tf.feature_column.categorical_column_with_vocabulary_file(
key,
vocabulary_file,
vocabulary_size=None,
num_oov_buckets=0,
default_value=None,
dtype=tf.dtypes.string
)
Defined in tensorflow/python/feature_column/feature_column_v2.py.
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, featureTensorobjects, 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 ofvocabulary_file, if less than length, later values are ignored. If None, it is set to the length ofvocabulary_file.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 positivenum_oov_bucketscan not be specified withdefault_value.default_value: The integer ID value to return for out-of-vocabulary feature values, defaults to-1. This can not be specified with a positivenum_oov_buckets.dtype: The type of features. Only string and integer types are supported.
Returns:
A CategoricalColumn with a vocabulary file.
Raises:
ValueError:vocabulary_fileis missing or cannot be opened.ValueError:vocabulary_sizeis missing or < 1.ValueError:num_oov_bucketsis a negative integer.ValueError:num_oov_bucketsanddefault_valueare both specified.ValueError:dtypeis neither string nor integer.