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| import torch | |
| import torch.nn as nn | |
| class SimpleCNN(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.conv = nn.Sequential( | |
| nn.Conv2d(3, 16, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), | |
| nn.Conv2d(16, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), | |
| nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), | |
| ) | |
| self.fc = nn.Sequential( | |
| nn.Flatten(), | |
| nn.Linear(64 * 32 * 32, 128), | |
| nn.ReLU(), | |
| nn.Linear(128, 3) | |
| ) | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.fc(x) | |
| return x | |