# tf.feature_column brief

tf.feature_column api_doc

categorical_column_with_hash_bucket(…): Represents sparse feature where ids are set by hashing.

categorical_column_with_identity(…): A CategoricalColumn that returns identity values.

categorical_column_with_vocabulary_file(…): A CategoricalColumn with a vocabulary file.

categorical_column_with_vocabulary_list(…): A CategoricalColumn with in-memory vocabulary.

# categorical_column_with_vocabulary_file

A CategoricalColumn 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 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:
File ‘/us/states.txt’ contains 50 lines, each with a 2-character U.S. state abbreviation. All inputs with values in that file are assigned an ID 0-49, corresponding to its line number. All other values are hashed and assigned an ID 50-54.

states = categorical_column_with_vocabulary_file(
key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
num_oov_buckets=5)
columns = [states, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)


Example with default_value:
File ‘/us/states.txt’ contains 51 lines – the first line is ‘XX’, and the other 50 each have a 2-character U.S. state abbreviation. Both a literal ‘XX’ in input, and other values missing from the file, will be assigned ID 0. All others are assigned the corresponding line number 1-50.

states = categorical_column_with_vocabulary_file(
key='states', vocabulary_file='/us/states.txt', vocabulary_size=51,
default_value=0)
columns = [states, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction, _, _ = linear_model(features, columns)


And to make an embedding with either:

columns = [embedding_column(states, 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_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. If None, it is set to the length of vocabulary_file.
dtype: The type of features. Only string and integer types are supported.
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 [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.

Returns:
A CategoricalColumn with a vocabulary file.

Raises:
ValueError: vocabulary_file is missing or cannot be opened.
ValueError: vocabulary_size is missing or < 1.
ValueError: num_oov_buckets is a negative integer.
ValueError: num_oov_buckets and default_value are both specified.
ValueError: dtype is neither string nor integer.