mindspore.nn.SoftmaxCrossEntropyWithLogits

SoftmaxCrossEntropyWithLogits is a loss function in MindSpore's neural network module that computes the cross-entropy loss between the predicted logits and the true labels.

It consists of two main steps:

1. Softmax function: the logits are transformed into probabilities using the softmax function. This ensures that the predicted probabilities sum up to 1.

2. Cross-entropy loss: the cross-entropy loss is computed between the predicted probabilities and the true labels. This measures how well the predicted probabilities match the true labels.

The SoftmaxCrossEntropyWithLogits function combines these two steps into a single operation, making it more efficient and numerically stable than computing the softmax and cross-entropy separately.

The function takes two inputs: logits and labels. Logits are the output of the last layer of the neural network before the activation function is applied, and labels are the true labels for the corresponding inputs. The function returns the average cross-entropy loss over all the samples in the batch.

SoftmaxCrossEntropyWithLogits is commonly used as a loss function in multi-class classification problems, where there are more than two classes.