Epoch [20/100], Loss: 0.0282
Epoch [21/100], Loss: 0.0259
Epoch [22/100], Loss: 0.0233
Epoch [23/100], Loss: 0.0221
Epoch [24/100], Loss: 0.0203
Epoch [25/100], Loss: 0.0190
Epoch [26/100], Loss: 0.0185
Epoch [27/100], Loss: 0.0178
Epoch [28/100], Loss: 0.0168
Epoch [29/100], Loss: 0.0159
Epoch [30/100], Loss: 0.0151
Epoch [31/100], Loss: 0.0142
Epoch [32/100], Loss: 0.0138
Epoch [33/100], Loss: 0.0134
Epoch [34/100], Loss: 0.0125
Epoch [35/100], Loss: 0.0117
Epoch [36/100], Loss: 0.0113
Epoch [37/100], Loss: 0.0109
Epoch [38/100], Loss: 0.0103
Epoch [39/100], Loss: 0.0100
Epoch [40/100], Loss: 0.0098
Epoch [41/100], Loss: 0.0096
Epoch [42/100], Loss: 0.0092
Epoch [43/100], Loss: 0.0090
Epoch [44/100], Loss: 0.0088
Epoch [45/100], Loss: 0.0085
Epoch [46/100], Loss: 0.0084
Epoch [47/100], Loss: 0.0084
Epoch [48/100], Loss: 0.0082
Epoch [49/100], Loss: 0.0081
Epoch [50/100], Loss: 0.0080
Epoch [51/100], Loss: 0.0080
Epoch [52/100], Loss: 0.0080
Epoch [53/100], Loss: 0.0078
Epoch [54/100], Loss: 0.0077
Epoch [55/100], Loss: 0.0074
Epoch [56/100], Loss: 0.0072
Epoch [57/100], Loss: 0.0072
Epoch [58/100], Loss: 0.0068
Epoch [59/100], Loss: 0.0067
Epoch [60/100], Loss: 0.0066
Epoch [61/100], Loss: 0.0066
Epoch [62/100], Loss: 0.0065
Epoch [63/100], Loss: 0.0063
Epoch [64/100], Loss: 0.0060
Epoch [65/100], Loss: 0.0058
Epoch [66/100], Loss: 0.0057
Epoch [67/100], Loss: 0.0055
Epoch [68/100], Loss: 0.0054
Epoch [69/100], Loss: 0.0052
Epoch [70/100], Loss: 0.0050
Epoch [71/100], Loss: 0.0049
Epoch [72/100], Loss: 0.0049
Epoch [73/100], Loss: 0.0048
Epoch [74/100], Loss: 0.0047
Epoch [75/100], Loss: 0.0046
Epoch [76/100], Loss: 0.0045
Epoch [77/100], Loss: 0.0044
Epoch [78/100], Loss: 0.0044
Epoch [79/100], Loss: 0.0043
Epoch [80/100], Loss: 0.0043
Epoch [81/100], Loss: 0.0042
Epoch [82/100], Loss: 0.0042
Epoch [83/100], Loss: 0.0043
Epoch [84/100], Loss: 0.0044
Epoch [85/100], Loss: 0.0044
Epoch [86/100], Loss: 0.0045
Epoch [87/100], Loss: 0.0044
Epoch [88/100], Loss: 0.0044
Epoch [89/100], Loss: 0.0044
Epoch [90/100], Loss: 0.0044
Epoch [91/100], Loss: 0.0044
Epoch [92/100], Loss: 0.0042
Epoch [93/100], Loss: 0.0042
Epoch [94/100], Loss: 0.0040
Epoch [95/100], Loss: 0.0040
Epoch [96/100], Loss: 0.0038
Epoch [97/100], Loss: 0.0039
Epoch [98/100], Loss: 0.0039
Epoch [99/100], Loss: 0.0040
Epoch [100/100], Loss: 0.0040
M: 500, Autoencoder(
(encoder): Encoder(
(conv2d_01): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_01): ReLU()
(conv2d_02): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_02): ReLU()
(conv2d_03): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_03): ReLU()
(flatten_04): Flatten(start_dim=1, end_dim=-1)
(linear_04): Linear(in_features=65536, out_features=1024, bias=True)
(relu_04): ReLU()
)
(decoder): Decoder(
(linear_01): Linear(in_features=1024, out_features=65536, bias=True)
(relu_01): ReLU()
(unflatten_01): Unflatten(dim=1, unflattened_size=(256, 16, 16))
(conv_transpose2d_02): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_02): ReLU()
(conv_transpose2d_03): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_03): ReLU()
(conv_transpose2d_04): ConvTranspose2d(64, 3, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(tanh_04): Tanh()
)
)
Epoch [1/100], Loss: 0.5974
Epoch [2/100], Loss: 0.4831
Epoch [3/100], Loss: 0.3327
Epoch [4/100], Loss: 0.2378
Epoch [5/100], Loss: 0.1760
Epoch [6/100], Loss: 0.1357
Epoch [7/100], Loss: 0.1095
Epoch [8/100], Loss: 0.0884
Epoch [9/100], Loss: 0.0720
Epoch [10/100], Loss: 0.0600
Epoch [11/100], Loss: 0.0516
Epoch [12/100], Loss: 0.0450
Epoch [13/100], Loss: 0.0396
Epoch [14/100], Loss: 0.0352
Epoch [15/100], Loss: 0.0320
Epoch [16/100], Loss: 0.0290
Epoch [17/100], Loss: 0.0268
Epoch [18/100], Loss: 0.0249
Epoch [19/100], Loss: 0.0234
Epoch [20/100], Loss: 0.0219
Epoch [21/100], Loss: 0.0206
Epoch [22/100], Loss: 0.0197
Epoch [23/100], Loss: 0.0188
Epoch [24/100], Loss: 0.0180
Epoch [25/100], Loss: 0.0167
Epoch [26/100], Loss: 0.0155
Epoch [27/100], Loss: 0.0145
Epoch [28/100], Loss: 0.0139
Epoch [29/100], Loss: 0.0131
Epoch [30/100], Loss: 0.0126
Epoch [31/100], Loss: 0.0123
Epoch [32/100], Loss: 0.0122
Epoch [33/100], Loss: 0.0117
Epoch [34/100], Loss: 0.0113
Epoch [35/100], Loss: 0.0109
Epoch [36/100], Loss: 0.0104
Epoch [37/100], Loss: 0.0099
Epoch [38/100], Loss: 0.0095
Epoch [39/100], Loss: 0.0092
Epoch [40/100], Loss: 0.0090
Epoch [41/100], Loss: 0.0088
Epoch [42/100], Loss: 0.0086
Epoch [43/100], Loss: 0.0084
Epoch [44/100], Loss: 0.0083
Epoch [45/100], Loss: 0.0082
Epoch [46/100], Loss: 0.0081
Epoch [47/100], Loss: 0.0079
Epoch [48/100], Loss: 0.0077
Epoch [49/100], Loss: 0.0075
Epoch [50/100], Loss: 0.0074
Epoch [51/100], Loss: 0.0072
Epoch [52/100], Loss: 0.0072
Epoch [53/100], Loss: 0.0071
Epoch [54/100], Loss: 0.0071
Epoch [55/100], Loss: 0.0068
Epoch [56/100], Loss: 0.0066
Epoch [57/100], Loss: 0.0067
Epoch [58/100], Loss: 0.0069
Epoch [59/100], Loss: 0.0067
Epoch [60/100], Loss: 0.0065
Epoch [61/100], Loss: 0.0064
Epoch [62/100], Loss: 0.0061
Epoch [63/100], Loss: 0.0060
Epoch [64/100], Loss: 0.0059
Epoch [65/100], Loss: 0.0057
Epoch [66/100], Loss: 0.0055
Epoch [67/100], Loss: 0.0054
Epoch [68/100], Loss: 0.0052
Epoch [69/100], Loss: 0.0051
Epoch [70/100], Loss: 0.0050
Epoch [71/100], Loss: 0.0049
Epoch [72/100], Loss: 0.0049
Epoch [73/100], Loss: 0.0048
Epoch [74/100], Loss: 0.0047
Epoch [75/100], Loss: 0.0047
Epoch [76/100], Loss: 0.0046
Epoch [77/100], Loss: 0.0045
Epoch [78/100], Loss: 0.0045
Epoch [79/100], Loss: 0.0044
Epoch [80/100], Loss: 0.0044
Epoch [81/100], Loss: 0.0045
Epoch [82/100], Loss: 0.0046
Epoch [83/100], Loss: 0.0046
Epoch [84/100], Loss: 0.0046
Epoch [85/100], Loss: 0.0045
Epoch [86/100], Loss: 0.0044
Epoch [87/100], Loss: 0.0043
Epoch [88/100], Loss: 0.0042
Epoch [89/100], Loss: 0.0042
Epoch [90/100], Loss: 0.0042
Epoch [91/100], Loss: 0.0042
Epoch [92/100], Loss: 0.0041
Epoch [93/100], Loss: 0.0041
Epoch [94/100], Loss: 0.0040
Epoch [95/100], Loss: 0.0040
Epoch [96/100], Loss: 0.0040
Epoch [97/100], Loss: 0.0040
Epoch [98/100], Loss: 0.0039
Epoch [99/100], Loss: 0.0039
Epoch [100/100], Loss: 0.0038
E: 500, Autoencoder(
(encoder): Encoder(
(conv2d_01): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_01): ReLU()
(conv2d_02): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_02): ReLU()
(conv2d_03): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(relu_03): ReLU()
(flatten_04): Flatten(start_dim=1, end_dim=-1)
(linear_04): Linear(in_features=65536, out_features=1024, bias=True)
(relu_04): ReLU()
)
(decoder): Decoder(
(linear_01): Linear(in_features=1024, out_features=65536, bias=True)
(relu_01): ReLU()
(unflatten_01): Unflatten(dim=1, unflattened_size=(256, 16, 16))
(conv_transpose2d_02): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_02): ReLU()
(conv_transpose2d_03): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(relu_03): ReLU()
(conv_transpose2d_04): ConvTranspose2d(64, 3, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
(tanh_04): Tanh()
)
)
Epoch [1/100], Loss: 0.5404
Epoch [2/100], Loss: 0.4630
Epoch [3/100], Loss: 0.3849
Epoch [4/100], Loss: 0.2614
Epoch [5/100], Loss: 0.1603
Epoch [6/100], Loss: 0.1130
Epoch [7/100], Loss: 0.0881
Epoch [8/100], Loss: 0.0698
Epoch [9/100], Loss: 0.0557
Epoch [10/100], Loss: 0.0468
Epoch [11/100], Loss: 0.0402
Epoch [12/100], Loss: 0.0356
Epoch [13/100], Loss: 0.0327
Epoch [14/100], Loss: 0.0297
Epoch [15/100], Loss: 0.0269
Epoch [16/100], Loss: 0.0249
Epoch [17/100], Loss: 0.0236
Epoch [18/100], Loss: 0.0233
Epoch [19/100], Loss: 0.0219
Epoch [20/100], Loss: 0.0208
Epoch [21/100], Loss: 0.0194
Epoch [22/100], Loss: 0.0180
Epoch [23/100], Loss: 0.0165
Epoch [24/100], Loss: 0.0154
Epoch [25/100], Loss: 0.0144
Epoch [26/100], Loss: 0.0136
Epoch [27/100], Loss: 0.0128
Epoch [28/100], Loss: 0.0123
Epoch [29/100], Loss: 0.0118
Epoch [30/100], Loss: 0.0114
Epoch [31/100], Loss: 0.0110
Epoch [32/100], Loss: 0.0107
Epoch [33/100], Loss: 0.0102
Epoch [34/100], Loss: 0.0101