5_3_NN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/minyoung347/python101/blob/main/5_3_NN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oALyyfgXRydv"
      },
      "source": [
        "## 영화 리뷰 분류: 이진 분류"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjZERUadRydw"
      },
      "source": [
        "### IMDB 데이터셋"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BQhTZrmRydx"
      },
      "source": [
        "**IMDB 데이터셋 로드하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:24.141645Z",
          "iopub.status.busy": "2022-05-21T00:58:24.140889Z",
          "iopub.status.idle": "2022-05-21T00:58:29.816007Z",
          "shell.execute_reply": "2022-05-21T00:58:29.814955Z"
        },
        "id": "EJIEvVXqRydy",
        "outputId": "19880e18-b20e-4c28-f17a-9b460ebd30c8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17464789/17464789 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import imdb\n",
        "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
        "    num_words=10000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
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          "shell.execute_reply": "2022-05-21T00:58:29.832695Z"
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        "id": "uAk9jmdMRyd0",
        "outputId": "80d8b722-0b13-49e8-8925-d2bdda5fd612",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
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            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "train_data[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:29.837815Z",
          "iopub.status.busy": "2022-05-21T00:58:29.837516Z",
          "iopub.status.idle": "2022-05-21T00:58:29.842773Z",
          "shell.execute_reply": "2022-05-21T00:58:29.841816Z"
        },
        "id": "-NAW-Oh2Ryd2",
        "outputId": "ea68576a-4094-4e3e-8ff1-a6c0c4c3df58",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "    train_labels[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:29.846598Z",
          "iopub.status.busy": "2022-05-21T00:58:29.846314Z",
          "iopub.status.idle": "2022-05-21T00:58:29.960078Z",
          "shell.execute_reply": "2022-05-21T00:58:29.959154Z"
        },
        "id": "_OKegKwsRyd3",
        "outputId": "3f2cb50e-7712-4b61-cc6d-834f4c3ca731",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9999"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "max([max(sequence) for sequence in train_data])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1syr49QyRyd4"
      },
      "source": [
        "**리뷰를 다시 텍스트로 디코딩하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:29.964258Z",
          "iopub.status.busy": "2022-05-21T00:58:29.963607Z",
          "iopub.status.idle": "2022-05-21T00:58:30.665703Z",
          "shell.execute_reply": "2022-05-21T00:58:30.664813Z"
        },
        "id": "FmGd2tohRyd5",
        "outputId": "69ae805c-8743-44bf-c08e-2d2c1e935290",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
            "1641221/1641221 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "word_index = imdb.get_word_index()\n",
        "reverse_word_index = dict(\n",
        "    [(value, key) for (key, value) in word_index.items()])\n",
        "decoded_review = \" \".join(\n",
        "    [reverse_word_index.get(i - 3, \"?\") for i in train_data[0]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMw5jhfYRyd6"
      },
      "source": [
        "**정수 시퀀스를 멀티-핫 인코딩으로 인코딩하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:30.670103Z",
          "iopub.status.busy": "2022-05-21T00:58:30.669714Z",
          "iopub.status.idle": "2022-05-21T00:58:35.758749Z",
          "shell.execute_reply": "2022-05-21T00:58:35.757888Z"
        },
        "id": "J2YPggO4Ryd6"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "def vectorize_sequences(sequences, dimension=10000):\n",
        "    results = np.zeros((len(sequences), dimension))\n",
        "    for i, sequence in enumerate(sequences):\n",
        "        for j in sequence:\n",
        "            results[i, j] = 1.\n",
        "    return results\n",
        "x_train = vectorize_sequences(train_data)\n",
        "x_test = vectorize_sequences(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:35.763362Z",
          "iopub.status.busy": "2022-05-21T00:58:35.762546Z",
          "iopub.status.idle": "2022-05-21T00:58:35.768031Z",
          "shell.execute_reply": "2022-05-21T00:58:35.767303Z"
        },
        "id": "cRP3GExeRyd7",
        "outputId": "5003dbb0-03d5-44fb-cbd7-6db0ba632f1f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1., 1., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "x_train[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:35.771822Z",
          "iopub.status.busy": "2022-05-21T00:58:35.771015Z",
          "iopub.status.idle": "2022-05-21T00:58:35.775500Z",
          "shell.execute_reply": "2022-05-21T00:58:35.774750Z"
        },
        "id": "iBw59ZljRyd7"
      },
      "outputs": [],
      "source": [
        "y_train = np.asarray(train_labels).astype(\"float32\")\n",
        "y_test = np.asarray(test_labels).astype(\"float32\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LANAjB0URyd8"
      },
      "source": [
        "### 신경망 모델 만들기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qObD7ZSGRyd9"
      },
      "source": [
        "**모델 정의하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:35.779731Z",
          "iopub.status.busy": "2022-05-21T00:58:35.779026Z",
          "iopub.status.idle": "2022-05-21T00:58:35.798811Z",
          "shell.execute_reply": "2022-05-21T00:58:35.797834Z"
        },
        "id": "iuTuYC4kRyd9"
      },
      "outputs": [],
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "model = keras.Sequential([\n",
        "    layers.Dense(16, activation=\"relu\"),\n",
        "    layers.Dense(16, activation=\"relu\"),\n",
        "    layers.Dense(1, activation=\"sigmoid\")\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoRYVfdjRyd-"
      },
      "source": [
        "**모델 컴파일하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:35.803010Z",
          "iopub.status.busy": "2022-05-21T00:58:35.802409Z",
          "iopub.status.idle": "2022-05-21T00:58:35.815382Z",
          "shell.execute_reply": "2022-05-21T00:58:35.814494Z"
        },
        "id": "KBQeB8kXRyd-"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"binary_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mjevp6k6Ryd_"
      },
      "source": [
        "### 훈련 검증"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KdUMmM7SRyd_"
      },
      "source": [
        "**검증 세트 준비하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:35.819667Z",
          "iopub.status.busy": "2022-05-21T00:58:35.819032Z",
          "iopub.status.idle": "2022-05-21T00:58:35.824128Z",
          "shell.execute_reply": "2022-05-21T00:58:35.823100Z"
        },
        "id": "SgqzuOPoRyeA"
      },
      "outputs": [],
      "source": [
        "x_val = x_train[:10000]\n",
        "partial_x_train = x_train[10000:]\n",
        "y_val = y_train[:10000]\n",
        "partial_y_train = y_train[10000:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "deqFApPTRyeA"
      },
      "source": [
        "**모델 훈련하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:35.828086Z",
          "iopub.status.busy": "2022-05-21T00:58:35.827799Z",
          "iopub.status.idle": "2022-05-21T00:58:47.348964Z",
          "shell.execute_reply": "2022-05-21T00:58:47.348136Z"
        },
        "id": "Wsgo70IxRyeA",
        "outputId": "0ec74574-eac5-4a7f-e24b-6e33c4bfeff1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "30/30 [==============================] - 4s 114ms/step - loss: 0.5038 - accuracy: 0.7819 - val_loss: 0.4035 - val_accuracy: 0.8397\n",
            "Epoch 2/20\n",
            "30/30 [==============================] - 1s 45ms/step - loss: 0.3107 - accuracy: 0.8937 - val_loss: 0.3038 - val_accuracy: 0.8877\n",
            "Epoch 3/20\n",
            "30/30 [==============================] - 2s 61ms/step - loss: 0.2321 - accuracy: 0.9234 - val_loss: 0.2954 - val_accuracy: 0.8808\n",
            "Epoch 4/20\n",
            "30/30 [==============================] - 2s 60ms/step - loss: 0.1922 - accuracy: 0.9361 - val_loss: 0.2749 - val_accuracy: 0.8902\n",
            "Epoch 5/20\n",
            "30/30 [==============================] - 2s 53ms/step - loss: 0.1590 - accuracy: 0.9481 - val_loss: 0.3000 - val_accuracy: 0.8812\n",
            "Epoch 6/20\n",
            "30/30 [==============================] - 1s 37ms/step - loss: 0.1380 - accuracy: 0.9546 - val_loss: 0.2901 - val_accuracy: 0.8863\n",
            "Epoch 7/20\n",
            "30/30 [==============================] - 1s 33ms/step - loss: 0.1163 - accuracy: 0.9646 - val_loss: 0.2968 - val_accuracy: 0.8849\n",
            "Epoch 8/20\n",
            "30/30 [==============================] - 1s 31ms/step - loss: 0.1030 - accuracy: 0.9680 - val_loss: 0.3101 - val_accuracy: 0.8829\n",
            "Epoch 9/20\n",
            "30/30 [==============================] - 1s 36ms/step - loss: 0.0851 - accuracy: 0.9761 - val_loss: 0.3286 - val_accuracy: 0.8822\n",
            "Epoch 10/20\n",
            "30/30 [==============================] - 1s 37ms/step - loss: 0.0763 - accuracy: 0.9785 - val_loss: 0.3446 - val_accuracy: 0.8812\n",
            "Epoch 11/20\n",
            "30/30 [==============================] - 2s 52ms/step - loss: 0.0638 - accuracy: 0.9833 - val_loss: 0.3649 - val_accuracy: 0.8800\n",
            "Epoch 12/20\n",
            "30/30 [==============================] - 2s 56ms/step - loss: 0.0572 - accuracy: 0.9849 - val_loss: 0.3842 - val_accuracy: 0.8786\n",
            "Epoch 13/20\n",
            "30/30 [==============================] - 2s 63ms/step - loss: 0.0487 - accuracy: 0.9876 - val_loss: 0.4200 - val_accuracy: 0.8709\n",
            "Epoch 14/20\n",
            "30/30 [==============================] - 2s 53ms/step - loss: 0.0432 - accuracy: 0.9889 - val_loss: 0.4327 - val_accuracy: 0.8723\n",
            "Epoch 15/20\n",
            "30/30 [==============================] - 1s 34ms/step - loss: 0.0354 - accuracy: 0.9921 - val_loss: 0.4504 - val_accuracy: 0.8729\n",
            "Epoch 16/20\n",
            "30/30 [==============================] - 1s 45ms/step - loss: 0.0313 - accuracy: 0.9943 - val_loss: 0.4726 - val_accuracy: 0.8726\n",
            "Epoch 17/20\n",
            "30/30 [==============================] - 2s 53ms/step - loss: 0.0259 - accuracy: 0.9956 - val_loss: 0.5030 - val_accuracy: 0.8699\n",
            "Epoch 18/20\n",
            "30/30 [==============================] - 2s 54ms/step - loss: 0.0225 - accuracy: 0.9963 - val_loss: 0.5251 - val_accuracy: 0.8685\n",
            "Epoch 19/20\n",
            "30/30 [==============================] - 2s 68ms/step - loss: 0.0195 - accuracy: 0.9976 - val_loss: 0.5442 - val_accuracy: 0.8690\n",
            "Epoch 20/20\n",
            "30/30 [==============================] - 2s 55ms/step - loss: 0.0204 - accuracy: 0.9960 - val_loss: 0.5554 - val_accuracy: 0.8696\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=20,\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:47.353728Z",
          "iopub.status.busy": "2022-05-21T00:58:47.352917Z",
          "iopub.status.idle": "2022-05-21T00:58:47.359185Z",
          "shell.execute_reply": "2022-05-21T00:58:47.358228Z"
        },
        "id": "8hVNM3-7RyeB",
        "outputId": "3a024fe4-be71-4a91-dfdf-a170e072b4d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "history_dict = history.history\n",
        "history_dict.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6HHYvTJZRyeB"
      },
      "source": [
        "**훈련과 검증 손실 그리기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:47.363128Z",
          "iopub.status.busy": "2022-05-21T00:58:47.362820Z",
          "iopub.status.idle": "2022-05-21T00:58:48.134762Z",
          "shell.execute_reply": "2022-05-21T00:58:48.133858Z"
        },
        "id": "wVEVb03vRyeC",
        "outputId": "664e8308-a6da-4d2c-edeb-f7548d202b07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "history_dict = history.history\n",
        "loss_values = history_dict[\"loss\"]\n",
        "val_loss_values = history_dict[\"val_loss\"]\n",
        "epochs = range(1, len(loss_values) + 1)\n",
        "plt.plot(epochs, loss_values, \"bo\", label=\"Training loss\")\n",
        "plt.plot(epochs, val_loss_values, \"b\", label=\"Validation loss\")\n",
        "plt.title(\"Training and validation loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vyz4nyT0RyeC"
      },
      "source": [
        "**훈련과 검증 정확도 그리기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:48.138805Z",
          "iopub.status.busy": "2022-05-21T00:58:48.138316Z",
          "iopub.status.idle": "2022-05-21T00:58:48.508495Z",
          "shell.execute_reply": "2022-05-21T00:58:48.507640Z"
        },
        "id": "4lhxzsBfRyeD",
        "outputId": "82207ba1-1aac-45da-9c10-a3b8ad7816be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.clf()\n",
        "acc = history_dict[\"accuracy\"]\n",
        "val_acc = history_dict[\"val_accuracy\"]\n",
        "plt.plot(epochs, acc, \"bo\", label=\"Training acc\")\n",
        "plt.plot(epochs, val_acc, \"b\", label=\"Validation acc\")\n",
        "plt.title(\"Training and validation accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLMQIrsARyeE"
      },
      "source": [
        "**모델을 처음부터 다시 훈련하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:48.512410Z",
          "iopub.status.busy": "2022-05-21T00:58:48.512097Z",
          "iopub.status.idle": "2022-05-21T00:58:53.740167Z",
          "shell.execute_reply": "2022-05-21T00:58:53.739318Z"
        },
        "id": "WdbiqqoXRyeE",
        "outputId": "dc633283-b7a5-4b57-bd1d-4e204e36963c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "49/49 [==============================] - 2s 28ms/step - loss: 0.4655 - accuracy: 0.8100\n",
            "Epoch 2/4\n",
            "49/49 [==============================] - 1s 30ms/step - loss: 0.2758 - accuracy: 0.9022\n",
            "Epoch 3/4\n",
            "49/49 [==============================] - 2s 31ms/step - loss: 0.2142 - accuracy: 0.9251\n",
            "Epoch 4/4\n",
            "49/49 [==============================] - 1s 30ms/step - loss: 0.1823 - accuracy: 0.9342\n",
            "782/782 [==============================] - 6s 6ms/step - loss: 0.2875 - accuracy: 0.8858\n"
          ]
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(16, activation=\"relu\"),\n",
        "    layers.Dense(16, activation=\"relu\"),\n",
        "    layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"binary_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit(x_train, y_train, epochs=4, batch_size=512)\n",
        "results = model.evaluate(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:53.744082Z",
          "iopub.status.busy": "2022-05-21T00:58:53.743592Z",
          "iopub.status.idle": "2022-05-21T00:58:53.748662Z",
          "shell.execute_reply": "2022-05-21T00:58:53.747996Z"
        },
        "id": "AutNeyvsRyeF",
        "outputId": "17f1f571-5cff-4d46-ca9d-cc8b7151de63",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.2874969244003296, 0.8857600092887878]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9b6hcHKARyeG"
      },
      "source": [
        "### 훈련된 모델로 새로운 데이터에 대해 예측하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:53.753112Z",
          "iopub.status.busy": "2022-05-21T00:58:53.752141Z",
          "iopub.status.idle": "2022-05-21T00:58:55.786729Z",
          "shell.execute_reply": "2022-05-21T00:58:55.785781Z"
        },
        "id": "453lRW_wRyeH",
        "outputId": "4eb499e5-f3eb-49d1-a7b0-0d24dfa7b35e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 2s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.27328938],\n",
              "       [0.9992004 ],\n",
              "       [0.85875785],\n",
              "       ...,\n",
              "       [0.11645874],\n",
              "       [0.11751027],\n",
              "       [0.72229904]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "model.predict(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjJG1lKSRyeL"
      },
      "source": [
        "## 뉴스 기사 분류: 다중 분류 문제"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jGWt2Xp0RyeM"
      },
      "source": [
        "### 로이터 데이터셋"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxiQdKzgRyeM"
      },
      "source": [
        "**로이터 데이터셋 로드하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:55.791733Z",
          "iopub.status.busy": "2022-05-21T00:58:55.790885Z",
          "iopub.status.idle": "2022-05-21T00:58:57.939572Z",
          "shell.execute_reply": "2022-05-21T00:58:57.938613Z"
        },
        "id": "8qf7A4Y5RyeM",
        "outputId": "7507b339-1564-4b88-bf2d-3f20d8e28ad3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/reuters.npz\n",
            "2110848/2110848 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import reuters\n",
        "(train_data, train_labels), (test_data, test_labels) = reuters.load_data(\n",
        "    num_words=10000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:57.945213Z",
          "iopub.status.busy": "2022-05-21T00:58:57.944384Z",
          "iopub.status.idle": "2022-05-21T00:58:57.949630Z",
          "shell.execute_reply": "2022-05-21T00:58:57.948847Z"
        },
        "id": "AQi1eT92RyeN",
        "outputId": "b9f6bbad-0143-402c-da60-08d70e54614c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8982"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "len(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:57.953105Z",
          "iopub.status.busy": "2022-05-21T00:58:57.952847Z",
          "iopub.status.idle": "2022-05-21T00:58:57.957221Z",
          "shell.execute_reply": "2022-05-21T00:58:57.956497Z"
        },
        "id": "HdsaMadiRyeN",
        "outputId": "01ceb9cd-f4ae-4d56-e22d-d58fc90cd451",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2246"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "len(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:57.960663Z",
          "iopub.status.busy": "2022-05-21T00:58:57.960413Z",
          "iopub.status.idle": "2022-05-21T00:58:57.965646Z",
          "shell.execute_reply": "2022-05-21T00:58:57.964973Z"
        },
        "id": "FWjzpVJWRyeO",
        "outputId": "d8634e0c-6292-4ff2-8369-25b23c3cf88b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1,\n",
              " 245,\n",
              " 273,\n",
              " 207,\n",
              " 156,\n",
              " 53,\n",
              " 74,\n",
              " 160,\n",
              " 26,\n",
              " 14,\n",
              " 46,\n",
              " 296,\n",
              " 26,\n",
              " 39,\n",
              " 74,\n",
              " 2979,\n",
              " 3554,\n",
              " 14,\n",
              " 46,\n",
              " 4689,\n",
              " 4329,\n",
              " 86,\n",
              " 61,\n",
              " 3499,\n",
              " 4795,\n",
              " 14,\n",
              " 61,\n",
              " 451,\n",
              " 4329,\n",
              " 17,\n",
              " 12]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "train_data[10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ou52UiQ2RyeO"
      },
      "source": [
        "**로이터 데이터셋을 텍스트로 디코딩하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:57.969038Z",
          "iopub.status.busy": "2022-05-21T00:58:57.968769Z",
          "iopub.status.idle": "2022-05-21T00:58:58.606123Z",
          "shell.execute_reply": "2022-05-21T00:58:58.605366Z"
        },
        "id": "UhI3hCQvRyeO",
        "outputId": "0165bf27-6541-4639-8d94-2c53a158a14b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/reuters_word_index.json\n",
            "550378/550378 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "word_index = reuters.get_word_index()\n",
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
        "decoded_newswire = \" \".join([reverse_word_index.get(i - 3, \"?\") for i in\n",
        "    train_data[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:58.610817Z",
          "iopub.status.busy": "2022-05-21T00:58:58.609932Z",
          "iopub.status.idle": "2022-05-21T00:58:58.615565Z",
          "shell.execute_reply": "2022-05-21T00:58:58.614707Z"
        },
        "id": "_dX4yM26RyeO",
        "outputId": "973fb1af-409b-43ff-e9af-f4de3c1b755b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "train_labels[10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwQGYNQORyeP"
      },
      "source": [
        "**데이터 인코딩하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:58.619093Z",
          "iopub.status.busy": "2022-05-21T00:58:58.618809Z",
          "iopub.status.idle": "2022-05-21T00:58:58.993179Z",
          "shell.execute_reply": "2022-05-21T00:58:58.992334Z"
        },
        "id": "Z3QCG9WCRyeP"
      },
      "outputs": [],
      "source": [
        "x_train = vectorize_sequences(train_data)\n",
        "x_test = vectorize_sequences(test_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdQHSLwjRyeQ"
      },
      "source": [
        "**레이블 인코딩하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:58.998170Z",
          "iopub.status.busy": "2022-05-21T00:58:58.997459Z",
          "iopub.status.idle": "2022-05-21T00:58:59.006393Z",
          "shell.execute_reply": "2022-05-21T00:58:59.005391Z"
        },
        "id": "S-E6ROygRyeQ"
      },
      "outputs": [],
      "source": [
        "def to_one_hot(labels, dimension=46):\n",
        "    results = np.zeros((len(labels), dimension))\n",
        "    for i, label in enumerate(labels):\n",
        "        results[i, label] = 1.\n",
        "    return results\n",
        "y_train = to_one_hot(train_labels)\n",
        "y_test = to_one_hot(test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:59.010347Z",
          "iopub.status.busy": "2022-05-21T00:58:59.010045Z",
          "iopub.status.idle": "2022-05-21T00:58:59.015326Z",
          "shell.execute_reply": "2022-05-21T00:58:59.014567Z"
        },
        "id": "I-7VmPp0RyeQ"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "y_train = to_categorical(train_labels)\n",
        "y_test = to_categorical(test_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7CVrNaGrRyeQ"
      },
      "source": [
        "### 모델 구성"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uv-3olCoRyeR"
      },
      "source": [
        "**모델 정의하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:59.019228Z",
          "iopub.status.busy": "2022-05-21T00:58:59.018698Z",
          "iopub.status.idle": "2022-05-21T00:58:59.027548Z",
          "shell.execute_reply": "2022-05-21T00:58:59.026784Z"
        },
        "id": "sLqB1E6MRyeR"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(64, activation=\"relu\"),\n",
        "    layers.Dense(64, activation=\"relu\"),\n",
        "    layers.Dense(46, activation=\"softmax\")\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guMVZpUNRyeR"
      },
      "source": [
        "**모델 컴파일하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:59.031190Z",
          "iopub.status.busy": "2022-05-21T00:58:59.030669Z",
          "iopub.status.idle": "2022-05-21T00:58:59.039143Z",
          "shell.execute_reply": "2022-05-21T00:58:59.038300Z"
        },
        "id": "YXWBBKnPRyeY"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QwUx1dQ_RyeY"
      },
      "source": [
        "### 훈련 검증"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOeH0p6lRyeZ"
      },
      "source": [
        "**검증 세트 준비하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:59.043039Z",
          "iopub.status.busy": "2022-05-21T00:58:59.042528Z",
          "iopub.status.idle": "2022-05-21T00:58:59.047024Z",
          "shell.execute_reply": "2022-05-21T00:58:59.046046Z"
        },
        "id": "wqLWYJVlRyeZ"
      },
      "outputs": [],
      "source": [
        "x_val = x_train[:1000]\n",
        "partial_x_train = x_train[1000:]\n",
        "y_val = y_train[:1000]\n",
        "partial_y_train = y_train[1000:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tIRPgk9zRyeZ"
      },
      "source": [
        "**모델 훈련하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:59.050590Z",
          "iopub.status.busy": "2022-05-21T00:58:59.050211Z",
          "iopub.status.idle": "2022-05-21T00:59:10.041884Z",
          "shell.execute_reply": "2022-05-21T00:59:10.040973Z"
        },
        "id": "aIGzzDunRyeZ",
        "outputId": "17e98a6c-919b-4316-d736-2e34a8b2efd9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "16/16 [==============================] - 2s 79ms/step - loss: 2.6469 - accuracy: 0.4395 - val_loss: 1.8580 - val_accuracy: 0.5790\n",
            "Epoch 2/20\n",
            "16/16 [==============================] - 1s 60ms/step - loss: 1.5836 - accuracy: 0.6543 - val_loss: 1.4290 - val_accuracy: 0.6850\n",
            "Epoch 3/20\n",
            "16/16 [==============================] - 1s 59ms/step - loss: 1.2262 - accuracy: 0.7379 - val_loss: 1.2186 - val_accuracy: 0.7380\n",
            "Epoch 4/20\n",
            "16/16 [==============================] - 1s 51ms/step - loss: 1.0048 - accuracy: 0.7865 - val_loss: 1.1060 - val_accuracy: 0.7540\n",
            "Epoch 5/20\n",
            "16/16 [==============================] - 1s 51ms/step - loss: 0.8377 - accuracy: 0.8207 - val_loss: 1.0344 - val_accuracy: 0.7800\n",
            "Epoch 6/20\n",
            "16/16 [==============================] - 1s 56ms/step - loss: 0.7002 - accuracy: 0.8505 - val_loss: 0.9859 - val_accuracy: 0.7870\n",
            "Epoch 7/20\n",
            "16/16 [==============================] - 1s 56ms/step - loss: 0.5894 - accuracy: 0.8757 - val_loss: 0.9381 - val_accuracy: 0.8040\n",
            "Epoch 8/20\n",
            "16/16 [==============================] - 1s 54ms/step - loss: 0.4933 - accuracy: 0.8946 - val_loss: 0.9197 - val_accuracy: 0.8110\n",
            "Epoch 9/20\n",
            "16/16 [==============================] - 1s 46ms/step - loss: 0.4147 - accuracy: 0.9144 - val_loss: 0.9331 - val_accuracy: 0.8100\n",
            "Epoch 10/20\n",
            "16/16 [==============================] - 1s 51ms/step - loss: 0.3556 - accuracy: 0.9223 - val_loss: 0.8956 - val_accuracy: 0.8170\n",
            "Epoch 11/20\n",
            "16/16 [==============================] - 1s 54ms/step - loss: 0.3060 - accuracy: 0.9334 - val_loss: 0.8933 - val_accuracy: 0.8220\n",
            "Epoch 12/20\n",
            "16/16 [==============================] - 1s 76ms/step - loss: 0.2612 - accuracy: 0.9414 - val_loss: 0.9022 - val_accuracy: 0.8250\n",
            "Epoch 13/20\n",
            "16/16 [==============================] - 1s 82ms/step - loss: 0.2298 - accuracy: 0.9456 - val_loss: 0.9199 - val_accuracy: 0.8130\n",
            "Epoch 14/20\n",
            "16/16 [==============================] - 1s 80ms/step - loss: 0.2071 - accuracy: 0.9486 - val_loss: 0.9385 - val_accuracy: 0.8170\n",
            "Epoch 15/20\n",
            "16/16 [==============================] - 1s 54ms/step - loss: 0.1872 - accuracy: 0.9523 - val_loss: 0.9334 - val_accuracy: 0.8170\n",
            "Epoch 16/20\n",
            "16/16 [==============================] - 1s 44ms/step - loss: 0.1699 - accuracy: 0.9526 - val_loss: 0.9778 - val_accuracy: 0.8070\n",
            "Epoch 17/20\n",
            "16/16 [==============================] - 1s 48ms/step - loss: 0.1571 - accuracy: 0.9559 - val_loss: 0.9565 - val_accuracy: 0.8160\n",
            "Epoch 18/20\n",
            "16/16 [==============================] - 1s 46ms/step - loss: 0.1479 - accuracy: 0.9558 - val_loss: 0.9552 - val_accuracy: 0.8080\n",
            "Epoch 19/20\n",
            "16/16 [==============================] - 1s 47ms/step - loss: 0.1380 - accuracy: 0.9555 - val_loss: 0.9685 - val_accuracy: 0.8100\n",
            "Epoch 20/20\n",
            "16/16 [==============================] - 1s 45ms/step - loss: 0.1358 - accuracy: 0.9559 - val_loss: 0.9835 - val_accuracy: 0.8160\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=20,\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IgfzW_8qRyea"
      },
      "source": [
        "**훈련과 검증 손실 그리기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:10.046902Z",
          "iopub.status.busy": "2022-05-21T00:59:10.045757Z",
          "iopub.status.idle": "2022-05-21T00:59:10.179653Z",
          "shell.execute_reply": "2022-05-21T00:59:10.178584Z"
        },
        "id": "SVm8Pe36Ryea",
        "outputId": "04aa33c8-a1a8-465c-e047-556a2d73a381",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "loss = history.history[\"loss\"]\n",
        "val_loss = history.history[\"val_loss\"]\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
        "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
        "plt.title(\"Training and validation loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqBSs9lwRyea"
      },
      "source": [
        "**훈련과 검증 정확도 그리기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:10.183642Z",
          "iopub.status.busy": "2022-05-21T00:59:10.183348Z",
          "iopub.status.idle": "2022-05-21T00:59:10.318032Z",
          "shell.execute_reply": "2022-05-21T00:59:10.317232Z"
        },
        "id": "SNmU0tPzRyeb",
        "outputId": "2b48abbe-6ccf-4f85-ca3f-94b47ddf864a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.clf()\n",
        "acc = history.history[\"accuracy\"]\n",
        "val_acc = history.history[\"val_accuracy\"]\n",
        "plt.plot(epochs, acc, \"bo\", label=\"Training accuracy\")\n",
        "plt.plot(epochs, val_acc, \"b\", label=\"Validation accuracy\")\n",
        "plt.title(\"Training and validation accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QIGN5jk2Ryeb"
      },
      "source": [
        "**모델을 처음부터 다시 훈련하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:10.321952Z",
          "iopub.status.busy": "2022-05-21T00:59:10.321440Z",
          "iopub.status.idle": "2022-05-21T00:59:13.836469Z",
          "shell.execute_reply": "2022-05-21T00:59:13.835680Z"
        },
        "id": "DUaioa29Ryeb",
        "outputId": "57ef0a17-64cf-41f9-b98c-11656a9ac47b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/9\n",
            "18/18 [==============================] - 1s 41ms/step - loss: 2.5619 - accuracy: 0.5067\n",
            "Epoch 2/9\n",
            "18/18 [==============================] - 1s 43ms/step - loss: 1.4344 - accuracy: 0.6917\n",
            "Epoch 3/9\n",
            "18/18 [==============================] - 1s 66ms/step - loss: 1.0949 - accuracy: 0.7648\n",
            "Epoch 4/9\n",
            "18/18 [==============================] - 1s 68ms/step - loss: 0.8894 - accuracy: 0.8084\n",
            "Epoch 5/9\n",
            "18/18 [==============================] - 1s 70ms/step - loss: 0.7272 - accuracy: 0.8431\n",
            "Epoch 6/9\n",
            "18/18 [==============================] - 1s 50ms/step - loss: 0.6056 - accuracy: 0.8687\n",
            "Epoch 7/9\n",
            "18/18 [==============================] - 1s 43ms/step - loss: 0.4972 - accuracy: 0.8923\n",
            "Epoch 8/9\n",
            "18/18 [==============================] - 1s 39ms/step - loss: 0.4226 - accuracy: 0.9060\n",
            "Epoch 9/9\n",
            "18/18 [==============================] - 1s 40ms/step - loss: 0.3514 - accuracy: 0.9215\n",
            "71/71 [==============================] - 0s 4ms/step - loss: 0.8962 - accuracy: 0.7974\n"
          ]
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "  layers.Dense(64, activation=\"relu\"),\n",
        "  layers.Dense(64, activation=\"relu\"),\n",
        "  layers.Dense(46, activation=\"softmax\")\n",
        "])\n",
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit(x_train,\n",
        "          y_train,\n",
        "          epochs=9,\n",
        "          batch_size=512)\n",
        "results = model.evaluate(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:13.840276Z",
          "iopub.status.busy": "2022-05-21T00:59:13.839752Z",
          "iopub.status.idle": "2022-05-21T00:59:13.845156Z",
          "shell.execute_reply": "2022-05-21T00:59:13.844286Z"
        },
        "id": "8Uvx5UfiRyec",
        "outputId": "921ce2a6-66f3-4d90-a080-50032d9ec722",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8962436318397522, 0.7974176406860352]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:13.848602Z",
          "iopub.status.busy": "2022-05-21T00:59:13.848317Z",
          "iopub.status.idle": "2022-05-21T00:59:13.854235Z",
          "shell.execute_reply": "2022-05-21T00:59:13.853462Z"
        },
        "id": "wiZnDW9HRyed",
        "outputId": "3ea8f750-6783-4095-faf6-828b23728b3d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.19100623330365094"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "import copy\n",
        "test_labels_copy = copy.copy(test_labels)\n",
        "np.random.shuffle(test_labels_copy)\n",
        "hits_array = np.array(test_labels) == np.array(test_labels_copy)\n",
        "hits_array.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zt0QjrLiRyed"
      },
      "source": [
        "### 새로운 데이터로 예측하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:13.858027Z",
          "iopub.status.busy": "2022-05-21T00:59:13.857648Z",
          "iopub.status.idle": "2022-05-21T00:59:14.132815Z",
          "shell.execute_reply": "2022-05-21T00:59:14.131779Z"
        },
        "id": "1rBJrITPRyee",
        "outputId": "039c5f32-43fd-4691-e7ae-bc259dacdd4f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "71/71 [==============================] - 0s 4ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:14.137393Z",
          "iopub.status.busy": "2022-05-21T00:59:14.136780Z",
          "iopub.status.idle": "2022-05-21T00:59:14.143161Z",
          "shell.execute_reply": "2022-05-21T00:59:14.142127Z"
        },
        "id": "oJm0-6OsRyee",
        "outputId": "850520ba-48ed-4a27-92d1-89fd34d78851",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(46,)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "predictions[0].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:14.147504Z",
          "iopub.status.busy": "2022-05-21T00:59:14.146927Z",
          "iopub.status.idle": "2022-05-21T00:59:14.152400Z",
          "shell.execute_reply": "2022-05-21T00:59:14.151516Z"
        },
        "id": "iUAzDrwWRyef",
        "outputId": "dbd8b6fe-4a65-490c-ae95-6282e44604de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.99999976"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "np.sum(predictions[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:14.155905Z",
          "iopub.status.busy": "2022-05-21T00:59:14.155423Z",
          "iopub.status.idle": "2022-05-21T00:59:14.161331Z",
          "shell.execute_reply": "2022-05-21T00:59:14.160581Z"
        },
        "id": "fTJBpMyrRyef",
        "outputId": "9aca2d1b-9249-42f8-b450-e0eaad364ef7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "np.argmax(predictions[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7BVjZPk3Ryef"
      },
      "source": [
        "### 레이블과 손실을 다루는 다른 방법"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:14.165413Z",
          "iopub.status.busy": "2022-05-21T00:59:14.164794Z",
          "iopub.status.idle": "2022-05-21T00:59:14.169329Z",
          "shell.execute_reply": "2022-05-21T00:59:14.168565Z"
        },
        "id": "eU3w1wAtRyef"
      },
      "outputs": [],
      "source": [
        "y_train = np.array(train_labels)\n",
        "y_test = np.array(test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:14.173213Z",
          "iopub.status.busy": "2022-05-21T00:59:14.172609Z",
          "iopub.status.idle": "2022-05-21T00:59:14.181387Z",
          "shell.execute_reply": "2022-05-21T00:59:14.180511Z"
        },
        "id": "sUHGzOpeRyeg"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"sparse_categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xoB6YZbLRyeh"
      },
      "source": [
        "### 충분히 큰 중간 층을 두어야 하는 이유"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L1OwV1cfRyeh"
      },
      "source": [
        "**정보 병목이 있는 모델**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:14.185530Z",
          "iopub.status.busy": "2022-05-21T00:59:14.185022Z",
          "iopub.status.idle": "2022-05-21T00:59:24.040585Z",
          "shell.execute_reply": "2022-05-21T00:59:24.039588Z"
        },
        "id": "E-_EV7p4Ryei",
        "outputId": "7ddcdae5-0d09-45bd-cd83-54ff554e519f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "63/63 [==============================] - 2s 19ms/step - loss: 2.8871 - accuracy: 0.2818 - val_loss: 2.2239 - val_accuracy: 0.5850\n",
            "Epoch 2/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 1.7586 - accuracy: 0.6488 - val_loss: 1.5090 - val_accuracy: 0.6650\n",
            "Epoch 3/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 1.3166 - accuracy: 0.7050 - val_loss: 1.3630 - val_accuracy: 0.6900\n",
            "Epoch 4/20\n",
            "63/63 [==============================] - 2s 25ms/step - loss: 1.1568 - accuracy: 0.7241 - val_loss: 1.2989 - val_accuracy: 0.6970\n",
            "Epoch 5/20\n",
            "63/63 [==============================] - 2s 25ms/step - loss: 1.0531 - accuracy: 0.7372 - val_loss: 1.2684 - val_accuracy: 0.7100\n",
            "Epoch 6/20\n",
            "63/63 [==============================] - 1s 21ms/step - loss: 0.9729 - accuracy: 0.7462 - val_loss: 1.2472 - val_accuracy: 0.7060\n",
            "Epoch 7/20\n",
            "63/63 [==============================] - 1s 19ms/step - loss: 0.9036 - accuracy: 0.7557 - val_loss: 1.2581 - val_accuracy: 0.7000\n",
            "Epoch 8/20\n",
            "63/63 [==============================] - 1s 20ms/step - loss: 0.8424 - accuracy: 0.7687 - val_loss: 1.2411 - val_accuracy: 0.7050\n",
            "Epoch 9/20\n",
            "63/63 [==============================] - 1s 21ms/step - loss: 0.7910 - accuracy: 0.7786 - val_loss: 1.2482 - val_accuracy: 0.6970\n",
            "Epoch 10/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 0.7440 - accuracy: 0.7898 - val_loss: 1.2658 - val_accuracy: 0.7080\n",
            "Epoch 11/20\n",
            "63/63 [==============================] - 1s 18ms/step - loss: 0.7034 - accuracy: 0.8018 - val_loss: 1.2712 - val_accuracy: 0.7050\n",
            "Epoch 12/20\n",
            "63/63 [==============================] - 1s 19ms/step - loss: 0.6687 - accuracy: 0.8078 - val_loss: 1.2716 - val_accuracy: 0.7090\n",
            "Epoch 13/20\n",
            "63/63 [==============================] - 1s 19ms/step - loss: 0.6362 - accuracy: 0.8201 - val_loss: 1.3118 - val_accuracy: 0.7100\n",
            "Epoch 14/20\n",
            "63/63 [==============================] - 1s 19ms/step - loss: 0.6050 - accuracy: 0.8316 - val_loss: 1.3390 - val_accuracy: 0.7190\n",
            "Epoch 15/20\n",
            "63/63 [==============================] - 2s 26ms/step - loss: 0.5794 - accuracy: 0.8434 - val_loss: 1.3441 - val_accuracy: 0.7170\n",
            "Epoch 16/20\n",
            "63/63 [==============================] - 2s 31ms/step - loss: 0.5495 - accuracy: 0.8527 - val_loss: 1.3581 - val_accuracy: 0.7210\n",
            "Epoch 17/20\n",
            "63/63 [==============================] - 2s 24ms/step - loss: 0.5269 - accuracy: 0.8616 - val_loss: 1.4329 - val_accuracy: 0.7110\n",
            "Epoch 18/20\n",
            "63/63 [==============================] - 1s 19ms/step - loss: 0.5026 - accuracy: 0.8641 - val_loss: 1.4156 - val_accuracy: 0.7180\n",
            "Epoch 19/20\n",
            "63/63 [==============================] - 1s 18ms/step - loss: 0.4870 - accuracy: 0.8697 - val_loss: 1.4435 - val_accuracy: 0.7250\n",
            "Epoch 20/20\n",
            "63/63 [==============================] - 1s 15ms/step - loss: 0.4687 - accuracy: 0.8745 - val_loss: 1.4647 - val_accuracy: 0.7070\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7f30f64bb970>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(64, activation=\"relu\"),\n",
        "    layers.Dense(4, activation=\"relu\"),\n",
        "    layers.Dense(46, activation=\"softmax\")\n",
        "])\n",
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit(partial_x_train,\n",
        "          partial_y_train,\n",
        "          epochs=20,\n",
        "          batch_size=128,\n",
        "          validation_data=(x_val, y_val))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f0v5NXFkRyej"
      },
      "source": [
        "## 주택 가격 예측: 회귀 문제"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EiShhudRRyej"
      },
      "source": [
        "### 보스턴 주택 가격 데이터셋"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PMOE4E-fRyek"
      },
      "source": [
        "**보스턴 주택 데이터셋 로드하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:24.045461Z",
          "iopub.status.busy": "2022-05-21T00:59:24.044756Z",
          "iopub.status.idle": "2022-05-21T00:59:24.724575Z",
          "shell.execute_reply": "2022-05-21T00:59:24.723670Z"
        },
        "id": "9TVqKPRpRyek",
        "outputId": "7d90a4ca-f11c-464c-8ac6-e967e3e4b661",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/boston_housing.npz\n",
            "57026/57026 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import boston_housing\n",
        "(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:24.729145Z",
          "iopub.status.busy": "2022-05-21T00:59:24.728397Z",
          "iopub.status.idle": "2022-05-21T00:59:24.734773Z",
          "shell.execute_reply": "2022-05-21T00:59:24.733860Z"
        },
        "id": "p_5GRHojRyek",
        "outputId": "b918fa37-788d-4a91-d7ec-d95b59ae5b6c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(404, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "train_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:24.738274Z",
          "iopub.status.busy": "2022-05-21T00:59:24.737701Z",
          "iopub.status.idle": "2022-05-21T00:59:24.743314Z",
          "shell.execute_reply": "2022-05-21T00:59:24.742477Z"
        },
        "id": "GtlvnaKPRyek",
        "outputId": "ebafb0a3-cfaf-4924-85e0-25d626869685",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(102, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "test_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:24.747014Z",
          "iopub.status.busy": "2022-05-21T00:59:24.746258Z",
          "iopub.status.idle": "2022-05-21T00:59:24.755724Z",
          "shell.execute_reply": "2022-05-21T00:59:24.754731Z"
        },
        "id": "Him5rrWrRyel",
        "outputId": "780479bb-68a1-4016-b8ec-a7fe85025184",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([15.2, 42.3, 50. , 21.1, 17.7, 18.5, 11.3, 15.6, 15.6, 14.4, 12.1,\n",
              "       17.9, 23.1, 19.9, 15.7,  8.8, 50. , 22.5, 24.1, 27.5, 10.9, 30.8,\n",
              "       32.9, 24. , 18.5, 13.3, 22.9, 34.7, 16.6, 17.5, 22.3, 16.1, 14.9,\n",
              "       23.1, 34.9, 25. , 13.9, 13.1, 20.4, 20. , 15.2, 24.7, 22.2, 16.7,\n",
              "       12.7, 15.6, 18.4, 21. , 30.1, 15.1, 18.7,  9.6, 31.5, 24.8, 19.1,\n",
              "       22. , 14.5, 11. , 32. , 29.4, 20.3, 24.4, 14.6, 19.5, 14.1, 14.3,\n",
              "       15.6, 10.5,  6.3, 19.3, 19.3, 13.4, 36.4, 17.8, 13.5, 16.5,  8.3,\n",
              "       14.3, 16. , 13.4, 28.6, 43.5, 20.2, 22. , 23. , 20.7, 12.5, 48.5,\n",
              "       14.6, 13.4, 23.7, 50. , 21.7, 39.8, 38.7, 22.2, 34.9, 22.5, 31.1,\n",
              "       28.7, 46. , 41.7, 21. , 26.6, 15. , 24.4, 13.3, 21.2, 11.7, 21.7,\n",
              "       19.4, 50. , 22.8, 19.7, 24.7, 36.2, 14.2, 18.9, 18.3, 20.6, 24.6,\n",
              "       18.2,  8.7, 44. , 10.4, 13.2, 21.2, 37. , 30.7, 22.9, 20. , 19.3,\n",
              "       31.7, 32. , 23.1, 18.8, 10.9, 50. , 19.6,  5. , 14.4, 19.8, 13.8,\n",
              "       19.6, 23.9, 24.5, 25. , 19.9, 17.2, 24.6, 13.5, 26.6, 21.4, 11.9,\n",
              "       22.6, 19.6,  8.5, 23.7, 23.1, 22.4, 20.5, 23.6, 18.4, 35.2, 23.1,\n",
              "       27.9, 20.6, 23.7, 28. , 13.6, 27.1, 23.6, 20.6, 18.2, 21.7, 17.1,\n",
              "        8.4, 25.3, 13.8, 22.2, 18.4, 20.7, 31.6, 30.5, 20.3,  8.8, 19.2,\n",
              "       19.4, 23.1, 23. , 14.8, 48.8, 22.6, 33.4, 21.1, 13.6, 32.2, 13.1,\n",
              "       23.4, 18.9, 23.9, 11.8, 23.3, 22.8, 19.6, 16.7, 13.4, 22.2, 20.4,\n",
              "       21.8, 26.4, 14.9, 24.1, 23.8, 12.3, 29.1, 21. , 19.5, 23.3, 23.8,\n",
              "       17.8, 11.5, 21.7, 19.9, 25. , 33.4, 28.5, 21.4, 24.3, 27.5, 33.1,\n",
              "       16.2, 23.3, 48.3, 22.9, 22.8, 13.1, 12.7, 22.6, 15. , 15.3, 10.5,\n",
              "       24. , 18.5, 21.7, 19.5, 33.2, 23.2,  5. , 19.1, 12.7, 22.3, 10.2,\n",
              "       13.9, 16.3, 17. , 20.1, 29.9, 17.2, 37.3, 45.4, 17.8, 23.2, 29. ,\n",
              "       22. , 18. , 17.4, 34.6, 20.1, 25. , 15.6, 24.8, 28.2, 21.2, 21.4,\n",
              "       23.8, 31. , 26.2, 17.4, 37.9, 17.5, 20. ,  8.3, 23.9,  8.4, 13.8,\n",
              "        7.2, 11.7, 17.1, 21.6, 50. , 16.1, 20.4, 20.6, 21.4, 20.6, 36.5,\n",
              "        8.5, 24.8, 10.8, 21.9, 17.3, 18.9, 36.2, 14.9, 18.2, 33.3, 21.8,\n",
              "       19.7, 31.6, 24.8, 19.4, 22.8,  7.5, 44.8, 16.8, 18.7, 50. , 50. ,\n",
              "       19.5, 20.1, 50. , 17.2, 20.8, 19.3, 41.3, 20.4, 20.5, 13.8, 16.5,\n",
              "       23.9, 20.6, 31.5, 23.3, 16.8, 14. , 33.8, 36.1, 12.8, 18.3, 18.7,\n",
              "       19.1, 29. , 30.1, 50. , 50. , 22. , 11.9, 37.6, 50. , 22.7, 20.8,\n",
              "       23.5, 27.9, 50. , 19.3, 23.9, 22.6, 15.2, 21.7, 19.2, 43.8, 20.3,\n",
              "       33.2, 19.9, 22.5, 32.7, 22. , 17.1, 19. , 15. , 16.1, 25.1, 23.7,\n",
              "       28.7, 37.2, 22.6, 16.4, 25. , 29.8, 22.1, 17.4, 18.1, 30.3, 17.5,\n",
              "       24.7, 12.6, 26.5, 28.7, 13.3, 10.4, 24.4, 23. , 20. , 17.8,  7. ,\n",
              "       11.8, 24.4, 13.8, 19.4, 25.2, 19.4, 19.4, 29.1])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "train_targets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1yxfVb8Ryel"
      },
      "source": [
        "### 데이터 준비"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8XtRErRzRyem"
      },
      "source": [
        "**데이터 정규화하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:24.759692Z",
          "iopub.status.busy": "2022-05-21T00:59:24.759382Z",
          "iopub.status.idle": "2022-05-21T00:59:24.764808Z",
          "shell.execute_reply": "2022-05-21T00:59:24.763724Z"
        },
        "id": "XYrfTZ5MRyem"
      },
      "outputs": [],
      "source": [
        "mean = train_data.mean(axis=0)\n",
        "train_data -= mean\n",
        "std = train_data.std(axis=0)\n",
        "train_data /= std\n",
        "test_data -= mean\n",
        "test_data /= std"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3WGqjsFRyem"
      },
      "source": [
        "### 모델 구성"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWKDgk12Ryem"
      },
      "source": [
        "**모델 정의하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:24.768846Z",
          "iopub.status.busy": "2022-05-21T00:59:24.768214Z",
          "iopub.status.idle": "2022-05-21T00:59:24.773358Z",
          "shell.execute_reply": "2022-05-21T00:59:24.772494Z"
        },
        "id": "MxJO2ZXlRyen"
      },
      "outputs": [],
      "source": [
        "def build_model():\n",
        "    model = keras.Sequential([\n",
        "        layers.Dense(64, activation=\"relu\"),\n",
        "        layers.Dense(64, activation=\"relu\"),\n",
        "        layers.Dense(1)\n",
        "    ])\n",
        "    model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3KyOwjXRyen"
      },
      "source": [
        "### K-겹 검증을 사용한 훈련 검증"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gvnz-7cIRyeo"
      },
      "source": [
        "**K-겹 검증하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:24.777180Z",
          "iopub.status.busy": "2022-05-21T00:59:24.776509Z",
          "iopub.status.idle": "2022-05-21T00:59:35.954278Z",
          "shell.execute_reply": "2022-05-21T00:59:35.953206Z"
        },
        "id": "3YFddtBwRyeo",
        "outputId": "4deb304a-e5b8-4b87-a2d2-4b3d27911b97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#0번째 폴드 처리중\n",
            "#1번째 폴드 처리중\n",
            "#2번째 폴드 처리중\n",
            "#3번째 폴드 처리중\n"
          ]
        }
      ],
      "source": [
        "k = 4\n",
        "num_val_samples = len(train_data) // k\n",
        "num_epochs = 100\n",
        "all_scores = []\n",
        "for i in range(k):\n",
        "    print(f\"#{i}번째 폴드 처리중\")\n",
        "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    partial_train_data = np.concatenate(\n",
        "        [train_data[:i * num_val_samples],\n",
        "         train_data[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "    partial_train_targets = np.concatenate(\n",
        "        [train_targets[:i * num_val_samples],\n",
        "         train_targets[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "    model = build_model()\n",
        "    model.fit(partial_train_data, partial_train_targets,\n",
        "              epochs=num_epochs, batch_size=16, verbose=0)\n",
        "    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\n",
        "    all_scores.append(val_mae)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:35.958698Z",
          "iopub.status.busy": "2022-05-21T00:59:35.958386Z",
          "iopub.status.idle": "2022-05-21T00:59:35.964375Z",
          "shell.execute_reply": "2022-05-21T00:59:35.963347Z"
        },
        "id": "vUv9Mo3yRyer",
        "outputId": "2fed6479-d427-4d93-d48c-308867a21fac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.9989207983016968, 2.546488046646118, 2.423715829849243, 2.3476626873016357]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "all_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:35.968402Z",
          "iopub.status.busy": "2022-05-21T00:59:35.967929Z",
          "iopub.status.idle": "2022-05-21T00:59:35.973477Z",
          "shell.execute_reply": "2022-05-21T00:59:35.972600Z"
        },
        "id": "e5AFRPuERyes",
        "outputId": "798b898c-ead8-454d-9cc7-22b9ad9b771f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.3291968405246735"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "np.mean(all_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AbzxHUVFRyes"
      },
      "source": [
        "**각 폴드의 검증 점수를 저장하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:59:35.977004Z",
          "iopub.status.busy": "2022-05-21T00:59:35.976727Z",
          "iopub.status.idle": "2022-05-21T01:01:15.236431Z",
          "shell.execute_reply": "2022-05-21T01:01:15.235480Z"
        },
        "id": "tBqJ3nGjRyes",
        "outputId": "d27db698-59c7-459d-ef2d-24ebc395f9f8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#0번째 폴드 처리중\n",
            "#1번째 폴드 처리중\n",
            "#2번째 폴드 처리중\n",
            "#3번째 폴드 처리중\n"
          ]
        }
      ],
      "source": [
        "num_epochs = 500\n",
        "all_mae_histories = []\n",
        "for i in range(k):\n",
        "    print(f\"#{i}번째 폴드 처리중\")\n",
        "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    partial_train_data = np.concatenate(\n",
        "        [train_data[:i * num_val_samples],\n",
        "         train_data[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "    partial_train_targets = np.concatenate(\n",
        "        [train_targets[:i * num_val_samples],\n",
        "         train_targets[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "    model = build_model()\n",
        "    history = model.fit(partial_train_data, partial_train_targets,\n",
        "                        validation_data=(val_data, val_targets),\n",
        "                        epochs=num_epochs, batch_size=16, verbose=0)\n",
        "    mae_history = history.history[\"val_mae\"]\n",
        "    all_mae_histories.append(mae_history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n7hitqM_Ryet"
      },
      "source": [
        "**K-겹 검증 점수 평균을 기록하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T01:01:15.240932Z",
          "iopub.status.busy": "2022-05-21T01:01:15.240614Z",
          "iopub.status.idle": "2022-05-21T01:01:15.249981Z",
          "shell.execute_reply": "2022-05-21T01:01:15.249150Z"
        },
        "id": "kt4SjG3NRyet"
      },
      "outputs": [],
      "source": [
        "average_mae_history = [\n",
        "    np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_TKKh0GDRyet"
      },
      "source": [
        "**검증 점수 그래프 그리기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T01:01:15.253642Z",
          "iopub.status.busy": "2022-05-21T01:01:15.253118Z",
          "iopub.status.idle": "2022-05-21T01:01:15.366352Z",
          "shell.execute_reply": "2022-05-21T01:01:15.365024Z"
        },
        "id": "pc47qQC4Ryet",
        "outputId": "cda43016-9143-44cd-b987-6000932cecad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Validation MAE\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFRXT2qYRyet"
      },
      "source": [
        "**처음 10개의 데이터 포인트를 제외한 검증 점수 그래프 그리기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T01:01:15.371320Z",
          "iopub.status.busy": "2022-05-21T01:01:15.371023Z",
          "iopub.status.idle": "2022-05-21T01:01:15.481039Z",
          "shell.execute_reply": "2022-05-21T01:01:15.479606Z"
        },
        "id": "x6VFtaG4Ryeu",
        "outputId": "2bf2a79f-3408-4be2-b8b3-87ade2dc470c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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OWQksKCYiIoqJoIObhIQEjBkzBkuWLMGWLVvQtWtXPPTQQ3C5XHjhhRewcOFCuN28kMsSuBSciIgoJhq0Wqp9+/b461//ioMHD+Lbb7+F3W7Htddei2bNmoV7fOcsZVqKmRsiIqKoCmlvKS2j0YiRI0di5MiROHHihM92DOczLgUnIiKKjbB1KG7atCkeffTRcD3cOU9eLcWl4ERERNEVdOamXbt2QR23b9++Bg8mnsjTUk63BJfbA7MpbHEkERERBRDSxpmtW7fGHXfcgZycnEiOKS7IBcUAUOPyIIXBDRERUVQEHdx8+umnmD59Ol599VWMHDkSd999N66++moYjbxo67GZ685LtcONFNtZlTcRERFRkIKOTG655RbMmzcPe/bsQZ8+ffDII48gLy8PTzzxBHbv3h3JMZ6TDAZD3eaZrLshIiKKmpDTLi1btsRTTz2F3bt3Y+bMmVizZg26dOmCM2fORGJ85zQWFRMREUVfg+ZKampqMHv2bEyfPh1r1qzBLbfcgqSkpHCP7ZyXYOZycCIiomgLKbhZs2YN3nvvPXz22Wdo164d7r77bvzvf/9TNtEktQQrG/kRERFFW9DBTffu3VFcXIw77rgDy5YtQ+/evSM5rrig1Ny4uHkmERFRtAQd3Gzfvh3Jycn44IMPAnYiPn36dFgGFg8SuAUDERFR1AUd3Lz//vuRHEdc4mopIiKi6As6uBk3blwkxxGXlMwNgxsiIqKoYQe+CJI3z2TmhoiIKHoY3ESQPC1VxZobIiKiqGFwE0HW2j43TjdXSxEREUULg5sIkoMbB5eCExERRQ2DmwhicENERBR9IW+/4Ha7MWPGDCxevBjFxcXweNQX7iVLloRtcOc6q4nTUkRERNEWcnAzadIkzJgxA9dccw169OgBg8EQiXHFBTm4cTC4ISIiipqQg5tZs2bhs88+w9VXXx2J8cQVeVrKzmkpIiKiqAm55sZqtaJDhw6RGEvcsSjTUlKMR0JERHT+CDm4eeyxx/D6669DknjBrk9dQTH73BAREUVLyNNSK1aswA8//IB58+ahe/fusFgsqu9/8cUXYRvcuY6rpYiIiKIv5OAmIyMDN954YyTGEnesnJYiIiKKupCDG+4OHjxmboiIiKIv5OBGduLECezcuRMA0LlzZzRt2jRsg4oXckGxnUvBiYiIoibkguLKykrcfffdyM3NxeDBgzF48GC0aNEC48ePR1VVVSTGeM5S9pZi5oaIiChqQg5uHn30USxbtgxff/01SkpKUFJSgq+++grLli3DY489FokxnrPYxI+IiCj6Qg5u/ve//+G9997DyJEjkZaWhrS0NFx99dV45513MHv27JAea+rUqejXrx9SU1ORk5ODUaNGKVNdgfzzn/9E586dkZiYiLy8PDzyyCOoqakJ9alEnNXs7d7MmhsiIqLoCTm4qaqqQrNmzXxuz8nJCXlaatmyZZg4cSJWr16NhQsXwul0Yvjw4aisrPR7n5kzZ+KJJ57AlClTsH37drz33nv49NNP8eSTT4b6VCLOajIB4N5SRERE0RRyQXFBQQGmTJmCDz74AAkJCQCA6upqPP/88ygoKAjpsebPn6/6esaMGcjJycGGDRswePBg3fv89NNPGDRoEO644w4AQJs2bXD77bdjzZo1oT6ViONqKSIiougLObh5/fXXMWLECLRq1Qq9e/cGAGzatAkJCQlYsGDBWQ2mtLQUAJCVleX3mIEDB+Kjjz7C2rVr0b9/f+zbtw/fffcd7rzzTt3j7XY77Ha78nVZWdlZjTEUFlPttBQzN0RERFETcnDTo0cP7N69Gx9//DF27NgBALj99tsxZswYJCYmNnggHo8HDz/8MAYNGoQePXr4Pe6OO+7AyZMncckll0CSJLhcLkyYMMHvtNTUqVPx/PPPN3hcZ4OZGyIiougzSI1kk6j77rsP8+bNw4oVK9CqVSu/xy1duhS33XYb/vrXv2LAgAHYs2cPJk2ahHvuuQfPPPOMz/F6mZu8vDyUlpYiLS0tIs9FVlxeg/4vLIbBAOx78WoYDIaI/jwiIqJ4VVZWhvT09KCu30FlbubOnYuRI0fCYrFg7ty5AY+9/vrrgx9prQceeADffPMNli9fHjCwAYBnnnkGd955J37/+98DAHr27InKykrce++9eOqpp2A0qmukbTYbbDZbyGMKB3kpuCQBbo8Es4nBDRERUaQFFdyMGjUKRUVFynJtfwwGA9zu4HfAliQJDz74IObMmYOlS5eibdu29d6nqqrKJ4Ax1a5KaiRJKIU8LQV4627MppAXpxEREVGIggpuPB6P7v+frYkTJ2LmzJn46quvkJqaiqKiIgBAenq6Ur8zduxYtGzZElOnTgUAXHfddXj11Vdx4YUXKtNSzzzzDK677jolyGksrEIw43B5kGSN4WCIiIjOEyEXFH/wwQcYPXq0z1SPw+HArFmzMHbs2KAfa9q0aQCAyy+/XHX7+++/j7vuugsAcOjQIVWm5umnn4bBYMDTTz+NI0eOoGnTprjuuuvwwgsvhPpUIs5kNMBg8E5LccUUERFRdIRcUGwymXDs2DHk5OSobj916hRycnJCmpaKhVAKksKh89PzYHd5sOLPQ9AqMyniP4+IiCgehXL9DrkIRJIk3VU/v/76K9LT00N9uLjH5eBERETRFfS01IUXXgiDwQCDwYChQ4fCbK67q9vtxv79+3HVVVdFZJDnMrnuxuluXMXORERE8Sro4EZeJVVYWIgRI0YgJSVF+Z7VakWbNm1w8803h32A5zpmboiIiKIr6OBmypQpALx7OY0ePVrZV4oCs9RmbhyNvBaJiIgoXoS8WmrcuHGRGEfcqsvccFqKiIgoGkIObtxuN1577TV89tlnOHToEBwOh+r7p0+fDtvg4oFVydxwWoqIiCgaQl4t9fzzz+PVV1/F6NGjUVpaikcffRQ33XQTjEYjnnvuuQgM8dxmYc0NERFRVIUc3Hz88cd455138Nhjj8FsNuP222/Hu+++i2effRarV6+OxBjPaTZltRSDGyIiomgIObgpKipCz549AQApKSkoLS0FAFx77bX49ttvwzu6OMDVUkRERNEVcnDTqlUrHDt2DADQvn17fP/99wCAdevWxWz37cbMUrsTOIMbIiKi6Ag5uLnxxhuxePFiAMCDDz6IZ555Bh07dsTYsWNx9913h32A57oEi3czzxoXl4ITERFFQ8irpV566SXl/0ePHo38/HysWrUKHTt2xHXXXRfWwcWDFJv3FJfXuGI8EiIiovNDyMGNVkFBAQoKCsIxlriUkuA9xRV2BjdERETREFRwM3fu3KAf8Prrr2/wYOJRam3mpoKZGyIioqgIKriR95WSGQwGSJLkcxvgbfJHdZi5ISIiiq6gCoo9Ho/y7/vvv8cFF1yAefPmoaSkBCUlJZg3bx4uuugizJ8/P9LjPeekJlgAsOaGiIgoWkKuuXn44Yfx1ltv4ZJLLlFuGzFiBJKSknDvvfdi+/btYR3guU4uKK6wO2M8EiIiovNDyEvB9+7di4yMDJ/b09PTceDAgTAMKb5wWoqIiCi6Qg5u+vXrh0cffRTHjx9Xbjt+/Dgef/xx9O/fP6yDiwcsKCYiIoqukIOb6dOn49ixY8jPz0eHDh3QoUMH5Ofn48iRI3jvvfciMcZzGjM3RERE0RVyzU2HDh2wefNmLFy4EDt27AAAdO3aFcOGDVNWTFEdNvEjIiKKrgY18TMYDBg+fDiGDx8e7vHEnVSbd7WU3eWBw+VRNtIkIiKiyAgquPnXv/6Fe++9FwkJCfjXv/4V8NiHHnooLAOLF8k2k/L/lXYXrGZrDEdDREQU/4IKbl577TWMGTMGCQkJeO211/weZzAYGNxomE1GJFpMqHa6UWF3ITOZwQ0REVEkBRXc7N+/X/f/KTgpCWZUO92suyEiIooCFoBEgbIcnCumiIiIIi6ozM2jjz4a9AO++uqrDR5MvJKXg5fXsEsxERFRpAUV3GzcuDGoB+NScH1ptftLlTG4ISIiiriggpsffvgh0uOIa3IR8elKBjdERESRxpqbKMhM8mZuSqocMR4JERFR/GtQE7/169fjs88+w6FDh+BwqC/YX3zxRVgGFk8ykuTMDYMbIiKiSAs5czNr1iwMHDgQ27dvx5w5c+B0OrFt2zYsWbIE6enpkRjjOS9LydxwWoqIiCjSQg5uXnzxRbz22mv4+uuvYbVa8frrr2PHjh249dZbkZ+fH4kxnvPqam6YuSEiIoq0kIObvXv34pprrgEAWK1WVFZWwmAw4JFHHsHbb78d9gHGg8zaaakzrLkhIiKKuJCDm8zMTJSXlwMAWrZsia1btwIASkpKUFVVFd7RxQk5uOG0FBERUeSFXFA8ePBgLFy4ED179sQtt9yCSZMmYcmSJVi4cCGGDh0aiTGe8zJqa25OVzkgSRL7AREREUVQ0MHN1q1b0aNHD7zxxhuoqakBADz11FOwWCz46aefcPPNN+Ppp5+O2EDPZVm1NTcOlwfVTjeSrA1apEZERERBCPoq26tXL/Tr1w+///3vcdtttwEAjEYjnnjiiYgNLl4kWU2wmoxwuD04XelgcENERBRBQdfcLFu2DN27d8djjz2G3NxcjBs3Dj/++GMkxxY3DAYDMpO9U1OnKlhUTEREFElBBzeXXnoppk+fjmPHjuHf//43Dhw4gMsuuwydOnXC3/72NxQVFUVynOe8Ts1SAQAzfjoQ24EQERHFuZBXSyUnJ+N3v/sdli1bhl27duGWW27Bm2++ifz8fFx//fWRGGNceHxEZxgMwJyNR3CstDrWwyEiIopbZ7W3VIcOHfDkk0/i6aefRmpqKr799ttwjSvu9GqVgeZpCQCAk+WcmiIiIoqUBle2Ll++HNOnT8f//vc/GI1G3HrrrRg/fnw4xxZ3Eq0mAECVwxXjkRAREcWvkIKbo0ePYsaMGZgxYwb27NmDgQMH4l//+hduvfVWJCcnR2qMcSNJDm6c7hiPhIiIKH4FHdyMHDkSixYtQpMmTTB27Fjcfffd6Ny5cyTHFncSLd7gptrB4IaIiChSgg5uLBYLZs+ejWuvvRYmkymSY4pbibX9baoY3BAREUVM0MHN3LlzIzmO80KSkrlhzQ0REVGknNVqKQqNUnPDzA0REVHEMLiJokQGN0RERBHH4CaK5MxNNVdLERERRQyDmyiqKyhmzQ0REVGkMLiJIiVz4/DEeCRERETxK6bBzdSpU9GvXz+kpqYiJycHo0aNws6dO+u9X0lJCSZOnIjc3FzYbDZ06tQJ3333XRRGfHbqpqWYuSEiIoqUBm+/EA7Lli3DxIkT0a9fP7hcLjz55JMYPnw4fvnlF78djx0OB6688krk5ORg9uzZaNmyJQ4ePIiMjIzoDr4BEiwsKCYiIoq0mAY38+fPV309Y8YM5OTkYMOGDRg8eLDufaZPn47Tp0/jp59+gsViAQC0adMm0kMNCy4FJyIiirxGVXNTWloKAMjKyvJ7zNy5c1FQUICJEyeiWbNm6NGjB1588UW43foBg91uR1lZmepfrNTV3DC4ISIiipRGE9x4PB48/PDDGDRoEHr06OH3uH379mH27Nlwu9347rvv8Mwzz+Af//gH/vrXv+oeP3XqVKSnpyv/8vLyIvUU6pVo4WopIiKiSGs0wc3EiROxdetWzJo1K+BxHo8HOTk5ePvtt9GnTx+MHj0aTz31FN566y3d4ydPnozS0lLl3+HDhyMx/KAwc0NERBR5Ma25kT3wwAP45ptvsHz5crRq1Srgsbm5ubBYLKrNO7t27YqioiI4HA5YrVbV8TabDTabLSLjDpVSc8MmfkRERBET08yNJEl44IEHMGfOHCxZsgRt27at9z6DBg3Cnj174PHU9YrZtWsXcnNzfQKbxobbLxAREUVeTIObiRMn4qOPPsLMmTORmpqKoqIiFBUVobq6Wjlm7NixmDx5svL1fffdh9OnT2PSpEnYtWsXvv32W7z44ouYOHFiLJ5CSJJqOxQ7XB64PVKMR0NERBSfYhrcTJs2DaWlpbj88suRm5ur/Pv000+VYw4dOoRjx44pX+fl5WHBggVYt24devXqhYceegiTJk3CE088EYunEBJ5WgoAlu4sjuFIiIiI4pdBkqTzKoVQVlaG9PR0lJaWIi0tLao/W5Ik9Hr+e5TXuGA1GbH1+RGwmhtNTTcREVGjFcr1m1fWKDIYDJg9YSAAwOH24GhJdT33ICIiolAxuImyzs1T0alZCgDg4OmqGI+GiIgo/jC4iYH8rCQAwKFTlTEeCRERUfxhcBMD+VneTUEPnmLmhoiIKNwY3MRA6+zazA2npYiIiMKOwU0M5AvBzYrdJ3Gi3B7jEREREcUPBjcxkJfpDW52FJXjt++twbBXl8V4RERERPGDwU0MNElRbxNRWu3EmUpHjEZDREQUXxjcxEBaggUmo0F12/LdJ2I0GiIiovjC4CYGjEYDMpMsqtuW7zoZo9EQERHFFwY3MZKVrJ6aOlbKbsVEREThwOAmRjKT1MFNWY0zRiMhIiKKLwxuYiRbU1RcXuOK0UiIiIjiC4ObGPHJ3FT7Zm6cbg8q7Qx6iIiIQsHgJkayk7XTUi5IkqS67Za3VuHC/1uIUp3Ah4iIiPQxuImRBKtJ9bXbI6Ha6VbdVni4BA6XByv3cCUVERE1DodOVWHGyv2o0VyzGhNzrAdAgNlogMsj4ciZahw8VYXBnZpCQl0Wp9rReF9ARER0frn6Xz+iwu7CiQo7Hh/RJdbD0cXMTYwkWeoyN6kJ3hjztrdX4/cfrMdby/aiQigwrnKw7oaIiMLjaEk1bnhzJb74+dcG3b+ithZ05Z5T4RxWWDG4iZHf9M1Dr1bpmDS0I9ISvQ39TtVuwTBn4xHV6qlT3JqBiIjC5MXvtmPT4RI8+tmms3ocTaP9RoXBTYyk2MyY+8AleOTKTkhLUHcrbppiUyJjANw1nIgozP42fwce/3yTz0KOeLD51xI8OWcLTlXoXzvKwtR6RLuNUGPCmptGIC1R/WtommpTNfVjcENEFD4ej4RpS/cCAP5wWTt0yEmt9z5yEGQwNN4Luuz6N1YCAEqrnHhzzEU+3/cXk0iSFNLzMzbic8HMTSOQalNnbhKtJlXNzQk/0TcFz+n2sHaJiAAAdpdH+X+nu/7MjSRJ+O17a3Dtv1fA5fbUe3xDfL7+MD5Zeyisj7n9WJnu7SadoOSfi3ZhwIuLcfBUZdCP35gzNwxuGgFt5qba6VbV3DBzc/ZG/HM5uj27gNtcEJFqCXMwyQeXR8LKPaew7WgZdhdXRGQ8j8/ejMlfbMGZKNRYarMzbo+Efy7ajeJyO77edDTgfe2uunPHzA0FpK25qXa4fWpu4nFeOJr2nfB+Gtlw8EyMR0JEsSb2FHO66n9vdQiZnkj0dhHbfYhZpbPl75lpEy6Fh+veF3NSE/w+3sFTlZj48c/K1404tmFw0xjIq6Vk1Q43yoUMg93lQXmcb8Pw056TYU/J6nEHkYImovgmBihiJsIfuyq4CU/wsXj7cfz+v+txotyOGmEMniA+yD795Rbc+d4auD0Nez/TZlyW7ChW/t8d4Off+8EGLNped2xj7sHG4KYRGNa1GbrmpqFpqg0AUOV0+wQzpyviezn4He+uweQvtqDwcElEf04wbxxEFN/EACWYTImYubn9ndV4/uttZz2G8f9dj0Xbj+O1RbtQJQQJjnrG4/FI+Gj1Ify4+yQ2/1oS8Fh/GX+jcOX3eCRsOVJXm+MMUFO083i56uvK2nE3xpkFBjeNQLcWaZg36VL8c/QFAIAah9tnl/DzpdfNodNVEX18BjdEJGZKgsncaAOO91ceCNtYSqudqgxIoOACACqEhRH1rWw6cKoKV7/+I0qq1NcPMXNjd3lwtKRa+drh8mD8jHW4/o0V9U7BVTtcePTTQlz52vJGl8VhcNOIJNR2La5yulSrpQBEpcisMXB7wr8SwSOkbiO00IGIziGqaakgppmCCYAaKi3BopkmCzye0qq6koVg3i9/OVaGt5fvU90mBkXVTrcquDlZ4cDiHcXY/Gsp1uw/HfCxKx1ufLHxCPYUV+CbzYELkaONwU0jklS7mWa1w6OquQGA07XBzY6iMgx4cRFmrol8fUosBLMsM1QuMbhh5obovGcPcVoqnEW+gDq4Sk+0qAqcHfV8Aiutrrs2VAWZLSkqrVF9LS5nLyqtUT3O3hN1q8E2C2UC2msSAFQJ5ROHTlfhhjdX4s+zNwc1pkhjcNOIJFrk4MalrJZKsHh/RfK01IMzN+J4mR1PztkS1GMeOFlZ7xxuY9LQArlgH9MTgccnonNL9VkUFMvOpt+N2N7Dajaqp6Xqy9wIwU2lPbjgpqRaHZiIz0cMZrRfrz1Ql7k5ImR3lJ8vjHvFnpPYdLgEn64/3CjalzC4aUSUzI3Q56ZNdjIA4EztnGkoPRaW7zqBy19Zit/NWBvmkYaXGHC4IhHcSOK0FIMbovNdKNNAgH6Rr9Mt4VhpNSbO/Blbfi0N6eeLjVkdLk+DMzfVTheOllSrWofoBV3amhsxoJPbZOh9/fPBM8r785EzvsGNaGdRXbHxyj0nAx4bDQxuGpGE2uDGIwHHatOIbZt4g5tTDVgtNeOnAwAa986tAOAU5o3dESiKEZd/c1qKiFSrpRpYc+NwefDawl34dvMxXPfGipB+vpjZsLvcqsxNfZl2Mbg5cLIKg/62BNfX/vzjZTV4dq7vSi5t5sYRIHMjqnS4leJrvcyNSJza+nE3gxsSyNNSQN0LuGerdADA6Uq7KsMhHuvPubLdgMsd2cyNSwyemLkhOu+F2udGL+Cwu92qrE992xaUVjnx1Jwt2HS4BMWq4EaTuaknuCkRCop/3H0CkuTNtpyudOC+jzbo1mNqPxwHmpbSkgNBbYYnEO+4Yvtey+CmEbGYjLCY6qrYEyxGdGiaAgA4XeXE0dK6yDknzeZzf+3+SY1taZ4/rihOS0VqXxgiajwOnKwMuNWKeil4wwqKnW5J9SHz2y3HAj7Gn/63CR+vOYQ73lmtztw4Gz4tJQY6Px88g58Plfi9T6UwdSUGUL/WTjela5rJyuRAUA6CRl3QAv3bZgUcY3G5HbuOh3+bilAwuGlkxD+WVplJyE6xAvBmbvYKkbOc7ThVYcfCX47D45Fw3b9XoNdz3ytV7dURaBMeCWLAEemCYgc7FBPFtT3F5bj8laW44pVlfo+pET74BbOdgl42xeHyqPqRFZfpF9HO33oMGw+dwYJtxwF4p3q001I1DZyWOij0BdtwKPDWMuJybzFYkx9PvtZoKcFNbb3nnQWt8dkfCtA6Oyngz/tx94mA3480BjeNTKK1LrhpmZGIzCTvC+5UhUNV0CW/4MZOX4t7PliP/646gB1F5XB5JGX/pGCXCcaaGHzYIxCQqYKbc2jlGBGFTt4e4GSF/xU7NcL7QFAFxTrZFIfLo8oOVepskbNi90lM+Ohn3Pj/flLdLhYA+0xL1Zu5qZtiEt/bfq5n3zxxrHrvg9nJ/oIbDyrtLhytrQNt18Q7m5BkNese36Q2SIp13Q2Dm0ZGfMG0zExEi4xEpCWYUeVw49mvtirfk4ObbUe9bbP/W1s8DNR14Y3EBm+R4BT+QGsiEHwwuCE6f+gFGVrie+OJcnu975V6H7qcbg/KhMxNldONTYdL8OYPe5Quw18VHvG5X6vMRNXjaYObUJaCi3YUleveLvuq8CiW7Dhe+zN9n0+WJriRV+/WuNzYf9I7a5CdbEVm7XHJVv26z2t65iLBYkSixRTTuhv90ItiJkE1LZWIBIsJf72xJx76ZKOqHkUbBBw4VZeelKeszpnMjTuymRvxvNXX2pyIzm3arWv0iMHMvK1F2Fn0I5b88XK/x+tlU+wudbPVaocbN7y5EoC3vODuS9pindAnxt/j2Z1u9d5SIdTcBHO77INVBzFr7WFsmjJcd4VYVrK6jjMzyYoqRzXu/+hntGvqXbUr/xcAkmz64UO/tlmYfHVX1bUsFpi5aWSsQkFxy4xEAMCQzk19jnN7JL8XajlTca7U3IhLwcO1467Io6q5aTzBjcvtwbjpa/GXr3+J9VCI4kZwmRv1+8C+k5UBG3zKwcDt/fPQIcc7LaOtuRF/7tajpThT6VB96JRVOdyqDLLD7VEFW4GyyxV2l1IA3BAOtwdHS6th13kfbKKpuZELjIvKavDTXm87EbnvGuA/c5OZZI15YAMwuGl0xArzSzo0AQCkJliQohMl+0ulVik7tUZggBHgVmWkIpu5aUzTUst3n8CyXScwfeX+WA+FKG5UCitG/U2L6L13ltcGJ/tOVOCRTwvx/sr9uOeD9Sguq1E+FFlNRlhM3sum0+1BmaqhXt1jejySqq5GNT67S1XnY3d61H1uAix6eGXBTtUKqYY4WlKt+z4oTktZzUZlWkqUn1VXRJzoJ7jJSNJfdRVtnJZqZMYNbIN3ftyHV27pheyUujRhszQbKk6o/1hqnB5kJ1t9dgyvPEf628jEDFQk6oTcjTRzU1Qa+xblROFWWuXEh6sP4Na+echJS4j6z68QtiRwuiVYzXXZ8E2HS7BkR7HuFM62I6WYOm8Hthzxdhues9FbL2MA0Ka2marNYoLV7A1utEGKOLXk9Eh+38vsLnUwY3e5g+5zs3Snt1j69v55+GTtYb/HBaKXTQLUwU2ixaSbfckXVkgl+ykozkjSL0yONmZuGpk/X9UZ654ahhsvbKW6vXm675tEjdOtfIoQVdrdPn9YsW6oFIhqtVSEC4rrK9aLptOVDG7o3ORye3xa+sumr9yPV77fhQFTF0d5VF6qfi6aDzM3vLkSry/erbvb9cOfFiqBjWjPiQol4LCajLCZ1Pv9ycRNJN1uKeAUuxhceQuKhWkqP+9RHo+krFjq2zpwn5lADp7Ub8YnBjcJFqOyr6EoT8jcJNnqgp+c1LoP4hl++uVEGzM3jYzBYPCpWgeA5mmJPrfZXW7dqvdKu8ungZXbI8Es1PM0JuJO4KFkbmqcbkiS//SozNVIMzfim6PbI8FkbJy/HyKt3763Bqv3ncaKPw9Bq0x1vxN5ZY0kAduOlqJ7i/SojGn+1iKcrLCjQqiDcbo8QO11t74eWsV+NnusdtR1IraajbDUZoK0XX+rhPeu+duKsHq//21vTgt/+95MjjBmP+9RpyodcLg8MBiAtkJhb6dmKThwsiro97YDOp2Uk63qTE2ixQSbTuYmL1M/c9OzZTr2n6xEaoJZdzorFpi5OUeI3SNTa+tvapwe3Si/wu5SzQUDwCfrDuNMZWj7U0mShOKymgaMNjSqmpsgC4olSUL/Fxahx3ML6m2f7pEaZ82N+PvgKi46l6ze5818fFV41Od7TYTp9Plbixr0+Et3FmPa0r1BZ5xPlNtx38cb8PSXW7HzeN2SaPGCv99PxqI+3ukn73uMzWyEtTZzc6JC/d6oXZ0aqDZGtSu5M7hpKbkJX7PUBKQl1F0PerfKqPcDnkjvPKQmWJTnBXhX7SaYfR9TLDoWg5hkmxnfPzIYX9w/CAZD4/iQxuDmHGER5o2zal9gNU637jROlcOF0mp13c0zX27FPR+sD+lnPv3lVvR/cTHmbw3cVlx0+HQVvvj515A6DbsaUHPjqO0x4fZIOFoSOAAT965qTEHEKQY3FGOSJOFUgGZ39dHb4kW8UIuridYdOI0F24ILdu56fx3+Nn8HluwoDur4rwqP6C6gEAOF7cfKgnosrWpn3eomq9mo1Nx8tNq7h5M8JdPQD07ezI169ZQeObhpkZGgCix65WX4Xbmk56BOzU1KgllV4pBgMelOS4mBS7KwyMVmNsJsMjaq7DODm3OEmAKUI+plu07o7sVUaXeruljK1tfTwfJEuR13vLMaczd5P419XLsB26sLdwU9zsEv/4BHP9uET9b6bt7mT0NWM4lTWfX9OUW6psef05UO/N83v2BHkf6bqpjWdnFbCIqBp7/cij5/XYTlu4JvlS9mU/R6aYm9quT6l6Ml1bjlrVX4w4cbcDyEbPAvR4MLSPQySIA6UGhocON0S8r7hs1s8qlzDNQJWUtu7yGyuzyqOqH9JytRXuPEiXI7Jn78M9bs805vyfU2uRmJquCmR4u0gJmbuwa2UX0tv9+mJtRdU1xujxK0AfoFxW/ecZHqa3EMNp1AKNYa34hI15gB+chJtWFsQWslov73kj26x1bYXThTGfpywdcW7cJPe0/hoU82qm4XX/T1kd/3Vu31P9+s5fKEnrkJpTBY3DgzHBmSGqcbP+09We9jPf3lFry3Yj+u+uePut8X3xTFXj9E0SJ/gPlHCB9gxMxMtdN3Zab4fTn4+feS3cptRbUX6SqHC1O+2qpcvGVi8HQmyGXPh2r3WNJeyOUPSx6PFHQWSI9e5kbWJjs56IyFXj0loD5nhYdLMOSVZXjk00J8u+UYRr+9GkBd5qZlRiJSEyxolZmIjCQLurVIU2VRxJ+1/S9XoV8b/eJjcWrrdKVDNS2VaFVnbp4Y2QXX9MpV3V/spm/TmcKKNQY354jsFBvWPDkUf7mhh26hl6jK4UJJPd0q9ZT6eSPRW5EVTmLWItjtF8TAwl3PvLxbCBzCUXPz+OzNuOOdNfjbvB0Bj9t02Hflhczl9qiCG2ZuKJZCWU0p9m8Rl13LxA8ocluKpTvrMkPySqHXFu7Cf1cdVC7edfev+xv1tyILAOZs/BWXv/wDdhSVoar254y/pK0qwJHfJxZuP44dReWwCYFJWkLw62nEmhubJrh56eZeAYtoxbgn2GLbkxV2rNij3ptJmZZKT4DJaMCChwdj1RNDYTObVI87aWhHtM5Owjtj+yDRakKyTf9nis+jrMblm7kRAha9aS/xNu05aQwa34jIL3m+M7Ge4KbC7g74puCPvxeotQHBjYQQam48oa+WEqeX6gsMxARLOFZLfV07bffuioY33yuvcUGcUWTNDcVSKJ0iKoWAZtXeU9iqWT6tl7k5I7wfyR+8Vu7Rz+6KwdOpSge+3nTUJ7sDAI98ugkHTlXhT7M3K9PU6UkWPHd9d7Sp7ccif5hZUFvYPE4IfAZ38u387o/8nK1mo+rD3riC1ujfNitg0HJtrxZ44cYe+PqBS3QzLMGodriVWhl5dVqyzaxMR4lZlBsuaIFljw9Bn9rl4v5+pjYDZRFW09rMRtW0lN5jJNmYuaEw0yv0Eu0+Xo49xRUBj9EjZoTEOpX6pqUOnqrEQ59sxG5hlUIoXA2oiRGDgfoCAzFz43Q1jgxJlSaIczJzE1bbjpbiyleX4fsgC1jPJ5IkYU9xuervJpQPI2J9yMkKO6799wpVYbGYealyuFDjdKtuK60NdE74qVWpEpZFL9t1Ag9+shG3vbMaN7y5Eu/+uM/n+APC6p+k2vcw+T1L/jAjL3/u3SoDU67rhsGdmuLPV3UJ8hlD6X9jFVZLAd5CXMD/DtmA9/16zIDW6NkqPWBtTKD32b0nKpTnIDcUFImPq+1m7y/wSkuw4OlrugIAJo/sovr5JqNBdZ3Re37JrLmhcKsvSnZ5JMxrwBJMMXMjNpmqL+V4xT+WYe6mo5gyd5tym6HeMt864mqpQHtmiZwhrIDS63Pj9kj4qvAIfj2j360zGGez4lG7ysTFmhu/TlXY8YcP1+OHncHXTEz4aAN2F1fg3g83RHBk0eFweVQX8LM1d9NRDHt1OZ79qu7vNbTMjW+dTYmwgEHMvlbZ3T5LouWvTwh9ZcRpMb1tCyTJ2134r99u9zm+svZvKcHiXbED1E2ly5mbQ6e9Uzr5WUn43aC2+ODu/sjLSsLbd/bBQ0M71vucZTZNzU1qbd1KoMyNKgOiOS5VCETa6QQtstX7TqHK4YbRoN4CQSa+FaVoptvEYEcsaO7WIg3jL2mLH/80BPcObqfKSJmMBtWHXb2prUROS1G4VUVoewUxa1JcXreiIdAniuLyGiXLU9+utHoq7S5s/lWd1g5makoMaPRWjIncOquxPl13GJNmFeKyl5eGMFq1+mKbQHUM2ufImhv/ps7bgQXbjuN3768L+j4n/DRkOxeNnb4Gl7+yFMtCWNEUyOuLvMW94orGUIIbvRVS4lSVtuamRLNys6Ta6dNzq0xYMq73+FplQqsL+e9bXFGqZG5qVyLJ9W3i9gEAMLx7c9zSR90NPhCbZlpKDhyCDW7EDIjVZFQFEO2bpvh9DDmoa5mZqPt+LP7+tGUL4s+UN/0EgN556TAYDMjLSoLBYFBlpIxGQ73TUsksKKZwC3YFgZ41+07hyTlbUF7j+xhi0HT4dN3Os/4Kih0uDx7/fLPydTNhH5lg09w3/b+fMOOnA6rb6mvkd6Lcjg3Csvb6Vk7p7S21srZYL5R+PFr1NasK9MjaN3DW3Pgnr64JRTxN88kN8z5afTAsj9dK55N/KGdLL7Mivneop6X0Mzd7TqinzcVgR3z8fm0yfS7WVQ4XjpT47owtXoCtyuaWkrKSKiPJomqGKstJs/nc5o/VZNJkbuqflhKzGmIGxKopTm7X1Ddzo11dJe7KLRLfx7TvS+LPzBW28endKkN1nFGofDYZDEgQx63z/MTtF5i50Zg6dSr69euH1NRU5OTkYNSoUdi5c2fQ9581axYMBgNGjRoVuUE2QmcaUCwsG/32asxccwhv6CwjFz99yW8IAODv+r9o+3HVp8lQ+j3IdurU6dRX9PuHD9erpsCc9QQoYmZHDiKMYWg2pX2ErwqPKD2C6lOtzdycRZAV7xoy/Xc2QWu4HTxVGZZVeuF6Tlk6uzaHslpKb1qqwu5CSZUDbo/kU1CsXdxQWu30CVhPC8dU1b4P9W+Thc8nDETLTHVvmBPldmXlkEgV3Cg1N27lvUxvOgfwZh0C7WT9wd39lf83GNQX8lAzN9oxikHaRa0zfe5718A2+PftFypfG/38MXgC/P7E4FDco9BfoATINTeBp6WsJiPMte+jrLnRWLZsGSZOnIjVq1dj4cKFcDqdGD58OCor659fPnDgAP74xz/i0ksvjcJIGxftJyGxGdM1PXNV7c/9LXfce8K34FiduakLbhx+tjeQ23g3r83YqOfQ/Q6/XoEuBG6P5LO5XX2ZG4/OtFQ4GmmK7zPlNU5MmlWIhz7ZGNS0obbmJtwbekqS1Kg3Sw1FsO3cY/mcP1p9ELf+Z5XP1OzKPSdx2ctLMWnWRj/3DF64AmC95dt6j11a7VTVw8nkGpfrerdA71befaN2HCvHBX9ZiLHT16impdweCcfL1B96SqsdPh+E5ADos3WHMXHmzwDqLqjajRiLy+04VqoT3AgBhlWouTl0KnBwA6jfQ0UpNjMGd2qKZ6/thtF989AtN02Vya6ruQkucyP2lhGngZqkWFWbT8oykiy4rncLtK/N6ozs0Vz3ZwR6aRgMBtw7uB2u7ZWLiUM64Pb+efj7b3oF/IBnMhpUexHqZW4MBoMS1Olt1RBrMd04c/78+aqvZ8yYgZycHGzYsAGDBw/2ez+3240xY8bg+eefx48//oiSkhK/x9rtdtjtdX9IZWUN61LZmEy5rhsmzSpUvm6SYlPanA/pkoP2TZPxr9rMTNNUm2o+W6a7m7hDP3PjcHlQ7XD7VPrLbzCdmqeiqKxGFdyczbRAoODmyJlqn8eurxhXrwOyKQz7n4hF02Iq/lSFA3+ctwnHAkynaJuf1Zd9kkmShA9XH0T3Funoo/NJTz7mrvfXoazGidkTBjaqlugNEczwa5xuXPvvFWiVmYjp4/pFflAaT3+5FQDw9vK9eHxE3SqcN3/w/h02pMBfyxOm4Ea7qS6g3tEa8E4FXvbyD+jTOhMz77kYgDfYSU+0KJmbFJtJKV6Vp8z0lnfLWZbc9AQcK61BSZXTpybqdG3T0T/9r26aW15qrM2qnCi344jOlitiVkQpKHZLSkfkFjrdgWUpNgsA34BJdvclbZX/t+pkbtISA62WqnvfFKfFbBYjRvfNw5eFR/DO2L66LTfk47+4fxCW7izGNT1zfY4B6s+8PXl1V+X/p97UK+CxgDe4Ed9W/S0nT7aZUVbjYuamPqWl3k/kWVmBt3P/y1/+gpycHIwfP77ex5w6dSrS09OVf3l5eWEZayzdcEFLfP3AJcrX4rysxyMhTfgDyk7Wn0/WS2+Kb3Bi5uaHnSfQfcp85ROQ7FjtG0znZt4iNfWS7vqLAv29WQe6776TvhknRz2BlJiydXkkeDxSeDZ3Ex5CnEp7Y8kefLcl8MVM3EsGgO4nZD0/7CzGs19tw83TfvL/2E43lu06gY2HSlRBaqzIreQbyl8qXnTodBX2FFdg6c4TQe9fFAmnNZ3B9epTtJ7/ehv+8vUv9R4Xrmkp7aa6gO84V+45CbvLg5/2nsK8Lcdw7wfr0fv57/G/Db8qjfmSrWYlY1EZIFspbxvQuraYt6Ta6ZO50dvUN8UqBzfqupPishrdthPaehbA+2FG7qsTaOop1c/FW++VZzX51tzo1fLI/AU3VpMRU2/qibVPDcOF+Zm6H0Lk9/L0RAtuuKClshpM6/b++QC8NUrhYDQYVB8a/S0qaVqbbcpM0u+8HEuNJrjxeDx4+OGHMWjQIPTo0cPvcStWrMB7772Hd955J6jHnTx5MkpLS5V/hw8fDteQY0rc8t4s/FG4PJLqE4q/iPq0zpuJWOR6UHNR9EjA9JXqpnXym1bHZqk+j6W3oZ6WvzfEQJkbvR1t6wsMtCuRHG5PeKalhP8X99PRC8C0tFNXTrcUVF3G3uL6p2zFzQrdUV5ivuXXUoybvlZ18en1/Pfo98KiBq2mA+pflQYAdiFzdjbNFc+W9nxX6GRNRaVVTry/8gCmr9yv+zepfuzwBDflOmMqq3GpAlDxA9J9H/+M7385DgBYufekkrlJspmVqaAqnaku+UIuN71sW7vU+US5HduPeV8fcj2IXh1hkp9pqZ/2nsLPh7wLCrrmpim3662Wcro9ypRXoAuwdvm0TO+MG4W3VDlzEzi4qbuDKrgxG2E0GpTb9IJ47XP3Z2jXHHz/yGB8OH5AUMfXx2w04MI8b6DUVGe6TPb33/TCq7f2RvcWaX6PiZVGE9xMnDgRW7duxaxZs/weU15ejjvvvBPvvPMOmjRpEtTj2mw2pKWlqf7Fg0Q/DfdcHg+Gd2uGURe0wDPXdvPbXViv+Fe84OpdaLUBS1HttFRnneCmJojMjd50mb+fLdMLbupv4ucb3Iifkhq6Ukl8LxKX0WtrovRol4Jv/rUEPZ9bgFe/D1xQH0zCSVwJp1dfEYrV+07h+jdWoPBwSVDHPzt3K5btOoErX1sOwJudkxNnDWksCQRXcyO+3hqyuipctLUr4mtcL+B3hrCvWn3bjNSnxunG5C+26K40AoB+LyxS+j75G8uJcruy8CDFZlKmKyo0wbrVZPRpJtdaKGCV6+Y61WZ99YKbFD/TUt//chxnqpwwGoArutR1GdadlnJ5lL/HQIGCdqyBiI1AU3QyN83SbLiiS47ytViPIgaN2hVGbZok477L2/sNhgIxGAzo1CzVZ7PLhhrYPhvpSRYUPnslfvzTEL/HdWmehpsuahWeTHiYNYrg5oEHHsA333yDH374Aa1a+e85sHfvXhw4cADXXXcdzGYzzGYzPvjgA8ydOxdmsxl79+6N4qhjy6TJ1sj6tcmC2WTEP2+7EOMvaev3RacX3FTWk20Ru+r+tOeksiS9TXayqnU3UP9ybgC6y9EBwB4g2NAPburpc6O5KJRVO1XnJdgtH7TEmhtVcOMnQ1HtcGNHURkkSfJZLfX/lu6F3eVRaqX8CaZ+plToAeLvHAfrtrdXY/Ovpfj9f4PrMSNe24vLalTP09zAdJkYn/vLXoi/w2ju/A6o6x3E8bk9EkqFHi96F3AxkNe2B9Bmus42c/PxmkOq3jZ6vttyDIDvar437vCu2DlSUl2XubGalYBCG3fZLEafFTZNU2x4YEgH1W2dm3s/GG07WubTfVh+7HQ/GRez0agKmJJ1liaL01LpAaalbvbT60bvFStOm8tBlBiEXNwuG7+/tK5Gx+YnWNH7W/7zVV1w/+UddI+PhlWTr8BH4wdgYAdv8iAjyRq2gCnaYhrcSJKEBx54AHPmzMGSJUvQtm3bgMd36dIFW7ZsQWFhofLv+uuvx5AhQ1BYWBgX9TQN4fJ4sPapofj6gUtUaVrA/xLBM1VOny0M6psWqa79dLb51xLc8e4a5fa0RLOyakAWTMAgNuIS2XUCoy2/luLRTwvxk85u46FmbkqqnKoMiPaNPFjie5M4LeVv+uW3763BVf/8ET/sLPa5kIlLSQMVjopviP4udqrMjZA5OFPpwD8X7VLVUwXrZEVw7QdaCVOi3245ppp6lNPukiSh8HCJaknx4dNV+KrwiO4UozqI1P9dia8Z8flHo5ZaDK7FKdCishrV9/SmncS/OfF8fL3pKHo//z3eE6bYAi33DUYwrRrk35H493v3oLbo2dK7KupoSbXyO02xmf0ugU6wmHxe471apeOPIzrj4nZ1NZUD2mYDADb/Wqo0qpPJjy0GxcO7NVP+f3CnJspKTe/x/qal5MyN/2mpyzo1xZz7fYvv9c64XvCsraURM+ticCCuXvUXhIu/52gHN7npibikY3CzIo1dTFdLTZw4ETNnzsRXX32F1NRUFBV5CwHT09ORmOh9kxw7dixatmyJqVOnIiEhwaceJyMjAwAC1unEO5dbQk5qAnJSE3y+F+jT3qkKh9L3IJiuoPIxYjOx1AQzDAYDUhPMqjfvYIIbf1kFvT43495f67cmof6NM9XfP13pUF1UahwNnZYSsj/C4/kLEuXGgzPXHEbTVPUbbWaSFVUO73TBsbIaVZt0vz/T6dZdxSBOhZQLF8wnvtiMBduO44ufj2B5gFQzAOwpLkduet0Y/O1nVu1ww1ZbOwCoA8U1+05jSOe69LzD7f3e3E1HMWlWIa7okoPpd3lXNl328g9K1ueGC1pqnrP65+ktuxWnpcSLhr8CzHASp5vE1+6vmiBSN3Pj1g9uHp+9CQDwf9/UFRqfbRfrYKZe5JVF8t/6TRe1xDPXdlXGWeP0KBs4JtvMukuEAe+0uVjM/uOfhiCvdil2x5xUpTFhQftsv2PR+zt6e2xfFJXW4K1le3H3oLaq33uKalrK+6KxuzxK9ixQQTEAXJififRES721T3qdhLWroMTXqDgtJb4e/dUlih9uovH6jVcxPXPTpk1DaWkpLr/8cuTm5ir/Pv30U+WYQ4cO4dixYzEcZeMXKIAJ9GlP/CQXVG8WpxtVDhe+3uT9fTRJseL567sD8O0TEcy0lN6SVED/TS3QG44zhKXggPciI14AG5q5Ednrq5fQ1EVp39jEYHCfTg8imTGIjJO4GkbM3CzZ4d2b6dDpKtz53hq/+xVtOHgGw15djj8Ly3L12qufqXSg53MLcMe7q3Wfx9oDp1WrcOTsymsLd6nG43J7lMDml2O+rRrE7Me3W45hyCtLfWqA/L3eXG5PxHvfVAnL+sXnry3cFV/Dby/fi+vfWKEq4hXPlV7xq7+/5UOnqvCbaT/Vuz1DMCu35EUC8uszyWqCwWCAzWxSCkt/PeMNwpul2fwuEdYGw3lCj5kuuXU1ei0yEn32W5LJr+MeLdJVtzdPT8Bz13dHfnYSmgkf6MSsi9XkfczSaqfy+qkvuNGjl/i7vHNTTL2pJ+Y+MEi5TQxuLCajKqPl94OBn7/fs62tIq+YZm6CedNZunRpwO/PmDEjPIM5hwVq7hUo8BGDG7lIMD3RAkmSdIt9q+xu7CwqR7XTjaapNqx9cqiSSUi1hT4tpbdqAwhcUKzn7/N3otrhxqNXdtKtMdKuYDlT6VA1JtQba+HhEpRWO3FZp6aq28XXrL+CYj1Ozeag2kyZmGHZW1yBSzuqf67yOGJQ5ueTn3hexQuaGCT8uPskJs3aiK+ElgKS5F0iv3qfd+pv0fbjyvf03qAX/nIcLo+kfAoH1G/YpysdqoaLci3VAU1LAbEVfyudjJWY3ZA3exw/Yx02PHNl3WP7ma7ySN6/D21NWDiJv0tVAbHmdSUud37xux0AgLeW1dUJilN4Oak2nz5J/v4upi3bg/UHz2Dc9LXYP/Vqv3V2wRS6H6stNpb/JsTplRbpCapgrHlagm4NnHy/jjkp2F1c4dOb5eaLWuHbzcdwYX4GAP89nq7o6p2C6tkqHdPv6ou8TN8mfGJ/GfG1bjF7z4G8R552qsgf7ZnTG5nBYFCWXteNo+79z+n2aDaV1P+5/gLyYV2b4c0f9uo29aPgMecVBwI1sdMLblrVtjMX34jlQsMkq0nV4Vh0psqBA6e8b2btmyar3kS1mRtXELt76/XbAHw7IgdTSPnvJXv8bn2gHcbpKme9mZtRb67EuOlrsak2QyCnisULrWopeAjBjdPtUX6mfArFC9c+4YIxf2sR7v94g1JsKf58fxf0MtVqKf+f1uULkyRJGDt9LW79zyp4PJKyjFt889UtKtS5hmrfsP+34de68Tr1syiFh0rqjtE5j3odsk9pMnmBMoXh2PogEDHILBde09rgc+H24xj9n1XYdrQu4BOnqsSVbdoaNsD/1LE4BTLy9R/9rhYrra6/bkoOqKr1ghsh8DQbDchOsfntzGuzmPDuuL549MpOmHpzT9X3EiwmzLznYqXZYbZm/ySz0YAFDw/GBXkZym1XdGmm23JCfA8SxyqvEi2u7Y6cnmQJakVPvzaBe6z5I/59VDs8QQVS/j4AXpifiW8fugQLH7msQWMhLwY3ccAdYC5eL5UtzxnLwcWe4nKlcNEA/5mgM1UO7DvhvSC2baKed9Z7M64ve+NvKbj2AifuUB7IW8v26d6uzdyUVKlrbrTBjRhMfVl4BMVlNej3wiI8//U21f3EN8v6mhZqi07lC1+aznkTN0b9+4Id+G5LEf767XYcOKneo0jbCFAm1jL5y44BdSubKuwuLN91AusOnMGxshrsOh7ckm3xUiEHLfLvfMwA7yfb9cIGpw63R5WNkIPoTcKu8HaXB+U1TpwSsorBrH4K9FqL9OqpYDM3K/ecwpr9p/HYZ5uU2/wVFOv1gPI3dSxOx+woKsdL87aj2uHG0p3FqkBKzNyIvUt652Uoy32Pl9XgmS+34oNV3rq6BCED0aZJ3cqkZmkJMBkNunsOAd5Ao3V2Mh4a2lH3NS6SV2KJjy2vogrG33/TC1d2a4Zb+9YtKJFXSxXXZpqC7Rfz4k09cd/l7ZWvG5Lvq3G5VcGNv405A71mu7dID7i6i+rH4OYcNqiDtxjvNk2KVKSX9ZDTnfIn/AMn66YJBndq6rcWxumWlAtRO+GNDgCap/v+AQf6NO32SKqdvUXaT9raTfKu6q6/v4pcDKklB2vyG45vQbH6TUa8KK3ccxLv/3QApyodeH/lAU1wU3ef+mqMxAuXU9hcUK9t+9ebjqLfC4uwdv9pnBJWKZXV1J9xAtSr0AItBZczfuKGqU6XR3ffsfIaF4o151cd3Hkfq1opRG3l84nc7nSret3IgbdY+1PtcOOi/1uIPn9dpGSdgsm8BKp5inTmRgw6xPPtL9Mi1t6IQa/4GtELSv09njbo+WbzMdw07Sfc9f46XP/GSiWTIwc3l3duitkTCvDJPRdjYPts/OOW3miRkQiz0QCPBHwoLBgQL9JdhICjWe0F219BcSh9Y/q0zlJ1XA+0CaWeW/vm4Z2xfVVTQfISbXnlYrAddLOSrfjzVXXbZzSk+qXG4YbRaMDm54aj8Nkr/S6l5ma5kcXg5hz2nzv74sPx/XG/8ElDSy+pI88PyxfBY7UXrSG1hXL+posAYP0Bb31FW01w0zFHp5Gf0w23R8Lv/7sOU79TL/N8ffFu/8GN24OTFXZc/fqPeH/lfhyt3eahb+tM/PzMlbi0k/5SRXlXYi15SqlJ7QqlknqmpcRtKHYdr1At7RYvRmLGrL7MjfgYLmFaSlurJDtRbsft76xWBZo1Tk/AjBMALNlxXDU9F2haSl59I16Qdx4vh93l8elJc7rSgf4vLsaafb5L8b1j845FXr2Snmj2+fTtcHtU45EDoaPCJohnqhzKOZaDnqCCmwDHBLMVSLD0Xl/i9JPd5VF+nvz7addU/bciXvjFoKzCHjgodXn0u1jLGbzJI7vgovwMuDwSttcWZrs9khKsyq/Bh4d1QuvsZBS0z8bMey5Gh5wUmIwGNEvzXW0pBjfi71NeZekvc+Nv3zN/xILcpBACI3+02wVEMwsi/w2kJVh8to4AvCvQAO+O3xQ5DG7OYSk2My7t2DTgckG9nilyfcye4nI8+MlGPFO76V9+VhIMBgNevfUCAMDDwzr63Ff+9NhW84bdsZnv8ki7y42Nh85g0fZi/Gf5PuWN2e2RMEunmZi8asLh8uDNH/bgl2NleP7rX5QNOltmJiIr2ep3B1qPpF/HI39CkqdBtJkbbZCgbWYorjgSU8mq+pd6MjfilECF3VU3LRVgwz230N0X8J5P8SI+bvpan/N494z1umPXuyjL50UsZt78awkA74VMm50DgP8sr5v6E6f75HMoP68Ei0m15xngPUdidqLa6YbHIyl7lAFQTVvJz12vNYBWoBR/QzM3M9ccwl3vr8W82sZ2xWU16P/CIjz95RbVcT7F4bXnXB5TX82FXvwkLwa94rnxt22DXhG5vAlrotWEzs19O7DLGU15GwJ/UzQtMnSCGyEb0k6YipZ/N/5WSw328wHEH/HvwBaG5c/a4Ka5TuAWiHz8wABL1bU65njPz9V+NreUvXhjT3z8+wGqzSwp/BjcxDm9i5o8B75oe7Gy7wsANK/tazLqwpZY+9RQPHiFb3ADeJcja1cu6PV+qHaoMw1ya/c1+06huNyO9EQL5j98qfL9xNoUt92lXiotFwXKnywD7UB7WqeXiDz9IQc33qXgdY+vvWBUarId4idqcYWZU1XcW09wIxRzllY76zI39dQjiGqcHp8L/RNfbPFztJc89kDZOHEKZNNh77Rjx5wUDOrge4ESL2biVFy1wxuoyOchwWLymZZyuD0+gcCRkmrVcxKDG3ns9QWO2rFohVJzc+hUFV5ftBul1U78c9EuLN15Avd9/DN+OVqG/yzfh1OVDny0Wh1QaoNj+XzK00VivyBAXfAtBtLy9KDbI/ntFj701WU+01Dy6zdR55wD3maCDpdHeUx/UzR6u2aLgZgYMMiBW7PUBN1NFfXeDwIRs1nhWApt0QRIF7XOCOn+n08owCPDOuFvN/cK6T4f3N0ft/XzXyYAeM/poA5N/G5GSeHBsxvnjJrfsMVkUC1bFOWm1326yUlN8NvqPy8ryecPU29eucblVgUb8kqrFXtOAvB2GxWLDeUUt93lUdVzyBc5eedef0srAf3dheXpF7mI8oymoFh78dNegMVanuNldcGNR6oLHusrnhYzN2XVTmWjQe0qs0BqnO6QsxDyhVavgRzgbRInFu/Kq8M6NkvF/UPa48YL1Q31xJ4k4nOudqqzSokWE7I1q+7sTo9PoeweTX3PMWGKSp6a8Ze5cQWxcsz7Pd/7n6ywK5kM0b0frsdri3bhkU8LlWJUAJiz8Ve/q5C0wbGcjZGnixI1NSTFZfqdgo+X1eC3767B20J2TG/c6w6op3Pl12uS1YzsFN/ApbjMrgTXBoP/15w2CAPgs+rn/svbw2w04KGh3g8+RqMBTwkZiIvyM/DCjT1C3mtIzD6HoxZFm526KD+0abK8rCRMGtYRmTrBot+fmWTF4E5Ng9oihSIvpn1uKPJeuqkX7nxvDQraZ2NvcSVevKmHqkhV1Dw9uNSttt5GZjIaVJmi4jK7qnhyf23hsrwaKC8rSRUUydNNDpdH1VdFDm7krIG/pljiY4vcmmmpGqdHNR3gk7nRXIDFjQa1RcsTPtqAV37Tu97sgPjzPJL3gm01G9FC54Lij91V/xYZ2t+BfO70zgsAvLdiv6qfhjxF1alZKnLTE/Ha6AswZ+MR1X1OVzqQlWxVZUtqnG5VsKM7LeVyw+VRv/Hv1WymqZ2+A/xPK3WbsgAPDOmAh4Z2rCdz49swcdiry5BkMWHZn4aoPuXvKPIug5cbDMrmbjqKVkK2ssbpVl672mBYDhbl6aIkqwm39cvDrHWHvc/HT7AmryyTg39/5KDs8Okq3DztJyUIS7Qafc454H3Nltae1/REi9JNWqu+aSkA+NNVXfDAFR1US8DvvLg1DAbvHnODO+n3ZwqF3hYcoeqrWdKdn+XbI4fiG4ObONejZTp+fuZK1SepVTr7MwHqzE0g/oKbRY9ehvlbi7D1SCm+3XIMu4vLVd+XC0TltHxagln1yVB+03W4PapCRTnrkNLAzI2c5k5LMMNiMsDpVqf9fQuK1V+LF9zjmmXpC385jr8v2FFv0apeluCi/Ay/NQt66svcON0eJbD5auIg3PDmSpTXOCFJku7Pl4kZClknnRoqAJi17jBmrTuMVZOvULW+r3F6lCDQajLCZDT4Tku5PNB+KN/npwkcUH9w43B58OrCXRjYPjvgLvTi/d/8YQ8W/nIcJVVOlMCJTYdL0LdNFkqrnNhR5NsdOTc9AcXldhwvs6s+FJRUOdE8vTa4caqDYbnRnVh/9My13VBud+HbzWffbV0OsJ/6cqvqd5doMSudeUVFZTXKKsemfnpYAcFlbgD49LYxGg0YW9AmqLEH42y3mQC8Qf7/G3MR7v/4Z9zat3HuWk2RxeDmPKD9w/ZXxKq3WkKPXqEp4A167ru8Pd5evrc2uKlQXeDkaSm5/iMt0aL0owDqthawO90woC6tLNdhBJO50au5kdPcZqMBmUlWn4u5b0Gx/xVGJ3SmFHYfr0BTP70sZHrdYS9ulx1S59wapztgca2YQZBX6Hgkb5DgL1unx2Y2qrIUeuZtKVJlahZsK6rrjVL7+/GZlnJ5fBo7yg0D87IScfi0esl/eY0LHo9Ub0Hx5l9LA9bliFm1lxfsVH1v+a4TSLCYcO2/V+jeNz8rCS6PhBPldtV0yenKun3ZtJk/uS6rbrrIhGSbGXcNbBOW4Kao1Pv48spFWaLVpPp7SrGZUWF34XhpDd5e7u2EfONF6mlGkd6Hm1jsCB2oKWkoru6Zi8WPXRZSdpTiB2tuzkNincu1vXLRLTcNw7o2C/hGJnc1BtT7xOiRl4XvOV6h6iQrb7gnNzpLS1CnyE1C5ka8UMuraeRsTsiZm9pPgiaTUbeYUtvnpirA8mlt5gbwBkP1Fb2K2xDILm6X7bPkOhBxmbEeOdgwGw1ITbAodVGl1U4sFLZSqE/zdP/1VjKXx6OaCpIDG6DugqidInG4fAuK5fqRvq19O8NW2F1BrZTaWVQedOZGa/nuk35bEgDeC75ex+7//fwrLvnbEszddFR5TvI5kzM32i0MgulaKxJrY67pmYvHR3QG4M3czN96THdnefHDhLx0+2iptzFjstWEMQNa+/15egXF2mmpaAimI3mw2jdNiclzoNhjcHMeEguKm6cl4NuHLsG74/oGvI+4lLJdk8ArIeRl4ftOVqgyHYdOV+GOd1YrRavawmY5w6S9CMqZlZQAmRv5QqC3waY8LWU2GpCZ7FtMrc3U+FupAugXg1Y53D5BRxdNjxe5lqN1dhJuvqgV7r+8Pfq3yQpp11+7n2kpuTtwlUN9MZV7h+w9UelTQxKIXm8OrRqnx2/jPPli4tPEz+XW7bJrMHiLy7UqaoILbnYcLw9Yc7PxUAkkSX87kM2/liir+PQ0T09EE50i3fdW7MevZ6rxyKeFSqZGruv476qD+O27a5RdsZXgJsSLrLgzvNVsRJtsbzau8HAJ7vv4Z5/jEy0mVQFs87QEVfB8x4B8VT8ZrcwkC7q3UC8lDzUgOxty8PbijT3rOZKofgxuzkOpQp1Hss0c1Hy0xWTEzHsGYNqYi5CfHThz0yI9EUlWE5xuCRsOqT8V/yTU+2inx+T3Ye+yVd+LYHKAmhv5QqC3Kkj+JGgyGHQLLsWOvkDgHdL1uiBX2OsyNw9e0QHLHr8cNj8XhcEdm+Ift/bGn67qAqPRENK0lL+CYvnCriwJrr2IyitGvvj5V7g9Enq1Uu+u3L9tlm6350xNw7O8LN9P9CfK7X6zJXJhuPZC6nB7VN2QZRfmZegWs5fXOINaHbb7eLmSJdHrjDt95X58uu6wbtNDjwQs2OY/q5WdbA1Yp+L2SFi607sbt5jRXLHnpFLELf8+Qg0UxEyKw+VRuoAfKamGJPk+XqLVpCqONhkNmFS7qslqMuJ3g9oG/HkGgwFfTRyEZ6/tptwWarfgszFxSAds/8tVGKjTgoAoVAxuzkPiVJC/DqNaZpMBA9s3wch6GlTJj9+jhfdCGijFrN1zRv7a7vL4FPUCYkGx78tWrhHR1tOUVjuVxnQmo0E3K1Gq6QGjdwGW6e+W7lIyNxfmZ6B1djL8zeokac63th9HIDWa5dbKeGuDMWWzQ6s6uJi/tQgAcLlmJctnfyhAv7a+00HaqbuZv78YN1/USnXb8bIav9kSqbZpvXZVjt3p0Q0c+7fN1p0SrbC7Aq5Cy0m1wWo2osrhVjYBTROmcsQL83+W7/OZfpTJGRY9bZoko4mf3Zm1GUS5iZuW/PsINVAQV/jYXW6lD5Vs1IUtVI+pfXwJwINDO2LWvRfjswkFutNOWmaTESnCOdT7W4skTiFRuDC4Oc8FuwuuMcTVBn3aqPtK6DWskqelnrm2GwZ1yMadF3vrARwuj+62AXXTUr5vgPI00IGTlaqdp299axVO1hbTmk0GZAUR3MgX4GD7VVQ63EpgIWeV/J0vbZYo0LTULX1aIcFiVHrN6DXxA7yru9buP40HZnqnKrTTUnKAcLFOt9W8TN8LXoZP5iYJjw3vpLrteLndb28f8Tm+cceFypSmXZhuFLOHrTITdX+n5TWugJmbP13VBXdo9lUTmyKKWZzW2Uk+mZs2ATKQ/727P569thuGdslRTUuJF/vLO+Wo7uNvs8cki//Xrd7jysQpIrvLg5xUm2o/sx4t01V/V9rO3b1rM3UXt8tW7bBdH3EsXGVE5yoGN+epJY9dhv/e3R8XBtncyl/jP3/ElvNmowGdm/m+8csN4cZf0hYf//5iZf8Xb0FxoGkp35dtl1zv45fVuFR1NzuP1y1HNxoMuk25tMuk5Zobsf9Lcj2fKOUVXfLYTH4uCtqfZRECKG0s9eJNPVH47HDlIlftp+am0uHCrf9ZpYxBydwIQYrVbMRF+Zl4pnbK4fnruwOA7hSjXtF1iqbxW3FZjd/gRgwWr+3VAs9e5/2ZYi2V2GyuZUai7pRNhV0/uElNMOPbhy7BzRe1xDPXdlNlOMSpTrGYvUmKzSe46Zrru1WBrE/rTNx9SVsYjQZkJ9e9DsQ6GO0eZ111tj4AgASr9zVhMxv9ZvRG98vzua1Hy7ppRIfLA4vJiGFd62qTerZMh1UIjuVM2TcPXoLHR3Ru8PLsXq0yGnQ/osaEwc15ql3TFFwWRMOtl27qiW65aXjy6i71HisSN867s6C1boMw7adC+Y1abBUvfk/+lKrXhCwzyapceOQl59qMjHcpeN0FX87MVDrUQYO8WqqpENwM6tAkYIAjdwKWMzf+PvBOHNJBPSbh4qT9ZG8xGZFgMSn1O3M3HcWvZ9TLpQHfGiFt5gbwXsgTLCaMv6Qt1j89DONqN+3TbqMB+NbcAECKprfJiXK7312qtUGEHPDZXW5lCk2sfWqZmahbJF4hTPeJ0hIs6N4iHQaDASajQZWVEKc6xSnRKofLZxqtdbZ+SwNAXc8iTkuJQVlbzf3FQFEsppYfy2AwqHrEiC/jni3TsejRwargpYMwzSVn3565phtsZiOyk63o3DxVNyPao2U6Jg7p0OD2/m2bJOOL+wdi+eNDGnR/osaAwQ0FdFv/fHw36VLdBl+BZCRZ8cTILrhrYBs8eXVX3eW0WlbhIqhdjl1fbVCCxYQ2TbwXl30nvMHNAU2DOKNRnblpkmJVgpDSaieW7izGn2ZvQlHtiiixkLRlZiJ6agpy9cj7XulNS217foTq0zjgnSoTn4PuY+pcpEZ0r7sIamuEkqy+wU17oTeR+LtItplVn/4B/dVS2oDS5ZFUnZsDqfu91tVSic+1RYb+tFSFMC0l/nhtINRNmL4Rl0+LjQgr7G6ffjSt/UxLGQ3qKUlxKnPy1V2Rn5WEJ0Z2UfXxsZgMSEsw463fXoTnruuGK7rUTVmJgZL4uD2FDInNYkKHnFR0za3LcIr1WHJwk5+dhEWPXoavHhgEm9kUsf2JLsrPrHfhAFFjxuCGImbCZe3x3PXdYTEZgyqcVT7hO30zN9pOvs9d1w0X5mcoXydaTErnZDlzs18T3JiN6pqbKodbqf1YsuM47np/HT5b/yu2H/N2qhUzN7npCbggr/4pPLnuQbunF6BfUGoxijUT+udI78L/1NXdlOyYNsiQjxf312njp/EiAKx9aij+/pu6DQL9bayoVe5n52ptYk3OZlU56poQioXCKTazKoCTly+LNTeBdnUWgxhx+vQft1yAbrVTT5V2l880WpMUm242TlsD3yy97nXQu1UGlv9pCCZc1l6VfUpPtMJgMOCqHrm4a1BbZAkZHjE7Vy5smtlJyMzIAebvL2mHmy5qiQ/H9wdQV1R8pbBUPi8rSSmg1wamROTFvwyKimC6jsqfQssDFBPL7hrUVrVjb6LVpPQBkTM32uDGZDSgW4s0ZcqgXZNkJUvxwrfbfX6mGNw0S0vALX1boXOzVNxwQQu/z0FeDaWXudErzrQEkbnRC3qsZqMSLO0+rt6fKUmn5iZQcJORZFVNgWgLikPRt3UmPrnnYtVtcuAiLtPXTjeJ50a+oDvcHqU/kJgl0U4v9REaAIqrbfKzkvBk7aaOFTUun+my9ERLUD19clIT8P5d/fDZHwpUmRf1FKf6Pk2S9TOVYuAkvr7kjF96kgWv3noBLu3onTKefV8BXhvdGxOHtNd9vGivZiI6V/Avg6KivsZ/gP6KKpneHkxig7Ikq0m5QO8prsCRkmq8tWyv6niT0QCLyYgVf74Cz13XDf83qodyIddb4i32XslNT0T7pilY8MhgvH7bhbpjFDND4rREIDY/Uxb+jpGpghvNHl56NTfa+hAtMavkbyfkR4Z1QuvsJJ/gRZaTasPs+wZiQDv1qiwlaK09x2ajATmp/jMxLTMTlSXdf/nmFwDA0K5151MvSPnxT0OwavIVgBA82CxGZTqzwu7ymZZKSzQHHcgN6ZKD/ppl84FWug2p/f3r9d2RqYIbP4+Vk5qAGy9s5bcrd6SmpYjOddxbiqJizMX5OFJSjRPldszddBTjCnzbwGcmWZX9cADv9Ib8SfeEzuaOogSLCZ1qV2TtP1mJp+Zs8emRIq9gSrSacFdtQ7NAHVvFDUKD2VS0SYpNqU258+LWyE6x4aFPNga8T9fcVAztkoPFO4rRrUUadmt2yQb0Mzc2sxHJtcWp2p21E6y+2SO5HskfYfW8bkExAEwa1hGThnmbwjVLs+G4pluzv+ksbXbB5ZHw3HXd4XR7cO/gdj7Hpyda0Do7WdmyonV2EiYO6YB/LtoNwHcvJ6CugZ5YRGwzG5XgosLum7mxmoyqGp1w6pCTgvkPXxqw1kwMbhoapIzo3hzrDpwJqqaN6HzC4Iaiwmb27owsSRIeHtZRd6WKxWTE5Z2b4pvazQWbptZdQPUarYkXskSLCWkJZiU4krvGvjO2L+75YD0A/W0V/E1LWM1GZCTWfS+nno0xtceYTUZc37sFHvusEM4AuxzbzCa8d1c/HC+rQVayFV8VHvU9xl/mpjYrcbRU3TVZ7qvSvmldtiw1IXCGQuyuG0wn3W65aThedkJ1W7bONgXyWEX5WUnIz07Ch+MH6B6fkWSBhCQluOmTn6mq2dLrNCxzC1GawWBQlrBXajI3l3ZsgjbZySE1UQxVFz9LwwFvs0GxYD3QfmmB3DWwDZqk2DCgXXD9qojOF8xpUlQZDAa0a5ridwpmhLAdQE5qAn5/iTfDcnt/3z4g8gXZajbCYjLAYDCoakfaZCdhmDCdIe8BJBILMq/rXVdL0zwtAZ2bp6KgXTZ+08f/tIAoR6eT7cW1UzRtA9S8AN6aHn8XWr1l0majelmxSE7Y5GUl4cuJg/Djn+pf0ptiM2P15KH4+Zkrg2rc1kloWNc7LwP922ThodpW/1racyf32NGSsygje+Sqetf4a46nx6OpBpanM10eSWkNcHv/PHw4fgCMRkNIG5cG0sFPd2IteQPaa3rlhiVzYzYZMerCliGvZiSKd8zcUKMytGsOBrTNgtVsxJTruqN1dhIK2mf71HEA3qmozc8Nh8VoVC7I7ZumoLB2Y85reuXCYDBgYPtsrD94RrXiRCZ2M/7doDb4epM3c9IszQaT0YBP7tWvL9HTVKeO5B+39sZHqw5itKaTbii0nWcBuWdK3e02s1GZhhNXBYXSmVZvfyd/xKaMX94/MGBAJF64Z917sRLwaS169DLsO1GJgvbZOCxk6kIJbsTMDQBl6g6om9oUC7dD2bhUz2d/KMA7P+7DlOu61X8wgE/uuRjztxbhjgH58AhjDedO2ETE4IYamSSrGZ/+oUB129CuvkGJTLs/1cD22fjfz7+ioF027r/c2zDvo/EDUONy62Y6JlzeHofPVOGRYZ1Uy40DFbwCwLW9cvHN5mP4/SVt8e6K/bX38c3c5KQm4NHhnQM+Vn1sOpkbQB1gXNMrF1/8fARA4GmbcLm8szcj1jIjsd5MT1qCWVm2Hmi7j2ZpCWhW+ztoJgRacifhgnbZWLXvFPoHeAzt7hQmozcIrHK4lcydOO12tpmb/m2zfAqNA8nLSsI9tXVGYmCtt/M4ETUcgxuKKzdd1BK989LRtknd1JcxwBROp2ap+HzCQADqjEd9mxy+NvoCPHhFR+Sk2pTg5myWUAciZm4K2mVjbG0x9sieuZg9oQBr9p/GHf3zleDGqbP/VLhlJVux9qmhsJnqn64zGAyYPaEgpH2K5I1XvSurvEHjG3dciNkbfsWNF7X0ez+9XdaTbWZUOdxYvKMYgDq4iWTNTX0MBgPmTboUJVVO5ATo40NEoWNwQ3HFW3cT/DSGSD1dEfhCbDEZ0bl5qqrG42ynOABvgFRS5dR05K0b1yNXdlJlCvq2yULf2kzGhMvaY+aag/jDYP2eKOFWX3ZLFOoGjE1TbVj06GCk2CzKfbNTbPjDZYGf28QhHfDDzmKM7ltXo5VqM6tW24m9cB65shMW/nIcv704Hxe3y8afZm/GK7f2DmmsZyPQ/lZE1HAMboh0BLsjuLgtQUud/bNC9dH4Afi/b37BEyPr9vISl1IHWrr+xMgueHxE56DH3tg1JEhtlpaAH/90heo2bSG5GCy2bZKMTVOGK3VB658exp2wieIAgxsiwU0XtsRXm45i/CW+/Vf8efOOi7DtaCmGdA6ucV8gPVqm+9QcGY0G3Du4HU5VOFRbDeiJl8AmnLQNGrVL3cWCZwY2RPHBIEnSeVWmX1ZWhvT0dJSWliItjSlhUvN4JFQ4XD6FynTumvDhBszfVqR8/f/GXISre+bGcERE1BChXL/Z54ZIYDQaGNjEmRdu7IEXb+ypfK237xcRxRcGN0QU17JTbKomkBU6G7MSUXxhcENEcc9gMOC+y9ujc7NUjOjuv28SEcUH1twQERFRo8eaGyIiIjpvMbghIiKiuMLghoiIiOIKgxsiIiKKKwxuiIiIKK4wuCEiIqK4wuCGiIiI4gqDGyIiIoorDG6IiIgorjC4ISIiorjC4IaIiIjiCoMbIiIiiisMboiIiCiuMLghIiKiuGKO9QCiTZIkAN6t04mIiOjcIF+35et4IOddcFNeXg4AyMvLi/FIiIiIKFTl5eVIT08PeIxBCiYEiiMejwdHjx5FamoqDAZD2B63rKwMeXl5OHz4MNLS0sL2uKSP5zu6eL6jj+c8uni+o6sh51uSJJSXl6NFixYwGgNX1Zx3mRuj0YhWrVpF7PHT0tL4hxFFPN/RxfMdfTzn0cXzHV2hnu/6MjYyFhQTERFRXGFwQ0RERHGFwU2Y2Gw2TJkyBTabLdZDOS/wfEcXz3f08ZxHF893dEX6fJ93BcVEREQU35i5ISIiorjC4IaIiIjiCoMbIiIiiisMboiIiCiuMLgJgzfffBNt2rRBQkICBgwYgLVr18Z6SOek5cuX47rrrkOLFi1gMBjw5Zdfqr4vSRKeffZZ5ObmIjExEcOGDcPu3btVx5w+fRpjxoxBWloaMjIyMH78eFRUVETxWZw7pk6din79+iE1NRU5OTkYNWoUdu7cqTqmpqYGEydORHZ2NlJSUnDzzTfj+PHjqmMOHTqEa665BklJScjJycHjjz8Ol8sVzadyTpg2bRp69eqlNC0rKCjAvHnzlO/zXEfWSy+9BIPBgIcffli5jec8vJ577jkYDAbVvy5duijfj+r5luiszJo1S7JardL06dOlbdu2Sffcc4+UkZEhHT9+PNZDO+d899130lNPPSV98cUXEgBpzpw5qu+/9NJLUnp6uvTll19KmzZtkq6//nqpbdu2UnV1tXLMVVddJfXu3VtavXq19OOPP0odOnSQbr/99ig/k3PDiBEjpPfff1/aunWrVFhYKF199dVSfn6+VFFRoRwzYcIEKS8vT1q8eLG0fv166eKLL5YGDhyofN/lckk9evSQhg0bJm3cuFH67rvvpCZNmkiTJ0+OxVNq1ObOnSt9++230q5du6SdO3dKTz75pGSxWKStW7dKksRzHUlr166V2rRpI/Xq1UuaNGmScjvPeXhNmTJF6t69u3Ts2DHl34kTJ5TvR/N8M7g5S/3795cmTpyofO12u6UWLVpIU6dOjeGozn3a4Mbj8UjNmzeXXn75ZeW2kpISyWazSZ988okkSZL0yy+/SACkdevWKcfMmzdPMhgM0pEjR6I29nNVcXGxBEBatmyZJEne82uxWKTPP/9cOWb79u0SAGnVqlWSJHkDUqPRKBUVFSnHTJs2TUpLS5Psdnt0n8A5KDMzU3r33Xd5riOovLxc6tixo7Rw4ULpsssuU4IbnvPwmzJlitS7d2/d70X7fHNa6iw4HA5s2LABw4YNU24zGo0YNmwYVq1aFcORxZ/9+/ejqKhIda7T09MxYMAA5VyvWrUKGRkZ6Nu3r3LMsGHDYDQasWbNmqiP+VxTWloKAMjKygIAbNiwAU6nU3XOu3Tpgvz8fNU579mzJ5o1a6YcM2LECJSVlWHbtm1RHP25xe12Y9asWaisrERBQQHPdQRNnDgR11xzjercAnx9R8ru3bvRokULtGvXDmPGjMGhQ4cARP98n3cbZ4bTyZMn4Xa7Vb8IAGjWrBl27NgRo1HFp6KiIgDQPdfy94qKipCTk6P6vtlsRlZWlnIM6fN4PHj44YcxaNAg9OjRA4D3fFqtVmRkZKiO1Z5zvd+J/D1S27JlCwoKClBTU4OUlBTMmTMH3bp1Q2FhIc91BMyaNQs///wz1q1b5/M9vr7Db8CAAZgxYwY6d+6MY8eO4fnnn8ell16KrVu3Rv18M7ghIkycOBFbt27FihUrYj2UuNa5c2cUFhaitLQUs2fPxrhx47Bs2bJYDysuHT58GJMmTcLChQuRkJAQ6+GcF0aOHKn8f69evTBgwAC0bt0an332GRITE6M6Fk5LnYUmTZrAZDL5VHsfP34czZs3j9Go4pN8PgOd6+bNm6O4uFj1fZfLhdOnT/P3EcADDzyAb775Bj/88ANatWql3N68eXM4HA6UlJSojteec73fifw9UrNarejQoQP69OmDqVOnonfv3nj99dd5riNgw4YNKC4uxkUXXQSz2Qyz2Yxly5bhX//6F8xmM5o1a8ZzHmEZGRno1KkT9uzZE/XXOIObs2C1WtGnTx8sXrxYuc3j8WDx4sUoKCiI4cjiT9u2bdG8eXPVuS4rK8OaNWuUc11QUICSkhJs2LBBOWbJkiXweDwYMGBA1Mfc2EmShAceeABz5szBkiVL0LZtW9X3+/TpA4vFojrnO3fuxKFDh1TnfMuWLaqgcuHChUhLS0O3bt2i80TOYR6PB3a7nec6AoYOHYotW7agsLBQ+de3b1+MGTNG+X+e88iqqKjA3r17kZubG/3XeMjl0KQya9YsyWazSTNmzJB++eUX6d5775UyMjJU1d4UnPLycmnjxo3Sxo0bJQDSq6++Km3cuFE6ePCgJEnepeAZGRnSV199JW3evFm64YYbdJeCX3jhhdKaNWukFStWSB07duRScD/uu+8+KT09XVq6dKlq6WZVVZVyzIQJE6T8/HxpyZIl0vr166WCggKpoKBA+b68dHP48OFSYWGhNH/+fKlp06ZcKqvjiSeekJYtWybt379f2rx5s/TEE09IBoNB+v777yVJ4rmOBnG1lCTxnIfbY489Ji1dulTav3+/tHLlSmnYsGFSkyZNpOLiYkmSonu+GdyEwb///W8pPz9fslqtUv/+/aXVq1fHekjnpB9++EEC4PNv3LhxkiR5l4M/88wzUrNmzSSbzSYNHTpU2rlzp+oxTp06Jd1+++1SSkqKlJaWJv3ud7+TysvLY/BsGj+9cw1Aev/995Vjqqurpfvvv1/KzMyUkpKSpBtvvFE6duyY6nEOHDggjRw5UkpMTJSaNGkiPfbYY5LT6Yzys2n87r77bql169aS1WqVmjZtKg0dOlQJbCSJ5zoatMENz3l4jR49WsrNzZWsVqvUsmVLafTo0dKePXuU70fzfBskSZIanHMiIiIiamRYc0NERERxhcENERERxRUGN0RERBRXGNwQERFRXGFwQ0RERHGFwQ0RERHFFQY3REREFFcY3BAREVFcYXBDROclg8GAL7/8MtbDIKIIYHBDRFF31113wWAw+Py76qqrYj00IooD5lgPgIjOT1dddRXef/991W02my1GoyGieMLMDRHFhM1mQ/PmzVX/MjMzAXinjKZNm4aRI0ciMTER7dq1w+zZs1X337JlC6644gokJiYiOzsb9957LyoqKlTHTJ8+Hd27d4fNZkNubi4eeOAB1fdPnjyJG2+8EUlJSejYsSPmzp2rfO/MmTMYM2YMmjZtisTERHTs2NEnGCOixonBDRE1Ss888wxuvvlmbNq0CWPGjMFtt92G7du3AwAqKysxYsQIZGZmYt26dfj888+xaNEiVfAybdo0TJw4Effeey+2bNmCuXPnokOHDqqf8fzzz+PWW2/F5s2bcfXVV2PMmDE4ffq08vN/+eUXzJs3D9u3b8e0adPQpEmT6J0AImq4s9zhnIgoZOPGjZNMJpOUnJys+vfCCy9IkiRJAKQJEyao7jNgwADpvvvukyRJkt5++20pMzNTqqioUL7/7bffSkajUSoqKpIkSZJatGghPfXUU37HAEB6+umnla8rKiokANK8efMkSZKk6667Tvrd734XnidMRFHFmhsiiokhQ4Zg2rRpqtuysrKU/y8oKFB9r6CgAIWFhQCA7du3o3fv3khOTla+P2jQIHg8HuzcuRMGgwFHjx7F0KFDA46hV69eyv8nJycjLS0NxcXFAID77rsPN998M37++WcMHz4co0aNwsCBAxv0XIkouhjcEFFMJCcn+0wThUtiYmJQx1ksFtXXBoMBHo8HADBy5EgcPHgQ3333HRYuXIihQ4di4sSJeOWVV8I+XiIKL9bcEFGjtHr1ap+vu3btCgDo2rUrNm3ahMrKSuX7K1euhNFoROfOnZGamoo2bdpg8eLFZzWGpk2bYty4cfjoo4/wz3/+E2+//fZZPR4RRQczN0QUE3a7HUVFRarbzGazUrT7+eefo2/fvrjkkkvw8ccfY+3atXjvvfcAAGPGjMGUKVMwbtw4PPfcczhx4gQefPBB3HnnnWjWrBkA4LnnnsOECROQk5ODkSNHory8HCtXrsSDDz4Y1PieffZZ9OnTB927d4fdbsc333yjBFdE1LgxuCGimJg/fz5yc3NVt3Xu3Bk7duwA4F3JNGvWLNx///3Izc3FJ598gm7dugEAkpKSsGDBAkyaNAn9+vVDUlISbr75Zrz66qvKY40bNw41NTV47bXX8Mc//hFNmjTBb37zm6DHZ7VaMXnyZBw4cACJiYm49NJLMWvWrDA8cyKKNIMkSVKsB0FEJDIYDJgzZw5GjRoV66EQ0TmINTdEREQUVxjcEBERUVxhzQ0RNTqcLSeis8HMDREREcUVBjdEREQUVxjcEBERUVxhcENERERxhcENERERxRUGN0RERBRXGNwQERFRXGFwQ0RERHHl/wNQP2Fn72C+9wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "truncated_mae_history = average_mae_history[10:]\n",
        "plt.plot(range(1, len(truncated_mae_history) + 1), truncated_mae_history)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Validation MAE\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bxO2cIKpRyeu"
      },
      "source": [
        "**최종 모델 훈련하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T01:01:15.488214Z",
          "iopub.status.busy": "2022-05-21T01:01:15.487884Z",
          "iopub.status.idle": "2022-05-21T01:01:20.038808Z",
          "shell.execute_reply": "2022-05-21T01:01:20.037674Z"
        },
        "id": "wSGcMeH3Ryev",
        "outputId": "12bf5536-66f1-458d-9d41-890151e5173c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 4ms/step - loss: 15.1859 - mae: 2.7261\n"
          ]
        }
      ],
      "source": [
        "model = build_model()\n",
        "model.fit(train_data, train_targets,\n",
        "          epochs=130, batch_size=16, verbose=0)\n",
        "test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T01:01:20.043289Z",
          "iopub.status.busy": "2022-05-21T01:01:20.042592Z",
          "iopub.status.idle": "2022-05-21T01:01:20.049022Z",
          "shell.execute_reply": "2022-05-21T01:01:20.048132Z"
        },
        "id": "_e_S3_P5Ryev",
        "outputId": "a74923da-1935-4a71-919a-31aa896e86a7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.7261292934417725"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "test_mae_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VesXPPJYRyev"
      },
      "source": [
        "### 새로운 데이터에 대해 예측하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T01:01:20.053135Z",
          "iopub.status.busy": "2022-05-21T01:01:20.052814Z",
          "iopub.status.idle": "2022-05-21T01:01:20.154058Z",
          "shell.execute_reply": "2022-05-21T01:01:20.153234Z"
        },
        "id": "rWLRtftSRyew",
        "outputId": "02560bf8-2080-4f1f-c60a-d9dc628d5302",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7.7293887], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "predictions = model.predict(test_data)\n",
        "predictions[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cyvfquV2Ryez"
      },
      "source": [
        "### 정리"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sPhk1YR1AKd1"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "chapter04_getting-started-with-neural-networks.i",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}