Construct a Categorical Column with a Vocabulary File

Use this when your inputs are in string or integer format, and you have a vocabulary file that maps each value to an integer ID. By default, out-of-vocabulary values are ignored. Use either (but not both) of num_oov_buckets and default_value to specify how to include out-of-vocabulary values. For input dictionary 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_vocabulary_file(..., vocabulary_file,
  vocabulary_size, num_oov_buckets = 0L, default_value = NULL,
  dtype = tf$string)

Arguments

...

Expression(s) identifying input feature(s). Used as the column name and the dictionary key for feature parsing configs, feature tensors, and feature columns.

vocabulary_file

The vocabulary file name.

vocabulary_size

Number of the elements in the vocabulary. This must be no greater than length of vocabulary_file, if less than length, later values are ignored.

num_oov_buckets

Non-negative integer, the number of out-of-vocabulary buckets. All out-of-vocabulary inputs will be assigned IDs in the range [vocabulary_size, vocabulary_size+num_oov_buckets) based on a hash of the input value. A positive num_oov_buckets can not be specified with default_value.

default_value

The integer ID value to return for out-of-vocabulary feature values, defaults to -1. This can not be specified with a positive num_oov_buckets.

dtype

The type of features. Only string and integer types are supported.

Value

A categorical column with a vocabulary file.

Raises

  • ValueError: vocabulary_file is missing.

  • ValueError: vocabulary_size is missing or < 1.

  • ValueError: num_oov_buckets is not a non-negative integer.

  • ValueError: dtype is neither string nor integer.

See also