Warning: This API is deprecated and will be removed in a future version of TensorFlow after the replacement is stable.

ParseExampleDatasetV2

public final class ParseExampleDatasetV2

Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features.

Nested Classes

class ParseExampleDatasetV2.Options Optional attributes for ParseExampleDatasetV2

Public Methods

Output <Object>
asOutput ()
Returns the symbolic handle of a tensor.
static ParseExampleDatasetV2
create ( Scope scope, Operand <?> inputDataset, Operand <Long> numParallelCalls, Iterable< Operand <?>> denseDefaults, List<String> sparseKeys, List<String> denseKeys, List<Class<?>> sparseTypes, List< Shape > denseShapes, List<Class<?>> outputTypes, List< Shape > outputShapes, List<Class<?>> raggedValueTypes, List<Class<?>> raggedSplitTypes, Options... options)
Factory method to create a class wrapping a new ParseExampleDatasetV2 operation.
static ParseExampleDatasetV2.Options
deterministic (String deterministic)
Output <?>
static ParseExampleDatasetV2.Options
raggedKeys (List<String> raggedKeys)

Inherited Methods

Public Methods

public Output <Object> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static ParseExampleDatasetV2 create ( Scope scope, Operand <?> inputDataset, Operand <Long> numParallelCalls, Iterable< Operand <?>> denseDefaults, List<String> sparseKeys, List<String> denseKeys, List<Class<?>> sparseTypes, List< Shape > denseShapes, List<Class<?>> outputTypes, List< Shape > outputShapes, List<Class<?>> raggedValueTypes, List<Class<?>> raggedSplitTypes, Options... options)

Factory method to create a class wrapping a new ParseExampleDatasetV2 operation.

Parameters
scope current scope
denseDefaults A dict mapping string keys to `Tensor`s. The keys of the dict must match the dense_keys of the feature.
sparseKeys A list of string keys in the examples features. The results for these keys will be returned as `SparseTensor` objects.
denseKeys A list of Ndense string Tensors (scalars). The keys expected in the Examples features associated with dense values.
sparseTypes A list of `DTypes` of the same length as `sparse_keys`. Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`), and `tf.string` (`BytesList`) are supported.
denseShapes List of tuples with the same length as `dense_keys`. The shape of the data for each dense feature referenced by `dense_keys`. Required for any input tensors identified by `dense_keys`. Must be either fully defined, or may contain an unknown first dimension. An unknown first dimension means the feature is treated as having a variable number of blocks, and the output shape along this dimension is considered unknown at graph build time. Padding is applied for minibatch elements smaller than the maximum number of blocks for the given feature along this dimension.
outputTypes The type list for the return values.
outputShapes The list of shapes being produced.
options carries optional attributes values
Returns
  • a new instance of ParseExampleDatasetV2

public static ParseExampleDatasetV2.Options deterministic (String deterministic)

Parameters
deterministic A string indicating the op-level determinism to use. Deterministic controls whether the dataset is allowed to return elements out of order if the next element to be returned isn't available, but a later element is. Options are "true", "false", and "default". "default" indicates that determinism should be decided by the `experimental_deterministic` parameter of `tf.data.Options`.

public Output <?> handle ()

public static ParseExampleDatasetV2.Options raggedKeys (List<String> raggedKeys)