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

Output distance: [tensor(23541.5781, device='cuda:0'), tensor(23569.7285, device='cuda:0'), tensor(23695.9258, device='cuda:0'), tensor(23556.4902, device='cuda:0'), tensor(23526.8340, device='cuda:0'), tensor(23597.0352, device='cuda:0'), tensor(23822.8633, device='cuda:0'), tensor(23580.7734, device='cuda:0'), tensor(23588.9277, device='cuda:0'), tensor(23646.8848, device='cuda:0')]

Prediction loss: [tensor(23876.3633, device='cuda:0'), tensor(22697.5020, device='cuda:0'), tensor(23479.1562, device='cuda:0'), tensor(23525.8594, device='cuda:0'), tensor(24217.2109, device='cuda:0'), tensor(22859.8613, device='cuda:0'), tensor(23812.0957, device='cuda:0'), tensor(23515.1152, device='cuda:0'), tensor(23418.7285, device='cuda:0'), tensor(23188.2031, device='cuda:0')]

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

Compressed training loss: [tensor(8910549., device='cuda:0'), tensor(8823381., device='cuda:0'), tensor(8824891., device='cuda:0'), tensor(8872882., device='cuda:0'), tensor(9040970., device='cuda:0'), tensor(8672105., device='cuda:0'), tensor(8954226., device='cuda:0'), tensor(8766719., device='cuda:0'), tensor(8862916., device='cuda:0'), tensor(8765189., device='cuda:0')]

Training loss: 8854965.0

Prediction time: [datetime.timedelta(microseconds=691066), datetime.timedelta(microseconds=724927), datetime.timedelta(microseconds=706006), datetime.timedelta(microseconds=700031), datetime.timedelta(microseconds=698039), datetime.timedelta(microseconds=803591), datetime.timedelta(microseconds=781685), datetime.timedelta(microseconds=713972), datetime.timedelta(microseconds=709992), datetime.timedelta(microseconds=799657)]

Phi time: [datetime.timedelta(seconds=1, microseconds=539771), datetime.timedelta(microseconds=964576), datetime.timedelta(microseconds=921941), datetime.timedelta(microseconds=924807), datetime.timedelta(microseconds=923740), datetime.timedelta(microseconds=925632), datetime.timedelta(microseconds=930905), datetime.timedelta(microseconds=936927), datetime.timedelta(microseconds=930253), datetime.timedelta(microseconds=963104)]

