in_features,out_features,bias=True):super(CustomLinear,self).__init__(in_features,out_features,bias)defreset_parameters(self):# 默认的权重初始化nn.init.kaiming_uniform_(self.weight,a=math.sqrt(5))# 自定义的偏置初始化ifself.biasisnotNone:# 使用均值为0,标准差为0.01的正态分布初始化fan_in...
He initialization的思想是:在ReLU网络中,假定每一层有一半的神经元被激活,另一半为0。推荐在ReLU网络中使用。 # he initialization for m in model.modules(): if isinstance(m, (nn.Conv2d, nn.Linear)): nn.init.kaiming_normal_(m.weight, mode='fan_in') 1. 2. 3. 4. 正交初始化(Orthogonal I...
IMHO there is a discrepancy between the docs and code of nn.Linear, when it comes to initialization. documentation says that the weights are initialized from uniform ( 1/sqrt(in_ feaures) , 1/sqrt(in_ feaures)): pytorch/torch/nn/modules/linear.py ...
torch.nn.init.normal_(m.weight, 0, 1e-2) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): torch.nn.init.normal_(m.weight, 0, 1e-2) m.bias.data.zero_() print('Weights Initialization complete!') 2024-07-06· ...
if isinstance(m, torch.nn.Linear): m.weight.data.normal_(mean = 0, std = 0.01) m.bias.data.fill_(0.0) PyTorch提供了几种常见的参数初始化方式的实现 Xavier Initialization: 基本思想是维持输入和输出的方差一致,避免了所有的输出值都为0, 使用于任何激活函数 ...
# This function is for model's parameters initializationdef init_weights(m):if isinstance(m, torch.nn.Linear):torch.nn.init.uniform_(m.weight , a = -0.1 , b = 0.1)torch.nn.init.uniform_(m.bias , a = -0.1 ...
bias is not None:torch.nn.init.constant_(layer.bias, val=0.0)elif isinstance(layer, torch.nn.BatchNorm2d):torch.nn.init.constant_(layer.weight, val=1.0)torch.nn.init.constant_(layer.bias, val=0.0)elif isinstance(layer, torch.nn.Linear):torch.nn.init.xavier_normal_(layer.weight)if...
权值初始化的作用有很多，通常，一个好的权值初始化将会加快模型的收敛，而比较差的权值初始化将会引发...
Linear: nn.init.xavier_uniform_(m.weight) nn.init.zeros_(m.bias) b. He初始化 He初始化是专为使用ReLU(修正线性单元)激活函数的神经网络设计的参数初始化方法。与Xavier初始化类似,He初始化也从均值为0的高斯分布中随机采样,但是使用方差𝜎^2 = 2/n进行缩放,其中n是输入的数量。He初始化通过...
torch.nn.Linear(hidden_num_units, output_num_units), ) loss_fn = torch.nn.CrossEntropyLoss() 现在你知道了PyTorch的基本组件,你可以轻松地从头构建自己的神经网络。如果你想知道具体怎么做,继续往下读。 构建神经网络之 Numpy VS. PyTorch 之前我提到PyT...