nsl.tools.build_graph | Neural Structured Learning | TensorFlow (original) (raw)
nsl.tools.build_graph
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Like nsl.tools.build_graph_from_config, but with individual parameters.
nsl.tools.build_graph(
embedding_files,
output_graph_path,
similarity_threshold=0.8,
id_feature_name='id',
embedding_feature_name='embedding',
lsh_splits=0,
lsh_rounds=2,
random_seed=None
)
This API exists to maintain backward compatibility, but is deprecated in favor of using nsl.tools.build_graph_from_config instead.
| Args | |
|---|---|
| embedding_files | A list of names of TFRecord files containingtf.train.Example objects, which in turn contain dense embeddings. |
| output_graph_path | Name of the file to which the output graph in TSV format should be written. |
| similarity_threshold | Threshold used to determine which edges to retain in the resulting graph. |
| id_feature_name | The name of the feature in the input tf.train.Exampleobjects representing the ID of examples. |
| embedding_feature_name | The name of the feature in the inputtf.train.Example objects representing the embedding of examples. |
| lsh_splits | Determines the maximum number of LSH buckets into which input data points will be bucketed by the graph builder. See thensl.tools.build_graph_from_config documentation for details. |
| lsh_rounds | The number of rounds of LSH bucketing to perform whenlsh_splits > 0. This is also the number of LSH buckets each point will be hashed into. |
| random_seed | Value used to seed the random number generator used to perform randomized LSH bucketing of the inputs when lsh_splits > 0. By default, the generator will be initialized randomly, but setting this to any integer will initialize it deterministically. |
| Raises | |
|---|---|
| ValueError | If lsh_splits < 0 or if lsh_splits > 0 and lsh_rounds < 1. |
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Last updated 2022-10-28 UTC.