tf.feature_column.categorical_column_with_vocabulary_list     View source on GitHub    A CategoricalColumn with in-memory vocabulary.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.feature_column.categorical_column_with_vocabulary_list  
tf.feature_column.categorical_column_with_vocabulary_list(
    key, vocabulary_list, dtype=None, default_value=-1, num_oov_buckets=0
)
 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 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 SparseTensor. If Tensor, missing values can be represented by -1 for int and '' for string, which will be dropped by this feature column. Example with num_oov_buckets: In the following example, each input in vocabulary_list is assigned an ID 0-3 corresponding to its index (e.g., input 'B' produces output 2). All other inputs are hashed and assigned an ID 4-5. colors = categorical_column_with_vocabulary_list(
    key='colors', vocabulary_list=('R', 'G', 'B', 'Y'),
    num_oov_buckets=2)
columns = [colors, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction, _, _ = linear_model(features, columns)
 Example with default_value: In the following example, each input in vocabulary_list is assigned an ID 0-4 corresponding to its index (e.g., input 'B' produces output 3). All other inputs are assigned default_value 0. colors = categorical_column_with_vocabulary_list(
    key='colors', vocabulary_list=('X', 'R', 'G', 'B', 'Y'), default_value=0)
columns = [colors, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction, _, _ = linear_model(features, columns)
 And to make an embedding with either: columns = [embedding_column(colors, 3),...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)

 


 Args
  key   A unique string identifying the input feature. It is used as the column name and the dictionary key for feature parsing configs, feature Tensor objects, 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 None, it will be inferred from vocabulary_list.  
  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.  
  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 [len(vocabulary_list), len(vocabulary_list)+num_oov_buckets) based on a hash of the input value. A positive num_oov_buckets can not be specified with default_value.   
 


 Returns   A CategoricalColumn with in-memory vocabulary.  

 


 Raises
  ValueError   if vocabulary_list is empty, or contains duplicate keys.  
  ValueError   num_oov_buckets is a negative integer.  
  ValueError   num_oov_buckets and default_value are both specified.  
  ValueError   if dtype is not integer or string.