polyu

train set size: 130846
test set size: 14539
valid set size: 36076
start training
epoch: 	0
### lr: 	0.0001
epoch: [0/150], epoch_loss: [0.2640]
--- Best ACC: 0.0000, Curr ACC: 0.7011 ---
--- Accuracy: 0.7011, Precision: 0.7553, Sensitivity: 0.7417, F-Score: 0.6688 ---
epoch: 	1
### lr: 	0.0001
epoch: [1/150], epoch_loss: [0.1439]
--- Best ACC: 0.7011, Curr ACC: 0.8056 ---
--- Accuracy: 0.8056, Precision: 0.7230, Sensitivity: 0.6862, F-Score: 0.6917 ---
epoch: 	2
### lr: 	0.0001
epoch: [2/150], epoch_loss: [0.1107]
--- Best ACC: 0.8056, Curr ACC: 0.7814 ---
--- Accuracy: 0.7814, Precision: 0.7385, Sensitivity: 0.7122, F-Score: 0.7122 ---
epoch: 	3
### lr: 	0.0001
epoch: [3/150], epoch_loss: [0.0918]
--- Best ACC: 0.8056, Curr ACC: 0.8294 ---
--- Accuracy: 0.8294, Precision: 0.8017, Sensitivity: 0.8320, F-Score: 0.7949 ---
epoch: 	4
### lr: 	0.0001
epoch: [4/150], epoch_loss: [0.0804]
--- Best ACC: 0.8294, Curr ACC: 0.7705 ---
--- Accuracy: 0.7705, Precision: 0.7328, Sensitivity: 0.6630, F-Score: 0.6719 ---
epoch: 	5
### lr: 	0.0001
epoch: [5/150], epoch_loss: [0.0726]
--- Best ACC: 0.8294, Curr ACC: 0.8657 ---
--- Accuracy: 0.8657, Precision: 0.8357, Sensitivity: 0.8199, F-Score: 0.8270 ---
epoch: 	6
### lr: 	0.0001
epoch: [6/150], epoch_loss: [0.0661]
--- Best ACC: 0.8657, Curr ACC: 0.8664 ---
--- Accuracy: 0.8664, Precision: 0.8215, Sensitivity: 0.8262, F-Score: 0.8237 ---
epoch: 	7
### lr: 	0.0001
epoch: [7/150], epoch_loss: [0.0640]
--- Best ACC: 0.8664, Curr ACC: 0.8620 ---
--- Accuracy: 0.8620, Precision: 0.8288, Sensitivity: 0.8067, F-Score: 0.8159 ---
epoch: 	8
### lr: 	0.0001
epoch: [8/150], epoch_loss: [0.0606]
--- Best ACC: 0.8664, Curr ACC: 0.8891 ---
--- Accuracy: 0.8891, Precision: 0.8615, Sensitivity: 0.8669, F-Score: 0.8608 ---
epoch: 	9
### lr: 	0.0001
epoch: [9/150], epoch_loss: [0.0594]
--- Best ACC: 0.8891, Curr ACC: 0.8656 ---
--- Accuracy: 0.8656, Precision: 0.8295, Sensitivity: 0.8400, F-Score: 0.8320 ---
epoch: 	10
### lr: 	0.0001
epoch: [10/150], epoch_loss: [0.0552]
--- Best ACC: 0.8891, Curr ACC: 0.8392 ---
--- Accuracy: 0.8392, Precision: 0.7956, Sensitivity: 0.7729, F-Score: 0.7807 ---
epoch: 	11
### lr: 	0.0001
epoch: [11/150], epoch_loss: [0.0544]
--- Best ACC: 0.8891, Curr ACC: 0.8671 ---
--- Accuracy: 0.8671, Precision: 0.8321, Sensitivity: 0.8755, F-Score: 0.8417 ---
epoch: 	12
### lr: 	0.0001
epoch: [12/150], epoch_loss: [0.0519]
--- Best ACC: 0.8891, Curr ACC: 0.8962 ---
--- Accuracy: 0.8962, Precision: 0.8639, Sensitivity: 0.8794, F-Score: 0.8671 ---
epoch: 	13
### lr: 	0.0001
epoch: [13/150], epoch_loss: [0.0515]
--- Best ACC: 0.8962, Curr ACC: 0.8845 ---
--- Accuracy: 0.8845, Precision: 0.8498, Sensitivity: 0.8706, F-Score: 0.8525 ---
epoch: 	14
### lr: 	0.0001
epoch: [14/150], epoch_loss: [0.0499]
--- Best ACC: 0.8962, Curr ACC: 0.8937 ---
--- Accuracy: 0.8937, Precision: 0.8654, Sensitivity: 0.8587, F-Score: 0.8609 ---
epoch: 	15
### lr: 	0.0001
epoch: [15/150], epoch_loss: [0.0481]
--- Best ACC: 0.8962, Curr ACC: 0.8777 ---
--- Accuracy: 0.8777, Precision: 0.8609, Sensitivity: 0.8373, F-Score: 0.8444 ---
epoch: 	16
### lr: 	0.0001
epoch: [16/150], epoch_loss: [0.0460]
--- Best ACC: 0.8962, Curr ACC: 0.9093 ---
--- Accuracy: 0.9093, Precision: 0.9088, Sensitivity: 0.8710, F-Score: 0.8862 ---
epoch: 	17
### lr: 	0.0001
epoch: [17/150], epoch_loss: [0.0450]
--- Best ACC: 0.9093, Curr ACC: 0.9046 ---
--- Accuracy: 0.9046, Precision: 0.8704, Sensitivity: 0.8952, F-Score: 0.8790 ---
epoch: 	18
### lr: 	0.0001
epoch: [18/150], epoch_loss: [0.0443]
--- Best ACC: 0.9093, Curr ACC: 0.8968 ---
--- Accuracy: 0.8968, Precision: 0.8751, Sensitivity: 0.8703, F-Score: 0.8704 ---
epoch: 	19
### lr: 	0.0001
epoch: [19/150], epoch_loss: [0.0414]
--- Best ACC: 0.9093, Curr ACC: 0.9304 ---
--- Accuracy: 0.9304, Precision: 0.9240, Sensitivity: 0.8852, F-Score: 0.9009 ---
epoch: 	20
### lr: 	1e-05
epoch: [20/150], epoch_loss: [0.0342]
--- Best ACC: 0.9304, Curr ACC: 0.9485 ---
--- Accuracy: 0.9485, Precision: 0.9442, Sensitivity: 0.9266, F-Score: 0.9347 ---
epoch: 	21
### lr: 	1e-05
epoch: [21/150], epoch_loss: [0.0301]
--- Best ACC: 0.9485, Curr ACC: 0.9424 ---
--- Accuracy: 0.9424, Precision: 0.9388, Sensitivity: 0.9151, F-Score: 0.9257 ---
epoch: 	22
### lr: 	1e-05
epoch: [22/150], epoch_loss: [0.0289]
--- Best ACC: 0.9485, Curr ACC: 0.9521 ---
--- Accuracy: 0.9521, Precision: 0.9515, Sensitivity: 0.9302, F-Score: 0.9399 ---
epoch: 	23
### lr: 	1e-05
epoch: [23/150], epoch_loss: [0.0275]
--- Best ACC: 0.9521, Curr ACC: 0.9494 ---
--- Accuracy: 0.9494, Precision: 0.9424, Sensitivity: 0.9236, F-Score: 0.9322 ---
epoch: 	24
### lr: 	1e-05
epoch: [24/150], epoch_loss: [0.0260]
--- Best ACC: 0.9521, Curr ACC: 0.9535 ---
--- Accuracy: 0.9535, Precision: 0.9461, Sensitivity: 0.9355, F-Score: 0.9405 ---
epoch: 	25
### lr: 	1e-05
epoch: [25/150], epoch_loss: [0.0265]
--- Best ACC: 0.9535, Curr ACC: 0.9502 ---
--- Accuracy: 0.9502, Precision: 0.9459, Sensitivity: 0.9278, F-Score: 0.9361 ---
epoch: 	26
### lr: 	1e-05
epoch: [26/150], epoch_loss: [0.0252]
--- Best ACC: 0.9535, Curr ACC: 0.9491 ---
--- Accuracy: 0.9491, Precision: 0.9432, Sensitivity: 0.9305, F-Score: 0.9364 ---
epoch: 	27
### lr: 	1e-05
epoch: [27/150], epoch_loss: [0.0260]
--- Best ACC: 0.9535, Curr ACC: 0.9439 ---
--- Accuracy: 0.9439, Precision: 0.9446, Sensitivity: 0.9126, F-Score: 0.9265 ---
epoch: 	28
### lr: 	1e-05
epoch: [28/150], epoch_loss: [0.0236]
--- Best ACC: 0.9535, Curr ACC: 0.9487 ---
--- Accuracy: 0.9487, Precision: 0.9472, Sensitivity: 0.9254, F-Score: 0.9353 ---
epoch: 	29
### lr: 	1e-05
epoch: [29/150], epoch_loss: [0.0252]
--- Best ACC: 0.9535, Curr ACC: 0.9382 ---
--- Accuracy: 0.9382, Precision: 0.9380, Sensitivity: 0.9107, F-Score: 0.9226 ---
epoch: 	30
### lr: 	1e-05
epoch: [30/150], epoch_loss: [0.0237]
--- Best ACC: 0.9535, Curr ACC: 0.9466 ---
--- Accuracy: 0.9466, Precision: 0.9411, Sensitivity: 0.9287, F-Score: 0.9343 ---
epoch: 	31
### lr: 	1e-05
epoch: [31/150], epoch_loss: [0.0246]
--- Best ACC: 0.9535, Curr ACC: 0.9444 ---
--- Accuracy: 0.9444, Precision: 0.9345, Sensitivity: 0.9307, F-Score: 0.9320 ---
epoch: 	32
### lr: 	1e-05
epoch: [32/150], epoch_loss: [0.0230]
--- Best ACC: 0.9535, Curr ACC: 0.9432 ---
--- Accuracy: 0.9432, Precision: 0.9327, Sensitivity: 0.9307, F-Score: 0.9311 ---
epoch: 	33
### lr: 	1e-05
epoch: [33/150], epoch_loss: [0.0231]
--- Best ACC: 0.9535, Curr ACC: 0.9510 ---
--- Accuracy: 0.9510, Precision: 0.9470, Sensitivity: 0.9339, F-Score: 0.9399 ---
epoch: 	34
### lr: 	1e-05
epoch: [34/150], epoch_loss: [0.0228]
--- Best ACC: 0.9535, Curr ACC: 0.9509 ---
--- Accuracy: 0.9509, Precision: 0.9457, Sensitivity: 0.9355, F-Score: 0.9402 ---
epoch: 	35
### lr: 	1e-05
epoch: [35/150], epoch_loss: [0.0224]
--- Best ACC: 0.9535, Curr ACC: 0.9517 ---
--- Accuracy: 0.9517, Precision: 0.9475, Sensitivity: 0.9334, F-Score: 0.9399 ---
epoch: 	36
### lr: 	1e-05
epoch: [36/150], epoch_loss: [0.0216]
--- Best ACC: 0.9535, Curr ACC: 0.9525 ---
--- Accuracy: 0.9525, Precision: 0.9476, Sensitivity: 0.9366, F-Score: 0.9417 ---
epoch: 	37
### lr: 	1e-05
epoch: [37/150], epoch_loss: [0.0213]
--- Best ACC: 0.9535, Curr ACC: 0.9494 ---
--- Accuracy: 0.9494, Precision: 0.9490, Sensitivity: 0.9257, F-Score: 0.9362 ---
epoch: 	38
### lr: 	1e-05
epoch: [38/150], epoch_loss: [0.0213]
--- Best ACC: 0.9535, Curr ACC: 0.9607 ---
--- Accuracy: 0.9607, Precision: 0.9577, Sensitivity: 0.9459, F-Score: 0.9515 ---
epoch: 	39
### lr: 	1e-05
epoch: [39/150], epoch_loss: [0.0219]
--- Best ACC: 0.9607, Curr ACC: 0.9622 ---
--- Accuracy: 0.9622, Precision: 0.9577, Sensitivity: 0.9500, F-Score: 0.9536 ---
epoch: 	40
### lr: 	1e-05
epoch: [40/150], epoch_loss: [0.0214]
--- Best ACC: 0.9622, Curr ACC: 0.9598 ---
--- Accuracy: 0.9598, Precision: 0.9549, Sensitivity: 0.9487, F-Score: 0.9515 ---
epoch: 	41
### lr: 	1e-05
epoch: [41/150], epoch_loss: [0.0207]
--- Best ACC: 0.9622, Curr ACC: 0.9576 ---
--- Accuracy: 0.9576, Precision: 0.9577, Sensitivity: 0.9384, F-Score: 0.9473 ---
epoch: 	42
### lr: 	1e-05
epoch: [42/150], epoch_loss: [0.0210]
--- Best ACC: 0.9622, Curr ACC: 0.9556 ---
--- Accuracy: 0.9556, Precision: 0.9525, Sensitivity: 0.9393, F-Score: 0.9455 ---
epoch: 	43
### lr: 	1e-05
epoch: [43/150], epoch_loss: [0.0211]
--- Best ACC: 0.9622, Curr ACC: 0.9523 ---
--- Accuracy: 0.9523, Precision: 0.9500, Sensitivity: 0.9343, F-Score: 0.9416 ---
epoch: 	44
### lr: 	1e-05
epoch: [44/150], epoch_loss: [0.0205]
--- Best ACC: 0.9622, Curr ACC: 0.9439 ---
--- Accuracy: 0.9439, Precision: 0.9454, Sensitivity: 0.9103, F-Score: 0.9253 ---
epoch: 	45
### lr: 	1e-05
epoch: [45/150], epoch_loss: [0.0206]
--- Best ACC: 0.9622, Curr ACC: 0.9514 ---
--- Accuracy: 0.9514, Precision: 0.9535, Sensitivity: 0.9264, F-Score: 0.9385 ---
epoch: 	46
### lr: 	1e-05
epoch: [46/150], epoch_loss: [0.0199]
--- Best ACC: 0.9622, Curr ACC: 0.9598 ---
--- Accuracy: 0.9598, Precision: 0.9613, Sensitivity: 0.9387, F-Score: 0.9490 ---
epoch: 	47
### lr: 	1e-05
epoch: [47/150], epoch_loss: [0.0193]
--- Best ACC: 0.9622, Curr ACC: 0.9508 ---
--- Accuracy: 0.9508, Precision: 0.9543, Sensitivity: 0.9248, F-Score: 0.9378 ---
epoch: 	48
### lr: 	1e-05
epoch: [48/150], epoch_loss: [0.0191]
--- Best ACC: 0.9622, Curr ACC: 0.9541 ---
--- Accuracy: 0.9541, Precision: 0.9558, Sensitivity: 0.9346, F-Score: 0.9443 ---
epoch: 	49
### lr: 	1e-05
epoch: [49/150], epoch_loss: [0.0197]
--- Best ACC: 0.9622, Curr ACC: 0.9547 ---
--- Accuracy: 0.9547, Precision: 0.9604, Sensitivity: 0.9262, F-Score: 0.9410 ---
epoch: 	50
### lr: 	1e-05
epoch: [50/150], epoch_loss: [0.0192]
--- Best ACC: 0.9622, Curr ACC: 0.9578 ---
--- Accuracy: 0.9578, Precision: 0.9579, Sensitivity: 0.9386, F-Score: 0.9475 ---
epoch: 	51