mindspore.mint.nn.functional.nll_loss

View Source On Gitee
mindspore.mint.nn.functional.nll_loss(input, target, weight=None, ignore_index=- 100, reduction='mean')[source]

Gets the negative log likelihood loss between input and target.

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

\[\ell(x, t)=L=\left\{l_{1}, \ldots, l_{N}\right\}^{\top}, \quad l_{n}=-w_{t_{n}} x_{n, t_{n}}, \quad w_{c}=\text { weight }[c] \cdot \mathbb{1} \{c \not= \text{ignore_index}\},\]

where \(x\) is the input, \(t\) 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

\[\begin{split}\ell(x, t)=\left\{\begin{array}{ll} \sum_{n=1}^{N} \frac{1}{\sum_{n=1}^{N} w_{t n}} l_{n}, & \text { if reduction }=\text { 'mean', } \\ \sum_{n=1}^{N} l_{n}, & \text { if reduction }=\text { 'sum' } \end{array}\right.\end{split}\]

Warning

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

Parameters

  • 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 only supports float32 or float16 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.

  • weight (Tensor, optional) – A rescaling weight applied to the loss of each batch element. If not None, the shape is \((C,)\). The 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. 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.

Returns

Tensor. The data type is the same as that of input.

Supported Platforms:

Ascend

Examples

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