Question

A machine learning engineer is training a neural network in a SageMaker training job to classify support tickets into 12 mutually exclusive categories. The labels are provided as integer class indices (0 through 11) rather than one-hot encoded vectors. The final layer uses a softmax activation with 12 units. During training, the loss function raises a shape-mismatch error expecting the target to have the same shape as the model output. Which change resolves the error with the least preprocessing effort?

Accepted Answer

Switch the loss function to sparse categorical cross-entropy, which accepts integer class indices directly.

Answer

Change the final layer activation from softmax to sigmoid so it outputs per-class probabilities independently.

Answer

Reduce the number of output units to 1 and treat the task as ordinal regression on the class index.

Answer

Convert the softmax output to a single argmax value before computing standard categorical cross-entropy.