Building LSTMs with PyTorch and Lightning AI Part 3: Finishing the LSTM Cell

Short summary

Part 3 finalizes the LSTM cell by implementing output gates that use sigmoid and tanh activation functions to control what information reaches the output layer. The forward pass shows how sequential stock prices are processed through the same LSTM unit over four days, with long-term and short-term memory states updated and carried forward at each step. The article concludes by configuring the Adam optimizer to prepare for the training phase.

• •Output gate implementation using sigmoid and tanh determines what portion of memory flows to output
• •Forward pass unrolls LSTM across four sequential inputs, maintaining and updating both memory types
• •Adam optimizer configured to prepare for training_step() method in next article

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