Fused implementation of map
and batch
. (deprecated)
tf.contrib.data.map_and_batch(
map_func, batch_size, num_parallel_batches=None, drop_remainder=False,
num_parallel_calls=None
)
Maps map_func
across batch_size
consecutive elements of this dataset
and then combines them into a batch. Functionally, it is equivalent to map
followed by batch
. However, by fusing the two transformations together, the
implementation can be more efficient. Surfacing this transformation in the API
is temporary. Once automatic input pipeline optimization is implemented,
the fusing of map
and batch
will happen automatically and this API will be
deprecated.
Args |
map_func
|
A function mapping a nested structure of tensors to another nested
structure of tensors.
|
batch_size
|
A tf.int64 scalar tf.Tensor , representing the number of
consecutive elements of this dataset to combine in a single batch.
|
num_parallel_batches
|
(Optional.) A tf.int64 scalar tf.Tensor ,
representing the number of batches to create in parallel. On one hand,
higher values can help mitigate the effect of stragglers. On the other
hand, higher values can increase contention if CPU is scarce.
|
drop_remainder
|
(Optional.) A tf.bool scalar tf.Tensor , representing
whether the last batch should be dropped in case its size is smaller than
desired; the default behavior is not to drop the smaller batch.
|
num_parallel_calls
|
(Optional.) A tf.int32 scalar tf.Tensor ,
representing the number of elements to process in parallel. If not
specified, batch_size * num_parallel_batches elements will be processed
in parallel.
|
Raises |
ValueError
|
If both num_parallel_batches and num_parallel_calls are
specified.
|