(self, x)
| 86 | return seq_block |
| 87 | |
| 88 | def forward(self, x): |
| 89 | x = self.conv1(x) |
| 90 | x = self.maxpool(x) |
| 91 | x = self.conv2(x) |
| 92 | x = self.conv3(x) |
| 93 | x = self.maxpool(x) |
| 94 | x = self.conv4(x) |
| 95 | x = self.conv5(x) |
| 96 | x = self.maxpool(x) |
| 97 | x = self.conv6(x) |
| 98 | x = self.maxpool(x) |
| 99 | x = x.reshape((-1, self.bigN)) |
| 100 | |
| 101 | x = F.relu(self.fc1(x)) |
| 102 | x = F.relu(self.fc2(x)) |
| 103 | x = F.relu(self.fc3(x)) |
| 104 | # x = F.relu(x) |
| 105 | # x = torch.sigmoid(self.fc3(x)) |
| 106 | return x |
| 107 | |
| 108 | class Ultra_Light_Classifier(torch.nn.Module): |
| 109 | def __init__(self): |
nothing calls this directly
no outgoing calls
no test coverage detected