model.py

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

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()

        self.conv1 = nn.Conv2d(1, 80, kernel_size = 5)
        self.conv2 = nn.Conv2d(80, 80, kernel_size = 5)

        self.pool1 = nn.MaxPool2d(kernel_size = 2, stride = 2, padding = 0)
        self.pool2 = nn.MaxPool2d(kernel_size = 2, stride = 2, padding = 0)

        self.batch_norm1 = nn.BatchNorm2d(80)
        self.batch_norm2 = nn.BatchNorm2d(80)

        self.fc1 = nn.Linear(1280, 250)
        self.fc2 = nn.Linear(250, 25)

    def forward(self, x):

        x = self.conv1(x)
        x = self.batch_norm1(x)
        x = F.relu(x)
        x = self.pool1(x)

        x = self.conv2(x)
        x = self.batch_norm2(x)
        x = F.relu(x)
        x = self.pool2(x)

        x = x.view(x.size(0), -1)

        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        x = F.log_softmax(x, dim=1)

        return x