mindspore.dataset.Dataset.create_tuple_iterator
- Dataset.create_tuple_iterator(columns=None, num_epochs=- 1, output_numpy=False, do_copy=True)[source]
Create an iterator over the dataset. The datatype retrieved back will be a list of numpy.ndarray .
To specify which columns to list and the order needed, use columns_list. If columns_list is not provided, the order of the columns will remain unchanged.
- Parameters
columns (list[str], optional) – List of columns to be used to specify the order of columns. Default:
None
, means all columns.num_epochs (int, optional) – Maximum number of epochs that iterator can be iterated. Default:
-1
, iterator can be iterated infinite number of epochs.output_numpy (bool, optional) – Whether or not to output NumPy datatype. If output_numpy is
False
, iterator will output MSTensor. Default:False
.do_copy (bool, optional) – When output data type is
mindspore.Tensor
, use this param to select the conversion method, only take False for better performance. Default:True
.
- Returns
Iterator, a dataset iterator that returns data of type Tuple.
Examples
>>> import mindspore.dataset as ds >>> dataset = ds.GeneratorDataset([i for i in range(10)], "column1") >>> iterator = dataset.create_tuple_iterator() >>> for item in iterator: ... # item is a list ... print(type(item)) ... break <class 'list'>