// 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;

package tensorflow;

// Example for a movie recommendation application:
//   features {
//     feature {
//       key: "age"
//       float_list {
//         value: 29.0
//       }
//     }
//     feature {
//       key: "movie"
//       bytes_list {
//         value: "The Shawshank Redemption"
//         value: "Fight Club"
//       }
//     }
//     feature {
//       key: "movie_ratings"
//       float_list {
//         value: 9.0
//         value: 9.7
//       }
//     }
//     feature {
//       key: "suggestion"
//       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"
//       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"
//       float_list {
//         value: 9.99
//       }
//     }
//  }
//
// A conformant 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;
};

// Example representing a ranking instance.
message RankingExample {
  Features context = 1;
  repeated Features positive = 2;
  repeated Features negative = 3;
};

// Example representing a sequence.
// The context contains features which apply to the entire sequence.
// Each element in example represents an entry in the sequence.
message SequenceExample {
  Features context = 1;
  repeated Features features = 2;
};

// Example representing a list of feature maps.
// The context contains features which apply to all feature maps.
message InferenceExample {
  Features context = 1;
  repeated Features features = 2;
};