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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:

(x,t)=L={l1,,lN},ln=wtnxn,tn,wc= weight [c]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

(x,t)={n=1N1n=1Nwtnln, if reduction = 'mean'; n=1Nln, if reduction = 'sum' 
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