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// Protocol messages for describing input data Examples for machine learning | |
// model training or inference. | |
syntax = "proto3"; | |
package tensorflow; | |
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/example_protos_go_proto"; | |
// 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. If you flatten a matrix into a FloatList it should | |
// be stored 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; | |
} |