material/deep_learning/template_attribute_data_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "template_attribute_data_regression.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "giMgHzNY8861"
      },
      "source": [
        "# 속성 데이터 회귀 Template"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDu8wpKXq7kV"
      },
      "source": [
        "# 데이터 준비"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROMsisKxw8MC"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwMz9NaYwx5e",
        "outputId": "cb4b6d89-1cee-4e79-d5b2-f36e9f72b08d"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/dhrim/cau_2022_summer/master/material/deep_learning/car_regression.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-09-24 16:26:31--  https://raw.githubusercontent.com/dhrim/cau_2022_summer/master/material/deep_learning/car_regression.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 58697 (57K) [text/plain]\n",
            "Saving to: ‘car_regression.csv’\n",
            "\n",
            "car_regression.csv  100%[===================>]  57.32K  --.-KB/s    in 0.006s  \n",
            "\n",
            "2021-09-24 16:26:31 (8.83 MB/s) - ‘car_regression.csv’ saved [58697/58697]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VckhCYiwxAZj"
      },
      "source": [
        "raw_data = pd.read_csv(\"car_regression.csv\")\n",
        "data = raw_data.to_numpy()\n",
        "np.random.shuffle(data)\n",
        "\n",
        "x = data[:,1:]\n",
        "y = data[:,:1]\n",
        "\n",
        "split_index = int(len(x)*0.8)\n",
        "train_x, test_x = x[:split_index], x[split_index:]\n",
        "train_y, test_y = y[:split_index], y[split_index:]"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JskspNe2Z5F5",
        "outputId": "4644fb02-ff9c-4b58-e10e-b3fbd85fcaac"
      },
      "source": [
        "print(train_x[:5])\n",
        "print(train_y[:5])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.30957112 -0.20461746  0.27358521 -0.32679933 -0.95739638  0.81993056\n",
            "   1.          0.          0.        ]\n",
            " [-0.86291079 -0.36707301  0.0137853  -0.20906952 -0.41369436  1.09139405\n",
            "   1.          0.          0.        ]\n",
            " [-0.86291079 -0.51997236 -0.47983453 -0.2208425   0.02126726  1.63432105\n",
            "   1.          0.          0.        ]\n",
            " [-0.86291079 -0.93088936 -0.42787455 -1.03317817  0.34748847 -1.08031391\n",
            "   0.          0.          1.        ]\n",
            " [ 1.48205303  1.07591459  0.66328508  1.31906339 -0.55868157 -1.08031391\n",
            "   1.          0.          0.        ]]\n",
            "[[ 0.42973458]\n",
            " [ 0.57066975]\n",
            " [ 0.45535916]\n",
            " [ 0.45535916]\n",
            " [-1.3383611 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XR8Ck1Er6HmY",
        "outputId": "e6758ce6-64db-4502-b669-ff071453c11f"
      },
      "source": [
        "print(train_x.shape)\n",
        "print(train_y.shape)\n",
        "print(test_x.shape)\n",
        "print(test_y.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(313, 9)\n",
            "(313, 1)\n",
            "(79, 9)\n",
            "(79, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvaZhPiSq5Gc"
      },
      "source": [
        "# Template"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-wBUWBH_mTQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 822
        },
        "outputId": "46887960-da3c-4f82-d0a5-34a30603873e"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Dense(10, activation='relu', input_shape=(9,)))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "model.compile(optimizer=\"SGD\", loss=\"mse\", metrics=[\"mape\"])\n",
        "model.summary()\n",
        "\n",
        "history = model.fit(train_x, train_y, epochs=1000, verbose=1, batch_size=128, validation_split=0.1)\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "loss, mape = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"mape=\", mape)\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "plt.scatter(test_y, y_)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 10)                100       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 221\n",
            "Trainable params: 221\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 4ms/step - loss: 0.1229 - mape: 203.4032\n",
            "loss= 0.12286374717950821\n",
            "mape= 203.4031524658203\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-OlstKv7zLpI"
      },
      "source": [
        ""
      ],
      "execution_count": 6,
      "outputs": []
    }
  ]
}