15_선형회귀_적용하기_Boston.ipynb

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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "15db6521-b3f0-452a-adcc-c5c34092433c"
      },
      "source": [
        "\n",
        "#데이터 입력\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "my_data = 'housing.csv'\n",
        "\n",
        "#-*- coding: utf-8 -*-\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import numpy\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "# seed 값 설정\n",
        "seed = 0\n",
        "numpy.random.seed(seed)\n",
        "tf.random.set_seed(3)\n",
        "\n",
        "df = pd.read_csv(my_data, delim_whitespace=True, header=None)\n",
        "'''\n",
        "print(df.info())\n",
        "print(df.head())\n",
        "'''\n",
        "dataset = df.values\n",
        "X = dataset[:,0:13]\n",
        "Y = dataset[:,13]\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=seed)\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(30, input_dim=13, activation='relu'))\n",
        "model.add(Dense(6, activation='relu'))\n",
        "model.add(Dense(1))\n",
        "# 선형회귀데이터는 참과 거짓을 구분할 필요가 없음으로 출력층에 활성화 함수를 지정하지 않음\n",
        "model.compile(loss='mean_squared_error',\n",
        "              optimizer='adam')\n",
        "\n",
        "model.fit(X_train, Y_train, epochs=200, batch_size=10)\n",
        "\n",
        "# 예측 값과 실제 값의 비교\n",
        "# 모델의 학습이 어느정도 되었는지 확인하기 위해 예측 값과 실체 값을 비교하는 부분\n",
        "Y_prediction = model.predict(X_test).flatten()\n",
        "for i in range(10):\n",
        "    label = Y_test[i]\n",
        "    prediction = Y_prediction[i]\n",
        "    print(\"실제가격: {:.3f}, 예상가격: {:.3f}\".format(label, prediction))\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1a7965b2-62ea-4c08-bcc7-1951df7daf61\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1a7965b2-62ea-4c08-bcc7-1951df7daf61\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving housing.csv to housing.csv\n",
            "Epoch 1/200\n",
            "36/36 [==============================] - 1s 1ms/step - loss: 20056.3143\n",
            "Epoch 2/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 1925.3600\n",
            "Epoch 3/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 703.1190\n",
            "Epoch 4/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 625.2917\n",
            "Epoch 5/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 613.6478\n",
            "Epoch 6/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 644.6751\n",
            "Epoch 7/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 605.0916\n",
            "Epoch 8/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 655.1075\n",
            "Epoch 9/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 657.1848\n",
            "Epoch 10/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 585.4776\n",
            "Epoch 11/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 588.6399\n",
            "Epoch 12/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 624.4650\n",
            "Epoch 13/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 582.7648\n",
            "Epoch 14/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 591.1735\n",
            "Epoch 15/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 575.8204\n",
            "Epoch 16/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 573.7284\n",
            "Epoch 17/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 556.6114\n",
            "Epoch 18/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 594.8052\n",
            "Epoch 19/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 591.9369\n",
            "Epoch 20/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 557.2388\n",
            "Epoch 21/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 573.6880\n",
            "Epoch 22/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 636.7092\n",
            "Epoch 23/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 638.0409\n",
            "Epoch 24/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 537.1685\n",
            "Epoch 25/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 580.2076\n",
            "Epoch 26/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 600.4361\n",
            "Epoch 27/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 543.4191\n",
            "Epoch 28/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 575.4675\n",
            "Epoch 29/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 589.4611\n",
            "Epoch 30/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 528.4308\n",
            "Epoch 31/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 584.8097\n",
            "Epoch 32/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 601.6177\n",
            "Epoch 33/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 547.6582\n",
            "Epoch 34/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 544.9698\n",
            "Epoch 35/200\n",
            "36/36 [==============================] - 0s 3ms/step - loss: 552.1075\n",
            "Epoch 36/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 589.0301\n",
            "Epoch 37/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 578.3629\n",
            "Epoch 38/200\n",
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            "Epoch 184/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 376.1984\n",
            "Epoch 185/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 340.2740\n",
            "Epoch 186/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 351.1768\n",
            "Epoch 187/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 395.9186\n",
            "Epoch 188/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 359.6727\n",
            "Epoch 189/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 359.4495\n",
            "Epoch 190/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 384.8935\n",
            "Epoch 191/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 383.9878\n",
            "Epoch 192/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 394.6139\n",
            "Epoch 193/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 350.4856\n",
            "Epoch 194/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 351.1759\n",
            "Epoch 195/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 382.2243\n",
            "Epoch 196/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 362.2807\n",
            "Epoch 197/200\n",
            "36/36 [==============================] - 0s 2ms/step - loss: 334.7218\n",
            "Epoch 198/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 319.1050\n",
            "Epoch 199/200\n",
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            "Epoch 200/200\n",
            "36/36 [==============================] - 0s 1ms/step - loss: 375.8077\n",
            "실제가격: 22.600, 예상가격: 6.342\n",
            "실제가격: 50.000, 예상가격: 6.342\n",
            "실제가격: 23.000, 예상가격: 6.342\n",
            "실제가격: 8.300, 예상가격: 6.342\n",
            "실제가격: 21.200, 예상가격: 6.342\n",
            "실제가격: 19.900, 예상가격: 6.342\n",
            "실제가격: 20.600, 예상가격: 6.342\n",
            "실제가격: 18.700, 예상가격: 6.342\n",
            "실제가격: 16.100, 예상가격: 6.342\n",
            "실제가격: 18.600, 예상가격: 6.342\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlmMSV2UiUq4"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}