mindspore.dataset.Dataset.create_tuple_iterator

Dataset.create_tuple_iterator(columns=None, num_epochs=- 1, output_numpy=False, do_copy=True)

Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column.

Parameters

• columns (list[str], optional) – Specify the output columns and the order. Default: None, keep all the output columns and their original order.

• num_epochs (int, optional) – The number of epochs to iterate over the entire dataset. Default: -1 , the dataset can be iterated indefinitely.

• output_numpy (bool, optional) – Whether to keep the output data as NumPy ndarray, or convert it to Tensor. Default: False .

• do_copy (bool, optional) – Whether to copy the data when converting output to Tensor, or reuse the buffer for better performance, only works when output_numpy is False . Default: True .

Returns

Iterator, a dataset iterator that yields samples of type list.

Examples

>>> import mindspore.dataset as ds
>>>
>>> dataset = ds.GeneratorDataset([i for i in range(10)], "data")
>>> num_epochs = 3
>>> iterator = dataset.create_tuple_iterator(num_epochs=num_epochs)
>>> for epoch in range(num_epochs):
...     for item in iterator:
...         # output is of type tuple
...         print(type(item))
...         break
...     break
<class 'list'>