.. _cn_api_fluid_regularizer_L2Decay:

L2Decay
-------------------------------

.. py:attribute::   paddle.fluid.regularizer.L2Decay

L2Decay实现L2权重衰减正则化，用于模型训练，有助于防止模型对训练数据过拟合。

该类生成的实例对象，需要设置在 :ref:`cn_api_fluid_ParamAttr` 或者 ``optimizer`` 
(例如 :ref:`cn_api_fluid_optimizer_SGDOptimizer` )中，在 ``ParamAttr`` 中设置时，
只对该网络层中的参数生效；在 ``optimizer`` 中设置时，会对所有的参数生效；如果同时设置，
在 ``ParamAttr`` 中设置的优先级会高于在 ``optimizer`` 中设置。

具体实现中，L2权重衰减正则化的计算公式如下：

.. math::
            \\L2WeightDecay=reg\_coeff*parameter\\

参数:
  - **regularization_coeff** (float) – 正则化系数，默认值为0.0。

**代码示例1**

.. code-block:: python

    import paddle.fluid as fluid

    main_prog = fluid.Program()
    startup_prog = fluid.Program()
    with fluid.program_guard(main_prog, startup_prog):
        data = fluid.layers.data(name='image', shape=[3, 28, 28], dtype='float32')
        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
        hidden = fluid.layers.fc(input=data, size=128, act='relu')
        prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
        loss = fluid.layers.cross_entropy(input=prediction, label=label)
        avg_loss = fluid.layers.mean(loss)
    optimizer = fluid.optimizer.Adagrad(
        learning_rate=1e-4,
        regularization=fluid.regularizer.L2Decay(
            regularization_coeff=0.1))
    optimizer.minimize(avg_loss)


**代码示例2**

.. code-block:: python
    
    # 在 ParamAttr 和 optimizer 中同时设置正则化
    import paddle.fluid as fluid
    l1 = fluid.regularizer.L1Decay(regularization_coeff=0.1)
    l2 = fluid.regularizer.L2Decay(regularization_coeff=0.1)
    x = fluid.layers.uniform_random([3,4])
    
    # 在ParamAttr中设置L1正则化
    w_param = fluid.ParamAttr(regularizer=l1)
    hidden1 = fluid.layers.fc(x, 8, param_attr=w_param)    # fc_0.w_0(L1), fc_0.b_0
    hidden2 = fluid.layers.fc(hidden1, 16, param_attr=w_param)  # fc_1.w_0(L1), fc_1.b_0
    predict = fluid.layers.fc(hidden2, 32)    # fc_3.w_0, fc_3.b_0
    avg_loss = fluid.layers.mean(predict)
    
    # 在optimizer中设置L2正则化
    optimizer = fluid.optimizer.SGD(learning_rate=1e-4, regularization=l2)
    optimizer.minimize(avg_loss)
    
    # 将会打印出提示信息:
    # Regularization of [fc_0.w_0, fc_1.w_0] have been set by ParamAttr or WeightNormParamAttr already. 
    # So, the Regularization of Optimizer will not take effect for these parameters!