Creates a dataset that fuses mapping with batching.
tf.raw_ops.MapAndBatchDataset(
input_dataset,
other_arguments,
batch_size,
num_parallel_calls,
drop_remainder,
f,
output_types,
output_shapes,
preserve_cardinality=False,
metadata='',
name=None
)
Creates a dataset that applies f
to the outputs of input_dataset
and then
batches batch_size
of them.
Unlike a "MapDataset", which applies f
sequentially, this dataset invokes up
to batch_size * num_parallel_batches
copies of f
in parallel.
Args | |
---|---|
input_dataset
|
A Tensor of type variant .
A variant tensor representing the input dataset.
|
other_arguments
|
A list of Tensor objects.
A list of tensors, typically values that were captured when building a closure
for f .
|
batch_size
|
A Tensor of type int64 .
A scalar representing the number of elements to accumulate in a
batch. It determines the number of concurrent invocations of f that process
elements from input_dataset in parallel.
|
num_parallel_calls
|
A Tensor of type int64 .
A scalar representing the maximum number of parallel invocations of the map_fn
function. Applying the map_fn on consecutive input elements in parallel has
the potential to improve input pipeline throughput.
|
drop_remainder
|
A Tensor of type 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 input_dataset .
|
output_types
|
A list of tf.DTypes that has length >= 1 .
|
output_shapes
|
A list of shapes (each a tf.TensorShape or list of ints ) that has length >= 1 .
|
preserve_cardinality
|
An optional bool . Defaults to False .
|
metadata
|
An optional string . Defaults to "" .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type variant .
|