Building LSTMs with PyTorch and Lightning AI Part 4: Training Step and Initial Predictions

Short summary

This tutorial walks through implementing PyTorch Lightning's training_step() function for LSTM neural networks, explaining how to calculate loss as squared residual error and log training metrics. It includes complete, working code that demonstrates making predictions from an untrained model on stock price data for comparison purposes. As part 4 of an ongoing series, this article establishes the foundation for the next installment, which will cover actual model training to improve the accuracy of predictions.

• •Implements training_step() function for LSTM models in PyTorch Lightning
• •Explains loss calculation as squared residual error and metric logging
• •Demonstrates initial predictions from untrained models using stock price data

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