dataset_batch
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
| Arguments | Description |
|---|---|
| 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.
See Also
Other dataset methods: dataset_cache(), dataset_collect(), dataset_concatenate(), dataset_decode_delim(), dataset_filter(), dataset_interleave(), dataset_map_and_batch(), dataset_map(), dataset_padded_batch(), dataset_prefetch_to_device(), dataset_prefetch(), dataset_reduce(), dataset_repeat(), dataset_shuffle_and_repeat(), dataset_shuffle(), dataset_skip(), dataset_take_while(), dataset_take(), dataset_window()