declarative programming (static graph)
WeightNormParamAttr(dim=None, name=None, initializer=None, learning_rate=1.0, regularizer=None, trainable=True, 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.
WeightNormParamAttrHAS 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, 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, do_model_average=False))