nonlinearity– the non-linear function (nn.functional name), recommended to use only with'relu'or'leaky_relu'(default). Examples: >>> w = torch.empty(3, 5) >>> nn.init.kaiming_normal_(w, mode='fan_out', nonlinearity='relu') 1. 2....
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')ifm.biasisnotNone: nn.init.zeros_(m.bias)elifisinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight,1) nn.init.constant_(m.bias,0)elifisinstance(m, nn.Linear): nn.init.normal_(m.weight,0...
kaiming_normal(m.weight, mode='fan_out') if m.bias: init.constant(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant(m.weight, 1) init.constant(m.bias, 0) elif isinstance(m, nn.Linear): init.normal(m.weight, std=1e-3) if m.bias: init.constant(m.bias, 0) #_,...
#10.kaiming_normal 初始化 #torch.nn.init.kaiming_normal_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu') print(nn.init.kaiming_normal_(w, mode='fan_out', nonlinearity='relu')) # === # tensor([[-0.0210, 0.5532, -0.8647, 0.9813, 0.0466], # [ 0.7713, -1.0418, 0.7264, ...
modules(): if isinstance(m, nn.Conv3d): m.weight = nn.init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm3d): m.weight.data.fill_(1) m.bias.data.zero_() ...
m.weight = nn.init.kaiming_normal_(m.weight, mode='fan_out')ifm.biasisnotNone: m.bias.data.zero_()elifisinstance(m, nn.BatchNorm3d): m.weight.data.fill_(1) m.bias.data.zero_() 开发者ID:TengdaHan,项目名称:DPC,代码行数:27,代码来源:resnet_2d3d.py ...
nn.Xxx不需要你自己定义和管理weight;而nn.functional.xxx需要你自己定义weight,每次调用的时候都需要手动传入weight, 不利于代码复用。 使用nn.Xxx定义一个CNN 。 class CNN(nn.Module): def __init__(self): super(CNN, self).__init__() self.cnn1 = nn.Conv2d(in_channels=1, out_channels=16, ke...
#torch.nn.init.normal_(tensor, mean=0.0, std= 1.0)print(nn.init.normal_(w))# === # tensor([[ 0.4388, 0.3083, -0.6803, - 1.1476, -0.6084],# [ 0.5148, -0.2876, - 1.2222, 0.6990, -0.1595],# [- 2.0834, - 1.6288, 0.5057, -0.5754, ...
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(m.weight) bound = 1 / math.sqrt(fan_in) nn.init.uniform_(weight, -bound, bound) And an explanation why we don't actually use Kaiming He's inititialization but values that are sqrt(3)/sqrt(6) smaller (with gain from 'linear...
nn.init.kaiming_normal_(m.weight, mode='fan_out') if m.bias is not None: nn.init.zeros_(m.bias) elif isinstance(m, nn.BatchNorm2d): nn.init.ones_(m.weight) nn.init.zeros_(m.bias) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.zeros_(m.bias...