MomentumOptimizer

class paddle.fluid.optimizer.MomentumOptimizer(learning_rate, momentum, use_nesterov=False, regularization=None, name=None)[源代码]

该接口实现含有速度状态的Simple Momentum 优化器

该优化器含有牛顿动量标志,公式更新如下:

\[\begin{split}& velocity = mu * velocity + gradient\\ & if (use\_nesterov):\\ &\quad param = param - (gradient + mu * velocity) * learning\_rate\\ & else:\\&\quad param = param - learning\_rate * velocity\end{split}\]
参数:
  • learning_rate (float|Variable) - 学习率,用于参数更新。作为数据参数,可以是浮点型值或含有一个浮点型值的变量。
  • momentum (float) - 动量因子。
  • use_nesterov (bool,可选) - 赋能牛顿动量,默认值False。
  • regularization - 正则化函数,,例如 fluid.regularizer.L2DecayRegularizer,默认值None。
  • name (str, 可选) - 可选的名称前缀,一般无需设置,默认值为None。

代码示例

import paddle
import paddle.fluid as fluid
import numpy as np

place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
    y_predict = fluid.layers.fc(input=x, size=1, act=None)
    cost = fluid.layers.square_error_cost(input=y_predict, label=y)
    avg_cost = fluid.layers.mean(cost)

    moment_optimizer = fluid.optimizer.MomentumOptimizer(learning_rate=0.001, momentum=0.9)
    moment_optimizer.minimize(avg_cost)

    fetch_list = [avg_cost]
    train_reader = paddle.batch(
        paddle.dataset.uci_housing.train(), batch_size=1)
    feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())
    for data in train_reader():
        exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
minimize(loss, startup_program=None, parameter_list=None, no_grad_set=None, grad_clip=None)

为网络添加反向计算过程,并根据反向计算所得的梯度,更新parameter_list中的Parameters,最小化网络损失值loss。

参数:
  • loss (Variable) – 需要最小化的损失值变量
  • startup_program (Program, 可选) – 用于初始化parameter_list中参数的 Program , 默认值为None,此时将使用 default_startup_program
  • parameter_list (list, 可选) – 待更新的Parameter组成的列表, 默认值为None,此时将更新所有的Parameter
  • no_grad_set (set, 可选) – 不需要更新的Parameter的集合,默认值为None
  • grad_clip (GradClipBase, 可选) – 梯度裁剪的策略,静态图模式不需要使用本参数,当前本参数只支持在dygraph模式下的梯度裁剪,未来本参数可能会调整,默认值为None

返回: (optimize_ops, params_grads),数据类型为(list, list),其中optimize_ops是minimize接口为网络添加的OP列表,params_grads是一个由(param, grad)变量对组成的列表,param是Parameter,grad是该Parameter对应的梯度值

返回类型: tuple

代码示例

import paddle
import paddle.fluid as fluid
import numpy as np

place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
    y_predict = fluid.layers.fc(input=x, size=1, act=None)
    cost = fluid.layers.square_error_cost(input=y_predict, label=y)
    avg_cost = fluid.layers.mean(cost)

    moment_optimizer = fluid.optimizer.MomentumOptimizer(learning_rate=0.001, momentum=0.9)
    moment_optimizer.minimize(avg_cost)

    fetch_list = [avg_cost]
    train_reader = paddle.batch(
        paddle.dataset.uci_housing.train(), batch_size=1)
    feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())
    for data in train_reader():
        exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)