public final class
ParseExampleDataset
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 | ParseExampleDataset.Options | Optional attributes for ParseExampleDataset
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Constants
| String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
| Output<TType> |
asOutput()
Returns the symbolic handle of the tensor.
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| static ParseExampleDataset |
create(Scope scope, Operand<?> inputDataset, Operand<TInt64> numParallelCalls, Iterable<Operand<?>> denseDefaults, List<String> sparseKeys, List<String> denseKeys, List<Class<? extends TType>> sparseTypes, List<Shape> denseShapes, List<Class<? extends TType>> outputTypes, List<Shape> outputShapes, List<Class<? extends TType>> raggedValueTypes, List<Class<? extends TNumber>> raggedSplitTypes, Options... options)
Factory method to create a class wrapping a new ParseExampleDataset operation.
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| static ParseExampleDataset.Options |
deterministic(String deterministic)
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| Output<?> |
handle()
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| static ParseExampleDataset.Options |
raggedKeys(List<String> raggedKeys)
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Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Constant Value:
"ParseExampleDatasetV2"
Public Methods
public Output<TType> asOutput ()
Returns the symbolic handle of the 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 ParseExampleDataset create (Scope scope, Operand<?> inputDataset, Operand<TInt64> numParallelCalls, Iterable<Operand<?>> denseDefaults, List<String> sparseKeys, List<String> denseKeys, List<Class<? extends TType>> sparseTypes, List<Shape> denseShapes, List<Class<? extends TType>> outputTypes, List<Shape> outputShapes, List<Class<? extends TType>> raggedValueTypes, List<Class<? extends TNumber>> raggedSplitTypes, Options... options)
Factory method to create a class wrapping a new ParseExampleDataset operation.
Parameters
| scope | current scope |
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| 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 ParseExampleDataset
public static ParseExampleDataset.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.
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