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ml1m_log3.txt
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ml1m_log3.txt
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----------- Configuration Arguments -----------
batch_size: 256
bert_config_path: ./bert_train/bert_config_ml-1m_128.json
candidate_path: ./bert_train/data/ml-1m.candidate
data_dir: ./bert_train/data/ml-1m-train.txt
data_name: ml-1m
epoch: 300
learning_rate: 0.001
max_seq_len: 200
num_train_steps: 800000
test_set_dir: ./bert_train/data/ml-1m-test.txt
validation_steps: 1000
vocab_path: ./bert_train/data/ml-1m2.0.2.vocab
warmup_steps: 1000
weight_decay: 0.01
------------------------------------------------
Start to train Bert4rec
attention_probs_dropout_prob: 0.2
hidden_act: gelu
hidden_dropout_prob: 0.5
hidden_size: 128
initializer_range: 0.02
intermediate_size: 512
max_position_embeddings: 200
num_attention_heads: 4
num_hidden_layers: 2
type_vocab_size: 2
vocab_size: 3420
------------------------------------------------
W0924 09:37:03.249991 4317 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0924 09:37:03.255316 4317 device_context.cc:422] device: 0, cuDNN Version: 7.6.
load candidate from :./bert_train/data/ml-1m.candidate
ac_ml1m.py:131: DeprecationWarning:
Warning:
API "paddle.nn.functional.loss.softmax_with_cross_entropy" is deprecated since 2.0.0, and will be removed in future versions. Please use "paddle.nn.functional.cross_entropy" instead.
reason: Please notice that behavior of "paddle.nn.functional.softmax_with_cross_entropy" and "paddle.nn.functional.cross_entropy" is different.
logits=fc_out, label=mask_label, return_softmax=True)
epoch: 0, aver loss is: [7.1570063]
epoch: 0, HR@10: 0.458899, NDCG@10: 0.251365, MRR: 0.207981
epoch: 1, aver loss is: [5.928174]
epoch: 1, HR@10: 0.587466, NDCG@10: 0.356925, MRR: 0.300707
epoch: 2, aver loss is: [5.608896]
epoch: 2, HR@10: 0.628227, NDCG@10: 0.395584, MRR: 0.336997
epoch: 3, aver loss is: [5.471108]
epoch: 3, HR@10: 0.650136, NDCG@10: 0.417672, MRR: 0.357947
epoch: 4, aver loss is: [5.386808]
epoch: 4, HR@10: 0.657609, NDCG@10: 0.433129, MRR: 0.375471
epoch: 5, aver loss is: [5.327096]
epoch: 5, HR@10: 0.669667, NDCG@10: 0.444092, MRR: 0.385483
epoch: 6, aver loss is: [5.2838674]
epoch: 6, HR@10: 0.675442, NDCG@10: 0.452514, MRR: 0.394507
epoch: 7, aver loss is: [5.2517924]
epoch: 7, HR@10: 0.677480, NDCG@10: 0.458856, MRR: 0.402324
epoch: 8, aver loss is: [5.2268586]
epoch: 8, HR@10: 0.680537, NDCG@10: 0.463605, MRR: 0.407357
epoch: 9, aver loss is: [5.2061653]
epoch: 9, HR@10: 0.684613, NDCG@10: 0.465712, MRR: 0.408702
epoch: 10, aver loss is: [5.1884]
epoch: 10, HR@10: 0.684443, NDCG@10: 0.469989, MRR: 0.414460
epoch: 11, aver loss is: [5.1734395]
epoch: 11, HR@10: 0.683254, NDCG@10: 0.466816, MRR: 0.410580
epoch: 12, aver loss is: [5.1598086]
epoch: 12, HR@10: 0.684783, NDCG@10: 0.469206, MRR: 0.413320
epoch: 13, aver loss is: [5.148016]
epoch: 13, HR@10: 0.687840, NDCG@10: 0.470518, MRR: 0.413813
epoch: 14, aver loss is: [5.1377363]
epoch: 14, HR@10: 0.685632, NDCG@10: 0.471458, MRR: 0.415894
epoch: 15, aver loss is: [5.1284986]
epoch: 15, HR@10: 0.685632, NDCG@10: 0.469460, MRR: 0.413437
epoch: 16, aver loss is: [5.120643]
epoch: 16, HR@10: 0.683933, NDCG@10: 0.470214, MRR: 0.414905
epoch: 17, aver loss is: [5.1132126]
epoch: 17, HR@10: 0.686821, NDCG@10: 0.472584, MRR: 0.417061
epoch: 18, aver loss is: [5.1062355]
epoch: 18, HR@10: 0.689029, NDCG@10: 0.472929, MRR: 0.416594
epoch: 19, aver loss is: [5.0999103]
epoch: 19, HR@10: 0.685292, NDCG@10: 0.472607, MRR: 0.417562
epoch: 20, aver loss is: [5.0948386]
epoch: 20, HR@10: 0.691576, NDCG@10: 0.473531, MRR: 0.416498
epoch: 21, aver loss is: [5.0887203]
epoch: 21, HR@10: 0.688349, NDCG@10: 0.473559, MRR: 0.417690
epoch: 22, aver loss is: [5.0843387]
epoch: 22, HR@10: 0.691067, NDCG@10: 0.472408, MRR: 0.415156
epoch: 23, aver loss is: [5.079666]
epoch: 23, HR@10: 0.686651, NDCG@10: 0.472634, MRR: 0.417058
epoch: 24, aver loss is: [5.075663]
epoch: 24, HR@10: 0.689538, NDCG@10: 0.474691, MRR: 0.418677
epoch: 25, aver loss is: [5.0713167]
epoch: 25, HR@10: 0.688859, NDCG@10: 0.474229, MRR: 0.418371
epoch: 26, aver loss is: [5.067827]
epoch: 26, HR@10: 0.689198, NDCG@10: 0.476291, MRR: 0.420888
epoch: 27, aver loss is: [5.0638046]
epoch: 27, HR@10: 0.690387, NDCG@10: 0.477446, MRR: 0.422064
epoch: 28, aver loss is: [5.060823]
epoch: 28, HR@10: 0.689878, NDCG@10: 0.477616, MRR: 0.422456
epoch: 29, aver loss is: [5.057021]
epoch: 29, HR@10: 0.689198, NDCG@10: 0.477486, MRR: 0.422506
epoch: 30, aver loss is: [5.053967]
epoch: 30, HR@10: 0.686481, NDCG@10: 0.475761, MRR: 0.421090
epoch: 31, aver loss is: [5.0510683]
epoch: 31, HR@10: 0.688689, NDCG@10: 0.477600, MRR: 0.422900
epoch: 32, aver loss is: [5.048488]
epoch: 32, HR@10: 0.687330, NDCG@10: 0.476131, MRR: 0.421426
epoch: 33, aver loss is: [5.0460196]
epoch: 33, HR@10: 0.688349, NDCG@10: 0.476937, MRR: 0.421944
epoch: 34, aver loss is: [5.043268]
epoch: 34, HR@10: 0.686481, NDCG@10: 0.475402, MRR: 0.420724
epoch: 35, aver loss is: [5.040637]
epoch: 35, HR@10: 0.689368, NDCG@10: 0.478467, MRR: 0.423674
epoch: 36, aver loss is: [5.0389423]
epoch: 36, HR@10: 0.693105, NDCG@10: 0.479535, MRR: 0.423658
epoch: 37, aver loss is: [5.0365644]
epoch: 37, HR@10: 0.689538, NDCG@10: 0.478690, MRR: 0.423864
epoch: 38, aver loss is: [5.0344653]
epoch: 38, HR@10: 0.691576, NDCG@10: 0.479210, MRR: 0.423812
epoch: 39, aver loss is: [5.032297]
epoch: 39, HR@10: 0.688179, NDCG@10: 0.479621, MRR: 0.425778
epoch: 40, aver loss is: [5.030576]
epoch: 40, HR@10: 0.691576, NDCG@10: 0.480293, MRR: 0.425300
epoch: 41, aver loss is: [5.0289245]
epoch: 41, HR@10: 0.687840, NDCG@10: 0.477511, MRR: 0.422953
epoch: 42, aver loss is: [5.0265613]
epoch: 42, HR@10: 0.689368, NDCG@10: 0.479445, MRR: 0.425005
epoch: 43, aver loss is: [5.0256534]
epoch: 43, HR@10: 0.690387, NDCG@10: 0.480094, MRR: 0.425499
epoch: 44, aver loss is: [5.024056]
epoch: 44, HR@10: 0.690048, NDCG@10: 0.480785, MRR: 0.426564
epoch: 45, aver loss is: [5.0223]
epoch: 45, HR@10: 0.691236, NDCG@10: 0.479595, MRR: 0.424511
epoch: 46, aver loss is: [5.0211267]
epoch: 46, HR@10: 0.689198, NDCG@10: 0.481117, MRR: 0.427104
epoch: 47, aver loss is: [5.018836]
epoch: 47, HR@10: 0.691746, NDCG@10: 0.480115, MRR: 0.424946
epoch: 48, aver loss is: [5.0181]
epoch: 48, HR@10: 0.691576, NDCG@10: 0.481286, MRR: 0.426619
epoch: 49, aver loss is: [5.016699]
epoch: 49, HR@10: 0.689538, NDCG@10: 0.480952, MRR: 0.426943
epoch: 50, aver loss is: [5.0151224]
epoch: 50, HR@10: 0.691067, NDCG@10: 0.480444, MRR: 0.425707
epoch: 51, aver loss is: [5.0141597]
epoch: 51, HR@10: 0.692595, NDCG@10: 0.482691, MRR: 0.428207
epoch: 52, aver loss is: [5.01321]
epoch: 52, HR@10: 0.691067, NDCG@10: 0.483033, MRR: 0.429185
epoch: 53, aver loss is: [5.011463]
epoch: 53, HR@10: 0.691067, NDCG@10: 0.480937, MRR: 0.426255
epoch: 54, aver loss is: [5.0105176]
epoch: 54, HR@10: 0.692255, NDCG@10: 0.481409, MRR: 0.426386
epoch: 55, aver loss is: [5.0091166]
epoch: 55, HR@10: 0.689538, NDCG@10: 0.481717, MRR: 0.428097
epoch: 56, aver loss is: [5.00842]
epoch: 56, HR@10: 0.691406, NDCG@10: 0.481744, MRR: 0.427328
epoch: 57, aver loss is: [5.006851]
epoch: 57, HR@10: 0.692425, NDCG@10: 0.482602, MRR: 0.428137
epoch: 58, aver loss is: [5.0059495]
epoch: 58, HR@10: 0.689538, NDCG@10: 0.481134, MRR: 0.427257
epoch: 59, aver loss is: [5.00505]
epoch: 59, HR@10: 0.692255, NDCG@10: 0.481237, MRR: 0.426384
epoch: 60, aver loss is: [5.003636]
epoch: 60, HR@10: 0.692595, NDCG@10: 0.481108, MRR: 0.426175
epoch: 61, aver loss is: [5.0031776]
epoch: 61, HR@10: 0.690217, NDCG@10: 0.480813, MRR: 0.426595
epoch: 62, aver loss is: [5.001794]
epoch: 62, HR@10: 0.693105, NDCG@10: 0.482140, MRR: 0.427467
epoch: 63, aver loss is: [5.001261]
epoch: 63, HR@10: 0.691406, NDCG@10: 0.480846, MRR: 0.426270
epoch: 64, aver loss is: [4.9999375]
epoch: 64, HR@10: 0.686990, NDCG@10: 0.480126, MRR: 0.426993
epoch: 65, aver loss is: [4.9996285]
epoch: 65, HR@10: 0.693444, NDCG@10: 0.482789, MRR: 0.427884
epoch: 66, aver loss is: [4.998254]
epoch: 66, HR@10: 0.691406, NDCG@10: 0.482996, MRR: 0.429071
epoch: 67, aver loss is: [4.9971466]
epoch: 67, HR@10: 0.693954, NDCG@10: 0.483285, MRR: 0.428440
epoch: 68, aver loss is: [4.9965725]
epoch: 68, HR@10: 0.695143, NDCG@10: 0.484791, MRR: 0.429972
epoch: 69, aver loss is: [4.995643]
epoch: 69, HR@10: 0.689368, NDCG@10: 0.480827, MRR: 0.426834
epoch: 70, aver loss is: [4.9950805]
epoch: 70, HR@10: 0.693105, NDCG@10: 0.484357, MRR: 0.430093
epoch: 71, aver loss is: [4.994242]
epoch: 71, HR@10: 0.692086, NDCG@10: 0.482594, MRR: 0.428275
epoch: 72, aver loss is: [4.9931526]
epoch: 72, HR@10: 0.693784, NDCG@10: 0.484008, MRR: 0.429579
epoch: 73, aver loss is: [4.992823]
epoch: 73, HR@10: 0.690387, NDCG@10: 0.481927, MRR: 0.428076
epoch: 74, aver loss is: [4.991815]
epoch: 74, HR@10: 0.692935, NDCG@10: 0.484021, MRR: 0.429904
epoch: 75, aver loss is: [4.9910026]
epoch: 75, HR@10: 0.694124, NDCG@10: 0.483154, MRR: 0.428381
epoch: 76, aver loss is: [4.990169]
epoch: 76, HR@10: 0.694633, NDCG@10: 0.483406, MRR: 0.428545
epoch: 77, aver loss is: [4.9895744]
epoch: 77, HR@10: 0.693274, NDCG@10: 0.482186, MRR: 0.427459
epoch: 78, aver loss is: [4.9890485]
epoch: 78, HR@10: 0.696162, NDCG@10: 0.484788, MRR: 0.429735
epoch: 79, aver loss is: [4.9879456]
epoch: 79, HR@10: 0.693614, NDCG@10: 0.483216, MRR: 0.428713
epoch: 80, aver loss is: [4.987139]
epoch: 80, HR@10: 0.693954, NDCG@10: 0.484465, MRR: 0.430121
epoch: 81, aver loss is: [4.9871035]
epoch: 81, HR@10: 0.690387, NDCG@10: 0.481422, MRR: 0.427452
epoch: 82, aver loss is: [4.986668]
epoch: 82, HR@10: 0.695992, NDCG@10: 0.484415, MRR: 0.429344
epoch: 83, aver loss is: [4.9861827]
epoch: 83, HR@10: 0.694293, NDCG@10: 0.484519, MRR: 0.430069
epoch: 84, aver loss is: [4.9854913]
epoch: 84, HR@10: 0.695992, NDCG@10: 0.483873, MRR: 0.428639
epoch: 85, aver loss is: [4.984349]
epoch: 85, HR@10: 0.692765, NDCG@10: 0.483916, MRR: 0.429843
epoch: 86, aver loss is: [4.9833407]
epoch: 86, HR@10: 0.696501, NDCG@10: 0.484214, MRR: 0.428942
epoch: 87, aver loss is: [4.98327]
epoch: 87, HR@10: 0.697181, NDCG@10: 0.485918, MRR: 0.430803
epoch: 88, aver loss is: [4.9827294]
epoch: 88, HR@10: 0.693444, NDCG@10: 0.484326, MRR: 0.430063
epoch: 89, aver loss is: [4.981972]
epoch: 89, HR@10: 0.692935, NDCG@10: 0.483012, MRR: 0.428546
epoch: 90, aver loss is: [4.981142]
epoch: 90, HR@10: 0.696501, NDCG@10: 0.485155, MRR: 0.430030
epoch: 91, aver loss is: [4.9810314]
epoch: 91, HR@10: 0.696671, NDCG@10: 0.486944, MRR: 0.432521
epoch: 92, aver loss is: [4.9801826]
epoch: 92, HR@10: 0.692765, NDCG@10: 0.482503, MRR: 0.428050
epoch: 93, aver loss is: [4.980001]
epoch: 93, HR@10: 0.695312, NDCG@10: 0.486678, MRR: 0.432616
epoch: 94, aver loss is: [4.9789705]
epoch: 94, HR@10: 0.695312, NDCG@10: 0.486123, MRR: 0.431917
epoch: 95, aver loss is: [4.979008]
epoch: 95, HR@10: 0.694463, NDCG@10: 0.483855, MRR: 0.429055
epoch: 96, aver loss is: [4.9782195]
epoch: 96, HR@10: 0.694293, NDCG@10: 0.485180, MRR: 0.430954
epoch: 97, aver loss is: [4.9780164]
epoch: 97, HR@10: 0.692255, NDCG@10: 0.481407, MRR: 0.426737
epoch: 98, aver loss is: [4.9775496]
epoch: 98, HR@10: 0.692935, NDCG@10: 0.485169, MRR: 0.431475
epoch: 99, aver loss is: [4.976219]
epoch: 99, HR@10: 0.691746, NDCG@10: 0.482820, MRR: 0.428764
epoch: 100, aver loss is: [4.975822]
epoch: 100, HR@10: 0.693614, NDCG@10: 0.484155, MRR: 0.429826
epoch: 101, aver loss is: [4.9756627]
epoch: 101, HR@10: 0.694633, NDCG@10: 0.488148, MRR: 0.434804
epoch: 102, aver loss is: [4.974898]
epoch: 102, HR@10: 0.693444, NDCG@10: 0.484638, MRR: 0.430640
epoch: 103, aver loss is: [4.9748306]
epoch: 103, HR@10: 0.692425, NDCG@10: 0.483259, MRR: 0.429002
epoch: 104, aver loss is: [4.973819]
epoch: 104, HR@10: 0.689538, NDCG@10: 0.481647, MRR: 0.427916
epoch: 105, aver loss is: [4.9735975]
epoch: 105, HR@10: 0.693444, NDCG@10: 0.484691, MRR: 0.430550
epoch: 106, aver loss is: [4.9725776]
epoch: 106, HR@10: 0.691746, NDCG@10: 0.484460, MRR: 0.430719
epoch: 107, aver loss is: [4.97221]
epoch: 107, HR@10: 0.692595, NDCG@10: 0.483562, MRR: 0.429302
epoch: 108, aver loss is: [4.9718704]
epoch: 108, HR@10: 0.691576, NDCG@10: 0.482286, MRR: 0.428075
epoch: 109, aver loss is: [4.9711833]
epoch: 109, HR@10: 0.692255, NDCG@10: 0.485899, MRR: 0.432690
epoch: 110, aver loss is: [4.9712176]
epoch: 110, HR@10: 0.692765, NDCG@10: 0.484847, MRR: 0.431065
epoch: 111, aver loss is: [4.9706664]
epoch: 111, HR@10: 0.691067, NDCG@10: 0.484333, MRR: 0.431007
epoch: 112, aver loss is: [4.970313]
epoch: 112, HR@10: 0.692086, NDCG@10: 0.486926, MRR: 0.434046
epoch: 113, aver loss is: [4.970151]
epoch: 113, HR@10: 0.690727, NDCG@10: 0.485627, MRR: 0.432788
epoch: 114, aver loss is: [4.9692993]
epoch: 114, HR@10: 0.693444, NDCG@10: 0.486009, MRR: 0.432320
epoch: 115, aver loss is: [4.9685354]
epoch: 115, HR@10: 0.691746, NDCG@10: 0.485689, MRR: 0.432595
epoch: 116, aver loss is: [4.9682493]
epoch: 116, HR@10: 0.693444, NDCG@10: 0.486895, MRR: 0.433390
epoch: 117, aver loss is: [4.9674726]
epoch: 117, HR@10: 0.693274, NDCG@10: 0.485480, MRR: 0.431775
epoch: 118, aver loss is: [4.9672303]
epoch: 118, HR@10: 0.691406, NDCG@10: 0.486003, MRR: 0.433032
epoch: 119, aver loss is: [4.9670167]
epoch: 119, HR@10: 0.691067, NDCG@10: 0.484708, MRR: 0.431520
epoch: 120, aver loss is: [4.966295]
epoch: 120, HR@10: 0.693784, NDCG@10: 0.486728, MRR: 0.433218
epoch: 121, aver loss is: [4.9660597]
epoch: 121, HR@10: 0.692765, NDCG@10: 0.485849, MRR: 0.432333
epoch: 122, aver loss is: [4.9653573]
epoch: 122, HR@10: 0.694124, NDCG@10: 0.486832, MRR: 0.433178
epoch: 123, aver loss is: [4.9649906]
epoch: 123, HR@10: 0.693444, NDCG@10: 0.486482, MRR: 0.432940
epoch: 124, aver loss is: [4.9644494]
epoch: 124, HR@10: 0.693274, NDCG@10: 0.488867, MRR: 0.436094
epoch: 125, aver loss is: [4.964131]
epoch: 125, HR@10: 0.694293, NDCG@10: 0.485830, MRR: 0.431677
epoch: 126, aver loss is: [4.963908]
epoch: 126, HR@10: 0.694293, NDCG@10: 0.487550, MRR: 0.433955
epoch: 127, aver loss is: [4.963502]
epoch: 127, HR@10: 0.693274, NDCG@10: 0.486157, MRR: 0.432576
epoch: 128, aver loss is: [4.9624715]
epoch: 128, HR@10: 0.696671, NDCG@10: 0.489216, MRR: 0.435263
epoch: 129, aver loss is: [4.9627123]
epoch: 129, HR@10: 0.691576, NDCG@10: 0.485403, MRR: 0.432190
epoch: 130, aver loss is: [4.9619274]
epoch: 130, HR@10: 0.694293, NDCG@10: 0.486764, MRR: 0.433012
epoch: 131, aver loss is: [4.9616733]
epoch: 131, HR@10: 0.693954, NDCG@10: 0.486676, MRR: 0.432855
epoch: 132, aver loss is: [4.9608493]
epoch: 132, HR@10: 0.694973, NDCG@10: 0.485715, MRR: 0.431233
epoch: 133, aver loss is: [4.9604077]
epoch: 133, HR@10: 0.693105, NDCG@10: 0.487224, MRR: 0.433966
epoch: 134, aver loss is: [4.9601746]
epoch: 134, HR@10: 0.694973, NDCG@10: 0.488766, MRR: 0.435359
epoch: 135, aver loss is: [4.959465]
epoch: 135, HR@10: 0.695652, NDCG@10: 0.488326, MRR: 0.434539
epoch: 136, aver loss is: [4.95974]
epoch: 136, HR@10: 0.692425, NDCG@10: 0.486894, MRR: 0.433836
epoch: 137, aver loss is: [4.9588037]
epoch: 137, HR@10: 0.691576, NDCG@10: 0.484684, MRR: 0.431192
epoch: 138, aver loss is: [4.9589834]
epoch: 138, HR@10: 0.693954, NDCG@10: 0.487264, MRR: 0.433696
epoch: 139, aver loss is: [4.957936]
epoch: 139, HR@10: 0.694803, NDCG@10: 0.485972, MRR: 0.431732
epoch: 140, aver loss is: [4.9577117]
epoch: 140, HR@10: 0.697181, NDCG@10: 0.488768, MRR: 0.434461
epoch: 141, aver loss is: [4.9577537]
epoch: 141, HR@10: 0.693105, NDCG@10: 0.486840, MRR: 0.433483
epoch: 142, aver loss is: [4.957046]
epoch: 142, HR@10: 0.692935, NDCG@10: 0.488887, MRR: 0.436213
epoch: 143, aver loss is: [4.956582]
epoch: 143, HR@10: 0.694124, NDCG@10: 0.487274, MRR: 0.433606
epoch: 144, aver loss is: [4.956256]
epoch: 144, HR@10: 0.694124, NDCG@10: 0.488999, MRR: 0.435983
epoch: 145, aver loss is: [4.9556627]
epoch: 145, HR@10: 0.695482, NDCG@10: 0.488965, MRR: 0.435300
epoch: 146, aver loss is: [4.9554634]
epoch: 146, HR@10: 0.694293, NDCG@10: 0.489038, MRR: 0.435928
epoch: 147, aver loss is: [4.955155]
epoch: 147, HR@10: 0.694633, NDCG@10: 0.490018, MRR: 0.436982
epoch: 148, aver loss is: [4.9543357]
epoch: 148, HR@10: 0.695652, NDCG@10: 0.487451, MRR: 0.433326
epoch: 149, aver loss is: [4.9537354]
epoch: 149, HR@10: 0.694633, NDCG@10: 0.489107, MRR: 0.435873
epoch: 150, aver loss is: [4.954237]
epoch: 150, HR@10: 0.695822, NDCG@10: 0.487535, MRR: 0.433363
epoch: 151, aver loss is: [4.9531593]
epoch: 151, HR@10: 0.692765, NDCG@10: 0.487286, MRR: 0.434236
epoch: 152, aver loss is: [4.952725]
epoch: 152, HR@10: 0.694633, NDCG@10: 0.486586, MRR: 0.432662
epoch: 153, aver loss is: [4.9527073]
epoch: 153, HR@10: 0.696332, NDCG@10: 0.487659, MRR: 0.433354
epoch: 154, aver loss is: [4.952792]
epoch: 154, HR@10: 0.694463, NDCG@10: 0.488152, MRR: 0.434644
epoch: 155, aver loss is: [4.951728]
epoch: 155, HR@10: 0.694803, NDCG@10: 0.487648, MRR: 0.434007
epoch: 156, aver loss is: [4.9516573]
epoch: 156, HR@10: 0.694463, NDCG@10: 0.488384, MRR: 0.434984
epoch: 157, aver loss is: [4.9507723]
epoch: 157, HR@10: 0.692765, NDCG@10: 0.485271, MRR: 0.431456
epoch: 158, aver loss is: [4.950887]
epoch: 158, HR@10: 0.696162, NDCG@10: 0.486633, MRR: 0.432039
epoch: 159, aver loss is: [4.9504266]
epoch: 159, HR@10: 0.690897, NDCG@10: 0.485962, MRR: 0.433052
epoch: 160, aver loss is: [4.950528]
epoch: 160, HR@10: 0.693784, NDCG@10: 0.485774, MRR: 0.431926
epoch: 161, aver loss is: [4.9498825]
epoch: 161, HR@10: 0.695312, NDCG@10: 0.486399, MRR: 0.432045
epoch: 162, aver loss is: [4.9491773]
epoch: 162, HR@10: 0.694124, NDCG@10: 0.487841, MRR: 0.434449
epoch: 163, aver loss is: [4.9487696]
epoch: 163, HR@10: 0.695482, NDCG@10: 0.488721, MRR: 0.435174
epoch: 164, aver loss is: [4.948843]
epoch: 164, HR@10: 0.694293, NDCG@10: 0.486904, MRR: 0.433340
epoch: 165, aver loss is: [4.948314]
epoch: 165, HR@10: 0.695143, NDCG@10: 0.487614, MRR: 0.433824
epoch: 166, aver loss is: [4.9479527]
epoch: 166, HR@10: 0.691746, NDCG@10: 0.485273, MRR: 0.431883
epoch: 167, aver loss is: [4.947782]
epoch: 167, HR@10: 0.695143, NDCG@10: 0.488192, MRR: 0.434641
epoch: 168, aver loss is: [4.946885]
epoch: 168, HR@10: 0.694463, NDCG@10: 0.487447, MRR: 0.433806
epoch: 169, aver loss is: [4.9466715]
epoch: 169, HR@10: 0.692255, NDCG@10: 0.487199, MRR: 0.434219
epoch: 170, aver loss is: [4.946774]
epoch: 170, HR@10: 0.693105, NDCG@10: 0.487367, MRR: 0.434156
epoch: 171, aver loss is: [4.946334]
epoch: 171, HR@10: 0.691406, NDCG@10: 0.486527, MRR: 0.433678
epoch: 172, aver loss is: [4.9463115]
epoch: 172, HR@10: 0.692425, NDCG@10: 0.487288, MRR: 0.434274
epoch: 173, aver loss is: [4.945]
epoch: 173, HR@10: 0.691406, NDCG@10: 0.486363, MRR: 0.433454
epoch: 174, aver loss is: [4.945348]
epoch: 174, HR@10: 0.694463, NDCG@10: 0.487424, MRR: 0.433754
epoch: 175, aver loss is: [4.945249]
epoch: 175, HR@10: 0.693105, NDCG@10: 0.487076, MRR: 0.433855
epoch: 176, aver loss is: [4.9446807]
epoch: 176, HR@10: 0.697181, NDCG@10: 0.488242, MRR: 0.433825
epoch: 177, aver loss is: [4.9439497]
epoch: 177, HR@10: 0.694633, NDCG@10: 0.488736, MRR: 0.435428
epoch: 178, aver loss is: [4.9436145]
epoch: 178, HR@10: 0.694463, NDCG@10: 0.487780, MRR: 0.434123
epoch: 179, aver loss is: [4.9435906]
epoch: 179, HR@10: 0.692765, NDCG@10: 0.486771, MRR: 0.433485
epoch: 180, aver loss is: [4.9434943]
epoch: 180, HR@10: 0.695652, NDCG@10: 0.487106, MRR: 0.432993
epoch: 181, aver loss is: [4.942623]
epoch: 181, HR@10: 0.694973, NDCG@10: 0.487635, MRR: 0.433780
epoch: 182, aver loss is: [4.9422684]
epoch: 182, HR@10: 0.693614, NDCG@10: 0.488805, MRR: 0.435984
epoch: 183, aver loss is: [4.942291]
epoch: 183, HR@10: 0.693954, NDCG@10: 0.487248, MRR: 0.433822
epoch: 184, aver loss is: [4.9415383]
epoch: 184, HR@10: 0.695312, NDCG@10: 0.487567, MRR: 0.433627
epoch: 185, aver loss is: [4.9415164]
epoch: 185, HR@10: 0.694293, NDCG@10: 0.487971, MRR: 0.434491
epoch: 186, aver loss is: [4.941257]
epoch: 186, HR@10: 0.690048, NDCG@10: 0.486895, MRR: 0.434841
epoch: 187, aver loss is: [4.940852]
epoch: 187, HR@10: 0.689708, NDCG@10: 0.485473, MRR: 0.433002
epoch: 188, aver loss is: [4.9403443]
epoch: 188, HR@10: 0.695312, NDCG@10: 0.484833, MRR: 0.430052
epoch: 189, aver loss is: [4.940311]
epoch: 189, HR@10: 0.694293, NDCG@10: 0.485462, MRR: 0.431272
epoch: 190, aver loss is: [4.9396324]
epoch: 190, HR@10: 0.691746, NDCG@10: 0.487137, MRR: 0.434465
epoch: 191, aver loss is: [4.9396796]
epoch: 191, HR@10: 0.695482, NDCG@10: 0.488805, MRR: 0.435302
epoch: 192, aver loss is: [4.9396615]
epoch: 192, HR@10: 0.692425, NDCG@10: 0.486431, MRR: 0.433324
epoch: 193, aver loss is: [4.9391284]
epoch: 193, HR@10: 0.694633, NDCG@10: 0.486666, MRR: 0.432789
epoch: 194, aver loss is: [4.938625]
epoch: 194, HR@10: 0.694463, NDCG@10: 0.488510, MRR: 0.435256
epoch: 195, aver loss is: [4.9381843]
epoch: 195, HR@10: 0.692086, NDCG@10: 0.485952, MRR: 0.432808
epoch: 196, aver loss is: [4.9379387]
epoch: 196, HR@10: 0.691746, NDCG@10: 0.484882, MRR: 0.431416
epoch: 197, aver loss is: [4.9376836]
epoch: 197, HR@10: 0.692765, NDCG@10: 0.486483, MRR: 0.433220
epoch: 198, aver loss is: [4.937574]
epoch: 198, HR@10: 0.691067, NDCG@10: 0.485791, MRR: 0.432947
epoch: 199, aver loss is: [4.937131]
epoch: 199, HR@10: 0.695992, NDCG@10: 0.487055, MRR: 0.432934
epoch: 200, aver loss is: [4.937027]
epoch: 200, HR@10: 0.693954, NDCG@10: 0.487218, MRR: 0.433764
epoch: 201, aver loss is: [4.9360704]
epoch: 201, HR@10: 0.694293, NDCG@10: 0.485738, MRR: 0.431605
epoch: 202, aver loss is: [4.9366574]
epoch: 202, HR@10: 0.693614, NDCG@10: 0.486792, MRR: 0.433296
epoch: 203, aver loss is: [4.9356346]
epoch: 203, HR@10: 0.692595, NDCG@10: 0.485436, MRR: 0.431971
epoch: 204, aver loss is: [4.935629]
epoch: 204, HR@10: 0.694124, NDCG@10: 0.485726, MRR: 0.431746
epoch: 205, aver loss is: [4.935095]
epoch: 205, HR@10: 0.695482, NDCG@10: 0.486213, MRR: 0.431763
epoch: 206, aver loss is: [4.9349265]
epoch: 206, HR@10: 0.692595, NDCG@10: 0.487915, MRR: 0.435148
epoch: 207, aver loss is: [4.934479]
epoch: 207, HR@10: 0.693784, NDCG@10: 0.487274, MRR: 0.433773
epoch: 208, aver loss is: [4.934374]
epoch: 208, HR@10: 0.693444, NDCG@10: 0.487676, MRR: 0.434480
epoch: 209, aver loss is: [4.9337773]
epoch: 209, HR@10: 0.695143, NDCG@10: 0.488208, MRR: 0.434611
epoch: 210, aver loss is: [4.9340544]
epoch: 210, HR@10: 0.693274, NDCG@10: 0.487917, MRR: 0.434877
epoch: 211, aver loss is: [4.933568]
epoch: 211, HR@10: 0.692425, NDCG@10: 0.485809, MRR: 0.432356
epoch: 212, aver loss is: [4.932937]
epoch: 212, HR@10: 0.693105, NDCG@10: 0.486452, MRR: 0.432994
epoch: 213, aver loss is: [4.9326587]
epoch: 213, HR@10: 0.694973, NDCG@10: 0.488074, MRR: 0.434636
epoch: 214, aver loss is: [4.932472]
epoch: 214, HR@10: 0.692086, NDCG@10: 0.486214, MRR: 0.433114
epoch: 215, aver loss is: [4.9324355]
epoch: 215, HR@10: 0.693444, NDCG@10: 0.488875, MRR: 0.436058
epoch: 216, aver loss is: [4.931822]
epoch: 216, HR@10: 0.689878, NDCG@10: 0.485518, MRR: 0.432890
epoch: 217, aver loss is: [4.931246]
epoch: 217, HR@10: 0.692425, NDCG@10: 0.486687, MRR: 0.433517
epoch: 218, aver loss is: [4.9317355]
epoch: 218, HR@10: 0.694973, NDCG@10: 0.488175, MRR: 0.434553
epoch: 219, aver loss is: [4.9310822]
epoch: 219, HR@10: 0.692935, NDCG@10: 0.486736, MRR: 0.433378
epoch: 220, aver loss is: [4.931144]
epoch: 220, HR@10: 0.693614, NDCG@10: 0.487543, MRR: 0.434264
epoch: 221, aver loss is: [4.9303384]
epoch: 221, HR@10: 0.695822, NDCG@10: 0.486774, MRR: 0.432459
epoch: 222, aver loss is: [4.930126]
epoch: 222, HR@10: 0.690217, NDCG@10: 0.486615, MRR: 0.434266
epoch: 223, aver loss is: [4.929732]
epoch: 223, HR@10: 0.694293, NDCG@10: 0.486182, MRR: 0.432210
epoch: 224, aver loss is: [4.9298296]
epoch: 224, HR@10: 0.694293, NDCG@10: 0.486656, MRR: 0.432817
epoch: 225, aver loss is: [4.9296656]
epoch: 225, HR@10: 0.692255, NDCG@10: 0.488181, MRR: 0.435633
epoch: 226, aver loss is: [4.929072]
epoch: 226, HR@10: 0.692086, NDCG@10: 0.484799, MRR: 0.431250
epoch: 227, aver loss is: [4.9289494]
epoch: 227, HR@10: 0.691916, NDCG@10: 0.486765, MRR: 0.433853
epoch: 228, aver loss is: [4.928564]
epoch: 228, HR@10: 0.691236, NDCG@10: 0.485273, MRR: 0.432030
epoch: 229, aver loss is: [4.9280424]
epoch: 229, HR@10: 0.692255, NDCG@10: 0.485740, MRR: 0.432179
epoch: 230, aver loss is: [4.9282064]
epoch: 230, HR@10: 0.694124, NDCG@10: 0.486939, MRR: 0.433258
epoch: 231, aver loss is: [4.9277983]
epoch: 231, HR@10: 0.690048, NDCG@10: 0.486180, MRR: 0.433698
epoch: 232, aver loss is: [4.9270043]
epoch: 232, HR@10: 0.692595, NDCG@10: 0.486252, MRR: 0.432891
epoch: 233, aver loss is: [4.927189]
epoch: 233, HR@10: 0.690727, NDCG@10: 0.486292, MRR: 0.433730
epoch: 234, aver loss is: [4.9269624]
epoch: 234, HR@10: 0.691916, NDCG@10: 0.487401, MRR: 0.434609
epoch: 235, aver loss is: [4.9262834]
epoch: 235, HR@10: 0.693105, NDCG@10: 0.485706, MRR: 0.431968
epoch: 236, aver loss is: [4.9266086]
epoch: 236, HR@10: 0.691916, NDCG@10: 0.484351, MRR: 0.430693
epoch: 237, aver loss is: [4.926061]
epoch: 237, HR@10: 0.692086, NDCG@10: 0.485928, MRR: 0.432625
epoch: 238, aver loss is: [4.9256473]
epoch: 238, HR@10: 0.690217, NDCG@10: 0.484165, MRR: 0.430904
epoch: 239, aver loss is: [4.9256496]
epoch: 239, HR@10: 0.692425, NDCG@10: 0.486903, MRR: 0.433876
epoch: 240, aver loss is: [4.9255986]
epoch: 240, HR@10: 0.691916, NDCG@10: 0.487062, MRR: 0.434260
epoch: 241, aver loss is: [4.9250484]
epoch: 241, HR@10: 0.691916, NDCG@10: 0.485442, MRR: 0.432152
epoch: 242, aver loss is: [4.9244566]
epoch: 242, HR@10: 0.689538, NDCG@10: 0.484020, MRR: 0.431071
epoch: 243, aver loss is: [4.9241896]
epoch: 243, HR@10: 0.690557, NDCG@10: 0.485755, MRR: 0.432988
epoch: 244, aver loss is: [4.9238157]
epoch: 244, HR@10: 0.691406, NDCG@10: 0.485505, MRR: 0.432438
epoch: 245, aver loss is: [4.9239683]
epoch: 245, HR@10: 0.687840, NDCG@10: 0.484467, MRR: 0.432298
epoch: 246, aver loss is: [4.923871]
epoch: 246, HR@10: 0.690387, NDCG@10: 0.485637, MRR: 0.432997
epoch: 247, aver loss is: [4.92294]
epoch: 247, HR@10: 0.688689, NDCG@10: 0.484084, MRR: 0.431429
epoch: 248, aver loss is: [4.9230294]
epoch: 248, HR@10: 0.691916, NDCG@10: 0.485912, MRR: 0.432693
epoch: 249, aver loss is: [4.922421]
epoch: 249, HR@10: 0.686990, NDCG@10: 0.482710, MRR: 0.430271
epoch: 250, aver loss is: [4.9227343]
epoch: 250, HR@10: 0.692255, NDCG@10: 0.485486, MRR: 0.432053
epoch: 251, aver loss is: [4.9217143]
epoch: 251, HR@10: 0.691746, NDCG@10: 0.485974, MRR: 0.432991
epoch: 252, aver loss is: [4.9221416]
epoch: 252, HR@10: 0.691236, NDCG@10: 0.487652, MRR: 0.435289
epoch: 253, aver loss is: [4.9219522]
epoch: 253, HR@10: 0.693274, NDCG@10: 0.486781, MRR: 0.433406
epoch: 254, aver loss is: [4.9213557]
epoch: 254, HR@10: 0.690217, NDCG@10: 0.486035, MRR: 0.433513
epoch: 255, aver loss is: [4.921193]
epoch: 255, HR@10: 0.690897, NDCG@10: 0.486422, MRR: 0.433857
epoch: 256, aver loss is: [4.9207067]
epoch: 256, HR@10: 0.691406, NDCG@10: 0.487029, MRR: 0.434404
epoch: 257, aver loss is: [4.9206905]
epoch: 257, HR@10: 0.690048, NDCG@10: 0.484117, MRR: 0.431146
epoch: 258, aver loss is: [4.9203997]
epoch: 258, HR@10: 0.692425, NDCG@10: 0.486656, MRR: 0.433557
epoch: 259, aver loss is: [4.920142]
epoch: 259, HR@10: 0.691746, NDCG@10: 0.487356, MRR: 0.434675
epoch: 260, aver loss is: [4.919726]
epoch: 260, HR@10: 0.693444, NDCG@10: 0.486259, MRR: 0.432588
epoch: 261, aver loss is: [4.919321]
epoch: 261, HR@10: 0.694463, NDCG@10: 0.486102, MRR: 0.432026
epoch: 262, aver loss is: [4.9189005]
epoch: 262, HR@10: 0.691746, NDCG@10: 0.486468, MRR: 0.433579
epoch: 263, aver loss is: [4.9188924]
epoch: 263, HR@10: 0.691576, NDCG@10: 0.486505, MRR: 0.433663
epoch: 264, aver loss is: [4.918664]
epoch: 264, HR@10: 0.694633, NDCG@10: 0.487281, MRR: 0.433591
epoch: 265, aver loss is: [4.918487]
epoch: 265, HR@10: 0.691406, NDCG@10: 0.486294, MRR: 0.433395
epoch: 266, aver loss is: [4.91845]
epoch: 266, HR@10: 0.692086, NDCG@10: 0.485356, MRR: 0.431931
epoch: 267, aver loss is: [4.9177675]
epoch: 267, HR@10: 0.690897, NDCG@10: 0.484810, MRR: 0.431764
epoch: 268, aver loss is: [4.917944]
epoch: 268, HR@10: 0.695312, NDCG@10: 0.487627, MRR: 0.433694
epoch: 269, aver loss is: [4.9171247]
epoch: 269, HR@10: 0.691916, NDCG@10: 0.486291, MRR: 0.433267
epoch: 270, aver loss is: [4.9175034]
epoch: 270, HR@10: 0.692595, NDCG@10: 0.488123, MRR: 0.435221
epoch: 271, aver loss is: [4.9166975]
epoch: 271, HR@10: 0.695143, NDCG@10: 0.488399, MRR: 0.434723
epoch: 272, aver loss is: [4.9163885]
epoch: 272, HR@10: 0.691067, NDCG@10: 0.485777, MRR: 0.432816
epoch: 273, aver loss is: [4.916753]
epoch: 273, HR@10: 0.691746, NDCG@10: 0.485976, MRR: 0.432939
epoch: 274, aver loss is: [4.9160056]
epoch: 274, HR@10: 0.695143, NDCG@10: 0.487607, MRR: 0.433804
epoch: 275, aver loss is: [4.916151]
epoch: 275, HR@10: 0.692595, NDCG@10: 0.487607, MRR: 0.434801
epoch: 276, aver loss is: [4.9154086]
epoch: 276, HR@10: 0.692765, NDCG@10: 0.487467, MRR: 0.434528
epoch: 277, aver loss is: [4.914973]
epoch: 277, HR@10: 0.695312, NDCG@10: 0.487226, MRR: 0.433075
epoch: 278, aver loss is: [4.9149504]
epoch: 278, HR@10: 0.693614, NDCG@10: 0.485812, MRR: 0.431898
epoch: 279, aver loss is: [4.914231]
epoch: 279, HR@10: 0.693274, NDCG@10: 0.486690, MRR: 0.433370
epoch: 280, aver loss is: [4.914604]
epoch: 280, HR@10: 0.694633, NDCG@10: 0.486244, MRR: 0.432288
epoch: 281, aver loss is: [4.914409]
epoch: 281, HR@10: 0.694124, NDCG@10: 0.487294, MRR: 0.433598
epoch: 282, aver loss is: [4.9140716]
epoch: 282, HR@10: 0.695312, NDCG@10: 0.485310, MRR: 0.430684
epoch: 283, aver loss is: [4.913621]
epoch: 283, HR@10: 0.694633, NDCG@10: 0.486795, MRR: 0.432924
epoch: 284, aver loss is: [4.913731]
epoch: 284, HR@10: 0.693444, NDCG@10: 0.485583, MRR: 0.431638
epoch: 285, aver loss is: [4.913327]
epoch: 285, HR@10: 0.694973, NDCG@10: 0.485518, MRR: 0.431002
epoch: 286, aver loss is: [4.912951]
epoch: 286, HR@10: 0.692086, NDCG@10: 0.486732, MRR: 0.433703
epoch: 287, aver loss is: [4.9127955]
epoch: 287, HR@10: 0.694293, NDCG@10: 0.485988, MRR: 0.431888
epoch: 288, aver loss is: [4.9122286]
epoch: 288, HR@10: 0.695822, NDCG@10: 0.488830, MRR: 0.435039
epoch: 289, aver loss is: [4.912735]
epoch: 289, HR@10: 0.695652, NDCG@10: 0.488664, MRR: 0.435016
epoch: 290, aver loss is: [4.911809]
epoch: 290, HR@10: 0.693444, NDCG@10: 0.485958, MRR: 0.432227
epoch: 291, aver loss is: [4.912119]
epoch: 291, HR@10: 0.694973, NDCG@10: 0.487079, MRR: 0.433186
epoch: 292, aver loss is: [4.91146]
epoch: 292, HR@10: 0.695143, NDCG@10: 0.488139, MRR: 0.434541
epoch: 293, aver loss is: [4.9117785]
epoch: 293, HR@10: 0.695312, NDCG@10: 0.486323, MRR: 0.431975
epoch: 294, aver loss is: [4.9110465]
epoch: 294, HR@10: 0.693954, NDCG@10: 0.487382, MRR: 0.433960
epoch: 295, aver loss is: [4.910812]
epoch: 295, HR@10: 0.695143, NDCG@10: 0.485173, MRR: 0.430648
epoch: 296, aver loss is: [4.9107475]
epoch: 296, HR@10: 0.692935, NDCG@10: 0.484898, MRR: 0.431044
epoch: 297, aver loss is: [4.910792]
epoch: 297, HR@10: 0.694124, NDCG@10: 0.487203, MRR: 0.433618
epoch: 298, aver loss is: [4.9097652]
epoch: 298, HR@10: 0.696501, NDCG@10: 0.488757, MRR: 0.434829
epoch: 299, aver loss is: [4.9097943]
epoch: 299, HR@10: 0.694803, NDCG@10: 0.485683, MRR: 0.431392