tf.compat.v1.feature_column.make_parse_example_spec

Creates parsing spec dictionary from input feature_columns. (deprecated)

Warning: tf.feature_column is not recommended for new code. Instead, feature preprocessing can be done directly using either Keras preprocessing layers or through the one-stop utility tf.keras.utils.FeatureSpace built on top of them. See the migration guide for details.

tf.compat.v1.feature_column.make_parse_example_spec(
    feature_columns
)

Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use Keras preprocessing layers instead, either directly or via the tf.keras.utils.FeatureSpace utility. Each of tf.feature_column.* has a functional equivalent in tf.keras.layers for feature preprocessing when training a Keras model.

The returned dictionary can be used as arg 'features' in tf.io.parse_example.

Typical usage example:

# Define features and transformations
feature_a = categorical_column_with_vocabulary_file(...)
feature_b = numeric_column(...)
feature_c_bucketized = bucketized_column(numeric_column("feature_c"), ...)
feature_a_x_feature_c = 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=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.