dataset_batch: Combines consecutive elements of this dataset into batches.
In tfdatasets: Interface to 'TensorFlow' Datasets

dataset_batch

R Documentation

Combines consecutive elements of this dataset into batches.

Description

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.

Usage

dataset_batch(
  dataset,
  batch_size,
  drop_remainder = FALSE,
  num_parallel_calls = NULL,
  deterministic = NULL
)

Arguments

`dataset`	A dataset
`batch_size`	An integer, representing the number of consecutive elements of this dataset to combine in a single batch.
`drop_remainder`	(Optional.) A boolean, 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.
`num_parallel_calls`	(Optional.) A scalar integer, representing the number of batches to compute asynchronously in parallel. If not specified, batches will be computed sequentially. If the value `tf$data$AUTOTUNE` is used, then the number of parallel calls is set dynamically based on available resources.
`deterministic`	(Optional.) When `num_parallel_calls` is specified, if this boolean is specified (`TRUE` or `FALSE`), it controls the order in which the transformation produces elements. If set to `FALSE`, the transformation is allowed to yield elements out of order to trade determinism for performance. If not specified, the `tf.data.Options.experimental_deterministic` option (`TRUE` by default) controls the behavior. See `dataset_options()` for how to set dataset options.

Value

A dataset

Note

If your program requires data to have a statically known shape (e.g., when using XLA), you should use drop_remainder=TRUE. Without drop_remainder=TRUE the shape of the output dataset will have an unknown leading dimension due to the possibility of a smaller final batch.

Other dataset methods: dataset_cache(), dataset_collect(), dataset_concatenate(), dataset_decode_delim(), dataset_filter(), dataset_interleave(), dataset_map(), dataset_map_and_batch(), dataset_padded_batch(), dataset_prefetch(), dataset_prefetch_to_device(), dataset_reduce(), dataset_repeat(), dataset_shuffle(), dataset_shuffle_and_repeat(), dataset_skip(), dataset_take(), dataset_take_while(), dataset_window()