column_crossed: Construct a Crossed Column

In rstudio/tflearn: Interface to 'TensorFlow' Estimators

Description

Returns a column for performing crosses of categorical features. Crossed features will be hashed according to hash_bucket_size.

Usage

column_crossed(keys, hash_bucket_size, hash_key = NULL)

Arguments

keys	An iterable identifying the features to be crossed. Each element can be either: string: Will use the corresponding feature which must be of string type. categorical column: Will use the transformed tensor produced by this column. Does not support hashed categorical columns.
hash_bucket_size	The number of buckets (> 1).
hash_key	Optional: specify the hash_key that will be used by the FingerprintCat64 function to combine the crosses fingerprints on SparseCrossOp.

Value

A crossed column.

Raises

• ValueError: If len(keys) < 2.

• ValueError: If any of the keys is neither a string nor categorical column.

• ValueError: If any of the keys is _HashedCategoricalColumn.

• ValueError: If hash_bucket_size < 1.

See Also

Other feature column constructors: column_bucketized(), column_categorical_weighted(), column_categorical_with_hash_bucket(), column_categorical_with_identity(), column_categorical_with_vocabulary_file(), column_categorical_with_vocabulary_list(), column_embedding(), column_numeric(), input_layer()

rstudio/tflearn documentation built on Nov. 25, 2021, 2:45 a.m.