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Specifications and Common Mistakes

- Specifications and Common Mistakes:

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mindspore.nn.MultiClassDiceLoss

class mindspore.nn.MultiClassDiceLoss(weights=None, ignore_indiex=None, activation='softmax')

When there are multiple classifications, label is transformed into multiple binary classifications by one hot. For each channel section in the channel, it can be regarded as a binary classification problem, so it can be obtained through the binary mindspore.nn.DiceLoss losses of each category, and then the average value of the binary losses.

Parameters

• weights (Union[Tensor, None]) – Tensor of shape $(num\mathrm{\_}classes,dim)$. The weight shape[0] should be equal to labels shape[1]. Default: None .

• ignore_indiex (Union[int, None]) – Class index to ignore. Default: None .

• activation (Union[str, Cell]) – Activate function applied to the output of the fully connected layer, eg. ‘ReLU’. Default: 'softmax' . Choose from: [ 'softmax' , 'logsoftmax' , 'relu' , 'relu6' , 'tanh' , 'Sigmoid' ]

Inputs:

• logits (Tensor) - Tensor of shape $(N,C,\ast )$ where $\ast$ means, any number of additional dimensions. The logits dimension should be greater than 1. The data type must be float16 or float32.

• labels (Tensor) - Tensor of shape $(N,C,\ast )$, same shape as the logits. The labels dimension should be greater than 1. The data type must be float16 or float32.

Outputs:

Tensor, a tensor of shape with the per-example sampled MultiClass Dice Losses.

Raises

• ValueError – If the shape of logits is different from labels.

• TypeError – If the type of logits or labels is not a tensor.

• ValueError – If the dimension of logits or labels is less than 2.

• ValueError – If the weights.shape[0] is not equal to labels.shape[1].

• ValueError – If weights is a tensor, but its dimension is not 2.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> loss = nn.MultiClassDiceLoss(weights=None, ignore_indiex=None, activation="softmax")
>>> logits = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.5], [0.9, 0.6, 0.3]]), mindspore.float32)
>>> labels = Tensor(np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]]), mindspore.float32)
>>> output = loss(logits, labels)
>>> print(output)
0.54958105