tensorflow / neural-structured-learning
File Size

The distribution of size of files (measured in lines of code).

Intro
  • File size measurements show the distribution of size of files.
  • Files are classified in four categories based on their size (lines of code): 1-100 (very small files), 101-200 (small files), 201-500 (medium size files), 501-1000 (long files), 1001+(very long files).
  • It is a good practice to keep files small. Long files may become "bloaters", code that have increased to such gargantuan proportions that they are hard to work with.
Learn more...
File Size Overall
  • There are 184 files with 19,394 lines of code.
    • 0 very long files (0 lines of code)
    • 6 long files (3,666 lines of code)
    • 19 medium size files (6,182 lines of codeclsfd_ftr_w_mp_ins)
    • 39 small files (5,295 lines of code)
    • 120 very small files (4,251 lines of code)
0% | 18% | 31% | 27% | 21%
Legend:
1001+
501-1000
201-500
101-200
1-100


explore: zoomable circles | sunburst | 3D view
File Size per Extension
1001+
501-1000
201-500
101-200
1-100
py0% | 24% | 27% | 26% | 22%
cc0% | 14% | 41% | 33% | 10%
bzl0% | 0% | 100% | 0% | 0%
h0% | 0% | 21% | 38% | 40%
proto0% | 0% | 0% | 23% | 76%
yaml0% | 0% | 0% | 0% | 100%
cfg0% | 0% | 0% | 0% | 100%
File Size per Logical Decomposition
primary
1001+
501-1000
201-500
101-200
1-100
research/gam0% | 49% | 23% | 17% | 8%
research/carls0% | 8% | 39% | 28% | 23%
research/a2n0% | 26% | 67% | 0% | 6%
neural_structured_learning/keras0% | 0% | 65% | 0% | 34%
neural_structured_learning/lib0% | 0% | 38% | 48% | 12%
research/kg_hyp_emb0% | 0% | 0% | 52% | 47%
research/gnn-survey0% | 0% | 0% | 84% | 15%
neural_structured_learning/tools0% | 0% | 0% | 76% | 23%
research/multi_representation_adversary0% | 0% | 0% | 41% | 58%
neural_structured_learning/experimental0% | 0% | 0% | 99% | <1%
neural_structured_learning/configs0% | 0% | 0% | 78% | 21%
research/neural_clustering0% | 0% | 0% | 0% | 100%
neural_structured_learning/estimator0% | 0% | 0% | 0% | 100%
g3doc0% | 0% | 0% | 0% | 100%
ROOT0% | 0% | 0% | 0% | 100%
neural_structured_learning0% | 0% | 0% | 0% | 100%
Longest Files (Top 50)
File# lines# units
trainer_agreement.py
in research/gam/gam/trainer
 656 27
input_context_helper.cc
in research/carls/base
 652 37
dataset.py
in research/gam/gam/data
 644 59
trainer_classification.py
in research/gam/gam/trainer
 602 13
trainer_classification_gcn.py
in research/gam/gam/trainer
 579 10
train.py
in research/a2n
 533 7
build_rules.bzl
in research/carls/bazel
 430 -
dynamic_embedding_manager.cc
in research/carls
 424 12
trainer_cotrain.py
in research/gam/gam/trainer
 414 6
encoders.py
in research/a2n
 412 30
run_train_gam.py
in research/gam/gam/experiments
 395 2
run_train_gam_graph.py
in research/gam/gam/experiments
 371 1
adversarial_regularization.py
in neural_structured_learning/keras
 363 27
knowledge_bank_grpc_service.cc
in research/carls
 363 9
dataset.py
in research/a2n
 352 14
gaussian_memory.cc
in research/carls/memory_store
 328 12
models.py
in research/a2n
 316 3
repo.bzl
in research/carls/bazel
 287 -
graph.py
in research/a2n
 265 9
async_node_hash_map.h
in research/carls/base
 256 15
utils.py
in neural_structured_learning/lib
 253 15
sparse_features.py
in research/carls/models/caml
 247 9
negative_sampler.cc
in research/carls/candidate_sampling
 242 8
dynamic_embedding_ops.cc
in research/carls/kernels
 236 7
leveldb_knowledge_bank.cc
in research/carls/knowledge_bank
 228 10
gnn.py
in neural_structured_learning/experimental
 195 12
build_graph.py
in neural_structured_learning/tools
 180 11
wide_resnet.py
in research/gam/gam/models
 179 7
adversarial_neighbor.py
in neural_structured_learning/lib
 178 11
attacks.py
in research/multi_representation_adversary/multi_representation_adversary
 178 17
gcn.py
in research/gam/gam/models
 173 12
models.py
in research/gnn-survey
 168 12
layers.py
in research/gnn-survey
 167 12
status_helper.cc
in research/carls/base
 163 10
sampled_logits_ops.cc
in research/carls/kernels
 162 4
dynamic_normalization.py
in research/carls
 160 10
loaders.py
in research/gam/gam/data
 160 4
top_n.h
in research/carls/base
 155 17
cnn.py
in research/gam/gam/models
 153 6
configs.py
in neural_structured_learning/configs
 151 7
distances.py
in neural_structured_learning/lib
 141 10
train.py
in research/kg_hyp_emb
 133 1
euclidean.py
in research/kg_hyp_emb/models
 131 17
in_proto_knowledge_bank.cc
in research/carls/knowledge_bank
 126 8
proto
knowledge_bank_service.proto
in research/carls
 125 -
utils.py
in research/gnn-survey
 122 7
pack_nbrs.py
in neural_structured_learning/tools
 119 4
evaluator.py
in research/multi_representation_adversary/multi_representation_adversary
 119 4
base.py
in research/kg_hyp_emb/models
 119 9
knowledge_bank.cc
in research/carls/knowledge_bank
 118 5
Files With Most Units (Top 20)
File# lines# units
dataset.py
in research/gam/gam/data
 644 59
input_context_helper.cc
in research/carls/base
 652 37
encoders.py
in research/a2n
 412 30
adversarial_regularization.py
in neural_structured_learning/keras
 363 27
trainer_agreement.py
in research/gam/gam/trainer
 656 27
top_n.h
in research/carls/base
 155 17
attacks.py
in research/multi_representation_adversary/multi_representation_adversary
 178 17
euclidean.py
in research/kg_hyp_emb/models
 131 17
utils.py
in neural_structured_learning/lib
 253 15
async_node_hash_map.h
in research/carls/base
 256 15
selectors.py
in research/multi_representation_adversary/multi_representation_adversary
 79 15
dataset.py
in research/a2n
 352 14
trainer_classification.py
in research/gam/gam/trainer
 602 13
hyperbolic.py
in research/kg_hyp_emb/models
 115 13
gnn.py
in neural_structured_learning/experimental
 195 12
gaussian_memory.cc
in research/carls/memory_store
 328 12
dynamic_embedding_manager.cc
in research/carls
 424 12
gcn.py
in research/gam/gam/models
 173 12
layers.py
in research/gnn-survey
 167 12
models.py
in research/gnn-survey
 168 12
Files With Long Lines (Top 1)

There is only one file with lines longer than 120 characters. In total, there are 2 long lines.

File# lines# units# long lines
_index.yaml
in g3doc
 61 - 2