| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class MNISTModel(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.conv1 = nn.Conv2d(1, 32, 3, 1) | |
| self.conv2 = nn.Conv2d(32, 64, 3, 1) | |
| self.dropout1 = nn.Dropout(0.25) | |
| self.dropout2 = nn.Dropout(0.5) | |
| self.fc1 = nn.Linear(9216, 128) | |
| self.fc2 = nn.Linear(128, 10) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = F.relu(x) | |
| x = self.conv2(x) | |
| x = F.relu(x) | |
| x = F.max_pool2d(x, 2) | |
| x = self.dropout1(x) | |
| x = torch.flatten(x, 1) | |
| x = self.fc1(x) | |
| x = F.relu(x) | |
| x = self.dropout2(x) | |
| x = self.fc2(x) | |
| return F.log_softmax(x, dim=1) |