declarative programming (static graph)

class paddle.fluid.WeightNormParamAttr(dim=None, name=None, initializer=None, learning_rate=1.0, regularizer=None, trainable=True, do_model_average=False)[source]

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.


gradient_clip of WeightNormParamAttr HAS BEEN DEPRECATED since 2.0. It is recommended to use minimize(loss, grad_clip=clip) to clip gradient. There are three clipping strategies: GradientClipByGlobalNorm , GradientClipByNorm , GradientClipByValue .

  • dim (int) – 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) – The method to initialize this parameter, such as initializer = fluid.initializer.ConstantInitializer(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) – 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 SGDOptimizer ), 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.


import paddle.fluid as fluid
data = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32")
fc = fluid.layers.fc(input=data,