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
CPU
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