chapter04_getting-started-with-neural-networks.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1csvZ7gIRydn"
      },
      "source": [
        "이 노트북은 [케라스 창시자에게 배우는 딥러닝 2판](https://tensorflow.blog/kerasdl2/)의 예제 코드를 담고 있습니다.\n",
        "\n",
        "<table align=\"left\">\n",
        "    <tr>\n",
        "        <td>\n",
        "            <a href=\"https://colab.research.google.com/github/rickiepark/deep-learning-with-python-2nd/blob/main/chapter04_getting-started-with-neural-networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "        </td>\n",
        "    </tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 코랩에서 메모리 부족을 피하기 위해 부동소수점 정밀도를 낮춥니다.\n",
        "import tensorflow as tf\n",
        "\n",
        "tf.keras.backend.set_floatx('float16')"
      ],
      "metadata": {
        "id": "LcAZD0hMLaHg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-mJrYOYRydu"
      },
      "source": [
        "# 신경망 시작하기: 분류와 회귀"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oALyyfgXRydv"
      },
      "source": [
        "## 영화 리뷰 분류: 이진 분류 예제"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjZERUadRydw"
      },
      "source": [
        "### IMDB 데이터셋"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BQhTZrmRydx"
      },
      "source": [
        "**IMDB 데이터셋 로드하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a2a4980-8e6b-4b67-c84e-142a98c80003"
      },
      "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",
      "source": [
        "len(train_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzNEZNnjRA5R",
        "outputId": "6934617e-1de3-413b-bf02-8a71369f165e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25000"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(test_labels)\n",
        "print(train_labels[:10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iD02ZMhcRc_m",
        "outputId": "054699d6-52af-47b6-d25e-428478fb59ad"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 0 1 0 0 1 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "execution": {
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        "outputId": "af1c30e5-9282-4da2-c20d-dbc6b43411bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              " 32]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "train_data[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "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": "9b9acbd9-9fbc-456c-8ddb-a8b3263a2193",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "train_labels[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "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": "4642241f-a793-4254-a02d-35d7dce4ab4f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9999"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "max([max(sequence) for sequence in train_data])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1syr49QyRyd4"
      },
      "source": [
        "**리뷰를 다시 텍스트로 디코딩하기**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "YnB6XR8BLpb3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "818357c6-ed43-41a9-b04a-6c22e861f70b"
      },
      "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": "code",
      "source": [
        "reverse_word_index.get(14 - 3, \"?\")"
      ],
      "metadata": {
        "id": "XLhrWO81afjE",
        "outputId": "21332086-059b-424a-cf02-104599556690",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'this'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(decoded_review)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ubQv1wcCNxbR",
        "outputId": "d66ed634-4374-4f71-c24f-a806dc4b5088"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TL-I0vCkRyd5"
      },
      "source": [
        "### 데이터 준비"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMw5jhfYRyd6"
      },
      "source": [
        "**정수 시퀀스를 멀티-핫 인코딩으로 인코딩하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "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": 18,
      "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": "2845c9b7-6f73-4348-9da2-8bb57500afe1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1., 1., ..., 0., 0., 0.],\n",
              "       [0., 1., 1., ..., 0., 0., 0.],\n",
              "       [0., 1., 1., ..., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "x_train[:3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d16385fc-7848-4ab8-c95a-7a2e9fed80c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "y_train = np.asarray(train_labels).astype(\"float32\")\n",
        "y_test = np.asarray(test_labels).astype(\"float32\")\n",
        "y_train[:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LANAjB0URyd8"
      },
      "source": [
        "### 신경망 모델 만들기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qObD7ZSGRyd9"
      },
      "source": [
        "**모델 정의하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "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": null,
      "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": null,
      "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": "ded63f96-a722-47f8-f886-8039c961f87e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "30/30 [==============================] - 8s 62ms/step - loss: 0.5278 - accuracy: 0.7871 - val_loss: 0.4009 - val_accuracy: 0.8662\n",
            "Epoch 2/20\n",
            "30/30 [==============================] - 0s 14ms/step - loss: 0.3330 - accuracy: 0.8911 - val_loss: 0.3313 - val_accuracy: 0.8750\n",
            "Epoch 3/20\n",
            "30/30 [==============================] - 0s 13ms/step - loss: 0.2520 - accuracy: 0.9175 - val_loss: 0.2991 - val_accuracy: 0.8794\n",
            "Epoch 4/20\n",
            "30/30 [==============================] - 0s 13ms/step - loss: 0.2064 - accuracy: 0.9302 - val_loss: 0.2749 - val_accuracy: 0.8882\n",
            "Epoch 5/20\n",
            "30/30 [==============================] - 0s 14ms/step - loss: 0.1768 - accuracy: 0.9414 - val_loss: 0.2903 - val_accuracy: 0.8833\n",
            "Epoch 6/20\n",
            "30/30 [==============================] - 0s 13ms/step - loss: 0.1538 - accuracy: 0.9492 - val_loss: 0.3237 - val_accuracy: 0.8750\n",
            "Epoch 7/20\n",
            "30/30 [==============================] - 0s 13ms/step - loss: 0.1361 - accuracy: 0.9546 - val_loss: 0.2861 - val_accuracy: 0.8857\n",
            "Epoch 8/20\n",
            "30/30 [==============================] - 0s 12ms/step - loss: 0.1182 - accuracy: 0.9639 - val_loss: 0.3352 - val_accuracy: 0.8721\n",
            "Epoch 9/20\n",
            "30/30 [==============================] - 0s 13ms/step - loss: 0.1053 - accuracy: 0.9692 - val_loss: 0.3477 - val_accuracy: 0.8750\n",
            "Epoch 10/20\n",
            "30/30 [==============================] - 0s 14ms/step - loss: 0.0926 - accuracy: 0.9717 - val_loss: 0.3567 - val_accuracy: 0.8706\n",
            "Epoch 11/20\n",
            "30/30 [==============================] - 0s 14ms/step - loss: 0.0833 - accuracy: 0.9756 - val_loss: 0.3428 - val_accuracy: 0.8784\n",
            "Epoch 12/20\n",
            "30/30 [==============================] - 0s 14ms/step - loss: 0.0701 - accuracy: 0.9824 - val_loss: 0.3777 - val_accuracy: 0.8706\n",
            "Epoch 13/20\n",
            "30/30 [==============================] - 0s 13ms/step - loss: 0.0638 - accuracy: 0.9844 - val_loss: 0.3696 - val_accuracy: 0.8770\n",
            "Epoch 14/20\n",
            "30/30 [==============================] - 0s 13ms/step - loss: 0.0552 - accuracy: 0.9878 - val_loss: 0.3911 - val_accuracy: 0.8721\n",
            "Epoch 15/20\n",
            "30/30 [==============================] - 0s 14ms/step - loss: 0.0511 - accuracy: 0.9878 - val_loss: 0.4199 - val_accuracy: 0.8735\n",
            "Epoch 16/20\n",
            "30/30 [==============================] - 0s 13ms/step - loss: 0.0433 - accuracy: 0.9917 - val_loss: 0.4319 - val_accuracy: 0.8706\n",
            "Epoch 17/20\n",
            "30/30 [==============================] - 0s 14ms/step - loss: 0.0378 - accuracy: 0.9932 - val_loss: 0.4707 - val_accuracy: 0.8647\n",
            "Epoch 18/20\n",
            "30/30 [==============================] - 1s 23ms/step - loss: 0.0349 - accuracy: 0.9941 - val_loss: 0.4775 - val_accuracy: 0.8682\n",
            "Epoch 19/20\n",
            "30/30 [==============================] - 1s 20ms/step - loss: 0.0269 - accuracy: 0.9980 - val_loss: 0.4993 - val_accuracy: 0.8662\n",
            "Epoch 20/20\n",
            "30/30 [==============================] - 1s 24ms/step - loss: 0.0251 - accuracy: 0.9980 - val_loss: 0.5142 - val_accuracy: 0.8721\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": null,
      "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": "c97208dc-af8d-412f-f5a1-015478227fb8",
        "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": 15
        }
      ],
      "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": null,
      "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": "f6ae7998-9f4e-4f42-c41b-1df8126d244d",
        "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": null,
      "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": "d26ff332-e966-4537-d3b6-9012f6955b21",
        "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": null,
      "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": "cbfb82fa-60ca-4ace-dc48-811840a316ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "49/49 [==============================] - 1s 9ms/step - loss: 0.4958 - accuracy: 0.8091\n",
            "Epoch 2/4\n",
            "49/49 [==============================] - 0s 8ms/step - loss: 0.2957 - accuracy: 0.8994\n",
            "Epoch 3/4\n",
            "49/49 [==============================] - 0s 9ms/step - loss: 0.2278 - accuracy: 0.9194\n",
            "Epoch 4/4\n",
            "49/49 [==============================] - 1s 10ms/step - loss: 0.1927 - accuracy: 0.9302\n",
            "782/782 [==============================] - 3s 3ms/step - loss: 0.2834 - accuracy: 0.9185\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": null,
      "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": "63bfe176-d52b-4f95-d5e2-d0bc67d6572b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.283447265625, 0.91845703125]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9b6hcHKARyeG"
      },
      "source": [
        "### 훈련된 모델로 새로운 데이터에 대해 예측하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "750444ec-302d-40c2-e8aa-15ffbc5a1c16",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 2s 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.2332 ],\n",
              "       [1.     ],\n",
              "       [0.867  ],\n",
              "       ...,\n",
              "       [0.10876],\n",
              "       [0.1023 ],\n",
              "       [0.6826 ]], dtype=float16)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "model.predict(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1GvnURGnRyeL"
      },
      "source": [
        "### 추가 실험"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wx8OWuKGRyeL"
      },
      "source": [
        "### 정리"
      ]
    },
    {
      "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": null,
      "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": "93ff29e2-bdb3-4077-e86c-27483bf24f44",
        "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 [==============================] - 1s 1us/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": null,
      "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": "3ae9b177-5a1d-481c-d10f-57bae933dba8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8982"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "len(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "2ecf03f1-8b7b-411c-c60e-e504f1f82583",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2246"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "len(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "6f54b0aa-a3db-4d16-b343-67f3d5388b05",
        "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": 24
        }
      ],
      "source": [
        "train_data[10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ou52UiQ2RyeO"
      },
      "source": [
        "**로이터 데이터셋을 텍스트로 디코딩하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "5774a6d4-55fc-40d6-a821-6a7b8144dcf5",
        "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 [==============================] - 1s 1us/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": null,
      "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": "eb4c44e0-3cca-43dd-dbe8-547aca45a37d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "train_labels[10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qt1koPzGRyeP"
      },
      "source": [
        "### 데이터 준비"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwQGYNQORyeP"
      },
      "source": [
        "**데이터 인코딩하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "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": null,
      "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": null,
      "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": null,
      "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": null,
      "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": null,
      "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": "71319e7e-8836-4f40-db5d-0fc4031c069d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "16/16 [==============================] - 1s 29ms/step - loss: 2.8555 - accuracy: 0.4966 - val_loss: 1.9209 - val_accuracy: 0.6089\n",
            "Epoch 2/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 1.5479 - accuracy: 0.6865 - val_loss: 1.4043 - val_accuracy: 0.7051\n",
            "Epoch 3/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 1.1562 - accuracy: 0.7534 - val_loss: 1.2119 - val_accuracy: 0.7388\n",
            "Epoch 4/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.9419 - accuracy: 0.7974 - val_loss: 1.0879 - val_accuracy: 0.7651\n",
            "Epoch 5/20\n",
            "16/16 [==============================] - 0s 12ms/step - loss: 0.7847 - accuracy: 0.8311 - val_loss: 1.0498 - val_accuracy: 0.7651\n",
            "Epoch 6/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.6616 - accuracy: 0.8628 - val_loss: 0.9561 - val_accuracy: 0.7959\n",
            "Epoch 7/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.5576 - accuracy: 0.8823 - val_loss: 0.9146 - val_accuracy: 0.8149\n",
            "Epoch 8/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.4722 - accuracy: 0.8989 - val_loss: 0.8960 - val_accuracy: 0.8101\n",
            "Epoch 9/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.3975 - accuracy: 0.9175 - val_loss: 0.9019 - val_accuracy: 0.8110\n",
            "Epoch 10/20\n",
            "16/16 [==============================] - 0s 12ms/step - loss: 0.3506 - accuracy: 0.9238 - val_loss: 0.8872 - val_accuracy: 0.8159\n",
            "Epoch 11/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.3003 - accuracy: 0.9331 - val_loss: 0.9082 - val_accuracy: 0.8101\n",
            "Epoch 12/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.2659 - accuracy: 0.9414 - val_loss: 0.9106 - val_accuracy: 0.8159\n",
            "Epoch 13/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.2394 - accuracy: 0.9429 - val_loss: 0.8779 - val_accuracy: 0.8208\n",
            "Epoch 14/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.2117 - accuracy: 0.9463 - val_loss: 0.8931 - val_accuracy: 0.8218\n",
            "Epoch 15/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.1953 - accuracy: 0.9507 - val_loss: 0.9390 - val_accuracy: 0.8052\n",
            "Epoch 16/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.1831 - accuracy: 0.9507 - val_loss: 0.9253 - val_accuracy: 0.8120\n",
            "Epoch 17/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.1682 - accuracy: 0.9531 - val_loss: 0.9214 - val_accuracy: 0.8140\n",
            "Epoch 18/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.1575 - accuracy: 0.9546 - val_loss: 0.9810 - val_accuracy: 0.8081\n",
            "Epoch 19/20\n",
            "16/16 [==============================] - 0s 11ms/step - loss: 0.1514 - accuracy: 0.9561 - val_loss: 0.9990 - val_accuracy: 0.8018\n",
            "Epoch 20/20\n",
            "16/16 [==============================] - 0s 10ms/step - loss: 0.1406 - accuracy: 0.9590 - val_loss: 0.9624 - val_accuracy: 0.8188\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": null,
      "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": "ed50e18c-1f0f-4e72-f43f-b5e6fe110d79",
        "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": null,
      "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": "3c6e8ad1-138b-48c2-fd9d-cd60eba4a9ba",
        "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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fTDIPBu+fRaQnBXUFb9Sokcb1Dx06JNq0aSPs7e2Fl5eX+PTTT8WuXbuK7F6s7O66YMGCfPsEoNbttKCu4KNHj863bd5uxEIIERcXJ5o3by5sbGxErVq1xP/93/+JTz75RNjZ2RXwLrxy6dIlERISIhwdHYWrq6sYPny4qst53q66FSpUyLe9ptgfP34sBg8eLJycnISzs7MYPHiwOH36dLG6gitdu3ZNABAAxMGDB/O9/vTpUzFkyBDh6uoqHB0dRWhoqLhy5Uq+96c4XcG1ifnevXuiV69ewsXFRTg7O4t+/fqJ+/fv57umQggxd+5c4e3tLSwsLNS6hWu6hjdu3BB9+/YVLi4uws7OTrRu3Vr8/vvvausoz2Xjxo1qyzV1rdakuO+Z8v0YM2aM8Pb2FjY2NqJatWoiIiJCJCcnq9bJzMwUU6dOFTVq1BDW1tbCw8ND9O3bV9y4cUO1zqNHj0SfPn2Eg4ODqFSpkvjggw/EhQsXiv35EqL4n1EhhLhw4YLq+tjZ2Yl69eqJ6dOn59tnVlaWqFSpknB2dhbPnz8v9H0j8yUTogz9q0pERerZsycuXryosT0IUXn38uVLeHl5oXv37lixYoWxwyEjYZsbojIs7zD0165dwx9//IEOHToYJyCiMm7r1q149OiRWiNlKn9YckNUhnl6eqrmO7p9+zaWLl2KrKwsnD59GnXq1DF2eERlxrFjx3Du3DnMnTsXrq6uJR54kcwDGxQTlWGdO3dGdHQ0kpKSYGtri6CgIHz++edMbIjyWLp0KX7++Wc0a9ZMbeJOKp9YckNERERmhW1uiIiIyKwwuSEiIiKzUu7a3CgUCty/fx8VK1Ys1Qy3REREZDhCCKSlpcHLy6vIATDLXXJz//59+Pj4GDsMIiIiKoG7d+9qnGU+t3KX3FSsWBGA9OY4OTkZORoiIiIqjtTUVPj4+Kju44Upd8mNsirKycmJyQ0REZGJKU6TEjYoJiIiIrPC5IaIiIjMCpMbIiIiMivlrs1NccnlcuTk5Bg7DCKds7GxKbIbJRGRKWNyk4cQAklJSXj27JmxQyHSCwsLC9SoUQM2NjbGDoWISC+Y3OShTGyqVq0KBwcHDvRHZkU5iGViYiKqV6/OzzcRmSUmN7nI5XJVYlOlShVjh0OkF25ubrh//z5evnwJa2trY4dDRKRzrHjPRdnGxsHBwciREOmPsjpKLpcbORIiIv1gcqMBi+rJnPHzTUTmjtVSREREpBNyOXDgAJCYCHh6AsHBgKWl4eNgyQ0VyM/PD1FRUcVef9++fZDJZOxpRkRUDsXEAH5+wBtvAO++K/3085OWGxqTGz2Ry4F9+4DoaOmnPps3yGSyQh+zZs0q0X5PnDiBESNGFHv9tm3bIjExEc7OziU6HhER6Ze+7k0xMUDfvsC9e+rLExKk5YZOcFgtpQcxMcC4ceoXuVo14Ntvgd69dX+8xMRE1e8bNmzAjBkzEB8fr1rm6Oio+l0IAblcDiuroi+9m5ubVnHY2NjAw8NDq23MRXZ2NseNIaJS02e1jr7uTXK5tF8h8r8mBCCTAePHAz16GK6KiiU3OmaM7NXDw0P1cHZ2hkwmUz2/cuUKKlasiD///BMBAQGwtbXFwYMHcePGDfTo0QPu7u5wdHREq1atsHv3brX95q2Wkslk+L//+z/06tULDg4OqFOnDrZv3656PW+11OrVq+Hi4oJdu3ahQYMGcHR0ROfOndWSsZcvX+Kjjz6Ci4sLqlSpgkmTJiEiIgI9e/Ys8HwfP36MgQMHwtvbGw4ODvD390d0dLTaOgqFAl999RVq164NW1tbVK9eHfPmzVO9fu/ePQwcOBCVK1dGhQoV0LJlSxw7dgwAEBkZme/448ePR4cOHVTPO3TogDFjxmD8+PFwdXVFaGgoAGDRokXw9/dHhQoV4OPjg1GjRiE9PV1tX4cOHUKHDh3g4OCASpUqITQ0FE+fPsXatWtRpUoVZGVlqa3fs2dPDB48uMD3g4gMS5+lH/qq1tHnvenAgfz7zU0I4O5daT1DYXKjQ0Vlr4CUvRqjB+7kyZPxxRdf4PLly2jSpAnS09PRtWtXxMXF4fTp0+jcuTO6d++OO3fuFLqf2bNno3///jh37hy6du2KQYMG4cmTJwWun5mZiYULF+Knn37C/v37cefOHUycOFH1+pdffol169Zh1apVOHToEFJTU7F169ZCY3jx4gUCAgKwY8cOXLhwASNGjMDgwYNx/Phx1TpTpkzBF198genTp+PSpUtYv3493N3dAQDp6elo3749EhISsH37dpw9exaffvopFApFMd7JV9asWQMbGxscOnQIy5YtAyCN/vvdd9/h4sWLWLNmDfbs2YNPP/1Utc2ZM2fQqVMnNGzYEEeOHMHBgwfRvXt3yOVy9OvXD3K5XC1hfPjwIXbs2IGhQ4dqFRsR6Ye+EhB9Jh/6vjfl+n9VJ+vphChnUlJSBACRkpKS77Xnz5+LS5cuiefPn5do33v3CiF9VAp/7N1bunMozKpVq4Szs3OumPYKAGLr1q1FbtuoUSOxePFi1XNfX1/xzTffqJ4DENOmTVM9T09PFwDEn3/+qXasp0+fqmIBIK5fv67aZsmSJcLd3V313N3dXSxYsED1/OXLl6J69eqiR48exT1lIYQQ3bp1E5988okQQojU1FRha2srfvzxR43r/vDDD6JixYri8ePHGl+PiIjId/xx48aJ9u3bq563b99eNG/evMi4Nm7cKKpUqaJ6PnDgQPHaa68VuP7IkSNFly5dVM+//vprUbNmTaFQKIo8VnGV9nNOVFovX0p/B9evl36+fGka+9+8WQiZLP/fdJlMemzeXPJ4q1Ur+J4hkwnh41Py89D3vclQ977C7t95seRGh8pk9vo/LVu2VHuenp6OiRMnokGDBnBxcYGjoyMuX75cZMlNkyZNVL9XqFABTk5OePjwYYHrOzg4oFatWqrnnp6eqvVTUlLw4MEDtG7dWvW6paUlAgICCo1BLpdj7ty58Pf3R+XKleHo6Ihdu3apYr98+TKysrLQqVMnjdufOXMGzZs3R+XKlQs9TlE0xbl792506tQJ3t7eqFixIgYPHozHjx8jMzNTdeyC4gKA4cOH46+//kJCQgIAqWovMjKSY9OQ2dB3jxp97V+fpR/6rtbR970pOFhqu1PQnymZDPDxkdYzFCY3OuTpqdv1dKlChQpqzydOnIgtW7bg888/x4EDB3DmzBn4+/sjOzu70P3kHa5fJpMVWp2jaX2h6a+DFhYsWIBvv/0WkyZNwt69e3HmzBmEhoaqYre3ty90+6Jet7CwyBejphni876nt27dwttvv40mTZpg8+bNOHnyJJYsWQIAxY6tefPmaNq0KdauXYuTJ0/i4sWLiIyMLHQbIlOh7zaJptquRN/Jh77vTZaWUqNkIH+Co3weFWXY8W6Y3OhQWcxeC3Lo0CFERkaiV69e8Pf3h4eHB27dumXQGJydneHu7o4TJ06olsnlcpw6darQ7Q4dOoQePXrgvffeQ9OmTVGzZk1cvXpV9XqdOnVgb2+PuLg4jds3adIEZ86cKbCtkJubm1qjZ0AqcSnKyZMnoVAo8PXXX6NNmzaoW7cu7t+/n+/YBcWl9P7772P16tVYtWoVQkJC4OPjU+SxiXRJHw1m9d3uw5Tbleg7+TDEval3b2DTJsDbW315tWrScn30FC4MkxsdKovZa0Hq1KmDmJgYnDlzBmfPnsW7776rdYNaXRg7dizmz5+Pbdu2IT4+HuPGjcPTp08LrYapU6cOYmNjcfjwYVy+fBkffPABHjx4oHrdzs4OkyZNwqeffoq1a9fixo0bOHr0KFasWAEAGDhwIDw8PNCzZ08cOnQI//77LzZv3owjR44AADp27Ih//vkHa9euxbVr1zBz5kxcuHChyHOpXbs2cnJysHjxYvz777/46aefVA2NlaZMmYITJ05g1KhROHfuHK5cuYKlS5ciOTlZtc67776Le/fu4ccff2RDYjI4fVXr6LvqRd/712cCou/kw1D3pt69gVu3gL17gfXrpZ83bxo+sQGY3OhcWcteC7Jo0SJUqlQJbdu2Rffu3REaGooWLVoYPI5JkyZh4MCBCA8PR1BQEBwdHREaGgo7O7sCt5k2bRpatGiB0NBQdOjQQZWo5DZ9+nR88sknmDFjBho0aICwsDBVWx8bGxv89ddfqFq1Krp27Qp/f3988cUXsPzfNzs0NBTTp0/Hp59+ilatWiEtLQ3h4eFFnkvTpk2xaNEifPnll2jcuDHWrVuH+fPnq61Tt25d/PXXXzh79ixat26NoKAgbNu2TW3cIWdnZ/Tp0weOjo6Fdokn0jV9Vuvou+rFlNuVGCL5MNS9ydIS6NABGDhQ+mmsf+ZlorQNIExMamoqnJ2dkZKSAicnJ7XXXrx4gZs3b6JGjRqF3lyLo6zMr2FqFAoFGjRogP79+2Pu3LnGDsdoOnXqhEaNGuG7777T+b51+Tkn49H13xi5XCqhKaj0QyaTboQ3b5bsOPv2SaVARdm7V7oplrX9A6+SP0C9+kuZgJQ2SdA0yJ6Pj5TY6Cr5MOV7U2H373xK1zHL9OizKzhp79atW2L58uUiPj5enDt3TowYMUJYW1uLS5cuGTs0o3jy5ImIiYkRFhYW4sqVK3o5Bj/nhqHP7s6bN+fvOlytWsm7Iguh/+68yu7OmrpS66K7s773r6TpvffxKd17n/c89NlN3pRp0xWc0y+QUVlYWGD16tWYOHEihBBo3Lgxdu/ejQYNGhg7NKNo3rw5nj59ii+//BL16tUzdjhUQvqcgkVZepC3zF1ZdVTS0gN9V+soq1769pVKOjSVfJSm6kXf+1fq3VuaRkBfpR/Kah0qHVZL5cLieioP+DnXr4KSD11UXeiz6sgQ1TqA/qteDFG1Q8ahTbUUk5tc+EefygN+zvXHlNutKGNPSNDcnbq0sec9lj7bfZhyuxIqmDbJDauliIh0RJvuyCUp/dBn1ZGhqnWUx9Jn1QurdohdwYmIdMTUR5o1laEsiIrCkhsiKpf0UXVhqJFmi6o6Ku1Is/psMEtkCExuiKjc0VdvJn0nH4aqOmK1Dpk6VksRUbmiz1F4zWmkWSJTxuSGVDp06IDx48ernvv5+SEqKqrQbWQyGbZu3VrqY+tqP0SF0ffkioBhko+yNIcPUVnEaikz0L17d+Tk5GDnzp35Xjtw4ADatWuHs2fPokmTJlrt98SJE6hQoYKuwgQAzJo1C1u3bs03y3ZiYiIqVaqk02MR5aXv3kxKhmi3wqojooIxuTEDw4YNQ58+fXDv3j1Uq1ZN7bVVq1ahZcuWWic2AODm5qarEIvk4eFhsGOVJdnZ2bCxsTF2GOWGvnsz5cbkg8h4WC1lBt5++224ublh9erVasvT09OxceNGDBs2DI8fP8bAgQPh7e0NBwcH+Pv7Izo6utD95q2WunbtGtq1awc7Ozs0bNgQsbGx+baZNGkS6tatCwcHB9SsWRPTp09HTk4OAGD16tWYPXs2zp49C5lMBplMpoo5b7XU+fPn0bFjR9jb26NKlSoYMWIE0tPTVa9HRkaiZ8+eWLhwITw9PVGlShWMHj1adSxNbty4gR49esDd3R2Ojo5o1aoVdu/erbZOVlYWJk2aBB8fH9ja2qJ27dpYsWKF6vWLFy/i7bffhpOTEypWrIjg4GDcuHEDQP5qPQDo2bMnIiMj1d7TuXPnIjw8HE5OThgxYkSR75vSb7/9hlatWsHOzg6urq7o1asXAGDOnDlo3LhxvvNt1qwZpk+fXuD7UdbJ5dKgddHR0s/SVBUp6bs3ExGVDSy5KYIQQGamcY7t4JC/UaImVlZWCA8Px+rVqzF16lTI/rfRxo0bIZfLMXDgQKSnpyMgIACTJk2Ck5MTduzYgcGDB6NWrVpo3bp1kcdQKBTo3bs33N3dcezYMaSkpOS7kQNAxYoVsXr1anh5eeH8+fMYPnw4KlasiE8//RRhYWG4cOECdu7cqUoqnJ2d8+0jIyMDoaGhCAoKwokTJ/Dw4UO8//77GDNmjFoCt3fvXnh6emLv3r24fv06wsLC0KxZMwwfPlzjOaSnp6Nr166YN28ebG1tsXbtWnTv3h3x8fGoXr06ACA8PBxHjhzBd999h6ZNm+LmzZtITk4GACQkJKBdu3bo0KED9uzZAycnJxw6dAgvX74s8v3LbeHChZgxYwZmzpxZrPcNAHbs2IFevXph6tSpWLt2LbKzs/HHH38AAIYOHYrZs2fjxIkTaNWqFQDg9OnTOHfuHGJK0zrWiEy1NxMRlRH6ncOz7NF2VvD09OLNlKuPR3p68c/r8uXLAoDYm2vK3uDgYPHee+8VuE23bt3EJ598onrevn17MW7cONVzX19f8c033wghhNi1a5ewsrISCQkJqtf//PNPAUBs2bKlwGMsWLBABAQEqJ7PnDlTNG3aNN96ufezfPlyUalSJZGe6w3YsWOHsLCwEElJSUIIISIiIoSvr694mWvK3H79+omwsLACY9GkUaNGYvHixUIIIeLj4wUAERsbq3HdKVOmiBo1aojs7GyNr+d9/4QQokePHiIiIkL13NfXV/Ts2bPIuPK+b0FBQWLQoEEFrt+lSxcxcuRI1fOxY8eKDh06aFy3rM8Kvnmz5pmdZTLpUdrZl5X7z3sMXe2fiPRDm1nBWS1lJurXr4+2bdti5cqVAIDr16/jwIEDGDZsGABALpdj7ty58Pf3R+XKleHo6Ihdu3bhzp07xdr/5cuX4ePjAy8vL9WyoKCgfOtt2LABr732Gjw8PODo6Ihp06YV+xi5j9W0aVO1xsyvvfYaFAoF4uPjVcsaNWoEy1wtND09PfHw4cMC95ueno6JEyeiQYMGcHFxgaOjIy5fvqyK78yZM7C0tET79u01bn/mzBkEBwfD2tpaq/PJq2XLlvmWFfW+nTlzBp06dSpwn8OHD0d0dDRevHiB7OxsrF+/HkOHDi1VnMZgLr2ZiMi4WC1VBAcHIFdTD4MfWxvDhg3D2LFjsWTJEqxatQq1atVS3agXLFiAb7/9FlFRUfD390eFChUwfvx4ZGdn6yzeI0eOYNCgQZg9ezZCQ0Ph7OyMX375BV9//bXOjpFb3iRDJpNBoVAUuP7EiRMRGxuLhQsXonbt2rC3t0ffvn1V74G9vX2hxyvqdQsLC4g8d2VNbYDy9kArzvtW1LG7d+8OW1tbbNmyBTY2NsjJyUHfvn0L3aYsMqfeTERkPExuiiCTATruDa03/fv3x7hx47B+/XqsXbsWI0eOVLW/OXToEHr06IH33nsPgNSG5urVq2jYsGGx9t2gQQPcvXsXiYmJ8Pxfa8ujR4+qrXP48GH4+vpi6tSpqmW3b99WW8fGxgbyIv7tbtCgAVavXo2MjAxVInDo0CFYWFigXr16xYpXk0OHDiEyMlLVEDc9PR23bt1Sve7v7w+FQoG///4bISEh+bZv0qQJ1qxZg5ycHI2lN25ubkjM1c1GLpfjwoULeKOIaZyL8741adIEcXFxGDJkiMZ9WFlZISIiAqtWrYKNjQ0GDBhQZEKkC7qewoC9mYhIF1gtZUYcHR0RFhaGKVOmIDExUa2XTp06dRAbG4vDhw/j8uXL+OCDD/DgwYNi7zskJAR169ZFREQEzp49iwMHDqjdjJXHuHPnDn755RfcuHED3333HbZs2aK2jp+fH27evIkzZ84gOTkZWVlZ+Y41aNAg2NnZISIiAhcuXMDevXsxduxYDB48GO7u7tq9KXnii4mJwZkzZ3D27Fm8++67aiU9fn5+iIiIwNChQ7F161bcvHkT+/btw6+//goAGDNmDFJTUzFgwAD8888/uHbtGn766SdVVVnHjh2xY8cO7NixA1euXMHIkSPx7NmzYsVV1Ps2c+ZMREdHY+bMmbh8+TLOnz+PL7/8Um2d999/H3v27MHOnTsNUiUVEwP4+QFvvAG8+67008+vdCP8sjcTEekCkxszM2zYMDx9+hShoaFq7WOmTZuGFi1aIDQ0FB06dICHhwd69uxZ7P1aWFhgy5YteP78OVq3bo33338f8+bNU1vnnXfewccff4wxY8agWbNmOHz4cL6uyH369EHnzp3xxhtvwM3NTWN3dAcHB+zatQtPnjxBq1at0LdvX3Tq1Anff/+9dm9GHosWLUKlSpXQtm1bdO/eHaGhoWjRooXaOkuXLkXfvn0xatQo1K9fH8OHD0dGRgYAoEqVKtizZw/S09PRvn17BAQE4Mcff1SV4gwdOhQREREIDw9H+/btUbNmzSJLbYDivW8dOnTAxo0bsX37djRr1gwdO3bE8ePH1dapU6cO2rZti/r16yMwMLA0b1WR9DWFgbI3U0G9BGUywMeHvZmIqHAykbeRgJlLTU2Fs7MzUlJS4OTkpPbaixcvcPPmTdSoUQN2dnZGipCoZIQQqFOnDkaNGoUJEyYUuF5pP+dyuVRCU1DbGGV36ps3S1ZFpUycAM0TQ7LRL1H5VNj9Oy+W3BCZgUePHuH7779HUlJSge1ydEWbRr8lwd5MRFRabFBMZAaqVq0KV1dXLF++XO9zdBmi0S97MxFRaTC5ITIDhqxdNlSjX/ZmIqKSYrUUEWmFjX6JqKxjcqNBOWtjTeVMaT/flpbSHE9A/gRH+TwqilVIRGQ8TG5yUXbpzTTWTJlEBqAckdmyFNkHG/0SUVnGNje5WFpawsXFRTU/kYODg2qEXyJzoFAo8OjRIzg4OMDKqnRffzb6JaKyislNHh4eHgBQ6ASMRKbMwsIC1atX10nizka/RFQWMbnJQyaTwdPTE1WrVtU46SGRqbOxsYGFBWukich8GT25WbJkCRYsWICkpCQ0bdoUixcvRuvWrTWum5OTg/nz52PNmjVISEhAvXr18OWXX6Jz5846j8vS0rJUbRKIiIjIOIz679uGDRswYcIEzJw5E6dOnULTpk0RGhpaYJXQtGnT8MMPP2Dx4sW4dOkSPvzwQ/Tq1QunT582cOREpkEuB/btA6KjpZ9FTMhORGQWjDq3VGBgIFq1aqWaEFGhUMDHxwdjx47F5MmT863v5eWFqVOnYvTo0aplffr0gb29PX7++ediHVObuSmITFlMDDBunPpUCdWqSd242ZuJiEyNScwtlZ2djZMnTyIkJORVMBYWCAkJwZEjRzRuk5WVlW+iP3t7exw8eFCvsRKZGn3N2k1EZAqMltwkJydDLpfD3d1dbbm7uzuSkpI0bhMaGopFixbh2rVrUCgUiI2NRUxMDBILmcQmKysLqampag8icyaXSyU2mspklcvGj2cVFRGZL5PqMvHtt9+iTp06qF+/PmxsbDBmzBgMGTKk0J4f8+fPh7Ozs+rh4+NjwIiJDE/fs3YTEZV1RktuXF1dYWlpiQcPHqgtf/DggWqsmbzc3NywdetWZGRk4Pbt27hy5QocHR1Rs2bNAo8zZcoUpKSkqB53797V6XkQlTWGmLWbiKgsM1pyY2Njg4CAAMTFxamWKRQKxMXFISgoqNBt7ezs4O3tjZcvX2Lz5s3o0aNHgeva2trCyclJ7UFkzgw1azcRUVll1HFuJkyYgIiICLRs2RKtW7dGVFQUMjIyMGTIEABAeHg4vL29MX/+fADAsWPHkJCQgGbNmiEhIQGzZs2CQqHAp59+aszTICpTlLN2JyRobncjk0mvc9ZuIjJXRk1uwsLC8OjRI8yYMQNJSUlo1qwZdu7cqWpkfOfOHbX2NC9evMC0adPw77//wtHREV27dsVPP/0EFxcXI50BUdmjnLW7b18pkcmd4HDWbiIqD4w6zo0xcJwbKi80jXPj4yMlNhznhohMjTb3b6NPv0BE+sFZu4movGJyQ2TGOGs3EZVHTG6IjEguZ8kKEZGuMbkhMhLO/UREpB8mNUIxkbng3E9ERPrD5IbIwDj3ExGRfjG5ITIwzv1ERKRfTG6IDIxzPxER6ReTGyID49xPRET6xeSGyMCUcz8pp0LISyaTRhLm3E9ERCXD5IbIwJRzPwH5ExzO/UREVHpMboiMoHdvYNMmwNtbfXm1atJyjnNDRFRyHMSPyEg49xMRkX4wuSEyIs79RESke6yWIiIiIrPC5IaIiIjMCquliArBWbuJiEwPkxuiAnDWbiIi08RqKSINOGs3EZHpYnJDlAdn7SYiMm1Mbojy4KzdRESmjckNUR6ctZuIyLQxuSHKg7N2ExGZNiY3RHlw1m4iItPG5IYoD87aTURk2pjcEGnAWbuJiEwXB/EjKgBn7SYiMk1MbogKwVm7iYhMD6uliIiIyKwwuSEiIiKzwuSGiIiIzAqTGyIiIjIrTG6IiIjIrDC5ISIiIrPC5IaIiIjMCpMbIiIiMitMboiIiMisMLkhIiIis8LkhoiIiMwK55YikyeXc3JLIiJ6hckNmbSYGGDcOODevVfLqlUDvv1WmtWbiIjKH1ZLkcmKiQH69lVPbAAgIUFaHhNjnLiIiMi4mNyQSZLLpRIbIfK/plw2fry0HhERlS9MbsgkHTiQv8QmNyGAu3el9YiIqHxhckMmKTFRt+sREZH5YHJDJsnTU7frERGR+WByQyYpOFjqFSWTaX5dJgN8fKT1iIiofGFyQybJ0lLq7g3kT3CUz6OiON4NEVF5xOSGTFbv3sCmTYC3t/ryatWk5RznhoiofOIgfmTSevcGevTgCMVEhZHLgaws4MWLVz9z/65p2YsXgIMD0LMn4Oho7DMg0g6TGzJ5lpZAhw7GjoJIvxQK4P59ID4euHpVety4AaSnF52wvHxZ8uO6uQGTJgEjR0rJDpEpkAmhaRg085WamgpnZ2ekpKTAycnJ2OEQEal59kw9gVH+fu0akJlZ+v1bWAB2dtLD1lb9Z97fz58H/v1X2s7dHZgyBfjgA+k1Ik2EALZvlz4vbdrodt/a3L+Z3BARGdiLF1KpS94E5upV4NGjgrezsgJq1gTq1gXq1QNq1wacnTUnJgX9bqVFef3Ll8BPPwFz5gC3bknLvLyAzz4D3n9f2i+RUlyc9Nk4fhwICgIOHSq4R2tJMLkpBJMbItORkQE8fCh169fmplwWCCGNon3lSv4E5tYtzVOHKHl5SclL3bqvHvXqAX5+gLW1oc7glexsYPVq4P/9P2nkb0C6JtOmAZGRgI2N4WOisuPoUWDqVGDPHum5g4M0Pc6sWbr9bDC5KQSTGyprzpwBjh0DatSQbmI+PuWrQfTLl9LNPm8CcPXqqyk2bG2Bhg2BJk3UH1WrGjV0lfR04MIFqRrn3LlXj2fPCt7GyUlzAlOnTtltwJuVBaxYAcybJ7X/AaSEa/p0YPBg4yReZDznz0sJ7vbt0nNra+DDD6XSGw8P3R+PyU0hmNxQWZGUJP0RWLVKfbmtrXSDy3vTq1sXqFJFt8W8hiIE8OBB/gQmPl5q05GTU/C21tYFv+7uDvj7qyc8DRror02IQiHFmzuBOXdOqmLSxMpKqjpSXr/cyUzVqqZ5LQGpWm35cmD+fOlzDAC1agEzZgDvvmt6pWyAlLg9ewakpEiPon7PzJTOWfm58/eXEtby4Pp1YOZMIDpa+m5bWAAREdIyX1/9HZfJTSGY3JCxZWcD330ntWNIS5OWtWsntbW4fr3wG32lSpr/269du2z0ZElLUy95yZ3MKM9VE3t7zclcnTqAiwtw+3b+hOLaNc1VO5aW0vZ5S3kKG9Fak6dP85fEnD9fcKNeT8/8x6xf37yrbDIzgWXLgC++eNVWqG5d6SYXFma8EsjkZODkSSmh1pSYaFqWlVX64/r55f8M1K5tPiWx9+4Bc+dKpXdyubSsXz/pb1n9+vo/PpObQjC5IWP6809g/HjpZg8ArVpJiY6yV8HLl8CdO/mrZ+LjX7V1KIiPj+bEx9dX/Y+rXK55TJOixjwp6PWMDODmTSnOwiYqtbB4VfWWtxTD21t6XRsZGcClS+rJx9mzUlKiibNz/htP48ZSSdnVq/mTp4JmnbezAxo1Ut+Pv7/UZbq8ysgAliwBvvoKePxYWtawodTmok8f7a+ttsc+dUpqxHrihPTz5s2S78/JSfqsKB8uLpp/t7WVvpfKz0tCgub92dlJn7O8JYyuriWP0dAePZIS2CVLXiWBXbtKbbCaNzdcHExuCsHkhozh2jXg44+BHTuk5+7uUpF+RETx//BnZkolO5oSn4Ju6IBUclCx4quERPkfl764u2tOYGrW1H/vGiGktiB5E5UrVwoe66Wwai9fX83/iZtitYshpKUBixcDCxe++kz6+wOzZ0uDAZa2Gi4nR2rblDuRuXhRqi7Mq25dKZkuKknJ/bxixZKXsjx+LJXs5S7tO38eeP5c8/qmUNKXkgIsWiQ90tOlZcHBwOefA6+/bvh4mNwUgskNGVJqqvTfTVSU9IfZykoquZk+XXf180JIf1g1VQVdu1Z4cbulpXbdiAv6vXr1V0mMs7NuzkuXsrKkBCf3TefcuVclTY6O+f+z9vcvm+diClJSpM/8okXSdwCQ/sOfPRt4++3iJTkKhZTMK5OYEyeA06elBD0vb2+pFLR1a+lny5ZSwmJscrnmNlrKsYPysrKSEhzlZ7BFC+l9M3QpT2amVErzxRfAkyfSshYtpKTmrbeM11aMyU0hmNyQISgU0vggkye/anDZpQvwzTdSaYYh47hzR/pjpWngtvJeAvHokVStUb26fqtOyqunT6UEJyrq1X/+LVtKbTQ6d1a/Sd6/r57InDihubeZs/OrREaZzHh5GeJsdCctTSqByp1onzsnJYWaVK/+KtFp0UJ6eHrqPsnIzpba08yd+yrxr19f+getd2/jN4BnclMIJjekb8ePA2PHSj8BqVHsN98A3boZNy4iY0lOBr7+WmpfpmyQ3aaN1G7j9Gnpu6KpzYqtrXRDz53I1K5tnomoEFK7OmWic+aM9N5cv655fXf3/AmPn1/JEhC5HFi/XmoIrmyv5OsrlbQNGlR2/gkyqeRmyZIlWLBgAZKSktC0aVMsXrwYrVu3LnD9qKgoLF26FHfu3IGrqyv69u2L+fPnw66YfT+Z3JC+JCVJw9OvXi09d3SUusaOG1e26tGJjOXhQ2DBAqnKI29bFAsLqRGyMolp3VpqiFvevzspKVKic+qUlOycOgVcvqy5nZGLS/6Ep06dgtsRCQFs3SqNVXPpkrTM3V2qNi+LI1CbTHKzYcMGhIeHY9myZQgMDERUVBQ2btyI+Ph4VNUwOtf69esxdOhQrFy5Em3btsXVq1cRGRmJAQMGYNGiRcU6JpMbw5PLzXvW7uxs4NtvpaJcZXfniAipwbCnp3FjIyqLkpKkqqo7d6QbcOvW0s+yOnhhWZOZKZXuKJOdU6ek6i1NDeMrVACaNn2V7LRoISWR+/ZJ42z984+0XqVK0gSpY8ZI25RFJpPcBAYGolWrVvj+++8BAAqFAj4+Phg7diwmT56cb/0xY8bg8uXLiIuLUy375JNPcOzYMRw8eLBYx2RyY1gxMVLJRe5utdWqSclA797Gi0tX/vhDaiB87Zr0vHVrqeg9MNCoYRFROZOdLZW+KJOdU6ekoRE0jctkZfWq92CFClJPzk8+KRuNsAujzf3baDWX2dnZOHnyJEJCQl4FY2GBkJAQHDlyROM2bdu2xcmTJ3H8f40Z/v33X/zxxx/o2rVrgcfJyspCamqq2oMMIyYG6Ns3/3ghCQnS8pgY48SlC1evSm1ounWTEht3d6k66sgRJjZEZHg2NkCzZsDQocD33wOHD0s91S5dAn7+GZgwAXjjDalB9suX0vrjx0s9t+bOLfuJjbaM1kwoOTkZcrkc7u7uasvd3d1x5coVjdu8++67SE5Oxuuvvw4hBF6+fIkPP/wQn332WYHHmT9/PmbPnq3T2KlocrlUYqOpXFAIqdHb+PFAjx6mVUWVmir9Ifj2W6kI2NpaOo9p08rP0OtEZBosLaXpSBo0kBoGA9Lf31u3pCSncmWjhqdXJtXmfN++ffj888/x3//+F6dOnUJMTAx27NiBuXPnFrjNlClTkJKSonrcLWqYV9KJAwcKHuEVeNUz4MABw8VUGgqFVDJTt640QFlOjlRqc+GCNCorExsiMgUymTS4oTknNoARS25cXV1haWmJBw8eqC1/8OABPAqYTnT69OkYPHgw3n//fQCAv78/MjIyMGLECEydOhUWGvoH2trawrasNfkuBwobhr8k6xnTlStSA2Fl1+66daWu3YXUhhIRkREZreTGxsYGAQEBao2DFQoF4uLiEBQUpHGbzMzMfAmM5f/qNMrZcD1lXnF7CZX13kQnT0rDjB8/Lg3NvmCB1CuBiQ0RUdll1KF5JkyYgIiICLRs2RKtW7dGVFQUMjIyMGTIEABAeHg4vL29MX/+fABA9+7dsWjRIjRv3hyBgYG4fv06pk+fju7du6uSHCobgoOlXlEJCZrb3chk0uvBwYaPrbgOHJCqntLSpHE3tm0r+8kYEREZObkJCwvDo0ePMGPGDCQlJaFZs2bYuXOnqpHxnTt31Epqpk2bBplMhmnTpiEhIQFubm7o3r075s2bZ6xToAJYWkqNbvv2lRKZ3AmOcgTNqKiy25h4506pq/rz50D79sBvv0klN0REVPYZfYRiQ+M4N4alaZwbHx8psSmr49xs2gS8++6rRsMbNwL29saOioiofNPm/l1GZowgc9W7t9Td21RGKF61Shp2XKEAwsKkyS+trY0dFRERaYPJDemdpSXQoYOxoyjat99KY9YAUoKzbFnZTcKIiKhgJjXODZE+CCENzKdMbD75BFi+nIkNEZGpYskNlWtCAP/5D/D119LzOXOk0YaVjZ6JiMj0MLmhcksuB0aOBH78UXoeFSU1fiYiItPG5IbKpZwcYPBgYMMGwMJCSnCGDjV2VEREpAtMbqjcef4c6NcP2LFD6gm1bp30nIiIzAOTGypX0tKAd94B9u2Txq6JiQE6dzZ2VEREpEtMbqjcePwY6NIFOHFCGm14x46yPf0DERGVDJMbKhcSE4G33gIuXACqVJGmV2jZ0thRERGRPjC5IbN36xYQEgLcuCGNkLx7N9CwobGjIiIifeEgfmTWrlyRqp5u3ABq1AAOHmRiQ0Rk7pjckNk6fRpo106atLNBA2l+q5o1jR0VERHpG5MbMkuHDgFvvAE8egQEBAD79wPe3saOioiIDIHJDZmd2Fip8XBKilQlFRcHuLoaOyoiIjIUJjdkVrZsAd5+G8jMlMav2bkTcHY2dlRERGRITG7IbKxdK400nJ0N9O0LbNsGODgYOyoiIjI0rbuC+/n5YejQoYiMjET16tX1EROZoaQk4OJF/e3/xAlgyhTp9yFDgOXLASsOdEBEVC5p/ed//PjxWL16NebMmYM33ngDw4YNQ69evWBra6uP+MgMbNggJRzPn+v/WOPGAYsWSZNhEhFR+SQTQoiSbHjq1CmsXr0a0dHRkMvlePfddzF06FC0aNFC1zHqVGpqKpydnZGSkgInJydjh2PW5HJg6lTgyy+l535+0rQH+mBpKc3y/fHHgEymn2MQEZHxaHP/LnFyo5STk4P//ve/mDRpEnJycuDv74+PPvoIQ4YMgawM3mWY3BjG06fAu+9KDXoBYNIkYN48KQkhIiLSljb37xK3SsjJycGWLVuwatUqxMbGok2bNhg2bBju3buHzz77DLt378b69etLunsyYZcuAT16ANevSzNvr1wJDBhg7KiIiKi80Dq5OXXqFFatWoXo6GhYWFggPDwc33zzDerXr69ap1evXmjVqpVOAyX9kcul0XsTE6W5l4KDS17Csm0b8N57QHo6UL06sHUr0Ly5TsMlIiIqlNbJTatWrfDmm29i6dKl6NmzJ6ytrfOtU6NGDQzgv+omISZGaoR7796rZdWqAd9+C/TuXfz9KBTA3LnArFnS8w4dgF9/BdzcdBktERFR0bRuc3P79m34+vrqKx69Y5ubV2JipPFg8n4ClE2lNm0qXoKTlgaEh0ulNAAwdizw9deAhryXiIioRLS5f2vdYfbhw4c4duxYvuXHjh3DP//8o+3uyEjkcqnERlNqq1w2fry0XmGuXwfatJESGxsbqX3Nd98xsSEiIuPROrkZPXo07t69m295QkICRo8erZOgSP8OHFCvispLCODuXWm9guzaBbRqJTUg9vQE/v5bGs+GiIjImLRObi5duqRxLJvmzZvj0qVLOgmK9C8xseTrCQEsWAB07Qo8eyaV3Jw8Kf0kIiIyNq2TG1tbWzx48CDf8sTERFhxvHuT4elZsvUyM4FBg4BPP5UaEQ8bBuzbV/z9ERER6ZvWyc1bb72FKVOmICUlRbXs2bNn+Oyzz/Dmm2/qNDjSn+BgqVdUQeMsymSAj4+0ntLt28DrrwPR0dK8TUuWAD/+CHDmDSIiKku0LmpZuHAh2rVrB19fXzT/3wAmZ86cgbu7O3766SedB0j6YWkpdffu21dKZHI3LFYmPFFRr8a7+ftvad3kZKl798aNQPv2Bg+biIioSCWafiEjIwPr1q3D2bNnYW9vjyZNmmDgwIEax7wpa9gVXJ2mcW58fKTEpndvKen573+lnlMvX0oD8m3dKg3QR0REZCgGnVvK1DC5ya+gEYqzsoBRo6Tu3QAwcCDwf/8HODgYN14iIip/DDK31KVLl3Dnzh1kZ2erLX/nnXdKuksyEktLaUTh3O7fB/r0AY4eBSwspJm9P/mEM24TEVHZp3Vy8++//6JXr144f/48ZDIZlAU/yhnA5UWN+kZl3tGjUpVUYiLg4gL88gsQGmrsqIiIiIpH695S48aNQ40aNfDw4UM4ODjg4sWL2L9/P1q2bIl9+/bpIUQypJUrpYbCiYlAo0bAiRNMbIiIyLRondwcOXIEc+bMgaurKywsLGBhYYHXX38d8+fPx0cffaSPGMkAcnKAMWOkcWuys4FevYAjR4DatY0dGRERkXa0Tm7kcjkqVqwIAHB1dcX9+/cBAL6+voiPj9dtdGQQWVlAjx7SuDUAMHu2NGnm/y4zERGRSdG6zU3jxo1x9uxZ1KhRA4GBgfjqq69gY2OD5cuXo2bNmvqIkfQoJwfo3x/480/A3l4aoK9HD2NHRUREVHJaJzfTpk1DRkYGAGDOnDl4++23ERwcjCpVqmDDhg06D5D05+VL4N13ge3bpVGGf/sN6NTJ2FERERGVjk7GuXny5AkqVaqk6jFVlnGcG4lcDoSHA+vXAzY20sB8XboYOyoiIiLNtLl/a9XmJicnB1ZWVrhw4YLa8sqVK5tEYkMShQIYMUJKbKysgF9/ZWJDRETmQ6vkxtraGtWrV+dYNiZMCKlX1MqV0uB869ezjQ0REZkXrXtLTZ06FZ999hmePHmij3hIj4QAJkwAli6VRhpeswbo18/YUREREemW1g2Kv//+e1y/fh1eXl7w9fVFhQoV1F4/deqUzoIj3REC+OwzaUJMQJoj6r33jBoSERGRXmid3PTs2VMPYZC+zZkDfPGF9Pt//wsMHWrceIiIiPSFs4KXA198AUyZIv2+aBHw8cfGjYeIiEhbeustRaYnKupVYjN/PhMbIiIyf1pXS1lYWBTa7Zs9qcqOpUtfJTMzZwKTJxs3HiIiIkPQOrnZsmWL2vOcnBycPn0aa9aswezZs3UWGJXOypXAqFHS75MmSckNERFReaCzNjfr16/Hhg0bsG3bNl3sTm/KQ5ub9eulnlBCAOPHS+1sOMYiERGZMqO0uWnTpg3i4uJ0tTsqoU2bpGkVhABGjmRiQ0RE5Y9Okpvnz5/ju+++g7e3ty52RyW0fTswcKA0b9SQIcD33zOxISKi8kfrNjd5J8gUQiAtLQ0ODg74+eefdRocFd/OndJow8qZvn/8UZpegYiIqLzROrn55ptv1JIbCwsLuLm5ITAwEJUqVdJpcFQ8e/YAvXoB2dlA377StAqWlsaOioiIyDi0Tm4iIyP1EAaV1MGDQPfuwIsXwDvvvJrpm4iIqLzSuuJi1apV2LhxY77lGzduxJo1a3QSFBXPsWNA165AZibQuTPw66+AtbWxoyIiIjIurZOb+fPnw9XVNd/yqlWr4vPPP9dJUFS0U6eA0FAgLQ144w0gJgawtTV2VERERMandXJz584d1KhRI99yX19f3LlzRydBUeHOnwfefBNISQFefx347TfA3t7YUREREZUNWic3VatWxblz5/ItP3v2LKpUqaKToKhgly8DnToBT54AgYHAjh1AhQrGjoqIiKjs0Dq5GThwID766CPs3bsXcrkccrkce/bswbhx4zBgwAB9xEj/c/26lNg8egS0aCF1/zbTQZaJiIhKTOt+NXPnzsWtW7fQqVMnWP2vW45CoUB4eDjb3OjRvXtAx45AYiLg7w/89Rfg4mLsqIiIiMoerUtubGxssGHDBsTHx2PdunWIiYnBjRs3sHLlStjY2JQoiCVLlsDPzw92dnYIDAzE8ePHC1y3Q4cOkMlk+R7dunUr0bFNxZdfAnfvAvXrA7GxAGsAiYiINCvxiCh16tRBnTp1Sh3Ahg0bMGHCBCxbtgyBgYGIiopCaGgo4uPjUbVq1Xzrx8TEIDs7W/X88ePHaNq0Kfr161fqWMoquVyaMwqQ5opydzduPERERGWZ1iU3ffr0wZdffplv+VdffVWiBGPRokUYPnw4hgwZgoYNG2LZsmVwcHDAypUrNa5fuXJleHh4qB6xsbFwcHAw6+Rm/34gKQmoVElqc0NEREQF0zq52b9/P7p27ZpveZcuXbB//36t9pWdnY2TJ08iJCTkVUAWFggJCcGRI0eKtY8VK1ZgwIABqFBAl6GsrCykpqaqPUzNhg3Sz969gRLW/BEREZUbWic36enpGtvWWFtba504JCcnQy6Xwz1PPYu7uzuSkpKK3P748eO4cOEC3n///QLXmT9/PpydnVUPHx8frWI0tpcvgc2bpd/DwowbCxERkSnQOrnx9/fHBmVRQi6//PILGjZsqJOgimvFihXw9/dH69atC1xnypQpSElJUT3u3r1rwAhLb88eIDkZcHOTRiImIiKiwmndoHj69Ono3bs3bty4gY4dOwIA4uLisH79emxStnotJldXV1haWuLBgwdqyx88eAAPD49Ct83IyMAvv/yCOXPmFLqera0tbE14XoJff5V+9unDCTGJiIiKQ+uSm+7du2Pr1q24fv06Ro0ahU8++QQJCQnYs2cPateurdW+bGxsEBAQgLi4ONUyhUKBuLg4BAUFFbrtxo0bkZWVhffee0/bUzAZ2dnSnFEAq6SIiIiKq0RlAd26dVONK5Oamoro6GhMnDgRJ0+ehFwu12pfEyZMQEREBFq2bInWrVsjKioKGRkZGDJkCAAgPDwc3t7emD9/vtp2K1asQM+ePc16yofdu4GnTwEPDyA42NjREBERmYYSV3Ts378fK1aswObNm+Hl5YXevXtjyZIlWu8nLCwMjx49wowZM5CUlIRmzZph586dqkbGd+7cgYWFegFTfHw8Dh48iL/++quk4ZsEZdMmZS/3ffukEYo9PaVkx9LSaKERERGVWTIhhCjuyklJSVi9ejVWrFiB1NRU9O/fH8uWLcPZs2cN3pi4pFJTU+Hs7IyUlBQ4leGJmV68kAbrS00F5s0Dli6VpmBQqlYN+PZbqXs4ERGRudPm/l3sNjfdu3dHvXr1cO7cOURFReH+/ftYvHhxqYMlzXbtkhKbKlWAqVPVExsASEgA+vZ91SaHiIiIJMVObv78808MGzYMs2fPRrdu3WDJOhG9UlZJZWVpfl1Z3jZ+vDQ9AxEREUmKndwcPHgQaWlpCAgIQGBgIL7//nskJyfrM7ZyKzMT2L5d+j09veD1hJAm0zxwwDBxERERmYJiJzdt2rTBjz/+iMTERHzwwQf45Zdf4OXlBYVCgdjYWKSlpekzznLljz+AjAxp4L7iSEzUbzxERESmROtxbipUqIChQ4fi4MGDOH/+PD755BN88cUXqFq1Kt555x19xFjuKKukijtJpqen/mIhIiIyNVonN7nVq1cPX331Fe7du4fo6GhdxVSupacDO3ZIv0+YIPWKksk0ryuTAT4+HAOHiIgot1IlN0qWlpbo2bMntisbilCJ/fYb8Pw5ULs20LKl1N0byJ/gKJ9HRXG8GyIiotx0ktyQ7ijnkgoLkxKY3r2BTZsAb2/19apVk5ZznBsiIiJ1Wg3iZw7K8iB+qalA1apS9+9z5wB//1evyeVSryiOUExEROWRNvdvzjNdhmzbJiU2DRoAjRurv2ZpCXToYJSwiIiITAqrpcoQZS8pZZUUERERaY/JTRnx9CmgnAc0LMy4sRAREZkyJjdlxJYtQE4O0KQJUL++saMhIiIyXUxuyghllVT//saNg4iIyNQxuSkDHj0C4uKk31klRUREVDpMbsqAmBipq3eLFtLgfURERFRyTG7KgNy9pIiIiKh0mNwYWVIS8Pff0u9sb0NERFR6TG6MbNMmQKEAAgMBPz9jR0NERGT6mNwYWe65pIiIiKj0mNwYUUICcPCg9Hu/fsaNhYiIyFwwuTGijRsBIYDXX5dm+SYiIqLSY3JjROwlRUREpHtMbozk9m3g6FFpgsy+fY0dDRERkflgcmMkyobE7dsDHh7GjYWIiMicMLkxElZJERER6QeTGyO4fh04eRKwtAT69DF2NEREROaFyY0RKKukOnYE3NyMGwsREZG5YXJjBKySIiIi0h8mNwZ25Qpw7hxgZQX06mXsaIiIiMwPkxsDU5bavPUWULmycWMhIiIyR0xuDEgIVkkRERHpG5MbA7p4Ebh8GbCxAXr0MHY0RERE5onJjQEpS226dAGcnY0bCxERkblicmMgrJIiIiIyDCY3BnLmDHDtGmBnB7z9trGjISIiMl9MbgxEWWrTrRtQsaJxYyEiIjJnTG4MgFVSREREhsPkxgBOnABu3QIqVJBKboiIiEh/mNwYgLLUpnt3wMHBuLEQERGZOyY3eqZQvJook1VSRERE+sfkRs+OHAHu3QOcnIDOnY0dDRERkfljcqNnyiqpHj2kbuBERESkX0xu9EguBzZulH5nlRQREZFhMLnRo4MHgaQkoFIl4M03jR0NERFR+cDkRo+UVVK9ekmTZRIREZH+MbnRk5cvgU2bpN/79zduLEREROUJkxs92bcPePQIqFIF6NjR2NEQERGVH0xu9ERZJdWnD2BtbdxYiIiIyhMmN3qQkwPExEi/s5cUERGRYTG50YPdu4EnTwB3d6B9e2NHQ0REVL4wudEDZZVU376ApaVxYyEiIipvmNzoWFYWsHWr9DurpIiIiAyPyY2O7doFpKQA3t7Aa68ZOxoiIqLyh8mNjimrpPr1Ayz47hIRERkcb7869Pw5sH279DurpIiIiIyDyY0O/fknkJ4OVK8OBAYaOxoiIqLyicmNDimrpPr3B2Qy48ZCRERUXjG50ZGMDOD336XfWSVFRERkPExudGTHDiAzE6hZEwgIMHY0RERE5ZeVsQMwF++8A2zbBrx4wSopIiIiYzJ6yc2SJUvg5+cHOzs7BAYG4vjx44Wu/+zZM4wePRqenp6wtbVF3bp18ccffxgo2oLZ2UkJTv/+xo6EiIiofDNqyc2GDRswYcIELFu2DIGBgYiKikJoaCji4+NRtWrVfOtnZ2fjzTffRNWqVbFp0yZ4e3vj9u3bcHFxMXzwREREVCbJhBDCWAcPDAxEq1at8P333wMAFAoFfHx8MHbsWEyePDnf+suWLcOCBQtw5coVWFtbl+iYqampcHZ2RkpKCpycnEoVPxERERmGNvdvo1VLZWdn4+TJkwgJCXkVjIUFQkJCcOTIEY3bbN++HUFBQRg9ejTc3d3RuHFjfP7555DL5YYKm4iIiMo4o1VLJScnQy6Xw93dXW25u7s7rly5onGbf//9F3v27MGgQYPwxx9/4Pr16xg1ahRycnIwc+ZMjdtkZWUhKytL9Tw1NVV3J0FERERljtEbFGtDoVCgatWqWL58OQICAhAWFoapU6di2bJlBW4zf/58ODs7qx4+Pj4GjJiIiIgMzWjJjaurKywtLfHgwQO15Q8ePICHh4fGbTw9PVG3bl1YWlqqljVo0ABJSUnIzs7WuM2UKVOQkpKiety9e1d3J0FERERljtGSGxsbGwQEBCAuLk61TKFQIC4uDkFBQRq3ee2113D9+nUoFArVsqtXr8LT0xM2NjYat7G1tYWTk5Pag4iIiMyXUaulJkyYgB9//BFr1qzB5cuXMXLkSGRkZGDIkCEAgPDwcEyZMkW1/siRI/HkyROMGzcOV69exY4dO/D5559j9OjRxjoFIiIiKmOMOs5NWFgYHj16hBkzZiApKQnNmjXDzp07VY2M79y5AwuLV/mXj48Pdu3ahY8//hhNmjSBt7c3xo0bh0mTJhnrFIiIiKiMMeo4N8bAcW6IiIhMj0mMc0NERESkD0xuiIiIyKwwuSEiIiKzwuSGiIiIzAqTGyIiIjIrTG6IiIjIrDC5ISIiIrPC5IaIiIjMCpMbIiIiMitMboiIiMisMLkhIiIis8LkhoiIiMwKkxsiIiIyK0xuiIiIyKwwuSEiIiKzwuSGiIiIzAqTGyIiIjIrTG6IiIjIrDC5ISIiIrPC5IaIiIjMCpMbIiIiMitMboiIiMisMLkhIiIis8LkhoiIiMwKkxsiIiIyK0xuiIiIyKwwuSEiIiKzwuSGiIiIzAqTGyIiIjIrTG6IiIjIrDC5ISIiIrPC5IaIiIjMCpMbIiIiMitMboiIiMisMLkhIiIis8LkhoiIiMwKkxsiIiIyK0xuiIiIyKwwuSEiIiKzwuSGiIiIzAqTGyIiIjIrTG6IiIjIrDC5ISIiIrPC5IaIiIjMCpMbIiIiMitMboiIiMisMLkhIiIis8LkhoiIiMwKkxsiIiIyK0xuiIiIyKwwuSEiIiKzwuSGiIiIzAqTGyIiIjIrTG6IiIjIrDC5ISIiIrPC5IaIiIjMCpMbIiIiMitMboiIiMisMLkhIiIis8LkhoiIiMwKkxsiIiIyK0xuiIiIyKyUieRmyZIl8PPzg52dHQIDA3H8+PEC1129ejVkMpnaw87OzoDREhERUVlm9ORmw4YNmDBhAmbOnIlTp06hadOmCA0NxcOHDwvcxsnJCYmJiarH7du3DRgxERERlWVGT24WLVqE4cOHY8iQIWjYsCGWLVsGBwcHrFy5ssBtZDIZPDw8VA93d3cDRqyZXA7s2wdER0s/5XJjR0RERFQ+GTW5yc7OxsmTJxESEqJaZmFhgZCQEBw5cqTA7dLT0+Hr6wsfHx/06NEDFy9eLHDdrKwspKamqj10LSYG8PMD3ngDePdd6aefn7SciIiIDMuoyU1ycjLkcnm+khd3d3ckJSVp3KZevXpYuXIltm3bhp9//hkKhQJt27bFvXv3NK4/f/58ODs7qx4+Pj46PYeYGKBvXyDv4RMSpOVMcIiIiAzL6NVS2goKCkJ4eDiaNWuG9u3bIyYmBm5ubvjhhx80rj9lyhSkpKSoHnfv3tVZLHI5MG4cIET+15TLxo9nFRUREZEhWRnz4K6urrC0tMSDBw/Ulj948AAeHh7F2oe1tTWaN2+O69eva3zd1tYWtra2pY5VkwMH8pfY5CYEcPeutF6HDnoJgYiIiPIwasmNjY0NAgICEBcXp1qmUCgQFxeHoKCgYu1DLpfj/Pnz8PT01FeYBUpM1O16REREVHpGLbkBgAkTJiAiIgItW7ZE69atERUVhYyMDAwZMgQAEB4eDm9vb8yfPx8AMGfOHLRp0wa1a9fGs2fPsGDBAty+fRvvv/++wWMvbj5lhLyLiIio3DJ6chMWFoZHjx5hxowZSEpKQrNmzbBz505VI+M7d+7AwuJVAdPTp08xfPhwJCUloVKlSggICMDhw4fRsGFDg8ceHAxUqyY1HtbU7kYmk14PDjZ4aEREROWWTAhNt2XzlZqaCmdnZ6SkpMDJyanU+1P2lgLUExyZTPq5aRPQu3epD0NERFSuaXP/NrneUmVN795SAuPtrb68WjUmNkRERMZg9Gopc9C7N9Cjh9QrKjFRamMTHAxYWho7MiIiovKHyY2OWFqyuzcREVFZwGopIiIiMitMboiIiMisMLkhIiIis8LkhoiIiMwKkxsiIiIyK0xuiIiIyKwwuSEiIiKzwuSGiIiIzAqTGyIiIjIr5W6EYuU8oampqUaOhIiIiIpLed8uznzf5S65SUtLAwD4+PgYORIiIiLSVlpaGpydnQtdRyaKkwKZEYVCgfv376NixYqQyWTGDkdvUlNT4ePjg7t37xY5Nbw5KE/ny3M1X+XpfHmu5ktf5yuEQFpaGry8vGBhUXirmnJXcmNhYYFq1aoZOwyDcXJyKhdfJqXydL48V/NVns6X52q+9HG+RZXYKLFBMREREZkVJjdERERkVpjcmClbW1vMnDkTtra2xg7FIMrT+fJczVd5Ol+eq/kqC+db7hoUExERkXljyQ0RERGZFSY3REREZFaY3BAREZFZYXJDREREZoXJjQmaP38+WrVqhYoVK6Jq1aro2bMn4uPjC91m9erVkMlkag87OzsDRVw6s2bNyhd7/fr1C91m48aNqF+/Puzs7ODv748//vjDQNGWjp+fX75zlclkGD16tMb1Te267t+/H927d4eXlxdkMhm2bt2q9roQAjNmzICnpyfs7e0REhKCa9euFbnfJUuWwM/PD3Z2dggMDMTx48f1dAbFV9i55uTkYNKkSfD390eFChXg5eWF8PBw3L9/v9B9luS7YAhFXdfIyMh8cXfu3LnI/ZbF6woUfb6avsMymQwLFiwocJ9l8doW517z4sULjB49GlWqVIGjoyP69OmDBw8eFLrfkn7PtcHkxgT9/fffGD16NI4ePYrY2Fjk5OTgrbfeQkZGRqHbOTk5ITExUfW4ffu2gSIuvUaNGqnFfvDgwQLXPXz4MAYOHIhhw4bh9OnT6NmzJ3r27IkLFy4YMOKSOXHihNp5xsbGAgD69etX4DamdF0zMjLQtGlTLFmyROPrX331Fb777jssW7YMx44dQ4UKFRAaGooXL14UuM8NGzZgwoQJmDlzJk6dOoWmTZsiNDQUDx8+1NdpFEth55qZmYlTp05h+vTpOHXqFGJiYhAfH4933nmnyP1q810wlKKuKwB07txZLe7o6OhC91lWrytQ9PnmPs/ExESsXLkSMpkMffr0KXS/Ze3aFude8/HHH+O3337Dxo0b8ffff+P+/fvo3bt3ofstyfdca4JM3sOHDwUA8ffffxe4zqpVq4Szs7PhgtKhmTNniqZNmxZ7/f79+4tu3bqpLQsMDBQffPCBjiPTv3HjxolatWoJhUKh8XVTvq4AxJYtW1TPFQqF8PDwEAsWLFAte/bsmbC1tRXR0dEF7qd169Zi9OjRqudyuVx4eXmJ+fPn6yXuksh7rpocP35cABC3b98ucB1tvwvGoOlcIyIiRI8ePbTajylcVyGKd2179OghOnbsWOg6pnBt895rnj17JqytrcXGjRtV61y+fFkAEEeOHNG4j5J+z7XFkhszkJKSAgCoXLlyoeulp6fD19cXPj4+6NGjBy5evGiI8HTi2rVr8PLyQs2aNTFo0CDcuXOnwHWPHDmCkJAQtWWhoaE4cuSIvsPUqezsbPz8888YOnRooZO8mvJ1ze3mzZtISkpSu3bOzs4IDAws8NplZ2fj5MmTattYWFggJCTE5K53SkoKZDIZXFxcCl1Pm+9CWbJv3z5UrVoV9erVw8iRI/H48eMC1zWn6/rgwQPs2LEDw4YNK3Ldsn5t895rTp48iZycHLXrVL9+fVSvXr3A61SS73lJMLkxcQqFAuPHj8drr72Gxo0bF7hevXr1sHLlSmzbtg0///wzFAoF2rZti3v37hkw2pIJDAzE6tWrsXPnTixduhQ3b95EcHAw0tLSNK6flJQEd3d3tWXu7u5ISkoyRLg6s3XrVjx79gyRkZEFrmPK1zUv5fXR5tolJydDLpeb/PV+8eIFJk2ahIEDBxY60aC234WyonPnzli7di3i4uLw5Zdf4u+//0aXLl0gl8s1rm8u1xUA1qxZg4oVKxZZVVPWr62me01SUhJsbGzyJeSFXaeSfM9LotzNCm5uRo8ejQsXLhRZNxsUFISgoCDV87Zt26JBgwb44YcfMHfuXH2HWSpdunRR/d6kSRMEBgbC19cXv/76a7H+GzJVK1asQJcuXeDl5VXgOqZ8XUmSk5OD/v37QwiBpUuXFrquqX4XBgwYoPrd398fTZo0Qa1atbBv3z506tTJiJHp38qVKzFo0KAiG/qX9Wtb3HtNWcGSGxM2ZswY/P7779i7dy+qVaum1bbW1tZo3rw5rl+/rqfo9MfFxQV169YtMHYPD498rfUfPHgADw8PQ4SnE7dv38bu3bvx/vvva7WdKV9X5fXR5tq5urrC0tLSZK+3MrG5ffs2YmNjCy210aSo70JZVbNmTbi6uhYYt6lfV6UDBw4gPj5e6+8xULaubUH3Gg8PD2RnZ+PZs2dq6xd2nUryPS8JJjcmSAiBMWPGYMuWLdizZw9q1Kih9T7kcjnOnz8PT09PPUSoX+np6bhx40aBsQcFBSEuLk5tWWxsrFoJR1m3atUqVK1aFd26ddNqO1O+rjVq1ICHh4fatUtNTcWxY8cKvHY2NjYICAhQ20ahUCAuLq7MX29lYnPt2jXs3r0bVapU0XofRX0Xyqp79+7h8ePHBcZtytc1txUrViAgIABNmzbVetuycG2LutcEBATA2tpa7TrFx8fjzp07BV6nknzPSxo8mZiRI0cKZ2dnsW/fPpGYmKh6ZGZmqtYZPHiwmDx5sur57Nmzxa5du8SNGzfEyZMnxYABA4SdnZ24ePGiMU5BK5988onYt2+fuHnzpjh06JAICQkRrq6u4uHDh0KI/Od66NAhYWVlJRYuXCguX74sZs6cKaytrcX58+eNdQpakcvlonr16mLSpEn5XjP165qWliZOnz4tTp8+LQCIRYsWidOnT6t6CH3xxRfCxcVFbNu2TZw7d0706NFD1KhRQzx//ly1j44dO4rFixernv/yyy/C1tZWrF69Wly6dEmMGDFCuLi4iKSkJIOfX26FnWt2drZ45513RLVq1cSZM2fUvsdZWVmqfeQ916K+C8ZS2LmmpaWJiRMniiNHjoibN2+K3bt3ixYtWog6deqIFy9eqPZhKtdViKI/x0IIkZKSIhwcHMTSpUs17sMUrm1x7jUffvihqF69utizZ4/4559/RFBQkAgKClLbT7169URMTIzqeXG+56XF5MYEAdD4WLVqlWqd9u3bi4iICNXz8ePHi+rVqwsbGxvh7u4uunbtKk6dOmX44EsgLCxMeHp6ChsbG+Ht7S3CwsLE9evXVa/nPVchhPj1119F3bp1hY2NjWjUqJHYsWOHgaMuuV27dgkAIj4+Pt9rpn5d9+7dq/GzqzwnhUIhpk+fLtzd3YWtra3o1KlTvvfB19dXzJw5U23Z4sWLVe9D69atxdGjRw10RgUr7Fxv3rxZ4Pd47969qn3kPdeivgvGUti5ZmZmirfeeku4ubkJa2tr4evrK4YPH54vSTGV6ypE0Z9jIYT44YcfhL29vXj27JnGfZjCtS3Oveb58+di1KhRolKlSsLBwUH06tVLJCYm5ttP7m2K8z0vLdn/DkxERERkFtjmhoiIiMwKkxsiIiIyK0xuiIiIyKwwuSEiIiKzwuSGiIiIzAqTGyIiIjIrTG6IiIjIrDC5IaJySSaTYevWrcYOg4j0gMkNERlcZGQkZDJZvkfnzp2NHRoRmQErYwdAROVT586dsWrVKrVltra2RoqGiMwJS26IyChsbW3h4eGh9qhUqRIAqcpo6dKl6NKlC+zt7VGzZk1s2rRJbfvz58+jY8eOsLe3R5UqVTBixAikp6errbNy5Uo0atQItra28PT0xJgxY9ReT05ORq9eveDg4IA6depg+/btqteePn2KQYMGwc3NDfb29qhTp06+ZIyIyiYmN0RUJk2fPh19+vTB2bNnMWjQIAwYMACXL18GAGRkZCA0NBSVKlXCiRMnsHHjRuzevVsteVm6dClGjx6NESNG4Pz589i+fTtq166tdozZs2ejf//+OHfuHLp27YpBgwbhyZMnquNfunQJf/75Jy5fvoylS5fC1dXVcG8AEZWcTqfhJCIqhoiICGFpaSkqVKig9pg3b54QQppF+MMPP1TbJjAwUIwcOVIIIcTy5ctFpUqVRHp6uur1HTt2CAsLC9Vs015eXmLq1KkFxgBATJs2TfU8PT1dABB//vmnEEKI7t27iyFDhujmhInIoNjmhoiM4o033sDSpUvVllWuXFn1e1BQkNprQUFBOHPmDADg8uXLaNq0KSpUqKB6/bXXXoNCoUB8fDxkMhnu37+PTp06FRpDkyZNVL9XqFABTk5OePjwIQBg5MiR6NOnD06dOoW33noLPXv2RNu2bUt0rkRkWExuiMgoKlSokK+aSFfs7e2LtZ61tbXac5lMBoVCAQDo0qULbt++jT/++AOxsbHo1KkTRo8ejYULF+o8XiLSLba5IaIy6ejRo/meN2jQAADQoEEDnD17FhkZGarXDx06BAsLC9SrVw8VK1aEn58f4uLiShWDm5sbIiIi8PPPPyMqKgrLly8v1f6IyDBYckNERpGVlYWkpCS1ZVZWVqpGuxs3bkTLli3x+uuvY926dTh+/DhWrFgBABg0aBBmzpyJiIgIzJo1C48ePcLYsWMxePBguLu7AwBmzZqFDz/8EFWrVkWXLl2QlpaGQ4cOYezYscWKb8aMGQgICECjRo2QlZWF33//XZVcEVHZxuSGiIxi586d8PT0VFtWr149XLlyBYDUk+mXX37BqFGj4OnpiejoaDRs2BAA4ODggF27dmHcuHFo1aoVHBwc0KdPHyxatEi1r4iICLx48QLffPMNJk6cCFdXV/Tt27fY8dnY2GDKlCm4desW7O3tERwcjF9++UUHZ05E+iYTQghjB0FElJtMJsOWLVvQs2dPY4dCRCaIbW6IiIjIrDC5ISIiIrPCNjdEVOawtpyISoMlN0RERGRWmNwQERGRWWFyQ0RERGaFyQ0RERGZFSY3REREZFaY3BAREZFZYXJDREREZoXJDREREZkVJjdERERkVv4/Pouaeoh70icAAAAASUVORK5CYII=\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": null,
      "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": "df2f6d38-2c86-4fcb-dba6-53fecb064a97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/9\n",
            "18/18 [==============================] - 1s 9ms/step - loss: 2.6152 - accuracy: 0.4507\n",
            "Epoch 2/9\n",
            "18/18 [==============================] - 0s 8ms/step - loss: 1.4795 - accuracy: 0.6821\n",
            "Epoch 3/9\n",
            "18/18 [==============================] - 0s 9ms/step - loss: 1.1260 - accuracy: 0.7500\n",
            "Epoch 4/9\n",
            "18/18 [==============================] - 0s 8ms/step - loss: 0.9209 - accuracy: 0.8022\n",
            "Epoch 5/9\n",
            "18/18 [==============================] - 0s 8ms/step - loss: 0.7642 - accuracy: 0.8359\n",
            "Epoch 6/9\n",
            "18/18 [==============================] - 0s 8ms/step - loss: 0.6392 - accuracy: 0.8613\n",
            "Epoch 7/9\n",
            "18/18 [==============================] - 0s 8ms/step - loss: 0.5444 - accuracy: 0.8828\n",
            "Epoch 8/9\n",
            "18/18 [==============================] - 0s 9ms/step - loss: 0.4631 - accuracy: 0.8994\n",
            "Epoch 9/9\n",
            "18/18 [==============================] - 0s 7ms/step - loss: 0.3972 - accuracy: 0.9155\n",
            "71/71 [==============================] - 0s 3ms/step - loss: 0.9180 - accuracy: 0.7891\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": null,
      "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": "82f9f6ae-196d-4dfa-b2cc-228ade23ebec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.91796875, 0.7890625]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "243b2131-580c-4369-a246-344f1ffffd24",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.19367764915405164"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "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": null,
      "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": "ea187e1b-622d-4ac0-ed18-5581854ad730",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "71/71 [==============================] - 0s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "34a65b9c-adc1-47a9-faa6-444b7006ec91",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(46,)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "predictions[0].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "61aa0eb7-5dc3-48df-dd32-449dfa5d64f1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "np.sum(predictions[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "5b255e50-da72-437a-d43b-8539ac850886",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "np.argmax(predictions[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7BVjZPk3Ryef"
      },
      "source": [
        "### 레이블과 손실을 다루는 다른 방법"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "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": null,
      "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": "8e03488c-d9cc-4d6d-a0ab-752e941feeaa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "63/63 [==============================] - 1s 9ms/step - loss: 2.9590 - accuracy: 0.1837 - val_loss: 2.1973 - val_accuracy: 0.5840\n",
            "Epoch 2/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 1.7695 - accuracy: 0.6040 - val_loss: 1.5518 - val_accuracy: 0.6201\n",
            "Epoch 3/20\n",
            "63/63 [==============================] - 0s 5ms/step - loss: 1.3965 - accuracy: 0.6401 - val_loss: 1.4033 - val_accuracy: 0.6450\n",
            "Epoch 4/20\n",
            "63/63 [==============================] - 0s 5ms/step - loss: 1.2285 - accuracy: 0.6880 - val_loss: 1.3076 - val_accuracy: 0.6660\n",
            "Epoch 5/20\n",
            "63/63 [==============================] - 0s 5ms/step - loss: 1.1064 - accuracy: 0.7139 - val_loss: 1.2480 - val_accuracy: 0.7021\n",
            "Epoch 6/20\n",
            "63/63 [==============================] - 0s 5ms/step - loss: 1.0010 - accuracy: 0.7549 - val_loss: 1.2129 - val_accuracy: 0.7192\n",
            "Epoch 7/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.9126 - accuracy: 0.7759 - val_loss: 1.1963 - val_accuracy: 0.7241\n",
            "Epoch 8/20\n",
            "63/63 [==============================] - 0s 5ms/step - loss: 0.8472 - accuracy: 0.7896 - val_loss: 1.1846 - val_accuracy: 0.7280\n",
            "Epoch 9/20\n",
            "63/63 [==============================] - 0s 5ms/step - loss: 0.7876 - accuracy: 0.8008 - val_loss: 1.1797 - val_accuracy: 0.7339\n",
            "Epoch 10/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.7383 - accuracy: 0.8086 - val_loss: 1.1973 - val_accuracy: 0.7349\n",
            "Epoch 11/20\n",
            "63/63 [==============================] - 0s 5ms/step - loss: 0.7002 - accuracy: 0.8169 - val_loss: 1.2051 - val_accuracy: 0.7402\n",
            "Epoch 12/20\n",
            "63/63 [==============================] - 0s 5ms/step - loss: 0.6641 - accuracy: 0.8228 - val_loss: 1.2461 - val_accuracy: 0.7310\n",
            "Epoch 13/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.6304 - accuracy: 0.8257 - val_loss: 1.2666 - val_accuracy: 0.7290\n",
            "Epoch 14/20\n",
            "63/63 [==============================] - 0s 5ms/step - loss: 0.6021 - accuracy: 0.8291 - val_loss: 1.2773 - val_accuracy: 0.7422\n",
            "Epoch 15/20\n",
            "63/63 [==============================] - 0s 8ms/step - loss: 0.5762 - accuracy: 0.8315 - val_loss: 1.3018 - val_accuracy: 0.7319\n",
            "Epoch 16/20\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 0.5552 - accuracy: 0.8350 - val_loss: 1.3857 - val_accuracy: 0.7109\n",
            "Epoch 17/20\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 0.5352 - accuracy: 0.8408 - val_loss: 1.3633 - val_accuracy: 0.7271\n",
            "Epoch 18/20\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 0.5166 - accuracy: 0.8457 - val_loss: 1.3760 - val_accuracy: 0.7358\n",
            "Epoch 19/20\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 0.5020 - accuracy: 0.8496 - val_loss: 1.3994 - val_accuracy: 0.7422\n",
            "Epoch 20/20\n",
            "63/63 [==============================] - 0s 5ms/step - loss: 0.4863 - accuracy: 0.8555 - val_loss: 1.4580 - val_accuracy: 0.7339\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7baea0e6e770>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "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": "QRgFhIntRyei"
      },
      "source": [
        "### 추가 실험"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFtnILDjRyej"
      },
      "source": [
        "### 정리"
      ]
    },
    {
      "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": null,
      "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": "55f1457c-cafd-4c60-eee6-3016e0c996e5",
        "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": null,
      "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": "05f1ca25-8435-4268-84b2-232c7d09b3c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(404, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "train_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "42156d32-52ff-40c8-a2c6-626c4f3783f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(102, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "test_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "faa62c9f-220c-4262-97f7-012b404c2826",
        "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": 49
        }
      ],
      "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": null,
      "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": "7e6dcc04-3dab-46f3-e889-6ff4d1987146",
        "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": "03611710-613b-4615-86d8-7c9338858ab1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.962890625, 2.4765625, 2.4921875, 2.42578125]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "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": "0fb6e24a-bbe4-4b58-cb59-47b04f777551",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.33935546875"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "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": "de2de12e-8d71-41d8-e20a-fa01cfd64c5a",
        "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": "c61de04a-744f-42d1-de03-ca47c626c955",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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1MQApTB4DxPxDRESkGgYghWl4J2giIiLVMQAprD7/sAuMiIhIRQxACuMsMCIiIvUxACmsoQtMCEAwBBEREamCAUhhDS1AAKfCExERqYUBSGENLUAAu8GIiIjUwgCkMO0lAYj5h4iISB0MQApjFxgREZH6mhyA/vWvf6Gqqkp+vWPHDlgsFvl1WVkZnnjiiZatrg3SXHLF2QVGRESkjiYHoDlz5qCsrEx+nZiYiNOnT8uvKysr8d///rdlq2uDNJe0APFeQEREROpocgD67ZRtTuG+PuwCIyIiUh/HACmMs8CIiIjUxwCkAvmBqDaVCyEiInJSuubs/OGHH8LT0xMAUFdXh6SkJPj7+wOA3fggapxWkmCFYAsQERGRSpocgMLDw/HBBx/Ir4ODg/G///3vsn3o2jQaAFYOgiYiIlJLkwNQdnZ2K5bhXBoGQtvYAkRERKSKFhsDZDKZ8O6777bU6dq0hoHQnAVGRESkjhsOQMnJyXj44YcREhKCefPmtURNbZ6GLUBERESquq4AlJeXh7///e+IjIzEXXfdBUmSsGrVKhQWFrZ0fW2SVm4BUrkQIiIiJ9XkAFRbW4sVK1YgISEBXbt2RVpaGt544w1oNBq88MILGDFiBFxcXFqz1jajoQWIXWBERETqaHIAat++Pd555x2MGTMGp0+fxsqVK/HAAw/c0Jtv27YNI0eORGhoKCRJwurVq+VttbW1+Nvf/obevXvDw8MDoaGhmDhxIvLz8xs95/z58yFJkt3SrVu3G6qzpWnrrzq7wIiIiNTR5ABUV1cnBwqtVtsib15RUYGYmBgsXrz4sm2VlZXYv38/XnrpJezfvx8rV65Eeno67rvvvmuet2fPnigoKJCX7du3t0i9LYWzwIiIiNTV5Gnw+fn5+Oabb/DRRx9h1qxZSExMxB//+EdIlzzbqrkSExORmJh4xW1GoxGbNm2yW/fuu+9i0KBByM3NbfSeQzqdDsHBwdddV2vjLDAiIiJ1NbkFyNXVFRMmTMDmzZtx6NAhdO/eHX/+859RV1eHf/zjH9i0aROsVmtr1orS0lJIkgQfH59G98vIyEBoaCiioqIwYcIE5ObmNrq/xWKB2Wy2W1qT/CgMtgARERGp4rpmgXXq1AmvvvoqcnJysHbtWlgsFtx7770ICgpq6fpk1dXV+Nvf/obx48fD29v7qvvFxsYiKSkJGzZswJIlS5CVlYWhQ4c2+qiOBQsWwGg0yktYWFhrfASZVuIsMCIiIjU161lgv6XRaORurDNnzlz2aIyWUltbiwcffBBCCCxZsqTRfS/tUuvTpw9iY2PRsWNHfPXVV5g6deoVj5kzZw5mz54tvzabza0aghp6DdkFRkREpI4bCkCXCggIsAsRLaUh/OTk5GDz5s2Ntv5ciY+PD7p06YITJ05cdR+DwQCDwXCjpTYZu8CIiIjU1eQAFBUV1aT9Tp48ed3F/FZD+MnIyMCWLVvQrl27Zp+jvLwcmZmZ+NOf/tRidd0o3geIiIhIXc16GGrHjh3x8MMPIzAwsEXevLy83K5lJisrC2lpafDz80NISAgeeOAB7N+/H99//z2sVqt8p2k/Pz/o9XoAwPDhwzF69GjMnDkTAPDMM89g5MiR6NixI/Lz8zFv3jxotVqMHz++RWpuCfKdoNkCREREpIomB6Avv/wSS5cuxcKFC5GYmIgpU6bg7rvvhkZz/Y8T27t3L4YNGya/buhCmzRpEubPn481a9YAAPr27Wt33JYtW3DnnXcCADIzM1FSUiJvO3XqFMaPH4+zZ88iICAAQ4YMwa5duxAQEHDddba0hgAkGICIiIhUIYlmfgufPn0aSUlJSEpKQmVlJf70pz9h6tSp6Ny5c2vVqDiz2Qyj0YjS0tJmjzlqilGLdyAtz4QPJg7A73u03sw5IiIiZ9Kc7+9mN9+0b98eL7zwAjIyMvD5559j9+7d6NatG86fP3/dBTsbLW+ESEREpKrrmgVWXV2Nr7/+GkuXLsXu3bsxduxYuLu7t3RtbVZ9/uEsMCIiIpU0KwDt3r0bH330Eb766itERUVhypQp+Oabb+Dr69ta9bVJnAVGRESkriYHoJ49e6K4uBgPP/wwUlJSEBMT05p1tWm8DxAREZG6mhyAjh07Bg8PD3zyySeN3vH53LlzLVJYW8YxQEREROpqcgBatmxZa9bhVBq6wJh/iIiI1NHkADRp0qTWrMOpyF1gTEBERESquP67GNJ1kwdBcwwQERGRKhiAVKCtv+ocA0RERKQOBiAVXBwDxABERESkBgYgFWg4C4yIiEhVDEAq0PJGiERERKpq9qMwrFYrkpKSkJycjOLiYthsNrvtmzdvbrHi2ireCJGIiEhdzQ5As2bNQlJSEu655x706tULUn1rBjUd7wNERESkrmYHoOXLl+Orr77C3Xff3Rr1OAXOAiMiIlJXs8cA6fV6REdHt0YtToM3QiQiIlJXswPQ008/jbfeeguC41euG2+ESEREpK5md4Ft374dW7Zswfr169GzZ0+4uLjYbV+5cmWLFddWyWOA2AJERESkimYHIB8fH4wePbo1anEa8tPg2QJERESkimYHID4V/sbJXWC2a+xIREREraLZAajBmTNnkJ6eDgDo2rUrAgICWqyotq5hFhjHUREREamj2YOgKyoqMGXKFISEhOD222/H7bffjtDQUEydOhWVlZWtUWObw0dhEBERqavZAWj27NlISUnBd999B5PJBJPJhG+//RYpKSl4+umnW6PGNkfLWWBERESqanYX2DfffIOvv/4ad955p7zu7rvvhpubGx588EEsWbKkJetrk3gfICIiInU1uwWosrISQUFBl60PDAxkF1gT8T5ARERE6mp2AIqLi8O8efNQXV0tr6uqqsLLL7+MuLi4Fi2ureIsMCIiInU1uwvsrbfeQkJCAjp06ICYmBgAwIEDB+Dq6ooffvihxQtsixpmgbELjIiISB3NDkC9evVCRkYGPvvsMxw/fhwAMH78eEyYMAFubm4tXmBbpOGNEImIiFR1XfcBcnd3x2OPPdbStTiNhllgNgYgIiIiVTQpAK1ZswaJiYlwcXHBmjVrGt33vvvua5HC2jLOAiMiIlJXkwLQqFGjUFhYiMDAQIwaNeqq+0mSBKvV2lK1tVkXZ4GpXAgREZGTalIAstlsV/wzXR+2ABEREamr2dPgP/nkE1gslsvW19TU4JNPPmmRoto6PgqDiIhIXc0OQJMnT0Zpaell68vKyjB58uQWKaqtq88/nAVGRESkkmYHICEEpPoxLJc6deoUjEZjixTV1smzwNgCREREpIomT4Pv168fJEmCJEkYPnw4dLqLh1qtVmRlZWHEiBGtUmRb09AFxmnwRERE6mhyAGqY/ZWWloaEhAR4enrK2/R6PSIiIjBmzJgWL7At0nIWGBERkaqaHIDmzZsHAIiIiMC4cePg6uraakW1dZwFRkREpK5mjwGaNGlSi4Wfbdu2YeTIkQgNDYUkSVi9erXddiEE5s6di5CQELi5uSE+Ph4ZGRnXPO/ixYsREREBV1dXxMbGYs+ePS1Sb0vhLDAiIiJ1NTsAWa1WvPnmmxg0aBCCg4Ph5+dntzRHRUUFYmJisHjx4itu/9e//oW3334b7733Hnbv3g0PDw8kJCTYPYn+t7788kvMnj0b8+bNw/79+xETE4OEhAQUFxc3q7bWdLELjAGIiIhIDc0OQC+//DIWLlyIcePGobS0FLNnz8Yf/vAHaDQazJ8/v1nnSkxMxKuvvorRo0dftk0Igf/85z948cUXcf/996NPnz745JNPkJ+ff1lL0aUWLlyIxx57DJMnT0aPHj3w3nvvwd3dHUuXLm3mJ209fBo8ERGRupodgD777DN88MEHePrpp6HT6TB+/Hh8+OGHmDt3Lnbt2tVihWVlZaGwsBDx8fHyOqPRiNjYWKSmpl7xmJqaGuzbt8/uGI1Gg/j4+KseAwAWiwVms9luaU0SW4CIiIhU1ewAVFhYiN69ewMAPD095Zsi3nvvvVi7dm2LFVZYWAgACAoKslsfFBQkb/utkpISWK3WZh0DAAsWLIDRaJSXsLCwG6y+cbwPEBERkbqaHYA6dOiAgoICAECnTp2wceNGAMDPP/8Mg8HQstUpZM6cOSgtLZWXvLy8Vn0/eRYY8w8REZEqmh2ARo8ejeTkZADAk08+iZdeegmdO3fGxIkTMWXKlBYrLDg4GABQVFRkt76oqEje9lv+/v7QarXNOgYADAYDvL297ZbWxFlgRERE6mryfYAavPbaa/Kfx40bh/DwcKSmpqJz584YOXJkixUWGRmJ4OBgJCcno2/fvgAAs9mM3bt3Y/r06Vc8Rq/Xo3///khOTpZv3Giz2ZCcnIyZM2e2WG03Su4C4xggIiIiVTQ7AP1WXFwc4uLiruvY8vJynDhxQn6dlZWFtLQ0+Pn5ITw8HE899RReffVVdO7cGZGRkXjppZcQGhoqhxsAGD58OEaPHi0HnNmzZ2PSpEkYMGAABg0ahP/85z+oqKhwqAe1aurb3dgCREREpI4mBaA1a9Y0+YT33Xdfk/fdu3cvhg0bJr+ePXs2gAs3W0xKSsJf//pXVFRUYNq0aTCZTBgyZAg2bNhgdyPGzMxMlJSUyK/HjRuHM2fOYO7cuSgsLETfvn2xYcOGywZGq4n3ASIiIlKXJMS1v4U1GvuhQpIk4beHyVO7rdYWLE8dZrMZRqMRpaWlrTIeaG/2OTzwXioi2rlj67PDrn0AERERXVNzvr+bNAjaZrPJy8aNG9G3b1+sX78eJpMJJpMJ69evxy233IINGza0yAdo63gfICIiInU1ewzQU089hffeew9DhgyR1yUkJMDd3R3Tpk3DsWPHWrTAtujiw1BVLoSIiMhJNXsafGZmJnx8fC5bbzQakZ2d3QIltX2cBUZERKSuZgeggQMHYvbs2Xb32ikqKsKzzz6LQYMGtWhxbRVngREREamr2QFo6dKlKCgoQHh4OKKjoxEdHY3w8HCcPn0aH330UWvU2OZcvBM0AxAREZEamj0GKDo6GgcPHsSmTZtw/PhxAED37t0RHx8vD+6lxsnT4NkCREREpIrruhGiJEm46667cNddd7V0PU6Bj8IgIiJSV5MC0Ntvv41p06bB1dUVb7/9dqP7/vnPf26RwtoyjcSHoRIREampSQFo0aJFmDBhAlxdXbFo0aKr7idJEgNQE7ALjIiISF1NCkBZWVlX/DNdn4ZZYBwETUREpI5mzwKjG8dZYEREROpqUgtQw0NKm2LhwoXXXYyzYBcYERGRupoUgH755ZcmnYzT4JtGo7k4CFoIwetGRESksCYFoC1btrR2HU5Fe0ngsQlAy/xDRESkKI4BUkFDCxDAbjAiIiI1XNeNEPfu3YuvvvoKubm5qKmpsdu2cuXKFimsLbsk/3AgNBERkQqa3QK0fPlyDB48GMeOHcOqVatQW1uLI0eOYPPmzTAaja1RY5uj1VzaBcYAREREpLRmB6B//vOfWLRoEb777jvo9Xq89dZbOH78OB588EGEh4e3Ro1tjkZiFxgREZGamh2AMjMzcc899wAA9Ho9KioqIEkS/vKXv+D9999v8QLbIrsWIJuKhRARETmpZgcgX19flJWVAQDat2+Pw4cPAwBMJhMqKytbtro26tJZYFZ2gRERESmu2YOgb7/9dmzatAm9e/fG2LFjMWvWLGzevBmbNm3C8OHDW6PGNoezwIiIiNTV5AB0+PBh9OrVC++++y6qq6sBAC+88AJcXFywc+dOjBkzBi+++GKrFdrWaDUSrDbBQdBEREQqaHIA6tOnDwYOHIhHH30UDz30EABAo9Hgueeea7Xi2jKtJMEKwRYgIiIiFTR5DFBKSgp69uyJp59+GiEhIZg0aRJ++umn1qytTWsYBsQAREREpLwmB6ChQ4di6dKlKCgowDvvvIPs7Gzccccd6NKlC15//XUUFha2Zp1tTsNMMPaAERERKa/Zs8A8PDwwefJkpKSk4Ndff8XYsWOxePFihIeH47777muNGtsk+YnwTEBERESKu6FngUVHR+P555/Hiy++CC8vL6xdu7al6mrzGmaCsQuMiIhIedf1LDAA2LZtG5YuXYpvvvkGGo0GDz74IKZOndqStbVpDV1gnAVGRESkvGYFoPz8fCQlJSEpKQknTpzA4MGD8fbbb+PBBx+Eh4dHa9XYJjU8DoMtQERERMprcgBKTEzEjz/+CH9/f0ycOBFTpkxB165dW7O2Nk1b3/nIAERERKS8JgcgFxcXfP3117j33nuh1Wpbsyan0DAIml1gREREymtyAFqzZk1r1uF0JDkAqVwIERGRE7qhWWB0/bScBUZERKQaBiCVcBYYERGRehiAVKLhozCIiIhUwwCkErkFiAGIiIhIcQxAKtHwURhERESqYQBSCQdBExERqYcBSCUcBE1ERKQehw9AERERkCTpsmXGjBlX3D8pKemyfV1dXRWu+trk+wDZVC6EiIjICV33w1CV8vPPP8NqtcqvDx8+jN///vcYO3bsVY/x9vZGenq6/LohbDgSbcMsMLYAERERKc7hA1BAQIDd69deew2dOnXCHXfccdVjJElCcHBwa5d2Q3SaC41vHANERESkPIfvArtUTU0NPv30U0yZMqXRVp3y8nJ07NgRYWFhuP/++3HkyJFGz2uxWGA2m+2W1uaiu1B/rZV9YEREREq7qQLQ6tWrYTKZ8Mgjj1x1n65du2Lp0qX49ttv8emnn8Jms2Hw4ME4derUVY9ZsGABjEajvISFhbVC9fZc6h8HX2tlCxAREZHSJCFunkEoCQkJ0Ov1+O6775p8TG1tLbp3747x48fjlVdeueI+FosFFotFfm02mxEWFobS0lJ4e3vfcN1X8tgne7HpaBH+Obo3Ho4Nb5X3ICIiciZmsxlGo7FJ398OPwaoQU5ODn788UesXLmyWce5uLigX79+OHHixFX3MRgMMBgMN1pis+jlFiB2gRERESntpukCW7ZsGQIDA3HPPfc06zir1YpDhw4hJCSklSq7PnodAxAREZFabooAZLPZsGzZMkyaNAk6nX2j1cSJEzFnzhz59d///nds3LgRJ0+exP79+/HHP/4ROTk5ePTRR5Uuu1Eu9fPgaxiAiIiIFHdTdIH9+OOPyM3NxZQpUy7blpubC43mYo47f/48HnvsMRQWFsLX1xf9+/fHzp070aNHDyVLviZ5EHTdTTMEi4iIqM24KQLQXXfdhauN1d66davd60WLFmHRokUKVHVjGgJQzSU3eSQiIiJl3BRdYG3RxTFAbAEiIiJSGgOQShpmgdXUcQwQERGR0hiAVOLCafBERESqYQBSCR+FQUREpB4GIJWwC4yIiEg9DEAq4bPAiIiI1MMApJKGWWC8ESIREZHyGIBUwkHQRERE6mEAUon8KAyOASIiIlIcA5BK+DR4IiIi9TAAqeTiozA4CJqIiEhpDEAqkR+FwS4wIiIixTEAqYSDoImIiNTDAKQSff2doDkNnoiISHkMQCqRW4DYBUZERKQ4BiCVcBA0ERGRehiAVCIPgmYXGBERkeIYgFTC+wARERGphwFIJS58GjwREZFqGIBU0vAojDqbgM3GcUBERERKYgBSiYvu4qWvtbEViIiISEkMQCppGAMEALWcCUZERKQoBiCVuFwagDgOiIiISFEMQCrRaiRoNbwbNBERkRoYgFTUMBCaM8GIiIiUxQCkIj4QlYiISB0MQCoyyHeD5iBoIiIiJTEAqYgtQEREROpgAFJRQwCycAwQERGRohiAVOTmogUAVNdaVa6EiIjIuTAAqcjdcCEAVVjqVK6EiIjIuTAAqchDrwMAVNawBYiIiEhJDEAqctfXtwDVsAWIiIhISQxAKvIw1LcAWdgCREREpCQGIBWxBYiIiEgdDEAqkluAOAaIiIhIUQxAKmqYBs9ZYERERMpiAFKRR/00eLYAERERKcuhA9D8+fMhSZLd0q1bt0aPWbFiBbp16wZXV1f07t0b69atU6ja5nOvnwbPFiAiIiJlOXQAAoCePXuioKBAXrZv337VfXfu3Inx48dj6tSp+OWXXzBq1CiMGjUKhw8fVrDipmMLEBERkTocPgDpdDoEBwfLi7+//1X3feuttzBixAg8++yz6N69O1555RXccsstePfddxWsuOnkFiDOAiMiIlKUwwegjIwMhIaGIioqChMmTEBubu5V901NTUV8fLzduoSEBKSmpjb6HhaLBWaz2W5RgnwnaN4HiIiISFEOHYBiY2ORlJSEDRs2YMmSJcjKysLQoUNRVlZ2xf0LCwsRFBRkty4oKAiFhYWNvs+CBQtgNBrlJSwsrMU+Q2PkZ4GxBYiIiEhRDh2AEhMTMXbsWPTp0wcJCQlYt24dTCYTvvrqqxZ9nzlz5qC0tFRe8vLyWvT8V8NngREREalDp3YBzeHj44MuXbrgxIkTV9weHByMoqIiu3VFRUUIDg5u9LwGgwEGg6HF6mwq+U7QnAVGRESkKIduAfqt8vJyZGZmIiQk5Irb4+LikJycbLdu06ZNiIuLU6K8Zmu4E7SlzoY6q03laoiIiJyHQwegZ555BikpKcjOzsbOnTsxevRoaLVajB8/HgAwceJEzJkzR95/1qxZ2LBhA/7973/j+PHjmD9/Pvbu3YuZM2eq9REa1dACBACVtewGIyIiUopDd4GdOnUK48ePx9mzZxEQEIAhQ4Zg165dCAgIAADk5uZCo7mY4QYPHozPP/8cL774Ip5//nl07twZq1evRq9evdT6CI0y6DTQaiRYbQKVFiu8XV3ULomIiMgpSEIIoXYRjsZsNsNoNKK0tBTe3t6t+l59/74RpspabPzL7egS5NWq70VERNSWNef726G7wJyBn7seAHCuokblSoiIiJwHA5DKfD0YgIiIiJTGAKQyPwYgIiIixTEAqaxdfQA6zwBERESkGAYglTV0gZ1lACIiIlIMA5DK5BagSgYgIiIipTAAqcyXs8CIiIgUxwCkMj9PBiAiIiKlMQCpjPcBIiIiUh4DkMounQbPm3ITEREpgwFIZe3qu8AsdTaUW+pUroaIiMg5MACpzF2vg5frhWfSFpmrVa6GiIjIOTAAOYBgb1cAQGGpReVKiIiInAMDkAMINtYHILYAERERKYIByAFcbAGqUrkSIiIi58AA5ABC2AJERESkKAYgBxDUEIBKGYCIiIiUwADkAOQuMLYAERERKYIByAE0DIIuMDEAERERKYEByAFE+ntAq5FwtqIGp00cCE1ERNTaGIAcgLteh56h3gCAvdnnVK6GiIio7WMAchADOvoBAPblnFe5EiIioraPAchBDIjwBQDsPskWICIiotbGAOQgBndqB51GQnpRGU4Ul6tdDhERUZvGAOQgfNz1GNrZHwDw/cF8lashIiJq2xiAHMg9fUIBABuPFKlcCRERUdvGAORA7ugSAAA4WmDGuYoalashIiJquxiAHEiAlwHdgr0AADszS1SuhoiIqO1iAHIwgztdGAe0+VixypUQERG1XQxADubemBAAwHcH85HPu0ITERG1CgYgB3NLuC/iotqh1irw/raTapdDRETUJjEAOaCZv4sGAHyxJxdnyiwqV0NERNT2MAA5oMGd2qFvmA8sdTZ8tD1L7XKIiIjaHAYgByRJEmYOu9AK9ElqNrJKKlSuiIiIqG1hAHJQw7sHonuINyprrLjn7Z+QmnlW7ZKIiIjaDAYgByVJEhY/3A99w3xQWWPFox//jNyzlWqXRURE1CYwADmwqABPLJ92KwZG+KKixoq5aw5DCKF2WURE5AT25ZxH5pmWeTh3rdWGxVtOYPdJx+nNYABycK4uWrw2pg/0Wg22pp/B//vfPmQUlaldFhFRm2K1qfPL5c7MEmz79UyLn7fOasP5ihoIIbDxSCGmfbLXblax1SYu+4W6utaKYnM1AOB4oRljluzEuP/uQk2dDTV1Nmz79QwqLHXy/jabgNUmUGu1YcvxYpw2VSHnbAU+3ZUDc3Wt3bn/l5qDN35Ix7j3d6Gypg4f/nQS1bXWFv/czSEJNilcxmw2w2g0orS0FN7e3mqXAwB4f1sm/rnuOABAr9PgtT/0xuh+7SEEUFJuQaC3q8oVEhE134bDBQj0dsUt4b6KvN/agwUI8jZgQISfvO5fG44jaWc2Pn00FgGeBtTZBCL9Pa55rpJyC6pqrAg2usJFa9+eIITA/23NhNUm8OTvoiFJErJLKvDfbZk4dLoUpVW1uC8mFIu3ZEIjAf83oT/6hfsg71wlNh8vxoMDwhBsdIVBp8FbyRk4mm/GsG6BeGhgGCRJQk2dDWl5JvQN84Fep5Hfc/3hQhSZq7Fw068oq65DB183nDp/4aa6026PwpO/i8bS7dn4cPtJRAd6YtGDfbHhSCG2Z5Tg0OlSlFXX4uHYcOw8cRYn6yfgDIn2l2sOMbqid3sjbAI4cMoEc1Ut9FoNyi4JRgDQLdgLib1C8HP2OeSXVuHkmcsn80wdEomX7u3RvB/gNTTn+9uhA9CCBQuwcuVKHD9+HG5ubhg8eDBef/11dO3a9arHJCUlYfLkyXbrDAYDqqurm/y+jhiAbDaBtzdn4PuDBThRfKFJ8pHBEThbUYPvD+bjzQdiMKZ/B1jqrNBKEk6dr4K/lwGeBp3KlRORMxJCYM2BfLT3cbMLGw3bRP0X6Oj/2wkA2P63YbDaBML93CFJEgCgsqYO5ZY6BHga8OOxYgR5GxDo5Qqjmwuqa604cMqEIdH+0P0mfBSXVWPF3lO4t08I2vu44VxlDQK9XLE9owR//Gg3dBoJH0wcgKU7sjB2QBj+/MUv8rEuWgkSJKyecRv8PfV4f9tJdPT3wLnyGsT3CERZdR1cXbSw2myY8OFuVNfaoNVIiItqh17tjaiutSLfVIUDp0woMl9scekS5InSqlq7dddjYIQvOvi6Y2t6Mc5X1qJPByMkSULu2Qqcr6y95vE6jYQ6lVq7LuXj7oJX7u+FkTGhLXreNhOARowYgYceeggDBw5EXV0dnn/+eRw+fBhHjx6Fh8eV03lSUhJmzZqF9PR0eZ0kSQgKCmry+zpiAGpgtQm8u/kE/pP8K377k+vYzh2nzlfJTbkxHYxY+cRtyDdVYWt6MW7vEoAzZRbcEu4LjUZSoXoiaoot6cXIKCrDI4Mj5d/uryXfVAVfdz2OFZrRI8Qbri5aedup85Ww2YDwdu7yOptN2P0/YLMJfLY7B2WWOvyhXwcEGy+2Kn+8Mxs/HivCrVHtUFZdh/jugfg4NQc6jYQ/3toR/cJ8cOp8Fdz0WmQUlWFAhB9+PFaEJz7bD0kC/jaiG2xCoHOgF4Z29se0/+3DwVMmhPm649Dp0ss+S89Qb3i56rDr5DnodRoMifbH5uNXfj5i3zAf9Az1xq9FZQjzc0e4nzs+352L4jIL3Fy08HV3QX5pNdr7uOF0Mx4v5OqigUGnRWlV46FCknDZ/8XXEuXvgZMlFc06tmeoN9ILy5ocXtz1Wnz35BBkFJXjwCkTlmzNlLdFB3piVN9QfH+wAMcLyxBqdMXjd3ZC1yAvrNx/Gqknz8pdW8X13WZR/h54dGgUjhWYsSOzBHf3CkGnQA+YKmvh5eqCe/uE4NT5KrjrL1yzZTuyUFljxcAIP0QHeuLXojKE+7njhyOFCPZ2xR/jOiLQq+V7LtpMAPqtM2fOIDAwECkpKbj99tuvuE9SUhKeeuopmEym634fRw5ADT786SQWrD9+zX7rO7oE4NDpUpyrqJHXebvq0C3EG48NjUJ890D5ty0iR1FUPw4h6Aa7dj/angVTZQ3+Et+l2aH/bLkFyceKcXefEOSbqnCmzIL0wjL4eegxql97eT+bTaC8pg7zvz2C3Vnn8O3M2+DvaUB1rRXrDhXgtmh/+XOUVV9oAYgO9ERNnQ0f78yG0c0Fw7sH4uOd2dh+ogQTYjviuZUHUWsVuLdPCN4Z3w+SJEEIgapaK9xctPhm/2mk5Z1HiNENhaXVGBDhi2dWHECt9cL/B/f3DcUD/TugS5AXzlfWYOx7qYAAkp+5A4dOlWL94UJ8dyAfo/q2x4AIXyQfK8ae7HPy/xNB3gZ8M30w8k3V+Oe6Y0jLM131Ol1PAHAUHdu5I+eS2bW3dwlAOw899mSdu2pY8nF3gam+pWVQhB+WTR6In7PP4d3NJxDh7wFfdxe093HDjsyzsNkE7usbiqP5Zqw5kI/uId6Ye28PRPh7wGYTsNTZUGO1Yfi/U2CqrEHvDkaM6BmMB/p3wLrDhRga7Y+zFTWotdpwa1Q75JytwMYjRaiutaLAXI3e7Y1ILyxDr/ZGdA3ywvYTJTDUB+Yeod64NaqdXPfiLSfw/cECzL23B26N8oMkSbDaBNLyzqNrsPdVewsyz5QjwMsAb1eXlrrsrarNBqATJ06gc+fOOHToEHr16nXFfZKSkvDoo4+iffv2sNlsuOWWW/DPf/4TPXv2vOp5LRYLLJaLzZJmsxlhYWEOHYAA4Fz9Pwx3vRZTk/Yio7gMk2+LxLubT6DGamvyeWIj/fBA/w4XEr9NYGCEL9xddAj1ccXaQwWI8vdE7w5GlFbVwuh25X8ENXU2rNiXh9KqWnyamoNXRvXC8O5Nb3UTQmD5z3mwCYFxA8Iua9IG7H9jPVNmwQurDuGePiG4v297/FpUhq3pxZgYF2H3m29bYbUJlFbVws9Dr+h7nquoQYCXodnHVtbU4Zt9pzCkc8BVx1LUWm344Ugh+rT3sWuZKCm3IH5hCsqq6/DMXV0x/c5OKKuuxeq0fEj15x4Y4Yd+9WNGqmuteGdzBkJ93BDRzgMDI/xQXWfFkdNmjP9gFwCgV3tvPDokyi64VNbU4eSZCvQM9caiTb8i80wFhnT2x7CugQjwMuCJz/bhhyNF6NXeG3nnquxaAv54azgstTaM7tceW9KL8cFPF+/Y/mxCV0y5LRIzP9+P5OPFiPL3wOsP9MGKvXn4Zv9pWG0CXgbdZWMmrsbX3QX9O/riaL4Z+aXVzTq2tXm76mCuvnotRjcXdAv2wu6sc5dt+/PvovH1vlMINrpizt3d8eraY4AQKCmvwR1dA5BZXI5Dp0thtQloJAmj+rXHXxO6wtVFi8wz5diZWYKBEX545fujyDtfhds6tcPaQwXwcdfjiTs7YVjXQCQfL4YQAv3CfXCmrAaSBPh56PHhTyfRu70R0++MxivfH8Xnu3Ox9JGBGNLZH8CFAcQ55yqRXVKBmDAfaCUJPu4uqLHaYNBpcaK4HGXVtegb5tMiv0CWW+qgkQB3PYcs3Kg2GYBsNhvuu+8+mEwmbN++/ar7paamIiMjA3369EFpaSnefPNNbNu2DUeOHEGHDh2ueMz8+fPx8ssvX7be0QPQpYQQqLUK6HUa5Juq4KHXISXjDDYeKUSXIC90CvBE6skS/L/bO8FcXYv5a47g5+zzjZ7z0t/sItq5I7v+N6WIdu6I9PfA73sEo0uQJzxddfh67yl8+JvHdqyfNRTHC83Yn2NChL8HHhzQAe9uPoEdmSXo08EHwd6u2JpejOIyC/50a0csWH9hkHe3YC/8vzuikNgrBAadBvtyzmPOykPIOVuJ2KgLzalf/pyHypoLMwhe+0NvvLvlBE6dr8KDAzpg7sie+Mfao1iTlo+hnQMwdWgkItp5IMDLgILSKhwrMGNY14stX6WVtfB01eFshQXuep38m1Cd1QarEDDoLgaqmjobUk+ehZerDv2a8J/fmTILcs9Von9H+wGeDefecaIEt0X7w6DTwlRZg6Sd2dDrNHj89k4AgGOFZnQJ8sJr649j2Y4sfDRpIIZ1C5TPU1lThy/25CHM1w3DuwdB20grR0m5BSv2nkLHdu7QazUYEOELN70WBp1Wbl0QAvjPj7/iq72nUFpVC40EvDamD3qEeOPfG9PRM9SIZxK6yp8t31Qlj0HYl3MOpVW1SEk/g49TcwBcaIbvGuyFknILwnzdodNqsOAPvfHPtcfww5FC1NkEXLQS3nggRg4nC9Yfw39TTsp/Bxc92BdrDuRf1gXyzvh+GBkTird+zMCiH3+123a1cQ4fTByAmA5GuGg1GPPeTpw8U4Eh0f7YfqLEbr9wP3fknru++24Z3VwQ4GWQx+pdi16rQY3VBk+DDuX1wcbfU48h0f5YnZbfrPcO8DJc8/mBvu4uiO8ehMgAD2w5XozMMxW4v28obu8cgOhAT+Sdr8SfPtojty7HRvrhhXu6w1xVh0OnSzFuYBhmLf8F4X7ueHVULzz79UF8ve8UgAs/r3fG98PACD/knau8MF7H3QVJO7IR3yMQ3x8swKepOfjHH3rjvvqxH0KIq/47stpEo3+nGzT8clRSboGnQdfsX4LqrLYr/tJFN582GYCmT5+O9evXY/v27VcNMldSW1uL7t27Y/z48XjllVeuuM/N2gJ0I/JNVZj68V74uLmgR6g39uWch9HNBadNVThZf98HtcfJtWTTuotWwpzE7vhoexZOm6qg00joGuwFrUbCodOl8vvoNBJui/bHwAhffLEnD2fKLejd3ohaqw39O/ri16Iy7Dhx4T4Wf7w1HFH+nggxusJUVQubEMguqcAdXQLRv6MvvjuQj1fWHkVZdR3+MboX/Nz12JZxBjlnK7E3+7zcShfl74GuwRear8vqf5uOjfSDuboOxwrMiO8ehB+PFcmf5a2H+uJ8RQ0qaqxYf7gAh0+bAQCDIv3wbEJXBHoZsPFIEUoqLtR+R5cAvPlDOr7cm4fqWvuWQV93F8z+fRe8l3KyyeMjRvQMRmWttcWn7v62O+JKugR5wkWrwZH8C59ZI7Xc31OdRoKHQWfX0tMv3AdFpdXIL62Gi1aSu5j0Og1CjK6N1hvoZcDdvUPw6a4cOYy567X4+/29EObrhjqbQAdfNwR6uWLz8WIMivSDXqfB/pzziI3yg0aSMO1/+1BeXYtuId7oF+aDuE7tcLygDL07GPH//rcPxwrM+GLarVi5/xSi/D0xZUgkfsk9jwXrjuPPwzsjJsyInLOVOHW+Cs+vOgQPgxbfPD74mrNG801VMOg0KC6zoEuQV6MhxGoT+CX3PHp3MOJcRQ1CjG6Nnpthg1pTmwtAM2fOxLfffott27YhMjKy2cePHTsWOp0OX3zxRZP2vxnGALUmq03AUmdFkdkCP3c9Pt2dA1cXLTwNWmw6WozbotuhutaGz/fkoKjUAqu4cC+I0f3ao1+4D+qsAh9tz0JZdS1CfdwQFeCBH48Wo8Zqg5dBhwERvtifa8LACF90DvKSB+dFBXjg48mDsOZAPv6XmoNC88WZewM6+uKvI7rhRHE5MorLsOV4MeI6+SPAy4BNR4uQVVKOIG9XeRC4i1aC0U2PkvIbm3GhtEtb2lpLiNEVNXU2nL1kXNil2tf/zGrqbCgorb6ulpDe7Y14dGgkXLQapOWZ8P62k3bbtRoJY25pj4cGheO1dcexJ9u+i2TMLR0QG+WHv359UF73+x5B+GDiANRZbZi0bI8cRIELwWj9rNsx7r+p2JtzHoMi/XBn1wBkFldg/KAwaDQS/peag1W/nJaPCfQyIMjbFYdOl6KDrxu+mzkERjcXfL3vFJ5beRDdQ7yx+OFbEOTtiuKyanRsd6Erb/2hAnTwdUfPUG8s3ZGF7w8WINjbFTnnKtEt2As/HClEiNEVn0yNRXsfN+SbqvBrURkGd/KHJOGy6dLXq8JShwpLXZNvgVFWXQudRgM3fdvrIiZq0GYCkBACTz75JFatWoWtW7eic+fOzT6H1WpFz549cffdd2PhwoVNOsbZA1BzCHFhIJ/VJuDRyJT7InM19mafR/cQL0QFeNptyy6pQFZJBW6L9re7n0WR2YJjhWak5ZrwyOAI+DZh/IsQAscKyuDqooGnqw5//uIX3Nk1EJuPF2NP1jm089BDp5VgqbNh6m2R6BzkifY+7tiTfQ6xkX44fLoUn+3ORUm5BZY6G564sxM+Ts1G3rkLrSOBXgYsGtcXEz7cLb9nTJgPAjz1qLUKpFzSKuJp0GHa7VE4kl+KH44UwV2vxQP9O8BcVQutRoOy6lrsyT4HU2UthkT7Y8qQCNzRJRDbT5Rg6fYsRLRzhwDwSX13kkGnQbcQbxy4ZEBqRDt3LJs8CLtOnsXzqw7JLVndgr3QI8QbKy/5wr+9SwBevq8nIusHYGYUl2Pc+6koq67D43dEYdLgCJwtr0Gkv4fchXDqfCV+9+8U1NTZ8ObYGPxz3TEEebvint7BGNErBMXmamw8WgQXrQQfdz3iuwfB6OZiN4MIALZnlGBHZgm2Z5Sg3FKHxQ/fgh6hF/5tnSmzYMW+PHgZdAjydkWdTSCxVzDqbAJvbkxHj5ALM4LiovzlL2+bTWDj0SJY6qzo2M4DHf3c4euhR965SnyxJxdThkTC3/PysUulVbUwV9ViW8YZjOgZjHb1g5X1Wo3dIGlzdS28DLrrGt/R0HXUlK4bImpZbSYAPfHEE/j888/x7bff2t37x2g0ws3tQjPrxIkT0b59eyxYsAAA8Pe//x233noroqOjYTKZ8MYbb2D16tXYt28fevRo2g2XGIDantKqWvxwpBDx3YPg4+aCOpu45vTihvEHDf9EjhaYEebnDm9XF3y1Nw+f7crBG2Nj0CXISz7GUmfF9wcK0D3EG91DvOTZO2fKLfAyuFz223ed1YajBWb0CjVecZaStX5qcmrmWTx/d3eE+bkj92wlquusCDG6wtVFa9ei0BA0Hr+9E4zuLth4pBBvbkzHkOgAzB15+d//4rJqWG2i0W6LfTnnUW6pwx1dApo8JuNqbDYBSQJnHhJRq2gzAehq/0kuW7YMjzzyCADgzjvvREREBJKSkgAAf/nLX7By5UoUFhbC19cX/fv3x6uvvop+/fo1+X0ZgIiIiG4+bSYAqYUBiIiI6ObTnO9vDsUnIiIip8MARERERE6HAYiIiIicDgMQEREROR0GICIiInI6DEBERETkdBiAiIiIyOkwABEREZHTYQAiIiIip8MARERERE6HAYiIiIicDgMQEREROR0GICIiInI6OrULcERCCAAXnipLREREN4eG7+2G7/HGMABdQVlZGQAgLCxM5UqIiIioucrKymA0GhvdRxJNiUlOxmazIT8/H15eXpAkqcXOazabERYWhry8PHh7e7fYecker7MyeJ2Vw2utDF5nZbTmdRZCoKysDKGhodBoGh/lwxagK9BoNOjQoUOrnd/b25v/uBTA66wMXmfl8Forg9dZGa11na/V8tOAg6CJiIjI6TAAERERkdNhAFKQwWDAvHnzYDAY1C6lTeN1Vgavs3J4rZXB66wMR7nOHARNREREToctQEREROR0GICIiIjI6TAAERERkdNhACIiIiKnwwCkkMWLFyMiIgKurq6IjY3Fnj171C7pprJt2zaMHDkSoaGhkCQJq1evttsuhMDcuXMREhICNzc3xMfHIyMjw26fc+fOYcKECfD29oaPjw+mTp2K8vJyBT+F41uwYAEGDhwILy8vBAYGYtSoUUhPT7fbp7q6GjNmzEC7du3g6emJMWPGoKioyG6f3Nxc3HPPPXB3d0dgYCCeffZZ1NXVKflRHN6SJUvQp08f+WZwcXFxWL9+vbyd17l1vPbaa5AkCU899ZS8jtf6xs2fPx+SJNkt3bp1k7c75DUW1OqWL18u9Hq9WLp0qThy5Ih47LHHhI+PjygqKlK7tJvGunXrxAsvvCBWrlwpAIhVq1bZbX/ttdeE0WgUq1evFgcOHBD33XefiIyMFFVVVfI+I0aMEDExMWLXrl3ip59+EtHR0WL8+PEKfxLHlpCQIJYtWyYOHz4s0tLSxN133y3Cw8NFeXm5vM/jjz8uwsLCRHJysti7d6+49dZbxeDBg+XtdXV1olevXiI+Pl788ssvYt26dcLf31/MmTNHjY/ksNasWSPWrl0rfv31V5Geni6ef/554eLiIg4fPiyE4HVuDXv27BERERGiT58+YtasWfJ6XusbN2/ePNGzZ09RUFAgL2fOnJG3O+I1ZgBSwKBBg8SMGTPk11arVYSGhooFCxaoWNXN67cByGazieDgYPHGG2/I60wmkzAYDOKLL74QQghx9OhRAUD8/PPP8j7r168XkiSJ06dPK1b7zaa4uFgAECkpKUKIC9fVxcVFrFixQt7n2LFjAoBITU0VQlwIqxqNRhQWFsr7LFmyRHh7ewuLxaLsB7jJ+Pr6ig8//JDXuRWUlZWJzp07i02bNok77rhDDkC81i1j3rx5IiYm5orbHPUaswusldXU1GDfvn2Ij4+X12k0GsTHxyM1NVXFytqOrKwsFBYW2l1jo9GI2NhY+RqnpqbCx8cHAwYMkPeJj4+HRqPB7t27Fa/5ZlFaWgoA8PPzAwDs27cPtbW1dte6W7duCA8Pt7vWvXv3RlBQkLxPQkICzGYzjhw5omD1Nw+r1Yrly5ejoqICcXFxvM6tYMaMGbjnnnvsrinAv9MtKSMjA6GhoYiKisKECROQm5sLwHGvMR+G2spKSkpgtVrtfqgAEBQUhOPHj6tUVdtSWFgIAFe8xg3bCgsLERgYaLddp9PBz89P3ofs2Ww2PPXUU7jtttvQq1cvABeuo16vh4+Pj92+v73WV/pZNGyjiw4dOoS4uDhUV1fD09MTq1atQo8ePZCWlsbr3IKWL1+O/fv34+eff75sG/9Ot4zY2FgkJSWha9euKCgowMsvv4yhQ4fi8OHDDnuNGYCI6IpmzJiBw4cPY/v27WqX0mZ17doVaWlpKC0txddff41JkyYhJSVF7bLalLy8PMyaNQubNm2Cq6ur2uW0WYmJifKf+/Tpg9jYWHTs2BFfffUV3NzcVKzs6tgF1sr8/f2h1WovG+1eVFSE4OBglapqWxquY2PXODg4GMXFxXbb6+rqcO7cOf4crmDmzJn4/vvvsWXLFnTo0EFeHxwcjJqaGphMJrv9f3utr/SzaNhGF+n1ekRHR6N///5YsGABYmJi8NZbb/E6t6B9+/ahuLgYt9xyC3Q6HXQ6HVJSUvD2229Dp9MhKCiI17oV+Pj4oEuXLjhx4oTD/n1mAGpler0e/fv3R3JysrzOZrMhOTkZcXFxKlbWdkRGRiI4ONjuGpvNZuzevVu+xnFxcTCZTNi3b5+8z+bNm2Gz2RAbG6t4zY5KCIGZM2di1apV2Lx5MyIjI+229+/fHy4uLnbXOj09Hbm5uXbX+tChQ3aBc9OmTfD29kaPHj2U+SA3KZvNBovFwuvcgoYPH45Dhw4hLS1NXgYMGIAJEybIf+a1bnnl5eXIzMxESEiI4/59bpWh1WRn+fLlwmAwiKSkJHH06FExbdo04ePjYzfanRpXVlYmfvnlF/HLL78IAGLhwoXil19+ETk5OUKIC9PgfXx8xLfffisOHjwo7r///itOg+/Xr5/YvXu32L59u+jcuTOnwf/G9OnThdFoFFu3brWbzlpZWSnv8/jjj4vw8HCxefNmsXfvXhEXFyfi4uLk7Q3TWe+66y6RlpYmNmzYIAICAjhl+Deee+45kZKSIrKyssTBgwfFc889JyRJEhs3bhRC8Dq3pktngQnBa90Snn76abF161aRlZUlduzYIeLj44W/v78oLi4WQjjmNWYAUsg777wjwsPDhV6vF4MGDRK7du1Su6SbypYtWwSAy5ZJkyYJIS5MhX/ppZdEUFCQMBgMYvjw4SI9Pd3uHGfPnhXjx48Xnp6ewtvbW0yePFmUlZWp8Gkc15WuMQCxbNkyeZ+qqirxxBNPCF9fX+Hu7i5Gjx4tCgoK7M6TnZ0tEhMThZubm/D39xdPP/20qK2tVfjTOLYpU6aIjh07Cr1eLwICAsTw4cPl8CMEr3Nr+m0A4rW+cePGjRMhISFCr9eL9u3bi3HjxokTJ07I2x3xGktCCNE6bUtEREREjoljgIiIiMjpMAARERGR02EAIiIiIqfDAEREREROhwGIiIiInA4DEBERETkdBiAiIiJyOgxARERE5HQYgIiIrkKSJKxevVrtMoioFTAAEZFDeuSRRyBJ0mXLiBEj1C6NiNoAndoFEBFdzYgRI7Bs2TK7dQaDQaVqiKgtYQsQETksg8GA4OBgu8XX1xfAhe6pJUuWIDExEW5uboiKisLXX39td/yhQ4fwu9/9Dm5ubmjXrh2mTZuG8vJyu32WLl2Knj17wmAwICQkBDNnzrTbXlJSgtGjR8Pd3R2dO3fGmjVr5G3nz5/HhAkTEBAQADc3N3Tu3PmywEZEjokBiIhuWi+99BLGjBmDAwcOYMKECXjooYdw7NgxAEBFRQUSEhLg6+uLn3/+GStWrMCPP/5oF3CWLFmCGTNmYNq0aTh06BDWrFmD6Ohou/d4+eWX8eCDD+LgwYO4++67MWHCBJw7d05+/6NHj2L9+vU4duwYlixZAn9/f+UuABFdv1Z7zjwR0Q2YNGmS0Gq1wsPDw275xz/+IYQQAoB4/PHH7Y6JjY0V06dPF0II8f777wtfX19RXl4ub1+7dq3QaDSisLBQCCFEaGioeOGFF65aAwDx4osvyq/Ly8sFALF+/XohhBAjR44UkydPbpkPTESK4hggInJYw4YNw5IlS+zW+fn5yX+Oi4uz2xYXF4e0tDQAwLFjxxATEwMPDw95+2233QabzYb09HRIkoT8/HwMHz680Rr69Okj/9nDwwPe3t4oLi4GAEyfPh1jxozB/v37cdddd2HUqFEYPHjwdX1WIlIWAxAROSwPD4/LuqRaipubW5P2c3FxsXstSRJsNhsAIDExETk5OVi3bh02bdqE4cOHY8aMGXjzzTdbvF4ialkcA0REN61du3Zd9rp79+4AgO7du+PAgQOoqKiQt+/YsQMajQZdu3aFl5cXIiIikJycfEM1BAQEYNKkSfj000/xn//8B++///4NnY+IlMEWICJyWBaLBYWFhXbrdDqdPNB4xYoVGDBgAIYMGYLPPvsMe/bswUcffQQAmDBhAubNm4dJkyZh/vz5OHPmDJ588kn86U9/QlBQEABg/vz5ePzxxxEYGIjExESUlZVhx44dePLJJ5tU39y5c9G/f3/07NkTFosF33//vRzAiMixMQARkcPasGEDQkJC7NZ17doVx48fB3Bhhtby5cvxxBNPICQkBF988QV69OgBAHB3d8cPP/yAWbNmYeDAgXB3d8eYMWOwcOFC+VyTJk1CdXU1Fi1ahGeeeQb+/v544IEHmlyfXq/HnDlzkJ2dDTc3NwwdOhTLly9vgU9ORK1NEkIItYsgImouSZKwatUqjBo1Su1SiOgmxDFARERE5HQYgIiIiMjpcAwQEd2U2HtPRDeCLUBERETkdBiAiIiIyOkwABEREZHTYQAiIiIip8MARERERE6HAYiIiIicDgMQEREROR0GICIiInI6/x/waKcChvSvxgAAAABJRU5ErkJggg==\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": "199957d3-25e4-4d9d-c7b7-fba812386612",
        "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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\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": "616ded91-fcf6-4fb6-8bb4-bcf17b1cebe4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 4ms/step - loss: 14.4219 - mae: 2.5352\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": "ea3d1930-b287-4c35-967c-0495545baddc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.53515625"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "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": "948e2981-7142-4e06-aeb7-38478a2a9d66",
        "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([8.93], dtype=float16)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "predictions = model.predict(test_data)\n",
        "predictions[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cyvfquV2Ryez"
      },
      "source": [
        "### 정리"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hN6PNjwSRyez"
      },
      "source": [
        "## 요약"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "chapter04_getting-started-with-neural-networks.i",
      "provenance": [],
      "gpuType": "V28"
    },
    "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"
    },
    "accelerator": "TPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}