A machine learning engineer is training a deep neural network image classifier using a SageMaker training job. During the first few hundred iterations, the training loss oscillates wildly and occasionally spikes to NaN before the job fails. The engineer confirms the input data has been normalized correctly and contains no missing values. Which change is MOST likely to stabilize training?