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 ) [source]


Please use ‘paddle.nn.utils.weight_norm’ in dygraph mode.


gradient_clip of ParamAttr HAS BEEN DEPRECATED since 2.0. Please use need_clip in ParamAttr to specify the clip scope. There are three clipping strategies: ClipGradByGlobalNorm , ClipGradByNorm , ClipGradByValue .

Parameter of weight Norm. Weight Norm is a reparameterization of the weight vectors in a neural network that decouples the magnitude of those weight vectors from their direction. Weight Norm has been implemented as discussed in this paper: Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks.

  • dim (int, optional) – Dimension over which to compute the norm. Dim is a non-negative number which is less than the rank of weight Tensor. For Example, dim can be chosen from 0, 1, 2, 3 for convolution whose weight shape is [cout, cin, kh, kw] and rank is 4. Default None, meaning that all elements will be normalized.

  • name (str, optional) – The parameter’s name. Default None, meaning that the name would be created automatically. Please refer to Name for more details.

  • initializer (Initializer, optional) – The method to initialize this parameter, such as initializer = paddle.nn.initializer.Constant(1.0). Default None, meaning that the weight parameter is initialized by Xavier initializer, and the bias parameter is initialized by 0.

  • learning_rate (float32, optional) – The parameter’s learning rate when optimizer is \(global\_lr * parameter\_lr * scheduler\_factor\). Default 1.0.

  • regularizer (WeightDecayRegularizer, optional) – Regularization strategy. There are two method: L1Decay , L2Decay. If regularizer is also set in optimizer (such as SGD ), that regularizer setting in optimizer will be ignored. Default None, meaning there is no regularization.

  • trainable (bool, optional) – Whether this parameter is trainable. Default True.

  • do_model_average (bool, optional) – Whether this parameter should do model average. Default False.

  • need_clip (bool, optional) – Whether the parameter gradient need to be clipped in optimizer. Default is True.


>>> 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))