(self, in_channel, out_channel)
| 61 | |
| 62 | class MLP(nn.Module): |
| 63 | def __init__(self, in_channel, out_channel): |
| 64 | super(MLP, self).__init__() |
| 65 | self.l1 = nn.Linear(in_features=in_channel, out_features=256) |
| 66 | self.l2 = nn.Linear(in_features=256, out_features=256) |
| 67 | self.l3 = nn.Linear(in_features=256, out_features=out_channel) |
| 68 | # self.dropout = nn.Dropout(p=0.5) |
| 69 | self.relu = nn.ReLU() |
| 70 | |
| 71 | def forward(self, x): |
| 72 | x = self.l1(x) |