(self)
| 175 | class LeNet(nn.Module): |
| 176 | |
| 177 | def __init__(self): |
| 178 | super(LeNet, self).__init__() |
| 179 | # 1 input image channel (black & white), 6 output channels, 5x5 square convolution |
| 180 | # kernel |
| 181 | self.conv1 = nn.Conv2d(1, 6, 5) |
| 182 | self.conv2 = nn.Conv2d(6, 16, 5) |
| 183 | # an affine operation: y = Wx + b |
| 184 | self.fc1 = nn.Linear(16 * 5 * 5, 120) # 5*5 from image dimension |
| 185 | self.fc2 = nn.Linear(120, 84) |
| 186 | self.fc3 = nn.Linear(84, 10) |
| 187 | |
| 188 | def forward(self, x): |
| 189 | # Max pooling over a (2, 2) window |