周期性学习率测验

当您参加测验时，就会出现答案和解释。

1. 周期性学习率挑战了什么核心假设？
   • 学习率只会在训练期间降低
   • 批量大小必须保持不变
   • 该模型需要 GPU
   • 该数据必须被打乱
2. What did Smith report for cyclical learning rates on tested classification tasks?
   • They always lowered training loss to zero
   • They improved accuracy versus fixed rates, often in fewer iterations
   • They removed the need for validation data
   • They guaranteed escape from every sharp minimum
3. “LR 范围测试”有什么作用？
   • 测量 GPU 内存使用情况
   • 计算模型参数的数量
   • 向上调整学习率，同时观察损失以选择合适的范围
   • 使用分布外数据测试模型
4. 在单周期策略中，动量相对于学习率通常如何表现？
   • 动量始终保持固定
   • 动量始终设置为零
   • 动量与速率完全同步增加
   • 动量反向移动——利率低时动量高，利率高时动量低
5. During a triangular cyclical learning-rate cycle, how does the rate move between its bounds?
   • It rises from the base rate to the maximum, then falls back toward the base
   • It only decreases from the first step
   • It stays fixed at the maximum
   • It jumps randomly at every minibatch
6. 谁引入了循环学习率和 LR 范围测试？
   • 杰弗里·辛顿
   • 莱斯利·史密斯
   • 严乐存
   • 安德烈·卡帕蒂
7. What sets the duration of a cyclical learning-rate cycle?
   • 模型层数
   • 词汇量大小
   • A chosen number of training iterations for the up-and-down pattern
   • The number of optimizer parameters
8. What distinguishes the one-cycle policy from repeating several triangular cycles?
   • It keeps the learning rate constant throughout
   • It removes the optimizer’s momentum
   • It measures the learning rate only once
   • It uses one long learning-rate cycle for the training run