| 105 | # can search over: |
| 106 | |
| 107 | class Net(nn.Module): |
| 108 | def __init__(self, l1=120, l2=84): |
| 109 | super().__init__() |
| 110 | self.conv1 = nn.Conv2d(3, 6, 5) |
| 111 | self.pool = nn.MaxPool2d(2, 2) |
| 112 | self.conv2 = nn.Conv2d(6, 16, 5) |
| 113 | self.fc1 = nn.Linear(16 * 5 * 5, l1) |
| 114 | self.fc2 = nn.Linear(l1, l2) |
| 115 | self.fc3 = nn.Linear(l2, 10) |
| 116 | |
| 117 | def forward(self, x): |
| 118 | x = self.pool(F.relu(self.conv1(x))) |
| 119 | x = self.pool(F.relu(self.conv2(x))) |
| 120 | x = torch.flatten(x, 1) # flatten all dimensions except batch |
| 121 | x = F.relu(self.fc1(x)) |
| 122 | x = F.relu(self.fc2(x)) |
| 123 | x = self.fc3(x) |
| 124 | return x |
| 125 | |
| 126 | ###################################################################### |
| 127 | # Define the search space |
no outgoing calls
no test coverage detected