(self, x)
| 161 | self.fc3 = torch.nn.Linear(84, 10) |
| 162 | |
| 163 | def forward(self, x): |
| 164 | # Max pooling over a (2, 2) window |
| 165 | x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) |
| 166 | # If the size is a square you can only specify a single number |
| 167 | x = F.max_pool2d(F.relu(self.conv2(x)), 2) |
| 168 | x = x.view(-1, self.num_flat_features(x)) |
| 169 | x = F.relu(self.fc1(x)) |
| 170 | x = F.relu(self.fc2(x)) |
| 171 | x = self.fc3(x) |
| 172 | return x |
| 173 | |
| 174 | def num_flat_features(self, x): |
| 175 | size = x.size()[1:] # all dimensions except the batch dimension |
nothing calls this directly
no test coverage detected