<ul><li>Inconsistent model predictions in PyTorch can be attributed to factors like Batch Normalization and Dropout layers, which behave differently when processing inputs individually versus in a batch.</li><li>To ensure consistent predictions, it is crucial to set the model to evaluation mode using model.eval(), disable gradient calculation during prediction using torch.no_grad(), and handle model settings consistently.</li><li>An enhanced prediction code should incorporate setting the model to evaluation mode, disabling gradient calculation, and verifying consistency between predictions for individual inputs and batch inputs.</li><li>Consistent environments, such as hardware and library versions, should be maintained to avoid variations in model predictions. Understanding model behavior and implementing the recommended steps can help achieve reliable predictions in PyTorch.</li></ul>

How to Ensure Consistent Model Predictions in PyTorch?

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