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   "source": [
    "# Automatic Differentiation\n",
    "\n",
    "### Neural Network\n",
    "\n",
    "Loss function: softmax layer in $\\mathbb{R}^3$\n",
    "\n",
    "Architecture: FC/ReLU 4-5-7-3"
   ],
   "id": "c897654e0a140cbd"
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     "end_time": "2025-03-24T15:16:27.015669Z",
     "start_time": "2025-03-24T15:16:23.856887Z"
    }
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   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracies = []\n",
    "\n",
    "for _ in range(10):\n",
    "    X, y = make_classification(n_samples=1000, n_features=4, n_classes=3, n_clusters_per_class=1)\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "    model = MLPClassifier(hidden_layer_sizes=(5, 7), activation='relu', max_iter=10000, solver='adam')\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracies.append(accuracy_score(y_test, y_pred))\n",
    "\n",
    "print(f'Mean Accuracy: {np.mean(accuracies) * 100:.0f}%')\n",
    "print(f'STD Accuracy: {np.std(accuracies) * 100:.0f}%')\n",
    "print(f\"Max accuracy: {np.max(accuracies) * 100:.0f}%\")\n",
    "print(f\"Min accuracy: {np.min(accuracies) * 100:.0f}%\")"
   ],
   "id": "70a4eb1d928b10d0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Accuracy: 94%\n",
      "STD Accuracy: 3%\n",
      "Max accuracy: 100%\n",
      "Min accuracy: 88%\n"
     ]
    }
   ],
   "execution_count": 33
  },
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   "cell_type": "code",
   "source": "",
   "id": "96b6d46883ed5570",
   "outputs": [],
   "execution_count": null
  }
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