model_architecture.py

import torch
import torch.nn as nn

class MLP(nn.Module):
    def __init__(self, input_dim, hidden_dims=[128, 64, 32], dropout_rate=0.3):
        """
        Multi-Layer Perceptron for xG prediction

        Args:
            input_dim: Number of input features
            hidden_dims: List of hidden layer dimensions
            dropout_rate: Dropout probability
        """
        super(MLP, self).__init__()

        layers = []
        prev_dim = input_dim

        # Build hidden layers
        for hidden_dim in hidden_dims:
            layers.append(nn.Linear(prev_dim, hidden_dim))
            layers.append(nn.ReLU())
            layers.append(nn.BatchNorm1d(hidden_dim))
            layers.append(nn.Dropout(dropout_rate))
            prev_dim = hidden_dim

        # Output layer
        layers.append(nn.Linear(prev_dim, 1))
        layers.append(nn.Sigmoid())

        self.network = nn.Sequential(*layers)

    def forward(self, x):
        return self.network(x)