| import torch | |
| import torch.nn as nn | |
| class MNISTModel(nn.Module): | |
| def __init__(self): | |
| super(MNISTModel, self).__init__() | |
| self.fc1 = nn.Linear(28 * 28, 128) # MNIST images are 28x28 | |
| self.fc2 = nn.Linear(128, 10) | |
| self.dropout = nn.Dropout(0.5) | |
| def forward(self, x): | |
| x = x.view(-1, 28 * 28) # Flatten the input | |
| x = torch.relu(self.fc1(x)) | |
| x = self.dropout(x) | |
| x = self.fc2(x) | |
| return x | |