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TensorFlow 1 version
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View source on GitHub
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A CategoricalColumn with in-memory vocabulary.
tf.feature_column.categorical_column_with_vocabulary_list(
key, vocabulary_list, dtype=None, default_value=-1, num_oov_buckets=0
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
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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.io.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.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(colors, 3),...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)
Args | |
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key
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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.
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vocabulary_list
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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.
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dtype
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The type of features. Only string and integer types are supported. If
None, it will be inferred from vocabulary_list.
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default_value
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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.
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num_oov_buckets
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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 positive num_oov_buckets can not be specified
with default_value.
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Returns | |
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A CategoricalColumn with in-memory vocabulary.
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Raises | |
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ValueError
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if vocabulary_list is empty, or contains duplicate keys.
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ValueError
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num_oov_buckets is a negative integer.
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ValueError
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num_oov_buckets and default_value are both specified.
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ValueError
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if dtype is not integer or string.
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