import torch
import torch.nn as nn

class DeepMLP(nn.Module):
    """Model 3: Głęboka sieć z progresywnym Dropout i L2"""
    def __init__(self):
        super(DeepMLP, self).__init__()
        self.fc1 = nn.Linear(5000, 1024)
        self.bn1 = nn.BatchNorm1d(1024)
        self.dropout1 = nn.Dropout(0.5)
        
        self.fc2 = nn.Linear(1024, 512)
        self.bn2 = nn.BatchNorm1d(512)
        self.dropout2 = nn.Dropout(0.4)
        
        self.fc3 = nn.Linear(512, 256)
        self.bn3 = nn.BatchNorm1d(256)
        self.dropout3 = nn.Dropout(0.3)
        
        self.fc4 = nn.Linear(256, 128)
        self.bn4 = nn.BatchNorm1d(128)
        self.dropout4 = nn.Dropout(0.2)
        
        self.fc5 = nn.Linear(128, 6)
        self.relu = nn.ReLU()
        
    def forward(self, x):
        x = self.relu(self.bn1(self.fc1(x)))
        x = self.dropout1(x)
        x = self.relu(self.bn2(self.fc2(x)))
        x = self.dropout2(x)
        x = self.relu(self.bn3(self.fc3(x)))
        x = self.dropout3(x)
        x = self.relu(self.bn4(self.fc4(x)))
        x = self.dropout4(x)
        x = self.fc5(x)
        return x