| 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] | |