View source: R/dataset_methods.R
dataset_interleave | R Documentation |
Maps map_func across this dataset, and interleaves the results
dataset_interleave(dataset, map_func, cycle_length, block_length = 1)
dataset |
A dataset |
map_func |
A function mapping a nested structure of tensors (having
shapes and types defined by |
cycle_length |
The number of elements from this dataset that will be processed concurrently. |
block_length |
The number of consecutive elements to produce from each input element before cycling to another input element. |
The cycle_length
and block_length
arguments control the order in which
elements are produced. cycle_length
controls the number of input elements
that are processed concurrently. In general, this transformation will apply
map_func
to cycle_length
input elements, open iterators on the returned
dataset objects, and cycle through them producing block_length
consecutive
elements from each iterator, and consuming the next input element each time
it reaches the end of an iterator.
Other dataset methods:
dataset_batch()
,
dataset_cache()
,
dataset_collect()
,
dataset_concatenate()
,
dataset_decode_delim()
,
dataset_filter()
,
dataset_map()
,
dataset_map_and_batch()
,
dataset_padded_batch()
,
dataset_prefetch()
,
dataset_prefetch_to_device()
,
dataset_reduce()
,
dataset_repeat()
,
dataset_shuffle()
,
dataset_shuffle_and_repeat()
,
dataset_skip()
,
dataset_take()
,
dataset_take_while()
,
dataset_window()
## Not run:
dataset <- tensor_slices_dataset(c(1,2,3,4,5)) %>%
dataset_interleave(cycle_length = 2, block_length = 4, function(x) {
tensors_dataset(x) %>%
dataset_repeat(6)
})
# resulting dataset (newlines indicate "block" boundaries):
c(1, 1, 1, 1,
2, 2, 2, 2,
1, 1,
2, 2,
3, 3, 3, 3,
4, 4, 4, 4,
3, 3,
4, 4,
5, 5, 5, 5,
5, 5,
)
## End(Not run)
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