Represents Sparse Feature where IDs are set by Hashing
Use this when your sparse features are in string or integer format, and you
want to distribute your inputs into a finite number of buckets by hashing.
output_id = Hash(input_feature_string)
features, features$key$ is either tensor or sparse tensor object. If it's
tensor object, missing values can be represented by -1 for int and '' for
string. Note that these values are independent of the default_value
argument.
column_categorical_with_hash_bucket(..., hash_bucket_size, dtype = tf$string)
... |
Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns. |
hash_bucket_size |
An int > 1. The number of buckets. |
dtype |
The type of features. Only string and integer types are supported. |
A _HashedCategoricalColumn.
ValueError: hash_bucket_size is not greater than 1.
ValueError: dtype is neither string nor integer.
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