feature.proto

// Protocol messages for describing features for machine learning model
// training or inference.
//
// There are three base Feature types:
//   - bytes
//   - float
//   - int64
//
// A Feature contains Lists which may hold zero or more values.  These
// lists are the base values BytesList, FloatList, Int64List.
//
// Features are organized into categories by name.  The Features message
// contains the mapping from name to Feature.
//
// Example Features for a movie recommendation application:
//   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"
//     }}
//   }
//   feature {
//     key: "suggestion_purchased"
//     value { int64_list {
//       value: 1
//     }}
//   }
//   feature {
//     key: "purchase_price"
//     value { float_list {
//       value: 9.99
//     }}
//   }
//

syntax = "proto3";

package tensorflow;

option cc_enable_arenas = true;
option java_outer_classname = "FeatureProtos";
option java_multiple_files = true;
option java_package = "org.tensorflow.example";
option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/example/example_protos_go_proto";

// LINT.IfChange
// Containers to hold repeated fundamental values.
message BytesList {
  repeated bytes value = 1;
}
message FloatList {
  repeated float value = 1 [packed = true];
}
message Int64List {
  repeated int64 value = 1 [packed = true];
}

// Containers for non-sequential data.
message Feature {
  // Each feature can be exactly one kind.
  oneof kind {
    BytesList bytes_list = 1;
    FloatList float_list = 2;
    Int64List int64_list = 3;
  }
}

message Features {
  // Map from feature name to feature.
  map<string, Feature> feature = 1;
}

// Containers for sequential data.
//
// A FeatureList contains lists of Features.  These may hold zero or more
// Feature values.
//
// FeatureLists are organized into categories by name.  The FeatureLists message
// contains the mapping from name to FeatureList.
//
message FeatureList {
  repeated Feature feature = 1;
}

message FeatureLists {
  // Map from feature name to feature list.
  map<string, FeatureList> feature_list = 1;
}
// LINT.ThenChange(
//     https://www.tensorflow.org/code/tensorflow/python/training/training.py)