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TensorFlow 1 version
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View source on GitHub
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Applies weight values to a CategoricalColumn.
tf.feature_column.weighted_categorical_column(
categorical_column, weight_feature_key, dtype=tf.dtypes.float32
)
Use this when each of your sparse inputs has both an ID and a value. For example, if you're representing text documents as a collection of word frequencies, you can provide 2 parallel sparse input features ('terms' and 'frequencies' below).
Example:
Input tf.Example objects:
[
features {
feature {
key: "terms"
value {bytes_list {value: "very" value: "model"} }
}
feature {
key: "frequencies"
value {float_list {value: 0.3 value: 0.1} }
}
},
features {
feature {
key: "terms"
value {bytes_list {value: "when" value: "course" value: "human"} }
}
feature {
key: "frequencies"
value {float_list {value: 0.4 value: 0.1 value: 0.2} }
}
}
]
categorical_column = categorical_column_with_hash_bucket(
column_name='terms', hash_bucket_size=1000)
weighted_column = weighted_categorical_column(
categorical_column=categorical_column, weight_feature_key='frequencies')
columns = [weighted_column, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction, _, _ = linear_model(features, columns)
This assumes the input dictionary contains a SparseTensor for key
'terms', and a SparseTensor for key 'frequencies'. These 2 tensors must have
the same indices and dense shape.
Args | |
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categorical_column
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A CategoricalColumn created by
categorical_column_with_* functions.
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weight_feature_key
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String key for weight values. |
dtype
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Type of weights, such as tf.float32. Only float and integer weights
are supported.
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Returns | |
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A CategoricalColumn composed of two sparse features: one represents id,
the other represents weight (value) of the id feature in that example.
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Raises | |
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ValueError
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if dtype is not convertible to float.
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