column_categorical_with_vocabulary_list: Construct a Categorical Column with In-Memory Vocabulary
In tfestimators: Interface to 'TensorFlow' Estimators

Description Usage Arguments Details Value Raises See Also

Use this when your inputs are in string or integer format, and you have an in-memory vocabulary mapping each value to an integer ID. By default, out-of-vocabulary values are ignored. Use default_value to specify how to include out-of-vocabulary values. For the 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.

column_categorical_with_vocabulary_list(
  ...,
  vocabulary_list,
  dtype = NULL,
  default_value = -1L,
  num_oov_buckets = 0L
)

`...`	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_list`	An ordered iterable defining the vocabulary. Each feature is mapped to the index of its value (if present) in `vocabulary_list`. Must be castable to `dtype`.
`dtype`	The type of features. Only string and integer types are supported. If `NULL`, it will be inferred from `vocabulary_list`.
`default_value`	The value to use for values not in `vocabulary_list`.
`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`.