piecewise_decay¶
- paddle.fluid.layers.learning_rate_scheduler. piecewise_decay ( boundaries, values ) [source]
-
Applies piecewise decay to the initial learning rate.
The algorithm can be described as the code below.
boundaries = [10000, 20000] values = [1.0, 0.5, 0.1] if step < 10000: learning_rate = 1.0 elif 10000 <= step < 20000: learning_rate = 0.5 else: learning_rate = 0.1- Args:
-
boundaries: A list of steps numbers. values: A list of learning rate values that will be picked during
different step boundaries.
- Returns:
-
The decayed learning rate.
- Examples:
-
import paddle.fluid as fluid import paddle paddle.enable_static() boundaries = [10000, 20000] values = [1.0, 0.5, 0.1] optimizer = fluid.optimizer.Momentum( momentum=0.9, learning_rate=fluid.layers.piecewise_decay(boundaries=boundaries, values=values), regularization=fluid.regularizer.L2Decay(1e-4))