torch.nn.BatchNorm1d(num_features,eps=1e-5,momentum=0.1,affine=True,track_running_stats=True,device=None,dtype=None) 具体参数的使用这里就不啰嗦了,紧接着 Applies Batch Normalization over a 2D or 3D input asdescribedin the paperBatch Normalization: Accelerating Deep Network Training by Reducing I...
torch.nn.BatchNorm1d(num_features,eps=1e-5,momentum=0.1,affine=True,track_running_stats=True,device=None,dtype=None) 具体参数的使用这里就不啰嗦了,紧接着 Applies Batch Normalization over a 2D or 3D input as described in the paper Batch Normalization: Accelerating Deep Network Training by Reduci...
track_running_stats (bool) : 是否跟踪训练期间的移动平均和移动方差 weight (torch.Tensor) : 权重参数,如果 affine=True bias (torch.Tensor) : 偏置参数,如果 affine=True 下面是torch.nn.BatchNorm1d的使用示例: import torch import torch.nn as nn # input tensor of size (batch_size, num_features)...
torch.nn.BatchNorm1d(num_features,eps=1e-5,momentum=0.1,affine=True,track_running_stats=True,device=None,dtype=None) 具体参数的使用这里就不啰嗦了,紧接着 Applies Batch Normalization over a 2D or 3D input as described in the paperBatch Normalization: Accelerating Deep Network Training by Reducing...
一、BatchNorm1d 对2D 或 3D 输入(具有可选附加通道维度的小批量 1D 输入)应用批量归一化。归一化的公式为: \[y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta \] 这里的\(x\)是张量的一个元素,\(\gamma\)和\(\beta\)分别是标准化处理后的数据放缩...
n: BatchNorm1d(2, eps=1e-05, momentum=0.1, affine=False, track_running_stats=True) input: tensor([[-2.2418, -0.1225], [ 0.1637, -0.1043], [-0.4440, -0.2567]]) output: tensor([[-2.2418, -0.1225], ...
2.1 BatchNorm 功能介绍:在C通道上做归一化,在通过gama和beta参数,将数据还原,BN的输入输出shape一致,数据基本一致,gama和beta是通过学习得到的 gama 和 beta是BN学习而来的的权重参数, torch.nn.BatchNorm1d/2d/3d(num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True, device=No...
(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # (linear2): Linear(in_features=1024, out_features=6272, bias=True) # (relu2): ReLU(inplace=True) # (batchnorm1d_2): BatchNorm1d(6272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # ...
(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # (linear2): Linear(in_features=1024, out_features=6272, bias=True) # (relu2): ReLU(inplace=True) # (batchnorm1d_2): BatchNorm1d(6272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # ...
(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # (linear2): Linear(in_features=1024, out_features=6272, bias=True) # (relu2): ReLU(inplace=True) # (batchnorm1d_2): BatchNorm1d(6272, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # ...