update optimizer doc for 2.0 #2424
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TCChenlong
reviewed
Aug 22, 2020
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| 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 |
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loat|LearningRateDecay 还是 float|Tensor？
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改为loat|LearningRateDecay
TCChenlong
reviewed
Aug 31, 2020
TCChenlong
reviewed
Aug 31, 2020
| AdamW | ||
| ------------------------------- | ||
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| .. py:class:: paddle.optimizer.AdamW(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, parameters=None, weight_decay=0.01, grad_clip=None, name=None, lazy_mode=False) |
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没有写【apply_decay_param_fun】
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| 返回：当前步骤的学习率。 | ||
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| 返回类型：float |
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不需要单独写返回类型，直接写到返回中即可，如：
返回：float，当前步骤的学习率。
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| 返回：当前步骤的学习率。 | ||
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| 返回类型：float |
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| 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。在静态图模式下，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。 | ||
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| 返回类型： tuple |
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| 返回：当前步骤的学习率。 | ||
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| 返回类型：float |
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| 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。在静态图模式下，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。 | ||
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| 返回类型： tuple |
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| 返回：当前步骤的学习率。 | ||
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| 返回类型：float |
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完善Adam、Adamax、Optimizer、RMSProp op
新增AdamW op
Optimizer类
参数parameter_list 变为 parameters
参数regularization 变为weight_decay，传入float类型时为L2Decay的系数
set_dict接口变为set_state_dict
动态图下新增step接口，替代minimize
AdamOptimzer变为Adam、AdamaxOptimizer变为Adamax、RMSPropOptimizer变为RMSProp，其余改动与基类Optimizer相同。
新增AdamW类
继承自DecoupledWeightDecay、Adam
英文文档PR：PaddlePaddle/Paddle#26288
英文文档修改PR：PaddlePaddle/Paddle#26711