01_regression_fuel_eff.ipynb

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    {
      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/mehrotrasan16/Keras-Deep-Learning/blob/01-AutoMPG-Regression/01_regression_fuel_eff.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EIdT9iu_Z4Rb"
      },
      "source": [
        "# Regression: Predicting fuel efficiency"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "AHp3M9ZmrIxj"
      },
      "source": [
        "In a *regression* problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a *classification* problem, where we aim to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1rRo8oNqZ-Rj",
        "colab": {}
      },
      "source": [
        "import pathlib\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9xQKvCJ85kCQ",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pGNXCMauOTp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install git+https://github.com/tensorflow/docs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Qz4HfsgRQUiV",
        "colab": {}
      },
      "source": [
        "import tensorflow_docs as tfdocs\n",
        "import tensorflow_docs.plots\n",
        "import tensorflow_docs.modeling"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "F_72b0LCNbjx"
      },
      "source": [
        "## The Auto MPG dataset\n",
        "\n",
        "The dataset is available from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "gFh9ne3FZ-On"
      },
      "source": [
        "### Get the data\n",
        "First download the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "p9kxxgzvzlyz",
        "colab": {}
      },
      "source": [
        "dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n",
        "dataset_path"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "nslsRLh7Zss4"
      },
      "source": [
        "Import it using pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CiX2FI4gZtTt",
        "colab": {}
      },
      "source": [
        "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n",
        "                'Acceleration', 'Model Year', 'Origin']\n",
        "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n",
        "                      na_values = \"?\", comment='\\t',\n",
        "                      sep=\" \", skipinitialspace=True)\n",
        "\n",
        "dataset = raw_dataset.copy()\n",
        "dataset.tail()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3MWuJTKEDM-f"
      },
      "source": [
        "### Clean the data\n",
        "\n",
        "The dataset contains a few unknown values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JEJHhN65a2VV",
        "colab": {}
      },
      "source": [
        "dataset.isna().sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9UPN0KBHa_WI"
      },
      "source": [
        "One simple option is to drop those rows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4ZUDosChC1UN",
        "colab": {}
      },
      "source": [
        "dataset = dataset.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "8XKitwaH4v8h"
      },
      "source": [
        "The `\"Origin\"` column is really categorical, not numeric. So convert that to a one-hot:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gWNTD2QjBWFJ",
        "colab": {}
      },
      "source": [
        "dataset['Origin'] = dataset['Origin'].map(lambda x: {1: 'USA', 2: 'Europe', 3: 'Japan'}.get(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ulXz4J7PAUzk",
        "colab": {}
      },
      "source": [
        "dataset = pd.get_dummies(dataset, prefix='', prefix_sep='')\n",
        "dataset.tail()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Cuym4yvk76vU"
      },
      "source": [
        "### Split the data into train and test\n",
        "\n",
        "Now split the dataset into a training set and a test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qn-IGhUE7_1H",
        "colab": {}
      },
      "source": [
        "train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
        "test_dataset = dataset.drop(train_dataset.index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "J4ubs136WLNp"
      },
      "source": [
        "### Inspect the data\n",
        "\n",
        "Have a quick look at the joint distribution of a few pairs of columns from the training set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oRKO_x8gWKv-",
        "colab": {}
      },
      "source": [
        "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "gavKO_6DWRMP"
      },
      "source": [
        "Also look at the overall statistics:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yi2FzC3T21jR",
        "colab": {}
      },
      "source": [
        "train_stats = train_dataset.describe()\n",
        "train_stats.pop(\"MPG\")\n",
        "train_stats = train_stats.transpose()\n",
        "train_stats"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Db7Auq1yXUvh"
      },
      "source": [
        "### Split features from labels\n",
        "\n",
        "Separate the target value, or \"label\", from the features. This label is the value that you will train the model to predict."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "t2sluJdCW7jN",
        "colab": {}
      },
      "source": [
        "train_labels = train_dataset.pop('MPG')\n",
        "test_labels = test_dataset.pop('MPG')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mRklxK5s388r"
      },
      "source": [
        "### Normalize the data\n",
        "\n",
        "Look again at the `train_stats` block above and note how different the ranges of each feature are."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-ywmerQ6dSox"
      },
      "source": [
        "It is good practice to normalize features that use different scales and ranges. Although the model *might* converge without feature normalization, it makes training more difficult, and it makes the resulting model dependent on the choice of units used in the input.\n",
        "\n",
        "Note: Although we intentionally generate these statistics from only the training dataset, these statistics will also be used to normalize the test dataset. We need to do that to project the test dataset into the same distribution that the model has been trained on."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JlC5ooJrgjQF",
        "colab": {}
      },
      "source": [
        "def norm(x):\n",
        "  return (x - train_stats['mean']) / train_stats['std']\n",
        "normed_train_data = norm(train_dataset)\n",
        "normed_test_data = norm(test_dataset)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "BuiClDk45eS4"
      },
      "source": [
        "This normalized data is what we will use to train the model.\n",
        "\n",
        "Caution: The statistics used to normalize the inputs here (mean and standard deviation) need to be applied to any other data that is fed to the model, along with the one-hot encoding that we did earlier.  That includes the test set as well as live data when the model is used in production."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bGxs6JEl1jnK"
      },
      "source": [
        "## The model #3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3I16pjjc1jnQ",
        "colab": {}
      },
      "source": [
        "#INSERT YOUR CODE TO BUILD AND COMPILE THE MODEL HERE\n",
        "#YOU CAN ALSO CHANGE THE MODEL TRAINING PARAMETERS BELOW\n",
        "model = keras.Sequential([\n",
        "                          layers.Dense(64,activation='relu',input_shape=[len(train_dataset.keys())]),\n",
        "                          layers.Dense(64,activation='relu'),\n",
        "                          layers.Dense(64,activation='relu'),\n",
        "                          layers.Dense(1)\n",
        "])\n",
        "optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
        "\n",
        "model.compile(loss='mse',optimizer=optimizer,metrics=['mae','mse'])\n"
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "z_7Jsemx1jnf"
      },
      "source": [
        "### Train the model\n",
        "\n",
        "Train the model for 1000 epochs, and record the training and validation accuracy in the `history` object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ucx4Zg5S1jnk",
        "colab": {}
      },
      "source": [
        "early_stop=keras.callbacks.EarlyStopping(monitor='val_loss',patience=10)"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MVOCPke91jnr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "e7b1fce6-1965-4689-db08-31f39399fdda"
      },
      "source": [
        "EPOCHS = 1000\n",
        "\n",
        "early_history = model.fit(\n",
        "  normed_train_data, train_labels,\n",
        "  epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
        "  callbacks=[early_stop,tfdocs.modeling.EpochDots()])"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch: 0, loss:567.5143,  mae:22.5051,  mse:567.5143,  val_loss:546.3226,  val_mae:21.9460,  val_mse:546.3226,  \n",
            "................................................................"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dZAue7fT1jnw"
      },
      "source": [
        "Visualize the model's training progress using the stats stored in the `history` object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VadpVdWy1jnx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e546b885-d4b4-4696-cb30-375145e8eb3f"
      },
      "source": [
        "hist = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "hist.tail()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>mae</th>\n",
              "      <th>mse</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_mae</th>\n",
              "      <th>val_mse</th>\n",
              "      <th>epoch</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>4.864207</td>\n",
              "      <td>1.469289</td>\n",
              "      <td>4.864207</td>\n",
              "      <td>8.595470</td>\n",
              "      <td>2.219290</td>\n",
              "      <td>8.595470</td>\n",
              "      <td>495</td>\n",
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              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>4.830314</td>\n",
              "      <td>1.495047</td>\n",
              "      <td>4.830314</td>\n",
              "      <td>8.531322</td>\n",
              "      <td>2.237597</td>\n",
              "      <td>8.531322</td>\n",
              "      <td>496</td>\n",
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              "      <th>497</th>\n",
              "      <td>4.783099</td>\n",
              "      <td>1.468698</td>\n",
              "      <td>4.783099</td>\n",
              "      <td>8.450450</td>\n",
              "      <td>2.192445</td>\n",
              "      <td>8.450450</td>\n",
              "      <td>497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>5.013025</td>\n",
              "      <td>1.516775</td>\n",
              "      <td>5.013025</td>\n",
              "      <td>8.580491</td>\n",
              "      <td>2.219481</td>\n",
              "      <td>8.580491</td>\n",
              "      <td>498</td>\n",
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              "      <th>499</th>\n",
              "      <td>4.907416</td>\n",
              "      <td>1.493987</td>\n",
              "      <td>4.907416</td>\n",
              "      <td>8.607849</td>\n",
              "      <td>2.232976</td>\n",
              "      <td>8.607849</td>\n",
              "      <td>499</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "         loss       mae       mse  val_loss   val_mae   val_mse  epoch\n",
              "495  4.864207  1.469289  4.864207  8.595470  2.219290  8.595470    495\n",
              "496  4.830314  1.495047  4.830314  8.531322  2.237597  8.531322    496\n",
              "497  4.783099  1.468698  4.783099  8.450450  2.192445  8.450450    497\n",
              "498  5.013025  1.516775  5.013025  8.580491  2.219481  8.580491    498\n",
              "499  4.907416  1.493987  4.907416  8.607849  2.232976  8.607849    499"
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          "metadata": {
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          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Jtez8cP91jn0",
        "colab": {}
      },
      "source": [
        "plotter = tfdocs.plots.HistoryPlotter(smoothing_std=2)"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7TMB1yuz1jn2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "1d7a5f1d-ac0d-4077-ecc8-c5327104f6af"
      },
      "source": [
        "plotter.plot({'Basic': early_history}, metric = \"mae\")\n",
        "plt.ylim([0, 10])\n",
        "plt.ylabel('MAE [MPG]')"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'MAE [MPG]')"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 130
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9DJCzwm51jn4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "ca3d2d40-5480-4ef6-bb46-c2e162e541c7"
      },
      "source": [
        "plotter.plot({'Basic': early_history}, metric = \"mse\")\n",
        "plt.ylim([0, 20])\n",
        "plt.ylabel('MSE [MPG^2]')"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'MSE [MPG^2]')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bNLJhKNg1jn7"
      },
      "source": [
        "Now determine how well the model generalizes by using the **test** set, which we did not use when training the model.  This tells us how well we can expect the model to predict when we use it in the real world."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "miAj05n_1jn7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c05b682d-4879-4372-e895-96cdc23ebad3"
      },
      "source": [
        "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
        "\n",
        "print(\"Testing set Mean Abs Error: {:5.2f} MPG\".format(mae))"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3/3 - 0s - loss: 5.4075 - mae: 1.7519 - mse: 5.4075\n",
            "Testing set Mean Abs Error:  1.75 MPG\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "samCwfkU1jn9"
      },
      "source": [
        "### Make predictions\n",
        "\n",
        "Finally, predict MPG values using data in the testing set:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zx2HRp0H1jn9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "a4c0f2b5-23cd-4426-8513-6f2c28dfe848"
      },
      "source": [
        "test_predictions = model.predict(normed_test_data).flatten()\n",
        "\n",
        "a = plt.axes(aspect='equal')\n",
        "plt.scatter(test_labels, test_predictions)\n",
        "plt.xlabel('True Values [MPG]')\n",
        "plt.ylabel('Predictions [MPG]')\n",
        "lims = [0, 50]\n",
        "plt.xlim(lims)\n",
        "plt.ylim(lims)\n",
        "_ = plt.plot(lims, lims)\n"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:5 out of the last 16 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f94bd860f28> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XDk0JvmP1jn_",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 84,
      "outputs": []
    }
  ]
}