NNIris.ipynb

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            "/usr/local/lib/python3.10/dist-packages/scikeras/wrappers.py:915: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead.\n",
            "  X, y = self._initialize(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/scikeras/wrappers.py:915: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead.\n",
            "  X, y = self._initialize(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/scikeras/wrappers.py:915: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead.\n",
            "  X, y = self._initialize(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/scikeras/wrappers.py:915: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead.\n",
            "  X, y = self._initialize(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/scikeras/wrappers.py:915: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead.\n",
            "  X, y = self._initialize(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/scikeras/wrappers.py:915: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead.\n",
            "  X, y = self._initialize(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/scikeras/wrappers.py:915: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead.\n",
            "  X, y = self._initialize(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/scikeras/wrappers.py:915: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead.\n",
            "  X, y = self._initialize(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/scikeras/wrappers.py:915: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead.\n",
            "  X, y = self._initialize(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/scikeras/wrappers.py:915: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead.\n",
            "  X, y = self._initialize(X, y)\n",
            "/usr/local/lib/python3.10/dist-packages/scikeras/wrappers.py:915: UserWarning: ``build_fn`` will be renamed to ``model`` in a future release, at which point use of ``build_fn`` will raise an Error instead.\n",
            "  X, y = self._initialize(X, y)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 96.67% (4.47%)\n",
            "Prediction [[1 0 0]]\n",
            "Iris_Setosa  \n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import keras.losses\n",
        "import numpy\n",
        "\n",
        "from pandas import read_csv\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from scikeras.wrappers import KerasClassifier\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.python.keras.utils.np_utils import to_categorical\n",
        "\n",
        "seed = 7\n",
        "numpy.random.seed(seed)\n",
        "# load dataset\n",
        "dataframe = read_csv(\"https://raw.githubusercontent.com/enuguru/GenAI/main/neural-networks/ann/iris.csv\", header=None)\n",
        "dataset = dataframe.values\n",
        "X = dataset[:, 0:4].astype(float)\n",
        "Y = dataset[:, 4]\n",
        "# encode class values as integers\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(Y)\n",
        "encoded_Y = encoder.transform(Y)\n",
        "# convert integers to dummy variables (i.e. one hot encoded)\n",
        "dummy_y = to_categorical(encoded_Y)\n",
        "\n",
        "\n",
        "# define baseline model\n",
        "# define baseline model\n",
        "def baseline_model():\n",
        "    # create model\n",
        "    model = Sequential()\n",
        "    model.add(Dense(8, input_dim=4, activation='relu'))\n",
        "    model.add(Dense(3, activation='softmax'))\n",
        "    # Compile model\n",
        "\n",
        "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    # print(model.summary())\n",
        "    return model\n",
        "\n",
        "\n",
        "estimator = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0)\n",
        "estimator.fit(X,dummy_y)\n",
        "kfold = KFold(n_splits=10, shuffle=True, random_state=seed)\n",
        "results = cross_val_score(estimator, X, dummy_y, cv=kfold)\n",
        "\n",
        "print(\"Accuracy: %.2f%% (%.2f%%)\" % (results.mean() * 100, results.std() * 100))\n",
        "inp=numpy.array([5.1,3.5,1.4,0.2]).reshape(1,-1)\n",
        "prediction = estimator.predict(inp)\n",
        "print(\"Prediction\",prediction)\n",
        "for val in prediction:\n",
        "    if val[0]==1:\n",
        "        print(\"Iris_Setosa\",end=\" \")\n",
        "    elif val[1] == 1:\n",
        "        print(\"Iris_Versicolor\",end=\" \")\n",
        "    else:\n",
        "        print(\"Iris_Viginica\",end=\" \")\n",
        "    print(end=\" \")\n",
        "print(\"\\n\\n\")\n"
      ]
    },
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    {
      "cell_type": "code",
      "source": [
        "pip install scikeras"
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          "text": [
            "Collecting scikeras\n",
            "  Downloading scikeras-0.12.0-py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: packaging>=0.21 in /usr/local/lib/python3.10/dist-packages (from scikeras) (24.0)\n",
            "Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from scikeras) (1.2.2)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->scikeras) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->scikeras) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->scikeras) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.0->scikeras) (3.3.0)\n",
            "Installing collected packages: scikeras\n",
            "Successfully installed scikeras-0.12.0\n"
          ]
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}