Creates a dataset that fuses mapping with batching.
Compat aliases for migration
See Migration guide for more details.
tf.raw_ops.MapAndBatchDataset( input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f, output_types, output_shapes, preserve_cardinality=False, name=None )
Creates a dataset that applies
f to the outputs of
input_dataset and then
batch_size of them.
Unlike a "MapDataset", which applies
f sequentially, this dataset invokes up
batch_size * num_parallel_batches copies of
f in parallel.
variant. A variant tensor representing the input dataset.
other_arguments: A list of
Tensorobjects. A list of tensors, typically values that were captured when building a closure for
int64. A scalar representing the number of elements to accumulate in a batch. It determines the number of concurrent invocations of
fthat process elements from
int64. A scalar representing the maximum number of parallel invocations of the
map_fnfunction. Applying the
map_fnon consecutive input elements in parallel has the potential to improve input pipeline throughput.
bool. A scalar representing whether the last batch should be dropped in case its size is smaller than desired.
f: A function decorated with @Defun. A function to apply to the outputs of
output_types: A list of
tf.DTypesthat has length
output_shapes: A list of shapes (each a
tf.TensorShapeor list of
ints) that has length
preserve_cardinality: An optional
bool. Defaults to
name: A name for the operation (optional).
Tensor of type