(self, input)
| 230 | self.softmax_cross_entropy = layer.SoftMaxCrossEntropy() |
| 231 | |
| 232 | def features(self, input): |
| 233 | x = self.conv1(input) |
| 234 | x = self.bn1(x) |
| 235 | x = self.relu1(x) |
| 236 | |
| 237 | x = self.conv2(x) |
| 238 | x = self.bn2(x) |
| 239 | x = self.relu2(x) |
| 240 | |
| 241 | x = self.block1(x) |
| 242 | x = self.block2(x) |
| 243 | x = self.block3(x) |
| 244 | x = self.block4(x) |
| 245 | x = self.block5(x) |
| 246 | x = self.block6(x) |
| 247 | x = self.block7(x) |
| 248 | x = self.block8(x) |
| 249 | x = self.block9(x) |
| 250 | x = self.block10(x) |
| 251 | x = self.block11(x) |
| 252 | x = self.block12(x) |
| 253 | |
| 254 | x = self.conv3(x) |
| 255 | x = self.bn3(x) |
| 256 | x = self.relu3(x) |
| 257 | |
| 258 | x = self.conv4(x) |
| 259 | x = self.bn4(x) |
| 260 | return x |
| 261 | |
| 262 | def logits(self, features): |
| 263 | x = self.relu4(features) |