model.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=3)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=3)
        self.fc1 = nn.Linear(20 * 5 * 5, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 20 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

# Create model and export
model = SimpleCNN()
dummy_input = torch.randn(1, 1, 28, 28)
torch.jit.trace(model, dummy_input).save("mnist_cnn.pt")