model.py

import torch
import torch.nn as nn


class Model(nn.Module):

    def __init__(self):
        super(Model, self).__init__()
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(2, 2)

        self.conv1 = nn.Conv2d(1, 32, 5, 1, 2)
        self.bn1 = nn.BatchNorm2d(32)

        self.conv2 = nn.Conv2d(32, 64, 5, 1, 2)
        self.bn2 = nn.BatchNorm2d(64)

        self.fc1 = nn.Linear(7 * 7 * 64, 1024)
        self.fc2 = nn.Linear(1024, 10)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)  # 28x28->14x14

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.maxpool(x)  # 14x14->7x7

        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = self.relu(x)

        x = self.fc2(x)
        x = self.softmax(x)

        return x