(self)
| 45 | class Net(nn.Module): |
| 46 | |
| 47 | def __init__(self): |
| 48 | super().__init__() |
| 49 | # 1 input image channel, 6 output channels, 5x5 square convolution |
| 50 | # kernel |
| 51 | self.conv1 = nn.Conv2d(1, 6, 5) |
| 52 | self.conv2 = nn.Conv2d(6, 16, 5) |
| 53 | # an affine operation: y = Wx + b |
| 54 | self.fc1 = nn.Linear(16 * 5 * 5, 120) # 5*5 from image dimension |
| 55 | self.fc2 = nn.Linear(120, 84) |
| 56 | self.fc3 = nn.Linear(84, 10) |
| 57 | |
| 58 | def forward(self, input): |
| 59 | # Convolution layer C1: 1 input image channel, 6 output channels, |
nothing calls this directly
no outgoing calls
no test coverage detected