mindspore.dataset.Dataset.repeat
- Dataset.repeat(count=None)[source]
Repeat this dataset count times. Repeat infinitely if the count is
None
or-1
.Note
The order of using repeat and batch reflects the number of batches. It is recommended that the repeat operation is used after the batch operation.
- Parameters
count (int) – Number of times the dataset is going to be repeated. Default:
None
.- Returns
Dataset, a new dataset with the above operation applied.
Examples
>>> import mindspore.dataset as ds >>> >>> # Create a dataset with 10 elements >>> dataset = ds.GeneratorDataset([i for i in range(10)], "column1") >>> ori_size = dataset.get_dataset_size() >>> >>> # Repeat the dataset 50 times. >>> dataset = dataset.repeat(50) >>> repeated_size = dataset.get_dataset_size() >>> print("ori_size", ori_size, ", repeated_size", repeated_size) ori_size 10 , repeated_size 500 >>> >>> # Since the original dataset size is less than batch_size, thus no data is returned >>> dataset1 = ds.GeneratorDataset([i for i in range(10)], "column1") >>> dataset1 = dataset1.batch(batch_size=20, drop_remainder=True) >>> dataset1 = dataset1.repeat(6) >>> >>> # Repeat the original dataset to 60 elements, thus 3 batches are returned >>> dataset2 = ds.GeneratorDataset([i for i in range(10)], "column1") >>> dataset2 = dataset2.repeat(6) >>> dataset2 = dataset2.batch(batch_size=20, drop_remainder=True) >>> print("dataset1 size", dataset1.get_dataset_size(), ", dataset2 size", dataset2.get_dataset_size()) dataset1 size 0 , dataset2 size 3