Housing_Classification_Project_3.ipynb

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  "metadata": {
    "colab": {
      "name": "Housing Classification Project 3.ipynb",
      "version": "0.3.2",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/danidavid/Tensorflow-Keras-Repo/blob/master/Housing_Classification_Project_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "QlneDcGqEfUu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Project 3: Housing Classification"
      ]
    },
    {
      "metadata": {
        "id": "2u3bxGd_7xbF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Step 1: Importing dataset"
      ]
    },
    {
      "metadata": {
        "id": "j77AgMTNmKY7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd #This will be used for online dataset import\n",
        "import io\n",
        "import requests\n",
        "url='https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.data'\n",
        "s=requests.get(url).content\n",
        "c=pd.read_csv(io.StringIO(s.decode('UTF-8')),delim_whitespace=True, header=None)\n",
        "#c"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "w0erLbBK74Wn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Importing packages"
      ]
    },
    {
      "metadata": {
        "id": "XGwftRKC3Jfa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import numpy\n",
        "import pandas\n",
        "import keras\n",
        "from keras import layers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.wrappers.scikit_learn import KerasRegressor\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kwFQUI6i3ZgR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Importing X and Y from dataset"
      ]
    },
    {
      "metadata": {
        "id": "7ub5xRvHx9Is",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dataset = c.values\n",
        "X = dataset[:,0:13]\n",
        "Y = dataset[:,13]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eE831m7j3YTs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Seed"
      ]
    },
    {
      "metadata": {
        "id": "R3V4SIeZxzN_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# fix random seed for reproducibility\n",
        "seed = 7\n",
        "numpy.random.seed(seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "myeXTnzM3KYZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Step 2: Making model"
      ]
    },
    {
      "metadata": {
        "id": "KMeG5uy-9Fu7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#baseline model\n",
        "def baseline_model():\n",
        "  #created model\n",
        "  model = keras.Sequential()\n",
        "  model.add(layers.Dense(13,activation='relu',input_shape=(13,)))\n",
        "  model.add(layers.Dense(3,activation='relu'))\n",
        "  model.add(layers.Dense(1))\n",
        "  #compiled model\n",
        "  model.compile(optimizer='Adam',loss='mse',metrics=['accuracy'])\n",
        "  #model.fit(X,Y,epochs=20,batch_size=32)\n",
        "  return model\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qRkLF9gPSIYG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#evaluate model with standardized dataset\n",
        "estimator=[]\n",
        "estimator = KerasRegressor(build_fn=baseline_model, epochs=100, batch_size=5, verbose=0)\n",
        "kfold = KFold(n_splits=10, random_state=seed)\n",
        "results = cross_val_score(estimator, X, Y, cv=kfold)\n",
        "print(\"Results: %.2f (%.2f) MSE\" % (results.mean(), results.std()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0vzm4tq8TQhs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Step 3: Modeling The Standardized Dataset"
      ]
    },
    {
      "metadata": {
        "id": "QZjdbGnJXueR",
        "colab_type": "code",
        "outputId": "7b5159f9-8fca-488c-cd48-ea378bbcec4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# evaluate model with standardized dataset\n",
        "numpy.random.seed(seed)\n",
        "estimators = []\n",
        "estimators.append(('standardize', StandardScaler()))\n",
        "estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=50, batch_size=5, verbose=0)))\n",
        "pipeline = Pipeline(estimators)\n",
        "kfold = KFold(n_splits=10, random_state=seed)\n",
        "results = cross_val_score(pipeline, X, Y, cv=kfold)\n",
        "print(\"Standardized: %.2f (%.2f) MSE\" % (results.mean(), results.std()))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Standardized: -26.92 (32.55) MSE\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2OrhzJBsjawO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Extension of Step 3: Modeling The Standardized Dataset"
      ]
    },
    {
      "metadata": {
        "id": "2h-XlWmPTT2L",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#baseline model\n",
        "def baseline_model():\n",
        "  #created model\n",
        "  model = keras.Sequential()\n",
        "  model.add(layers.Dense(13,activation='relu',input_shape=(13,)))\n",
        "  #model.add(layers.Dense(3,activation='relu'))\n",
        "  model.add(layers.Dense(1))\n",
        "  #compiled model\n",
        "  model.compile(optimizer='Adam',loss='mse',metrics=['accuracy'])\n",
        "  model.fit(X,Y,epochs=20,batch_size=32)\n",
        "  return model\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NELVaiu2j4Ft",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "a64aa25a-4036-4a29-ccc2-de8c71cdba41"
      },
      "cell_type": "code",
      "source": [
        "# evaluate model with standardized dataset\n",
        "estimators = []\n",
        "estimators.append(('standardize', StandardScaler()))\n",
        "estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=50, batch_size=5, verbose=0)))\n",
        "pipeline = Pipeline(estimators)\n",
        "kfold = KFold(n_splits=10, random_state=seed)\n",
        "results = cross_val_score(pipeline, X, Y, cv=kfold)\n",
        "print(\"Standardized: %.2f (%.2f) MSE\" % (results.mean(), results.std()))\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-426653329916>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mestimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mestimators\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'standardize'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mestimators\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mlp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKerasRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuild_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbaseline_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mpipeline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimators\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mkfold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKFold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_splits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'StandardScaler' is not defined"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "Wi7Nef76AMKq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Step 4. Tune The Neural Network Topology"
      ]
    },
    {
      "metadata": {
        "id": "zVBDUgMsbJrB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Step 4.1. Evaluate a Deeper Network Topology"
      ]
    },
    {
      "metadata": {
        "id": "VyjOZ5CPj7Pw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#larger model\n",
        "def larger_model():\n",
        "  #created model\n",
        "  model = keras.Sequential()\n",
        "  model.add(layers.Dense(13,activation='relu',input_shape=(13,)))\n",
        "  model.add(layers.Dense(6,activation='relu'))\n",
        "  model.add(layers.Dense(1))\n",
        "  #compiled model\n",
        "  model.compile(optimizer='Adam',loss='mse',metrics=['accuracy'])\n",
        "  model.fit(X,Y,epochs=20,batch_size=32)\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uleR4P9qbdbS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "numpy.random.seed(seed)\n",
        "estimators = []\n",
        "estimators.append(('standardize', StandardScaler()))\n",
        "estimators.append(('mlp', KerasRegressor(build_fn=larger_model, epochs=50, batch_size=5, verbose=0)))\n",
        "pipeline = Pipeline(estimators)\n",
        "kfold = KFold(n_splits=10, random_state=seed)\n",
        "results = cross_val_score(pipeline, X, Y, cv=kfold)\n",
        "print(\"Larger: %.2f (%.2f) MSE\" % (results.mean(), results.std()))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7F_Gxc3qc3eo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Step 4.2. Evaluate a Wider Network Topology"
      ]
    },
    {
      "metadata": {
        "id": "CyYJLRZndBCQ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#wider model\n",
        "def wider_model():\n",
        "  #created model\n",
        "  model = keras.Sequential()\n",
        "  model.add(layers.Dense(20,activation='relu',input_shape=(13,)))\n",
        "  #model.add(layers.Dense(6,activation='relu'))\n",
        "  model.add(layers.Dense(1))\n",
        "  #compiled model\n",
        "  model.compile(optimizer='Adam',loss='mse',metrics=['accuracy'])\n",
        "  model.fit(X,Y,epochs=20,batch_size=32)\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DwsPru8nbg0g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "estimators = []\n",
        "estimators.append(('standardize', StandardScaler()))\n",
        "estimators.append(('mlp', KerasRegressor(build_fn=wider_model, epochs=100, batch_size=5, verbose=0)))\n",
        "pipeline = Pipeline(estimators)\n",
        "kfold = KFold(n_splits=10, random_state=seed)\n",
        "results = cross_val_score(pipeline, X, Y, cv=kfold)\n",
        "print(\"Wider: %.2f (%.2f) MSE\" % (results.mean(), results.std()))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "K3fOKf2WU3Xd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Step 5. Developing a model that overfits"
      ]
    },
    {
      "metadata": {
        "id": "XyQmRmqK26I2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#overfitting model\n",
        "def create_overfit():\n",
        "  #create model\n",
        "  model = keras.Sequential()\n",
        "  #adding more layers & making them bigger\n",
        "  model.add(layers.Dense(13,activation='relu',input_shape=(13,)))\n",
        "  model.add(layers.Dense(5,activation='relu'))\n",
        "  model.add(layers.Dense(6,activation='relu'))\n",
        "  model.add(layers.Dense(5,activation='relu'))\n",
        "  model.add(layers.Dense(4,activation='relu'))\n",
        "  model.add(layers.Dense(1))\n",
        "  #compiled model\n",
        "  model.compile(optimizer='Adam',loss='mse',metrics=['accuracy'])\n",
        "  #training for more epochs\n",
        "  model.fit(X,Y,epochs=20,batch_size=32,verbose=0)\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iADQN2J2_Ac5",
        "colab_type": "code",
        "outputId": "52572cba-b9a8-4a2f-d404-ddc73a07c64c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6774
        }
      },
      "cell_type": "code",
      "source": [
        "estimators = []\n",
        "estimators.append(('standardize', StandardScaler()))\n",
        "estimators.append(('mlp', KerasRegressor(build_fn=create_overfit, epochs=100, batch_size=5, verbose=0)))\n",
        "pipeline = Pipeline(estimators)\n",
        "kfold = KFold(n_splits=10, random_state=seed)\n",
        "results = cross_val_score(pipeline, X, Y, cv=kfold)\n",
        "print(\"Overfit: %.2f (%.2f) MSE\" % (results.mean(), results.std()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 3ms/step - loss: 756.1964 - acc: 0.0000e+00\n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 274us/step - loss: 589.6234 - acc: 0.0020\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 277us/step - loss: 464.0067 - acc: 0.0000e+00\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 257us/step - loss: 188.7046 - acc: 0.0079\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 273us/step - loss: 116.9159 - acc: 0.0099\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 283us/step - loss: 99.9269 - acc: 0.0119\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 281us/step - loss: 90.2563 - acc: 0.0158\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 281us/step - loss: 84.0403 - acc: 0.0059\n",
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 263us/step - loss: 78.2781 - acc: 0.0119\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 286us/step - loss: 74.2480 - acc: 0.0020\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 274us/step - loss: 71.2435 - acc: 0.0059\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 263us/step - loss: 69.3449 - acc: 0.0138\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 281us/step - loss: 67.6366 - acc: 0.0119\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 271us/step - loss: 66.5248 - acc: 0.0099\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 272us/step - loss: 64.8056 - acc: 0.0040\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 261us/step - loss: 63.6242 - acc: 0.0079\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 285us/step - loss: 62.7613 - acc: 0.0040\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 283us/step - loss: 61.8522 - acc: 0.0099\n",
            "Epoch 19/20\n",
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            "506/506 [==============================] - 0s 279us/step - loss: 17941.2878 - acc: 0.0000e+00\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 288us/step - loss: 10386.8913 - acc: 0.0000e+00\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 285us/step - loss: 6791.9856 - acc: 0.0000e+00\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 282us/step - loss: 4872.6890 - acc: 0.0000e+00\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 279us/step - loss: 3686.2534 - acc: 0.0000e+00\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 284us/step - loss: 2936.0980 - acc: 0.0000e+00\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 283us/step - loss: 2417.4661 - acc: 0.0000e+00\n",
            "Epoch 9/20\n",
            "448/506 [=========================>....] - ETA: 0s - loss: 2043.0704 - acc: 0.0000e+00"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "506/506 [==============================] - 0s 289us/step - loss: 2027.4339 - acc: 0.0000e+00\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 268us/step - loss: 1722.9238 - acc: 0.0000e+00\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 294us/step - loss: 1471.3219 - acc: 0.0000e+00\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 269us/step - loss: 1257.1525 - acc: 0.0000e+00\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 278us/step - loss: 1085.4470 - acc: 0.0000e+00\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 285us/step - loss: 948.5795 - acc: 0.0000e+00\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 267us/step - loss: 847.7196 - acc: 0.0000e+00\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 282us/step - loss: 788.4876 - acc: 0.0000e+00\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 291us/step - loss: 750.5983 - acc: 0.0000e+00\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 279us/step - loss: 724.6099 - acc: 0.0000e+00\n",
            "Epoch 19/20\n",
            "506/506 [==============================] - 0s 279us/step - loss: 706.1200 - acc: 0.0000e+00\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 302us/step - loss: 691.9333 - acc: 0.0000e+00\n",
            "Overfit: -22.27 (26.22) MSE\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "l7q40ZieQU6t",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Step 6. Tuning the Model"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "f18Tb_6OAc3U",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#baseline model\n",
        "def tuned_model():\n",
        "  #created model\n",
        "  model = keras.Sequential()\n",
        "  model.add(layers.Dense(13,activation='relu',input_shape=(13,)))\n",
        "  model.add(layers.Dense(9,activation='relu'))\n",
        "  model.add(layers.Dense(3,activation='relu'))\n",
        "  model.add(layers.Dense(1))\n",
        "  #compiled model\n",
        "  model.compile(optimizer='Adam',loss='mse',metrics=['accuracy'])\n",
        "  #model.fit(X,Y,epochs=20,batch_size=32)\n",
        "  return model\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "I2RtvG6ZAc3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5c1860f0-74f5-4b09-df95-95ba14e76b50"
      },
      "cell_type": "code",
      "source": [
        "#evaluate model with standardized dataset\n",
        "estimator=[]\n",
        "estimator = KerasRegressor(build_fn=tuned_model, epochs=600, batch_size=5, verbose=0)\n",
        "kfold = KFold(n_splits=10, random_state=seed)\n",
        "results = cross_val_score(estimator, X, Y, cv=kfold)\n",
        "print(\"Results: %.2f (%.2f) MSE\" % (results.mean(), results.std()))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Results: -35.78 (39.36) MSE\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rjW5aGjPD59T",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Step 7. Rewriting the code using Keras functional API"
      ]
    },
    {
      "metadata": {
        "id": "WP9pJYFvK2QJ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Creating baseline model"
      ]
    },
    {
      "metadata": {
        "id": "xBCXNIINEEQW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#creating baseline model\n",
        "def kf_baseline():\n",
        "  #create model\n",
        "  input = keras.Input(shape=(13,))\n",
        "  a = layers.Dense(13,activation='relu')(input)\n",
        "  a = layers.Dense(3,activation='relu')(a)\n",
        "  outputs = layers.Dense(1)(a)\n",
        "  #compile model\n",
        "  model = keras.Model(input, outputs)\n",
        "  model.compile(optimizer='Adam',loss='mse',metrics=['accuracy'])\n",
        "  model.fit(X,Y,epochs=20,batch_size=32)\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "va-PRGeaFD0-",
        "colab_type": "code",
        "outputId": "7cea4309-e7ab-45bf-919e-f838f88576e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6774
        }
      },
      "cell_type": "code",
      "source": [
        "#evaluate model with standardized dataset\n",
        "estimator=[]\n",
        "estimator = KerasRegressor(build_fn=kf_baseline,epochs=100,batch_size=5,verbose=0)\n",
        "kfold = KFold(n_splits=10,shuffle=True,random_state=seed)\n",
        "results = cross_val_score(estimator,X,Y,cv=kfold)\n",
        "print(\"Results: %.2f (%.2f) MSE\" % (results.mean(), results.std()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 1ms/step - loss: 1307.2054 - acc: 0.0000e+00\n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 172us/step - loss: 716.8546 - acc: 0.0000e+00\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 170us/step - loss: 393.6707 - acc: 0.0020\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 174us/step - loss: 235.8854 - acc: 0.0040\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 168us/step - loss: 158.9681 - acc: 0.0000e+00\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 171us/step - loss: 125.2641 - acc: 0.0059\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 166us/step - loss: 108.3440 - acc: 0.0040\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 169us/step - loss: 96.4335 - acc: 0.0020\n",
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 170us/step - loss: 88.6343 - acc: 0.0059\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 173us/step - loss: 84.0491 - acc: 0.0079\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 191us/step - loss: 80.1122 - acc: 0.0079\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 176us/step - loss: 76.9925 - acc: 0.0040\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 170us/step - loss: 74.1317 - acc: 0.0040\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 177us/step - loss: 72.0285 - acc: 0.0040\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 176us/step - loss: 70.2738 - acc: 0.0040\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 194us/step - loss: 68.5014 - acc: 0.0079\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 217us/step - loss: 66.9802 - acc: 0.0040\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 217us/step - loss: 65.7122 - acc: 0.0040\n",
            "Epoch 19/20\n",
            "506/506 [==============================] - 0s 206us/step - loss: 64.6193 - acc: 0.0059\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 209us/step - loss: 63.9372 - acc: 0.0099\n",
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 1ms/step - loss: 387.1990 - acc: 0.0020\n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 210us/step - loss: 207.8596 - acc: 0.0000e+00\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 206us/step - loss: 132.4449 - acc: 0.0099\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 192us/step - loss: 106.4089 - acc: 0.0059\n",
            "Epoch 5/20\n",
            "288/506 [================>.............] - ETA: 0s - loss: 92.9552 - acc: 0.0035    "
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "506/506 [==============================] - 0s 209us/step - loss: 93.3075 - acc: 0.0059\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 203us/step - loss: 85.5775 - acc: 0.0099\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 217us/step - loss: 79.7722 - acc: 0.0020\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 194us/step - loss: 74.9497 - acc: 0.0059\n",
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 213us/step - loss: 71.1821 - acc: 0.0040\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 203us/step - loss: 66.4516 - acc: 0.0059\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 197us/step - loss: 62.7893 - acc: 0.0040\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 213us/step - loss: 61.6592 - acc: 0.0099\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 205us/step - loss: 59.1384 - acc: 0.0040\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 201us/step - loss: 57.4743 - acc: 0.0099\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 200us/step - loss: 55.5567 - acc: 0.0059\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 203us/step - loss: 55.0559 - acc: 0.0040\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 219us/step - loss: 53.8007 - acc: 0.0099\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 199us/step - loss: 52.4592 - acc: 0.0138\n",
            "Epoch 19/20\n",
            "506/506 [==============================] - 0s 200us/step - loss: 51.3446 - acc: 0.0059\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 218us/step - loss: 48.8940 - acc: 0.0020\n",
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 2ms/step - loss: 4651.4271 - acc: 0.0000e+00\n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 296us/step - loss: 1375.1342 - acc: 0.0000e+00\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 306us/step - loss: 640.9076 - acc: 0.0000e+00\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 298us/step - loss: 459.3203 - acc: 0.0020\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 293us/step - loss: 401.0535 - acc: 0.0000e+00\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 309us/step - loss: 379.5780 - acc: 0.0000e+00\n",
            "Epoch 7/20\n",
            "384/506 [=====================>........] - ETA: 0s - loss: 367.7875 - acc: 0.0000e+00"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r506/506 [==============================] - 0s 300us/step - loss: 365.8511 - acc: 0.0000e+00\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 295us/step - loss: 353.5595 - acc: 0.0000e+00\n",
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 329us/step - loss: 343.5642 - acc: 0.0000e+00\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 287us/step - loss: 334.9892 - acc: 0.0020\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 323us/step - loss: 325.9282 - acc: 0.0020\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 299us/step - loss: 314.8115 - acc: 0.0000e+00\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 294us/step - loss: 305.5946 - acc: 0.0000e+00\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 301us/step - loss: 296.9942 - acc: 0.0000e+00\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 291us/step - loss: 289.2182 - acc: 0.0020\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 311us/step - loss: 281.8143 - acc: 0.0040\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 303us/step - loss: 273.9472 - acc: 0.0020\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 335us/step - loss: 266.1645 - acc: 0.0040\n",
            "Epoch 19/20\n",
            "506/506 [==============================] - 0s 290us/step - loss: 258.2427 - acc: 0.0059\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 328us/step - loss: 250.6789 - acc: 0.0079\n",
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 2ms/step - loss: 436.7217 - acc: 0.0000e+00\n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 264us/step - loss: 160.2886 - acc: 0.0059\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 277us/step - loss: 108.7519 - acc: 0.0040\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 250us/step - loss: 85.8106 - acc: 0.0079\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 256us/step - loss: 76.8668 - acc: 0.0119\n",
            "Epoch 6/20\n",
            "480/506 [===========================>..] - ETA: 0s - loss: 73.3077 - acc: 0.0104"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "506/506 [==============================] - 0s 256us/step - loss: 71.4154 - acc: 0.0119\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 259us/step - loss: 68.4734 - acc: 0.0138\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 276us/step - loss: 66.0518 - acc: 0.0099\n",
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 261us/step - loss: 64.2894 - acc: 0.0079\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 235us/step - loss: 64.1578 - acc: 0.0059\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 273us/step - loss: 62.9509 - acc: 0.0059\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 260us/step - loss: 62.7154 - acc: 0.0079\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 272us/step - loss: 62.2736 - acc: 0.0138\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 262us/step - loss: 62.1147 - acc: 0.0099\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 272us/step - loss: 61.7302 - acc: 0.0099\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 262us/step - loss: 61.6614 - acc: 0.0040\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 261us/step - loss: 61.6595 - acc: 0.0138\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 230us/step - loss: 60.1393 - acc: 0.0059\n",
            "Epoch 19/20\n",
            "506/506 [==============================] - 0s 215us/step - loss: 59.7094 - acc: 0.0099\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 251us/step - loss: 59.3106 - acc: 0.0059\n",
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 1ms/step - loss: 2054.5592 - acc: 0.0000e+00\n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 196us/step - loss: 1436.4127 - acc: 0.0000e+00\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 209us/step - loss: 1064.9094 - acc: 0.0000e+00\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 196us/step - loss: 841.4573 - acc: 0.0000e+00\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 191us/step - loss: 698.5090 - acc: 0.0000e+00\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 291us/step - loss: 621.0652 - acc: 0.0000e+00\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 204us/step - loss: 599.7934 - acc: 0.0000e+00\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 203us/step - loss: 595.8582 - acc: 0.0000e+00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 208us/step - loss: 594.1737 - acc: 0.0000e+00\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 200us/step - loss: 592.7514 - acc: 0.0000e+00\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 212us/step - loss: 591.5343 - acc: 0.0000e+00\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 215us/step - loss: 590.2871 - acc: 0.0000e+00\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 226us/step - loss: 589.0903 - acc: 0.0000e+00\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 207us/step - loss: 587.9765 - acc: 0.0000e+00\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 226us/step - loss: 586.8299 - acc: 0.0000e+00\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 213us/step - loss: 585.7437 - acc: 0.0000e+00\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 210us/step - loss: 584.7096 - acc: 0.0000e+00\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 227us/step - loss: 583.6863 - acc: 0.0000e+00\n",
            "Epoch 19/20\n",
            "506/506 [==============================] - 0s 207us/step - loss: 582.7212 - acc: 0.0000e+00\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 214us/step - loss: 581.8302 - acc: 0.0000e+00\n",
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 1ms/step - loss: 1004.0656 - acc: 0.0000e+00\n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 198us/step - loss: 753.0287 - acc: 0.0000e+00\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 218us/step - loss: 683.6202 - acc: 0.0000e+00\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 205us/step - loss: 642.0173 - acc: 0.0000e+00\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 206us/step - loss: 620.8616 - acc: 0.0000e+00\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 197us/step - loss: 606.6322 - acc: 0.0000e+00\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 212us/step - loss: 597.6899 - acc: 0.0000e+00\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 202us/step - loss: 592.3406 - acc: 0.0000e+00\n",
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 201us/step - loss: 589.1137 - acc: 0.0000e+00\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 204us/step - loss: 586.9769 - acc: 0.0000e+00\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 197us/step - loss: 585.7604 - acc: 0.0000e+00\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 201us/step - loss: 584.7167 - acc: 0.0000e+00\n",
            "Epoch 13/20\n",
            "320/506 [=================>............] - ETA: 0s - loss: 617.6130 - acc: 0.0000e+00"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "506/506 [==============================] - 0s 199us/step - loss: 583.9138 - acc: 0.0000e+00\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 205us/step - loss: 583.1625 - acc: 0.0000e+00\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 213us/step - loss: 582.4644 - acc: 0.0000e+00\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 198us/step - loss: 581.7610 - acc: 0.0000e+00\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 202us/step - loss: 581.0634 - acc: 0.0000e+00\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 219us/step - loss: 580.3605 - acc: 0.0000e+00\n",
            "Epoch 19/20\n",
            "506/506 [==============================] - 0s 214us/step - loss: 579.6596 - acc: 0.0000e+00\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 198us/step - loss: 578.9639 - acc: 0.0000e+00\n",
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 2ms/step - loss: 31583.3230 - acc: 0.0000e+00\n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 211us/step - loss: 19105.6555 - acc: 0.0000e+00\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 250us/step - loss: 9073.0051 - acc: 0.0000e+00\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 271us/step - loss: 3440.5850 - acc: 0.0000e+00\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 241us/step - loss: 1407.2172 - acc: 0.0040\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 235us/step - loss: 918.4524 - acc: 0.0020\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 278us/step - loss: 649.4308 - acc: 0.0020\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 304us/step - loss: 502.4813 - acc: 0.0000e+00\n",
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 257us/step - loss: 427.0802 - acc: 0.0059\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 210us/step - loss: 384.8589 - acc: 0.0000e+00\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 304us/step - loss: 355.3131 - acc: 0.0020\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 212us/step - loss: 331.7939 - acc: 0.0020\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 208us/step - loss: 314.8452 - acc: 0.0020\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 221us/step - loss: 298.4835 - acc: 0.0020\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 296us/step - loss: 283.7057 - acc: 0.0000e+00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 210us/step - loss: 270.8496 - acc: 0.0040\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 211us/step - loss: 258.5284 - acc: 0.0020\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 268us/step - loss: 247.4424 - acc: 0.0020\n",
            "Epoch 19/20\n",
            "506/506 [==============================] - 0s 257us/step - loss: 237.9341 - acc: 0.0040\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 219us/step - loss: 228.1922 - acc: 0.0040\n",
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 2ms/step - loss: 661.5235 - acc: 0.0000e+00\n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 194us/step - loss: 596.2765 - acc: 0.0020\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 314us/step - loss: 580.4171 - acc: 0.0000e+00\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 223us/step - loss: 578.8910 - acc: 0.0000e+00\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 202us/step - loss: 577.6339 - acc: 0.0000e+00\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 212us/step - loss: 576.5936 - acc: 0.0000e+00\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 262us/step - loss: 575.3132 - acc: 0.0000e+00\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 252us/step - loss: 574.3914 - acc: 0.0000e+00\n",
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 206us/step - loss: 573.1704 - acc: 0.0000e+00\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 209us/step - loss: 571.7646 - acc: 0.0000e+00\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 225us/step - loss: 569.1909 - acc: 0.0000e+00\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 260us/step - loss: 552.1570 - acc: 0.0000e+00\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 200us/step - loss: 492.7395 - acc: 0.0000e+00\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 208us/step - loss: 318.3214 - acc: 0.0040\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 210us/step - loss: 94.2594 - acc: 0.0040\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 200us/step - loss: 64.8140 - acc: 0.0020\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 202us/step - loss: 57.2812 - acc: 0.0040\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 308us/step - loss: 55.1977 - acc: 0.0059\n",
            "Epoch 19/20\n",
            " 32/506 [>.............................] - ETA: 0s - loss: 117.9551 - acc: 0.0000e+00"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "506/506 [==============================] - 0s 211us/step - loss: 55.3021 - acc: 0.0040\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 221us/step - loss: 51.2044 - acc: 0.0059\n",
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 2ms/step - loss: 2308.7117 - acc: 0.0000e+00\n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 210us/step - loss: 1113.2045 - acc: 0.0000e+00\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 203us/step - loss: 656.6162 - acc: 0.0000e+00\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 248us/step - loss: 591.8038 - acc: 0.0000e+00\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 223us/step - loss: 589.8025 - acc: 0.0000e+00\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 219us/step - loss: 589.0918 - acc: 0.0000e+00\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 208us/step - loss: 588.5097 - acc: 0.0000e+00\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 225us/step - loss: 587.9150 - acc: 0.0000e+00\n",
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 224us/step - loss: 587.3307 - acc: 0.0000e+00\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 235us/step - loss: 586.7218 - acc: 0.0000e+00\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 215us/step - loss: 586.1182 - acc: 0.0000e+00\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 216us/step - loss: 585.4922 - acc: 0.0000e+00\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 231us/step - loss: 584.8681 - acc: 0.0000e+00\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 224us/step - loss: 584.2450 - acc: 0.0000e+00\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 211us/step - loss: 583.6124 - acc: 0.0000e+00\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 233us/step - loss: 582.9798 - acc: 0.0000e+00\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 205us/step - loss: 582.3394 - acc: 0.0000e+00\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 204us/step - loss: 581.6998 - acc: 0.0000e+00\n",
            "Epoch 19/20\n",
            "506/506 [==============================] - 0s 233us/step - loss: 581.0533 - acc: 0.0000e+00\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 201us/step - loss: 580.4058 - acc: 0.0000e+00\n",
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 2ms/step - loss: 12327.3546 - acc: 0.0000e+00\n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 204us/step - loss: 8917.5277 - acc: 0.0000e+00\n",
            "Epoch 3/20\n",
            "320/506 [=================>............] - ETA: 0s - loss: 6676.8208 - acc: 0.0000e+00"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "506/506 [==============================] - 0s 210us/step - loss: 6174.4998 - acc: 0.0000e+00\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 200us/step - loss: 4111.6872 - acc: 0.0000e+00\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 198us/step - loss: 2696.1799 - acc: 0.0000e+00\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 210us/step - loss: 1779.6267 - acc: 0.0000e+00\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 203us/step - loss: 1236.8240 - acc: 0.0000e+00\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 194us/step - loss: 963.1812 - acc: 0.0000e+00\n",
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 207us/step - loss: 808.2402 - acc: 0.0000e+00\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 189us/step - loss: 735.6751 - acc: 0.0000e+00\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 196us/step - loss: 703.9201 - acc: 0.0000e+00\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 197us/step - loss: 683.5053 - acc: 0.0000e+00\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 195us/step - loss: 666.7541 - acc: 0.0000e+00\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 203us/step - loss: 652.8680 - acc: 0.0000e+00\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 212us/step - loss: 640.8374 - acc: 0.0000e+00\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 193us/step - loss: 630.7686 - acc: 0.0000e+00\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 194us/step - loss: 621.1364 - acc: 0.0000e+00\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 213us/step - loss: 613.6746 - acc: 0.0000e+00\n",
            "Epoch 19/20\n",
            "506/506 [==============================] - 0s 194us/step - loss: 607.2782 - acc: 0.0000e+00\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 201us/step - loss: 601.9119 - acc: 0.0000e+00\n",
            "Results: -79.48 (95.56) MSE\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3aOCBfOSPrGq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Now with smaller network\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "D3z34F57P--0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#smaller model \n",
        "def create_kfsmaller():  \n",
        "  #create model\n",
        "  input = keras.Input(shape=(4,))\n",
        "  a = layers.Dense(8,activation='relu')(input)\n",
        "  a = layers.Dense(5,activation='relu')(a)\n",
        "  outputs = layers.Dense(3,activation='softmax')(a)\n",
        "  #compile model\n",
        "  model = keras.Model(input, outputs)\n",
        "  model.compile(optimizer='Adam',loss='mse',metrics=['accuracy'])\n",
        "  model.fit(X,Y,epochs=20,batch_size=32)\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ICH2HHM6PwwA",
        "colab_type": "code",
        "outputId": "e012960e-06a8-46a6-b934-2ad01bb63ff4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 7094
        }
      },
      "cell_type": "code",
      "source": [
        "estimator=[]\n",
        "estimator = KerasClassifier(build_fn=create_kfsmaller,epochs=100,batch_size=5,verbose=0)\n",
        "kfold = KFold(n_splits=10,shuffle=True,random_state=seed)\n",
        "results = cross_val_score(estimator,X,Y,cv=kfold)\n",
        "print(\"Smaller: %.2f%% (%.2f%%)\" % (results.mean()*100,results.std()*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "150/150 [==============================] - 2s 10ms/step - loss: 2.3213 - acc: 0.3333\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 68us/step - loss: 2.2035 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 85us/step - loss: 2.0908 - acc: 0.3333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 69us/step - loss: 1.9831 - acc: 0.3333\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 75us/step - loss: 1.8772 - acc: 0.3333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 75us/step - loss: 1.7721 - acc: 0.3333\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 81us/step - loss: 1.6611 - acc: 0.3333\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 82us/step - loss: 1.5528 - acc: 0.3333\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 66us/step - loss: 1.4412 - acc: 0.3333\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 98us/step - loss: 1.3424 - acc: 0.3333\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 79us/step - loss: 1.2470 - acc: 0.3333\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 96us/step - loss: 1.1598 - acc: 0.3333\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 100us/step - loss: 1.0849 - acc: 0.3333\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 93us/step - loss: 1.0218 - acc: 0.3333\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 88us/step - loss: 0.9627 - acc: 0.3333\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 75us/step - loss: 0.9186 - acc: 0.3333\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 82us/step - loss: 0.8827 - acc: 0.3333\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 78us/step - loss: 0.8525 - acc: 0.3533\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 80us/step - loss: 0.8269 - acc: 0.4667\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 76us/step - loss: 0.8051 - acc: 0.6133\n",
            "Epoch 1/20\n",
            "150/150 [==============================] - 1s 8ms/step - loss: 1.5754 - acc: 0.5133\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 103us/step - loss: 1.5050 - acc: 0.5200\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 90us/step - loss: 1.4363 - acc: 0.5200\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 100us/step - loss: 1.3790 - acc: 0.5333\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 95us/step - loss: 1.3212 - acc: 0.5333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 95us/step - loss: 1.2603 - acc: 0.5400\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 92us/step - loss: 1.2128 - acc: 0.5333\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 1.1593 - acc: 0.5333\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 1.1102 - acc: 0.5333\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 102us/step - loss: 1.0625 - acc: 0.5333\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 91us/step - loss: 1.0184 - acc: 0.5267\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 111us/step - loss: 0.9743 - acc: 0.5267\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 0.9330 - acc: 0.5333\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 103us/step - loss: 0.8956 - acc: 0.5333\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 115us/step - loss: 0.8568 - acc: 0.5333\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 117us/step - loss: 0.8194 - acc: 0.5200\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 130us/step - loss: 0.7880 - acc: 0.5200\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 119us/step - loss: 0.7560 - acc: 0.5133\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 113us/step - loss: 0.7260 - acc: 0.5200\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 118us/step - loss: 0.6985 - acc: 0.5333\n",
            "Epoch 1/20\n",
            "150/150 [==============================] - 1s 9ms/step - loss: 1.1135 - acc: 0.3933\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 98us/step - loss: 1.0988 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 1.0898 - acc: 0.3533\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 98us/step - loss: 1.0870 - acc: 0.4400\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 102us/step - loss: 1.0820 - acc: 0.4800\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 92us/step - loss: 1.0747 - acc: 0.4733\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 82us/step - loss: 1.0680 - acc: 0.4533\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 89us/step - loss: 1.0608 - acc: 0.4467\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 1.0551 - acc: 0.4667\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 108us/step - loss: 1.0512 - acc: 0.4867\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 102us/step - loss: 1.0483 - acc: 0.4733\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 1.0460 - acc: 0.4667\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 98us/step - loss: 1.0436 - acc: 0.4733\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 1.0420 - acc: 0.4533\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 113us/step - loss: 1.0404 - acc: 0.4667\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 113us/step - loss: 1.0385 - acc: 0.4667\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 134us/step - loss: 1.0369 - acc: 0.4733\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 112us/step - loss: 1.0352 - acc: 0.4733\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 112us/step - loss: 1.0334 - acc: 0.4733\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 122us/step - loss: 1.0315 - acc: 0.4800\n",
            "Epoch 1/20\n",
            " 32/150 [=====>........................] - ETA: 4s - loss: 1.1581 - acc: 0.2500"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "150/150 [==============================] - 1s 9ms/step - loss: 1.1396 - acc: 0.2133\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 103us/step - loss: 1.1226 - acc: 0.1000\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 100us/step - loss: 1.1107 - acc: 0.1333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 100us/step - loss: 1.1012 - acc: 0.1933\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 96us/step - loss: 1.0930 - acc: 0.2267\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 101us/step - loss: 1.0869 - acc: 0.2667\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 99us/step - loss: 1.0826 - acc: 0.3133\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 99us/step - loss: 1.0778 - acc: 0.3267\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 98us/step - loss: 1.0741 - acc: 0.3333\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 100us/step - loss: 1.0699 - acc: 0.3333\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 92us/step - loss: 1.0659 - acc: 0.3333\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 1.0622 - acc: 0.3333\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 102us/step - loss: 1.0577 - acc: 0.3333\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 1.0534 - acc: 0.3333\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 108us/step - loss: 1.0491 - acc: 0.3333\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 1.0447 - acc: 0.3400\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 144us/step - loss: 1.0394 - acc: 0.3400\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 128us/step - loss: 1.0344 - acc: 0.3400\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 111us/step - loss: 1.0296 - acc: 0.3467\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 103us/step - loss: 1.0240 - acc: 0.3600\n",
            "Epoch 1/20\n",
            "150/150 [==============================] - 1s 9ms/step - loss: 1.0185 - acc: 0.5600\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 126us/step - loss: 1.0111 - acc: 0.4000\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 110us/step - loss: 1.0041 - acc: 0.3533\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 106us/step - loss: 0.9971 - acc: 0.3600\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 95us/step - loss: 0.9901 - acc: 0.3667\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 115us/step - loss: 0.9827 - acc: 0.3800\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 0.9757 - acc: 0.3933\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 0.9685 - acc: 0.4333\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 101us/step - loss: 0.9614 - acc: 0.4533\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 96us/step - loss: 0.9543 - acc: 0.4667\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 99us/step - loss: 0.9474 - acc: 0.4800\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 111us/step - loss: 0.9406 - acc: 0.5000\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 0.9339 - acc: 0.5133\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 0.9274 - acc: 0.5267\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 144us/step - loss: 0.9206 - acc: 0.5467\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 114us/step - loss: 0.9142 - acc: 0.5667\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 97us/step - loss: 0.9078 - acc: 0.5667\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 114us/step - loss: 0.9011 - acc: 0.5667\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 0.8949 - acc: 0.5667\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 103us/step - loss: 0.8885 - acc: 0.5667\n",
            "Epoch 1/20\n",
            "150/150 [==============================] - 1s 10ms/step - loss: 1.7824 - acc: 0.3333\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 96us/step - loss: 1.6117 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 93us/step - loss: 1.4570 - acc: 0.3333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 100us/step - loss: 1.3223 - acc: 0.3333\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 1.2189 - acc: 0.3333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 1.1371 - acc: 0.3333\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 94us/step - loss: 1.0867 - acc: 0.3333\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 100us/step - loss: 1.0583 - acc: 0.3533\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 101us/step - loss: 1.0354 - acc: 0.3867\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 96us/step - loss: 1.0219 - acc: 0.3667\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 103us/step - loss: 1.0074 - acc: 0.3600\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 0.9946 - acc: 0.3533\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 0.9823 - acc: 0.3533\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 116us/step - loss: 0.9696 - acc: 0.3533\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 151us/step - loss: 0.9574 - acc: 0.3667\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 113us/step - loss: 0.9460 - acc: 0.3800\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 94us/step - loss: 0.9338 - acc: 0.4000\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 126us/step - loss: 0.9225 - acc: 0.4333\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 0.9107 - acc: 0.4733\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 0.8997 - acc: 0.4800\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "150/150 [==============================] - 1s 10ms/step - loss: 1.8932 - acc: 0.3333\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 98us/step - loss: 1.7585 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 1.6434 - acc: 0.3333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 1.5503 - acc: 0.3333\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 108us/step - loss: 1.4622 - acc: 0.3333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 98us/step - loss: 1.3889 - acc: 0.3333\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 102us/step - loss: 1.3336 - acc: 0.3333\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 1.2793 - acc: 0.3333\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 101us/step - loss: 1.2344 - acc: 0.3333\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 1.1884 - acc: 0.3333\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 95us/step - loss: 1.1512 - acc: 0.3333\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 97us/step - loss: 1.1150 - acc: 0.3333\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 99us/step - loss: 1.0816 - acc: 0.3333\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 99us/step - loss: 1.0518 - acc: 0.3333\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 99us/step - loss: 1.0253 - acc: 0.3333\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 100us/step - loss: 0.9984 - acc: 0.3333\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 106us/step - loss: 0.9762 - acc: 0.3333\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 0.9555 - acc: 0.3333\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 111us/step - loss: 0.9335 - acc: 0.3333\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 0.9145 - acc: 0.3333\n",
            "Epoch 1/20\n",
            "150/150 [==============================] - 2s 10ms/step - loss: 1.5010 - acc: 0.3333\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 91us/step - loss: 1.4332 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 1.3624 - acc: 0.3333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 101us/step - loss: 1.3074 - acc: 0.3333\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 1.2665 - acc: 0.3333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 97us/step - loss: 1.2217 - acc: 0.3333\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 97us/step - loss: 1.1778 - acc: 0.3333\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 98us/step - loss: 1.1366 - acc: 0.3333\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 90us/step - loss: 1.0951 - acc: 0.3333\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 101us/step - loss: 1.0609 - acc: 0.3333\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 90us/step - loss: 1.0271 - acc: 0.3333\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 101us/step - loss: 0.9941 - acc: 0.3333\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 127us/step - loss: 0.9647 - acc: 0.3333\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 103us/step - loss: 0.9362 - acc: 0.3333\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 106us/step - loss: 0.9077 - acc: 0.3333\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 128us/step - loss: 0.8811 - acc: 0.3333\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 110us/step - loss: 0.8566 - acc: 0.3333\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 96us/step - loss: 0.8337 - acc: 0.3333\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 97us/step - loss: 0.8110 - acc: 0.3400\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 103us/step - loss: 0.7899 - acc: 0.3667\n",
            "Epoch 1/20\n",
            "150/150 [==============================] - 2s 11ms/step - loss: 2.2385 - acc: 0.3333\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 2.0916 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 121us/step - loss: 1.9502 - acc: 0.3333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 123us/step - loss: 1.8220 - acc: 0.3333\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 126us/step - loss: 1.6800 - acc: 0.3333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 96us/step - loss: 1.5701 - acc: 0.3333\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 102us/step - loss: 1.4744 - acc: 0.3333\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 119us/step - loss: 1.3823 - acc: 0.3333\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 103us/step - loss: 1.3062 - acc: 0.3333\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 99us/step - loss: 1.2299 - acc: 0.3333\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 110us/step - loss: 1.1750 - acc: 0.3333\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 97us/step - loss: 1.1210 - acc: 0.3333\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 1.0723 - acc: 0.3267\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 103us/step - loss: 1.0304 - acc: 0.3667\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 108us/step - loss: 0.9922 - acc: 0.4467\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 0.9554 - acc: 0.5533\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 150us/step - loss: 0.9254 - acc: 0.6067\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 130us/step - loss: 0.8964 - acc: 0.6533\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 120us/step - loss: 0.8725 - acc: 0.6600\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 93us/step - loss: 0.8476 - acc: 0.6667\n",
            "Epoch 1/20\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "150/150 [==============================] - 2s 11ms/step - loss: 1.7131 - acc: 0.3333\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 1.6413 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 117us/step - loss: 1.5737 - acc: 0.3333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 116us/step - loss: 1.5161 - acc: 0.3333\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 113us/step - loss: 1.4612 - acc: 0.3333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 111us/step - loss: 1.4149 - acc: 0.3333\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 125us/step - loss: 1.3705 - acc: 0.3333\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 123us/step - loss: 1.3340 - acc: 0.3333\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 124us/step - loss: 1.3027 - acc: 0.3333\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 123us/step - loss: 1.2725 - acc: 0.3333\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 115us/step - loss: 1.2475 - acc: 0.3333\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 137us/step - loss: 1.2288 - acc: 0.3333\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 110us/step - loss: 1.2110 - acc: 0.3333\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 133us/step - loss: 1.1989 - acc: 0.3333\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 121us/step - loss: 1.1859 - acc: 0.3333\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 113us/step - loss: 1.1742 - acc: 0.3333\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 117us/step - loss: 1.1657 - acc: 0.3333\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 112us/step - loss: 1.1573 - acc: 0.3333\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 138us/step - loss: 1.1509 - acc: 0.3333\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 119us/step - loss: 1.1446 - acc: 0.3333\n",
            "Smaller: 96.67% (5.37%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MBDWr9gWSSrT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Now with larger network"
      ]
    },
    {
      "metadata": {
        "id": "iIbmEn-XR33e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#larger model \n",
        "def create_kflarger():  \n",
        "  #create model\n",
        "  input = keras.Input(shape=(4,))\n",
        "  a = layers.Dense(8,activation='relu')(input)\n",
        "  a = layers.Dense(6,activation='relu')(a)\n",
        "  a = layers.Dense(5,activation='relu')(a)\n",
        "  outputs = layers.Dense(3,activation='softmax')(a)\n",
        "  #compile model\n",
        "  model = keras.Model(input, outputs)\n",
        "  model.compile(optimizer='Adam',loss='mse',metrics=['accuracy'])\n",
        "  model.fit(X,Y,epochs=20,batch_size=32)\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iSynPQNgSRv7",
        "colab_type": "code",
        "outputId": "05ea3097-5b71-43fb-b68a-b191ab4de928",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 7328
        }
      },
      "cell_type": "code",
      "source": [
        "estimator=[]\n",
        "estimator = KerasClassifier(build_fn=create_kfsmaller,epochs=100,batch_size=5,verbose=0)\n",
        "kfold = KFold(n_splits=10,shuffle=True,random_state=seed)\n",
        "results = cross_val_score(estimator,X,Y,cv=kfold)\n",
        "print(\"Larger: %.2f%% (%.2f%%)\" % (results.mean()*100,results.std()*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "150/150 [==============================] - 2s 13ms/step - loss: 2.5760 - acc: 0.3333\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 121us/step - loss: 2.3186 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 77us/step - loss: 2.0832 - acc: 0.3333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 100us/step - loss: 1.8732 - acc: 0.3333\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 93us/step - loss: 1.6834 - acc: 0.3333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 1.5266 - acc: 0.3333\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 110us/step - loss: 1.3916 - acc: 0.3333\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 117us/step - loss: 1.2785 - acc: 0.3333\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 1.1906 - acc: 0.3333\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 1.1169 - acc: 0.3333\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 112us/step - loss: 1.0675 - acc: 0.3467\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 126us/step - loss: 1.0210 - acc: 0.4067\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 102us/step - loss: 0.9922 - acc: 0.5400\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 121us/step - loss: 0.9670 - acc: 0.6000\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 110us/step - loss: 0.9492 - acc: 0.6600\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 111us/step - loss: 0.9336 - acc: 0.6667\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 132us/step - loss: 0.9217 - acc: 0.6667\n",
            "Epoch 18/20\n",
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            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r150/150 [==============================] - 0s 114us/step - loss: 0.8609 - acc: 0.6600\n",
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          ],
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            "Epoch 9/20\n",
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            "150/150 [==============================] - 0s 118us/step - loss: 0.6909 - acc: 0.4200\n",
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            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r150/150 [==============================] - 0s 160us/step - loss: 1.1941 - acc: 0.3533\n",
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            "Epoch 1/20\n",
            "150/150 [==============================] - 2s 15ms/step - loss: 1.8177 - acc: 0.3333\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 125us/step - loss: 1.7345 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 118us/step - loss: 1.6576 - acc: 0.3333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 112us/step - loss: 1.5797 - acc: 0.3333\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 116us/step - loss: 1.5087 - acc: 0.3333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 111us/step - loss: 1.4430 - acc: 0.3333\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 116us/step - loss: 1.3765 - acc: 0.3400\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 100us/step - loss: 1.3165 - acc: 0.3667\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 1.2608 - acc: 0.3867\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 106us/step - loss: 1.2071 - acc: 0.3533\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 106us/step - loss: 1.1591 - acc: 0.3400\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 1.1172 - acc: 0.3333\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 101us/step - loss: 1.0769 - acc: 0.3333\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 111us/step - loss: 1.0407 - acc: 0.3333\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 112us/step - loss: 1.0100 - acc: 0.3333\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 100us/step - loss: 0.9833 - acc: 0.3333\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 102us/step - loss: 0.9648 - acc: 0.3333\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 121us/step - loss: 0.9492 - acc: 0.3600\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 169us/step - loss: 0.9363 - acc: 0.4067\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 123us/step - loss: 0.9266 - acc: 0.4600\n",
            "Larger: 97.33% (4.42%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iG0Lz6QtbUvP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Developing a model that overfits"
      ]
    },
    {
      "metadata": {
        "id": "orBddnRiU5Ay",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#overfit model \n",
        "def create_kfoverfit():  \n",
        "  #create model\n",
        "  input = keras.Input(shape=(4,))\n",
        "  a = layers.Dense(8,activation='relu')(input)\n",
        "  a = layers.Dense(5,activation='relu')(a)\n",
        "  a = layers.Dense(6,activation='relu')(a)\n",
        "  a = layers.Dense(5,activation='relu')(a)\n",
        "  a = layers.Dense(4,activation='relu')(a)\n",
        "  outputs = layers.Dense(3,activation='softmax')(a)\n",
        "  #compile model\n",
        "  model = keras.Model(input, outputs)\n",
        "  model.compile(optimizer='Adamax',loss='mse',metrics=['accuracy'])\n",
        "  model.fit(X,Y,epochs=20,batch_size=32)\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pDfBVgMGVDJk",
        "colab_type": "code",
        "outputId": "1e1c4627-8c2f-4f85-a364-08420e6bcb03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3574
        }
      },
      "cell_type": "code",
      "source": [
        "estimator=[]\n",
        "estimator = KerasClassifier(build_fn=create_kfoverfit, epochs=200, batch_size=5, verbose=0)\n",
        "kfold = KFold(n_splits=5,shuffle=True,random_state=seed)\n",
        "results = cross_val_score(estimator,X,Y,cv=kfold)\n",
        "print(\"Overfit: %.2f%% (%.2f%%)\" % (results.mean()*100,results.std()*100)) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "150/150 [==============================] - 2s 16ms/step - loss: 1.1095 - acc: 0.3333\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 118us/step - loss: 1.1033 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 123us/step - loss: 1.1002 - acc: 0.3333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 128us/step - loss: 1.0958 - acc: 0.3400\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 135us/step - loss: 1.0925 - acc: 0.4267\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 114us/step - loss: 1.0908 - acc: 0.5933\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 125us/step - loss: 1.0903 - acc: 0.6467\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 114us/step - loss: 1.0890 - acc: 0.6533\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 119us/step - loss: 1.0876 - acc: 0.6267\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 112us/step - loss: 1.0866 - acc: 0.5800\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 120us/step - loss: 1.0849 - acc: 0.5733\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 115us/step - loss: 1.0831 - acc: 0.5800\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 110us/step - loss: 1.0810 - acc: 0.6000\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 126us/step - loss: 1.0787 - acc: 0.6000\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 110us/step - loss: 1.0757 - acc: 0.5867\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 1.0724 - acc: 0.5800\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 129us/step - loss: 1.0686 - acc: 0.5800\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 113us/step - loss: 1.0644 - acc: 0.5933\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 118us/step - loss: 1.0595 - acc: 0.5867\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 126us/step - loss: 1.0541 - acc: 0.5867\n",
            "Epoch 1/20\n",
            "150/150 [==============================] - 2s 15ms/step - loss: 1.3244 - acc: 0.3533\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 1.2453 - acc: 0.4000\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 1.1871 - acc: 0.3600\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 106us/step - loss: 1.1484 - acc: 0.3467\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 115us/step - loss: 1.1187 - acc: 0.3333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 115us/step - loss: 1.1023 - acc: 0.3333\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 96us/step - loss: 1.0863 - acc: 0.3067\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 92us/step - loss: 1.0742 - acc: 0.2800\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 96us/step - loss: 1.0643 - acc: 0.2000\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 97us/step - loss: 1.0523 - acc: 0.1867\n",
            "Epoch 11/20\n",
            " 32/150 [=====>........................] - ETA: 0s - loss: 1.0623 - acc: 0.1562"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r150/150 [==============================] - 0s 101us/step - loss: 1.0414 - acc: 0.2600\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 132us/step - loss: 1.0309 - acc: 0.4733\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 122us/step - loss: 1.0209 - acc: 0.5600\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 1.0113 - acc: 0.6200\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 113us/step - loss: 1.0028 - acc: 0.6467\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 0.9940 - acc: 0.6467\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 0.9853 - acc: 0.6533\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 108us/step - loss: 0.9758 - acc: 0.6533\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 114us/step - loss: 0.9671 - acc: 0.6600\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 131us/step - loss: 0.9578 - acc: 0.6600\n",
            "Epoch 1/20\n",
            "150/150 [==============================] - 2s 16ms/step - loss: 1.1889 - acc: 0.3333\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 107us/step - loss: 1.1338 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 112us/step - loss: 1.0845 - acc: 0.3333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 126us/step - loss: 1.0601 - acc: 0.3333\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 132us/step - loss: 1.0401 - acc: 0.3333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 117us/step - loss: 1.0248 - acc: 0.3333\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 1.0108 - acc: 0.3333\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 124us/step - loss: 0.9984 - acc: 0.3333\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 0.9875 - acc: 0.3333\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 127us/step - loss: 0.9776 - acc: 0.3333\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 0.9690 - acc: 0.3333\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 118us/step - loss: 0.9608 - acc: 0.3333\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 127us/step - loss: 0.9536 - acc: 0.3333\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 120us/step - loss: 0.9467 - acc: 0.3333\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 108us/step - loss: 0.9397 - acc: 0.3400\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 102us/step - loss: 0.9329 - acc: 0.3533\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 130us/step - loss: 0.9257 - acc: 0.3933\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 0.9191 - acc: 0.4933\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 0.9119 - acc: 0.5800\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 119us/step - loss: 0.9051 - acc: 0.6467\n",
            "Epoch 1/20\n",
            "150/150 [==============================] - 3s 17ms/step - loss: 1.1003 - acc: 0.6667\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 152us/step - loss: 1.0232 - acc: 0.6667\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 125us/step - loss: 0.9717 - acc: 0.6667\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 114us/step - loss: 0.9233 - acc: 0.6667\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 108us/step - loss: 0.8755 - acc: 0.6667\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 106us/step - loss: 0.8316 - acc: 0.6600\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 0.7940 - acc: 0.6600\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 116us/step - loss: 0.7669 - acc: 0.6533\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 117us/step - loss: 0.7433 - acc: 0.6600\n",
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 118us/step - loss: 0.7239 - acc: 0.7733\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 132us/step - loss: 0.7062 - acc: 0.8267\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 133us/step - loss: 0.6930 - acc: 0.8267\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 104us/step - loss: 0.6830 - acc: 0.8067\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 128us/step - loss: 0.6732 - acc: 0.8000\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 135us/step - loss: 0.6635 - acc: 0.7800\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 125us/step - loss: 0.6521 - acc: 0.7800\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 129us/step - loss: 0.6410 - acc: 0.8133\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 122us/step - loss: 0.6300 - acc: 0.8267\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 119us/step - loss: 0.6186 - acc: 0.8400\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 122us/step - loss: 0.6062 - acc: 0.8467\n",
            "Epoch 1/20\n",
            "150/150 [==============================] - 3s 18ms/step - loss: 2.5160 - acc: 0.3333\n",
            "Epoch 2/20\n",
            "150/150 [==============================] - 0s 129us/step - loss: 2.0507 - acc: 0.3333\n",
            "Epoch 3/20\n",
            "150/150 [==============================] - 0s 103us/step - loss: 1.7312 - acc: 0.3333\n",
            "Epoch 4/20\n",
            "150/150 [==============================] - 0s 120us/step - loss: 1.5148 - acc: 0.3333\n",
            "Epoch 5/20\n",
            "150/150 [==============================] - 0s 108us/step - loss: 1.3628 - acc: 0.3333\n",
            "Epoch 6/20\n",
            "150/150 [==============================] - 0s 106us/step - loss: 1.2441 - acc: 0.3400\n",
            "Epoch 7/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 1.1487 - acc: 0.3467\n",
            "Epoch 8/20\n",
            "150/150 [==============================] - 0s 105us/step - loss: 1.0897 - acc: 0.4600\n",
            "Epoch 9/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 1.0491 - acc: 0.5933\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 10/20\n",
            "150/150 [==============================] - 0s 109us/step - loss: 1.0209 - acc: 0.6200\n",
            "Epoch 11/20\n",
            "150/150 [==============================] - 0s 116us/step - loss: 1.0070 - acc: 0.6533\n",
            "Epoch 12/20\n",
            "150/150 [==============================] - 0s 123us/step - loss: 0.9953 - acc: 0.6533\n",
            "Epoch 13/20\n",
            "150/150 [==============================] - 0s 120us/step - loss: 0.9854 - acc: 0.6533\n",
            "Epoch 14/20\n",
            "150/150 [==============================] - 0s 120us/step - loss: 0.9755 - acc: 0.6667\n",
            "Epoch 15/20\n",
            "150/150 [==============================] - 0s 118us/step - loss: 0.9680 - acc: 0.6667\n",
            "Epoch 16/20\n",
            "150/150 [==============================] - 0s 141us/step - loss: 0.9616 - acc: 0.6667\n",
            "Epoch 17/20\n",
            "150/150 [==============================] - 0s 161us/step - loss: 0.9579 - acc: 0.6667\n",
            "Epoch 18/20\n",
            "150/150 [==============================] - 0s 132us/step - loss: 0.9549 - acc: 0.6667\n",
            "Epoch 19/20\n",
            "150/150 [==============================] - 0s 115us/step - loss: 0.9519 - acc: 0.6667\n",
            "Epoch 20/20\n",
            "150/150 [==============================] - 0s 146us/step - loss: 0.9493 - acc: 0.6667\n",
            "Overfit: 96.67% (3.65%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bhZxlpkGXUW1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Tuning the model"
      ]
    },
    {
      "metadata": {
        "id": "ac_c16TlXcz5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#tuning model \n",
        "def create_kftune():  \n",
        "  #create model\n",
        "  input = keras.Input(shape=(4,))\n",
        "  a = layers.Dense(8,activation='relu')(input)\n",
        "  a = layers.Dense(5,activation='relu')(a)\n",
        "  a = layers.Dense(6,activation='relu')(a)\n",
        "  a = layers.Dense(5,activation='relu')(a)\n",
        "  a = layers.Dense(4,activation='relu')(a)\n",
        "  outputs = layers.Dense(3,activation='softmax')(a)\n",
        "  #compile model\n",
        "  model = keras.Model(input, outputs)\n",
        "  model.compile(optimizer='Adamax',loss='mse',metrics=['accuracy'])\n",
        "  model.fit(X,Y,epochs=200,batch_size=32)\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3VHJ5ID3XYGL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "estimator=[]\n",
        "estimator = KerasClassifier(build_fn=create_kftune, epochs=200, batch_size=5, verbose=0)\n",
        "kfold = KFold(n_splits=5,shuffle=True,random_state=seed)\n",
        "results = cross_val_score(estimator,X,Y,cv=kfold)\n",
        "print(\"Tuned: %.2f%% (%.2f%%)\" % (results.mean()*100,results.std()*100)) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4A0FWI-9eTOq",
        "colab_type": "code",
        "outputId": "6ed76279-4746-4239-9171-16cced3ea7c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65510
        }
      },
      "cell_type": "code",
      "source": [
        "estimator=[]\n",
        "estimator = KerasClassifier(build_fn=create_kftune, epochs=200, batch_size=5, verbose=0)\n",
        "kfold = KFold(n_splits=9,shuffle=True,random_state=seed) # n_splits cahnged to 9 (just experimenting) \n",
        "results = cross_val_score(estimator,X,Y,cv=kfold)\n",
        "print(\"Tuned: %.2f%% (%.2f%%)\" % (results.mean()*100,results.std()*100)) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "150/150 [==============================] - 4s 25ms/step - loss: 1.1135 - acc: 0.3333\n",
            "Epoch 2/200\n",
            "150/150 [==============================] - 0s 106us/step - loss: 1.1072 - acc: 0.3333\n",
            "Epoch 3/200\n",
            "150/150 [==============================] - 0s 125us/step - loss: 1.1027 - acc: 0.3333\n",
            "Epoch 4/200\n",
            "150/150 [==============================] - 0s 136us/step - loss: 1.0985 - acc: 0.3333\n",
            "Epoch 5/200\n",
            "150/150 [==============================] - 0s 112us/step - loss: 1.0942 - acc: 0.3333\n",
            "Epoch 6/200\n",
            "150/150 [==============================] - 0s 118us/step - loss: 1.0912 - acc: 0.3333\n",
            "Epoch 7/200\n",
            "150/150 [==============================] - 0s 108us/step - loss: 1.0881 - acc: 0.3333\n",
            "Epoch 8/200\n",
            "150/150 [==============================] - 0s 145us/step - loss: 1.0851 - acc: 0.3333\n",
            "Epoch 9/200\n",
            "150/150 [==============================] - 0s 147us/step - loss: 1.0821 - acc: 0.3333\n",
            "Epoch 10/200\n",
            "150/150 [==============================] - 0s 135us/step - loss: 1.0800 - acc: 0.3467\n",
            "Epoch 11/200\n",
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            "Epoch 28/200\n",
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            "Epoch 81/200\n",
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            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r150/150 [==============================] - 0s 113us/step - loss: 0.3657 - acc: 0.9067\n",
            "Epoch 160/200\n",
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            "Epoch 11/200\n",
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            "Epoch 39/200\n",
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            "Epoch 94/200\n",
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          ],
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            "Epoch 6/200\n",
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            "Epoch 33/200\n",
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            "\n",
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            "Epoch 169/200\n",
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            "Epoch 28/200\n",
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            "Epoch 84/200\n",
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            "Epoch 192/200\n",
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            "Epoch 21/200\n",
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            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r150/150 [==============================] - 0s 122us/step - loss: 0.7002 - acc: 0.7267\n",
            "Epoch 48/200\n",
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            "Epoch 99/200\n",
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            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r150/150 [==============================] - 0s 155us/step - loss: 0.4543 - acc: 0.9333\n",
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            "Epoch 59/200\n",
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            "Epoch 87/200\n",
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            "Epoch 142/200\n",
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            "Epoch 170/200\n",
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            "Epoch 55/200\n",
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            "Epoch 83/200\n",
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            "Epoch 112/200\n",
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            "Epoch 140/200\n",
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            "150/150 [==============================] - 0s 188us/step - loss: 0.4125 - acc: 0.9133\n",
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            "Epoch 198/200\n",
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            "Epoch 26/200\n",
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            "Epoch 52/200\n",
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          ],
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          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r150/150 [==============================] - 0s 113us/step - loss: 0.6051 - acc: 0.6600\n",
            "Epoch 81/200\n",
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            " 32/150 [=====>........................] - ETA: 0s - loss: 0.4296 - acc: 0.8438"
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            "150/150 [==============================] - 0s 164us/step - loss: 0.5458 - acc: 0.6467\n",
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            "Epoch 16/200\n",
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            "Epoch 100/200\n",
            "150/150 [==============================] - 0s 135us/step - loss: 0.7498 - acc: 0.6667\n",
            "Epoch 101/200\n",
            "150/150 [==============================] - 0s 155us/step - loss: 0.7469 - acc: 0.6667\n",
            "Epoch 102/200\n",
            "150/150 [==============================] - 0s 188us/step - loss: 0.7444 - acc: 0.6667\n",
            "Epoch 103/200\n",
            "150/150 [==============================] - 0s 114us/step - loss: 0.7415 - acc: 0.6667\n",
            "Epoch 104/200\n",
            "150/150 [==============================] - 0s 121us/step - loss: 0.7388 - acc: 0.6667\n",
            "Epoch 105/200\n",
            "150/150 [==============================] - 0s 151us/step - loss: 0.7362 - acc: 0.6667\n",
            "Epoch 106/200\n",
            "150/150 [==============================] - 0s 142us/step - loss: 0.7337 - acc: 0.6667\n",
            "Epoch 107/200\n",
            "150/150 [==============================] - 0s 123us/step - loss: 0.7313 - acc: 0.6667\n",
            "Epoch 108/200\n",
            "150/150 [==============================] - 0s 128us/step - loss: 0.7290 - acc: 0.6667\n",
            "Epoch 109/200\n",
            "150/150 [==============================] - 0s 104us/step - loss: 0.7265 - acc: 0.6667\n",
            "Epoch 110/200\n",
            "150/150 [==============================] - 0s 112us/step - loss: 0.7241 - acc: 0.6667\n",
            "Epoch 111/200\n",
            "150/150 [==============================] - 0s 149us/step - loss: 0.7218 - acc: 0.6667\n",
            "Epoch 112/200\n",
            "150/150 [==============================] - 0s 132us/step - loss: 0.7196 - acc: 0.6667\n",
            "Epoch 113/200\n",
            "150/150 [==============================] - 0s 138us/step - loss: 0.7173 - acc: 0.6667\n",
            "Epoch 114/200\n",
            "150/150 [==============================] - 0s 122us/step - loss: 0.7151 - acc: 0.6667\n",
            "Epoch 115/200\n",
            "150/150 [==============================] - 0s 164us/step - loss: 0.7129 - acc: 0.6667\n",
            "Epoch 116/200\n",
            "150/150 [==============================] - 0s 121us/step - loss: 0.7109 - acc: 0.6667\n",
            "Epoch 117/200\n",
            "150/150 [==============================] - 0s 141us/step - loss: 0.7087 - acc: 0.6667\n",
            "Epoch 118/200\n",
            "150/150 [==============================] - 0s 131us/step - loss: 0.7065 - acc: 0.6667\n",
            "Epoch 119/200\n",
            "150/150 [==============================] - 0s 101us/step - loss: 0.7044 - acc: 0.6667\n",
            "Epoch 120/200\n",
            "150/150 [==============================] - 0s 132us/step - loss: 0.7025 - acc: 0.6667\n",
            "Epoch 121/200\n",
            "150/150 [==============================] - 0s 133us/step - loss: 0.7003 - acc: 0.6667\n",
            "Epoch 122/200\n",
            "150/150 [==============================] - 0s 95us/step - loss: 0.6983 - acc: 0.6667\n",
            "Epoch 123/200\n",
            "150/150 [==============================] - 0s 121us/step - loss: 0.6963 - acc: 0.6667\n",
            "Epoch 124/200\n",
            "150/150 [==============================] - 0s 111us/step - loss: 0.6944 - acc: 0.6667\n",
            "Epoch 125/200\n",
            " 32/150 [=====>........................] - ETA: 0s - loss: 0.6939 - acc: 0.6875"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r150/150 [==============================] - 0s 152us/step - loss: 0.6923 - acc: 0.6667\n",
            "Epoch 126/200\n",
            "150/150 [==============================] - 0s 123us/step - loss: 0.6906 - acc: 0.6667\n",
            "Epoch 127/200\n",
            "150/150 [==============================] - 0s 117us/step - loss: 0.6885 - acc: 0.6667\n",
            "Epoch 128/200\n",
            "150/150 [==============================] - 0s 124us/step - loss: 0.6865 - acc: 0.6667\n",
            "Epoch 129/200\n",
            "150/150 [==============================] - 0s 115us/step - loss: 0.6846 - acc: 0.6667\n",
            "Epoch 130/200\n",
            "150/150 [==============================] - 0s 126us/step - loss: 0.6827 - acc: 0.6667\n",
            "Epoch 131/200\n",
            "150/150 [==============================] - 0s 104us/step - loss: 0.6809 - acc: 0.6667\n",
            "Epoch 132/200\n",
            "150/150 [==============================] - 0s 108us/step - loss: 0.6792 - acc: 0.6667\n",
            "Epoch 133/200\n",
            "150/150 [==============================] - 0s 102us/step - loss: 0.6770 - acc: 0.6667\n",
            "Epoch 134/200\n",
            "150/150 [==============================] - 0s 151us/step - loss: 0.6752 - acc: 0.6667\n",
            "Epoch 135/200\n",
            "150/150 [==============================] - 0s 121us/step - loss: 0.6733 - acc: 0.6667\n",
            "Epoch 136/200\n",
            "150/150 [==============================] - 0s 175us/step - loss: 0.6716 - acc: 0.6667\n",
            "Epoch 137/200\n",
            "150/150 [==============================] - 0s 146us/step - loss: 0.6697 - acc: 0.6667\n",
            "Epoch 138/200\n",
            "150/150 [==============================] - 0s 131us/step - loss: 0.6678 - acc: 0.6733\n",
            "Epoch 139/200\n",
            "150/150 [==============================] - 0s 100us/step - loss: 0.6662 - acc: 0.6733\n",
            "Epoch 140/200\n",
            "150/150 [==============================] - 0s 111us/step - loss: 0.6643 - acc: 0.6733\n",
            "Epoch 141/200\n",
            "150/150 [==============================] - 0s 118us/step - loss: 0.6625 - acc: 0.6733\n",
            "Epoch 142/200\n",
            "150/150 [==============================] - 0s 118us/step - loss: 0.6608 - acc: 0.6667\n",
            "Epoch 143/200\n",
            "150/150 [==============================] - 0s 116us/step - loss: 0.6590 - acc: 0.6733\n",
            "Epoch 144/200\n",
            "150/150 [==============================] - 0s 120us/step - loss: 0.6570 - acc: 0.6733\n",
            "Epoch 145/200\n",
            "150/150 [==============================] - 0s 131us/step - loss: 0.6554 - acc: 0.6733\n",
            "Epoch 146/200\n",
            "150/150 [==============================] - 0s 127us/step - loss: 0.6538 - acc: 0.6733\n",
            "Epoch 147/200\n",
            "150/150 [==============================] - 0s 110us/step - loss: 0.6522 - acc: 0.6733\n",
            "Epoch 148/200\n",
            "150/150 [==============================] - 0s 154us/step - loss: 0.6506 - acc: 0.6733\n",
            "Epoch 149/200\n",
            "150/150 [==============================] - 0s 116us/step - loss: 0.6490 - acc: 0.6733\n",
            "Epoch 150/200\n",
            "150/150 [==============================] - 0s 128us/step - loss: 0.6475 - acc: 0.6733\n",
            "Epoch 151/200\n",
            "150/150 [==============================] - 0s 120us/step - loss: 0.6456 - acc: 0.6733\n",
            "Epoch 152/200\n",
            "150/150 [==============================] - 0s 139us/step - loss: 0.6442 - acc: 0.6733\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 153/200\n",
            "150/150 [==============================] - 0s 170us/step - loss: 0.6426 - acc: 0.6733\n",
            "Epoch 154/200\n",
            "150/150 [==============================] - 0s 153us/step - loss: 0.6411 - acc: 0.6733\n",
            "Epoch 155/200\n",
            "150/150 [==============================] - 0s 142us/step - loss: 0.6397 - acc: 0.6733\n",
            "Epoch 156/200\n",
            "150/150 [==============================] - 0s 130us/step - loss: 0.6381 - acc: 0.6733\n",
            "Epoch 157/200\n",
            "150/150 [==============================] - 0s 119us/step - loss: 0.6367 - acc: 0.6733\n",
            "Epoch 158/200\n",
            "150/150 [==============================] - 0s 114us/step - loss: 0.6350 - acc: 0.6733\n",
            "Epoch 159/200\n",
            "150/150 [==============================] - 0s 122us/step - loss: 0.6335 - acc: 0.6733\n",
            "Epoch 160/200\n",
            "150/150 [==============================] - 0s 143us/step - loss: 0.6320 - acc: 0.6733\n",
            "Epoch 161/200\n",
            "150/150 [==============================] - 0s 119us/step - loss: 0.6310 - acc: 0.6733\n",
            "Epoch 162/200\n",
            "150/150 [==============================] - 0s 139us/step - loss: 0.6291 - acc: 0.6733\n",
            "Epoch 163/200\n",
            "150/150 [==============================] - 0s 113us/step - loss: 0.6277 - acc: 0.6800\n",
            "Epoch 164/200\n",
            "150/150 [==============================] - 0s 133us/step - loss: 0.6261 - acc: 0.6800\n",
            "Epoch 165/200\n",
            "150/150 [==============================] - 0s 136us/step - loss: 0.6244 - acc: 0.6733\n",
            "Epoch 166/200\n",
            "150/150 [==============================] - 0s 136us/step - loss: 0.6229 - acc: 0.6800\n",
            "Epoch 167/200\n",
            "150/150 [==============================] - 0s 140us/step - loss: 0.6213 - acc: 0.6800\n",
            "Epoch 168/200\n",
            "150/150 [==============================] - 0s 130us/step - loss: 0.6200 - acc: 0.6733\n",
            "Epoch 169/200\n",
            "150/150 [==============================] - 0s 155us/step - loss: 0.6183 - acc: 0.6733\n",
            "Epoch 170/200\n",
            "150/150 [==============================] - 0s 161us/step - loss: 0.6167 - acc: 0.6733\n",
            "Epoch 171/200\n",
            "150/150 [==============================] - 0s 142us/step - loss: 0.6150 - acc: 0.6867\n",
            "Epoch 172/200\n",
            "150/150 [==============================] - 0s 160us/step - loss: 0.6136 - acc: 0.6867\n",
            "Epoch 173/200\n",
            "150/150 [==============================] - 0s 132us/step - loss: 0.6120 - acc: 0.6867\n",
            "Epoch 174/200\n",
            "150/150 [==============================] - 0s 126us/step - loss: 0.6104 - acc: 0.6933\n",
            "Epoch 175/200\n",
            "150/150 [==============================] - 0s 131us/step - loss: 0.6088 - acc: 0.7000\n",
            "Epoch 176/200\n",
            "150/150 [==============================] - 0s 148us/step - loss: 0.6072 - acc: 0.7000\n",
            "Epoch 177/200\n",
            "150/150 [==============================] - 0s 139us/step - loss: 0.6056 - acc: 0.6933\n",
            "Epoch 178/200\n",
            "150/150 [==============================] - 0s 115us/step - loss: 0.6039 - acc: 0.7000\n",
            "Epoch 179/200\n",
            "150/150 [==============================] - 0s 119us/step - loss: 0.6023 - acc: 0.6867\n",
            "Epoch 180/200\n",
            "150/150 [==============================] - 0s 131us/step - loss: 0.6007 - acc: 0.6867\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 181/200\n",
            "150/150 [==============================] - 0s 150us/step - loss: 0.5991 - acc: 0.6933\n",
            "Epoch 182/200\n",
            "150/150 [==============================] - 0s 131us/step - loss: 0.5975 - acc: 0.6933\n",
            "Epoch 183/200\n",
            "150/150 [==============================] - 0s 119us/step - loss: 0.5959 - acc: 0.7000\n",
            "Epoch 184/200\n",
            "150/150 [==============================] - 0s 179us/step - loss: 0.5940 - acc: 0.7000\n",
            "Epoch 185/200\n",
            "150/150 [==============================] - 0s 141us/step - loss: 0.5923 - acc: 0.7000\n",
            "Epoch 186/200\n",
            "150/150 [==============================] - 0s 131us/step - loss: 0.5906 - acc: 0.7000\n",
            "Epoch 187/200\n",
            "150/150 [==============================] - 0s 131us/step - loss: 0.5894 - acc: 0.7000\n",
            "Epoch 188/200\n",
            "150/150 [==============================] - 0s 119us/step - loss: 0.5873 - acc: 0.7000\n",
            "Epoch 189/200\n",
            "150/150 [==============================] - 0s 183us/step - loss: 0.5855 - acc: 0.7000\n",
            "Epoch 190/200\n",
            "150/150 [==============================] - 0s 141us/step - loss: 0.5841 - acc: 0.7000\n",
            "Epoch 191/200\n",
            "150/150 [==============================] - 0s 134us/step - loss: 0.5820 - acc: 0.7000\n",
            "Epoch 192/200\n",
            "150/150 [==============================] - 0s 153us/step - loss: 0.5804 - acc: 0.7000\n",
            "Epoch 193/200\n",
            "150/150 [==============================] - 0s 151us/step - loss: 0.5787 - acc: 0.7000\n",
            "Epoch 194/200\n",
            "150/150 [==============================] - 0s 117us/step - loss: 0.5770 - acc: 0.7000\n",
            "Epoch 195/200\n",
            "150/150 [==============================] - 0s 151us/step - loss: 0.5751 - acc: 0.7000\n",
            "Epoch 196/200\n",
            "150/150 [==============================] - 0s 116us/step - loss: 0.5734 - acc: 0.7000\n",
            "Epoch 197/200\n",
            "150/150 [==============================] - 0s 146us/step - loss: 0.5716 - acc: 0.7000\n",
            "Epoch 198/200\n",
            "150/150 [==============================] - 0s 154us/step - loss: 0.5698 - acc: 0.7000\n",
            "Epoch 199/200\n",
            "150/150 [==============================] - 0s 138us/step - loss: 0.5681 - acc: 0.7000\n",
            "Epoch 200/200\n",
            "150/150 [==============================] - 0s 124us/step - loss: 0.5665 - acc: 0.7000\n",
            "Tuned: 96.57% (4.26%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PxQANb7iQdpb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Step 8. Rewriting the code by doing Model Subclassing"
      ]
    },
    {
      "metadata": {
        "id": "yIDidq0xHA9C",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "input = keras.Input(shape=(13,))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "D969F9Mxk_pH",
        "colab_type": "code",
        "outputId": "2058780f-902b-4381-94cc-9d3a3fa375b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "cell_type": "code",
      "source": [
        "#making a class\n",
        "class MyModel(keras.Model):\n",
        "    \n",
        "  def __init__(self):\n",
        "    super(MyModel, self).__init__()\n",
        "    self.dense1 = layers.Dense(13,activation='relu')\n",
        "    self.dense2 = layers.Dense(3,activation='relu')\n",
        "    self.dense3 = layers.Dense(1)\n",
        "    \n",
        "  def call(self,inputs):\n",
        "    x = self.dense1(inputs)\n",
        "    x = self.dense2(x)\n",
        "    return self.dense3(x)\n",
        "    \n",
        "  \n",
        "model = MyModel()\n",
        "model.compile(optimizer='Adam',loss='mse',metrics=['accuracy'])\n",
        "model.fit(X,Y,epochs=20,batch_size=32)  "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "506/506 [==============================] - 1s 2ms/step - loss: 140.0744 - acc: 0.0079 \n",
            "Epoch 2/20\n",
            "506/506 [==============================] - 0s 54us/step - loss: 109.4166 - acc: 0.0059\n",
            "Epoch 3/20\n",
            "506/506 [==============================] - 0s 55us/step - loss: 97.6317 - acc: 0.0000e+00\n",
            "Epoch 4/20\n",
            "506/506 [==============================] - 0s 55us/step - loss: 90.0027 - acc: 0.0000e+00\n",
            "Epoch 5/20\n",
            "506/506 [==============================] - 0s 54us/step - loss: 85.5909 - acc: 0.0059\n",
            "Epoch 6/20\n",
            "506/506 [==============================] - 0s 57us/step - loss: 83.4652 - acc: 0.0040\n",
            "Epoch 7/20\n",
            "506/506 [==============================] - 0s 60us/step - loss: 80.3127 - acc: 0.0059\n",
            "Epoch 8/20\n",
            "506/506 [==============================] - 0s 55us/step - loss: 78.1569 - acc: 0.0059\n",
            "Epoch 9/20\n",
            "506/506 [==============================] - 0s 52us/step - loss: 76.2403 - acc: 0.0020\n",
            "Epoch 10/20\n",
            "506/506 [==============================] - 0s 58us/step - loss: 74.6232 - acc: 0.0020\n",
            "Epoch 11/20\n",
            "506/506 [==============================] - 0s 53us/step - loss: 73.0258 - acc: 0.0059\n",
            "Epoch 12/20\n",
            "506/506 [==============================] - 0s 57us/step - loss: 72.6108 - acc: 0.0020\n",
            "Epoch 13/20\n",
            "506/506 [==============================] - 0s 55us/step - loss: 70.6574 - acc: 0.0059\n",
            "Epoch 14/20\n",
            "506/506 [==============================] - 0s 59us/step - loss: 71.0062 - acc: 0.0059\n",
            "Epoch 15/20\n",
            "506/506 [==============================] - 0s 54us/step - loss: 69.0651 - acc: 0.0059\n",
            "Epoch 16/20\n",
            "506/506 [==============================] - 0s 63us/step - loss: 67.5253 - acc: 0.0020\n",
            "Epoch 17/20\n",
            "506/506 [==============================] - 0s 53us/step - loss: 65.5182 - acc: 0.0040\n",
            "Epoch 18/20\n",
            "506/506 [==============================] - 0s 62us/step - loss: 64.9633 - acc: 0.0079\n",
            "Epoch 19/20\n",
            "506/506 [==============================] - 0s 55us/step - loss: 63.5220 - acc: 0.0040\n",
            "Epoch 20/20\n",
            "506/506 [==============================] - 0s 70us/step - loss: 63.5634 - acc: 0.0059\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f953e65cdd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "zsD7KMJ3QhNF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Step 9. Rewriting the code without using scikit-learn"
      ]
    },
    {
      "metadata": {
        "id": "WsuMlxErl8Wz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def create_baseline():\n",
        "  #create model\n",
        "  input = keras.Input(shape=(13,))\n",
        "  a = layers.Dense(13,activation='relu')(input)\n",
        "  a = layers.Dense(3,activation='relu')(a)\n",
        "  outputs = layers.Dense(1)(a)\n",
        "  #compile model\n",
        "  model = keras.Model(input, outputs)\n",
        "  model.compile(optimizer='Adam',loss='mse',metrics=['accuracy'])\n",
        "  model.fit(X,Y,epochs=20,batch_size=32,verbose=0)\n",
        "  return model\n",
        "  \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kPqczKKRQ6Uw",
        "colab_type": "code",
        "outputId": "ff8602c4-82e5-4e8e-a493-d094803d92c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "cell_type": "code",
      "source": [
        "k = 9\n",
        "num_val_samples = len(X) // k\n",
        "num_epochs = 900\n",
        "all_scores = []\n",
        "for i in range(k):\n",
        "    print('processing fold #', i)\n",
        "    val_data = X[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    val_targets = Y[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    partial_train_data = np.concatenate([X[:i * num_val_samples],X[(i + 1) * num_val_samples:]],axis=0)\n",
        "    partial_train_targets = np.concatenate([Y[:i * num_val_samples],Y[(i + 1) * num_val_samples:]],axis=0)\n",
        "model = create_baseline()\n",
        "model.fit(partial_train_data, partial_train_targets,epochs=num_epochs, batch_size=32, verbose=0)\n",
        "val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\n",
        "all_scores.append(val_mae)\n",
        "\n",
        "score = np.average(all_scores)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "processing fold # 0\n",
            "processing fold # 1\n",
            "processing fold # 2\n",
            "processing fold # 3\n",
            "processing fold # 4\n",
            "processing fold # 5\n",
            "processing fold # 6\n",
            "processing fold # 7\n",
            "processing fold # 8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YopZZSbc4oeW",
        "colab_type": "code",
        "outputId": "783c7ed0-22c1-4032-921a-ea89d360af06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"Result: %.2f%% (%.2f%%)\" % (score.mean()*100,score.std()*100))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Result: 1.79% (0.00%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-_LvHuZxBSSM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}