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| import torch.nn as nn | |
| class Model(nn.Module): | |
| def __init__(self, infeatures, hidden=64, outfeature=1): | |
| super().__init__() | |
| self.network = nn.Sequential( | |
| nn.Linear(infeatures, hidden), | |
| nn.BatchNorm1d(hidden), | |
| nn.ReLU(), | |
| nn.Dropout(0.3), | |
| nn.Linear(hidden, hidden // 2), | |
| nn.BatchNorm1d(hidden // 2), | |
| nn.ReLU(), | |
| nn.Dropout(0.3), | |
| nn.Linear(hidden // 2, outfeature), | |
| ) | |
| def forward(self, x): | |
| return self.network(x) | |