mindspore.nn.CrossEntropyLoss

class mindspore.nn.CrossEntropyLoss(weight=None, ignore_index=- 100, reduction='mean', label_smoothing=0.0)[source]

The cross entropy loss between input and target.

The CrossEntropyLoss support two kind of targets:

  • Class indices (int) in the range \([0, C)\) where \(C\) is the number of classes, the loss with reduction=none can be described as:

    \[\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad l_n = - w_{y_n} \log \frac{\exp(x_{n,y_n})}{\sum_{c=1}^C \exp(x_{n,c})} \cdot \mathbb{1}\{y_n \not= \text{ignore_index}\}\]

    where \(x\) is the inputs, \(t\) is the target, \(w\) 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

    \[\begin{split}\ell(x, y) = \begin{cases} \sum_{n=1}^N \frac{1}{\sum_{n=1}^N w_{y_n} \cdot \mathbb{1}\{y_n \not= \text{ignore_index}\}} l_n, & \text{if reduction} = \text{'mean',}\\ \sum_{n=1}^N l_n, & \text{if reduction} = \text{'sum'.} \end{cases}\end{split}\]

  • Probabilities (float) for each class, useful when labels beyond a single class per minibatch item are required, the loss with reduction=none can be described as:

    \[\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad l_n = - \sum_{c=1}^C w_c \log \frac{\exp(x_{n,c})}{\sum_{i=1}^C \exp(x_{n,i})} y_{n,c}\]

    where \(x\) is the inputs, \(t\) is the target, \(w\) 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

    \[\begin{split}\ell(x, y) = \begin{cases} \frac{\sum_{n=1}^N l_n}{N}, & \text{if reduction} = \text{'mean',}\\ \sum_{n=1}^N l_n, & \text{if reduction} = \text{'sum'.} \end{cases}\end{split}\]
Parameters

  • weight (Tensor) – The rescaling weight to each class. If the value is not None, the shape is (C,). The data type only supports float32 or float16. Default: None.

  • ignore_index (int) – Specifies a target value that is ignored (typically for padding value) and does not contribute to the gradient. Default: -100.

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

  • label_smoothing (float) – Label smoothing values, a regularization tool used to prevent the model from overfitting when calculating Loss. The value range is [0.0, 1.0]. Default value: 0.0.

Inputs:

  • logits (Tensor) - Tensor of shape \((C,)\) \((N, C)\) or \((N, C, d_1, d_2, ..., d_K)\) , where C = number of classes. Data type must be float16 or float32.

  • labels (Tensor) - For class indices, tensor of shape \(()\), \((N)\) or \((N, d_1, d_2, ..., d_K)\) , data type must be int32. For probabilities, tensor of shape \((C,)\) \((N, C)\) or \((N, C, d_1, d_2, ..., d_K)\) , data type must be float16 or float32.

Returns

Tensor, the computed cross entropy loss value.

Raises

  • TypeError – If weight is not a Tensor.

  • TypeError – If ignore_index is not an int.

  • TypeError – If the data type of weight is not float16 or float32.

  • ValueError – If reduction is not one of ‘none’, ‘mean’, ‘sum’.

  • TypeError – If label_smoothing is not a float.

  • TypeError – If logits is not a Tensor.

  • TypeError – If labels is not a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> # Case 1: Indices labels
>>> inputs = mindspore.Tensor(np.random.randn(3, 5), mindspore.float32)
>>> target = mindspore.Tensor(np.array([1, 0, 4]), mindspore.int32)
>>> loss = nn.CrossEntropyLoss()
>>> output = loss(inputs, target)
>>> # Case 2: Probability labels
>>> inputs = mindspore.Tensor(np.random.randn(3, 5), mindspore.float32)
>>> target = mindspore.Tensor(np.random.randn(3, 5), mindspore.float32)
>>> loss = nn.CrossEntropyLoss()
>>> output = loss(inputs, target)