(self, in_dim, out_dim, bias=True, w_init_gain='linear')
| 27 | |
| 28 | class LinearNorm(torch.nn.Module): |
| 29 | def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'): |
| 30 | super(LinearNorm, self).__init__() |
| 31 | self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias) |
| 32 | |
| 33 | torch.nn.init.xavier_uniform_( |
| 34 | self.linear_layer.weight, |
| 35 | gain=torch.nn.init.calculate_gain(w_init_gain)) |
| 36 | |
| 37 | def forward(self, x): |
| 38 | return self.linear_layer(x) |