model.py

import torch
import torch.nn as nn

class Model(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv_layers = nn.Sequential(
            # Block 1: 1 -> 32 channels, 28x28 -> 14x14
            nn.Conv2d(1, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout2d(0.25),

            # Block 2: 32 -> 64 channels, 14x14 -> 7x7
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout2d(0.25),

            # Block 3: 64 -> 128 channels, 7x7 -> 3x3
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout2d(0.25),

            # Block 3: 128 -> 256 channels, 3x3 -> 1x1
            nn.Conv2d(128, 256, kernel_size=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Dropout2d(0.25),
        )

        self.fc_layers = nn.Sequential(
            nn.Flatten(),                        # 256 * 1 * 1 = 256
            nn.Linear(256 * 1 * 1, 128),
            nn.ReLU(),
            nn.Dropout(0.25),
            nn.Linear(128, 10)
        )

    def forward(self, x):
        x = self.conv_layers(x)
        x = self.fc_layers(x)
        return x