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import torch |
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import torch.nn as nn |
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from torch.utils.data import Dataset |
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class SimpleNN(nn.Module): |
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def __init__(self): |
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super(SimpleNN, self).__init__() |
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self.fc1 = nn.Linear(512, 512) |
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self.fc2 = nn.Linear(512, 256) |
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self.fc3 = nn.Linear(256, 1) |
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def forward(self, x): |
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x = torch.relu(self.fc1(x)) |
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x = torch.relu(self.fc2(x)) |
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x = torch.sigmoid(self.fc3(x)) |
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return x |
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class CustomDataset(Dataset): |
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<<<<<<< HEAD |
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def __init__(self, X, Y): |
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======= |
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def __init__(self,X,Y): |
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>>>>>>> docker |
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self.X = torch.tensor(X, dtype=torch.float32) |
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self.Y = torch.tensor(Y, dtype=torch.float32) |
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def __len__(self): |
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return len(self.X) |
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def __getitem__(self, index): |
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return self.X[index], self.Y[index] |
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