Creates a dataset that applies f to the outputs of input_dataset.
tf.raw_ops.LegacyParallelInterleaveDatasetV2( input_dataset, other_arguments, cycle_length, block_length, buffer_output_elements, prefetch_input_elements, f, output_types, output_shapes, deterministic='default', metadata='', 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. |
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. |
deterministic | An optional string. Defaults to "default". |
metadata | An optional string. Defaults to "". |
name | A name for the operation (optional). |
Returns | |
|---|---|
A Tensor of type variant. |