(self, x)
| 43 | return seq_block |
| 44 | |
| 45 | def forward(self, x): |
| 46 | x = self.conv1(x) |
| 47 | x = self.maxpool(x) |
| 48 | |
| 49 | x = self.conv2(x) |
| 50 | x = self.maxpool(x) |
| 51 | |
| 52 | x = self.conv3(x) |
| 53 | x = self.maxpool(x) |
| 54 | x = x.reshape((-1, self.bigN)) |
| 55 | |
| 56 | x = F.relu(self.fc1(x)) |
| 57 | x = F.relu(self.fc2(x)) |
| 58 | x = torch.sigmoid(self.fc3(x)) |
| 59 | return x |
| 60 | |
| 61 | class Heavy_Classifier(torch.nn.Module): |
| 62 | def __init__(self): |
nothing calls this directly
no outgoing calls
no test coverage detected