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| import torch.nn as nn | |
| class WasteCNN(nn.Module): | |
| def __init__(self): | |
| super(WasteCNN, self).__init__() | |
| self.conv_layer = nn.Sequential( | |
| nn.Conv2d(3, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), | |
| nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), | |
| nn.Conv2d(64, 128, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), | |
| ) | |
| self.fc_layer = nn.Sequential( | |
| nn.Flatten(), | |
| nn.Linear(128 * 16 * 16, 128), | |
| nn.ReLU(), | |
| nn.Dropout(0.5), | |
| nn.Linear(128, 2) # 2 classes: dry/wet | |
| ) | |
| def forward(self, x): | |
| x = self.conv_layer(x) | |
| x = self.fc_layer(x) | |
| return x |