tf.feature_column.categorical_column_with_vocabulary_list(
key,
vocabulary_list,
dtype=None,
default_value=-1,
num_oov_buckets=0
)
Defined in tensorflow/python/feature_column/feature_column_v2.py
.
A CategoricalColumn
with in-memory vocabulary.
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
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'),
num_oov_buckets=2)
columns = [colors, ...]
features = tf.parse_example(..., 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 = 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(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, featureTensor
objects, and feature columns.vocabulary_list
: An ordered iterable defining the vocabulary. Each feature is mapped to the index of its value (if present) invocabulary_list
. Must be castable todtype
.dtype
: The type of features. Only string and integer types are supported. IfNone
, it will be inferred fromvocabulary_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 positivenum_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 positivenum_oov_buckets
can not be specified withdefault_value
.
Returns:
A CategoricalColumn
with in-memory vocabulary.
Raises:
ValueError
: ifvocabulary_list
is empty, or contains duplicate keys.ValueError
:num_oov_buckets
is a negative integer.ValueError
:num_oov_buckets
anddefault_value
are both specified.ValueError
: ifdtype
is not integer or string.