polynomial_decay

  • paddle.fluid.layers.polynomial_decay(learning_rate, decay_steps, end_learning_rate=0.0001, power=1.0, cycle=False)[源代码]

对初始学习率使用多项式衰减

  1. if cycle:
  2.     decay_steps = decay_steps * ceil(global_step / decay_steps)
  3. else:
  4.     global_step = min(global_step, decay_steps)
  5.     decayed_learning_rate = (learning_rate - end_learning_rate) *
  6.         (1 - global_step / decay_steps) ^ power + end_learning_rate
  • 参数：
    • learning_rate (Variable|float) - 训练过程中的初始学习率，数据类型为float的常数或变量。
    • decay_steps (int) - 衰减步数
    • end_learning_rate (float) - 训练过程的最终学习率
    • power (float) - 多项式衰减系数
    • cycle (bool) - step 超出 decay_steps 后是否继续循环，默认为False

返回：衰减的学习率

返回类型：变量（Variable）

代码示例：

  1. import paddle.fluid as fluid
  2. start_lr = 0.01
  3. total_step = 5000
  4. end_lr = 0
  5. lr = fluid.layers.polynomial_decay(
  6.     start_lr, total_step, end_lr, power=1)