# mindspore.ops.NLLLoss

class mindspore.ops.NLLLoss(reduction='mean')[source]

Gets the negative log likelihood loss between logits and labels.

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

$\ell(x, t)=L=\left\{l_{1}, \ldots, l_{N}\right\}^{\top}, \quad l_{n}=-w_{t_{n}} x_{n, t_{n}}, \quad w_{c}=\text { weight }[c] \cdot 1$

where $$x$$ is the logits, $$t$$ is the labels, $$w$$ 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

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

reduction (str) – Apply specific reduction method to the output: ‘none’, ‘mean’, or ‘sum’. Default: ‘mean’.

Inputs:
• logits (Tensor) - Input logits, with shape $$(N, C)$$. Data type only supports float32 or float16.

• labels (Tensor) - Ground truth labels, with shape $$(N,)$$. Data type only supports int32.

• weight (Tensor) - The rescaling weight to each class, with shape $$(C,)$$ and data type only supports float32 or float16.

Outputs:

Tuple of 2 tensors composed with loss and total_weight.

• loss (Tensor) - When reduction is ‘none’ and logits is a 2D tensor, the loss shape is $$(N,)$$. Otherwise, the loss is a scalar. The data type is the same with input’s.

• total_weight (Tensor) - The total_weight is a scalar. The data type is the same with weight’s.

Raises
• TypeError – If dtype of logits or weight is neither float16 nor float32, labels is not int32.

• ValueError – If logits is not a one or two dimension tensor, labels and weight are not one dimension tensors. When logits is a two dimension tensor, the first dimension of logits is not equal to labels, and second dimension of logits is not equal to weight. When logits is a one dimension tensor, the dimensions of logits, labels and weight should be equal to each other.

Supported Platforms:

Ascend GPU

Examples

>>> logits = Tensor(np.array([[0.5488135, 0.71518934],
...                           [0.60276335, 0.5448832],
...                           [0.4236548, 0.6458941]]).astype(np.float32))
>>> labels = Tensor(np.array([0, 0, 0]).astype(np.int32))
>>> weight = Tensor(np.array([0.3834415, 0.79172504]).astype(np.float32))
>>> nll_loss = ops.NLLLoss(reduction="mean")
>>> loss, weight = nll_loss(logits, labels, weight)
>>> print(loss)
-0.52507716
>>> print(weight)
1.1503246