/raid/cs21mtech12001/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/raid/cs21mtech12001/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/raid/cs21mtech12001/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/raid/cs21mtech12001/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/raid/cs21mtech12001/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/raid/cs21mtech12001/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
2022-07-09 10:16:32.695444: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
Dictionaries loaded.
Loaded subtoken vocab. size: 12895
Loaded target word vocab. size: 8914
Loaded nodes vocab. size: 108
Created model
Starting training
WARNING:tensorflow:From /raid/cs21mtech12001/.local/lib/python3.6/site-packages/tensorflow/python/ops/sparse_ops.py:1165: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
Training batch size:                     512
Dataset path:                            /raid/cs21mtech12001/Research/Code2Seq/Data/extracted_data/final_data/CodeSearchNet//CodeSearchNet
Training file path:                      /raid/cs21mtech12001/Research/Code2Seq/Data/extracted_data/final_data/CodeSearchNet//CodeSearchNet.train.c2s
Validation path:                         /raid/cs21mtech12001/Research/Code2Seq/Data/extracted_data/final_data/CodeSearchNet//CodeSearchNet.val.c2s
Taking max contexts from each example:   200
Random path sampling:                    True
Embedding size:                          512
Using BiLSTMs, each of size:             256
Decoder size:                            512
Decoder layers:                          2
Max path lengths:                        9
Max subtokens in a token:                5
Max target length:                       37
Embeddings dropout keep_prob:            0.3
LSTM dropout keep_prob:                  0.75
============================================
Number of trainable params: 24180224
Initalized variables
Started reader...
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m13s
Accuracy after 1 epochs: 0.00000
After 1 epochs: Precision: 0.00000, recall: 0.00000, F1: 0.00000
Rouge:  0
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m10s
Accuracy after 2 epochs: 0.00000
After 2 epochs: Precision: 0.40872, recall: 0.16968, F1: 0.23981
Rouge:  {'rouge-1': {'r': 0.057577618028034946, 'p': 0.4141791044776119, 'f': 0.09925430934285011}, 'rouge-2': {'r': 0.0, 'p': 0.0, 'f': 0.0}, 'rouge-l': {'r': 0.057577618028034946, 'p': 0.4141791044776119, 'f': 0.09925430934285011}}
Saved after 2 epochs in: models/python150k-default_2/model_iter2
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m11s
Accuracy after 3 epochs: 0.00000
After 3 epochs: Precision: 0.42660, recall: 0.23238, F1: 0.30087
Rouge:  {'rouge-1': {'r': 0.05994019637009089, 'p': 0.4216417910447761, 'f': 0.10285341540799756}, 'rouge-2': {'r': 0.0, 'p': 0.0, 'f': 0.0}, 'rouge-l': {'r': 0.05994019637009089, 'p': 0.4216417910447761, 'f': 0.10285341540799756}}
Saved after 3 epochs in: models/python150k-default_2/model_iter3
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m10s
Accuracy after 4 epochs: 0.00000
After 4 epochs: Precision: 0.55590, recall: 0.35391, F1: 0.43248
Rouge:  {'rouge-1': {'r': 0.05891394235876226, 'p': 0.39925373134328357, 'f': 0.10029996355778756}, 'rouge-2': {'r': 0.0, 'p': 0.0, 'f': 0.0}, 'rouge-l': {'r': 0.05891394235876226, 'p': 0.39925373134328357, 'f': 0.10029996355778756}}
Saved after 4 epochs in: models/python150k-default_2/model_iter4
Average loss at batch 100: 62.967107,   throughput: 16 samples/sec
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m11s
Accuracy after 5 epochs: 0.00000
After 5 epochs: Precision: 0.59709, recall: 0.41072, F1: 0.48667
Rouge:  {'rouge-1': {'r': 0.085090836811008, 'p': 0.28544776119403004, 'f': 0.12650573399190743}, 'rouge-2': {'r': 0.0009350568585643213, 'p': 0.0029539800995024876, 'f': 0.0013987081864064313}, 'rouge-l': {'r': 0.08460528380679826, 'p': 0.28358208955223896, 'f': 0.12573674241134866}}
Saved after 5 epochs in: models/python150k-default_2/model_iter5
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m10s
Accuracy after 6 epochs: 0.00000
After 6 epochs: Precision: 0.73643, recall: 0.49142, F1: 0.58948
Rouge:  {'rouge-1': {'r': 0.0874779009284826, 'p': 0.3774875621890548, 'f': 0.13717743727825318}, 'rouge-2': {'r': 0.0021344171997157075, 'p': 0.00503731343283582, 'f': 0.0026962169391789204}, 'rouge-l': {'r': 0.0872913337643035, 'p': 0.37686567164179113, 'f': 0.1368904108718238}}
Saved after 6 epochs in: models/python150k-default_2/model_iter6
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m11s
Accuracy after 7 epochs: 0.00000
After 7 epochs: Precision: 0.69214, recall: 0.56118, F1: 0.61982
Rouge:  {'rouge-1': {'r': 0.10488029783270711, 'p': 0.4264614427860699, 'f': 0.16108224226808643}, 'rouge-2': {'r': 0.0015569474058280029, 'p': 0.0038868159203980096, 'f': 0.002135157508231125}, 'rouge-l': {'r': 0.10271207930741524, 'p': 0.41962064676616945, 'f': 0.15787069428041903}}
Saved after 7 epochs in: models/python150k-default_2/model_iter7
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m9s
Accuracy after 8 epochs: 0.00000
After 8 epochs: Precision: 0.59689, recall: 0.29284, F1: 0.39291
Rouge:  {'rouge-1': {'r': 0.12382448399262394, 'p': 0.48289800995024923, 'f': 0.1894008014549637}, 'rouge-2': {'r': 0.010636616700049535, 'p': 0.022885572139303485, 'f': 0.01384280082379927}, 'rouge-l': {'r': 0.11787168458387055, 'p': 0.46455223880597074, 'f': 0.18061167391583616}}
Average loss at batch 200: 51.581963,   throughput: 17 samples/sec
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m9s
Accuracy after 9 epochs: 0.00000
After 9 epochs: Precision: 0.67562, recall: 0.30187, F1: 0.41729
Rouge:  {'rouge-1': {'r': 0.1426108282453862, 'p': 0.5380907960199006, 'f': 0.2155513739245295}, 'rouge-2': {'r': 0.01505365488574444, 'p': 0.03762437810945273, 'f': 0.0203970015567941}, 'rouge-l': {'r': 0.13631041742072159, 'p': 0.5188121890547267, 'f': 0.20625056900805294}}
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m9s
Accuracy after 10 epochs: 0.00000
After 10 epochs: Precision: 0.72370, recall: 0.30490, F1: 0.42905
Rouge:  {'rouge-1': {'r': 0.15001095983525095, 'p': 0.5656094527363184, 'f': 0.22692803502035536}, 'rouge-2': {'r': 0.013776558226185093, 'p': 0.03902363184079601, 'f': 0.019497724669347915}, 'rouge-l': {'r': 0.14246084514894214, 'p': 0.541355721393035, 'f': 0.21560451194310082}}
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m9s
Accuracy after 11 epochs: 0.00000
After 11 epochs: Precision: 0.73620, recall: 0.31003, F1: 0.43631
Rouge:  {'rouge-1': {'r': 0.15927354715410294, 'p': 0.5634328358208952, 'f': 0.23717860998395074}, 'rouge-2': {'r': 0.018623165288277226, 'p': 0.047947761194029846, 'f': 0.025303651662633566}, 'rouge-l': {'r': 0.1518843153245727, 'p': 0.5394900497512438, 'f': 0.22616724365542015}}
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m10s
Accuracy after 12 epochs: 0.00000
After 12 epochs: Precision: 0.72833, recall: 0.31117, F1: 0.43604
Rouge:  {'rouge-1': {'r': 0.17187817508756265, 'p': 0.5334266169154225, 'f': 0.2498421281911023}, 'rouge-2': {'r': 0.024586718733173953, 'p': 0.05373134328358206, 'f': 0.03150185318011848}, 'rouge-l': {'r': 0.16470142252834744, 'p': 0.513215174129353, 'f': 0.23949918547875684}}
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m9s
Accuracy after 13 epochs: 0.00000
After 13 epochs: Precision: 0.72706, recall: 0.31330, F1: 0.43790
Rouge:  {'rouge-1': {'r': 0.1787070582690577, 'p': 0.5132151741293527, 'f': 0.25632071690862607}, 'rouge-2': {'r': 0.0260802992612694, 'p': 0.05883084577114425, 'f': 0.03412805157087623}, 'rouge-l': {'r': 0.170905272575481, 'p': 0.4942475124378107, 'f': 0.2455746895114793}}
Average loss at batch 300: 48.555464,   throughput: 18 samples/sec
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m9s
Accuracy after 14 epochs: 0.00000
After 14 epochs: Precision: 0.73579, recall: 0.32065, F1: 0.44665
Rouge:  {'rouge-1': {'r': 0.1824805546731511, 'p': 0.5208333333333328, 'f': 0.26135101059924315}, 'rouge-2': {'r': 0.02845317514160797, 'p': 0.06361940298507462, 'f': 0.03721954957912176}, 'rouge-l': {'r': 0.17515418494006502, 'p': 0.5026430348258701, 'f': 0.25116729768012636}}
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m9s
Accuracy after 15 epochs: 0.00000
After 15 epochs: Precision: 0.74297, recall: 0.32417, F1: 0.45139
Rouge:  {'rouge-1': {'r': 0.18620523484372684, 'p': 0.5286069651741288, 'f': 0.26655020607331453}, 'rouge-2': {'r': 0.02884865445499773, 'p': 0.06548507462686567, 'f': 0.0383257350534484}, 'rouge-l': {'r': 0.17922830837053175, 'p': 0.5125932835820891, 'f': 0.25710513055328277}}
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m9s
Accuracy after 16 epochs: 0.00000
After 16 epochs: Precision: 0.74397, recall: 0.32454, F1: 0.45193
Rouge:  {'rouge-1': {'r': 0.1890232328027476, 'p': 0.5345149253731338, 'f': 0.2704033628177182}, 'rouge-2': {'r': 0.029339503779802278, 'p': 0.06791044776119404, 'f': 0.03915894852910035}, 'rouge-l': {'r': 0.18077661312888918, 'p': 0.5163246268656714, 'f': 0.25932494965688613}}
Finished 1 epochs
Done testing, epoch reached
Evaluation time: 0h0m9s
Accuracy after 17 epochs: 0.00000
After 17 epochs: Precision: 0.74019, recall: 0.32086, F1: 0.44767
Rouge:  {'rouge-1': {'r': 0.19121911241576953, 'p': 0.5424440298507457, 'f': 0.27367855507684213}, 'rouge-2': {'r': 0.028492548082100316, 'p': 0.0654539800995025, 'f': 0.03811185614157057}, 'rouge-l': {'r': 0.18181322497816568, 'p': 0.5212997512437808, 'f': 0.26095349205821006}}
Not improved for 10 epochs, stopping training
Best scores - epoch 7: 
Precision: 0.69214, recall: 0.56118, F1: 0.61982