Computes softmax cross entropy between logits and labels. (deprecated arguments)

Used in the notebooks

Used in the tutorials

Measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in exactly one class). For example, each CIFAR-10 image is labeled with one and only one label: an image can be a dog or a truck, but not both.

If using exclusive labels (wherein one and only one class is true at a time), see sparse_softmax_cross_entropy_with_logits.

A common use case is to have logits and labels of shape [batch_size, num_classes], but higher dimensions are supported, with the axis argument specifying the class dimension.

logits and labels must have the same dtype (either float16, float32, or float64).

Backpropagation will happen into bot