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tf.train.SequenceExample

A SequenceExample is a format for representing one or more sequences and some context.

The context contains features which apply to the entire example. The feature_lists contain a key, value map where each key is associated with a repeated set of tf.train.Features (a tf.train.FeatureList). A FeatureList represents the values of a feature identified by its key over time / frames.

Below is a SequenceExample for a movie recommendation application recording a sequence of ratings by a user. The time-independent features ("locale", "age", "favorites") describing the user are part of the context. The sequence of movies the user rated are part of the feature_lists. For each movie in the sequence we have information on its name and actors and the user's rating. This information is recorded in three separate feature_lists. In the example below there are only two movies. All three feature_lists, namely "movie_ratings", "movie_names", and "actors" have a feature value for both movies. Note, that "actors" is itself a bytes_list with multiple strings per movie.

  context: {
    feature: {
      key  : "locale"
      value: {
        bytes_list: {
          value: [ "pt_BR" ]
        }
      }
    }
    feature: {
      key  : "age"
      value: {
        float_list: {
          value: [ 19.0 ]
        }
      }
    }
    feature: {
      key  : "favorites"
      value: {
        bytes_list: {
          value: [ "Majesty Rose", "Savannah Outen", "One Direction" ]
        }
      }
    }
  }
  feature_lists: {
    feature_list: {
      key  : "movie_ratings"
      value: {
        feature: {
          float_list: {
            value: [ 4.5 ]
          }
        }
        feature: {
          float_list: {
            value: [ 5.0 ]
          }
        }
      }
    }
    feature_list: {
      key  : "movie_names"
      value: {
        feature: {
          bytes_list: {
            value: [ "The Shawshank Redemption" ]
          }
        }
        feature: {
          bytes_list: {
            value: [ "Fight Club" ]
          }
        }
      }
    }
    feature_list: {
      key  : "actors"
      value: {
        feature: {
          bytes_list: {
            value: [ "Tim Robbins", "Morgan Freeman" ]
          }
        }
        feature: {
          bytes_list: {
            value: [ "Brad Pitt", "Edward Norton", "Helena Bonham Carter" ]
          }
        }
      }
    }
  }

A conformant SequenceExample data set obeys the following conventions:

context:

  • All conformant context features K must obey the same conventions as a conformant Example's features (see above).

feature_lists:

  • A FeatureList L may be missing in an example; it is up to the parser configuration to determine if this is allowed or considered an empty list (zero length).
  • If a FeatureList L exists, it may be empty (zero length).
  • If a FeatureList L is non-empty, all features within the FeatureList must have the same data type T. Even across SequenceExamples, the type T of the FeatureList identified by the same key must be the same. An entry without any values may serve as an empty feature.
  • If a FeatureList L is non-empty, it is up to the parser configuration to determine if all features within the FeatureList must have the same size. The same holds for this FeatureList across multiple examples.
  • For sequence modeling (example), the feature lists represent a sequence of frames. In this scenario, all FeatureLists in a SequenceExample have the same number of Feature messages, so that the i-th element in each FeatureList is part of the i-th frame (or time step).

Examples of conformant and non-conformant examples' FeatureLists:

Conformant FeatureLists:

    feature_lists: { feature_list: {
      key: "movie_ratings"
      value: { feature: { float_list: { value: [ 4.5 ] } }
               feature: { float_list: { value: [ 5.0 ] } } }
    } }

Non-conformant FeatureLists (mismatched types):

    feature_lists: { feature_list: {
      key: "movie_ratings"
      value: { feature: { float_list: { value: [ 4.5 ] } }
               feature: { int64_list: { value: [ 5 ] } } }
    } }

Conditionally conformant FeatureLists, the parser configuration determines if the feature sizes must match:

    feature_lists: { feature_list: {
      key: "movie_ratings"
      value: { feature: { float_list: { value: [ 4.5 ] } }
               feature: { float_list: { value: [ 5.0, 6.0 ] } } }
    } }

Examples of conformant and non-conformant SequenceExamples:

Conformant pair of SequenceExample:

    feature_lists: { feature_list: {
      key: "movie_ratings"
      value: { feature: { float_list: { value: [ 4.5 ] } }
               feature: { float_list: { value: [ 5.0 ] } } }
     } }

    feature_lists: { feature_list: {
      key: "movie_ratings"
      value: { feature: { float_list: { value: [ 4.5 ] } }
               feature: { float_list: { value: [ 5.0 ] } }
               feature: { float_list: { value: [ 2.0 ] } } }
     } }

Conformant pair of SequenceExamples:

    feature_lists: { feature_