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