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# torch.nn.functional.cross_entropy

torch.nn.functional.cross_entropy(input, target, weight=None, size_average=None, ignore_index=- 100, reduce=None, reduction='mean', label_smoothing=0.0)

  This criterion computes the cross entropy loss between input logits and target.

  See `CrossEntropyLoss` for details.

  Parameters:

    - input (Tensor) – Predicted unnormalized logits; see Shape section below for supported shapes.

    - target (Tensor) – Ground truth class indices or class probabilities; see Shape section below for supported shapes.

    - weight (Tensor, optional) – a manual rescaling weight given to each class. If given, has to be a Tensor of size C

    - size_average (bool, optional) – Deprecated (see `reduction`). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there multiple elements per sample. If the field `size_average` is set to `False`, the losses are instead summed for each minibatch. Ignored when reduce is `False`. Default: `True`

    - ignore_index (int, optional) – Specifies a target value that is ignored and does not contribute to the input gradient. When `size_average` is `True`, the loss is averaged over non-ignored targets. Note that `ignore_index` is only applicable when the target contains class indices. Default: -100

    - reduce (bool, optional) – Deprecated (see `reduction`). By default, the losses are averaged or summed over observations for each minibatch depending on `size_average`. When `reduce` is `False`, returns a loss per batch element instead and ignores `size_average`. Default: `True`

    - reduction (str, optional) – Specifies the reduction to apply to the output: `'none'` | `'mean'` | `'sum'`. `'none'`: no reduction will be applied, `'mean'`: the sum of the output will be divided by the number of elements in the output, `'sum'`: the output will be summed. Note: `size_average` and `reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override `reduction`. Default: `'mean'`

    - label_smoothing (float, optional) – A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets become a mixture of the original ground truth and a uniform distribution as described in Rethinking the Inception Architecture for Computer Vision. Default: 0.0 0.0 0.0.

  Return type:

    Tensor

  Shape:

    - Input: Shape (C)(C)(C), (N,C)(N, C)(N,C) or (N,C,d 1,d 2,...,d K)(N, C, d_1, d_2, ..., d_K)(N,C,d 1​,d 2​,...,d K​) with K≥1 K \geq 1 K≥1 in the case of K-dimensional loss.

    - Target: If containing class indices, shape ()()(), (N)(N)(N) or (N,d 1,d 2,...,d K)(N, d_1, d_2, ..., d_K)(N,d 1​,d 2​,...,d K​) with K≥1 K \geq 1 K≥1 in the case of K-dimensional loss where each value should be between [0,C)[0, C)[0,C). If containing class probabilities, same shape as the input and each value should be between [0,1][0, 1][0,1].

    where:

    C=number of classes N=batch size\begin{aligned} C ={} & \text{number of classes} \\ N ={} & \text{batch size} \\ \end{aligned} C=N=​number of classes batch size​

  Examples:

      >>> # Example of target with class indices
      >>> input = torch.randn(3, 5, requires_grad=True)
      >>> target = torch.randint(5, (3,), dtype=torch.int64)
      >>> loss = F.cross_entropy(input, target)
      >>> loss.backward()
      >>>
      >>> # Example of target with class probabilities
      >>> input = torch.randn(3, 5, requires_grad=True)
      >>> target = torch.randn(3, 5).softmax(dim=1)
      >>> loss = F.cross_entropy(input, target)
      >>> loss.backward()

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