mindspore.dataset.transforms.OneHot

class mindspore.dataset.transforms.OneHot(num_classes, smoothing_rate=0.0)[source]

Apply One-Hot encoding to the input labels.

For a 1-D input of shape \((*)\), an output of shape \((*, num\_classes)\) will be returned, where the elements with index values equal to the input values will be set to 1, and the rest will be set to 0. If a label smoothing rate is specified, the element values are further smoothed to enhance generalization.

Parameters

num_classes (int) – Total number of classes. Must be greater than the maximum value of the input labels.
smoothing_rate (float, optional) – The amount of label smoothing. Must be between [0.0, 1.0]. Default: 0.0, no label smoothing.

Raises

TypeError – If num_classes is not of type int.
TypeError – If smoothing_rate is not of type float.
ValueError – If smoothing_rate is not in range of [0.0, 1.0].
RuntimeError – If input label is not of type int.
RuntimeError – If the dimension of the input label is not 1.

Supported Platforms:: CPU

Examples

>>> import numpy as np
>>> import mindspore.dataset as ds
>>> import mindspore.dataset.transforms as transforms
>>>
>>> # Use the transform in dataset pipeline mode
>>> data = [1, 2, 3, 4, 5, 6, 7, 8]
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data, ["label"])
>>>
>>> # Assume that dataset has 10 classes, thus the label ranges from 0 to 9
>>> onehot_op = transforms.OneHot(num_classes=10)
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=onehot_op, input_columns=["label"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["label"].shape, item["label"].dtype)
...     break
(10,) int64
>>>
>>> # Use the transform in eager mode
>>> data = np.array([1, 2, 3])
>>> output = transforms.OneHot(num_classes=5, smoothing_rate=0)(data)
>>> print(output.shape, output.dtype)
(3, 5) int64