| 20 | |
| 21 | |
| 22 | class MLP(nn.Module): |
| 23 | def __init__(self, sizes, bias=True): |
| 24 | super(MLP, self).__init__() |
| 25 | layers = [] |
| 26 | |
| 27 | for i in range(0, len(sizes) - 1): |
| 28 | |
| 29 | if i < (len(sizes) - 2): |
| 30 | layers.append(nn.Linear(sizes[i], sizes[i + 1])) |
| 31 | |
| 32 | layers.append(nn.ReLU()) |
| 33 | else: |
| 34 | layers.append(nn.Linear(sizes[i], sizes[i + 1], bias=bias)) |
| 35 | |
| 36 | self.net = nn.Sequential(*layers) |
| 37 | self.net.apply(Xavier) |
| 38 | |
| 39 | def forward(self, x): |
| 40 | return self.net(x) |
| 41 | |
| 42 | |
| 43 | def conv3x3(in_planes, out_planes, stride=1): |