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A CategoricalColumn with a vocabulary file.
tf.compat.v1.feature_column.categorical_column_with_vocabulary_file(
key, vocabulary_file, vocabulary_size=None, num_oov_buckets=0,
default_value=None, dtype=tf.dtypes.string
)
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.io.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.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(states, 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_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.
|
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.
|
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.
|
dtype
|
The type of features. Only string and integer types are supported. |
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.
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