| 11 | |
| 12 | |
| 13 | class Net(nn.Module): |
| 14 | def __init__(self): |
| 15 | super(Net, self).__init__() |
| 16 | self.conv1 = nn.Conv2d(3, 6, 5) |
| 17 | self.pool1 = nn.MaxPool2d(2, 2) |
| 18 | self.conv2 = nn.Conv2d(6, 16, 5) |
| 19 | self.pool2 = nn.MaxPool2d(2, 2) |
| 20 | self.fc1 = nn.Linear(16 * 5 * 5, 120) |
| 21 | self.fc2 = nn.Linear(120, 84) |
| 22 | self.fc3 = nn.Linear(84, 10) |
| 23 | |
| 24 | def forward(self, x): |
| 25 | x = self.pool1(F.relu(self.conv1(x))) |
| 26 | x = self.pool2(F.relu(self.conv2(x))) |
| 27 | x = x.view(-1, 16 * 5 * 5) |
| 28 | x = F.relu(self.fc1(x)) |
| 29 | x = F.relu(self.fc2(x)) |
| 30 | x = self.fc3(x) |
| 31 | return x |
| 32 | |
| 33 | # 定义权值初始化 |
| 34 | def initialize_weights(self): |
| 35 | for m in self.modules(): |
| 36 | if isinstance(m, nn.Conv2d): |
| 37 | torch.nn.init.xavier_normal_(m.weight.data) |
| 38 | if m.bias is not None: |
| 39 | m.bias.data.zero_() |
| 40 | elif isinstance(m, nn.BatchNorm2d): |
| 41 | m.weight.data.fill_(1) |
| 42 | m.bias.data.zero_() |
| 43 | elif isinstance(m, nn.Linear): |
| 44 | torch.nn.init.normal_(m.weight.data, 0, 0.01) |
| 45 | m.bias.data.zero_() |
| 46 | |
| 47 | class MyDataset(Dataset): |
| 48 | def __init__(self, txt_path, transform = None, target_transform = None): |
no outgoing calls
no test coverage detected