Maps map_func across this dataset, and interleaves the results
Maps map_func across this dataset, and interleaves the results
dataset_interleave(dataset, map_func, cycle_length, block_length = 1)Arguments
| 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. |
Details
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.
See also
Other dataset methods: dataset_batch,
dataset_cache,
dataset_concatenate,
dataset_decode_delim,
dataset_filter, dataset_map,
dataset_padded_batch,
dataset_prefetch,
dataset_repeat,
dataset_shuffle,
dataset_skip, dataset_take
Examples
# 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,
)
# }