def_initialize_weights(self):forminself.modules():ifisinstance(m,nn.Conv2d):nn.init.kaiming_normal_(m.weight,mode='fan_out',nonlinearity='relu')ifm.bias is not None:nn.init.constant_(m.bias,0)elifisinstance(m,nn.BatchNorm2d):nn.init.constant_(m.weight,1)nn.init.constant_(m.bias,0...
for m in model.modules(): if isinstance(m, (nn.Conv2d, nn.Linear)): nn.init.xavier_uniform_(m.weight) 1. 2. 3. 4. 也可以使用gain参数来自定义初始化的标准差来匹配特定的激活函数: for m in model.modules(): if isinstance(m, (nn.Conv2d, nn.Linear)): nn.init.xavier_uniform_(m....
If ``None``, use the foreach implementation for CUDA and CPU native tensors and silently fall back to the slow implementation for other device types. Default: ``None`` """ if isinstance(parameters, torch.Tensor): parameters = [parameters] clip_value = float(clip_value) grads = [p.grad...
AI代码解释 definitialize(self):forminself.modules():ifisinstance(m,nn.Linear):nn.init.kaiming_normal_(m.weight.data)# nn.init.normal_(m.weight.data,std=np.sqrt(2/self.neural_num))# 这两句话其实作用一样,不过自己写还得计算出标准差 我们可以看一下结果: 所以从上面的学习中,我们对权值的初...
bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1, dilate=False): norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = ...
if isinstance(m, nn.Conv2d): torch.nn.init.kaiming_normal_(m.weight) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
# Common practise for initialization.for layer in model.modules():if isinstance(layer, torch.nn.Conv2d):torch.nn.init.kaiming_normal_(layer.weight, mode='fan_out',nonlinearity='relu')if layer.bias is not None:torch.nn.init.constant_(layer.bias, val=0.0)el...
ifisinstance(elem, torch.Tensor): out=None iftorch.utils.data.get_worker_info()isnotNone: # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy numel=sum([x.numel()forxinbatch]) ...
classGMMClassifier(torch.nn.Module):def__init__(self, n_features, n_classes, n_components=2):super().__init__()self.n_classes = n_classesself.n_features = n_featuresself.n_components = n_componentsifisinstance( n_components, list)else[n_co...
if isinstance(layer,nn.Linear): x = layer(x) else: x = layer(x) x = self.output_layer(x) return x # 示例:构建一个具有两个隐藏层的网络,每个隐藏层有100个神经元,输入层大小为784(例如,展平的MNIST图像),输出层大小为10(例如,10个类别的分类问题) ...