07: Autokeras_classification_Python.ipynb

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      "name": "Autokeras_classification_Python.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/gulabpatel/AutoML/blob/main/Autokeras_classification_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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        "outputId": "1c4c5286-f57a-4448-a97c-bcebd09ec622",
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          "base_uri": "https://localhost:8080/",
          "height": 649
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      "source": [
        "!pip install git+https://github.com/keras-team/keras-tuner.git@1.0.2rc3"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/keras-team/keras-tuner.git@1.0.2rc3\n",
            "  Cloning https://github.com/keras-team/keras-tuner.git (to revision 1.0.2rc3) to /tmp/pip-req-build-j9r_581x\n",
            "  Running command git clone -q https://github.com/keras-team/keras-tuner.git /tmp/pip-req-build-j9r_581x\n",
            "  Running command git checkout -q 172be42f1310cdbad48941494f84d65367de4f11\n",
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            "Building wheels for collected packages: keras-tuner\n",
            "  Building wheel for keras-tuner (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-tuner: filename=keras_tuner-1.0.2rc3-cp36-none-any.whl size=91635 sha256=bef9e19e5021df69fd4b68ac5da39fb325d076914a597f87e2eb6795d5f015d7\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-gmmzni9a/wheels/2d/b8/84/5f9abbcad32fc56a0fdd5d1b8c267221abb5ec582dde4371f7\n",
            "Successfully built keras-tuner\n",
            "Installing collected packages: keras-tuner\n",
            "  Found existing installation: keras-tuner 1.0.2rc2\n",
            "    Uninstalling keras-tuner-1.0.2rc2:\n",
            "      Successfully uninstalled keras-tuner-1.0.2rc2\n",
            "Successfully installed keras-tuner-1.0.2rc3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
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        "colab": {
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      "source": [
        "!pip install -U keras-tuner"
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      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: keras-tuner in /usr/local/lib/python3.6/dist-packages (1.0.2rc3)\n",
            "Requirement already satisfied, skipping upgrade: terminaltables in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (3.1.0)\n",
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            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (2.23.0)\n",
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            "Requirement already satisfied, skipping upgrade: packaging in /usr/local/lib/python3.6/dist-packages (from keras-tuner) (20.4)\n",
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            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2020.6.20)\n",
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            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->keras-tuner) (2.10)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7lM-q-xzW2n"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import autokeras\n",
        "from autokeras import StructuredDataClassifier\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ha6PEi87zW5a"
      },
      "source": [
        "df = pd.read_excel(\"/content/Data.xlsx\")"
      ],
      "execution_count": 3,
      "outputs": []
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Dependents</th>\n",
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              "      <th>Self_Employed</th>\n",
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              "      <th>LoanAmount</th>\n",
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              "   Gender  Married  Dependents  ...  Credit_History  Property_Area  Loan_Status\n",
              "0       1        0           0  ...               1              2            1\n",
              "1       1        1           1  ...               1              0            0\n",
              "2       1        1           0  ...               1              2            1\n",
              "3       1        1           0  ...               1              2            1\n",
              "4       1        0           0  ...               1              2            1\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 4
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aR1rkt8KzW-_"
      },
      "source": [
        "target_col = \"Loan_Status\"\n",
        "X = df.loc[:, df.columns != target_col]\n",
        "y = df.loc[:, target_col]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfwOZEfq0NgT"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                                                    test_size=0.20, \n",
        "                                                    random_state=2021)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qP96_Q2J0ZOK"
      },
      "source": [
        "search = StructuredDataClassifier(max_trials = 20)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DD5hmwL0uMn",
        "outputId": "47066255-d652-4ffa-da3a-8a85533a2f42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        }
      },
      "source": [
        "search.fit(x=X_train, y=y_train)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Trial 20 Complete [00h 00m 02s]\n",
            "val_accuracy: 0.746666669845581\n",
            "\n",
            "Best val_accuracy So Far: 0.7733333110809326\n",
            "Total elapsed time: 00h 05m 19s\n",
            "INFO:tensorflow:Oracle triggered exit\n",
            "WARNING:tensorflow:Layer multi_category_encoding is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because its dtype defaults to floatx.\n",
            "\n",
            "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
            "\n",
            "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
            "\n",
            "Epoch 1/8\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.7163 - accuracy: 0.4786\n",
            "Epoch 2/8\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.6435 - accuracy: 0.6986\n",
            "Epoch 3/8\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.6061 - accuracy: 0.7210\n",
            "Epoch 4/8\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.5798 - accuracy: 0.7332\n",
            "Epoch 5/8\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.5578 - accuracy: 0.7597\n",
            "Epoch 6/8\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.5390 - accuracy: 0.7739\n",
            "Epoch 7/8\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.5232 - accuracy: 0.7760\n",
            "Epoch 8/8\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.5104 - accuracy: 0.7841\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
            "INFO:tensorflow:Assets written to: ./structured_data_classifier/best_model/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OaztLYky1tYY",
        "outputId": "6132dae2-6bc0-44c9-b458-9bfd9e7fc832",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "loss, acc = search.evaluate(X_train, y_train, verbose=0)\n",
        "print('Accuracy is {}'.format(acc))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:9 out of the last 9 calls to <function recreate_function.<locals>.restored_function_body at 0x7f13083cf268> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:10 out of the last 10 calls to <function recreate_function.<locals>.restored_function_body at 0x7f13081a2158> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f13083cf2f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f12f6921730> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f13081a2378> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f13081a4268> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f13081a22f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f13083032f0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "Accuracy is 0.7861506938934326\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RcgHFIEP3xLI",
        "outputId": "ef2fa82a-2584-4208-a48d-98d9856b3ff6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "source": [
        "loss, acc = search.evaluate(X_test, y_test, verbose=0)\n",
        "print('Accuracy is {}'.format(acc))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f12f6616b70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f12f6636c80> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f12f661aae8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f12f6619e18> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f12f65d2d08> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f12f65d2488> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f12f661a730> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:11 out of the last 11 calls to <function recreate_function.<locals>.restored_function_body at 0x7f12f6616488> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "Accuracy is 0.6991869807243347\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}