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A CategoricalColumn金铨达配资 with in-memory vocabulary.


  • tf.compat.v1.feature_column.categorical_column_with_vocabulary_list
  • tf.compat.v2.feature_column.categorical_column_with_vocabulary_list

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金铨达配资 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'),
columns = [colors, ...]
features =, 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 =, 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 =, features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)


  • 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.


A CategoricalColumn with in-memory vocabulary.


  • 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.

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