learning_rate_scheduler

cosine_decay

paddle.fluid.layers.cosine_decay(learning_rate, step_each_epoch, epochs)[source]

Applies cosine decay to the 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 following cosine decay strategy.

\[decayed\_lr = learning\_rate * 0.5 * (math.cos * (epoch * \frac{math.pi}{epochs} ) + 1)\]
Parameters:
  • learning_rate (Variable|float) – The initial learning rate.
  • step_each_epoch (int) – the number of steps in an epoch.
  • epochs (int) – the number of epochs.
Returns:

The decayed learning rate.

Return type:

Variable

Examples

import paddle.fluid as fluid
base_lr = 0.1
lr = fluid.layers.cosine_decay(
learning_rate = base_lr, step_each_epoch=10000, epochs=120)

exponential_decay

paddle.fluid.layers.exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False)[source]

Applies exponential decay to the 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 ‘decay_rate’ every ‘decay_steps’ steps.

>>> if staircase == True:
>>>     decayed_learning_rate = learning_rate * decay_rate ^ floor(global_step / decay_steps)
>>> else:
>>>     decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
Parameters:
  • learning_rate (Variable|float) – The initial learning rate.
  • decay_steps (int) – See the decay computation above.
  • decay_rate (float) – The decay rate. See the decay computation above.
  • staircase (Boolean) – If True, decay the learning rate at discrete intervals. Default: False
Returns:

The decayed learning rate

Return type:

Variable

Examples

import paddle.fluid as fluid
base_lr = 0.1
sgd_optimizer = fluid.optimizer.SGD(
    learning_rate=fluid.layers.exponential_decay(
          learning_rate=base_lr,
          decay_steps=10000,
          decay_rate=0.5,
          staircase=True))

inverse_time_decay

paddle.fluid.layers.inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False)[source]

Applies inverse time 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, an inverse decay function will be applied to the initial learning rate.

>>> if staircase == True:
>>>     decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step / decay_step))
>>> else:
>>>     decayed_learning_rate = learning_rate / (1 + decay_rate * global_step / decay_step)
Parameters:
  • learning_rate (Variable|float) – The initial learning rate.
  • decay_steps (int) – See the decay computation above.
  • decay_rate (float) – The decay rate. See the decay computation above.
  • staircase (Boolean) – If True, decay the learning rate at discrete intervals. Default: False
Returns:

The decayed learning rate

Return type:

Variable

Examples

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))

linear_lr_warmup

paddle.fluid.layers.linear_lr_warmup(learning_rate, warmup_steps, start_lr, end_lr)[source]

Applies linear learning rate warmup before the normal learning rate scheduling.

if global_step < warmup_steps:
    linear_step = end_lr - start_lr
    lr = start_lr + linear_step * (global_step / warmup_steps)
Parameters:
  • learning_rate (float | Variable) – A float value or Variable.
  • warmup_steps (int) – The warmup steps.
  • start_lr (float) – The start learning of warmup.
  • end_lr (float) – The end learning of warmup.
Returns:

The decayed learning rate in warmup period.

Examples

import paddle.fluid as fluid
boundaries = [100, 200]
lr_steps = [0.1, 0.01, 0.001]
warmup_steps = 50
start_lr = 1. / 3.
end_lr = 0.1
decayed_lr = fluid.layers.linear_lr_warmup(
    fluid.layers.piecewise_decay(boundaries, lr_steps),
    warmup_steps, start_lr, end_lr)

natural_exp_decay

paddle.fluid.layers.natural_exp_decay(learning_rate, decay_steps, decay_rate, staircase=False)[source]

Applies natural exponential decay to the initial learning rate.

>>> if not staircase:
>>>     decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
>>> else:
>>>     decayed_learning_rate = learning_rate * exp(- decay_rate * (global_step / decay_steps))
Parameters:
  • learning_rate – A scalar float32 value or a Variable. This will be the initial learning rate during training
  • decay_steps – A Python int32 number.
  • decay_rate – A Python float number.
  • staircase – Boolean. If set true, decay the learning rate every decay_steps.
Returns:

The decayed learning rate

Examples

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))

noam_decay

paddle.fluid.layers.noam_decay(d_model, warmup_steps)[source]

Noam decay method. The numpy implementation of noam decay as follows.

import padde.fluid as fluid
import numpy as np
# set hyper parameters
d_model = 2
current_steps = 20
warmup_steps = 200
# compute
lr_value = np.power(d_model, -0.5) * np.min([
                        np.power(current_steps, -0.5),
                        np.power(warmup_steps, -1.5) * current_steps])

Please reference attention is all you need.

Parameters:
  • d_model (Variable) – The dimensionality of input and output of model.
  • warmup_steps (Variable) – A super parameter.
Returns:

The decayed learning rate.

Examples

import padde.fluid as fluid
warmup_steps = 100
learning_rate = 0.01
lr = fluid.layers.learning_rate_scheduler.noam_decay(
               1/(warmup_steps *(learning_rate ** 2)),
               warmup_steps)

piecewise_decay

paddle.fluid.layers.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
Parameters:
  • 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
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))

polynomial_decay

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

Applies polynomial decay to the initial learning rate.

if cycle:
  decay_steps = decay_steps * ceil(global_step / decay_steps)
else:
  global_step = min(global_step, decay_steps)
  decayed_learning_rate = (learning_rate - end_learning_rate) *
       (1 - global_step / decay_steps) ^ power + end_learning_rate
Parameters:
  • learning_rate (Variable|float32) – A scalar float32 value or a Variable. This will be the initial learning rate during training.
  • decay_steps (int32) – A Python int32 number.
  • end_learning_rate (float) – A Python float number.
  • power (float) – A Python float number.
  • cycle (bool) – If set true, decay the learning rate every decay_steps.
Returns:

The decayed learning rate

Return type:

Variable

Examples

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