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model_architecture.py

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+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)