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.
dataset_batch(
dataset,
batch_size,
drop_remainder = FALSE,
num_parallel_calls = NULL,
deterministic = NULL
)A dataset
A dataset
An integer, representing the number of consecutive elements of this dataset to combine in a single batch.
(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.
(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.
(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.
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()