| 28 | """ |
| 29 | |
| 30 | def __init__( |
| 31 | self, hidden_size, out_size, n_layers, weight_scale, bias=True, output_act=False |
| 32 | ): |
| 33 | super().__init__() |
| 34 | |
| 35 | self.linear = nn.ModuleList( |
| 36 | [nn.Linear(hidden_size, hidden_size, bias) for _ in range(n_layers)] |
| 37 | ) |
| 38 | self.output_linear = nn.Linear(hidden_size, out_size) |
| 39 | self.output_act = output_act |
| 40 | |
| 41 | for lin in self.linear: |
| 42 | lin.weight.data.uniform_( |
| 43 | -np.sqrt(weight_scale / hidden_size), |
| 44 | np.sqrt(weight_scale / hidden_size), |
| 45 | ) |
| 46 | |
| 47 | return |
| 48 | |
| 49 | def forward(self, x): |
| 50 | out = self.filters[0](x) |