# tf.contrib.layers.create_feature_spec_for_parsing

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/layers/python/layers/feature_column.py#L2581-L2625) |

Helper that prepares features config from input feature_columns.

    tf.contrib.layers.create_feature_spec_for_parsing(
        feature_columns
    )

The returned feature config can be used as arg 'features' in tf.parse_example.

#### Typical usage example:

    # Define features and transformations
    feature_a = sparse_column_with_vocabulary_file(...)
    feature_b = real_valued_column(...)
    feature_c_bucketized = bucketized_column(real_valued_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])
    batch_examples = tf.io.parse_example(
        serialized=serialized_examples,
        features=create_feature_spec_for_parsing(feature_columns))

For the above example, create_feature_spec_for_parsing 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 ||
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| `feature_columns` | An iterable containing all the feature columns. All items should be instances of classes derived from _FeatureColumn, unless feature_columns is a dict -- in which case, this should be true of all values in the dict. |

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| ## Returns ||
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| A dict mapping feature keys to FixedLenFeature or VarLenFeature values. ||

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