example.proto

// Protocol messages for describing input data Examples for machine learning
// model training or inference.
syntax = "proto3";

import "tensorflow/core/example/feature.proto";
option cc_enable_arenas = true;
option java_outer_classname = "ExampleProtos";
option java_multiple_files = true;
option java_package = "org.tensorflow.example";
option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/example";
package tensorflow;

// An Example is a mostly-normalized data format for storing data for
// training and inference.  It contains a key-value store (features); where
// each key (string) maps to a Feature message (which is oneof packed BytesList,
// FloatList, or Int64List).  This flexible and compact format allows the
// storage of large amounts of typed data, but requires that the data shape
// and use be determined by the configuration files and parsers that are used to
// read and write this format.  That is, the Example is mostly *not* a
// self-describing format.  In TensorFlow, Examples are read in row-major
// format, so any configuration that describes data with rank-2 or above
// should keep this in mind.  For example, to store an M x N matrix of Bytes,
// the BytesList must contain M*N bytes, with M rows of N contiguous values
// each.  That is, the BytesList value must store the matrix as:
//     .... row 0 .... .... row 1 .... // ...........  // ... row M-1 ....
//
// An Example for a movie recommendation application:
//   features {
//     feature {
//       key: "age"
//       value { float_list {
//         value: 29.0
//       }}
//     }
//     feature {
//       key: "movie"
//       value { bytes_list {
//         value: "The Shawshank Redemption"
//         value: "Fight Club"
//       }}
//     }
//     feature {
//       key: "movie_ratings"
//       value { float_list {
//         value: 9.0
//         value: 9.7
//       }}
//     }
//     feature {
//       key: "suggestion"
//       value { bytes_list {
//         value: "Inception"
//       }}
//     }
//     # Note that this feature exists to be used as a label in training.
//     # E.g., if training a logistic regression model to predict purchase
//     # probability in our learning tool we would set the label feature to
//     # "suggestion_purchased".
//     feature {
//       key: "suggestion_purchased"
//       value { float_list {
//         value: 1.0
//       }}
//     }
//     # Similar to "suggestion_purchased" above this feature exists to be used
//     # as a label in training.
//     # E.g., if training a linear regression model to predict purchase
//     # price in our learning tool we would set the label feature to
//     # "purchase_price".
//     feature {
//       key: "purchase_price"
//       value { float_list {
//         value: 9.99
//       }}
//     }
//  }
//
// A conformant Example data set obeys the following conventions:
//   - If a Feature K exists in one example with data type T, it must be of
//       type T in all other examples when present. It may be omitted.
//   - The number of instances of Feature K list data may vary across examples,
//       depending on the requirements of the model.
//   - If a Feature K doesn't exist in an example, a K-specific default will be
//       used, if configured.
//   - If a Feature K exists in an example but contains no items, the intent
//       is considered to be an empty tensor and no default will be used.

message Example {
  Features features = 1;
};

// A SequenceExample is an Example 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 Features (a FeatureList).
// A FeatureList thus 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_list(s).
// In the example below there are only two movies. All three feature_list(s),
// 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, e.g.:
//        http://colah.github.io/posts/2015-08-Understanding-LSTMs/
//        https://github.com/tensorflow/nmt
//     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 ith element in each FeatureList
//     is part of the ith 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 ] } } }
//    } }
//
// Conformant pair of SequenceExample
//    feature_lists: { feature_list: {
//      key: "movie_ratings"
//      value: { feature: { float_list: { value: [ 4.5 ] } }
//               feature: { float_list: { value: [ 5.0 ] } } }
//    } }
// and:
//    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 SequenceExample
//    feature_lists: { feature_list: {
//      key: "movie_ratings"
//      value: { feature: { float_list: { value: [ 4.5 ] } }
//               feature: { float_list: { value: [ 5.0 ] } } }
//    } }
// and:
//    feature_lists: { feature_list: {
//      key: "movie_ratings"
//      value: { }
//    } }
//
// Conditionally conformant pair of SequenceExample, the parser configuration
// determines if the second feature_lists is consistent (zero-length) or
// invalid (missing "movie_ratings"):
//    feature_lists: { feature_list: {
//      key: "movie_ratings"
//      value: { feature: { float_list: { value: [ 4.5 ] } }
//               feature: { float_list: { value: [ 5.0 ] } } }
//    } }
// and:
//    feature_lists: { }
//
// Non-conformant pair of SequenceExample (mismatched types)
//    feature_lists: { feature_list: {
//      key: "movie_ratings"
//      value: { feature: { float_list: { value: [ 4.5 ] } }
//               feature: { float_list: { value: [ 5.0 ] } } }
//    } }
// and:
//    feature_lists: { feature_list: {
//      key: "movie_ratings"
//      value: { feature: { int64_list: { value: [ 4 ] } }
//               feature: { int64_list: { value: [ 5 ] } }
//               feature: { int64_list: { value: [ 2 ] } } }
//    } }
//
// Conditionally conformant pair of SequenceExample; 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 ] } } }
//    } }
// and:
//    feature_lists: { feature_list: {
//      key: "movie_ratings"
//      value: { feature: { float_list: { value: [ 4.0 ] } }
//               feature: { float_list: { value: [ 5.0, 3.0 ] } }
//    } }

message SequenceExample {
  Features context = 1;
  FeatureLists feature_lists = 2;
};