Learning rate schedules are an essential part of training neural networks. They define how the learning rate changes over time, which can significantly affect the performance and convergence of the model.
Common Learning Rate Schedules
- Step Decay: The learning rate is reduced by a fixed factor at regular intervals.
- Example:
lr = 0.1, decay = 0.01, step_size = 100
- Example:
- Exponential Decay: The learning rate is reduced exponentially over time.
- Example:
lr = 0.1, decay_rate = 0.99
- Example:
- Learning Rate Warmup: The learning rate starts at a low value and gradually increases to the initial value.
- Example:
lr = 0.01, warmup_steps = 1000
- Example:
Choosing the Right Schedule
The choice of learning rate schedule depends on the specific problem and the neural network architecture. It is often recommended to experiment with different schedules to find the one that works best for your model.
For more information on learning rate schedules, you can check out our Neural Network Optimization Guide.
Useful Resources
Learning Rate Schedule Visualization