mindspore.dataset.transforms.c_transforms.OneHot

class mindspore.dataset.transforms.c_transforms.OneHot(num_classes)[source]

Tensor operation to apply one hot encoding.

Parameters: num_classes (int) – Number of classes of the label. It should be larger than the largest label number in the dataset.
Raises: RuntimeError – feature size is bigger than num_classes.

Examples

>>> import mindspore.dataset.transforms.c_transforms as c_transforms
>>> import mindspore.dataset.vision.c_transforms as c_vision
>>>
>>> onehot_op = c_transforms.OneHot(num_classes=10)
>>> data1 = data1.map(operations=onehot_op, input_columns=["label"])
>>> mixup_batch_op = c_vision.MixUpBatch(alpha=0.8)
>>> data1 = data1.batch(4)
>>> data1 = data1.map(operations=mixup_batch_op, input_columns=["image", "label"])