(self, x)
| 36 | self.fc2 = nn.Linear(32, num_classes) |
| 37 | |
| 38 | def forward(self, x): |
| 39 | # Convolution 1 |
| 40 | out = self.conv1(x) |
| 41 | out = self.relu1(out) |
| 42 | out = out.reshape(out.shape[0], out.shape[1], -1) |
| 43 | # print('After convolution1:', out.shape) |
| 44 | |
| 45 | # Convolution 2 |
| 46 | out = self.conv2(out) |
| 47 | out = self.relu2(out) |
| 48 | # print('After convolution2:', out.shape) |
| 49 | |
| 50 | # Max pool 1 |
| 51 | out = self.maxpool1(out) |
| 52 | # print('After maxpool1:', out.shape) |
| 53 | |
| 54 | # Convolution 3 |
| 55 | out = self.conv3(out) |
| 56 | out = self.relu3(out) |
| 57 | # print('After convolution3:', out.shape) |
| 58 | |
| 59 | # Convolution 4 |
| 60 | out = self.conv4(out) |
| 61 | out = self.relu4(out) |
| 62 | # print('After convolution4:', out.shape) |
| 63 | |
| 64 | # Max pool 2 |
| 65 | out = self.maxpool2(out) |
| 66 | # print('After maxcpool2:', out.shape) |
| 67 | |
| 68 | # flatten |
| 69 | out = out.view(out.size(0), -1) |
| 70 | # print('After flatten:', out.shape) |
| 71 | |
| 72 | # Linear function 1 |
| 73 | out = self.fc1(out) |
| 74 | out = self.relu5(out) |
| 75 | # print('After linear1:', out.shape) |
| 76 | |
| 77 | # Linear function (readout) |
| 78 | out = self.fc2(out) |
| 79 | # print('After linear2:', out.shape) |
| 80 | |
| 81 | return out |
nothing calls this directly
no outgoing calls
no test coverage detected