ParseSingleExample

public final class ParseSingleExample

Transforms a tf.Example proto (as a string) into typed tensors.

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

static ParseSingleExample
create ( Scope scope, Operand < TString > serialized, Iterable< Operand <?>> denseDefaults, Long numSparse, List<String> sparseKeys, List<String> denseKeys, List<Class<? extends TType >> sparseTypes, List< Shape > denseShapes)
Factory method to create a class wrapping a new ParseSingleExample operation.
List< Output <?>>
List< Output < TInt64 >>
List< Output < TInt64 >>
List< Output <?>>

Inherited Methods

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "ParseSingleExample"

Public Methods

public static ParseSingleExample create ( Scope scope, Operand < TString > serialized, Iterable< Operand <?>> denseDefaults, Long numSparse, List<String> sparseKeys, List<String> denseKeys, List<Class<? extends TType >> sparseTypes, List< Shape > denseShapes)

Factory method to create a class wrapping a new ParseSingleExample operation.

Parameters
scope current scope
serialized A vector containing a batch of binary serialized Example protos.
denseDefaults A list of Tensors (some may be empty), whose length matches the length of `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.
numSparse The number of sparse features to be parsed from the example. This must match the lengths of `sparse_keys` and `sparse_types`.
sparseKeys A list of `num_sparse` strings. The keys expected in the Examples' features associated with sparse values.
denseKeys The keys expected in the Examples' features associated with dense values.
sparseTypes A list of `num_sparse` types; the data types of data in each Feature given in sparse_keys. Currently the ParseSingleExample op supports DT_FLOAT (FloatList), DT_INT64 (Int64List), and DT_STRING (BytesList).
denseShapes The shapes of data in each Feature given in dense_keys. The length of this list must match the length of `dense_keys`. 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 (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1, ..., DN), the shape of the output Tensor dense_values[j] will be (M, D1, .., DN), where M is the number of blocks of elements of length D1 * .... * DN, in the input.
Returns
  • a new instance of ParseSingleExample

public List< Output <?>> denseValues ()

public List< Output < TInt64 >> sparseIndices ()

public List< Output < TInt64 >> sparseShapes ()

public List< Output <?>> sparseValues ()