natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False)
Applies natural exponential decay to the initial learning rate.
When training a model, it is often recommended to lower the learning rate as the training progresses. By using this function, the learning rate will be decayed by natural exponential power ‘decay_rate’ every ‘decay_steps’ steps.
Decayed learning rate calculates as follows:
>>> if not staircase: >>> decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps)) >>> else: >>> decayed_learning_rate = learning_rate * exp(- decay_rate * floor(global_step / decay_steps))
learning_rate (Variable|float) – The initial learning rate. It should be a Variable or a float
decay_steps (int) – The learning rate decay steps. See the decay computation above.
decay_rate (float) – The learning rate decay rate. See the decay computation above.
staircase (bool) – If True, decay the learning rate at discrete intervals, which means the learning rate will be decayed by natural exponential power decay_rate every decay_steps. If False, learning rate will be decayed continuously and following the formula above. Default: False
The decayed learning rate. The data type is float32.
import paddle.fluid as fluid base_lr = 0.1 sgd_optimizer = fluid.optimizer.SGD( learning_rate=fluid.layers.natural_exp_decay( learning_rate=base_lr, decay_steps=10000, decay_rate=0.5, staircase=True))