.. _cn_api_fluid_optimizer_AdamOptimizer:

AdamOptimizer
-------------------------------

.. py:class:: paddle.fluid.optimizer.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, parameter_list=None, regularization=None, grad_clip=None, name=None, lazy_mode=False)




Adam优化器出自 `Adam论文 <https://arxiv.org/abs/1412.6980>`_ 的第二节，能够利用梯度的一阶矩估计和二阶矩估计动态调整每个参数的学习率。

其参数更新的计算公式如下：

.. math::
    \\t = t + 1
.. math::
    moment\_1\_out=\beta_1∗moment\_1+(1−\beta_1)∗grad
.. math::
    moment\_2\_out=\beta_2∗moment\_2+(1−\beta_2)∗grad*grad
.. math::
    learning\_rate=learning\_rate*\frac{\sqrt{1-\beta_2^t}}{1-\beta_1^t}
.. math::
    param\_out=param-learning\_rate*\frac{moment\_1}{\sqrt{moment\_2}+\epsilon}\\

相关论文：`Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_ 

参数: 
    - **learning_rate** (float|Variable，可选) - 学习率，用于参数更新的计算。可以是一个浮点型值或者一个值为浮点型的Variable，默认值为0.001
    - **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。
    - **beta1** (float|Variable, 可选) - 一阶矩估计的指数衰减率，是一个float类型或者一个shape为[1]，数据类型为float32的Variable类型。默认值为0.9
    - **beta2** (float|Variable, 可选) - 二阶矩估计的指数衰减率，是一个float类型或者一个shape为[1]，数据类型为float32的Variable类型。默认值为0.999
    - **epsilon** (float, 可选) - 保持数值稳定性的短浮点类型值，默认值为1e-08
    - **regularization** (WeightDecayRegularizer，可选) - 正则化方法。支持两种正则化策略: :ref:`cn_api_fluid_regularizer_L1Decay` 、 
      :ref:`cn_api_fluid_regularizer_L2Decay` 。如果一个参数已经在 :ref:`cn_api_fluid_ParamAttr` 中设置了正则化，这里的正则化设置将被忽略；
      如果没有在 :ref:`cn_api_fluid_ParamAttr` 中设置正则化，这里的设置才会生效。默认值为None，表示没有正则化。
    - **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。
    - **name** (str, 可选)- 该参数供开发人员打印调试信息时使用，具体用法请参见 :ref:`api_guide_Name` ，默认值为None
    - **lazy_mode** （bool, 可选） - 设为True时，仅更新当前具有梯度的元素。官方Adam算法有两个移动平均累加器（moving-average accumulators）。累加器在每一步都会更新。在密集模式和稀疏模式下，两条移动平均线的每个元素都会更新。如果参数非常大，那么更新可能很慢。 lazy mode仅更新当前具有梯度的元素，所以它会更快。但是这种模式与原始的算法有不同的描述，可能会导致不同的结果，默认为False


**代码示例**

.. code-block:: python

    import paddle
    import paddle.fluid as fluid
     
    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)
        adam_optimizer = fluid.optimizer.AdamOptimizer(0.01)
        adam_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)

.. code-block:: python

    # Adam with beta1/beta2 as Variable
    import paddle
    import paddle.fluid as fluid
    import paddle.fluid.layers.learning_rate_scheduler as lr_scheduler

    place = fluid.CPUPlace()
    main = fluid.Program()
    with fluid.program_guard(main):
        x = fluid.data(name='x', shape=[None, 13], dtype='float32')
        y = fluid.data(name='y', shape=[None, 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)

        # define beta decay variable
        def get_decayed_betas(beta1_init, beta2_init, decay_steps, decay_rate):
            global_step = lr_scheduler._decay_step_counter()

            beta1 = fluid.layers.create_global_var(
                shape=[1],
                value=float(beta1_init),
                dtype='float32',
                # set persistable for save checkpoints and resume
                persistable=True,
                name="beta1")
            beta2 = fluid.layers.create_global_var(
                shape=[1],
                value=float(beta2_init),
                dtype='float32',
                # set persistable for save checkpoints and resume
                persistable=True,
                name="beta2")

            div_res = global_step / decay_steps
            decayed_beta1 = beta1_init * (decay_rate**div_res)
            decayed_beta2 = beta2_init * (decay_rate**div_res)
            fluid.layers.assign(decayed_beta1, beta1)
            fluid.layers.assign(decayed_beta2, beta2)

            return beta1, beta2

        beta1, beta2 = get_decayed_betas(0.9, 0.99, 1e5, 0.9)
        adam_optimizer = fluid.optimizer.AdamOptimizer(
                                            learning_rate=0.01,
                                            beta1=beta1,
                                            beta2=beta2)
        adam_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)

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
         
返回: tuple(optimize_ops, params_grads)，其中optimize_ops为参数优化OP列表；param_grads为由(param, param_grad)组成的列表，其中param和param_grad分别为参数和参数的梯度。该返回值可以加入到 ``Executor.run()`` 接口的 ``fetch_list`` 参数中，若加入，则会重写 ``use_prune`` 参数为True，并根据 ``feed`` 和 ``fetch_list`` 进行剪枝，详见 ``Executor`` 的文档。

返回类型： tuple

**代码示例**

.. code-block:: python

    import numpy
    import paddle.fluid as fluid
     
    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)
    loss = fluid.layers.mean(cost)
    adam = fluid.optimizer.AdamOptimizer(learning_rate=0.2)
    adam.minimize(loss)

    place = fluid.CPUPlace() # fluid.CUDAPlace(0)
    exe = fluid.Executor(place)
     
    x = numpy.random.random(size=(10, 13)).astype('float32')
    y = numpy.random.random(size=(10, 1)).astype('float32')
    exe.run(fluid.default_startup_program())
    outs = exe.run(program=fluid.default_main_program(),
                   feed={'X': x, 'Y': y},
                   fetch_list=[loss.name])


.. 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.Adam(learning_rate=0.02,
                                         parameter_list=linear.parameters())
        out = linear(a)
        out.backward()
        optimizer.minimize(out)
        optimizer.clear_gradients()

.. py:method:: set_lr()

**注意：**

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

手动设置当前 ``optimizer`` 的学习率。当使用LearningRateDecay时，无法使用该API手动设置学习率，因为这将导致冲突。

参数：
    value (float|Variable) - 需要设置的学习率的值。

返回：无

**代码示例**

.. code-block:: python

    import paddle.fluid as fluid
            
    with fluid.dygraph.guard():
        linear = fluid.dygraph.nn.Linear(10, 10)
        adam = fluid.optimizer.Adam(0.1, parameter_list=linear.parameters())
        # 通过Python float数值手动设置学习率
        lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
        for i in range(5):
            adam.set_lr(lr_list[i])
            print("current lr is {}".format(adam.current_step_lr()))
        # 打印结果:
        #    current lr is 0.2
        #    current lr is 0.3
        #    current lr is 0.4
        #    current lr is 0.5
        #    current lr is 0.6


        # 通过 框架的Variable 设置学习率
        lr_var = fluid.layers.create_global_var(shape=[1], value=0.7, dtype='float32')
        adam.set_lr(lr_var)
        print("current lr is {}".format(adam.current_step_lr()))
        # 打印结果:
        #    current lr is 0.7



.. 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

