- SoftmaxCrossEntropyLossInternal#

SoftmaxCrossEntropyLossInternal - 1 (


This version of the operator has been available since version 1 of domain




  • reduction: Type of reduction to apply to loss: none, sum, mean(default). ‘none’: the output is the loss for each sample in the batch.’sum’: the output will be summed. ‘mean’: the sum of the output will be divided by the batch_size. Default value is ?.


Between 2 and 4 inputs.

  • scores (heterogeneous) - T: The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , …, Dk], where K is the number of dimensions.

  • labels (heterogeneous) - Tind: The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, …, Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.

  • weights (optional, heterogeneous) - T: A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.

  • ignore_index (optional, heterogeneous) - I: Scalar tensor to specify a target value that is ignored and does not contribute to the input gradient.


  • output (heterogeneous) - T: Weighted loss float Tensor. If reduction is ‘none’, this has the shape of [batch_size], or [batch_size, D1, D2, …, Dk] in case of K-dimensional loss. Otherwise, it is a scalar.

  • log_prob (heterogeneous) - T: Log probability tensor. If the output of softmax is prob, its value is log(prob).