Note: This API is only avaliable in [Static Graph] mode
WeightNormParamAttr(dim=None, name=None, initializer=None, learning_rate=1.0, regularizer=None, trainable=True, gradient_clip=None, do_model_average=False)
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) – 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) – Regularization factor, such as
regularizer = fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.1). Default None, meaning that there is no regularization.
trainable (bool, optional) – Whether this parameter is trainable. Default True.
gradient_clip – The method to clip this parameter’s gradient, such as
gradient_clip = fluid.clip.GradientClipByNorm(clip_norm=2.0)). Default None, meaning that there is no gradient clip.
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, size=1000, param_attr=fluid.WeightNormParamAttr( dim=None, name='weight_norm_param', initializer=fluid.initializer.ConstantInitializer(1.0), learning_rate=1.0, regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.1), trainable=True, gradient_clip=fluid.clip.GradientClipByNorm(clip_norm=2.0), do_model_average=False))