Creates a dataset that applies f
to the outputs of input_dataset
.
tf.raw_ops.ExperimentalParallelInterleaveDataset(
input_dataset,
other_arguments,
cycle_length,
block_length,
sloppy,
buffer_output_elements,
prefetch_input_elements,
f,
output_types,
output_shapes,
name=None
)
The resulting dataset is similar to the InterleaveDataset
, with the exception
that if retrieving the next value from a dataset would cause the requester to
block, it will skip that input dataset. This dataset is especially useful
when loading data from a variable-latency datastores (e.g. HDFS, GCS), as it
allows the training step to proceed so long as some data is available.
!! WARNING !! This dataset is not deterministic!
Args | |
---|---|
input_dataset
|
A Tensor of type variant .
|
other_arguments
|
A list of Tensor objects.
|
cycle_length
|
A Tensor of type int64 .
|
block_length
|
A Tensor of type int64 .
|
sloppy
|
A Tensor of type bool .
|
buffer_output_elements
|
A Tensor of type int64 .
|
prefetch_input_elements
|
A Tensor of type int64 .
|
f
|
A function decorated with @Defun.
A function mapping elements of input_dataset , concatenated with
other_arguments , to a Dataset variant that contains elements matching
output_types and output_shapes .
|
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 .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type variant .
|