tf.data.experimental.dense_to_ragged_batch

A transformation that batches ragged elements into tf.RaggedTensors.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.data.experimental.dense_to_ragged_batch

tf.data.experimental.dense_to_ragged_batch(
    batch_size, drop_remainder=False, row_splits_dtype=tf.dtypes.int64
)

This transformation combines multiple consecutive elements of the input dataset into a single element.

Like tf.data.Dataset.batch, the components of the resulting element will have an additional outer dimension, which will be batch_size (or N % batch_size for the last element if batch_size does not divide the number of input elements N evenly and drop_remainder is False). If your program depends on the batches having the same outer dimension, you should set the drop_remainder argument to True to prevent the smaller batch from being produced.

Unlike tf.data.Dataset.batch, the input elements to be batched may have different shapes:

• If an input element is a tf.Tensor whose static tf.TensorShape is fully defined, then it is batched as normal.
• If an input element is a tf.Tensor whose static tf.TensorShape contains one or more axes with unknown size (i.e., shape[i]=None), then the output will contain a tf.RaggedTensor that is ragged up to any of such dimensions.
• If an input element is a tf.RaggedTensor or any other type, then it is batched as normal.

Example:

dataset = tf.data.Dataset.from_tensor_slices(np.arange(6))
dataset = dataset.map(lambda x: tf.range(x))
dataset.element_spec.shape
TensorShape([None])
dataset = dataset.apply(
    tf.data.experimental.dense_to_ragged_batch(batch_size=2))
for batch in dataset:
  print(batch)
<tf.RaggedTensor [[], [0]]>
<tf.RaggedTensor [[0, 1], [0, 1, 2]]>
<tf.RaggedTensor [[0, 1, 2, 3], [0, 1, 2, 3, 4]]>