# tf.compat.v1.feature_column.make_parse_example_spec

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/feature_column/feature_column.py#L773-L832) |

Creates parsing spec dictionary from input feature_columns. (deprecated)

> [!WARNING]
> **Warning:** tf.feature_column is not recommended for new code. Instead, feature preprocessing can be done directly using either [Keras preprocessing
> layers](https://www.tensorflow.org/guide/migrate/migrating_feature_columns) or through the one-stop utility [`tf.keras.utils.FeatureSpace`](https://www.tensorflow.org/api_docs/python/tf/keras/utils/FeatureSpace) built on top of them. See the [migration guide](https://tensorflow.org/guide/migrate) for details.

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

> [!WARNING]
> **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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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`](https://www.tensorflow.org/versions/r2.13/api_docs/python/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)
    }

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| ## Args ||
|---|---|
| `feature_columns` | An iterable containing all feature columns. All items should be instances of classes derived from `_FeatureColumn`. |

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| ## Returns ||
|---|---|
| A dict mapping each feature key to a `FixedLenFeature` or `VarLenFeature` value. ||

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| ## Raises ||
|---|---|
| `ValueError` | If any of the given `feature_columns` is not a `_FeatureColumn` instance. |

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