tensorflow::ops::ParseExampleV2

Transforms a vector of tf.Example protos (as strings) into typed tensors.

Summary

Arguments:

• scope: A Scope object
• serialized: A scalar or vector containing binary serialized Example protos.
• names: A tensor containing the names of the serialized protos. Corresponds 1:1 with the serialized tensor. May contain, for example, table key (descriptive) names for the corresponding serialized protos. These are 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 names are available. If non-empty, this tensor must have the same shape as "serialized".
• sparse_keys: Vector of strings. The keys expected in the Examples' features associated with sparse values.
• dense_keys: Vector of strings. The keys expected in the Examples' features associated with dense values.
• ragged_keys: Vector of strings. The keys expected in the Examples' features associated with ragged values.
• dense_defaults: A list of Tensors (some may be empty). Corresponds 1:1 with dense_keys. dense_defaults[j] provides default values when the example's feature_map lacks dense_key[j]. If an empty Tensor is provided for dense_defaults[j], then the Feature dense_keys[j] is required. The input type is inferred from dense_defaults[j], even when it's empty. If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined, then the shape of dense_defaults[j] must match that of dense_shapes[j]. If dense_shapes[j] has an undefined major dimension (variable strides dense feature), dense_defaults[j] must contain a single element: the padding element.
• num_sparse: The number of sparse keys.
• sparse_types: A list of num_sparse types; the data types of data in each Feature given in sparse_keys. Currently the ParseExample supports DT_FLOAT (FloatList), DT_INT64 (Int64List), and DT_STRING (BytesList).
• ragged_value_types: A list of num_ragged types; the data types of data in each Feature given in ragged_keys (where num_ragged = sparse_keys.size()). Currently the ParseExample supports DT_FLOAT (FloatList), DT_INT64 (Int64List), and DT_STRING (BytesList).
• ragged_split_types: A list of num_ragged types; the data types of row_splits in each Feature given in ragged_keys (where num_ragged = sparse_keys.size()). May be DT_INT32 or DT_INT64.
• dense_shapes: A list of num_dense shapes; the shapes of data in each Feature given in dense_keys (where num_dense = dense_keys.size()). The number of elements in the Feature corresponding to dense_key[j] must always equal dense_shapes[j].NumEntries(). If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN): The dense outputs are just the inputs row-stacked by batch. This works for dense_shapes[j] = (-1, D1, ..., DN). In this case the shape of the output Tensor dense_values[j] will be (|serialized|, M, D1, .., DN), where M is the maximum number of blocks of elements of length D1 * .... * DN, across all minibatch entries in the input. Any minibatch entry with less than M blocks of elements of length D1 * ... * DN will be padded with the corresponding default_value scalar element along the second dimension.

Returns:

• OutputList sparse_indices
• OutputList sparse_values
• OutputList sparse_shapes
• OutputList dense_values
• OutputList ragged_values
• OutputList ragged_row_splits

Public attributes

::tensorflow::OutputList dense_values

Operation operation

::tensorflow::OutputList ragged_row_splits

::tensorflow::OutputList ragged_values

::tensorflow::OutputList sparse_indices

::tensorflow::OutputList sparse_shapes

::tensorflow::OutputList sparse_values

 ParseExampleV2(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input serialized,
  ::tensorflow::Input names,
  ::tensorflow::Input sparse_keys,
  ::tensorflow::Input dense_keys,
  ::tensorflow::Input ragged_keys,
  ::tensorflow::InputList dense_defaults,
  int64 num_sparse,
  const DataTypeSlice & sparse_types,
  const DataTypeSlice & ragged_value_types,
  const DataTypeSlice & ragged_split_types,
  const gtl::ArraySlice< PartialTensorShape > & dense_shapes
)