model.py

import torch
import torch.nn as nn


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

        self.conv_layer = nn.Sequential(

            # Conv Layer block 1
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.LeakyReLU(),
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1),
            nn.LeakyReLU(),
            nn.AvgPool2d(kernel_size=2, stride=2),

            # Conv Layer block 2
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(),
            nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1),
            nn.LeakyReLU(),
            nn.AvgPool2d(kernel_size=2, stride=2),

            # Conv Layer block 3
            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(),
            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1),
            nn.LeakyReLU(),
            nn.AvgPool2d(kernel_size=2, stride=2),
        )


        self.fc_layer = nn.Sequential(
            nn.Linear(4096, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512),
            nn.ReLU(),
            nn.Dropout(p=0.2),
            nn.Linear(512, 10)
        )


    def forward(self, x):
        
        # conv layers
        x = self.conv_layer(x)
        
        # flatten
        x = x.view(x.size(0), -1)
        
        # fc layer
        x = self.fc_layer(x)

        return x