# model.py
import torch
import torch.nn as nn
import torch.nn.functional as F

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)       # (1, 28, 28) -> (32, 26, 26)
        self.pool1 = nn.MaxPool2d(2, 2)                    # (32, 26, 26) -> (32, 13, 13)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)      # (32, 13, 13) -> (64, 11, 11)
        self.pool2 = nn.MaxPool2d(2, 2)                    # (64, 11, 11) -> (64, 5, 5)
        self.fc1 = nn.Linear(64 * 5 * 5, 64)
        self.fc2 = nn.Linear(64, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool1(x)
        x = F.relu(self.conv2(x))
        x = self.pool2(x)
        x = x.view(-1, 64 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x