mindspore.ops.NLLLoss

class mindspore.ops.NLLLoss(*args, **kwargs)[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 input, t is the target. w is the weight. and 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

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

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

Inputs:

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

  • target (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 input 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 x and weight data type are not float16 or float32 tensor, target data type is not int32 tensor.

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

Supported Platforms:

Ascend GPU

Examples

>>> input_tensor = Tensor(np.array([[0.5488135, 0.71518934],
...                                 [0.60276335, 0.5448832],
...                                 [0.4236548, 0.6458941]]).astype(np.float32))
>>> target = 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(input_tensor, target, weight)
>>> print(loss)
-0.52507716
>>> print(weight)
1.1503246