tf.feature_column.categorical_column_with_vocabulary_list

tf.feature_column.categorical_column_with_vocabulary_list

tf.feature_column.categorical_column_with_vocabulary_list

categorical_column_with_vocabulary_list(
    key,
    vocabulary_list,
    dtype=None,
    default_value=-1
)

Defined in tensorflow/python/feature_column/feature_column.py.

A _CategoricalColumn with in-memory vocabulary.

Logic for feature f is: id = vocabulary_list.index_of(f) if f in vocabulary_list else default_value

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 input dictionary features, features[key] is either Tensor or SparseTensor. If Tensor, missing values can be represented by -1 for int and '' for string. Note that these values are independent of the default_value argument.

In the following examples, each input in vocabulary_list is assigned an ID 0-4 corresponding to its index (e.g., input 'B' produces output 2). All other inputs are assigned default_value 0.

Linear model:

colors = categorical_column_with_vocabulary_list(
    key='colors', vocabulary_list=('X', 'R', 'G', 'B', 'Y'), default_value=0)
columns = [colors, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction, _, _ = linear_model(features, columns)

Embedding for a DNN model:

columns = [embedding_column(colors, 3),...]
features = tf.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 value to use for values not in vocabulary_list.

Returns:

A _CategoricalColumn with in-memory vocabulary.

Raises:

• ValueError: if vocabulary_list is empty, or contains duplicate keys.
• ValueError: if dtype is not integer or string.