Dataset

Groups elements of this dataset into batches.

Groups elements of this dataset into batches. Includes the last batch, even if it has fewer than `batchSize` elements.

Creates an in-memory `Dataset` whose elements are slices of the given tensors. Each element of this dataset will be a List<Operand<?>>, representing slices (e.g. batches) of the provided tensors.

Get a list of shapes for each component of this dataset.

Get a list of output types for each component of this dataset.

Get the variant tensor representing this dataset.

Creates an iterator which iterates through all batches of this Dataset in an eager fashion. Each batch is a list of components, returned as `Output` objects.

This method enables for-each iteration through batches when running in eager mode. For Graph mode batch iteration, see `makeOneShotIterator`.

Creates a `DatasetIterator` that can be used to iterate over elements of this dataset.

This iterator will have to be initialized with a call to `iterator.makeInitializer(Dataset)` before elements can be retreived in a loop.

Creates a `DatasetIterator` that can be used to iterate over elements of this dataset. Using `makeOneShotIterator` ensures that the iterator is automatically initialized on this dataset. skips In graph mode, the initializer op will be added to the Graph's intitializer list, which must be run via `tf.init()`:

Ex:

     try (Session session = new Session(graph) {
         // Immediately run initializers
         session.run(tf.init());
     }
 

In eager mode, the initializer will be run automatically as a result of this call.

Returns a new Dataset which maps a function over all elements returned by this dataset.

For example, suppose each element is a List<Operand<?>> with 2 components: (features, labels).

Calling

dataset.map(components -> {
      Operand<?> features = components.get(0);
      Operand<?> labels   = components.get(1);

      return Arrays.asList(
        tf.math.mul(features, tf.constant(2)),
        tf.math.mul(labels, tf.constant(5))
      );
 );
 }

Returns a new Dataset which maps a function across all elements from this dataset, on all components of each element.

For example, suppose each element is a List<Operand<?>> with 2 components: (features, labels).

Calling dataset.mapAllComponents(component -> tf.math.mul(component, tf.constant(2))) will map the function over the both the `features` and `labels` components of each element, multiplying them all by 2

Returns a new Dataset which maps a function across all elements from this dataset, on a single component of each element.

For example, suppose each element is a List<Operand<?>> with 2 components: (features, labels).

Calling dataset.mapOneComponent(0, features -> tf.math.mul(features, tf.constant(2))) will map the function over the `features` component of each element, multiplying each by 2.

Returns a new `Dataset` which skips `count` initial elements from this dataset

Returns a new `Dataset` with only the first `count` elements from this dataset.