MomentumOptimizer_cn.rst

.. _cn_api_fluid_optimizer_MomentumOptimizer:

MomentumOptimizer
-------------------------------

.. py:class::  paddle.fluid.optimizer.MomentumOptimizer(learning_rate, momentum, parameter_list=None, use_nesterov=False, regularization=None, name=None)

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：

.. math::
    & velocity = mu * velocity + gradient\\
    & if (use\_nesterov):\\
    &\quad   param = param - (gradient + mu * velocity) * learning\_rate\\
    & else:\\&\quad   param = param - learning\_rate * velocity

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。
    - **parameter_list** (list, 可选) - 指定优化器需要优化的参数。在动态图模式下必须提供该参数；在静态图模式下默认值为None，这时所有的参数都将被优化。
    - **use_nesterov** (bool，可选) - 赋能牛顿动量，默认值False。
    - **regularization** - 正则化函数，，例如 :code:`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**：

.. code-block:: python

    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)



.. py:method:: 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 :ref:`cn_api_fluid_Program` , 默认值为None，此时将使用 :ref:`cn_api_fluid_default_startup_program` 
    - **parameter_list** (list, 可选) – 待更新的Parameter或者Parameter.name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或者Parameter.name组成的集合，默认值为None
    - **grad_clip** (GradientClipBase, 可选) – 梯度裁剪的策略，支持三种裁剪策略： :ref:`cn_api_fluid_clip_GradientClipByGlobalNorm` 、 :ref:`cn_api_fluid_clip_GradientClipByNorm` 、 :ref:`cn_api_fluid_clip_GradientClipByValue` 。
      默认值为None，此时将不进行梯度裁剪。
        
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**：

.. code-block:: python

    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)



.. py:method:: clear_gradients()

**注意：**

  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**


清除需要优化的参数的梯度。

**代码示例**

.. code-block:: python

    import paddle.fluid as fluid
    import numpy as np

    with fluid.dygraph.guard():
        value = np.arange(26).reshape(2, 13).astype("float32")
        a = fluid.dygraph.to_variable(value)
        linear = fluid.Linear(13, 5, dtype="float32")
        optimizer = fluid.optimizer.MomentumOptimizer(learning_rate=0.001, momentum=0.9,
                                                      parameter_list=linear.parameters())
        out = linear(a)
        out.backward()
        optimizer.minimize(out)
        optimizer.clear_gradients()


.. py:method:: current_step_lr()

**注意：**

  **1. 该API只在** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **模式下生效**

获取当前步骤的学习率。当不使用LearningRateDecay时，每次调用的返回值都相同，否则返回当前步骤的学习率。

返回：当前步骤的学习率。

返回类型：float

**代码示例**

.. code-block:: python

    import paddle.fluid as fluid
    import numpy as np

    # example1: LearningRateDecay is not used, return value is all the same
    with fluid.dygraph.guard():
        emb = fluid.dygraph.Embedding([10, 10])
        adam = fluid.optimizer.Adam(0.001, parameter_list = emb.parameters())
        lr = adam.current_step_lr()
        print(lr) # 0.001

    # example2: PiecewiseDecay is used, return the step learning rate
    with fluid.dygraph.guard():
        inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
        linear = fluid.dygraph.nn.Linear(10, 10)
        inp = fluid.dygraph.to_variable(inp)
        out = linear(inp)
        loss = fluid.layers.reduce_mean(out)

        bd = [2, 4, 6, 8]
        value = [0.2, 0.4, 0.6, 0.8, 1.0]
        adam = fluid.optimizer.Adam(fluid.dygraph.PiecewiseDecay(bd, value, 0),
                           parameter_list=linear.parameters())

        # first step: learning rate is 0.2
        np.allclose(adam.current_step_lr(), 0.2, rtol=1e-06, atol=0.0) # True

        # learning rate for different steps
        ret = [0.2, 0.2, 0.4, 0.4, 0.6, 0.6, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0]
        for i in range(12):
            adam.minimize(loss)
            lr = adam.current_step_lr()
            np.allclose(lr, ret[i], rtol=1e-06, atol=0.0) # True