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mindspore.ops.NLLLoss

class mindspore.ops.NLLLoss(reduction='mean')

Gets the negative log likelihood loss between logits and labels.

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

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

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

If reduction is not ‘none’ (default ‘mean’), then

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

Parameters

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

Inputs:

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

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

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

Outputs:

Tuple of 2 tensors composed with loss and total_weight.

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

• total_weight (Tensor) - The total_weight is a scalar. The data type is 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 not a one dimension tensor. 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