Ensembling_with_Bagging_and_Stacking.ipynb

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      "source": [
        "import torch\n",
        "\n",
        "class EnsembleModel:\n",
        "    def __init__(self, models):\n",
        "        self.models = models\n",
        "\n",
        "    def predict(self, x):\n",
        "        # Ensure the models are in evaluation mode\n",
        "        for model in self.models:\n",
        "            model.eval()\n",
        "\n",
        "        with torch.no_grad():  # Disable gradient tracking\n",
        "            # Ensure input is on the same device as the models\n",
        "            device = next(self.models[0].parameters()).device\n",
        "            x = x.to(device)\n",
        "\n",
        "            # Collect predictions from all models\n",
        "            predictions = [model(x) for model in self.models]\n",
        "            # Average the predictions\n",
        "            return torch.mean(torch.stack(predictions), dim=0)\n",
        "\n",
        "# Example usage:\n",
        "# models = [model1, model2, model3]\n",
        "# ensemble_model = EnsembleModel(models)\n",
        "# input_data = torch.randn(1, 3, 32, 32)  # Example input data\n",
        "# output = ensemble_model.predict(input_data)"
      ]
    }
  ]
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