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ml1m_log2.txt
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ml1m_log2.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: 0.0001
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 00:47:27.598799 3957 device_context.cc:404] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0924 00:47:27.604296 3957 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: [7.712618]
epoch: 0, HR@10: 0.118037, NDCG@10: 0.053447, MRR: 0.055795
epoch: 1, aver loss is: [7.461514]
epoch: 1, HR@10: 0.168818, NDCG@10: 0.082068, MRR: 0.079973
epoch: 2, aver loss is: [6.963717]
epoch: 2, HR@10: 0.308254, NDCG@10: 0.153634, MRR: 0.132260
epoch: 3, aver loss is: [6.5467153]
epoch: 3, HR@10: 0.406250, NDCG@10: 0.212852, MRR: 0.176053
epoch: 4, aver loss is: [6.3384337]
epoch: 4, HR@10: 0.435802, NDCG@10: 0.235195, MRR: 0.195259
epoch: 5, aver loss is: [6.1889896]
epoch: 5, HR@10: 0.465693, NDCG@10: 0.257440, MRR: 0.213997
epoch: 6, aver loss is: [6.0555706]
epoch: 6, HR@10: 0.493886, NDCG@10: 0.281878, MRR: 0.235892
epoch: 7, aver loss is: [5.9675293]
epoch: 7, HR@10: 0.517154, NDCG@10: 0.301126, MRR: 0.252962
epoch: 8, aver loss is: [5.9036393]
epoch: 8, HR@10: 0.535496, NDCG@10: 0.315819, MRR: 0.265669
epoch: 9, aver loss is: [5.851625]
epoch: 9, HR@10: 0.553668, NDCG@10: 0.329754, MRR: 0.277501
epoch: 10, aver loss is: [5.806733]
epoch: 10, HR@10: 0.563519, NDCG@10: 0.338506, MRR: 0.285372
epoch: 11, aver loss is: [5.76572]
epoch: 11, HR@10: 0.575238, NDCG@10: 0.348384, MRR: 0.294079
epoch: 12, aver loss is: [5.726767]
epoch: 12, HR@10: 0.580673, NDCG@10: 0.355253, MRR: 0.301170
epoch: 13, aver loss is: [5.6904516]
epoch: 13, HR@10: 0.588315, NDCG@10: 0.362705, MRR: 0.308241
epoch: 14, aver loss is: [5.6565056]
epoch: 14, HR@10: 0.594260, NDCG@10: 0.367424, MRR: 0.312297
epoch: 15, aver loss is: [5.625626]
epoch: 15, HR@10: 0.602582, NDCG@10: 0.375929, MRR: 0.320402
epoch: 16, aver loss is: [5.5961204]
epoch: 16, HR@10: 0.609545, NDCG@10: 0.382275, MRR: 0.326312
epoch: 17, aver loss is: [5.5699353]
epoch: 17, HR@10: 0.616338, NDCG@10: 0.388851, MRR: 0.332408
epoch: 18, aver loss is: [5.5474677]
epoch: 18, HR@10: 0.621094, NDCG@10: 0.393214, MRR: 0.336515
epoch: 19, aver loss is: [5.52788]
epoch: 19, HR@10: 0.627717, NDCG@10: 0.398974, MRR: 0.341635
epoch: 20, aver loss is: [5.5102296]
epoch: 20, HR@10: 0.633152, NDCG@10: 0.403663, MRR: 0.345795
epoch: 21, aver loss is: [5.493874]
epoch: 21, HR@10: 0.636209, NDCG@10: 0.406213, MRR: 0.348007
epoch: 22, aver loss is: [5.4785156]
epoch: 22, HR@10: 0.634681, NDCG@10: 0.407260, MRR: 0.349880
epoch: 23, aver loss is: [5.4635386]
epoch: 23, HR@10: 0.638247, NDCG@10: 0.411866, MRR: 0.354617
epoch: 24, aver loss is: [5.4489813]
epoch: 24, HR@10: 0.643173, NDCG@10: 0.414735, MRR: 0.356603
epoch: 25, aver loss is: [5.4353375]
epoch: 25, HR@10: 0.643003, NDCG@10: 0.416467, MRR: 0.358974
epoch: 26, aver loss is: [5.4226804]
epoch: 26, HR@10: 0.644531, NDCG@10: 0.417494, MRR: 0.359737
epoch: 27, aver loss is: [5.411148]
epoch: 27, HR@10: 0.647079, NDCG@10: 0.420702, MRR: 0.362942
epoch: 28, aver loss is: [5.3999543]
epoch: 28, HR@10: 0.649626, NDCG@10: 0.423138, MRR: 0.365195
epoch: 29, aver loss is: [5.389137]
epoch: 29, HR@10: 0.651834, NDCG@10: 0.423529, MRR: 0.364970
epoch: 30, aver loss is: [5.379419]
epoch: 30, HR@10: 0.653533, NDCG@10: 0.425588, MRR: 0.366972
epoch: 31, aver loss is: [5.3693166]
epoch: 31, HR@10: 0.653533, NDCG@10: 0.427665, MRR: 0.369781
epoch: 32, aver loss is: [5.359927]
epoch: 32, HR@10: 0.654042, NDCG@10: 0.432112, MRR: 0.375507
epoch: 33, aver loss is: [5.351122]
epoch: 33, HR@10: 0.656080, NDCG@10: 0.431001, MRR: 0.373267
epoch: 34, aver loss is: [5.3423977]
epoch: 34, HR@10: 0.657439, NDCG@10: 0.433783, MRR: 0.376441
epoch: 35, aver loss is: [5.3334045]
epoch: 35, HR@10: 0.659137, NDCG@10: 0.434815, MRR: 0.377228
epoch: 36, aver loss is: [5.3255577]
epoch: 36, HR@10: 0.659137, NDCG@10: 0.435890, MRR: 0.378587
epoch: 37, aver loss is: [5.31738]
epoch: 37, HR@10: 0.661005, NDCG@10: 0.438380, MRR: 0.381252
epoch: 38, aver loss is: [5.309427]
epoch: 38, HR@10: 0.661515, NDCG@10: 0.439414, MRR: 0.382362
epoch: 39, aver loss is: [5.302401]
epoch: 39, HR@10: 0.662534, NDCG@10: 0.440255, MRR: 0.383127
epoch: 40, aver loss is: [5.294998]
epoch: 40, HR@10: 0.667120, NDCG@10: 0.444093, MRR: 0.386479
epoch: 41, aver loss is: [5.287908]
epoch: 41, HR@10: 0.668139, NDCG@10: 0.445576, MRR: 0.388108
epoch: 42, aver loss is: [5.2817583]
epoch: 42, HR@10: 0.668818, NDCG@10: 0.445723, MRR: 0.387947
epoch: 43, aver loss is: [5.275173]
epoch: 43, HR@10: 0.672554, NDCG@10: 0.447355, MRR: 0.388742
epoch: 44, aver loss is: [5.269254]
epoch: 44, HR@10: 0.670686, NDCG@10: 0.447938, MRR: 0.390249
epoch: 45, aver loss is: [5.2632256]
epoch: 45, HR@10: 0.669837, NDCG@10: 0.447453, MRR: 0.389912
epoch: 46, aver loss is: [5.2581134]
epoch: 46, HR@10: 0.672045, NDCG@10: 0.448261, MRR: 0.390137
epoch: 47, aver loss is: [5.252054]
epoch: 47, HR@10: 0.673064, NDCG@10: 0.450700, MRR: 0.393015
epoch: 48, aver loss is: [5.247217]
epoch: 48, HR@10: 0.671875, NDCG@10: 0.450856, MRR: 0.393655
epoch: 49, aver loss is: [5.2423553]
epoch: 49, HR@10: 0.675272, NDCG@10: 0.452857, MRR: 0.395132
epoch: 50, aver loss is: [5.237395]
epoch: 50, HR@10: 0.674083, NDCG@10: 0.452717, MRR: 0.395367
epoch: 51, aver loss is: [5.2326856]
epoch: 51, HR@10: 0.675102, NDCG@10: 0.455947, MRR: 0.399215
epoch: 52, aver loss is: [5.22814]
epoch: 52, HR@10: 0.674932, NDCG@10: 0.453929, MRR: 0.396635
epoch: 53, aver loss is: [5.2240257]
epoch: 53, HR@10: 0.675272, NDCG@10: 0.455849, MRR: 0.399003
epoch: 54, aver loss is: [5.220014]
epoch: 54, HR@10: 0.675781, NDCG@10: 0.455464, MRR: 0.398370
epoch: 55, aver loss is: [5.2160964]
epoch: 55, HR@10: 0.676461, NDCG@10: 0.456254, MRR: 0.399119
epoch: 56, aver loss is: [5.2115464]
epoch: 56, HR@10: 0.676461, NDCG@10: 0.454910, MRR: 0.397401
epoch: 57, aver loss is: [5.2076564]
epoch: 57, HR@10: 0.673743, NDCG@10: 0.454424, MRR: 0.397671
epoch: 58, aver loss is: [5.2041607]
epoch: 58, HR@10: 0.674592, NDCG@10: 0.455641, MRR: 0.398961
epoch: 59, aver loss is: [5.200794]
epoch: 59, HR@10: 0.678159, NDCG@10: 0.458087, MRR: 0.400902
epoch: 60, aver loss is: [5.1968613]
epoch: 60, HR@10: 0.678668, NDCG@10: 0.457793, MRR: 0.400344
epoch: 61, aver loss is: [5.1938024]
epoch: 61, HR@10: 0.679178, NDCG@10: 0.458904, MRR: 0.401611
epoch: 62, aver loss is: [5.1906886]
epoch: 62, HR@10: 0.679008, NDCG@10: 0.457715, MRR: 0.400171
epoch: 63, aver loss is: [5.187229]
epoch: 63, HR@10: 0.678838, NDCG@10: 0.459726, MRR: 0.402809
epoch: 64, aver loss is: [5.1844597]
epoch: 64, HR@10: 0.678159, NDCG@10: 0.458029, MRR: 0.400870
epoch: 65, aver loss is: [5.1811438]
epoch: 65, HR@10: 0.681556, NDCG@10: 0.460246, MRR: 0.402484
epoch: 66, aver loss is: [5.1782956]
epoch: 66, HR@10: 0.679857, NDCG@10: 0.460115, MRR: 0.402989
epoch: 67, aver loss is: [5.1751513]
epoch: 67, HR@10: 0.678838, NDCG@10: 0.460862, MRR: 0.404399
epoch: 68, aver loss is: [5.172713]
epoch: 68, HR@10: 0.678159, NDCG@10: 0.460459, MRR: 0.404033
epoch: 69, aver loss is: [5.1698527]
epoch: 69, HR@10: 0.678668, NDCG@10: 0.461181, MRR: 0.404810
epoch: 70, aver loss is: [5.167133]
epoch: 70, HR@10: 0.682065, NDCG@10: 0.463170, MRR: 0.406229
epoch: 71, aver loss is: [5.164237]
epoch: 71, HR@10: 0.680707, NDCG@10: 0.461523, MRR: 0.404552
epoch: 72, aver loss is: [5.1620984]
epoch: 72, HR@10: 0.681046, NDCG@10: 0.463082, MRR: 0.406520
epoch: 73, aver loss is: [5.1596217]
epoch: 73, HR@10: 0.682405, NDCG@10: 0.463608, MRR: 0.406612
epoch: 74, aver loss is: [5.1571302]
epoch: 74, HR@10: 0.681216, NDCG@10: 0.463829, MRR: 0.407496
epoch: 75, aver loss is: [5.1548214]
epoch: 75, HR@10: 0.681556, NDCG@10: 0.463381, MRR: 0.406739
epoch: 76, aver loss is: [5.152235]
epoch: 76, HR@10: 0.680367, NDCG@10: 0.463340, MRR: 0.407117
epoch: 77, aver loss is: [5.149909]
epoch: 77, HR@10: 0.683084, NDCG@10: 0.464780, MRR: 0.408012
epoch: 78, aver loss is: [5.1474833]
epoch: 78, HR@10: 0.680027, NDCG@10: 0.465060, MRR: 0.409539
epoch: 79, aver loss is: [5.145616]
epoch: 79, HR@10: 0.682235, NDCG@10: 0.464113, MRR: 0.407510
epoch: 80, aver loss is: [5.143162]
epoch: 80, HR@10: 0.682065, NDCG@10: 0.465452, MRR: 0.409296
epoch: 81, aver loss is: [5.1410995]
epoch: 81, HR@10: 0.682065, NDCG@10: 0.465167, MRR: 0.408957
epoch: 82, aver loss is: [5.139235]
epoch: 82, HR@10: 0.684443, NDCG@10: 0.466460, MRR: 0.409799
epoch: 83, aver loss is: [5.1372137]
epoch: 83, HR@10: 0.680707, NDCG@10: 0.464352, MRR: 0.408270
epoch: 84, aver loss is: [5.1351795]
epoch: 84, HR@10: 0.682745, NDCG@10: 0.465936, MRR: 0.409658
epoch: 85, aver loss is: [5.1334834]
epoch: 85, HR@10: 0.684443, NDCG@10: 0.466515, MRR: 0.409796
epoch: 86, aver loss is: [5.1315455]
epoch: 86, HR@10: 0.682745, NDCG@10: 0.467124, MRR: 0.411227
epoch: 87, aver loss is: [5.1293316]
epoch: 87, HR@10: 0.681386, NDCG@10: 0.465887, MRR: 0.410093
epoch: 88, aver loss is: [5.127845]
epoch: 88, HR@10: 0.681216, NDCG@10: 0.466969, MRR: 0.411599
epoch: 89, aver loss is: [5.125809]
epoch: 89, HR@10: 0.682235, NDCG@10: 0.467517, MRR: 0.411994
epoch: 90, aver loss is: [5.1239176]
epoch: 90, HR@10: 0.680367, NDCG@10: 0.466693, MRR: 0.411581
epoch: 91, aver loss is: [5.122371]
epoch: 91, HR@10: 0.683424, NDCG@10: 0.466709, MRR: 0.410431
epoch: 92, aver loss is: [5.1203566]
epoch: 92, HR@10: 0.683764, NDCG@10: 0.467931, MRR: 0.411899
epoch: 93, aver loss is: [5.1189947]
epoch: 93, HR@10: 0.682745, NDCG@10: 0.467780, MRR: 0.412100
epoch: 94, aver loss is: [5.116958]
epoch: 94, HR@10: 0.682745, NDCG@10: 0.469654, MRR: 0.414556
epoch: 95, aver loss is: [5.1155334]
epoch: 95, HR@10: 0.683084, NDCG@10: 0.468596, MRR: 0.413047
epoch: 96, aver loss is: [5.1139374]
epoch: 96, HR@10: 0.682914, NDCG@10: 0.468617, MRR: 0.413103
epoch: 97, aver loss is: [5.112507]
epoch: 97, HR@10: 0.683084, NDCG@10: 0.468710, MRR: 0.413082
epoch: 98, aver loss is: [5.110381]
epoch: 98, HR@10: 0.685632, NDCG@10: 0.469331, MRR: 0.413028
epoch: 99, aver loss is: [5.109199]
epoch: 99, HR@10: 0.683424, NDCG@10: 0.468915, MRR: 0.413331
epoch: 100, aver loss is: [5.1076775]
epoch: 100, HR@10: 0.682235, NDCG@10: 0.469517, MRR: 0.414555
epoch: 101, aver loss is: [5.1066227]
epoch: 101, HR@10: 0.684952, NDCG@10: 0.469882, MRR: 0.413988
epoch: 102, aver loss is: [5.1050005]
epoch: 102, HR@10: 0.683594, NDCG@10: 0.468437, MRR: 0.412634
epoch: 103, aver loss is: [5.1035647]
epoch: 103, HR@10: 0.682914, NDCG@10: 0.469178, MRR: 0.413921
epoch: 104, aver loss is: [5.1021934]
epoch: 104, HR@10: 0.682405, NDCG@10: 0.469180, MRR: 0.414055
epoch: 105, aver loss is: [5.100729]
epoch: 105, HR@10: 0.684443, NDCG@10: 0.471644, MRR: 0.416590
epoch: 106, aver loss is: [5.0990443]
epoch: 106, HR@10: 0.684443, NDCG@10: 0.471398, MRR: 0.416260
epoch: 107, aver loss is: [5.0979314]
epoch: 107, HR@10: 0.685122, NDCG@10: 0.470915, MRR: 0.415374
epoch: 108, aver loss is: [5.0966945]
epoch: 108, HR@10: 0.684613, NDCG@10: 0.471066, MRR: 0.415783
epoch: 109, aver loss is: [5.095379]
epoch: 109, HR@10: 0.684443, NDCG@10: 0.470821, MRR: 0.415476
epoch: 110, aver loss is: [5.094312]
epoch: 110, HR@10: 0.683933, NDCG@10: 0.470148, MRR: 0.414757
epoch: 111, aver loss is: [5.0928183]
epoch: 111, HR@10: 0.685292, NDCG@10: 0.471215, MRR: 0.415740
epoch: 112, aver loss is: [5.091882]
epoch: 112, HR@10: 0.685462, NDCG@10: 0.471507, MRR: 0.415989
epoch: 113, aver loss is: [5.090871]
epoch: 113, HR@10: 0.684952, NDCG@10: 0.471489, MRR: 0.416161
epoch: 114, aver loss is: [5.0895486]
epoch: 114, HR@10: 0.684443, NDCG@10: 0.471910, MRR: 0.416982
epoch: 115, aver loss is: [5.088516]
epoch: 115, HR@10: 0.684952, NDCG@10: 0.472583, MRR: 0.417706
epoch: 116, aver loss is: [5.08697]
epoch: 116, HR@10: 0.684952, NDCG@10: 0.472928, MRR: 0.418198
epoch: 117, aver loss is: [5.085992]
epoch: 117, HR@10: 0.685971, NDCG@10: 0.471991, MRR: 0.416420
epoch: 118, aver loss is: [5.0846124]
epoch: 118, HR@10: 0.684443, NDCG@10: 0.471614, MRR: 0.416621
epoch: 119, aver loss is: [5.0835023]
epoch: 119, HR@10: 0.686990, NDCG@10: 0.473663, MRR: 0.418375
epoch: 120, aver loss is: [5.0824003]
epoch: 120, HR@10: 0.687840, NDCG@10: 0.472947, MRR: 0.417048
epoch: 121, aver loss is: [5.0814857]
epoch: 121, HR@10: 0.685802, NDCG@10: 0.472018, MRR: 0.416625
epoch: 122, aver loss is: [5.0806084]
epoch: 122, HR@10: 0.685632, NDCG@10: 0.472143, MRR: 0.416796
epoch: 123, aver loss is: [5.079206]
epoch: 123, HR@10: 0.688179, NDCG@10: 0.473738, MRR: 0.417992
epoch: 124, aver loss is: [5.0788107]
epoch: 124, HR@10: 0.686141, NDCG@10: 0.472303, MRR: 0.416852
epoch: 125, aver loss is: [5.0774417]
epoch: 125, HR@10: 0.687670, NDCG@10: 0.473644, MRR: 0.417974
epoch: 126, aver loss is: [5.07642]
epoch: 126, HR@10: 0.687160, NDCG@10: 0.472349, MRR: 0.416571
epoch: 127, aver loss is: [5.0752983]
epoch: 127, HR@10: 0.685292, NDCG@10: 0.473137, MRR: 0.418340
epoch: 128, aver loss is: [5.074687]
epoch: 128, HR@10: 0.685632, NDCG@10: 0.472478, MRR: 0.417241
epoch: 129, aver loss is: [5.073423]
epoch: 129, HR@10: 0.687670, NDCG@10: 0.473211, MRR: 0.417466
epoch: 130, aver loss is: [5.0723147]
epoch: 130, HR@10: 0.688349, NDCG@10: 0.473057, MRR: 0.416999
epoch: 131, aver loss is: [5.0718365]
epoch: 131, HR@10: 0.685971, NDCG@10: 0.472519, MRR: 0.417173
epoch: 132, aver loss is: [5.071066]
epoch: 132, HR@10: 0.685632, NDCG@10: 0.472335, MRR: 0.417054
epoch: 133, aver loss is: [5.069601]
epoch: 133, HR@10: 0.688010, NDCG@10: 0.473892, MRR: 0.418186
epoch: 134, aver loss is: [5.0690875]
epoch: 134, HR@10: 0.687160, NDCG@10: 0.473125, MRR: 0.417555
epoch: 135, aver loss is: [5.0680265]
epoch: 135, HR@10: 0.687500, NDCG@10: 0.473687, MRR: 0.418129
epoch: 136, aver loss is: [5.066715]
epoch: 136, HR@10: 0.688689, NDCG@10: 0.472798, MRR: 0.416490
epoch: 137, aver loss is: [5.06649]
epoch: 137, HR@10: 0.686651, NDCG@10: 0.473053, MRR: 0.417650
epoch: 138, aver loss is: [5.065525]
epoch: 138, HR@10: 0.684952, NDCG@10: 0.471730, MRR: 0.416521
epoch: 139, aver loss is: [5.064719]
epoch: 139, HR@10: 0.688519, NDCG@10: 0.473151, MRR: 0.417081
epoch: 140, aver loss is: [5.0639753]
epoch: 140, HR@10: 0.686311, NDCG@10: 0.472396, MRR: 0.416930
epoch: 141, aver loss is: [5.0632615]
epoch: 141, HR@10: 0.685802, NDCG@10: 0.472492, MRR: 0.417176
epoch: 142, aver loss is: [5.062105]
epoch: 142, HR@10: 0.687330, NDCG@10: 0.474029, MRR: 0.418741
epoch: 143, aver loss is: [5.0613694]
epoch: 143, HR@10: 0.687160, NDCG@10: 0.473562, MRR: 0.418017
epoch: 144, aver loss is: [5.0607033]
epoch: 144, HR@10: 0.686311, NDCG@10: 0.473740, MRR: 0.418688
epoch: 145, aver loss is: [5.059524]
epoch: 145, HR@10: 0.688010, NDCG@10: 0.474239, MRR: 0.418749
epoch: 146, aver loss is: [5.058726]
epoch: 146, HR@10: 0.686651, NDCG@10: 0.472772, MRR: 0.417240
epoch: 147, aver loss is: [5.0579915]
epoch: 147, HR@10: 0.686481, NDCG@10: 0.472638, MRR: 0.417171
epoch: 148, aver loss is: [5.057207]
epoch: 148, HR@10: 0.685802, NDCG@10: 0.473593, MRR: 0.418689
epoch: 149, aver loss is: [5.056898]
epoch: 149, HR@10: 0.687330, NDCG@10: 0.473554, MRR: 0.418070
epoch: 150, aver loss is: [5.055701]
epoch: 150, HR@10: 0.686821, NDCG@10: 0.473235, MRR: 0.417830
epoch: 151, aver loss is: [5.055479]
epoch: 151, HR@10: 0.685462, NDCG@10: 0.472866, MRR: 0.417788
epoch: 152, aver loss is: [5.0543094]
epoch: 152, HR@10: 0.685462, NDCG@10: 0.473461, MRR: 0.418564
epoch: 153, aver loss is: [5.053931]
epoch: 153, HR@10: 0.687670, NDCG@10: 0.473549, MRR: 0.417893
epoch: 154, aver loss is: [5.0531187]
epoch: 154, HR@10: 0.684952, NDCG@10: 0.472604, MRR: 0.417575
epoch: 155, aver loss is: [5.0523553]
epoch: 155, HR@10: 0.686481, NDCG@10: 0.473008, MRR: 0.417581
epoch: 156, aver loss is: [5.051606]
epoch: 156, HR@10: 0.685122, NDCG@10: 0.473087, MRR: 0.418264
epoch: 157, aver loss is: [5.0507545]
epoch: 157, HR@10: 0.685971, NDCG@10: 0.473979, MRR: 0.419065
epoch: 158, aver loss is: [5.050324]
epoch: 158, HR@10: 0.685462, NDCG@10: 0.472800, MRR: 0.417769
epoch: 159, aver loss is: [5.049668]
epoch: 159, HR@10: 0.687500, NDCG@10: 0.473572, MRR: 0.418030
epoch: 160, aver loss is: [5.0487623]
epoch: 160, HR@10: 0.686311, NDCG@10: 0.473333, MRR: 0.418090
epoch: 161, aver loss is: [5.048502]
epoch: 161, HR@10: 0.685122, NDCG@10: 0.473400, MRR: 0.418645
epoch: 162, aver loss is: [5.047461]
epoch: 162, HR@10: 0.685971, NDCG@10: 0.473361, MRR: 0.418266
epoch: 163, aver loss is: [5.046992]
epoch: 163, HR@10: 0.685971, NDCG@10: 0.473091, MRR: 0.417853
epoch: 164, aver loss is: [5.046551]
epoch: 164, HR@10: 0.686651, NDCG@10: 0.474105, MRR: 0.418986
epoch: 165, aver loss is: [5.046006]
epoch: 165, HR@10: 0.684783, NDCG@10: 0.472753, MRR: 0.417943
epoch: 166, aver loss is: [5.044945]
epoch: 166, HR@10: 0.687500, NDCG@10: 0.473517, MRR: 0.417933
epoch: 167, aver loss is: [5.044948]
epoch: 167, HR@10: 0.686311, NDCG@10: 0.474155, MRR: 0.419224
epoch: 168, aver loss is: [5.0437756]
epoch: 168, HR@10: 0.685462, NDCG@10: 0.472987, MRR: 0.417963
epoch: 169, aver loss is: [5.043099]
epoch: 169, HR@10: 0.686481, NDCG@10: 0.474048, MRR: 0.418997
epoch: 170, aver loss is: [5.0425696]
epoch: 170, HR@10: 0.685292, NDCG@10: 0.472803, MRR: 0.417836
epoch: 171, aver loss is: [5.042181]
epoch: 171, HR@10: 0.687840, NDCG@10: 0.474016, MRR: 0.418491
epoch: 172, aver loss is: [5.041404]
epoch: 172, HR@10: 0.684613, NDCG@10: 0.472610, MRR: 0.417789
epoch: 173, aver loss is: [5.040658]
epoch: 173, HR@10: 0.683933, NDCG@10: 0.473570, MRR: 0.419327
epoch: 174, aver loss is: [5.0400105]
epoch: 174, HR@10: 0.685802, NDCG@10: 0.472714, MRR: 0.417514
epoch: 175, aver loss is: [5.0399]
epoch: 175, HR@10: 0.685462, NDCG@10: 0.473671, MRR: 0.418955
epoch: 176, aver loss is: [5.0391455]
epoch: 176, HR@10: 0.685122, NDCG@10: 0.471611, MRR: 0.416313
epoch: 177, aver loss is: [5.0386457]
epoch: 177, HR@10: 0.685292, NDCG@10: 0.472890, MRR: 0.417963
epoch: 178, aver loss is: [5.0377345]
epoch: 178, HR@10: 0.686651, NDCG@10: 0.473840, MRR: 0.418747
epoch: 179, aver loss is: [5.037169]
epoch: 179, HR@10: 0.683594, NDCG@10: 0.472522, MRR: 0.418040
epoch: 180, aver loss is: [5.03687]
epoch: 180, HR@10: 0.684613, NDCG@10: 0.473198, MRR: 0.418674
epoch: 181, aver loss is: [5.0363398]
epoch: 181, HR@10: 0.684103, NDCG@10: 0.473051, MRR: 0.418658
epoch: 182, aver loss is: [5.035549]
epoch: 182, HR@10: 0.686311, NDCG@10: 0.474338, MRR: 0.419484
epoch: 183, aver loss is: [5.0349283]
epoch: 183, HR@10: 0.685122, NDCG@10: 0.473483, MRR: 0.418886
epoch: 184, aver loss is: [5.0350304]
epoch: 184, HR@10: 0.686141, NDCG@10: 0.472488, MRR: 0.417195
epoch: 185, aver loss is: [5.033939]
epoch: 185, HR@10: 0.686141, NDCG@10: 0.473713, MRR: 0.418762
epoch: 186, aver loss is: [5.033221]
epoch: 186, HR@10: 0.684952, NDCG@10: 0.473284, MRR: 0.418681
epoch: 187, aver loss is: [5.033051]
epoch: 187, HR@10: 0.683254, NDCG@10: 0.472006, MRR: 0.417583
epoch: 188, aver loss is: [5.032141]
epoch: 188, HR@10: 0.684613, NDCG@10: 0.473390, MRR: 0.418915
epoch: 189, aver loss is: [5.031513]
epoch: 189, HR@10: 0.686311, NDCG@10: 0.474769, MRR: 0.420154
epoch: 190, aver loss is: [5.0310316]
epoch: 190, HR@10: 0.684952, NDCG@10: 0.474386, MRR: 0.420116
epoch: 191, aver loss is: [5.030855]
epoch: 191, HR@10: 0.684613, NDCG@10: 0.472764, MRR: 0.418079
epoch: 192, aver loss is: [5.0301123]
epoch: 192, HR@10: 0.683424, NDCG@10: 0.473094, MRR: 0.418998
epoch: 193, aver loss is: [5.029903]
epoch: 193, HR@10: 0.684952, NDCG@10: 0.473622, MRR: 0.419139
epoch: 194, aver loss is: [5.02963]
epoch: 194, HR@10: 0.682745, NDCG@10: 0.473422, MRR: 0.419668
epoch: 195, aver loss is: [5.0289874]
epoch: 195, HR@10: 0.685122, NDCG@10: 0.474482, MRR: 0.420248
epoch: 196, aver loss is: [5.0281787]
epoch: 196, HR@10: 0.682914, NDCG@10: 0.473114, MRR: 0.419192
epoch: 197, aver loss is: [5.028045]
epoch: 197, HR@10: 0.683084, NDCG@10: 0.472497, MRR: 0.418375
epoch: 198, aver loss is: [5.0275645]
epoch: 198, HR@10: 0.683764, NDCG@10: 0.472934, MRR: 0.418667
epoch: 199, aver loss is: [5.02703]
epoch: 199, HR@10: 0.682575, NDCG@10: 0.474152, MRR: 0.420690
epoch: 200, aver loss is: [5.0269156]
epoch: 200, HR@10: 0.684952, NDCG@10: 0.474011, MRR: 0.419702
epoch: 201, aver loss is: [5.0263567]
epoch: 201, HR@10: 0.684952, NDCG@10: 0.473888, MRR: 0.419484
epoch: 202, aver loss is: [5.0255704]
epoch: 202, HR@10: 0.684273, NDCG@10: 0.473432, MRR: 0.419170
epoch: 203, aver loss is: [5.0250573]
epoch: 203, HR@10: 0.684952, NDCG@10: 0.473596, MRR: 0.419118
epoch: 204, aver loss is: [5.0248165]
epoch: 204, HR@10: 0.685632, NDCG@10: 0.474321, MRR: 0.419789
epoch: 205, aver loss is: [5.02445]
epoch: 205, HR@10: 0.685122, NDCG@10: 0.474389, MRR: 0.420184
epoch: 206, aver loss is: [5.0241184]
epoch: 206, HR@10: 0.685122, NDCG@10: 0.475210, MRR: 0.421288
epoch: 207, aver loss is: [5.023226]
epoch: 207, HR@10: 0.686821, NDCG@10: 0.474425, MRR: 0.419535
epoch: 208, aver loss is: [5.022435]
epoch: 208, HR@10: 0.685462, NDCG@10: 0.474633, MRR: 0.420332
epoch: 209, aver loss is: [5.02268]
epoch: 209, HR@10: 0.683594, NDCG@10: 0.473114, MRR: 0.418938
epoch: 210, aver loss is: [5.022278]
epoch: 210, HR@10: 0.685122, NDCG@10: 0.474005, MRR: 0.419582
epoch: 211, aver loss is: [5.021613]
epoch: 211, HR@10: 0.685122, NDCG@10: 0.474603, MRR: 0.420408
epoch: 212, aver loss is: [5.021055]
epoch: 212, HR@10: 0.685802, NDCG@10: 0.474255, MRR: 0.419723
epoch: 213, aver loss is: [5.020632]
epoch: 213, HR@10: 0.684783, NDCG@10: 0.474449, MRR: 0.420319
epoch: 214, aver loss is: [5.020327]
epoch: 214, HR@10: 0.683933, NDCG@10: 0.473213, MRR: 0.419003
epoch: 215, aver loss is: [5.0200677]
epoch: 215, HR@10: 0.684103, NDCG@10: 0.473926, MRR: 0.419920
epoch: 216, aver loss is: [5.01962]
epoch: 216, HR@10: 0.686311, NDCG@10: 0.475790, MRR: 0.421583
epoch: 217, aver loss is: [5.0186677]
epoch: 217, HR@10: 0.684613, NDCG@10: 0.473781, MRR: 0.419555
epoch: 218, aver loss is: [5.0184355]
epoch: 218, HR@10: 0.682914, NDCG@10: 0.472959, MRR: 0.419070
epoch: 219, aver loss is: [5.0183835]
epoch: 219, HR@10: 0.687330, NDCG@10: 0.475011, MRR: 0.420147
epoch: 220, aver loss is: [5.0176897]
epoch: 220, HR@10: 0.686821, NDCG@10: 0.474744, MRR: 0.420012
epoch: 221, aver loss is: [5.017419]
epoch: 221, HR@10: 0.684443, NDCG@10: 0.474530, MRR: 0.420638
epoch: 222, aver loss is: [5.0168614]
epoch: 222, HR@10: 0.684103, NDCG@10: 0.473669, MRR: 0.419581
epoch: 223, aver loss is: [5.0170274]
epoch: 223, HR@10: 0.683764, NDCG@10: 0.472388, MRR: 0.418010
epoch: 224, aver loss is: [5.016244]
epoch: 224, HR@10: 0.683764, NDCG@10: 0.473763, MRR: 0.419789
epoch: 225, aver loss is: [5.01563]
epoch: 225, HR@10: 0.684783, NDCG@10: 0.474456, MRR: 0.420379
epoch: 226, aver loss is: [5.0154185]
epoch: 226, HR@10: 0.686311, NDCG@10: 0.475167, MRR: 0.420766
epoch: 227, aver loss is: [5.0154366]
epoch: 227, HR@10: 0.685122, NDCG@10: 0.474137, MRR: 0.419841
epoch: 228, aver loss is: [5.014886]
epoch: 228, HR@10: 0.683254, NDCG@10: 0.473220, MRR: 0.419236
epoch: 229, aver loss is: [5.014299]
epoch: 229, HR@10: 0.684103, NDCG@10: 0.473653, MRR: 0.419537
epoch: 230, aver loss is: [5.014062]
epoch: 230, HR@10: 0.685122, NDCG@10: 0.474590, MRR: 0.420479
epoch: 231, aver loss is: [5.013674]
epoch: 231, HR@10: 0.685292, NDCG@10: 0.474729, MRR: 0.420588
epoch: 232, aver loss is: [5.01337]
epoch: 232, HR@10: 0.685971, NDCG@10: 0.474833, MRR: 0.420452
epoch: 233, aver loss is: [5.012602]
epoch: 233, HR@10: 0.687500, NDCG@10: 0.475729, MRR: 0.421058
epoch: 234, aver loss is: [5.0122566]
epoch: 234, HR@10: 0.687670, NDCG@10: 0.475446, MRR: 0.420616
epoch: 235, aver loss is: [5.011613]
epoch: 235, HR@10: 0.686821, NDCG@10: 0.474798, MRR: 0.420140
epoch: 236, aver loss is: [5.011898]
epoch: 236, HR@10: 0.687500, NDCG@10: 0.474909, MRR: 0.420015
epoch: 237, aver loss is: [5.0116982]
epoch: 237, HR@10: 0.685292, NDCG@10: 0.474023, MRR: 0.419599
epoch: 238, aver loss is: [5.0110044]
epoch: 238, HR@10: 0.685462, NDCG@10: 0.473320, MRR: 0.418645
epoch: 239, aver loss is: [5.0108843]
epoch: 239, HR@10: 0.686141, NDCG@10: 0.474551, MRR: 0.420014
epoch: 240, aver loss is: [5.010201]
epoch: 240, HR@10: 0.687670, NDCG@10: 0.475286, MRR: 0.420399
epoch: 241, aver loss is: [5.0101833]
epoch: 241, HR@10: 0.686141, NDCG@10: 0.473920, MRR: 0.419213
epoch: 242, aver loss is: [5.0096602]
epoch: 242, HR@10: 0.686481, NDCG@10: 0.474906, MRR: 0.420358
epoch: 243, aver loss is: [5.009296]
epoch: 243, HR@10: 0.684103, NDCG@10: 0.473586, MRR: 0.419509
epoch: 244, aver loss is: [5.0085115]
epoch: 244, HR@10: 0.685971, NDCG@10: 0.474483, MRR: 0.419956
epoch: 245, aver loss is: [5.00867]
epoch: 245, HR@10: 0.685802, NDCG@10: 0.474407, MRR: 0.419906
epoch: 246, aver loss is: [5.0086813]
epoch: 246, HR@10: 0.685632, NDCG@10: 0.474608, MRR: 0.420247
epoch: 247, aver loss is: [5.008149]
epoch: 247, HR@10: 0.685122, NDCG@10: 0.474656, MRR: 0.420478
epoch: 248, aver loss is: [5.007559]
epoch: 248, HR@10: 0.684952, NDCG@10: 0.474757, MRR: 0.420743
epoch: 249, aver loss is: [5.007914]
epoch: 249, HR@10: 0.684952, NDCG@10: 0.475396, MRR: 0.421571
epoch: 250, aver loss is: [5.0067425]
epoch: 250, HR@10: 0.684613, NDCG@10: 0.474775, MRR: 0.420880
epoch: 251, aver loss is: [5.0068254]
epoch: 251, HR@10: 0.683254, NDCG@10: 0.474583, MRR: 0.421072
epoch: 252, aver loss is: [5.0061584]
epoch: 252, HR@10: 0.685122, NDCG@10: 0.475134, MRR: 0.421175
epoch: 253, aver loss is: [5.006479]
epoch: 253, HR@10: 0.685462, NDCG@10: 0.474571, MRR: 0.420311
epoch: 254, aver loss is: [5.0059147]
epoch: 254, HR@10: 0.684952, NDCG@10: 0.474194, MRR: 0.420023
epoch: 255, aver loss is: [5.0054173]
epoch: 255, HR@10: 0.684613, NDCG@10: 0.474962, MRR: 0.421120
epoch: 256, aver loss is: [5.0047274]
epoch: 256, HR@10: 0.683254, NDCG@10: 0.473806, MRR: 0.420077
epoch: 257, aver loss is: [5.004773]
epoch: 257, HR@10: 0.684443, NDCG@10: 0.473687, MRR: 0.419522
epoch: 258, aver loss is: [5.00478]
epoch: 258, HR@10: 0.683594, NDCG@10: 0.473587, MRR: 0.419677
epoch: 259, aver loss is: [5.0043693]
epoch: 259, HR@10: 0.686990, NDCG@10: 0.475497, MRR: 0.420950
epoch: 260, aver loss is: [5.0037255]
epoch: 260, HR@10: 0.688519, NDCG@10: 0.475537, MRR: 0.420456
epoch: 261, aver loss is: [5.0032673]
epoch: 261, HR@10: 0.685292, NDCG@10: 0.475551, MRR: 0.421604
epoch: 262, aver loss is: [5.0035243]
epoch: 262, HR@10: 0.684613, NDCG@10: 0.474593, MRR: 0.420615
epoch: 263, aver loss is: [5.0028167]
epoch: 263, HR@10: 0.686311, NDCG@10: 0.475184, MRR: 0.420793
epoch: 264, aver loss is: [5.0025797]
epoch: 264, HR@10: 0.686481, NDCG@10: 0.475235, MRR: 0.420770
epoch: 265, aver loss is: [5.0022807]
epoch: 265, HR@10: 0.683084, NDCG@10: 0.473878, MRR: 0.420282
epoch: 266, aver loss is: [5.001901]
epoch: 266, HR@10: 0.683764, NDCG@10: 0.474423, MRR: 0.420777
epoch: 267, aver loss is: [5.001747]
epoch: 267, HR@10: 0.686141, NDCG@10: 0.474692, MRR: 0.420268
epoch: 268, aver loss is: [5.001146]
epoch: 268, HR@10: 0.686821, NDCG@10: 0.475872, MRR: 0.421575
epoch: 269, aver loss is: [5.00094]
epoch: 269, HR@10: 0.686141, NDCG@10: 0.475671, MRR: 0.421538
epoch: 270, aver loss is: [5.0008326]
epoch: 270, HR@10: 0.684103, NDCG@10: 0.474938, MRR: 0.421267
epoch: 271, aver loss is: [5.0009675]
epoch: 271, HR@10: 0.684443, NDCG@10: 0.475112, MRR: 0.421397
epoch: 272, aver loss is: [5.0005183]
epoch: 272, HR@10: 0.685122, NDCG@10: 0.476464, MRR: 0.422980
epoch: 273, aver loss is: [4.999725]
epoch: 273, HR@10: 0.684613, NDCG@10: 0.476109, MRR: 0.422688
epoch: 274, aver loss is: [4.9995666]
epoch: 274, HR@10: 0.682914, NDCG@10: 0.474397, MRR: 0.421027
epoch: 275, aver loss is: [4.999337]
epoch: 275, HR@10: 0.686481, NDCG@10: 0.475235, MRR: 0.420813
epoch: 276, aver loss is: [4.999672]
epoch: 276, HR@10: 0.688010, NDCG@10: 0.475975, MRR: 0.421263
epoch: 277, aver loss is: [4.998893]
epoch: 277, HR@10: 0.687670, NDCG@10: 0.476411, MRR: 0.421942
epoch: 278, aver loss is: [4.998388]
epoch: 278, HR@10: 0.683933, NDCG@10: 0.474484, MRR: 0.420721
epoch: 279, aver loss is: [4.998621]
epoch: 279, HR@10: 0.684783, NDCG@10: 0.474750, MRR: 0.420788
epoch: 280, aver loss is: [4.997649]
epoch: 280, HR@10: 0.687330, NDCG@10: 0.476968, MRR: 0.422791
epoch: 281, aver loss is: [4.9976106]
epoch: 281, HR@10: 0.686141, NDCG@10: 0.477188, MRR: 0.423552
epoch: 282, aver loss is: [4.997268]
epoch: 282, HR@10: 0.684443, NDCG@10: 0.475545, MRR: 0.421970
epoch: 283, aver loss is: [4.9972534]
epoch: 283, HR@10: 0.685802, NDCG@10: 0.476230, MRR: 0.422383
epoch: 284, aver loss is: [4.996629]
epoch: 284, HR@10: 0.686311, NDCG@10: 0.476042, MRR: 0.421950
epoch: 285, aver loss is: [4.9966745]
epoch: 285, HR@10: 0.687670, NDCG@10: 0.477103, MRR: 0.422865
epoch: 286, aver loss is: [4.996505]
epoch: 286, HR@10: 0.685802, NDCG@10: 0.476032, MRR: 0.422184
epoch: 287, aver loss is: [4.996372]
epoch: 287, HR@10: 0.686311, NDCG@10: 0.476895, MRR: 0.423014
epoch: 288, aver loss is: [4.995744]
epoch: 288, HR@10: 0.686481, NDCG@10: 0.476291, MRR: 0.422179
epoch: 289, aver loss is: [4.9959383]
epoch: 289, HR@10: 0.686651, NDCG@10: 0.477010, MRR: 0.423073
epoch: 290, aver loss is: [4.995217]
epoch: 290, HR@10: 0.684443, NDCG@10: 0.475994, MRR: 0.422574
epoch: 291, aver loss is: [4.995298]
epoch: 291, HR@10: 0.687330, NDCG@10: 0.476197, MRR: 0.421821
epoch: 292, aver loss is: [4.994839]
epoch: 292, HR@10: 0.686481, NDCG@10: 0.476088, MRR: 0.421947
epoch: 293, aver loss is: [4.9943023]
epoch: 293, HR@10: 0.685632, NDCG@10: 0.475692, MRR: 0.421782
epoch: 294, aver loss is: [4.9942017]
epoch: 294, HR@10: 0.685292, NDCG@10: 0.476795, MRR: 0.423346
epoch: 295, aver loss is: [4.994054]
epoch: 295, HR@10: 0.686141, NDCG@10: 0.476207, MRR: 0.422259
epoch: 296, aver loss is: [4.9937334]
epoch: 296, HR@10: 0.686141, NDCG@10: 0.477245, MRR: 0.423643
epoch: 297, aver loss is: [4.993572]
epoch: 297, HR@10: 0.688179, NDCG@10: 0.476952, MRR: 0.422465
epoch: 298, aver loss is: [4.993238]
epoch: 298, HR@10: 0.688519, NDCG@10: 0.477697, MRR: 0.423354
epoch: 299, aver loss is: [4.992975]
epoch: 299, HR@10: 0.689878, NDCG@10: 0.478233, MRR: 0.423546