tf.data.experimental.dense_to_ragged_batch

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A transformation that batches ragged elements into tf.RaggedTensors.

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, and each batch will be encoded as a tf.RaggedTensor. Example:

dataset = tf.data.Dataset.from_tensor_slices(np.arange(6)) 
dataset = dataset.map(lambda x: tf.range(x)) 
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]]> 

Args:

  • batch_size: A tf.int64 scalar tf.Tensor, representing the number of consecutive elements of this dataset to combine in a single batch.
  • drop_remainder: (Optional.) A tf.bool scalar tf.Tensor, representing whether the last batch should be dropped in the case it has fewer than batch_size elements; the default behavior is not to drop the smaller batch.
  • row_splits_dtype: The dtype that should be used for the row_splits of any new ragged tensors. Existing tf.RaggedTensor elements do not have their row_splits dtype changed.

Returns:

  • Dataset: A Dataset.