src/models/cnn.py

import torch
import torch.nn as nn

class ImprovedCNN(nn.Module):
    def __init__(self, input_channels, hidden_units, num_classes=4):
        super().__init__()
        self.block1 = nn.Sequential(
            nn.Conv2d(in_channels=input_channels, out_channels=hidden_units, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(hidden_units),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)
        )
        
        self.block2 = nn.Sequential(
            nn.Conv2d(in_channels=hidden_units, out_channels=hidden_units*2, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(hidden_units*2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)
        )
        
        self.block3 = nn.Sequential(
            nn.Conv2d(in_channels=hidden_units*2, out_channels=hidden_units*4, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(hidden_units*4),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d(output_size=(4, 4))
        )
        
        self.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(hidden_units*4*4*4, 256),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(256, 128)
        )
    
    def forward(self, x):
        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.classifier(x)
        return x