torch.nn.init.kaiming_normal_(tensor, a=0, mode='fan_in', nonlinearity='leaky_relu')[source] 1. Fills the input Tensor with values according to the method described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), us...
参考torch.nn.init - 云+社区 - 腾讯云 代码语言:javascript 复制 torch.nn.init.kaiming_normal_(tensor,a=0,mode='fan_in',nonlinearity='leaky_relu')[source] Fills the input Tensor with values according to the method described in Delving deep into rectifiers: Surpassing human-level performance on ...
find('Linear') == 0) and hasattr(m, 'weight'): if init_type == 'normal': init.normal(m.weight.data, 0.0, 0.02) elif init_type == 'xavier': init.xavier_normal(m.weight.data, gain=math.sqrt(2)) elif init_type == 'kaiming': init.kaiming_normal(m.weight.data, a=0, mode=...
#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, ...
w = torch.empty(2,2)print('before init w = \n',w)torch.nn.init.kaiming_normal_(w, mode='fan_out', nonlinearity='relu')print('after init w = \n',w) 结果: before init w =tensor([[-0.8456, 1.3498],[-0.8480, -1.1506]])after init w =tensor([[-1.0357, -1.1732],[ 0.1517,...
torch.nn.init.kaiming_normal(tensor, a=0, mode='fan_in') 根据He, K等人2015年在"深入研究了超越人类水平的性能:整流器在ImageNet分类"中描述的方法,采用正态分布,填充张量或变量。结果张量中的值采样自均值为0,标准差为sqrt(2/((1 + a^2) * fan_in))的正态分布。该方法也被称为He的初始化。
torch.nn.init.kaiming_normal_(tensor,a=0,mode='fan_in',nonlinearity='leaky_relu') 解释: Fills the input Tensor with values according to the method described in Delving deep into rectifiers: Surpassing humanlevel performance on ImageNet classification - He, K. et...
torch.nn.init.kaiming_uniform_(tensor, a=0, mode=‘fan_in’,nonlinearity='leaky_relu'),均匀分布∼U(−bound,bound)。其中,bound的计算公式: bound=6(1+a2)×fan_in torch.nn.init.kaiming_normal_(tensor, a=0, mode=‘fan_in’,nonlinearity='leaky_relu'),正太分布∼N(0,std)。 其中,...
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) nn.init.constant_(m.bias, 0) ...
kaiming_normalw = torch.empty(3, 5) torch_init.kaiming_normal_(w, mode='fan_in', nonlinearity='relu') 复制tensor([[-0.1372, 0.7091, -1.0794, -0.2982, -0.5171], [ 0.2817, 0.6475, -0.3793, -0.0194, 0.1257], [-0.2764, -1.0841, 0.5978, 0.1805, 0.0318]]) 复制w = ms_init....