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mindspore.mint.nn.NLLLoss

class mindspore.mint.nn.NLLLoss(weight=None, ignore_index=- 100, reduction='mean')

Gets the negative log likelihood loss between inputs and target.

The nll loss with reduction=none can be described as:

$$\ell (x,t)=L={\{l_1,\dots ,l_N\}}^{\mathrm{\top }},l_n=-w_{t_n}{x}_{n,t_n},w_c=\text{ weight }[c]\cdot \mathbb{1}\{c\ne \text{ignore\_index}\},$$

where $x$ is the inputs, $t$ is the target, $w$ is the weight, $N$ is the batch size, $c$ belonging to $[0,C-1]$ is class index, where $C$ is the number of classes.

If reduction is not 'None' (default 'mean'), then

$$\begin{array}{r}\ell (x,t)=\{\begin{array}{ll}\sum _{n=1}^N\frac{1}{\sum _{n=1}^N{w}_{tn}}{l}_n,& \text{ if reduction }=\text{ 'mean', }\\ \sum _{n=1}^N{l}_n,& \text{ if reduction }=\text{ 'sum' }\end{array}\end{array}$$

Warning

This is an experimental API that is subject to change or deletion.

Parameters

• weight (Tensor, optional) – A rescaling weight applied to the loss of each batch element. If not None, the shape is $(C,)$, data type must be float16 or float32 or bfloat16(only supported by Atlas A2 training series products). It should have the same data type as input . Default: None .

• ignore_index (int, optional) – Specifies a target value that is ignored and does not contribute to the input gradient. Only valid in class indices, please set it to a negative number in probabilities. Default: -100 .

• reduction (str, optional) –

  Apply specific reduction method to the output: 'none' , 'mean' , 'sum' . Default: 'mean' .

  • 'none': no reduction will be applied.

  • 'mean': compute and return the weighted mean of elements in the output.

  • 'sum': the output elements will be summed.

Inputs:

• input (Tensor) - $(N)$ or $(N,C)$ where C = number of classes , N = batch size , or $(N,C,d_1,d_2,...,d_K)$ (for high-dimensional data). input is expected to be log-probabilities, data type must be float16 or float32 or bfloat16(only supported by Atlas A2 training series products).

• target (Tensor) - $()$ or $(N)$ , where the value range is $[0,C-1]$, or $(N,d_1,d_2,...,d_K)$ for high-dimensional loss, data type must be int32 or int64 or uint8.

Outputs:

Tensor, the data type is the same as input .

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> inputs = mindspore.Tensor(np.random.randn(3, 5), mindspore.float32)
>>> target = mindspore.Tensor(np.array([1, 0, 4]), mindspore.int32)
>>> op = mindspore.mint.nn.NLLLoss()
>>> output = op(inputs, target)