What are the key parameters of LSTM networks?

What are the key parameters of LSTM networks?

The key parameters of LSTM networks include the number of layers, number of units in each layer, learning rate, and dropout rate.

Can you explain what each of these parameters does?

Sure! The number of layers affects the depth of the network, while the number of units determines the capacity of each layer. The learning rate controls how quickly the model learns, and dropout rate helps prevent overfitting.

How do I choose the right learning rate?

Choosing the right learning rate often involves experimentation. You can start with a common value like 0.001 and adjust based on the model's performance.

What happens if the learning rate is too high?

If the learning rate is too high, the model may converge too quickly to a suboptimal solution or even diverge, leading to poor performance.