| import torch | |
| import torch.nn as nn | |
| # Define the model | |
| class Net(nn.Module): | |
| def __init__(self): | |
| super(Net, self).__init__() | |
| self.fc1 = nn.Linear(28*28, 128) # MNIST images are 28x28 | |
| self.fc2 = nn.Linear(128, 64) | |
| self.fc3 = nn.Linear(64, 10) # There are 10 classes (0 through 9) | |
| def forward(self, x): | |
| x = x.view(x.shape[0], -1) # Flatten the input | |
| x = torch.relu(self.fc1(x)) | |
| x = torch.relu(self.fc2(x)) | |
| return self.fc3(x) |