HackathonF23-dotsandboxes/5kLog.log

PS C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes> python -m src.model.train
Using cuda
Data loaded
Model created
Optimizer and loss created
Epoch 1/10
Batch:  0
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9671375751495361
Batch:  1
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3357730805873871
Batch:  2
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7758014798164368
Batch:  3
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.10815244913101196
Batch:  4
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.36683541536331177
Batch:  5
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.604379415512085
Batch:  6
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8250358700752258
Batch:  7
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.702534556388855
Batch:  8
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.875644326210022
Batch:  9
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5796182155609131
Batch:  10
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.348760724067688
Batch:  11
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.510147213935852
Batch:  12
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.627685546875
Batch:  13
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.312531054019928
Batch:  14
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.4717888832092285
Batch:  15
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9486780166625977
Batch:  16
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5000591278076172
Batch:  17
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8765230178833008
Batch:  18
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.799582839012146
Batch:  19
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8773936033248901
Batch:  20
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9767993688583374
Batch:  21
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7391752004623413
Batch:  22
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.06409857422113419
Batch:  23
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7439115643501282
Batch:  24
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.21947161853313446
Batch:  25
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6262499094009399
Batch:  26
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.45701873302459717
Batch:  27
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.185937762260437
Batch:  28
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8371411561965942
Batch:  29
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6778520941734314
Batch:  30
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.14422258734703064
Batch:  31
Error
Batch:  32
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.05408451333642006
Batch:  33
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3011379539966583
Batch:  34
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9189160466194153
Batch:  35
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6175788640975952
Batch:  36
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.606583833694458
Batch:  37
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  38
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9083439111709595
Batch:  39
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.28578275442123413
Batch:  40
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2884284257888794
Batch:  41
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8158679604530334
Batch:  42
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9501778483390808
Batch:  43
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5349695682525635
Batch:  44
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.627077043056488
Batch:  45
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7644076943397522
Batch:  46
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7351073026657104
Batch:  47
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.14422348141670227
Batch:  48
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.13329266011714935
Batch:  49
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3382028341293335
Batch:  50
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.856562614440918
Batch:  51
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9715302586555481
Batch:  52
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.6213864088058472
Batch:  53
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4195018708705902
Batch:  54
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.30492836236953735
Batch:  55
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  56
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7306706309318542
Batch:  57
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1934454441070557
Batch:  58
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1315622329711914
Batch:  59
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.31865227222442627
Batch:  60
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6776185035705566
Batch:  61
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5712005496025085
Batch:  62
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5283052921295166
Batch:  63
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.302706241607666
Batch:  64
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5152894258499146
Batch:  65
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.18344828486442566
Batch:  66
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.10180754214525223
Batch:  67
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4467272162437439
Batch:  68
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6470417976379395
Batch:  69
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.519213080406189
Batch:  70
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.10180605202913284
Batch:  71
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3552370071411133
Batch:  72
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.13312585651874542
Batch:  73
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.24230259656906128
Batch:  74
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7845883965492249
Batch:  75
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.694536566734314
Batch:  76
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.11889225244522095
Batch:  77
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.407955527305603
Batch:  78
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2884470224380493
Batch:  79
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.07099834829568863
Batch:  80
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.23900917172431946
Batch:  81
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6765679717063904
Batch:  82
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.685997486114502
Batch:  83
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.25578513741493225
Batch:  84
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5069621801376343
Batch:  85
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.939524233341217
Batch:  86
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6981882452964783
Batch:  87
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.45135483145713806
Batch:  88
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4903637766838074
Batch:  89
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1362071931362152
Batch:  90
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1722727119922638
Batch:  91
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6506141424179077
Batch:  92
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4171068072319031
Batch:  93
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3117816150188446
Batch:  94
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6943203210830688
Batch:  95
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4945167303085327
Batch:  96
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1479922533035278
Batch:  97
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  98
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5394001603126526
Batch:  99
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5768826007843018
Batch:  100
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9562923908233643
Batch:  101
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5533633232116699
Batch:  102
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5430610179901123
Batch:  103
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.197507381439209
Batch:  104
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.16239020228385925
Batch:  105
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5952204465866089
Batch:  106
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6208470463752747
Batch:  107
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.32691463828086853
Batch:  108
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4465545117855072
Batch:  109
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2202731966972351
Batch:  110
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.41666868329048157
Batch:  111
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.6773345470428467
Batch:  112
Error
Batch:  113
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9205189347267151
Batch:  114
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1726571321487427
Batch:  115
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6203481554985046
Batch:  116
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6790733337402344
Batch:  117
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2563943564891815
Batch:  118
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.171336054801941
Batch:  119
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5107123255729675
Batch:  120
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3867741823196411
Batch:  121
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2680294513702393
Batch:  122
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.15049752593040466
Batch:  123
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5865037441253662
Batch:  124
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7007516622543335
Epoch 2/10
Batch:  0
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8216699957847595
Batch:  1
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.4645968675613403
Batch:  2
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1060538291931152
Batch:  3
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.32798492908477783
Batch:  4
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.17417189478874207
Batch:  5
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.36387449502944946
Batch:  6
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.4034414291381836
Batch:  7
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.17307165265083313
Batch:  8
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5389558672904968
Batch:  9
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7624702453613281
Batch:  10
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9147507548332214
Batch:  11
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.13312022387981415
Batch:  12
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7083905935287476
Batch:  13
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3251414895057678
Batch:  14
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.30569905042648315
Batch:  15
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3244859576225281
Batch:  16
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.49008744955062866
Batch:  17
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.07867049425840378
Batch:  18
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.28597351908683777
Batch:  19
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.069812297821045
Batch:  20
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8570716977119446
Batch:  21
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.16483362019062042
Batch:  22
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3008725643157959
Batch:  23
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7473375797271729
Batch:  24
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5934094190597534
Batch:  25
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.750707745552063
Batch:  26
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3021131753921509
Batch:  27
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6097243428230286
Batch:  28
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5246003866195679
Batch:  29
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2733412981033325
Batch:  30
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.30748918652534485
Batch:  31
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1841813325881958
Batch:  32
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.923446774482727
Batch:  33
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2773888111114502
Batch:  34
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4615168571472168
Batch:  35
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6779862642288208
Batch:  36
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.0792465209960938
Batch:  37
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6698505878448486
Batch:  38
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.15731731057167053
Batch:  39
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6129428148269653
Batch:  40
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5848506093025208
Batch:  41
Error
Batch:  42
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.473865270614624
Batch:  43
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8414234519004822
Batch:  44
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.43582767248153687
Batch:  45
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5430217385292053
Batch:  46
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0227267742156982
Batch:  47
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.15761633217334747
Batch:  48
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.14631341397762299
Batch:  49
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.27648305892944336
Batch:  50
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9377621412277222
Batch:  51
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  52
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2307640016078949
Batch:  53
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3984891176223755
Batch:  54
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2325260639190674
Batch:  55
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0580955743789673
Batch:  56
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6535903215408325
Batch:  57
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4038296937942505
Batch:  58
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6552420854568481
Batch:  59
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.19230084121227264
Batch:  60
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2307557314634323
Batch:  61
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8158867359161377
Batch:  62
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.34610724449157715
Batch:  63
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3918859362602234
Batch:  64
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.22250822186470032
Batch:  65
Error
Batch:  66
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2636241614818573
Batch:  67
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3805955648422241
Batch:  68
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6620373129844666
Batch:  69
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5900037884712219
Batch:  70
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.13312630355358124
Batch:  71
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0269193649291992
Batch:  72
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.483146607875824
Batch:  73
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5252665877342224
Batch:  74
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5657846927642822
Batch:  75
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.09614921361207962
Batch:  76
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5516667366027832
Batch:  77
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.25641006231307983
Batch:  78
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5031712651252747
Batch:  79
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.47777244448661804
Batch:  80
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.47354888916015625
Batch:  81
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6250122785568237
Batch:  82
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0788853168487549
Batch:  83
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0639724731445312
Batch:  84
Error
Batch:  85
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5027806758880615
Batch:  86
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.33540284633636475
Batch:  87
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.017976999282837
Batch:  88
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.2471442222595215
Batch:  89
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.393146276473999
Batch:  90
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5210620164871216
Batch:  91
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5725550651550293
Batch:  92
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6207171678543091
Batch:  93
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7211016416549683
Batch:  94
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4072118401527405
Batch:  95
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.34613466262817383
Batch:  96
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7139992117881775
Batch:  97
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6466346979141235
Batch:  98
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.18426513671875
Batch:  99
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.10487249493598938
Batch:  100
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.387503981590271
Batch:  101
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3416404724121094
Batch:  102
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.216884732246399
Batch:  103
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4944817125797272
Batch:  104
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.620098352432251
Batch:  105
Error
Batch:  106
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.23255205154418945
Batch:  107
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1633925437927246
Batch:  108
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2417421042919159
Batch:  109
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3412394523620605
Batch:  110
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1594233512878418
Batch:  111
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5933969616889954
Batch:  112
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.849651575088501
Batch:  113
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.46872371435165405
Batch:  114
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.44267702102661133
Batch:  115
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7704805135726929
Batch:  116
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.709647536277771
Batch:  117
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  118
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5183135867118835
Batch:  119
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.648858904838562
Batch:  120
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.601204514503479
Batch:  121
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.05967683345079422
Batch:  122
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.6440906524658203
Batch:  123
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2573587894439697
Batch:  124
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9582837224006653
Epoch 3/10
Batch:  0
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7278819680213928
Batch:  1
Error
Batch:  2
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6459208726882935
Batch:  3
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.45147448778152466
Batch:  4
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.13825926184654236
Batch:  5
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  6
Error
Batch:  7
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7120490074157715
Batch:  8
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.34613004326820374
Batch:  9
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.43266192078590393
Batch:  10
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8927388191223145
Batch:  11
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3585691452026367
Batch:  12
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.687412679195404
Batch:  13
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4207839071750641
Batch:  14
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5485715866088867
Batch:  15
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6028484106063843
Batch:  16
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6542356014251709
Batch:  17
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3747721910476685
Batch:  18
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8434720039367676
Batch:  19
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6236392259597778
Batch:  20
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.34962406754493713
Batch:  21
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8068771958351135
Batch:  22
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.19277679920196533
Batch:  23
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7446494102478027
Batch:  24
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.42694246768951416
Batch:  25
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4576033353805542
Batch:  26
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7533493041992188
Batch:  27
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9402579665184021
Batch:  28
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4793030321598053
Batch:  29
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0610723495483398
Batch:  30
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7499773502349854
Batch:  31
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.32965439558029175
Batch:  32
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5645320415496826
Batch:  33
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  34
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.06181241199374199
Batch:  35
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0543919801712036
Batch:  36
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.5146477222442627
Batch:  37
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4619579613208771
Batch:  38
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3296763598918915
Batch:  39
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2209529876708984
Batch:  40
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.37808194756507874
Batch:  41
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9647338390350342
Batch:  42
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1537007093429565
Batch:  43
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5006552934646606
Batch:  44
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.25640833377838135
Batch:  45
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7129522562026978
Batch:  46
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.24050123989582062
Batch:  47
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.16884639859199524
Batch:  48
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.38806629180908203
Batch:  49
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9439498782157898
Batch:  50
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.17903417348861694
Batch:  51
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  52
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2788226902484894
Batch:  53
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5128114819526672
Batch:  54
Error
Batch:  55
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3532533049583435
Batch:  56
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6211085915565491
Batch:  57
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.264704704284668
Batch:  58
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.40838971734046936
Batch:  59
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8898868560791016
Batch:  60
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8116297721862793
Batch:  61
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8390936851501465
Batch:  62
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6558694839477539
Batch:  63
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.30598610639572144
Batch:  64
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.46151456236839294
Batch:  65
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.965258777141571
Batch:  66
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2563852071762085
Batch:  67
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.0038955211639404
Batch:  68
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.24034979939460754
Batch:  69
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.19229565560817719
Batch:  70
Error
Batch:  71
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3018265664577484
Batch:  72
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5768895745277405
Batch:  73
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4737894833087921
Batch:  74
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.36054667830467224
Batch:  75
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.42040392756462097
Batch:  76
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.8124957084655762
Batch:  77
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6386255025863647
Batch:  78
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8088455200195312
Batch:  79
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5948607921600342
Batch:  80
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7026998996734619
Batch:  81
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9699044823646545
Batch:  82
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7624577283859253
Batch:  83
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5465285778045654
Batch:  84
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.19229809939861298
Batch:  85
Error
Batch:  86
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5466693043708801
Batch:  87
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1507691740989685
Batch:  88
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.51142817735672
Batch:  89
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8223261833190918
Batch:  90
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.29838359355926514
Batch:  91
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7615168690681458
Batch:  92
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.186139464378357
Batch:  93
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5342870950698853
Batch:  94
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.307042121887207
Batch:  95
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.41824039816856384
Batch:  96
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.15949343144893646
Batch:  97
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.30207112431526184
Batch:  98
Error
Batch:  99
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.588372826576233
Batch:  100
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3695206940174103
Batch:  101
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3845994174480438
Batch:  102
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2629271149635315
Batch:  103
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.26892513036727905
Batch:  104
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6459289789199829
Batch:  105
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7446944713592529
Batch:  106
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5802391171455383
Batch:  107
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1414464712142944
Batch:  108
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4660806655883789
Batch:  109
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2902428209781647
Batch:  110
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.35248395800590515
Batch:  111
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.23601096868515015
Batch:  112
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5716551542282104
Batch:  113
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7524344325065613
Batch:  114
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3556723594665527
Batch:  115
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8533833026885986
Batch:  116
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.34612202644348145
Batch:  117
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4713132977485657
Batch:  118
Error
Batch:  119
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8784006834030151
Batch:  120
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9391247034072876
Batch:  121
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4900531768798828
Batch:  122
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7640222907066345
Batch:  123
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.6718268394470215
Batch:  124
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5199609994888306
Epoch 4/10
Batch:  0
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6922632455825806
Batch:  1
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.14888140559196472
Batch:  2
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.44711950421333313
Batch:  3
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5480469465255737
Batch:  4
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.17102950811386108
Batch:  5
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5360546112060547
Batch:  6
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6970803737640381
Batch:  7
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.23016518354415894
Batch:  8
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1648205816745758
Batch:  9
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.09109208732843399
Batch:  10
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.36057132482528687
Batch:  11
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6477615833282471
Batch:  12
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4698120355606079
Batch:  13
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4305943250656128
Batch:  14
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6651465892791748
Batch:  15
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1573398858308792
Batch:  16
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.40012386441230774
Batch:  17
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0559180974960327
Batch:  18
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.05089643597602844
Batch:  19
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  20
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1537196636199951
Batch:  21
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5404183864593506
Batch:  22
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3461456894874573
Batch:  23
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6188719272613525
Batch:  24
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7629416584968567
Batch:  25
Error
Batch:  26
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.150776982307434
Batch:  27
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5499976873397827
Batch:  28
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.47388264536857605
Batch:  29
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.35208505392074585
Batch:  30
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9259459376335144
Batch:  31
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.19180238246917725
Batch:  32
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1734892725944519
Batch:  33
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7136067152023315
Batch:  34
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.43385300040245056
Batch:  35
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3955739140510559
Batch:  36
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5768712162971497
Batch:  37
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.33995622396469116
Batch:  38
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7760007977485657
Batch:  39
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1935755014419556
Batch:  40
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4420817494392395
Batch:  41
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8059060573577881
Batch:  42
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2579205632209778
Batch:  43
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.20431718230247498
Batch:  44
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6817824244499207
Batch:  45
Error
Batch:  46
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8735362887382507
Batch:  47
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  48
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7930512428283691
Batch:  49
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1573403775691986
Batch:  50
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7840093374252319
Batch:  51
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.649664044380188
Batch:  52
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9739811420440674
Batch:  53
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.37220293283462524
Batch:  54
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5446142554283142
Batch:  55
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6623204350471497
Batch:  56
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2897769212722778
Batch:  57
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4163522720336914
Batch:  58
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.25079649686813354
Batch:  59
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.16482272744178772
Batch:  60
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.10180752724409103
Batch:  61
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4564470946788788
Batch:  62
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.49771708250045776
Batch:  63
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2827431559562683
Batch:  64
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.37174129486083984
Batch:  65
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.18282704055309296
Batch:  66
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0232051610946655
Batch:  67
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7622325420379639
Batch:  68
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7686375379562378
Batch:  69
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.31592631340026855
Batch:  70
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3882167339324951
Batch:  71
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6273937821388245
Batch:  72
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4716740846633911
Batch:  73
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.47387218475341797
Batch:  74
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.46150901913642883
Batch:  75
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.15063020586967468
Batch:  76
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3580213487148285
Batch:  77
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8583611249923706
Batch:  78
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4247787594795227
Batch:  79
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5047405958175659
Batch:  80
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5369718670845032
Batch:  81
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2979367971420288
Batch:  82
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5384169220924377
Batch:  83
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7833620309829712
Batch:  84
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3182750642299652
Batch:  85
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.49447235465049744
Batch:  86
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7848111391067505
Batch:  87
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3556877076625824
Batch:  88
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0910857617855072
Batch:  89
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.222510427236557
Batch:  90
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2163364589214325
Batch:  91
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4128417372703552
Batch:  92
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5624570846557617
Batch:  93
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9423930048942566
Batch:  94
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.647646427154541
Batch:  95
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.31592196226119995
Batch:  96
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7478219270706177
Batch:  97
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1548545360565186
Batch:  98
Error
Batch:  99
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6837530136108398
Batch:  100
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.466111421585083
Batch:  101
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  3.3172287940979004
Batch:  102
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.08546465635299683
Batch:  103
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5403462052345276
Batch:  104
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5176123380661011
Batch:  105
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1442287266254425
Batch:  106
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2998274564743042
Batch:  107
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7131085395812988
Batch:  108
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7866928577423096
Batch:  109
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.818934977054596
Batch:  110
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5546977519989014
Batch:  111
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8401613235473633
Batch:  112
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8721956014633179
Batch:  113
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4246814250946045
Batch:  114
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9922517538070679
Batch:  115
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.463589608669281
Batch:  116
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4134780764579773
Batch:  117
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.853995680809021
Batch:  118
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  119
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.15047423541545868
Batch:  120
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7129990458488464
Batch:  121
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1193607896566391
Batch:  122
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.156897783279419
Batch:  123
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0600188970565796
Batch:  124
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.915186882019043
Epoch 5/10
Batch:  0
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8971927165985107
Batch:  1
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.743038535118103
Batch:  2
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7066560983657837
Batch:  3
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  4
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.615856409072876
Batch:  5
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3141632080078125
Batch:  6
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.21634584665298462
Batch:  7
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6439026594161987
Batch:  8
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.18216435611248016
Batch:  9
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3840988874435425
Batch:  10
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1274313926696777
Batch:  11
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0008164644241333
Batch:  12
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6839984655380249
Batch:  13
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.0131030082702637
Batch:  14
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.263435959815979
Batch:  15
Error
Batch:  16
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5564086437225342
Batch:  17
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5760500431060791
Batch:  18
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.37080150842666626
Batch:  19
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.25847816467285156
Batch:  20
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6296982169151306
Batch:  21
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2490970343351364
Batch:  22
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.774893581867218
Batch:  23
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6812818050384521
Batch:  24
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.569995105266571
Batch:  25
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9877709150314331
Batch:  26
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6179949045181274
Batch:  27
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.38508981466293335
Batch:  28
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9265202879905701
Batch:  29
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.22771534323692322
Batch:  30
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9655395150184631
Batch:  31
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.19967934489250183
Batch:  32
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.38859593868255615
Batch:  33
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5434062480926514
Batch:  34
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5768726468086243
Batch:  35
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.278775930404663
Batch:  36
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6844310760498047
Batch:  37
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3146532475948334
Batch:  38
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6466431617736816
Batch:  39
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9477319717407227
Batch:  40
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5980005264282227
Batch:  41
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2307475209236145
Batch:  42
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7336395978927612
Batch:  43
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.31728947162628174
Batch:  44
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.23177650570869446
Batch:  45
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.41650867462158203
Batch:  46
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.307653784751892
Batch:  47
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.7911734580993652
Batch:  48
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9592520594596863
Batch:  49
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2214302271604538
Batch:  50
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4738696217536926
Batch:  51
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.11935894936323166
Batch:  52
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4913233518600464
Batch:  53
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8325319886207581
Batch:  54
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.4077162742614746
Batch:  55
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7323758006095886
Batch:  56
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.45407113432884216
Batch:  57
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5384114980697632
Batch:  58
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3027467727661133
Batch:  59
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.32505378127098083
Batch:  60
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0689892768859863
Batch:  61
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0756731033325195
Batch:  62
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.004233479499817
Batch:  63
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.39081770181655884
Batch:  64
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5006827712059021
Batch:  65
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4282821714878082
Batch:  66
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7733926177024841
Batch:  67
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0973968505859375
Batch:  68
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0897326469421387
Batch:  69
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3708232641220093
Batch:  70
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6902000904083252
Batch:  71
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4837237000465393
Batch:  72
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.21152591705322266
Batch:  73
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4012894034385681
Batch:  74
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0042024850845337
Batch:  75
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.104366421699524
Batch:  76
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9922798871994019
Batch:  77
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4935346245765686
Batch:  78
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.47199973464012146
Batch:  79
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5841437578201294
Batch:  80
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.7307100296020508
Batch:  81
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.349251389503479
Batch:  82
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5995259284973145
Batch:  83
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.28844931721687317
Batch:  84
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7133349180221558
Batch:  85
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8668351173400879
Batch:  86
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1908094882965088
Batch:  87
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9464229941368103
Batch:  88
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4326609969139099
Batch:  89
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.306373953819275
Batch:  90
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6641676425933838
Batch:  91
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8524676561355591
Batch:  92
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6206752061843872
Batch:  93
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.31357571482658386
Batch:  94
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.5223159790039062
Batch:  95
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.42441660165786743
Batch:  96
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5899932980537415
Batch:  97
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1782273054122925
Batch:  98
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.433566153049469
Batch:  99
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8485879898071289
Batch:  100
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1501132249832153
Batch:  101
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2891663610935211
Batch:  102
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6841820478439331
Batch:  103
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6977110505104065
Batch:  104
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5537427663803101
Batch:  105
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3460692167282104
Batch:  106
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9163570404052734
Batch:  107
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.33141249418258667
Batch:  108
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7692042589187622
Batch:  109
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.09613628685474396
Batch:  110
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2390090972185135
Batch:  111
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7899326682090759
Batch:  112
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.37952086329460144
Batch:  113
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.20361103117465973
Batch:  114
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.237602949142456
Batch:  115
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3845984935760498
Batch:  116
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.12145458161830902
Batch:  117
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8459117412567139
Batch:  118
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2547228336334229
Batch:  119
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5698932409286499
Batch:  120
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.22944140434265137
Batch:  121
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.49232348799705505
Batch:  122
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7970023155212402
Batch:  123
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.8315078020095825
Batch:  124
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Epoch 6/10
Batch:  0
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5872188806533813
Batch:  1
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4982620179653168
Batch:  2
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3902386426925659
Batch:  3
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5142786502838135
Batch:  4
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6666727662086487
Batch:  5
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4933289885520935
Batch:  6
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5943757891654968
Batch:  7
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.08241332322359085
Batch:  8
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7599675059318542
Batch:  9
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.47389304637908936
Batch:  10
Error
Batch:  11
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4395395517349243
Batch:  12
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0464372634887695
Batch:  13
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.4584144353866577
Batch:  14
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.0163378715515137
Batch:  15
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.32722821831703186
Batch:  16
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.680080771446228
Batch:  17
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6042599081993103
Batch:  18
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.45665523409843445
Batch:  19
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2641598582267761
Batch:  20
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.10488580167293549
Batch:  21
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8932242393493652
Batch:  22
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0192949771881104
Batch:  23
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8327299356460571
Batch:  24
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9033570289611816
Batch:  25
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.640706479549408
Batch:  26
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5829495191574097
Batch:  27
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5127749443054199
Batch:  28
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2604151964187622
Batch:  29
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4759315252304077
Batch:  30
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.08241833001375198
Batch:  31
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.38639312982559204
Batch:  32
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7945055961608887
Batch:  33
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.45493584871292114
Batch:  34
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6043891310691833
Batch:  35
Error
Batch:  36
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.10301395505666733
Batch:  37
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6180881857872009
Batch:  38
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3217375874519348
Batch:  39
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.36055606603622437
Batch:  40
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4996114671230316
Batch:  41
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6788814067840576
Batch:  42
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9394853711128235
Batch:  43
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.571834146976471
Batch:  44
Error
Batch:  45
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8401619791984558
Batch:  46
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.845962405204773
Batch:  47
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8935343623161316
Batch:  48
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6590392589569092
Batch:  49
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.124898076057434
Batch:  50
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6485705375671387
Batch:  51
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.057687435299158096
Batch:  52
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3886370658874512
Batch:  53
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3453896641731262
Batch:  54
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.780446469783783
Batch:  55
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8722987174987793
Batch:  56
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4601173400878906
Batch:  57
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2996470034122467
Batch:  58
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8053526878356934
Batch:  59
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0868163108825684
Batch:  60
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2142058610916138
Batch:  61
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5797408819198608
Batch:  62
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8574618101119995
Batch:  63
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6691722869873047
Batch:  64
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.283566951751709
Batch:  65
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7262375354766846
Batch:  66
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.41398435831069946
Batch:  67
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1404801309108734
Batch:  68
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3125988841056824
Batch:  69
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5222074389457703
Batch:  70
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.270747423171997
Batch:  71
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.595139741897583
Batch:  72
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5118536353111267
Batch:  73
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8188916444778442
Batch:  74
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.19924595952033997
Batch:  75
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.25960999727249146
Batch:  76
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.20768290758132935
Batch:  77
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.19819048047065735
Batch:  78
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.139389991760254
Batch:  79
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.44930434226989746
Batch:  80
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3044555187225342
Batch:  81
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.36056289076805115
Batch:  82
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5042380094528198
Batch:  83
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.21043413877487183
Batch:  84
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4058431088924408
Batch:  85
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.26538652181625366
Batch:  86
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8709694147109985
Batch:  87
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.24289165437221527
Batch:  88
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.06656099855899811
Batch:  89
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.081365704536438
Batch:  90
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1650922298431396
Batch:  91
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.42945215106010437
Batch:  92
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.25402122735977173
Batch:  93
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.13635703921318054
Batch:  94
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6014596223831177
Batch:  95
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9129011631011963
Batch:  96
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.39636820554733276
Batch:  97
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8984119892120361
Batch:  98
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.16482236981391907
Batch:  99
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4279031455516815
Batch:  100
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.566428542137146
Batch:  101
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4622645974159241
Batch:  102
Error
Batch:  103
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.36261290311813354
Batch:  104
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9044058322906494
Batch:  105
Error
Batch:  106
Error
Batch:  107
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.3467931747436523
Batch:  108
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.17750409245491028
Batch:  109
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.30156418681144714
Batch:  110
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2724047899246216
Batch:  111
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5294079780578613
Batch:  112
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.24772390723228455
Batch:  113
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.05768502131104469
Batch:  114
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6302156448364258
Batch:  115
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7567689418792725
Batch:  116
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7388069033622742
Batch:  117
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.46236443519592285
Batch:  118
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1305345296859741
Batch:  119
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4193326234817505
Batch:  120
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5682539939880371
Batch:  121
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0713679790496826
Batch:  122
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5109177231788635
Batch:  123
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.49997276067733765
Batch:  124
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6521342992782593
Epoch 7/10
Batch:  0
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5344933271408081
Batch:  1
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.32173314690589905
Batch:  2
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.43267130851745605
Batch:  3
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.260547935962677
Batch:  4
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.6482957601547241
Batch:  5
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6272170543670654
Batch:  6
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6908732652664185
Batch:  7
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5238074660301208
Batch:  8
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7714064717292786
Batch:  9
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.33397653698921204
Batch:  10
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.731061577796936
Batch:  11
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.31730198860168457
Batch:  12
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5918083190917969
Batch:  13
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.849858283996582
Batch:  14
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7336049675941467
Batch:  15
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6825923919677734
Batch:  16
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1246528625488281
Batch:  17
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5800769925117493
Batch:  18
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9626617431640625
Batch:  19
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4195582866668701
Batch:  20
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5135859251022339
Batch:  21
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.576809823513031
Batch:  22
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.15380859375
Batch:  23
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4944906234741211
Batch:  24
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.668677031993866
Batch:  25
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8478403091430664
Batch:  26
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6730390787124634
Batch:  27
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6083324551582336
Batch:  28
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.15037256479263306
Batch:  29
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  30
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5440798997879028
Batch:  31
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0522446632385254
Batch:  32
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.262948989868164
Batch:  33
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.14422261714935303
Batch:  34
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1821753829717636
Batch:  35
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2884156107902527
Batch:  36
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5040158033370972
Batch:  37
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7760797739028931
Batch:  38
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4968183636665344
Batch:  39
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.18722890317440033
Batch:  40
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4326663315296173
Batch:  41
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.19228464365005493
Batch:  42
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6388431787490845
Batch:  43
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.14421185851097107
Batch:  44
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1297365427017212
Batch:  45
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.28203150629997253
Batch:  46
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9812024831771851
Batch:  47
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.14423015713691711
Batch:  48
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8268833160400391
Batch:  49
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5903046727180481
Batch:  50
Error
Batch:  51
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.44505831599235535
Batch:  52
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5980119705200195
Batch:  53
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0225470066070557
Batch:  54
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.23173290491104126
Batch:  55
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.40232181549072266
Batch:  56
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8538024425506592
Batch:  57
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.42345815896987915
Batch:  58
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6244103312492371
Batch:  59
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7517243027687073
Batch:  60
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.098273754119873
Batch:  61
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.16483630239963531
Batch:  62
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6233187913894653
Batch:  63
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.41946467757225037
Batch:  64
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.5719823837280273
Batch:  65
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.22890393435955048
Batch:  66
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8847097158432007
Batch:  67
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9059339165687561
Batch:  68
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1153717041015625
Batch:  69
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5635969638824463
Batch:  70
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.37783920764923096
Batch:  71
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9441249966621399
Batch:  72
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.28583627939224243
Batch:  73
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.20191261172294617
Batch:  74
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3351415395736694
Batch:  75
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.974639892578125
Batch:  76
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.11166210472583771
Batch:  77
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7714897394180298
Batch:  78
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.49572113156318665
Batch:  79
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.38037872314453125
Batch:  80
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6979902982711792
Batch:  81
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.647039532661438
Batch:  82
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9106612205505371
Batch:  83
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2699090540409088
Batch:  84
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.562467098236084
Batch:  85
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6885561347007751
Batch:  86
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7772623896598816
Batch:  87
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.37106671929359436
Batch:  88
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.32956793904304504
Batch:  89
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7847123742103577
Batch:  90
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0046944618225098
Batch:  91
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.49612224102020264
Batch:  92
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1047985553741455
Batch:  93
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6188446879386902
Batch:  94
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0529016256332397
Batch:  95
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7884951829910278
Batch:  96
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5473349094390869
Batch:  97
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.24952289462089539
Batch:  98
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5695134997367859
Batch:  99
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9201067686080933
Batch:  100
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6526135206222534
Batch:  101
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  102
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.424051433801651
Batch:  103
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4026048481464386
Batch:  104
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7551800608634949
Batch:  105
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.34418338537216187
Batch:  106
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9205433130264282
Batch:  107
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7393696904182434
Batch:  108
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.38460034132003784
Batch:  109
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5467325448989868
Batch:  110
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6596071124076843
Batch:  111
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6526113748550415
Batch:  112
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.8067023754119873
Batch:  113
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7691866159439087
Batch:  114
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3061504364013672
Batch:  115
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.27913954854011536
Batch:  116
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8720118999481201
Batch:  117
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7150678634643555
Batch:  118
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5696760416030884
Batch:  119
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8718154430389404
Batch:  120
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8320630788803101
Batch:  121
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.46800559759140015
Batch:  122
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1573297381401062
Batch:  123
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.080875039100647
Batch:  124
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6168820261955261
Epoch 8/10
Batch:  0
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6568893194198608
Batch:  1
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8045917749404907
Batch:  2
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.48456981778144836
Batch:  3
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8109951615333557
Batch:  4
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3062421083450317
Batch:  5
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.09614618122577667
Batch:  6
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7282026410102844
Batch:  7
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4679592251777649
Batch:  8
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6842526793479919
Batch:  9
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.009570598602295
Batch:  10
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.19229160249233246
Batch:  11
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9848917722702026
Batch:  12
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7953740954399109
Batch:  13
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.35502538084983826
Batch:  14
Error
Batch:  15
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.12145349383354187
Batch:  16
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2337255477905273
Batch:  17
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6238337159156799
Batch:  18
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.26441600918769836
Batch:  19
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6518197059631348
Batch:  20
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6200940608978271
Batch:  21
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  22
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8779008388519287
Batch:  23
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3714977204799652
Batch:  24
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9294655919075012
Batch:  25
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  26
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.29840096831321716
Batch:  27
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.12361302226781845
Batch:  28
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.36449864506721497
Batch:  29
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.519143283367157
Batch:  30
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.36536020040512085
Batch:  31
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7383953332901001
Batch:  32
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8049402832984924
Batch:  33
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8258803486824036
Batch:  34
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.665472388267517
Batch:  35
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1649491786956787
Batch:  36
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.382983922958374
Batch:  37
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6192185878753662
Batch:  38
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5896351933479309
Batch:  39
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5177075862884521
Batch:  40
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.40792927145957947
Batch:  41
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.11935115605592728
Batch:  42
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8718539476394653
Batch:  43
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8557208776473999
Batch:  44
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0069327354431152
Batch:  45
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7532863616943359
Batch:  46
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.38110071420669556
Batch:  47
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0235415697097778
Batch:  48
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2769177258014679
Batch:  49
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.409635066986084
Batch:  50
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7096498012542725
Batch:  51
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5223997831344604
Batch:  52
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.29669153690338135
Batch:  53
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7860143780708313
Batch:  54
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.576876163482666
Batch:  55
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.34278786182403564
Batch:  56
Error
Batch:  57
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.46194833517074585
Batch:  58
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7372310161590576
Batch:  59
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.30394625663757324
Batch:  60
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8488500118255615
Batch:  61
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.649250864982605
Batch:  62
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8697556853294373
Batch:  63
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5396344661712646
Batch:  64
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8099991083145142
Batch:  65
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  3.017470359802246
Batch:  66
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6138456463813782
Batch:  67
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3543851375579834
Batch:  68
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.32451730966567993
Batch:  69
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2940172553062439
Batch:  70
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6004223823547363
Batch:  71
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6812889575958252
Batch:  72
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0889700651168823
Batch:  73
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3124282360076904
Batch:  74
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5519167184829712
Batch:  75
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.757331132888794
Batch:  76
Error
Batch:  77
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5901093482971191
Batch:  78
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5347081422805786
Batch:  79
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6238465905189514
Batch:  80
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  3.0383763313293457
Batch:  81
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3547956347465515
Batch:  82
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.278642177581787
Batch:  83
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.21191361546516418
Batch:  84
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.22814206779003143
Batch:  85
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2144943475723267
Batch:  86
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8954859972000122
Batch:  87
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.35827916860580444
Batch:  88
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.346129447221756
Batch:  89
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7376165390014648
Batch:  90
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7202056646347046
Batch:  91
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2567553222179413
Batch:  92
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5088526010513306
Batch:  93
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3731154799461365
Batch:  94
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8054153919219971
Batch:  95
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3230556845664978
Batch:  96
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4944888949394226
Batch:  97
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8220988512039185
Batch:  98
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.38552361726760864
Batch:  99
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  100
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.15733161568641663
Batch:  101
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.17606478929519653
Batch:  102
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2891421318054199
Batch:  103
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3552209734916687
Batch:  104
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5434703230857849
Batch:  105
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.440895140171051
Batch:  106
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.969868540763855
Batch:  107
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3865986466407776
Batch:  108
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.589996337890625
Batch:  109
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3582358658313751
Batch:  110
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7287222146987915
Batch:  111
Error
Batch:  112
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8029482364654541
Batch:  113
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.36881983280181885
Batch:  114
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.39705637097358704
Batch:  115
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7227243185043335
Batch:  116
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4845777153968811
Batch:  117
Error
Batch:  118
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.43129369616508484
Batch:  119
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6437532305717468
Batch:  120
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6173020601272583
Batch:  121
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3574254512786865
Batch:  122
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7533405423164368
Batch:  123
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4100012481212616
Batch:  124
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.317295104265213
Epoch 9/10
Batch:  0
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7345656156539917
Batch:  1
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6730391979217529
Batch:  2
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.1718015670776367
Batch:  3
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4413965344429016
Batch:  4
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.31171685457229614
Batch:  5
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2016597986221313
Batch:  6
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5527026057243347
Batch:  7
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5192282795906067
Batch:  8
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9264284372329712
Batch:  9
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5091806054115295
Batch:  10
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5257461071014404
Batch:  11
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.31729334592819214
Batch:  12
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4063711166381836
Batch:  13
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.842191219329834
Batch:  14
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5052682757377625
Batch:  15
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.19280755519866943
Batch:  16
Error
Batch:  17
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8004270792007446
Batch:  18
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.20978474617004395
Batch:  19
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.37367379665374756
Batch:  20
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7313725352287292
Batch:  21
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.100931167602539
Batch:  22
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.40718942880630493
Batch:  23
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.767785906791687
Batch:  24
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.5767652988433838
Batch:  25
Error
Batch:  26
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6517310738563538
Batch:  27
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7123125195503235
Batch:  28
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7652304172515869
Batch:  29
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2641660273075104
Batch:  30
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.33636242151260376
Batch:  31
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7447634339332581
Batch:  32
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.964715838432312
Batch:  33
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.18063151836395264
Batch:  34
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9044499397277832
Batch:  35
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.47897177934646606
Batch:  36
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7158205509185791
Batch:  37
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0137046575546265
Batch:  38
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.33767205476760864
Batch:  39
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.73110431432724
Batch:  40
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6517393589019775
Batch:  41
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5126274824142456
Batch:  42
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0135185718536377
Batch:  43
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3970211148262024
Batch:  44
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.4303746223449707
Batch:  45
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2810763716697693
Batch:  46
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  47
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0480544567108154
Batch:  48
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.49821123480796814
Batch:  49
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  50
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7211185693740845
Batch:  51
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.918916642665863
Batch:  52
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3085004985332489
Batch:  53
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7951086163520813
Batch:  54
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6742643117904663
Batch:  55
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3743208646774292
Batch:  56
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9442601799964905
Batch:  57
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4790322482585907
Batch:  58
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5724614262580872
Batch:  59
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7444860935211182
Batch:  60
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.904697060585022
Batch:  61
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2428647130727768
Batch:  62
Error
Batch:  63
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.4081653356552124
Batch:  64
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3708758354187012
Batch:  65
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.44867634773254395
Batch:  66
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.544798731803894
Batch:  67
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2567532062530518
Batch:  68
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5811418294906616
Batch:  69
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1992332637310028
Batch:  70
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7944881916046143
Batch:  71
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.02068030834198
Batch:  72
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2021524906158447
Batch:  73
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.17276522517204285
Batch:  74
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.558301568031311
Batch:  75
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6207790970802307
Batch:  76
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6149213314056396
Batch:  77
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.213158369064331
Batch:  78
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4250856935977936
Batch:  79
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.28850701451301575
Batch:  80
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9530126452445984
Batch:  81
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2853034734725952
Batch:  82
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5403205156326294
Batch:  83
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.10815663635730743
Batch:  84
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.20768165588378906
Batch:  85
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0576233863830566
Batch:  86
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.17103588581085205
Batch:  87
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1159979104995728
Batch:  88
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8412510752677917
Batch:  89
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2591487169265747
Batch:  90
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.40780162811279297
Batch:  91
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6323535442352295
Batch:  92
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9930872917175293
Batch:  93
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5573930144309998
Batch:  94
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8268994688987732
Batch:  95
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8402301669120789
Batch:  96
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9913444519042969
Batch:  97
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  98
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.44796568155288696
Batch:  99
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5628897547721863
Batch:  100
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6278419494628906
Batch:  101
Error
Batch:  102
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.42935559153556824
Batch:  103
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6947746276855469
Batch:  104
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.44230809807777405
Batch:  105
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8535305261611938
Batch:  106
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2270166426897049
Batch:  107
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.3484891653060913
Batch:  108
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9879856109619141
Batch:  109
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.33067646622657776
Batch:  110
Error
Batch:  111
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.30099430680274963
Batch:  112
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0054309368133545
Batch:  113
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.57536780834198
Batch:  114
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3576895594596863
Batch:  115
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.38454973697662354
Batch:  116
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7640138864517212
Batch:  117
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.34275442361831665
Batch:  118
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8335493206977844
Batch:  119
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6442902088165283
Batch:  120
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5538132786750793
Batch:  121
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7761893272399902
Batch:  122
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2884468734264374
Batch:  123
Error
Batch:  124
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8021494746208191
Epoch 10/10
Batch:  0
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9509953856468201
Batch:  1
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5866695642471313
Batch:  2
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1538478434085846
Batch:  3
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9012120366096497
Batch:  4
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0145281553268433
Batch:  5
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.645067036151886
Batch:  6
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8653911352157593
Batch:  7
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6538023352622986
Batch:  8
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3744964301586151
Batch:  9
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6026909351348877
Batch:  10
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.33316296339035034
Batch:  11
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.7307106256484985
Batch:  12
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3924136757850647
Batch:  13
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5690389275550842
Batch:  14
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7717849016189575
Batch:  15
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.36538344621658325
Batch:  16
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7048656940460205
Batch:  17
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2498241662979126
Batch:  18
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0953290462493896
Batch:  19
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1385852098464966
Batch:  20
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5488225817680359
Batch:  21
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7305606007575989
Batch:  22
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.49614959955215454
Batch:  23
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2955164611339569
Batch:  24
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.35532793402671814
Batch:  25
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4326772689819336
Batch:  26
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.34012335538864136
Batch:  27
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.17718052864074707
Batch:  28
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0080759525299072
Batch:  29
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5191957354545593
Batch:  30
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7552229166030884
Batch:  31
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1573370099067688
Batch:  32
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5424433946609497
Batch:  33
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6434647440910339
Batch:  34
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.794025182723999
Batch:  35
Error
Batch:  36
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.42442917823791504
Batch:  37
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.16399729251861572
Batch:  38
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.16022813320159912
Batch:  39
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6854473948478699
Batch:  40
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.549430787563324
Batch:  41
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.721182107925415
Batch:  42
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6508143544197083
Batch:  43
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6431180834770203
Batch:  44
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5436357259750366
Batch:  45
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.13574649393558502
Batch:  46
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6105443239212036
Batch:  47
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3937390446662903
Batch:  48
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.249205231666565
Batch:  49
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3288542628288269
Batch:  50
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.10987840592861176
Batch:  51
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5768960118293762
Batch:  52
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6470632553100586
Batch:  53
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.791163444519043
Batch:  54
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6873786449432373
Batch:  55
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.39249488711357117
Batch:  56
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1689293384552002
Batch:  57
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.23075592517852783
Batch:  58
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.674311637878418
Batch:  59
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4369685649871826
Batch:  60
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1774943768978119
Batch:  61
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3230464458465576
Batch:  62
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6264789700508118
Batch:  63
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5480329990386963
Batch:  64
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0427312850952148
Batch:  65
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.0749969482421875
Batch:  66
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8190129995346069
Batch:  67
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.03328290954232216
Batch:  68
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.22295287251472473
Batch:  69
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6557236909866333
Batch:  70
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.910079836845398
Batch:  71
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7284246683120728
Batch:  72
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2100384533405304
Batch:  73
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8935866355895996
Batch:  74
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6249580383300781
Batch:  75
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.44502538442611694
Batch:  76
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.16483111679553986
Batch:  77
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.46148842573165894
Batch:  78
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.40692687034606934
Batch:  79
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7702367901802063
Batch:  80
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.863802433013916
Batch:  81
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.07866603881120682
Batch:  82
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4715064764022827
Batch:  83
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1315429210662842
Batch:  84
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8527708649635315
Batch:  85
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.510165810585022
Batch:  86
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.8028428554534912
Batch:  87
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.1236215829849243
Batch:  88
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.0
Batch:  89
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.41957002878189087
Batch:  90
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.15766873955726624
Batch:  91
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.42363977432250977
Batch:  92
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4467853009700775
Batch:  93
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.33041155338287354
Batch:  94
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4355665147304535
Batch:  95
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5356876254081726
Batch:  96
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.35537898540496826
Batch:  97
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.596323549747467
Batch:  98
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6994932889938354
Batch:  99
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4326479434967041
Batch:  100
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.20458360016345978
Batch:  101
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6101669073104858
Batch:  102
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6288650631904602
Batch:  103
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.0103554725646973
Batch:  104
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5903040766716003
Batch:  105
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  1.2546981573104858
Batch:  106
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.64098060131073
Batch:  107
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.3146357834339142
Batch:  108
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.33075428009033203
Batch:  109
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8458899259567261
Batch:  110
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.8351468443870544
Batch:  111
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.2884502410888672
Batch:  112
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.941017210483551
Batch:  113
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5892589092254639
Batch:  114
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4298489987850189
Batch:  115
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.1442013680934906
Batch:  116
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.6231182813644409
Batch:  117
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4542675018310547
Batch:  118
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.5075332522392273
Batch:  119
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7377285957336426
Batch:  120
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  2.034531354904175
Batch:  121
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4120592474937439
Batch:  122
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.4326831102371216
Batch:  123
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.9930051565170288
Batch:  124
C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes\src\model\loss.py:87: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  torch.tensor(sum(loss) / len(loss), requires_grad=True)
Batch Loss:  0.7418548464775085
----------
Model trained
Model saved
PS C:\Users\sauqu\Documents\Purdue\Hackathon\mlp_new\HackathonF23-dotsandboxes>