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Howuhh's group workspace

Timestamps visible
2023-06-12 15:26:07
Training:  81% 5337901/6600000 [69:29:21<12:42:02, 27.60it/s]
2023-06-12 15:26:09
Training:  81% 5337955/6600000 [69:29:23<12:57:37, 27.05it/s]
2023-06-12 15:26:11
Training:  81% 5338009/6600000 [69:29:25<13:18:25, 26.34it/s]
2023-06-12 15:26:13
Training:  81% 5338060/6600000 [69:29:27<13:17:15, 26.38it/s]
2023-06-12 15:26:15
Training:  81% 5338114/6600000 [69:29:29<13:39:16, 25.67it/s]
2023-06-12 15:26:17
Training:  81% 5338168/6600000 [69:29:32<13:42:43, 25.56it/s]
2023-06-12 15:26:19
Training:  81% 5338223/6600000 [69:29:33<12:25:42, 28.20it/s]
2023-06-12 15:26:21
Training:  81% 5338276/6600000 [69:29:35<13:01:18, 26.91it/s]
2023-06-12 15:26:23
Training:  81% 5338330/6600000 [69:29:37<13:07:35, 26.70it/s]
2023-06-12 15:26:26
Training:  81% 5338387/6600000 [69:29:40<12:29:44, 28.05it/s]
2023-06-12 15:26:28
Training:  81% 5338441/6600000 [69:29:42<13:19:53, 26.29it/s]
2023-06-12 15:26:30
Training:  81% 5338492/6600000 [69:29:44<13:23:16, 26.17it/s]
2023-06-12 15:26:32
           -80.1921]]], device='cuda:0', grad_fn=<SubBackward0>).3796,of distribution Categorical(logits: torch.Size([64, 16, 121])) to satisfy the constraint IndependentConstraint(Real(), 1), but found invalid values:
2023-06-12 15:26:32
           -80.1921]]], device='cuda:0', grad_fn=<SubBackward0>).3796,of distribution Categorical(logits: torch.Size([64, 16, 121])) to satisfy the constraint IndependentConstraint(Real(), 1), but found invalid values:
2023-06-12 15:26:32
           -80.1921]]], device='cuda:0', grad_fn=<SubBackward0>).3796,of distribution Categorical(logits: torch.Size([64, 16, 121])) to satisfy the constraint IndependentConstraint(Real(), 1), but found invalid values: