Creates a dataset that fuses mapping with batching.
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tf.raw_ops.ExperimentalMapAndBatchDataset(
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
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_
|
A Tensor of type variant .
A variant tensor representing the input dataset.
|
other_
|
A list of Tensor objects.
A list of tensors, typically values that were captured when building a closure
for f .
|
batch_
|
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_ in parallel.
|
num_
|
A Tensor of type int64 .
A scalar representing the maximum number of parallel invocations of the map_
function. Applying the map_ on consecutive input elements in parallel has
the potential to improve input pipeline throughput.
|
drop_
|
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_ .
|
output_
|
A list of tf. that has length >= 1 .
|
output_
|
A list of shapes (each a tf.TensorShape or list of ints ) that has length >= 1 .
|
preserve_
|
An optional bool . Defaults to False .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type variant .
|