Function SoftmaxCrossEntropyWithLogits

tensorflow/go/op/wrappers.go:24228–24240

Computes softmax cross entropy cost and gradients to backpropagate. Inputs are the logits, not probabilities. Arguments: features: batch_size x num_classes matrix labels: batch_size x num_classes matrix The caller must ensure that each batch of labels represents a valid probability distribution.

(scope *Scope, features tf.Output, labels tf.Output)

//
// Returns Per example loss (batch_size vector).backpropagated gradients (batch_size x num_classes matrix).
func SoftmaxCrossEntropyWithLogits(scope *Scope, features tf.Output, labels tf.Output) (loss tf.Output, backprop tf.Output) {
	if scope.Err() != nil {
		return
	}
	opspec := tf.OpSpec{
		Type: "SoftmaxCrossEntropyWithLogits",
		Input: []tf.Input{
			features, labels,
		},
	}
	op := scope.AddOperation(opspec)
	return op.Output(0), op.Output(1)
}

// Debugging/model interpretability outputs for each example.
//