Precision: [tensor(0.9995, device='cuda:0'), tensor(0.9993, device='cuda:0'), tensor(0.9998, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9995, device='cuda:0'), tensor(0.9997, device='cuda:0'), tensor(0.9992, device='cuda:0'), tensor(0.9995, device='cuda:0')]

Output distance: [tensor(23342.2539, device='cuda:0'), tensor(23326.3164, device='cuda:0'), tensor(23246.8828, device='cuda:0'), tensor(23246.9121, device='cuda:0'), tensor(23481.9297, device='cuda:0'), tensor(23151.2695, device='cuda:0'), tensor(23240.0352, device='cuda:0'), tensor(23164.7793, device='cuda:0'), tensor(23262.4238, device='cuda:0'), tensor(23172.7969, device='cuda:0')]

Prediction loss: [tensor(22441.9570, device='cuda:0'), tensor(23705.4746, device='cuda:0'), tensor(21950.3340, device='cuda:0'), tensor(23827.1914, device='cuda:0'), tensor(22394.1172, device='cuda:0'), tensor(22861.7969, device='cuda:0'), tensor(22576.9844, device='cuda:0'), tensor(24278.1602, device='cuda:0'), tensor(24884.6777, device='cuda:0'), tensor(23749.7969, device='cuda:0')]

Others: [{'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 11, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}, {'iter_num': 9, 'num_positive': tensor(6000, device='cuda:0'), 'num_positive_true': tensor(18000, device='cuda:0')}]

Compressed training loss: [tensor(8836449., device='cuda:0'), tensor(9275055., device='cuda:0'), tensor(8783844., device='cuda:0'), tensor(9052505., device='cuda:0'), tensor(8710852., device='cuda:0'), tensor(8815389., device='cuda:0'), tensor(8693354., device='cuda:0'), tensor(8947436., device='cuda:0'), tensor(9359166., device='cuda:0'), tensor(9125184., device='cuda:0')]

Training loss: 8905986.0

Prediction time: [datetime.timedelta(microseconds=572623), datetime.timedelta(microseconds=557635), datetime.timedelta(microseconds=526766), datetime.timedelta(microseconds=542695), datetime.timedelta(microseconds=585516), datetime.timedelta(microseconds=544640), datetime.timedelta(microseconds=563560), datetime.timedelta(microseconds=537719), datetime.timedelta(microseconds=541704), datetime.timedelta(microseconds=537719)]

Phi time: [datetime.timedelta(seconds=1, microseconds=275177), datetime.timedelta(microseconds=766747), datetime.timedelta(microseconds=698184), datetime.timedelta(microseconds=702479), datetime.timedelta(microseconds=706708), datetime.timedelta(microseconds=704794), datetime.timedelta(microseconds=705454), datetime.timedelta(microseconds=708880), datetime.timedelta(microseconds=702610), datetime.timedelta(microseconds=703966)]

