tf.feature_column.make_parse_example_spec
Creates parsing spec dictionary from input feature_columns.
tf.feature_column.make_parse_example_spec(
feature_columns
)
The returned dictionary can be used as arg 'features' in
tf.io.parse_example
.
Typical usage example:
# Define features and transformations
feature_a = tf.feature_column.categorical_column_with_vocabulary_file(...)
feature_b = tf.feature_column.numeric_column(...)
feature_c_bucketized = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("feature_c"), ...)
feature_a_x_feature_c = tf.feature_column.crossed_column(
columns=["feature_a", feature_c_bucketized], ...)
feature_columns = set(
[feature_b, feature_c_bucketized, feature_a_x_feature_c])
features = tf.io.parse_example(
serialized=serialized_examples,
features=tf.feature_column.make_parse_example_spec(feature_columns))
For the above example, make_parse_example_spec would return the dict:
{
"feature_a": parsing_ops.VarLenFeature(tf.string),
"feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
"feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
}
Args |
feature_columns
|
An iterable containing all feature columns. All items
should be instances of classes derived from FeatureColumn .
|
Returns |
A dict mapping each feature key to a FixedLenFeature or VarLenFeature
value.
|
Raises |
ValueError
|
If any of the given feature_columns is not a FeatureColumn
instance.
|
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Last updated 2021-02-18 UTC.
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