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paddle.static / WeightNormParamAttr
WeightNormParamAttr¶
-
class
paddle.static.
WeightNormParamAttr
( dim=None, name=None, initializer=None, learning_rate=1.0, regularizer=None, trainable=True, do_model_average=False, need_clip=True ) [源代码] ¶
- api_attr
-
声明式编程模式(静态图)
注解
动态图模式下请使用 paddle.nn.utils.weight_norm
。
注解
该类中的 gradient_clip
属性在2.0版本会废弃,推荐在初始化 optimizer
时设置梯度裁剪。共有三种裁剪策略: cn_api_paddle_nn_ClipGradByGlobalNorm 、 cn_api_paddle_nn_ClipGradByNorm 、 cn_api_paddle_nn_ClipGradByValue 。
该类定义了权重归一化(Weight Normalization)的参数。权重归一化可以将神经网络中权重向量的长度与其方向解耦,详细的定义与实现可以参考论文:Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
- 参数:
-
dim (int,可选) - 进行归一化操作(norm)的切片所在维度,是小于权重Tensor rank的非负数。比如卷积的权重shape是 \([cout, cin, kh, kw]\) , rank是4,则dim可以选0,1,2,3;fc的权重shape是 \([cout, cin]\) ,rank是2,dim可以选0,1。 dim 默认为None,如果为None就对所有元素做归一化(norm)。
name (None|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 Name ,默认为None。
initializer (Initializer,可选) - 初始化参数方法,例如
initializer = fluid.nn.initializer.Constant(1.0)
。默认为None,如果为None则使用默认初始化函数 Xavier() 。learning_rate (float32,可选) - 学习率,优化过程 \(global\_lr∗parameter\_lr∗scheduler\_factor\) 的学习速率,默认为1.0。
regularizer (WeightDecayRegularizer,可选) - 正则化方法。支持两种正则化策略: L1Decay 、 L2Decay ,如果在
optimizer
(例如 SGD ) 中也 设置了正则化,optimizer
中的正则化将被忽略。默认值为None,表示没有正则化。trainable (bool) - 可选,指明参数是否可训练,默认为True。
do_model_average (bool) - 可选,指明参数是否需要模型平均化操作(Model Average),默认为False。
need_clip (bool) - 可选,指明参数梯度是否需要在优化器中进行clip,默认为True。
代码示例
import paddle
paddle.enable_static()
data = paddle.static.data(name="data", shape=[3, 32, 32], dtype="float32")
fc = paddle.static.nn.fc(x=data,
size=1000,
weight_attr=paddle.static.WeightNormParamAttr(
dim=None,
name='weight_norm_param',
initializer=paddle.nn.initializer.Constant(1.0),
learning_rate=1.0,
regularizer=paddle.regularizer.L2Decay(0.1),
trainable=True,
do_model_average=False,
need_clip=True))
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