(self, x)
| 33 | self.fc2 = nn.Linear(50, 10, **kwargs) |
| 34 | |
| 35 | def forward(self, x): |
| 36 | x = F.relu(F.max_pool2d(self.conv1(x), 2)) |
| 37 | x = F.relu(F.max_pool2d(self.conv2(x), 2)) |
| 38 | x = x.view(-1, 320) |
| 39 | x = F.relu(self.fc1(x)) |
| 40 | x = F.dropout(x, training=self.training) |
| 41 | x = self.fc2(x) |
| 42 | return F.log_softmax(x, dim=1) |
| 43 | |
| 44 | |
| 45 | class QuantLeNet(nn.Module): |