1. BN层

• torch.nn.BatchNorm1d(num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  输入：\((N,C) \ or \ (N, C, L)\), \(\ C\)对应num_features.
• torch.nn.BatchNorm2d(num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  输入：\((N,C, H, W) \ or \ (N, C H, W)\), \(\ C\)对应num_features.

2. 卷积层

• torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros')
  输入：\((N, C, L)\), \(\ C\)对应num_features.
• torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros')
  输入：\((N, C, H, W)\), \(\ C\)对应num_features.
• torch.nn.Conv3d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros')
  输入：\((N, C, D, H, W)\), \(\ C\)对应num_features.

3. 激活层

• torch.nn.ReLU(inplace=False)
  输入：\((N,*)\).

4. 全连接层

• torch.nn.Linear(in_features, out_features, bias=True)
  输入：Input: \((N, *, C_{in})\).

5. LSTM

6. Transformer