tf.raw_ops.ParseSequenceExample

Transforms a vector of brain.SequenceExample protos (as strings) into typed tensors.

tf.raw_ops.ParseSequenceExample(
    serialized, debug_name, context_dense_defaults,
    feature_list_dense_missing_assumed_empty, context_sparse_keys,
    context_dense_keys, feature_list_sparse_keys, feature_list_dense_keys,
    Ncontext_sparse=0, Ncontext_dense=0, Nfeature_list_sparse=0,
    Nfeature_list_dense=0, context_sparse_types=[], feature_list_dense_types=[],
    context_dense_shapes=[], feature_list_sparse_types=[],
    feature_list_dense_shapes=[], name=None
)

Args:

  • serialized: A Tensor of type string. A vector containing binary serialized SequenceExample protos.
  • debug_name: A Tensor of type string. A vector containing the names of the serialized protos. May contain, for example, table key (descriptive) name for the corresponding serialized proto. This is purely useful for debugging purposes, and the presence of values here has no effect on the output. May also be an empty vector if no name is available.
  • context_dense_defaults: A list of Tensor objects with types from: float32, int64, string. A list of Ncontext_dense Tensors (some may be empty). context_dense_defaults[j] provides default values when the SequenceExample's context map lacks context_dense_key[j]. If an empty Tensor is provided for context_dense_defaults[j], then the Feature context_dense_keys[j] is required. The input type is inferred from context_dense_defaults[j], even when it's empty. If context_dense_defaults[j] is not empty, its shape must match context_dense_shapes[j].
  • feature_list_dense_missing_assumed_empty: A list of strings. A vector listing the FeatureList keys which may be missing from the SequenceExamples. If the associated FeatureList is missing, it is treated as empty. By default, any FeatureList not listed in this vector must exist in the SequenceExamples.
  • context_sparse_keys: A list of strings. A list of Ncontext_sparse string Tensors (scalars). The keys expected in the Examples' features associated with context_sparse values.
  • context_dense_keys: A list of strings. A list of Ncontext_dense string Tensors (scalars). The keys expected in the SequenceExamples' context features associated with dense values.
  • feature_list_sparse_keys: A list of strings. A list of Nfeature_list_sparse string Tensors (scalars). The keys expected in the FeatureLists associated with sparse values.
  • feature_list_dense_keys: A list of strings. A list of Nfeature_list_dense string Tensors (scalars). The keys expected in the SequenceExamples' feature_lists associated with lists of dense values.
  • Ncontext_sparse: An optional int that is >= 0. Defaults to 0.
  • Ncontext_dense: An optional int that is >= 0. Defaults to 0.
  • Nfeature_list_sparse: An optional int that is >= 0. Defaults to 0.
  • Nfeature_list_dense: An optional int that is >= 0. Defaults to 0.
  • context_sparse_types: An optional list of tf.DTypes from: tf.float32, tf.int64, tf.string. Defaults to []. A list of Ncontext_sparse types; the data types of data in each context Feature given in context_sparse_keys. Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), DT_INT64 (Int64List), and DT_STRING (BytesList).
  • feature_list_dense_types: An optional list of tf.DTypes from: tf.float32, tf.int64, tf.string. Defaults to [].
  • context_dense_shapes: An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. A list of Ncontext_dense shapes; the shapes of data in each context Feature given in context_dense_keys. The number of elements in the Feature corresponding to context_dense_key[j] must always equal context_dense_shapes[j].NumEntries(). The shape of context_dense_values[j] will match context_dense_shapes[j].
  • feature_list_sparse_types: An optional list of tf.DTypes from: tf.float32, tf.int64, tf.string. Defaults to []. A list of Nfeature_list_sparse types; the data types of data in each FeatureList given in feature_list_sparse_keys. Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), DT_INT64 (Int64List), and DT_STRING (BytesList).
  • feature_list_dense_shapes: An optional list of shapes (each a tf.TensorShape or list of ints). Defaults to []. A list of Nfeature_list_dense shapes; the shapes of data in each FeatureList given in feature_list_dense_keys. The shape of each Feature in the FeatureList corresponding to feature_list_dense_key[j] must always equal feature_list_dense_shapes[j].NumEntries().
  • name: A name for the operation (optional).

Returns:

A tuple of Tensor objects (context_sparse_indices, context_sparse_values, context_sparse_shapes, context_dense_values, feature_list_sparse_indices, feature_list_sparse_values, feature_list_sparse_shapes, feature_list_dense_values, feature_list_dense_lengths).

  • context_sparse_indices: A list of Ncontext_sparse Tensor objects with type int64.
  • context_sparse_values: A list of Tensor objects of type context_sparse_types.
  • context_sparse_shapes: A list of Ncontext_sparse Tensor objects with type int64.
  • context_dense_values: A list of Tensor objects. Has the same type as context_dense_defaults.
  • feature_list_sparse_indices: A list of Nfeature_list_sparse Tensor objects with type int64.
  • feature_list_sparse_values: A list of Tensor objects of type feature_list_sparse_types.
  • feature_list_sparse_shapes: A list of Nfeature_list_sparse Tensor objects with type int64.
  • feature_list_dense_values: A list of Tensor objects of type feature_list_dense_types.
  • feature_list_dense_lengths: A list of Nfeature_list_dense Tensor objects with type int64.