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Functions158 in github.com/bxshi/ConMask

↓ 1 callersFunctiontrain
()
ndkgc/models/dkrl.py:885
↓ 1 callersMethodtrain_op
(self, num_epochs=10, batch_size=200)
ndkgc/models/dkrl.py:374
↓ 1 callersMethodtrain_ops
(self, lr=0.01, num_epoch=10, batch_size=200, sampled_true=1, sampled_false=1, devices=list(
ndkgc/models/content_averaging_model.py:802
↓ 1 callersFunctionvalid_vocab_file
(file_path)
ndkgc/utils/__init__.py:14
MethodCompute
ndkgc/ops/__sampling/pairwise_sampler.cpp:62
MethodCompute
ndkgc/ops/__sampling/pairwise_sampler.cpp:98
MethodCompute
ndkgc/ops/__sampling/pairwise_sampler.cpp:138
MethodMultipleNegativeSamplingOp
ndkgc/ops/__sampling/pairwise_sampler.cpp:58
FunctionMultipleNegativeSamplingShapeFn
ndkgc/ops/__sampling/pairwise_sampler.cpp:46
MethodPairwiseSamplingOp
ndkgc/ops/__sampling/pairwise_sampler.cpp:132
FunctionPairwiseSamplingShapeFn
ndkgc/ops/__sampling/pairwise_sampler.cpp:25
MethodSingleNegativeSamplingOp
ndkgc/ops/__sampling/pairwise_sampler.cpp:94
FunctionSingleNegativeSamplingShapeFn
ndkgc/ops/__sampling/pairwise_sampler.cpp:41
Method__init__
Initialize all parameters and variable scopes for a content-based model. If you have new parameters you want to add, inherit from this class
ndkgc/models/content_averaging_model.py:29
Method__init__
(self, **kwargs)
ndkgc/models/fcn_model_v2.py:29
Method__init__
Initialize all parameters and variable scopes for a content-based model. If you have new parameters you want to add, inherit from this class
ndkgc/models/content_model.py:25
Method__init__
(self, **kwargs)
ndkgc/models/cnn_model.py:29
Method__init__
(self, entity_file, relation_file, vocab_file,
ndkgc/models/dkrl.py:23
Method_calculate_rank
(target_scores, pred_scores)
ndkgc/models/content_averaging_model.py:958
Method_calculate_rank
(target_scores, pred_scores)
ndkgc/models/content_model.py:960
Method_create_embeddings
Create all embedding matrices in this function. :param device: The storage device of all the embeddings. If
ndkgc/models/fcn_model_v2.py:62
Method_create_nontrainable_variables
(self)
ndkgc/models/fcn_model_v2.py:43
Method_create_nontrainable_variables
(self)
ndkgc/models/cnn_model.py:36
Method_eval_padded_targets
(self, heads, rels, tails, masks, device)
ndkgc/models/content_averaging_model.py:939
Method_eval_padded_targets
(self, heads, rels, tails, masks, device)
ndkgc/models/content_model.py:941
Method_eval_targets
For a set of targets, calculate heads, rels -> targets and targets, rels -> tails :param heads: :param rels: :param tails:
ndkgc/models/content_averaging_model.py:883
Method_eval_targets
For a set of targets, calculate heads, rels -> targets and targets, rels -> tails :param heads: :param rels: :param tails:
ndkgc/models/content_model.py:885
Method_matched_ents_helper
(hrt, hrt_matrix)
ndkgc/models/dkrl.py:668
Method_true_target_helper
(entity, relation, targets_lookup_table, entity_table, targets, name=None)
ndkgc/models/content_averaging_model.py:924
Method_true_target_helper
(entity, relation, targets_lookup_table, entity_table, targets, name=None)
ndkgc/models/content_model.py:926
Methodentity_in_set_indicator
(ents, all_ents, name=None)
ndkgc/models/content_averaging_model.py:965
Methodentity_in_set_indicator
(ents, all_ents, name=None)
ndkgc/models/content_model.py:967
Functionget_content_matrix
Return content matrix :param variable_scope: :param size: :param reuse: :param device: :return:
ndkgc/ops/content.py:6
Functionget_lookup_table_from_tensor
(tensor, oov_buckets, device='/cpu:0', name='lookup_table')
ndkgc/ops/content.py:23
Functionget_true_tails
Given ent \t rel pair return a list of string targets :param ent_rel_str: :param targets_lookup_table: :param name: :return:
ndkgc/ops/corruption.py:267
Functionget_true_targets
:param entity: :param relation: :param targets_lookup_table: :param entity_table: :param targets: :param name: :return:
ndkgc/ops/corruption.py:235
Methodhead_conv_helper
(self, x)
ndkgc/models/dkrl.py:537
Functionload_closed_manual_evaluation_file_by_rel
Load normal head, tail, rel files, divide them into dicts { relation : { head : [tails] } } We also skip
ndkgc/utils/__init__.py:140
Functionload_manual_evaluation_file
Load normal head, tail, rel files, divide them into dicts where the key is unique head rel pair and the values are the targets in
ndkgc/utils/__init__.py:238
Functionload_rev_list
(file_path)
ndkgc/utils/__init__.py:22
Methodlookup_entity_description_and_title
(self, ents, name=None)
ndkgc/models/fcn_model_v2.py:56
Methodlookup_entity_description_and_title
(self, ents, name=None)
ndkgc/models/cnn_model.py:44
Functionmain
(_)
ndkgc/models/content_averaging_model.py:1272
Functionmain
(_)
ndkgc/models/fcn_model_v2.py:533
Functionmain
(_)
ndkgc/models/content_model.py:1275
Functionmain
(_)
ndkgc/models/cnn_model.py:383
Functionmain
(_)
ndkgc/models/dkrl.py:856
Methodmanual_eval_ops
Manually evaluate one single partial triple with a given set of targets Given a <head, rel> pair, a set of evaluation targets, and a set
ndkgc/models/content_averaging_model.py:1185
Methodmanual_eval_ops
Manually evaluate one single partial triple with a given set of targets Given a <head, rel> pair, a set of evaluation targets, and a set
ndkgc/models/content_model.py:1188
Methodmanual_eval_ops
This is the baseline random model, this takes all the targets, randomly assign values to it and then report the result. :param devic
ndkgc/models/random_model.py:5
Methodmetric_helper
(x)
ndkgc/models/dkrl.py:702
Methodpredict_helper
(a, b)
ndkgc/models/fcn_model_v2.py:170
Methodpredict_helper
(a, b)
ndkgc/models/cnn_model.py:106
Methodtail_conv_helper
(self, x)
ndkgc/models/dkrl.py:545
Functiontail_target_checker
(head, rel, dic)
ndkgc/models/fcn_model_v2.py:842
Methodtranslate_triple
(self, heads, tails, rels, device, reuse=True)
ndkgc/models/fcn_model_v2.py:80
Methodtranslate_triple
(self, heads, tails, rels, device, reuse=True)
ndkgc/models/cnn_model.py:50
Functiontriple_id_lookup
(heads, rels, tails, ent_table, rel_table, name=None)
ndkgc/ops/lookup.py:11
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