chapter04_getting-started-with-neural-networks.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/FennexFox/-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>"
      ]
    },
    {
      "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": 1,
      "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"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.datasets import imdb\n",
        "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
        "    num_words=10000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:29.821048Z",
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          "shell.execute_reply": "2022-05-21T00:58:29.832695Z"
        },
        "id": "uAk9jmdMRyd0",
        "outputId": "f3304c70-6d9e-4b4d-abb9-683d37916660",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 32]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "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": "6861ea9e-e2ad-4107-c508-81aa86f95821",
        "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": "43410037-7737-423c-8b13-69e2e7f3b1f8",
        "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": "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"
      },
      "outputs": [],
      "source": [
        "word_index = imdb.get_word_index()\n",
        "reverse_word_index = dict(\n",
        "    [(value, key) for (key, value) in word_index.items()])\n",
        "decoded_review = \" \".join(\n",
        "    [reverse_word_index.get(i - 3, \"?\") for i in train_data[0]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TL-I0vCkRyd5"
      },
      "source": [
        "### 데이터 준비"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMw5jhfYRyd6"
      },
      "source": [
        "**정수 시퀀스를 멀티-핫 인코딩으로 인코딩하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "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": 8,
      "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": "6131f0fe-e7d5-4dce-bd85-65552bca338e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1., 1., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "x_train[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-21T00:58:35.771822Z",
          "iopub.status.busy": "2022-05-21T00:58:35.771015Z",
          "iopub.status.idle": "2022-05-21T00:58:35.775500Z",
          "shell.execute_reply": "2022-05-21T00:58:35.774750Z"
        },
        "id": "iBw59ZljRyd7"
      },
      "outputs": [],
      "source": [
        "y_train = np.asarray(train_labels).astype(\"float32\")\n",
        "y_test = np.asarray(test_labels).astype(\"float32\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LANAjB0URyd8"
      },
      "source": [
        "### 신경망 모델 만들기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qObD7ZSGRyd9"
      },
      "source": [
        "**모델 정의하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "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": 11,
      "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": 12,
      "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": 13,
      "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": "f5d65abd-9263-4c54-b229-55765b71e424",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 138ms/step - accuracy: 0.6962 - loss: 0.6070 - val_accuracy: 0.8657 - val_loss: 0.4116\n",
            "Epoch 2/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.8889 - loss: 0.3575 - val_accuracy: 0.8774 - val_loss: 0.3289\n",
            "Epoch 3/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.9137 - loss: 0.2677 - val_accuracy: 0.8887 - val_loss: 0.2881\n",
            "Epoch 4/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9287 - loss: 0.2123 - val_accuracy: 0.8862 - val_loss: 0.2854\n",
            "Epoch 5/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9462 - loss: 0.1773 - val_accuracy: 0.8896 - val_loss: 0.2747\n",
            "Epoch 6/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.9539 - loss: 0.1521 - val_accuracy: 0.8809 - val_loss: 0.3064\n",
            "Epoch 7/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.9567 - loss: 0.1367 - val_accuracy: 0.8770 - val_loss: 0.3083\n",
            "Epoch 8/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9624 - loss: 0.1239 - val_accuracy: 0.8823 - val_loss: 0.3125\n",
            "Epoch 9/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9651 - loss: 0.1087 - val_accuracy: 0.8809 - val_loss: 0.3267\n",
            "Epoch 10/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9756 - loss: 0.0894 - val_accuracy: 0.8809 - val_loss: 0.3359\n",
            "Epoch 11/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9746 - loss: 0.0811 - val_accuracy: 0.8809 - val_loss: 0.3413\n",
            "Epoch 12/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9822 - loss: 0.0692 - val_accuracy: 0.8794 - val_loss: 0.3528\n",
            "Epoch 13/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9862 - loss: 0.0592 - val_accuracy: 0.8774 - val_loss: 0.3711\n",
            "Epoch 14/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9884 - loss: 0.0518 - val_accuracy: 0.8774 - val_loss: 0.3931\n",
            "Epoch 15/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.9892 - loss: 0.0500 - val_accuracy: 0.8735 - val_loss: 0.4128\n",
            "Epoch 16/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9903 - loss: 0.0423 - val_accuracy: 0.8760 - val_loss: 0.4500\n",
            "Epoch 17/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.9937 - loss: 0.0339 - val_accuracy: 0.8750 - val_loss: 0.4568\n",
            "Epoch 18/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.9958 - loss: 0.0293 - val_accuracy: 0.8721 - val_loss: 0.4751\n",
            "Epoch 19/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.9958 - loss: 0.0252 - val_accuracy: 0.8735 - val_loss: 0.4932\n",
            "Epoch 20/20\n",
            "\u001b[1m30/30\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9990 - loss: 0.0204 - val_accuracy: 0.8721 - val_loss: 0.5405\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": 14,
      "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": "0f61787e-2019-4641-ce95-ca78062b9a7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['accuracy', 'loss', 'val_accuracy', 'val_loss'])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "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": 15,
      "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": "440ebfbd-9962-4f27-85cd-e5a898083189",
        "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": 16,
      "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": "3831bfdf-3e77-4048-faac-1cdb9fb916d6",
        "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": 17,
      "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": "0c815df6-92f5-4045-d666-8a4e6f0537c8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 36ms/step - accuracy: 0.7294 - loss: 0.5726\n",
            "Epoch 2/4\n",
            "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.8956 - loss: 0.3024\n",
            "Epoch 3/4\n",
            "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9174 - loss: 0.2276\n",
            "Epoch 4/4\n",
            "\u001b[1m49/49\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.9355 - loss: 0.1827\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 4ms/step - accuracy: 0.8896 - loss: 0.2870\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": 18,
      "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": "f47fc6a5-e7ae-48a8-b50f-df1db084bcb3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.287353515625, 0.91748046875]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9b6hcHKARyeG"
      },
      "source": [
        "### 훈련된 모델로 새로운 데이터에 대해 예측하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "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": "f72af20f-152a-47c9-8e46-914471bcef4c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.1782 ],\n",
              "       [0.999  ],\n",
              "       [0.714  ],\n",
              "       ...,\n",
              "       [0.0788 ],\n",
              "       [0.05072],\n",
              "       [0.5337 ]], dtype=float16)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "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": "code",
      "source": [],
      "metadata": {
        "id": "qo9iP6aqaDSV"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxiQdKzgRyeM"
      },
      "source": [
        "**로이터 데이터셋 로드하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "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"
      },
      "outputs": [],
      "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": 55,
      "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": "ad2698f6-4353-4377-cad4-dd691fb90196",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8982"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "len(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "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": "0b89952c-7859-4478-817e-0176a3f36afd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2246"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "len(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "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": "a0bc7972-6423-4751-9aa8-bf89855e6fd2",
        "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": 57
        }
      ],
      "source": [
        "train_data[10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ou52UiQ2RyeO"
      },
      "source": [
        "**로이터 데이터셋을 텍스트로 디코딩하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "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": "f708ad3d-d501-4ee3-adb6-97b548f8c365",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "? ? ? said as a result of its december acquisition of space co it expects earnings per share in 1987 of 1 15 to 1 30 dlrs per share up from 70 cts in 1986 the company said pretax net should rise to nine to 10 mln dlrs from six mln dlrs in 1986 and rental operation revenues to 19 to 22 mln dlrs from 12 5 mln dlrs it said cash flow per share this year should be 2 50 to three dlrs reuter 3\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]])\n",
        "print(decoded_newswire)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "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": "c4c2a0b2-d390-4b52-8b9d-3a2354d61496",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "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": 60,
      "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": "code",
      "source": [
        "print(x_train[10])\n",
        "print(len(x_train[10]))\n",
        "print(len(x_train))"
      ],
      "metadata": {
        "id": "uQ1zXifAV1H6",
        "outputId": "54995712-f54c-45c5-863f-590a3edce3b6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 1. 0. ... 0. 0. 0.]\n",
            "10000\n",
            "8982\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(train_labels[10])\n",
        "print(len(train_labels))"
      ],
      "metadata": {
        "id": "yisFptO0WEpr",
        "outputId": "670ce941-5112-4670-b959-d8b0df6c374d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n",
            "8982\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdQHSLwjRyeQ"
      },
      "source": [
        "**레이블 인코딩하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "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": 62,
      "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": 31,
      "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": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "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": 32,
      "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": 33,
      "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": 34,
      "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": "4bf1e98d-142a-49dc-f6b4-0fc062b961e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 293ms/step - accuracy: 0.4214 - loss: 3.1891 - val_accuracy: 0.6362 - val_loss: 1.8340\n",
            "Epoch 2/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6744 - loss: 1.6050 - val_accuracy: 0.6909 - val_loss: 1.3984\n",
            "Epoch 3/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7316 - loss: 1.2217 - val_accuracy: 0.7280 - val_loss: 1.2197\n",
            "Epoch 4/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7847 - loss: 0.9910 - val_accuracy: 0.7471 - val_loss: 1.1123\n",
            "Epoch 5/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8174 - loss: 0.8475 - val_accuracy: 0.7690 - val_loss: 1.0400\n",
            "Epoch 6/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8451 - loss: 0.7036 - val_accuracy: 0.7578 - val_loss: 1.0342\n",
            "Epoch 7/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.8694 - loss: 0.6156 - val_accuracy: 0.8018 - val_loss: 0.9346\n",
            "Epoch 8/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8911 - loss: 0.5152 - val_accuracy: 0.7959 - val_loss: 0.9502\n",
            "Epoch 9/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9121 - loss: 0.4247 - val_accuracy: 0.8091 - val_loss: 0.8984\n",
            "Epoch 10/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9213 - loss: 0.3675 - val_accuracy: 0.8101 - val_loss: 0.8760\n",
            "Epoch 11/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9296 - loss: 0.3307 - val_accuracy: 0.8062 - val_loss: 0.8872\n",
            "Epoch 12/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9374 - loss: 0.2890 - val_accuracy: 0.8032 - val_loss: 0.9111\n",
            "Epoch 13/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9449 - loss: 0.2620 - val_accuracy: 0.8081 - val_loss: 0.8799\n",
            "Epoch 14/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9488 - loss: 0.2208 - val_accuracy: 0.8159 - val_loss: 0.8921\n",
            "Epoch 15/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9541 - loss: 0.2028 - val_accuracy: 0.7910 - val_loss: 0.9600\n",
            "Epoch 16/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9533 - loss: 0.1901 - val_accuracy: 0.8062 - val_loss: 0.9150\n",
            "Epoch 17/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9542 - loss: 0.1670 - val_accuracy: 0.7842 - val_loss: 0.9995\n",
            "Epoch 18/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9563 - loss: 0.1544 - val_accuracy: 0.8008 - val_loss: 0.9268\n",
            "Epoch 19/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9528 - loss: 0.1515 - val_accuracy: 0.7959 - val_loss: 0.9780\n",
            "Epoch 20/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9532 - loss: 0.1524 - val_accuracy: 0.8110 - val_loss: 0.9355\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": 35,
      "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": "4b5698af-f517-44dc-8343-48282b65698c",
        "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",
        "\n",
        "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": 36,
      "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": "62889246-f8d6-4c1d-cc67-0bc79fb72e9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.clf()\n",
        "acc = history.history[\"accuracy\"]\n",
        "val_acc = history.history[\"val_accuracy\"]\n",
        "plt.plot(epochs, acc, \"bo\", label=\"Training accuracy\")\n",
        "plt.plot(epochs, val_acc, \"b\", label=\"Validation accuracy\")\n",
        "plt.title(\"Training and validation accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QIGN5jk2Ryeb"
      },
      "source": [
        "**모델을 처음부터 다시 훈련하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "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": "7a0f9de3-71d5-4512-e90c-f8a46970b116",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/9\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 125ms/step - accuracy: 0.3658 - loss: 3.1437\n",
            "Epoch 2/9\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.6676 - loss: 1.5869\n",
            "Epoch 3/9\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7413 - loss: 1.1904\n",
            "Epoch 4/9\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7823 - loss: 0.9928\n",
            "Epoch 5/9\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8200 - loss: 0.7994\n",
            "Epoch 6/9\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8543 - loss: 0.6703\n",
            "Epoch 7/9\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8767 - loss: 0.5692\n",
            "Epoch 8/9\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9031 - loss: 0.4781\n",
            "Epoch 9/9\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9151 - loss: 0.3958\n",
            "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - accuracy: 0.7949 - loss: 0.9017\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": 38,
      "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": "b02d0092-d413-4500-ff9f-d1a7cbf7a636",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9208984375, 0.78857421875]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "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": "ffcf2330-82a7-4b52-b7bd-27d6aa2f886c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.19857524487978628"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "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": 40,
      "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": "47c91b00-edc7-4ef6-af1a-ea2fa2644fd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "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": "e39978d3-0298-4b29-d20a-b3554fcb9dc6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(46,)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "predictions[0].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "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": "19c4a5bb-d277-485d-92b8-4cfbd4531648",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9995"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "np.sum(predictions[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "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": "497658b8-30ec-45e4-ac29-8c1a64058ef6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "np.argmax(predictions[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7BVjZPk3Ryef"
      },
      "source": [
        "### 레이블과 손실을 다루는 다른 방법"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "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": 45,
      "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": 46,
      "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": "695ccf3f-6e46-4c68-879a-4e9aa7f7df6d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 76ms/step - accuracy: 0.2197 - loss: 3.3469 - val_accuracy: 0.4031 - val_loss: 2.3379\n",
            "Epoch 2/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.4350 - loss: 2.1181 - val_accuracy: 0.5742 - val_loss: 1.8096\n",
            "Epoch 3/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6370 - loss: 1.5792 - val_accuracy: 0.6011 - val_loss: 1.5244\n",
            "Epoch 4/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6369 - loss: 1.3363 - val_accuracy: 0.6362 - val_loss: 1.4092\n",
            "Epoch 5/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6845 - loss: 1.1871 - val_accuracy: 0.6670 - val_loss: 1.3662\n",
            "Epoch 6/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7231 - loss: 1.0674 - val_accuracy: 0.6738 - val_loss: 1.3379\n",
            "Epoch 7/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7384 - loss: 0.9921 - val_accuracy: 0.6968 - val_loss: 1.3057\n",
            "Epoch 8/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7576 - loss: 0.9141 - val_accuracy: 0.7061 - val_loss: 1.2959\n",
            "Epoch 9/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7780 - loss: 0.8391 - val_accuracy: 0.7002 - val_loss: 1.2988\n",
            "Epoch 10/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7820 - loss: 0.8153 - val_accuracy: 0.7061 - val_loss: 1.3145\n",
            "Epoch 11/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7976 - loss: 0.7617 - val_accuracy: 0.7070 - val_loss: 1.3486\n",
            "Epoch 12/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8087 - loss: 0.7105 - val_accuracy: 0.7070 - val_loss: 1.3516\n",
            "Epoch 13/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8184 - loss: 0.6615 - val_accuracy: 0.7051 - val_loss: 1.3857\n",
            "Epoch 14/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8135 - loss: 0.6634 - val_accuracy: 0.7002 - val_loss: 1.4072\n",
            "Epoch 15/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8202 - loss: 0.6262 - val_accuracy: 0.6938 - val_loss: 1.4600\n",
            "Epoch 16/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8242 - loss: 0.5949 - val_accuracy: 0.7061 - val_loss: 1.4707\n",
            "Epoch 17/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8236 - loss: 0.5861 - val_accuracy: 0.7021 - val_loss: 1.5361\n",
            "Epoch 18/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8354 - loss: 0.5451 - val_accuracy: 0.7119 - val_loss: 1.5273\n",
            "Epoch 19/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8411 - loss: 0.5310 - val_accuracy: 0.7031 - val_loss: 1.6035\n",
            "Epoch 20/20\n",
            "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8482 - loss: 0.5154 - val_accuracy: 0.7168 - val_loss: 1.5752\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7c8afd30ce20>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "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",
      "source": [
        "#### 가중치 규제를 적용한 경우\n",
        "1. 과대적합 시점을 알기 위한 탐색적 훈련"
      ],
      "metadata": {
        "id": "WoPQcrvjVKVU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import regularizers\n",
        "\n",
        "model = keras.Sequential([\n",
        "    layers.Dense(64, activation=\"relu\", kernel_regularizer = regularizers.l2(0.002)),\n",
        "    layers.Dense(64, activation=\"relu\", kernel_regularizer = regularizers.l2(0.002)),\n",
        "    # layers.Dense(256, activation=\"relu\", kernel_regularizer = regularizers.l2()),\n",
        "    # layers.Dense(64, activation=\"relu\"),\n",
        "    # layers.Dense(64, activation=\"relu\"),\n",
        "    # layers.Dense(256, activation=\"relu\"),\n",
        "    layers.Dense(46, activation=\"softmax\")\n",
        "])\n",
        "\n",
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "history_weight_reg = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=40,\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val))"
      ],
      "metadata": {
        "id": "-JEjQsR1VFbu",
        "outputId": "b7591fd9-5ee0-4b7e-9db7-bf54baf2a5d7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 336ms/step - accuracy: 0.4030 - loss: 3.5347 - val_accuracy: 0.6152 - val_loss: 2.1621\n",
            "Epoch 2/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - accuracy: 0.6572 - loss: 1.9943 - val_accuracy: 0.6748 - val_loss: 1.7305\n",
            "Epoch 3/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.7281 - loss: 1.5562 - val_accuracy: 0.7231 - val_loss: 1.5234\n",
            "Epoch 4/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.7705 - loss: 1.3436 - val_accuracy: 0.7402 - val_loss: 1.4102\n",
            "Epoch 5/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7974 - loss: 1.2115 - val_accuracy: 0.7749 - val_loss: 1.3340\n",
            "Epoch 6/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8298 - loss: 1.0767 - val_accuracy: 0.7842 - val_loss: 1.2881\n",
            "Epoch 7/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8475 - loss: 0.9917 - val_accuracy: 0.7920 - val_loss: 1.2598\n",
            "Epoch 8/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8691 - loss: 0.9191 - val_accuracy: 0.7832 - val_loss: 1.2773\n",
            "Epoch 9/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8719 - loss: 0.8852 - val_accuracy: 0.7900 - val_loss: 1.2549\n",
            "Epoch 10/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8847 - loss: 0.8458 - val_accuracy: 0.8071 - val_loss: 1.2148\n",
            "Epoch 11/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8971 - loss: 0.8096 - val_accuracy: 0.8179 - val_loss: 1.2119\n",
            "Epoch 12/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9121 - loss: 0.7606 - val_accuracy: 0.8071 - val_loss: 1.2080\n",
            "Epoch 13/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9130 - loss: 0.7463 - val_accuracy: 0.7920 - val_loss: 1.2334\n",
            "Epoch 14/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9214 - loss: 0.7020 - val_accuracy: 0.8008 - val_loss: 1.2676\n",
            "Epoch 15/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9199 - loss: 0.6992 - val_accuracy: 0.8110 - val_loss: 1.1904\n",
            "Epoch 16/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9259 - loss: 0.6736 - val_accuracy: 0.8232 - val_loss: 1.1963\n",
            "Epoch 17/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9336 - loss: 0.6429 - val_accuracy: 0.8159 - val_loss: 1.1904\n",
            "Epoch 18/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9331 - loss: 0.6457 - val_accuracy: 0.8008 - val_loss: 1.1973\n",
            "Epoch 19/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9403 - loss: 0.6301 - val_accuracy: 0.7832 - val_loss: 1.3076\n",
            "Epoch 20/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.9374 - loss: 0.6286 - val_accuracy: 0.7949 - val_loss: 1.2236\n",
            "Epoch 21/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.9371 - loss: 0.6128 - val_accuracy: 0.8149 - val_loss: 1.1924\n",
            "Epoch 22/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.9460 - loss: 0.5995 - val_accuracy: 0.8071 - val_loss: 1.1992\n",
            "Epoch 23/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.9460 - loss: 0.5875 - val_accuracy: 0.7881 - val_loss: 1.2480\n",
            "Epoch 24/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9463 - loss: 0.5795 - val_accuracy: 0.7959 - val_loss: 1.2393\n",
            "Epoch 25/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.9474 - loss: 0.5860 - val_accuracy: 0.8008 - val_loss: 1.1963\n",
            "Epoch 26/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.9466 - loss: 0.5715 - val_accuracy: 0.8159 - val_loss: 1.1699\n",
            "Epoch 27/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.9472 - loss: 0.5609 - val_accuracy: 0.8169 - val_loss: 1.1816\n",
            "Epoch 28/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9497 - loss: 0.5548 - val_accuracy: 0.8101 - val_loss: 1.1992\n",
            "Epoch 29/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9556 - loss: 0.5424 - val_accuracy: 0.8130 - val_loss: 1.1943\n",
            "Epoch 30/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9514 - loss: 0.5436 - val_accuracy: 0.8042 - val_loss: 1.1904\n",
            "Epoch 31/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9525 - loss: 0.5356 - val_accuracy: 0.8091 - val_loss: 1.1777\n",
            "Epoch 32/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9517 - loss: 0.5282 - val_accuracy: 0.7871 - val_loss: 1.2422\n",
            "Epoch 33/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9524 - loss: 0.5287 - val_accuracy: 0.8149 - val_loss: 1.1885\n",
            "Epoch 34/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.9571 - loss: 0.5116 - val_accuracy: 0.8052 - val_loss: 1.1943\n",
            "Epoch 35/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9538 - loss: 0.5227 - val_accuracy: 0.8081 - val_loss: 1.1738\n",
            "Epoch 36/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9592 - loss: 0.5043 - val_accuracy: 0.8140 - val_loss: 1.1797\n",
            "Epoch 37/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9574 - loss: 0.5031 - val_accuracy: 0.7998 - val_loss: 1.1934\n",
            "Epoch 38/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9547 - loss: 0.5035 - val_accuracy: 0.7969 - val_loss: 1.2158\n",
            "Epoch 39/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9602 - loss: 0.4943 - val_accuracy: 0.7920 - val_loss: 1.1855\n",
            "Epoch 40/40\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9549 - loss: 0.4988 - val_accuracy: 0.7910 - val_loss: 1.2197\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history_weight_reg.history[\"loss\"]\n",
        "val_loss = history_weight_reg.history[\"val_loss\"]\n",
        "acc = history_weight_reg.history[\"accuracy\"]\n",
        "val_acc = history_weight_reg.history[\"val_accuracy\"]\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "fig = plt.figure(figsize = (10, 5))\n",
        "\n",
        "train_val_loss = fig.add_subplot(1, 2, 1)\n",
        "train_val_loss.plot(epochs, loss, 'o', label = \"Training loss\")\n",
        "train_val_loss.plot(epochs, val_loss, '', label = \"Validation loss\")\n",
        "train_val_loss.set_title(\"Loss\")\n",
        "train_val_loss.set_xlabel(\"Epochs\")\n",
        "train_val_loss.set_ylabel(\"Loss\")\n",
        "# train_val_loss.set_ylim([0.5, 1.5])\n",
        "train_val_loss.legend()\n",
        "\n",
        "train_val_acc = fig.add_subplot(1, 2, 2)\n",
        "train_val_acc.plot(epochs, acc, 'o', label = \"Training Accuracy\")\n",
        "train_val_acc.plot(epochs, val_acc, '', label = \"Validation Accuracy\")\n",
        "train_val_acc.set_title(\"Accuracy\")\n",
        "train_val_acc.set_xlabel(\"Epochs\")\n",
        "train_val_acc.set_ylabel(\"Accuracy\")\n",
        "# train_val_acc.set_ylim([0.9, 1])\n",
        "train_val_acc.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ALm05wwoeqLO",
        "outputId": "2b494510-a5d9-4938-b172-a357ab73773a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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rFosl7fZff/2Fn58f3377LS1atMDLy4v169ezd+9eevbsSVBQEOXKlaNly5asXr063XFr1arFpEmT0m5bLBY+/vhjevfuja+vL/Xq1WPp0qVp969btw6LxZJWQjdr1izKly/PypUradiwIeXKlSM8PDxdonfx4kUee+wxypcvT8WKFRk9ejSDBg2iV69euXoNpk6dSp06dfD09KR+/fp89tlnaffZ7XbGjRtHjRo18PLyokqVKjz22GNp97///vvUq1cPb29vgoKCuOuuu3J1bhERyVmKzc7GvSf4atshNu49kau/hSuiYunw6lr6f7SJEfO20f+jTXR4dS0romJztU9ez5H6pejVn3/j4hP5v9mRPL3oj2z/9o9b+ifjlmb/+eDpRX9keY5H5v7m0GfvKWv35Os1yAuXJk5Tp06lcePG+Pv74+/vT9u2bfn222+zfcyCBQto0KAB3t7eNGrUiG+++aZAYzx6Out/uLzsJ1JSFOUvFZ5++mleeeUVdu7cSePGjTlz5gzdu3dnzZo1/Pbbb4SHhxMREUFMTEy2xxk/fjx9+vTh999/p3v37tx7772cPJn18zl37hxvvPEGn332GT/++CMxMTE8+eSTafe/+uqrzJkzh5kzZ7JhwwYSEhJYsmRJrp7b4sWLGTFiBE888QRRUVH85z//YciQIXz//fcAfPnll7z99tt88MEH/P333yxZsoRGjRoBsHXrVh577DEmTJjArl27WLFiBR07dszV+UVEJPvEyJGkJqvHZ5e0DJ0dmWNik7pPdvKbGJ06l5zlse1AXEIScQnZfz44dS4523M44u3Vu/P8GuSVS0v1qlWrxiuvvEK9evWw2+188skn9OzZk99++43rrrsuw/4///wz/fv3Z+LEidx2223MnTuXXr16ERkZSVhYWIHEGOjn2OKNju4nUlIU5S8VJkyYQNeuXdNuBwQE0KRJk7TbL7zwAosXL2bp0qUMHz48y+MMHjyY/v37A/Dyyy/zzjvvsHnzZsLDwzPdPzk5mWnTplGnTh0Ahg8fzoQJE9Luf/fddxkzZgy9e/cGYMqUKbn+8ueNN95g8ODBPPLIIwCMGjWKTZs28cYbb3DTTTcRExNDcHAwXbp0wcPDgxo1atCqVSsAYmJiKFu2LLfddht+fn7UrFmTZs2a5er8IiLFXX5L3JxRxpbXErVxS/8ELHkuY0s9d14To6KuoKfRuDRxioiISHf7pZdeYurUqWzatCnTxGny5MmEh4fz3//+FzAfflatWsWUKVOYNm1agcTYqnYAIVZv4uITM/0lswDB1su/jCKlRVH+UuH6669Pd/vMmTOMGzeO5cuXExsby8WLFzl//nyOI06NGzdOu162bFn8/f05evRolvv7+vqmJU0AISEhafvHx8dz5MiRtCQGwN3dnRYtWmCz2Rx+bjt37uThhx9Ot619+/ZMnjwZgLvvvptJkyZxzTXXEB4eTvfu3YmIiKBMmTJ07dqVmjVrpt0XHh6eVoooIlIaONrQKC9NC/5vdiTlfT2yTWqeXvQH8ZmMtqSWqGUndTQnp31Sy9jmbYnJ8Dz7tayebbVISXBlxUvbOhWdeuwiM8cpJSWFefPmcfbsWdq2bZvpPhs3bqRLly7ptnXr1o2NGzdmedykpCQSEhLSXXLD3c2S9g3C1Tlr6u2xEaFqDCGlTuqXCln95lswb9Ku+FLh6u54Tz75JIsXL+bll1/mp59+Ytu2bTRq1IgLFy5kexwPD490ty0WS7ZJTmb72+3OneOVk+rVq7Nr1y7ef/99fHx8eOSRR+jYsSPJycn4+fkRGRnJ559/TkhICM8//zxNmjRRq3MRKVHyUwaXul9mpXbf/H44X6M1zipRc0RWZWxvr/7byWdKzwIE+3sR7J/154PcHi+729kpiIoXlydOf/zxB+XKlcPLy4v/+7//Y/HixYSGhma6b1xcHEFBQem2BQUFERcXl+XxJ06ciNVqTbtUr1491zGGh4UwdUBzgq3pvzkPtnqrFbmUWsXpS4UNGzYwePBgevfuTaNGjQgODmbfvn2FGoPVaiUoKIgtW7akbUtJSSEyMjJXx2nYsCEbNmxIt23Dhg3p3jd9fHyIiIjgnXfeYd26dWzcuJE//vgDgDJlytClSxdee+01fv/9d/bt28fatWvz8cxERBznSNOEnPbJy/wiR5Ke8ct28M3vWSdXOTUtKOqckZxZgPK+HljI+m//uNuvY9zt2X8+SD1GVucIsXrz/j2Zf/Z+vEs9h2ItiIoXl7cjr1+/Ptu2bSM+Pp6FCxcyaNAgfvjhhyyTp9waM2YMo0aNSrudkJCQ5+Spa2hwsV/kU8SZUr9UuLrsIbiILQ5dr149Fi1aREREBBaLheeeey5X5XHO8uijjzJx4kTq1q1LgwYNePfdd/n3339zXJvqSv/973/p06cPzZo1o0uXLixbtoxFixaldQmcNWsWKSkptG7dGl9fX2bPno2Pjw81a9bk66+/5p9//qFjx45UqFCBb775BpvNRv369QvqKYuIpHHG2j95nV/kSBlcbHwiz34VVSgjQnlhAYL8vQALRxIyn0LijHNYfT2IvzRyZr/qPoBX7jANh3L625/d5wMw/1aWLM6RepxuYRk/ewPM23LAJdNoXJ44eXp6UrduXQBatGjBli1bmDx5Mh988EGGfYODgzly5Ei6bUeOHMl2LRUvLy+8vLycEqu7m8XptZIixV1x+FLhrbfe4v7776ddu3ZUqlSJ0aNH57ps1xlGjx5NXFwcAwcOxN3dnYcffphu3brh7u7u8DF69erF5MmTeeONNxgxYgS1a9dm5syZdOrUCYDy5cvzyiuvMGrUKFJSUmjUqBHLli2jYsWKlC9fnkWLFjFu3DgSExOpV68en3/+eaZzSkVEnMmRdf8g+8YKD3eszYc/RudpfpGjTp7NvoS7sGSVUIy73bxfZ5V05Oa5ZnUORxOjnP725/T5wJEvXrP67D02IjTHxKsgPodY7IVdgJ+Dzp07U6NGDWbNmpXhvr59+3Lu3DmWLVuWtq1du3Y0btzY4eYQCQkJWK1W4uPj8ff3d1bYIsVSYmIi0dHR1K5dG29vdYYsbDabjYYNG9KnTx9eeOEFV4fjNNn9Xuk9OHN6XaQkS7HZ6fDq2izL3K4cScmujbWbBZy8NKBT5TRaY8eUqGXWHCJ1n2CrN8/1COWF5XkbmevXsrpD85ge73Jtps0jrjxHfrsPOiI/53C00UdOcvP+69IRpzFjxnDrrbdSo0YNTp8+zdy5c1m3bh0rV64EYODAgVStWpWJEycCMGLECG688UbefPNNevTowbx589i6dSsffvihK5+GiIhD9u/fz3fffceNN95IUlISU6ZMITo6mnvuucfVoYmIOCQvH3QdWfcvp25xULBJkwWoUNaDk2cda8Wd19EayHuJmiOjOeBYGdvwznUZ3rlutucojEqr/JzDFRUvLk2cjh49ysCBA4mNjcVqtdK4cWNWrlyZtv5KTEwMbm6X+1e0a9eOuXPn8uyzz/K///2PevXqsWTJkgJbw0lExJnc3NyYNWsWTz75JHa7nbCwMFavXk3Dhg1dHZqISI7y+g2/K9bzy05WScuLPcN4YfnOHJOOzEaEclPGlp8StSs5o4ytuE9BKexpNEWuVK+gqRxC5DKV6klBUKle7ul1kaIit+sXpX4YT+0ynNnjN0efpP9Hmwr7qWSI05EyuNTnCZknHdk9z9yMdBR0GZyzythKg2JTqiciIiIiRUNWH7af69GQF5bvzHZh1/HLdmCzkWlS8lyPhoRYvbMdyXGkW5ybBez2zBsgONINzpEyOEe7xeZ3pKOgR0qKQ+Om4kgjTiKlmEacpCBoxCn39LqIq2U3opSfD4qpH9NTO+JB1iM5QLajPY4ewxkjLYXRGEGKBo04iYiIiIhDUmz2HBeHzavUEaml22N5757mOc4Nymm0p1mNCvluk+0ILUEjmVHiJCIiIlJKZDUHKbuud/mVurhshbKerB/dOV9r/zhSgqakRwqKEicRERGREiCn8rKs5jB1DwsulPiOnk7MV7c4R+8XKShKnERERESKuZy6qGU1hykuPpHpG/Y5fJ7MWlw7Ws4X6Ke5tFK8ueW8i4hIydOpUydGjhyZdrtWrVpMmjQp28dYLBaWLFmS73M76zjZGTduHE2bNi3Qc4hI0ZCaFF1dbhcXn8jQ2ZF88/vhHOcwuVkuN1m4mgWThL1/T3OCremTn2CrN+/f04wQq3eOj09doFWkuNKIk4gUKxERESQnJ7NixYoM9/3000907NiR7du307hx41wdd8uWLZQtW9ZZYQImeVmyZAnbtm1Ltz02NpYKFSo49VwiUjrl1NjBAjz7VRQnzyZnexzbpQNkt2hqdq283dwsDi+6KlJcacRJRIqVBx54gFWrVnHw4MEM982cOZPrr78+10kTQOXKlfH19XVGiDkKDg7Gy8urUM4lIiVbTo0d7JBj0pTq/va1Mh1RSl30FS7PL+rZtCpt61TMsP5RTo8XKc6UOInIZXY7XDjrmouDS8rddtttVK5cmVmzZqXbfubMGRYsWMADDzzAiRMn6N+/P1WrVsXX15dGjRrx+eefZ3vcq0v1/v77bzp27Ii3tzehoaGsWrUqw2NGjx7Ntddei6+vL9dccw3PPfccycnmA8qsWbMYP34827dvx2KxYLFY0mK+ulTvjz/+oHPnzvj4+FCxYkUefvhhzpw5k3b/4MGD6dWrF2+88QYhISFUrFiRYcOGpZ3LETabjQkTJlCtWjW8vLxo2rRpulG7CxcuMHz4cEJCQvD29qZmzZpMnDgRALvdzrhx46hRowZeXl5UqVKFxx57zOFzi4hzpNjsbNx7gq+2HWLj3hOk2OwcPe28bnhdQ4NZP7oznz/Uhsn9mvL5Q21YP7qzw0lPeFhIvh4vUtSpVE9ELks+By9Xcc25/3cYPHMulStTpgwDBw5k1qxZPPPMM1gs5tvOBQsWkJKSQv/+/Tlz5gwtWrRg9OjR+Pv7s3z5cu677z7q1KlDq1atcjyHzWbjjjvuICgoiF9++YX4+Ph086FS+fn5MWvWLKpUqcIff/zBQw89hJ+fH0899RR9+/YlKiqKFStWsHr1agCsVmuGY5w9e5Zu3brRtm1btmzZwtGjR3nwwQcZPnx4uuTw+++/JyQkhO+//549e/bQt29fmjZtykMPPZTj8wGYPHkyb775Jh988AHNmjVjxowZ3H777fz555/Uq1ePd955h6VLl/LFF19Qo0YNDhw4wIEDBwD48ssvefvtt5k3bx7XXXcdcXFxbN++3aHziohzZNX8oV/L6g49PqCsJ/+evZBpSZ8FMzKUWnaXn4516ngnJZkSJxEpdu6//35ef/11fvjhBzp16gSYMr0777wTq9WK1WrlySefTNv/0UcfZeXKlXzxxRcOJU6rV6/mr7/+YuXKlVSpYhLJl19+mVtvvTXdfs8++2za9Vq1avHkk08yb948nnrqKXx8fChXrhxlypQhODjrVr9z584lMTGRTz/9NG2O1ZQpU4iIiODVV18lKCgIgAoVKjBlyhTc3d1p0KABPXr0YM2aNQ4nTm+88QajR4+mX79+ALz66qt8//33TJo0iffee4+YmBjq1atHhw4dsFgs1KxZM+2xMTExBAcH06VLFzw8PKhRo4ZDr6OIOEd2HfHeXv035X09iD+XnG1S9FyPUIbN1RwkkfxQ4iQil3n4mpEfV53bQQ0aNKBdu3bMmDGDTp06sWfPHn766ScmTJgAQEpKCi+//DJffPEFhw4d4sKFCyQlJTk8h2nnzp1Ur149LWkCaNu2bYb95s+fzzvvvMPevXs5c+YMFy9exN/f3+HnkXquJk2apGtM0b59e2w2G7t27UpLnK677jrc3d3T9gkJCeGPP/5w6BwJCQkcPnyY9u3bp9vevn37tJGjwYMH07VrV+rXr094eDi33XYbt9xyCwB33303kyZN4pprriE8PJzu3bsTERFBmTL6EyLiTJmtwwTk2PwhVU6NHaa6Nc8wahV8RctyEcme/uqJyGUWi0PlckXBAw88wKOPPsp7773HzJkzqVOnDjfeeCMAr7/+OpMnT2bSpEk0atSIsmXLMnLkSC5cuOC082/cuJF7772X8ePH061bN6xWK/PmzePNN9902jmu5OHhke62xWLBZrM57fjNmzcnOjqab7/9ltWrV9OnTx+6dOnCwoULqV69Ort27WL16tWsWrWKRx55JG3E7+q4RCRvsivFy6n5w6lzyTze5VrmbYnJNikKDwuha2jmXfFEJGdKnESkWOrTpw8jRoxg7ty5fPrppwwdOjRtvtOGDRvo2bMnAwYMAMycpd27dxMaGurQsRs2bMiBAweIjY0lJMR84Ni0aVO6fX7++Wdq1qzJM888k7Zt//796fbx9PQkJSUlx3PNmjWLs2fPpo06bdiwATc3N+rXr+9QvDnx9/enSpUqbNiwIS25TD3PlSV3/v7+9O3bl759+3LXXXcRHh7OyZMnCQgIwMfHh4iICCIiIhg2bBgNGjTgjz/+oHnz5k6JUaQ0y6kUzxG1KvmyfnTnHJMizUESyTslTiJSLJUrV46+ffsyZswYEhISGDx4cNp99erVY+HChfz8889UqFCBt956iyNHjjicOHXp0oVrr72WQYMG8frrr5OQkJAuQUo9R0xMDPPmzaNly5YsX76cxYsXp9unVq1aREdHs23bNqpVq4afn1+GNuT33nsvY8eOZdCgQYwbN45jx47x6KOPct9996WV6TnDf//7X8aOHUudOnVo2rQpM2fOZNu2bcyZMweAt956i5CQEJo1a4abmxsLFiwgODiY8uXLM2vWLFJSUmjdujW+vr7Mnj0bHx+fdPOgRCRneSnFc1Sgn7eSIpECpsRJRIqtBx54gOnTp9O9e/d085GeffZZ/vnnH7p164avry8PP/wwvXr1Ij4+3qHjurm5sXjxYh544AFatWpFrVq1eOeddwgPD0/b5/bbb+fxxx9n+PDhJCUl0aNHD5577jnGjRuXts+dd97JokWLuOmmmzh16hQzZ85Ml+AB+Pr6snLlSkaMGEHLli3x9fXlzjvv5K233srXa3O1xx57jPj4eJ544gmOHj1KaGgoS5cupV69eoDpEPjaa6/x999/4+7uTsuWLfnmm29wc3OjfPnyvPLKK4waNYqUlBQaNWrEsmXLqFhRH9BEHJXXUrycXNkRT0QKlsVud3DxlBIiISEBq9VKfHx8ridxi5Q0iYmJREdHU7t2bby9vXN+gIgDsvu90ntw5vS6lGxZleJd3cwhJ1k1f9ACsyJ5l5v3Xy2AKyIiIuIEmS1Qm2KzO6UU7/Eu1xJsTf9FRLDVW0mTSCFSqZ6IiIhIPhV0Kd7wznUZ3rmuOuKJuJASJxEREZF8cEZXPMh+HabUBEnNH0RcR6V6IiIiIjnIrAwvdbtK8URKB404iQilrEeMFDD9PklJk1UZ3tiIUKw+nirFEykllDiJlGIeHh4AnDt3Dh8fHxdHIyXFuXPngMu/XyLFWXZleENnR3J/+1oOH0uleCLFmxInkVLM3d2d8uXLc/ToUcCsKWSx6NtNyRu73c65c+c4evQo5cuXx93d3dUhieRLTmV4FmDxtkMOHevxLtcyb0tMutGp4EujVirFEykelDiJlHLBwcEAacmTSH6VL18+7fdKpDjbHH0y2zI8O3DybDIBZT359+yFTBMsleKJlBxKnERKOYvFQkhICIGBgSQnJ7s6HCnmPDw8NNIkJcbR047NXerVtAozN+xTKZ5ICafESUQAU7anD7wiIpcF+nnnvBPQNTSYVrUDMjSQUCmeSMmiduQiIiJXee+996hVqxbe3t60bt2azZs3Z7lvcnIyEyZMoE6dOnh7e9OkSRNWrFhRiNFKQWlVO4AQqzdZFdNZMN31WtUOIDwshPWjO/P5Q22Y3K8pnz/UhvWjOytpEilBlDiJiIhcYf78+YwaNYqxY8cSGRlJkyZN6NatW5bzAJ999lk++OAD3n33XXbs2MH//d//0bt3b3777bdCjlzyI7N1mtzdLIyNCAXIkDxlVobn7mahbZ2K9GxalbZ1Kmr+kkgJY7GXsgU3EhISsFqtxMfH4+/v7+pwRERKleLwHty6dWtatmzJlClTALDZbFSvXp1HH32Up59+OsP+VapU4ZlnnmHYsGFp2+688058fHyYPXu2Q+csDq9LSZbdOk3hYSE53i8ixVdu3n81x0lEROSSCxcu8OuvvzJmzJi0bW5ubnTp0oWNGzdm+pikpCS8vdPPhfHx8WH9+vVZnicpKYmkpKS02wkJCfmMXPIqp3Wapg5oTnhYCF1Dg9URT6SUU6meiIjIJcePHyclJYWgoKB024OCgoiLi8v0Md26deOtt97i77//xmazsWrVKhYtWkRsbGyW55k4cSJWqzXtUr16dac+D3FMTus0AYxftiOtbE9leCKlmxInERGRfJg8eTL16tWjQYMGeHp6Mnz4cIYMGYKbW9Z/YseMGUN8fHza5cCBA4UYsaRyZJ2m2PhENkefLLygRKTIUuIkIiJySaVKlXB3d+fIkSPpth85ciTLRX0rV67MkiVLOHv2LPv37+evv/6iXLlyXHPNNVmex8vLC39//3QXKXyOrtPk6H4iUrIpcRIREbnE09OTFi1asGbNmrRtNpuNNWvW0LZt22wf6+3tTdWqVbl48SJffvklPXv2LOhwJZ8cXafJ0f1EpGRTcwgREZErjBo1ikGDBnH99dfTqlUrJk2axNmzZxkyZAgAAwcOpGrVqkycOBGAX375hUOHDtG0aVMOHTrEuHHjsNlsPPXUU658GuKA1HWa4uITM53nZMEsYtuqdkBhhyYiRZASJxERkSv07duXY8eO8fzzzxMXF0fTpk1ZsWJFWsOImJiYdPOXEhMTefbZZ/nnn38oV64c3bt357PPPqN8+fIuegaSmRSbPdOueGMjQhk6OxILpEueMlunSURKN63jJCIihUbvwZnT61KwtE6TiGRF6ziJiIiIoHWaRMR5lDiJiIhIiZTTOk0WzDpNXUOD09ZpEhHJirrqiYiISImkdZpExJmUOImIiEiJpHWaRMSZlDiJiIhIiaR1mkTEmZQ4iYiISImUuk5TVi0eLJjueVqnSUQcocRJRERESqTUdZqADMmT1mkSkdxS4iQiIiIlVnhYCFMHNCfYmr4cL9jqndaKXETEEWpHLiIiIiWa1mkSEWdQ4iQiIiLFXorNnm1ipHWaRCS/lDiJiIhIsbYiKpbxy3akW7MpxOrN2IhQleKJiNNojpOIiIgUWyuiYhk6OzLDQrdx8YkMnR3JiqhYF0UmIiWNEicREREpllJsdsYv24E9k/tSt41ftoMUW2Z7iIjkjhInERERKZY2R5/MMNJ0JTsQG5/I5uiThReUiJRYSpxERESkWDp6OuukKS/7iYhkR4mTiIiIFEuBft4575SL/UREsqPESURERIqlVrUDCLF6k9VqTBZMd71WtQMKMywRKaGUOImIiEiRlmKzs3HvCb7adoiNe0+kNXtwd7MwNiIUIEPylHp7bESoFroVEafQOk4iIiJSZOW0RlN4WAhTBzTPsE+w1nESESdT4iQiIiJFUuoaTVc3E09do2nqgOZpyVPX0GA2R5/k6OlEAv1MeZ5GmkTEmVxaqjdx4kRatmyJn58fgYGB9OrVi127dmX7mFmzZmGxWNJdvL016VNERKQkye0aTe5uFtrWqUjPplVpW6eikiYRcTqXJk4//PADw4YNY9OmTaxatYrk5GRuueUWzp49m+3j/P39iY2NTbvs37+/kCIWERGRwqA1mkSkqHFpqd6KFSvS3Z41axaBgYH8+uuvdOzYMcvHWSwWgoODHTpHUlISSUlJabcTEhLyFqyIiIgUGq3RJCJFTZHqqhcfHw9AQED2bUPPnDlDzZo1qV69Oj179uTPP//Mct+JEyditVrTLtWrV3dqzCIiIuJ8WqNJRIqaIpM42Ww2Ro4cSfv27QkLC8tyv/r16zNjxgy++uorZs+ejc1mo127dhw8eDDT/ceMGUN8fHza5cCBAwX1FERERMRJtEaTiBQ1Raar3rBhw4iKimL9+vXZ7te2bVvatm2bdrtdu3Y0bNiQDz74gBdeeCHD/l5eXnh5eTk9XhERESk4qWs0DZ0diQXSNYnQGk0i4gpFYsRp+PDhfP3113z//fdUq1YtV4/18PCgWbNm7Nmzp4CiExEREVdIXaMp2Jq+HC/Y6p3WilxEpLC4dMTJbrfz6KOPsnjxYtatW0ft2rVzfYyUlBT++OMPunfvXgARioiIiCtpjSYRKSpcmjgNGzaMuXPn8tVXX+Hn50dcXBwAVqsVHx8fAAYOHEjVqlWZOHEiABMmTKBNmzbUrVuXU6dO8frrr7N//34efPBBlz0PERERKTipazSJiLiSSxOnqVOnAtCpU6d022fOnMngwYMBiImJwc3tckXhv//+y0MPPURcXBwVKlSgRYsW/Pzzz4SGhhZW2CIiIiIiUspY7HZ7Zotyl1gJCQlYrVbi4+Px9/d3dTgiIqWK3oMzp9dFRMQ1cvP+WySaQ4iIiIiIiBRlRaYduYiIiJROKTa7mj+ISJGnxElERERcZkVULOOX7SA2PjFtW4jVm7ERoWo3LiJFikr1RERExCVWRMUydHZkuqQJIC4+kaGzI1kRFeuiyEREMlLiJCIiIoUuxWZn/LIdZNahKnXb+GU7SLGVqh5WIlKEKXESERGRQrc5+mSGkaYr2YHY+EQ2R58svKBERLKhxElEREQK3dHTWSdNedlPRKSgKXESERGRQhfo5+3U/URECpoSJxERESl0rWoHEGL1Jqum4xZMd71WtQMKMywRkSwpcRIREZFC5+5mYWxEKECG5Cn19tiIUK3nJCJFhhInERERcYnwsBCmDmhOsDV9OV6w1ZupA5prHScRKVK0AK6IiIi4THhYCF1Dg9kcfZKjpxMJ9DPleRppEpGiRomTiIiIuJS7m4W2dSq6OgwRkWypVE9ERERERCQHSpxERERERERyoFI9ERERKVApNrvmMIlIsafESURERArMiqhYxi/bQWx8Ytq2EKs3YyNC1TVPRIoVleqJiIhIgVgRFcvQ2ZHpkiaAuPhEhs6OZEVUrIsiExHJPSVOIiIi4nQpNjvjl+3Ansl9qdvGL9tBii2zPUREih4lTiIiIuJ0m6NPZhhpupIdiI1PZHP0ycILSkQkH5Q4iYiIiNMdPZ110pSX/UREXE2Jk4iIiDhdoJ+3U/eTS/5cAiufgbPHXR2JSKmjxElEREScrlXtAEKs3mTVdNyC6a7XqnZAYYZVvP21HBYMho1TYGo7+Hu1qyMSKVWUOImIiIjTubtZGBsRCpAheUq9PTYiVOs5OerwNvjyQcAOXv5w5gjMuRO++S8kn3d1dCKlghInERERKRDhYSFMHdCcYGv6crxgqzdTBzTXOk6OSjgMn/eD5HNwzU0wage0+o+5b/OH8GEniP3dpSGKlAZaAFdEREQKTHhYCF1Dg9kcfZKjpxMJ9DPleUVypOnYbjgdC9fc6OpILks6A3P7mrgq1Ye7Z4GXH3R/DerdAl89Asf+go86w83PQ9vh4KbvxUUKghInERERKVDubhba1qno6jCyt289zL4LLp6Hm8fCDaNcHRHYUmDRwxD3O/hWgnu/AJ/yl++v1wWG/gxLH4Ndy2HVc/D3d9B7GliruSzsYsWWYhptnDkCZ45e+nnpevI5aDYAqrdydZRSRChxEhERkdLtwGaY08ckTQBrxkMZb2j7iGvjWj3WJETuXtBvLlSolXGfspWg3xyI/BRWPA37fjKNIwYshmotCj1kAOx2sF0Edw/XnN8Rv34C6yaaJMluy3q/yE+hzSPQ+Vnw9C28+JwlORHKeIGlCI7wFkMayxUREZHS61AkzL4Tks/CNZ3ghifM9pVjYMt018W1dSb8/K653ut9qNE6630tFmgxCP5vPVRpDonxsOhBuHC2cGK92lfDYGJ12DTNJFFFzcFf4evHTfmj3QZYoGwgBDWCOjdDk3ug/UgIuwuww6b3YFp72LfBxYHn0om98No1MPNWOFdAC00nxsP+n8GWTfJZgmjESUREREqnuCj4rDckJUCNdmZUx8PXjJZsmAzLR5mRp2b3Fm5ce7+H5ZcSuE7/g0Z3Ofa4inXgvsVmxOnkP7DqeejxZsHFmZkDm2HbHHN9xWjYvx5un5K+xNCVks/D4v+APQWu6w3hr4JvRXDP4iNx477w9Ujzes7qDq0eNqWcXuWyPofNBnHbzb9jhZoQdmeBPJUc/TrTfCEQsxFm3WZ+N/yCnHPslIsQOQu+fxnOnYA2wyD8ZeccuwjTiJOIiIiUPsd2wac9IfEUVGtp5g95ljWjN13GQ+v/M/stHQ5/LCzcuL4YZD7YN+4LNz6Vu8f7lIeeU8z1LR/DnjVODzFba18wP4Mbg5sH7FwGH94Ih38r3Diysno8nPgb/EKgx1smkcgqaQK49hZ4ZCM0H2Rub/4QpraFf9al3+/sCfh9gZmT9kY90+lwzXhYeD98+7SZS1WYUi7C71+Y62W84eifMDMcTsXk/9h/rzYjcMufMEkTmFG53Svzf+wiTomTiIiIlC4n9sInt8O54xDSBO5daDrVpbJYIPwVaDHYlHItehh2LC34uE4dgDl3QVI81GgLt7+bt7kpdTqbkRGAr4bD+X+dG2dW/vkBon8Ed08zenf/SihfA/7dB9Nvgc0fubZ0758f4Jep5vrtU8DXwcWXva1w+ztw3xKw1jDJx6c9TVOO7182HQ1fr2PKI3+fb36vPMtBrRvM43+ZCp/3h6TTBfK0MvXP92b+lk8A/OdHE/fJf2DGrXD877wd8+hOU9Y6507TydGnAtz6+uXftSVDTev8EkyJk4iIiJQe/+43SdOZOAgMNR+GMysjs1igx9vQpL8Z/Vl4P+z+ruDiOhUDs3qYnxVqQ985ZlJ/XnUZDwF14PRh+Ha08+LMit1+ebSpxRAoX900p/jPj1C/B6RcgG+ehAWDzbyYwpYYb+ZeAVx/v+lImFt1bjKjTy0fMrcjP4EfXoVDvwJ2M0eq/UgY9DU8FQ2Dv4a7PzEjPn+vhOndTHLsqLgo2D7PjB7lVmq5ZKO7oXJ9uH8FVLoWEg7CjPDcrft19jh8PQqmtoc9q81IYtvh8Nhv0PphuOVFM8J47oT5kqGwR9cKkcVuL4qz9gpOQkICVquV+Ph4/P39XR2OiEipovfgzOl1KSTxh8w8lX/3QcV6MOQbKBeY/WNSLsKih+DPRaa73T3zzQdoZ/p3H8yKgPgYCLgGBi1zTjvxA1tgxi1m1KzPpxDaM//HzMrulTC3D5TxgRHb08+lsdth0/tmzpXtokkM+3xiRvsKy5JHTDJRoRb834bs5yg5Yt8GWP+WGVmq19U0lfDPYkHnQ7+aEaczR0wTiv7zsu94uP9nWP+2aS0P0Pk56Pik47Gd/xfeqA8pSfDwD1Clqdl+9riZ0xf3O3hZ4d4FWTcduZgEMZtMDJGfmnmAAA1ug64TzHy6Kx3fAx90NHOqbnoWbvyv4/G6WG7ef5U4iYhIodF7cOaK++uSYrMX/QVuk86YeScn/jYfnod8C/5VHHtsSrIZKfnra5MY3P8tVGnmnLhORsMnERB/ACrWNUmTo3E5Ys0E+OlNU7L1yCbnNQe4ks0GH3aEuD+g/QjzwTozB7bAwiHmuRZUEpqZv5bDvHsAixl5qdGm4M95tVMH4PN+cCTKjED1mgphd1y+3243yef6t+HApvSP9a0II6Mcb4e+ZbppbBIYatb5urLc8/wps6DygU2mEUq/uZf/Df7dZ0aU/l5tSi6Tr+jKGNIEur0MtTpkfd7t80zjDYsbDF4ONds5Fq+L5eb9V6V6IiIikmcromLp8Opa+n+0iRHzttH/o010eHUtK6JiXR1aej++dqkpQJXcJyfuHnDXDDOqcPE8LPqP6c6WXyf2mvK8+ANmBGzwcucmTQA3Pm1KyM6fhGUjCmaO0c6vTNLk6WdK1bJSvaUp3avb1YyGfPtU3srQcuPscfO8Ado/5pqkCUzp4v0roF43uJhoEsgfXjdJ+e9fmE6In19KaNw9zfy6YVugfE1TAvfbZ46fa/vn5mfTezLOkfMpD/ctMvPgks+ZUcKvhsG7LWByE9PwYfe3JmkqG2has/f5DB5al33SBNCkHzTuZ0Y4v3yo4Fqgu5ASJxEREcmTFVGxDJ0dSWx8YrrtcfGJDJ0dWXSSp6N/wcb3zPXb3jYNC3KrjBfc+TGUC4Lju2DNC/mL6cRe0yI64RBUqm+SJr/g/B0zM2U84Y4PzIfx3d9envviLLYU0yABoO2wnBsu+AbAXdPNCNjx3blLCHLLbjdJ09ljZvTlpmcK7lyO8PKD/p+b1t0A378Ir9c1paBHd1xKPEfAyD8gYjJUvtYke2DW9EpJzvkcx/+Gg1vA4g6N+mS+j2dZUy7YMMLMPfttNpzYYx5Tsz3c/LxJcJ/YBb2nQujt4OZgytDjDTO3LuEgLH20aK7jlQ9KnERERK7y3nvvUatWLby9vWndujWbN2/Odv9JkyZRv359fHx8qF69Oo8//jiJiYnZPqa4S7HZGb9sB5l9LErdNn7ZDlJsLv7gZLebb9FtF6F+d6gfnvdj+QaYbmxg2i9H/5S34xz/G2Z2N40bKjc0TQQKooQuVdB1l5OGb582DTKc5fcvTALkU8EkTo7wtsKNlxpWrJtYcAv1/j7flFe6eUDvD/LXbMNZ3NzNekc93jKJSuIp8K1k5jE9HmXKHK9MoJsOMCM/8QfgjwU5H3/bXPOzbpfsf6fKeMFds6DjU6aZR5/PYHS0mfd3wxOmNM/RZOlKXn5mdNbd07z2Wz7O/TGKMCVOubXxPfjyQdOSUURESpz58+czatQoxo4dS2RkJE2aNKFbt24cPXo00/3nzp3L008/zdixY9m5cyfTp09n/vz5/O9//yvkyAvX5uiTGUaarmQHYuMT2Rzt4nKdPxaYRVjL+JgW4/l17S2X1/RZ8ggkJuTu8cd2mfK8M3EQeJ0pG8ypQYUztHsUqreBC6dNaZbNlv9jXrxgEh8wJXreuZifd/39Zq7ZmSOXRwOdKf4gfHNpDaxOT0NIY+efIz9aPgAPfAe9ppkRpo5PZt7d0cMb2j5irq+flP2/my3FzDMCaNo/5xjcy0DnZyBikhlV8rbm8klkoUrTy/PcVj5jyjhLCCVOubXza/MmfORPV0ciIiIF4K233uKhhx5iyJAhhIaGMm3aNHx9fZkxY0am+//888+0b9+ee+65h1q1anHLLbfQv3//HEepirujpx0bUXN0vwJx/pT54Abmg2mFms45breXzNyT+BhYOcbxxx3+zZTnnTli5h0NWgblKjsnppy4uZuyK4+ysO8ns5Brfv32GZzab0ZEUtfycVQZT1MSBrBhMpw5lv94wIww7v3eNGJIijeLG2c378qVql1vEpycmj5c/4Dpgnd8F+xanvV+0T+YUUzv8nDtrU4NNdda/x9cG27msi0YUnCjioVMiVNuBdQ2P09GuzYOERFxugsXLvDrr7/SpcvlNV7c3Nzo0qULGzduzPQx7dq149dff01LlP755x+++eYbunfvnuV5kpKSSEhISHcpbgL9vJ26X4H4/mU4e9R0q2v3qPOO6+UHvacBFjM/ZNe3OT9mx1Kz+OjZoxDcCAYthbIVnReTIwKugVsujQSsfRFOH8n7sZLPw4+vm+sdn3S849uVQnub7oQXzpj1kPLDlgJ/LjGdEz/rZUY5vPzNiI57mfwd29W8/aHVg+b6T29lPW9o26WmEGF3mpEqV7JYoOf7phnLib9hUiNTsbV9nvOSZBdQ4pRbqYnTv0qcRERKmuPHj5OSkkJQUPq5AUFBQcTFxWX6mHvuuYcJEybQoUMHPDw8qFOnDp06dcq2VG/ixIlYrda0S/Xq1Z36PApDq9oBhFi9yarpuAUIsZrW5C5xeBts+chc7/6G8+e31GwH7Yab60sfg7MnMt/Pbjctpr+4z3Tkq9sFBn+TcxOFgtLifqjS3JTsrR6X9+NsnQGnY8Fa3XSAyws3t8slXb/ONA0zcuviBbPO0HutYMEgiN1myjJb/59pxV2pbt5iK2paDzVtzA9HmpGlqyUmwM5l5nrTews3tqyUrQh3zzTt1M+dMBVbi/8Db9SFD240DVb2byz4zopOpMQptypoxElERC5bt24dL7/8Mu+//z6RkZEsWrSI5cuX88ILWXddGzNmDPHx8WmXAwcOFGLEzuHuZmFsRChAhuQp9fbYiFDXrOdks5mGEHYbXHdHwa0VdNOzprnD2aPw9ciMIwEXL8BXwy8nKK0ehv7zczcXyNnc3KD7pZGi7XPhQB5KSpPOmJEPgBufyl9SWrsj1LvFNO9YMz53Mfw8xbTQXvqo6QrnbTXNDh6PgltfNS3AS4pylaH5QHN9/dsZ79+xxCTmla6Fqs0LNbRs1WhjuvMN+fZy0wkwCe5Pb8DMcHjtGvP/5Nhul4bqCCVOuaURJxGREqtSpUq4u7tz5Ej6EqYjR44QHJx5q+jnnnuO++67jwcffJBGjRrRu3dvXn75ZSZOnIgti4ncXl5e+Pv7p7sUR+FhIUwd0Jxga/qyoGCrN1MHNCc8LMQ1gf32KRzaCp7lzHykguLhbUr23MrAzqXpu56dOwmf9YZts82CoLe+bhKWolA2Vu16060N4JsnTZlbbvwyFc4dN6V/TRxoQpCTLuPNa7TjK7NIbk72rIZ3msJ3z5g5PX4hcMuL8PifptlB2Ur5j6koajvcdOL7Zx0cikx/X2o3vczWbnI1dw8zQpvW5ny3KaEMu8u0pU+KN/Pl3msF8wfAoV9dHXGWlDjlVuqI0+lYuHDOtbGIiIhTeXp60qJFC9asWZO2zWazsWbNGtq2bZvpY86dO4fbVW173d3dAbCXsDVMMhMeFsL60Z35/KE2TO7XlM8fasP60Z1dlzSdPXF5hOem/zl/QdmrVWl6ubX2N09C/CE4vgc+7mK6+Xn6wT0LoHUumycUtC5jzRyg2O2m1M1RR/6E9ZPN9U7/Mx+K8yso1Cy0CrDq+azn8KRcNOVds+8yazMFXAMR78CI7WYOm5df/mMpyirUhEZ3m+vr37q8/cReiNloks/GfV0TW274BZmmGHdNh//uMaWr9XsAdlNu+FFn+OR20+SjiL2HKnHKLd+Ay+0a/93n0lBERMT5Ro0axUcffcQnn3zCzp07GTp0KGfPnmXIkCEADBw4kDFjLndSi4iIYOrUqcybN4/o6GhWrVrFc889R0RERFoCVdK5u1loW6ciPZtWpW2diq4pz0u1Zhyc/9e0+m71n8I5Z4dRULUFJMabb8w/vhlO7gVrDdNyul6XnI9R2MoFmsQSYM0EM0KWk/iDJmm5cBpqdoCwO5wXz03/M3N4Yn7OvNnG6Tj4tKcp78Ju2pkP3QgtBhWN9ZkKS4eR5ufOry+XtqW2IL/mpoL/osDZ3NyhVnvoPxce2WRGMC3uZh7XZ73go5vMSKQz2uc7QREYLy6GKtQ2tZn/RptvSUREpMTo27cvx44d4/nnnycuLo6mTZuyYsWKtIYRMTEx6UaYnn32WSwWC88++yyHDh2icuXKRERE8NJLBVgiVtJcOAsrxsD+DaaRQ17nJB3YfHn0pMebhVcW517GlB59cIOZvA+mDXa/uYWzRlNetXwQfv0Eju00HQh7vJH1vudPmaTp9GGo3AD6zTYfep3FWhXaDDXzd1aPM/OeUv/99n4Pix4yo0ye5SBiMjS6y3nnLk4CG5rRmV3LYcMksyBz2tpN97g0tHwLbGhKX2/6n5m/FvmpaeH/xUDTYt2/qvn/VC7oip9BZgSrXJBpVJKX7o65YLGXhjqCKyQkJGC1WomPj897TfmCwfDnYrjlpcsddUREJEdOeQ8ugUr163L0L9MN7dhf5rbF3XyAv/7+3B0nJdl8Ox33h+kq1ut958eak60zTVOKsDvh9nfAw6fwY8it6B/hkwhT5vWfH02r9KtdTILP7jClh+WC4cHVBdN4ITEeJjeF8ydNctTsPtOm/IfXADsEhcHds6BSPeefuzg5uNWMarqVMYnTkv8zZZdP7i4ev3OOOnscfplm1hxLjM95/zun5ymhzs37r0ac8qKCGkSIiIjk27bPYfkoSD5nvjGuer35Jv3rx808oVtecGxUI+4P+GqY+eld/nKL68J2/RAzx6SAv/V2qtodIbSX6cr2zVMw5Jv0zQVsNlj8f5fnaw1YWHDd6ryt0PG/ZlHh7ydC1JcmsQNoPsh0yitJiUFeVbseat1gFjJeNsJsu653yXttylaCzs+aUtiT/5iFo88cvepn6vU48x5SwJQ45YUWwRUREcm7C+fg2/+axWMBat8Id34MZSubRVW/fwk2vWc+LN35MXiVy/w4Fy/AT2+aeS+2iyZpuuMj13ZVK05JU6pbXoTdK838oqgv039rv/p5+HORGd3o+1nmI1LO1PIBM8pwar/5MOxRFiImQeM+BXve4uaGUSZxSkkyt4t7mV52PH0hOAwIc3Ukag6RJxpxEhERyZtju02Z0W+zAYvpzHbfYjNnwWIx6wLdNcM0Ctj9LcwIN00Jrnb4N/iwE/zwikmaGtwGwzbDtbcU9jMq/spXN2vsAHz3rFkjCWDTNPj5XXO95/sFtx7Wlcp4mRbyFjcIDIWH1ylpysw1N0FIU3M94Bqo3tql4ZQWGnHKi9QRp1MxpjVmUViTQUREpKj7fYEpLUo+C2UDzWjSNTdm3C/sTihfEz7vB0f+MO2J+39uOtclJ5pkacM7YE8B34qmocR1vYve+jXFSbtHzZpT/+4zI3hVmsGKp819N4+FJoXY5rphhFmTqWygPmNlxWIxCebC++HGp/W7X0j025gXflXA3csMjyYchAq1XB2RiIhI0ZVyEb59CrZON7dr3WAmcvtlMyeh2vXw0FqY2xeO7oCZPeCmMfDbHDi+y+xz3R1mUdmSuuBpYfLwhvBXTLL68xQz4oMdrn8AOjxe+PEUt7barlCrg2kIIYVGpXp54eZmFiEDzXMSERHJzoWzMO+eS0mTxSwWO/Cr7JOmVOVrwP0roW5XuHjeLI56fJcZieg7G+6eqaTJma4NN6+1Ldl8OVy/h0lMNZohAihxyjvNcxIREcne2eOm1fXfK82cpX5zzRotuVn/x9sf+s+DNo+YVuWN+8GwX0w5lziXxWI61/lUMAvc3vmxc9dqEinmVKqXV+qsJyIipUSKzc7m6JMcPZ1IoJ83rWoH4O6WwyjEyWiYfSec3Gs+iN/zBVRvlbcA3MtA+EToMh7KeObtGOKYinXgyb9Nkuqm79dFrqTEKa804iQiIqXAiqhYxi/bQWx8Ytq2EKs3YyNCCQ8LyfxBh7fBnLvh7FGw1oABX0Lla/MfjJKmwuHu4eoIRIokfZWQV2kjTvtcGoaIiEhBWREVy9DZkemSJoC4+ESGzo5kRVRsxgftWQOzepikKagRPPCdc5ImEREXc2niNHHiRFq2bImfnx+BgYH06tWLXbt25fi4BQsW0KBBA7y9vWnUqBHffPNNIUR7lStHnOz2wj+/iIhIAUqx2Rm/bAeZ/YVL3TZ+2Q5SbFfssX0ezO0DF86YRW2HfAP+WYxKiYgUMy5NnH744QeGDRvGpk2bWLVqFcnJydxyyy2cPXs2y8f8/PPP9O/fnwceeIDffvuNXr160atXL6Kiogoxci511bOYPw5njxfuuUVERArY5uiTGUaarmQHYuMT2Rx90mzYMBkW/8csRht2F9y70DR2EBEpIVw6x2nFihXpbs+aNYvAwEB+/fVXOnbsmOljJk+eTHh4OP/9738BeOGFF1i1ahVTpkxh2rRpBR5zmjJe4F/VrOP0bzSUq1x45xYRESlgR09nnTRl2C8uyrQKB2g7HLq+oMYCIlLiFKl3tfj4eAACAgKy3Gfjxo106dIl3bZu3bqxcePGTPdPSkoiISEh3cVp1FlPRERKqEA/b8f3+2u5uVGvG3R7SUmTiJRIReadzWazMXLkSNq3b09YWFiW+8XFxREUlH7RvKCgIOLi4jLdf+LEiVit1rRL9erVnRd0hVrmpzrriYhICdOqdgAhVm+yajpuwXTXa1U7AHZ/azY2vK2wwhMRKXRFJnEaNmwYUVFRzJs3z6nHHTNmDPHx8WmXAwcOOO/gaSNO/zjvmCIiIkWAu5uFsRGhABmSp9TbYyNCcT97BA7/ZjbU61Zo8YmIFLYikTgNHz6cr7/+mu+//55q1aplu29wcDBHjhxJt+3IkSMEBwdnur+Xlxf+/v7pLk4TcI35qVI9EREpgcLDQpg6oDnB1vRle8FWb6YOaG7Wcdq90mys2gL8gjI5iohIyeDS5hB2u51HH32UxYsXs27dOmrXrp3jY9q2bcuaNWsYOXJk2rZVq1bRtm3bAow0C1oEV0RESrjwsBC6hgazOfokR08nEuhnyvPc3S6NO+2+1Ojp2nDXBSkiUghcmjgNGzaMuXPn8tVXX+Hn55c2T8lqteLj4wPAwIEDqVq1KhMnTgRgxIgR3Hjjjbz55pv06NGDefPmsXXrVj788MPCfwKppXpnj0HSafDyK/wYRESEWrVqcf/99zN48GBq1Kjh6nBKHHc3C23rVMx4R/J5+Gedua7ESURKOJeW6k2dOpX4+Hg6depESEhI2mX+/Plp+8TExBAbe3ll8nbt2jF37lw+/PBDmjRpwsKFC1myZEm2DSUKjLcVfC51APx3X+GfX0REABg5ciSLFi3immuuoWvXrsybN4+kpCRXh1XyRf8EyefM8hzBjVwdjYhIgbLY7fbMFgUvsRISErBarcTHxztnvtNHneHQr9DnMwi9Pf/HExEpwZz+HnyVyMhIZs2axeeff05KSgr33HMP999/P82bN3f6uZypoF+XAvP147B1Blx/P9z2tqujERHJtdy8/xaJ5hDFmuY5iYgUGc2bN+edd97h8OHDjB07lo8//piWLVvStGlTZsyYQSn7rrBg2e2XG0Nce6trYxERKQQuneNUImgRXBGRIiM5OZnFixczc+ZMVq1aRZs2bXjggQc4ePAg//vf/1i9ejVz5851dZglQ9wfkHAIyvhA7RtcHY2ISIFT4pRfGnESEXG5yMhIZs6cyeeff46bmxsDBw7k7bffpkGDBmn79O7dm5YtW7owyhImtZtenZvAw8e1sYiIFAIlTvmlEScREZdr2bIlXbt2ZerUqfTq1QsPD48M+9SuXZt+/fq5ILoSSm3IRaSUUeKUX6kjTvEHISUZ3DP+sRYRkYL1zz//ULNmzWz3KVu2LDNnziykiEq400dMYySAa7u5NhYRkUKi5hD55Rds6rvtKXAqhhSbnY17T/DVtkNs3HuCFJsmIouIFLSjR4/yyy+/ZNj+yy+/sHXrVhdEVML9fakpRJVm5u+giEgpoMQpvywWqFALgK2//UqHV9fS/6NNjJi3jf4fbaLDq2tZERWb/TFERCRfhg0bxoEDBzJsP3ToEMOGDXNBRCWcuumJSCmkxMkZLs1z+ur7n4mNT0x3V1x8IkNnRyp5EhEpQDt27Mh0raZmzZqxY8cOF0RUgiUnwt615rrK9ESkFFHi5AS28rUAqGE5kuG+1EK98ct2qGxPRKSAeHl5ceRIxvfg2NhYypTRdF6n2vcTJJ8DvyoQ0sTV0YiIFBolTk6wzx4IQM1MEicwyVNsfCKbo08WYlQiIqXHLbfcwpgxY4iPj0/bdurUKf73v//RtWtXF0ZWAqV10+tmytVFREoJfQ3nBEfcQ7gGqGE5mu1+R08nZnu/iIjkzRtvvEHHjh2pWbMmzZo1A2Dbtm0EBQXx2WefuTi6EsRuh11qQy4ipZMSJyfwDqwLpI442YHMv4EL9PMuvKBEREqRqlWr8vvvvzNnzhy2b9+Oj48PQ4YMoX///pmu6SR5dORPSDhouslec6OroxERKVRKnJygcVgjUr5yw8dygUBOcZQK6e63AMFWb1rVDnBNgCIipUDZsmV5+OGHXR1Gybb7W/Pzmk7g4ePSUERECpsSJydw9/DinG8IvucOUctyhKP2y4lT6tjT2IhQ3N1UCy4iUpB27NhBTEwMFy5cSLf99ttvd1FEJcyuK+Y3iYiUMnlKnA4cOIDFYqFatWoAbN68mblz5xIaGlpqv+3zDaoL0YdoVPYkm89c3h5s9WZsRCjhYSGuC05EpIT7559/6N27N3/88QcWiwW73XQxtVxqXpCSkuLK8EqGM0fh0K/muuY3iUgplKeuevfccw/ff/89AHFxcXTt2pXNmzfzzDPPMGHCBKcGWGxcWsvpmbbefP5QGyb3a8rnD7Vh/ejOSppERArYiBEjqF27NkePHsXX15c///yTH3/8keuvv55169a5OryS4e/vADuENAV//V0TkdInT4lTVFQUrVq1AuCLL74gLCyMn3/+mTlz5jBr1ixnxld8VDCJk9u/+2hbpyI9m1albZ2KKs8TESkEGzduZMKECVSqVAk3Nzfc3Nzo0KEDEydO5LHHHnN1eCXDrkvzmzTaJCKlVJ4Sp+TkZLy8vABYvXp1Wu14gwYNiI2NdV50xUnANebnv9GujUNEpBRKSUnBz88PgEqVKnH48GEAatasya5du1wZWslwMQn2mkoT6itxEpHSKU+J03XXXce0adP46aefWLVqFeHh5k308OHDVKxY0akBFhuXSvU4qcRJRKSwhYWFsX37dgBat27Na6+9xoYNG5gwYQLXXHONi6MrAfauheSz4BdiSvVEREqhPDWHePXVV+nduzevv/46gwYNokmTJgAsXbo0rYSv1KlQy/w8fxIS48Hb6tJwRERKk2effZazZ88CMGHCBG677TZuuOEGKlasyPz5810cXdGWYrOzOfokR08nEuhnls5IV2aekgyrx5nr1/UGi0rQRaR0ylPi1KlTJ44fP05CQgIVKlxuvf3www/j6+vrtOCKFS8/KFsZzh4zo05Vmro6IhGRUqNbt8vtsevWrctff/3FyZMnqVChQlpnPcloRVQs45ftIDY+MW1byNXdYDe9D8f+At+K0PG/LopURMT18lSqd/78eZKSktKSpv379zNp0iR27dpFYGCgUwMsVi41iNA8JxGRwpOcnEyZMmWIiopKtz0gIEBJUzZWRMUydHZkuqQJIC4+kaGzI1kRFQvxB2HdK+aOrhPAVwu5i0jplafEqWfPnnz66acAnDp1itatW/Pmm2/Sq1cvpk6d6tQAixXNcxIRKXQeHh7UqFFDazXlQorNzvhlO7Bncl/qtvHLdmBfMQaSz0H1NtDknsIMUUSkyMlT4hQZGckNN9wAwMKFCwkKCmL//v18+umnvPPOO04NsFjRiJOIiEs888wz/O9//+PkyZOuDqVY2Bx9MsNI05XswLWnf8GycylY3KHHm+CWp48MIiIlRp7mOJ07dy6t7et3333HHXfcgZubG23atGH//v1ODbBY0YiTiIhLTJkyhT179lClShVq1qxJ2bJl090fGRnposiKpqOns06aALy4wPgys8yN1v8HwWEFH5SISBGXp8Spbt26LFmyhN69e7Ny5Uoef/xxAI4ePYq/v79TAyxW0kac9rk0DBGR0qZXr16uDqFYCfTzzvb+/7h/TS23I1zwCcSz09OFFJWISNGWp8Tp+eef55577uHxxx+nc+fOtG3bFjCjT82aNXNqgMVK6ohT/EGzWGAZL9fGIyJSSowdO9bVIRQrrWoHEGL1Ji4+McM8pxqWIwwr8xUAZbq/At6l+AtREZEr5Klg+a677iImJoatW7eycuXKtO0333wzb7/9ttOCK3bKVgaPsoAdTsW4OhoREZFMubtZGBsRCkD6voN2xpX5BC9LMscD2+EWdocrwhMRKZLyPNMzODiYZs2acfjwYQ4ePAhAq1ataNCggdOCK3YsFs1zEhFxATc3N9zd3bO8SEbhYSFMHdCcYOvlsr1ublvp7L4Nm8WDSn3e0WK3IiJXyFOpns1m48UXX+TNN9/kzJkzAPj5+fHEE0/wzDPP4FaaO+9UqAVHotRZT0SkEC1evDjd7eTkZH777Tc++eQTxo8f76Koir7wsBC6hgazOfokJ/49Sde1T8A5cOvwGFSq5+rwRESKlDwlTs888wzTp0/nlVdeoX379gCsX7+ecePGkZiYyEsvveTUIIuVinXNz7jfXRuHiEgp0rNnzwzb7rrrLq677jrmz5/PAw884IKoigd3Nwtt61SEVe/AuViw1oAbnnR1WCIiRU6eEqdPPvmEjz/+mNtvvz1tW+PGjalatSqPPPJI6U6caneEDZNgz1qw21XmICLiQm3atOHhhx92dRhF37FdsHGKuX7rq+Dp69p4RESKoDzV1J08eTLTuUwNGjTQ4oM124OHL5w+DEf+dHU0IiKl1vnz53nnnXeoWrWqq0Mp+jZOAdtFuDYcGnR3dTQiIkVSnkacmjRpwpQpU3jnnXfSbZ8yZQqNGzd2SmDFloc31LoB/l4Je1Zp0UARkUJQoUIFLFeM8Nvtdk6fPo2vry+zZ892YWTFxIHN5mfzQa6NQ0SkCMtT4vTaa6/Ro0cPVq9enbaG08aNGzlw4ADffPONUwMslup1NYnT36uhw+OujkZEpMR7++230yVObm5uVK5cmdatW1OhQgUXRlYMJCaYUj2Aate7NhYRkSIsT4nTjTfeyO7du3nvvff466+/ALjjjjt4+OGHefHFF7nhhhucGmSxU7eL+XlgEyTGg7fVtfGIiJRwgwcPdnUIxdfh3wC7aQpRLtDV0YiIFFl5SpwAqlSpkqEJxPbt25k+fToffvhhvgMr1gJqm+56J/bAP+sgNGO3JxERcZ6ZM2dSrlw57r777nTbFyxYwLlz5xg0SCVoWTq01fys1sK1cYiIFHGleMGlAla3q/n59yrXxiEiUgpMnDiRSpUqZdgeGBjIyy+/7IKIipGDv5qfVVWmJyKSHSVOBaXepXK9PWtMW3IRESkwMTEx1K5dO8P2mjVrEhMT44KIigm7/YoRJyVOIiLZUeJUUGp2gDI+aksuIlIIAgMD+f33jAuPb9++nYoVK7ogomIi/iCcOQJuZSCkiaujEREp0nI1x+mOO+7I9v5Tp07lJ5aSxcMbat8Af3+ntuQiIgWsf//+PPbYY/j5+dGxY0cAfvjhB0aMGEG/fv1cHF0RljraFHQdePi4NhYRkSIuV4mT1Zp9dzir1crAgQPzFVCJUrerSZzUllxEpEC98MIL7Nu3j5tvvpkyZcyfNpvNxsCBAzXHKTsHLyVOmt8kIpKjXCVOM2fOLKg4SqZ6XeBbLrUlTwBvf1dHJCJSInl6ejJ//nxefPFFtm3bho+PD40aNaJmzZquDq1oO3SpMYTmN4mI5CjP7cjFAQHXQEAdOLn3Ulvy210dkYhIiVavXj3q1avn6jCKh5RkOLzNXNeIk4hIjtQcoqDVu8X83KO25CIiBeXOO+/k1VdfzbD9tddey7C2k1xydAdcPA9eVrP2oIiIZEuJU0FLbUv+92q1JRcRKSA//vgj3bt3z7D91ltv5ccff3RBRMVA2vym5uCmjwMiIjnRO2VBU1tyEZECd+bMGTw9PTNs9/DwICEhwQURFQOa3yQikitKnApaaltyULmeiEgBadSoEfPnz8+wfd68eYSGhub6eO+99x61atXC29ub1q1bs3nz5iz37dSpExaLJcOlR48euT5voVJHPRGRXFFziMKgtuQiIgXqueee44477mDv3r107twZgDVr1jB37lwWLlyYq2PNnz+fUaNGMW3aNFq3bs2kSZPo1q0bu3btIjAwMMP+ixYt4sKFC2m3T5w4QZMmTYr23KrEeDi+21zXiJOIiEM04lQYUuc5pbYlFxERp4qIiGDJkiXs2bOHRx55hCeeeIJDhw6xdu1a6tbNXeODt956i4ceeoghQ4YQGhrKtGnT8PX1ZcaMGZnuHxAQQHBwcNpl1apV+Pr6Fu3E6VAkYIfyNaFsJVdHIyJSLChxKgypbcltF01bchERcboePXqwYcMGzp49yz///EOfPn148sknadKkicPHuHDhAr/++itdunRJ2+bm5kaXLl3YuHGjQ8eYPn06/fr1o2zZslnuk5SUREJCQrpLoTp0qUxPo00iIg5T4lRY6nU1PzXPSUSkwPz4448MGjSIKlWq8Oabb9K5c2c2bdrk8OOPHz9OSkoKQUFB6bYHBQURFxeX4+M3b95MVFQUDz74YLb7TZw4EavVmnapXr26wzE6xcFLjSE0v0lExGFKnApL3UuJk9qSi4g4VVxcHK+88gr16tXj7rvvxt/fn6SkJJYsWcIrr7xCy5YtCy2W6dOn06hRI1q1apXtfmPGjCE+Pj7tcuDAgUKKEPM3SCNOIiK5psSpsNRqD2W8TVvyoztcHY2ISIkQERFB/fr1+f3335k0aRKHDx/m3XffzfPxKlWqhLu7O0eOHEm3/ciRIwQHB2f72LNnzzJv3jweeOCBHM/j5eWFv79/ukuhORUDZ4+BmwcENy6884qIFHNKnAqLhw/UMm3J9//yFV9tO8TGvSdIsWn0SUQkr7799lseeOABxo8fT48ePXB3d8/X8Tw9PWnRogVr1qxJ22az2VizZg1t27bN9rELFiwgKSmJAQMG5CuGApc62hQcZpbMEBERhyhxKkQ7ypnSjditSxkxbxv9P9pEh1fXsiIq1sWRiYgUT+vXr+f06dO0aNGC1q1bM2XKFI4fP56vY44aNYqPPvqITz75hJ07dzJ06FDOnj3LkCFDABg4cCBjxozJ8Ljp06fTq1cvKlasmK/zF7i0+U0tXBuHiEgxo8SpkKyIiuWRX8wf0xaW3ZTjHABx8YkMnR2p5ElEJA/atGnDRx99RGxsLP/5z3+YN28eVapUwWazsWrVKk6fPp3rY/bt25c33niD559/nqZNm7Jt2zZWrFiR1jAiJiaG2Nj079m7du1i/fr1DpXpudwhLXwrIpIXFru9dHUqSEhIwGq1Eh8fX2g15Sk2Ox1eXUtsfCLfez5Obbcj/OfCSFbazAiUBQi2erN+dGfc3SyFEpOIiCsUxnvwrl27mD59Op999hmnTp2ia9euLF26tEDO5SyF9rcpJRkmVoOLiTB8K1SqV3DnEhEpBnLz/qsRp0KwOfoksfGJAKyzNQWgs9u2tPvtQGx8IpujTxZ+cCIiJUz9+vV57bXXOHjwIJ9//rmrwylajvxpkiZvq1lfUEREHObSxOnHH38kIiKCKlWqYLFYWLJkSbb7r1u3DovFkuHiyNoarnT0dGLa9e9spjTidvefqcy/We4nIiL54+7uTq9evYr8aFOhSivTawFu+u5URCQ3XPquefbsWZo0acJ7772Xq8ft2rWL2NjYtEtgYGABRegcgX6XuxZttIUSaauLj+UCw8ssyXI/ERERp9PCtyIieVbGlSe/9dZbufXWW3P9uMDAQMqXL+/QvklJSSQlJaXdTkhIyPX58qtV7QBCrN7ExSdix8LrF/vyuedL9Hdfy0cpt3HIXplgqzetagcUemwiIlKKaOFbEZE8K5bj9E2bNiUkJISuXbuyYcOGbPedOHEiVqs17VK9evVCivIydzcLYyNCAdMIYqPtOn5KCcPTksII9y8BGBsRqsYQIiJScM6fguO7zXW1IhcRybVilTiFhIQwbdo0vvzyS7788kuqV69Op06diIyMzPIxY8aMIT4+Pu1y4MCBQoz4svCwEKYOaE6w1ZTjvXGxDwB3lFnPp7dbCQ8LcUlcIiJSShy+9LeyQi0oW8mloYiIFEcuLdXLrfr161O/fv202+3atWPv3r28/fbbfPbZZ5k+xsvLCy8vr8IKMVvhYSF0DQ1mc/RJjp5uysmtPxFwcBU3HPwI6ODq8EREpCTT/CYRkXwpViNOmWnVqhV79uxxdRgOc3ez0LZORXo2rUpAxHjAAjuWQOx2V4cmIiIlUIrNzsa9J4jbsR4Am8r0RETypNgnTtu2bSMkpJiWuQVdB43uMtfXvujaWEREpMRZERVLh1fX0v+jjZSJM6V6D68120VEJHdcWqp35syZdKNF0dHRbNu2jYCAAGrUqMGYMWM4dOgQn376KQCTJk2idu3aXHfddSQmJvLxxx+zdu1avvvuO1c9hfzrNAaiFsHf30HMJqjRxtURiYhICbAiKpahsyOxA9Usx6hkSeCC3Z2fToewZnYkUwc01/xaEZFccOmI09atW2nWrBnNmjUDYNSoUTRr1oznn38egNjYWGJiYtL2v3DhAk888QSNGjXixhtvZPv27axevZqbb77ZJfE7RcU60GyAub5mAtjtro1HRESKvRSbnfHLdpD6F6WZxXxJucNekyQ8ARi/bAcpNv3NERFxlEtHnDp16oQ9m0Rh1qxZ6W4/9dRTPPXUUwUclQvc+BRs/xz2b4C9a6FuMU4ERUTE5TZHnyQ2PjHtdlO3vQBss9UFwA7ExieyOfokbetUdEWIIiLFTrGf41QiWKtBywfN9bUvaNRJRETy5ejpxHS3m1yVOGW1n4iIZE2JU1HRYRR4lIXDv8FfX7s6GhERKcYC/bzTrluw0cBiyt6j7LWy3E9ERLKnxKmoKFcZ2gw119e+CLYU18YjIiLFVqvaAYRYvbEAVS0nKGdJJMlehn32YAAsQIjVm1a1A1wap4hIcaLEqShp9yh4W+HYX/DHQldHIyIixZS7m4WxEaEAaaNN/9ircJEyWC7tMzYiFHc3SxZHEBGRqylxKkp8ykP7Eeb62hfh/ClXRiMiIsVYeFgIUwc0p4VPHAB/2asDEGz1VityEZE8cGlXPclE6/+DX2fBqRhYMhT6zgE35bciIpJ74WEh2P5Kgii4tlFrPm/Rhla1AzTSJCKSB/pEXtR4loU+n4K7J+z6Bja87eqIRESkGHM7ugOA65q2oW2dikqaRETySIlTUVSlGXR/3Vxf+yL8s86l4YiISDF18QIc322uB4a6NhYRkWJOiVNR1XwQNB0AdhssfADiD7k6IhERKW5O7AHbRfDyN2sGiohInilxKqosFujxBgQ3gnPHYcEg882hiIiIoy6V6RHY0PxdERGRPFPiVJR5+ECfz0yL8oNb4LtnXB2RiIgUJ2mJk8r0RETyS4lTEZNis7Nx7wm+2naIjXtPkFK+FvT+0Ny5+UP4/QuXxiciIsXIESVOIiLOonbkRciKqFjGL9tBbHxi2rYQqzdjI5oQfsOT8NMbsGwEBIVBkP4IiohIDlJHnPQ3Q0Qk3zTiVESsiIpl6OzIdEkTQFx8IkNnR7IicAhc0wmSz8EX90FigmsCFRGR4iHpNJzab65rxElEJN+UOBUBKTY745ftwJ7Jfanbxn+9i5TeH4N/NdMlaclQSLlYmGGKiEhxcmyX+VkuGHwDXBuLiEgJoMSpCNgcfTLDSNOV7EBsfCKbj7pBn0/AzQP++ho+6wVnjhZanCIiUowc+dP8VJmeiIhTKHEqAo6ezjppyrBftevh7lngWQ72/QQfdISYXwo2QBERKX7UUU9ExKmUOBUBgX7euduv4W3w0FqoVB9Ox8Ks7rBpGtgzK/YTEZFSSYmTiIhTKXEqAlrVDiDE6k1WSxNaMN31WtW+oka9cn2TPF13h1kVfsVo+PIBSDpTGCGLs3w7Gub21b+biDjfkSsWvxURkXxT4lQEuLtZGBthvhG8OnlKvT02IhR3t6vu9SoHd82A8FfArQxEfQkf3wzHdhd4zOIEe9fCL9Ng9wpYOcbV0YhISXLmGJw7DligcgNXRyMiUiIocSoiwsNCmDqgOcHW9GV7wVZvpg5oTnhYSOYPtFigzVAY9LXpnHTsL/joJvhzScEHLXlns8HqcZdvR34KO5e5LBwRKWGOXmoMEXANePq6NhYRkRJCC+AWIeFhIXQNDWZz9EmOnk4k0M+U52UYacpMzbbwnx9h4f2wfz0sGAS/3gQdn4Sa7U2CJUXHjsUQux08/SDsDoj8BJY+BtVagl+wq6MTkeJOZXoiIk6nEacixt3NQts6FenZtCpt61R0LGlK5RcEA7+C9iPB4g7/fA+zesCMcNj9nWPNI87/C38shGUj4ae3IOFwXp+KZCUlGda+aK63exS6vwHBjeD8SVjyiJp8iEj+pTaGCLrOtXGIiJQgGnEqadzLQNfxcP398PM7EPkZHNgEc+82H85veAIa3g5u7mZ/u90sqLt7BexeCft/BnvK5eOtfQHq3AzN7oX63aGMl2ueV16cjAbsplSlKIn8BE7+A2UrQ9thUMYT7vgYPrwR9q6BzR9B64ddHaWIFGdHNeIkIuJsSpxKqgo1oceb0PG/sHEKbJkBcX/AgsFQsR60ehj+jTYJ08l/0j+2cgO45iaI3QYxG2HPKnPxqQCN+pgkKqSJK56VY86egO9fhF9nmaYZ96+Aqi1cHZVx4Sz88Jq53vEp0+ADILABdH0Bvv0vrHoOanc020REcstmg6N/meuBGnESEXEWi91euuqCEhISsFqtxMfH4+/v7+pwCs+5k/DLB6aLW+Kp9Pe5eUCtDnBtOFzbDQJqX77v+B7YNge2z4PTV5TtBTUyoyJNB4BbEan4TEmGLR/DuomQGH95u7UG/OcH8A3I+rGF5cfXTZlehVowbIsZbUplt8Ocu2DPajM6+ODa9PeLlACl9j04B059XU5GwztNwd0L/nfYVCKIiEimcvP+q8SptEk6DVtnmA5uleqbRKnOTeDll/3jbCmw93vYNhv+Wg4pF8z22h2h53tQvkbBx56dPathxRg4fqkVe3Aj6PycWSfp32iodwv0n+/aJO/sCZjcBC6cNqV5je/OuM/pOHi/rZnv1H6kKbssSLYUMzJ3KgYq1r18KVtJDUWkQJT69+AsOPV1+Ws5zLvHvA/+33rnBCgiUkLl5v1XX0OVNl5+0H6EueSGmzvU62Iu506a9tnrXoHoH+H9dhD+MjS7r/A/bJ/YCyv/Z0oOAXwrws3Pm1jc3MEvBKZ3hb+/g/VvmS6DrvLTmyZpCm4EYXdmvo9fMNz+DswfABsmQ72uZjSwICTGw8IHTBnm1bysULEOVKpnEqlq15vyzdKaTJ0/BSueNq9JhyeKziirSGbS5jepTE9ExJn0119yzzcAOoyEoRugemuTDCx9FOb2gYTYwokh/hCsfAbea22SJrcy0GYYPBoJLQZfbn4R0th0rQP4/iX454fCie9qp2Jgy0fm+s3jsv/g3TDCJH7YYfH/mQ/tznZiL3zcxSRNZXyg+UCo0/nSyKEFkuLhcCT8Pt+8bp/1hi8fhMQE58dS1F04B5/3g+2fmzLLxQ/DxQuujkoka6mtyINCXRuHiEgJoxEnybuKdWDIt7DxPdN97+/v4P02JlFpdJfzRyfsdjjwi5mntWPp5e5/dbtCt5eh8rWZP675fRCzyZQZfvkA/Ocn8M9iQeGC8v1EU95Y6waoe3PO+4e/AvvWmzLDb56EOz92Xiz/rIMvBpm5bn5VoP9cqNLs8v3J580ciRN7zOXYX6ZFfdRCOLgF7pphRqBKg5RkWDjENEnx9IOL5+GPBWbUtc+nl5t7lCZ2e+kdeSwu0kaclDiJiDiTRpyKmRSbnY17T/DVtkNs3HuCFJuLp6i5uUP7x8ziuyFNzYfxRQ/CF/fBmWPOOUdyImyba9p1z+gGfy42SVPNDnDvQhiwMOukKVX31yEoDM4eMx+EU5KdE5sjjuwwoxUAXcY79qHTqxzc8ZFZj+uPBfDZHXB4W/7isNtNq/PP7jD/TlWvh4e/T580AXj4mG+qQ2+HG0bBHR/C/SvNaNSp/ebfYP3bpnNXSWazmUWJd6+AMt5w7xdmnpyHr2kb/+ntZt5acZKSDCkX8/74/Rvhw05m2QIpmi4mmS88QImTiIiTqTlEMbIiKpbxy3YQG5+Yti3E6s3YiFDCwwp5BCUzKcnmA/UPr4LtovnQ7+VnLp7lTDLgWfbS9Uvbva3gXd789Clvrqf+tKfAb7Nh60w4d9yco4w3NO4Drf4DwWG5i+/EXvOhLykB2j0Gt7zgzGeftbn9YPe3Zv2svp/l7rGbpsF3z5jXEyC0F3R+1sw9yo2UZPj2KdMYBKBxP4iYDB7ejh8jMd4sjPznInP7mk7Q+wMzL6uksdvhu2dNK3+LO/SbA/VvNfcd3Apz7jYNPCrWg/sWub45SnbsdlN2+dscM2p48QK0etA0HylbybFjnDoAq8dC1Jfmdq0bYPDXeQqnOL8HFySnvS5xUTCtvZmn+PR+jQ6KiORAXfWyUVz/aK+IimXo7Eiu/sdK/ZM4dUDzopE8AcRuhyWPwJEo5x3Tv5r5sNd8UP7aiu9YakbDAPrOgYa3OSe+rOzfCDPDzYfvYb/kPuEBs87W9xPNyBN2c6xm98KNT4O1as6PP3cSvhgI+34CLNBlnGkOkpcPVHa7SWa/fQqSz5lmHL2mwbW35P5YRdn6t2H1OHO911Roek/6+4/thtl3QPwB04BkwKKiN5/kzDEzR23bnMulW1fyKAtt/g/aDs/6/9SFc2Yh7fWTTJkiFmgxCG56FspVzlNYxfU9uKA57XX5fYEZ9a/R1qxhJyIi2VLilI3i+Ec7xWanw6tr0400XckCBFu9WT+6M+5uReTbRbsdEg7DhTOQdMb8zHD9tGl8kHjKjGakXk/9mXIBarQzH+7q93DeWiQrnzEjCV5W+M86CLjm8n0Xk0xL8NNxcCbOxOIbAOWCzchKuaDs11ZKOm0aV8QfhISDZl2puD9Mwnf7O/mLOy7KzCVL7SDo7gWtHoIbnjAjdmeOmPOmXhIuxXFwq3kunuXgzulQPzx/cYBJHL683zw3gNZD4aYxJg5nOhUDu1ea+XPnTpoREt9KULbipZ9X3fYpb+Yi5afrXeSnptkJwC0vQrtHM98v/hDMvhOO7TTPu/98qNk2833tdjj/r/lZtmLeY8tJSjL8vcokt3+vvDxSWcb7UtORAWaftS+aBa4BvPyh7TBoM/Tyv5/dbkYWv3ve/B4D1Gxv5t6FNM5XiMXxPbgwOO11WT3OJP7XPwC3veW0+ERESiolTtkojn+0N+49Qf+PNuW43+cPtaFtnQL8UFaY7Hbzoc/dw/nHTkmGWT1Mo4kKtaBC7SsSpX9zfrxvxcuJVNnKJsmLP2hGH65ceDdVGW947Dfwr+Kc+GM2werxEHNpnom7lylrtGUzd6VCLeg/DwIbOicGMEnmqrHwy1Rz29PPdOdr/R+oUDNvx7SlmERv97cmYcpspCQnFjeTDFxd+lm2kmkFX/V68zqkdl680s6vzYik3ebYOlrnTpqOewd+Mf/OncaYf4vU5Dv19+r0EUhJMo+p1hLC7oLreoNfUO6f35UunDNleDGbzOXg5vS/g1VbmGTpujvM65DKbodd38D3L18eGfYub+Yr1mxvPnzHbDTbrdVNWWtoL6eUfRXH9+DC4LTXZU4fkzR3f8N8sSIiItlS4pSN4vhH+6tthxgxb1uO+03u15SeTR0o3RIzGjbthstzp67k7nk5MfIpbz4cn44zIzo2B5pKeFtNaaG1mimlaxhhWn07k91uFv1dM/7yqI/F3SRn1kvn9q966Xp1sxZUQXWA2/0drHrejLyASVwa3m5KwKq3zPl5nPwHDkWa5/P3d2buUCqLO9RoYxZqDqgD506YBh/nTsDZ4+bf7+xxc/vcCbiY+ahsBp7lTFOMqi1Mh8Cq15sJ9bPvNAlOswFw+xTHEoUL50zDkdSRQEdZ3MxcoUZ3md8RnwrZ72+zmSTs4FaTqMVsMqNGVyfMZStDk37Q9N6cE2WbDXYsgXUTLy8enaqMj2kO0u5R0zDESYrje3BhcNrr8nYjiI8xHU9rtnNegCIiJZQSp2wUxz/apXLEqTAc2WE+rJetdKkMLzVZqpD5B2abzYxInY69NIoQZz7Ee5c3yYm1qklWvAvx98pmMx94vfxM7JmNohQGu910mtv4Huxde3l7tVbQbjg0uA2wmCQpdhsc/s3MhYv93awZdSXv8mbh32vDTcKZmzltyYnpyz2v/Hn6sEnQDv9mSkWvZnEzI00NboO7P8ldaWjKRfjxdTi01ZRzXvn7lFriWS7IxPLnYtPe/dDWy4938zDPuUEPMyJ65kjGEaszRy634L+SX4hZT61GG/MzuHHuy1ptKSamH14x/0Zhd5nRNmu13B3HAcXxPbgwOOV1SUyAV6qb66P35ZyMi4iIEqfsFMc/2qlznOLiEzM0h4AiOsdJSq8jf8Km9+H3L8w8NTBJxIWzZrHkq7l7mQ6JNdvBtbeaD//Oms+WGVsKHNtlEpeDW+HQr6Yk0G4zLe4HfJm7boN5dTLadKmLWgRH/3TwQRbTYrpGazP5v3pr09HPWZ3TUi6akbv8lhBmozi+BxcGp7wuBzbD9K5mfbYndjo3QBGREio3779aALcYcHezMDYilKGzI7FAuuQp9ePS2IhQJU1SNARdBz3fg87Pm+YYWz42oyZgyr+Cw8yaX1Wamp+V6xfMXLasuLmbDnhBoWZOFpimJcd3m7W+smv+4UwBtaHjk+ZydKcZ8dm/wZR6po1YBZkRpdRRrLKBBZtUupcp0KRJCtiRSwl4UevwKCJSQihxKibCw0KYOqB5hnWcgovSOk4iV/ILgs7PQIfHTaMBvxCodG3BfvDPK69yULW5684f2BBufs5155eSIbWZijObwIiISJoi+AlGshIeFkLX0GA2R5/k6OlEAv28aVU7QCNNUrR5+kLdm10dhUjJd/RSeV7gda6NQ0SkhFLiVMy4u1nUAEJERNKz2y+X6mnESUSkQORjlUgREREpEs4cNa38LW5m3qCIiDidEicREZHiLrUzY0Adp667JSIilylxEhERKe6OqDGEiEhB0xynEibFZlfzCBGR0ia1MUSQGkOIiBQUJU4lyIqo2AztykPUrlxEpOQ7qsYQIiIFTaV6JcSKqFiGzo5MlzQBxMUnMnR2JCuiYl0UmYiIFCibDY7+Za6rFbmISIFR4lQCpNjsjF+2A3sm96VuG79sBym2zPYQEZFi7exR8PaHMt4QUNvV0YiIlFgq1SsBNkefzDDSdCU7EBufyObok1oDSkSkpPELhid3w/lT4Obu6mhEREosjTiVAEdPZ5005WU/EREphnzKuzoCEZESTYlTCRDo5+3U/UREREREJD0lTiVAq9oBhFi9yarpuAXTXa9V7YDCDEtEREREpMRQ4lQCuLtZGBsRCpAheUq9PTYiVOs5iYiIiIjkkRKnEiI8LISpA5oTbE1fjhds9WbqgOZax0lEREREJB/UVa8ECQ8LoWtoMJujT3L0dCKBfqY8TyNNIiIiIiL5oxGnEsbdzULbOhXp2bQqbetUVNIkIpIH7733HrVq1cLb25vWrVuzefPmbPc/deoUw4YNIyQkBC8vL6699lq++eabQopWREQKg0acSqEUm12jUiIiWZg/fz6jRo1i2rRptG7dmkmTJtGtWzd27dpFYGBghv0vXLhA165dCQwMZOHChVStWpX9+/dTvnz5wg9eREQKjEtHnH788UciIiKoUqUKFouFJUuW5PiYdevW0bx5c7y8vKhbty6zZs0q8DhLkhVRsXR4dS39P9rEiHnb6P/RJjq8upYVUbGuDk1EpEh46623eOihhxgyZAihoaFMmzYNX19fZsyYken+M2bM4OTJkyxZsoT27dtTq1YtbrzxRpo0aVLIkYuISEFyaeJ09uxZmjRpwnvvvefQ/tHR0fTo0YObbrqJbdu2MXLkSB588EFWrlxZwJGWDCuiYhk6O5LY+PQL4cbFJzJ0dqSSJxEp9S5cuMCvv/5Kly5d0ra5ubnRpUsXNm7cmOljli5dStu2bRk2bBhBQUGEhYXx8ssvk5KSkuV5kpKSSEhISHcREZGizaWlerfeeiu33nqrw/tPmzaN2rVr8+abbwLQsGFD1q9fz9tvv023bt0KKswSIcVmZ/yyHdgzuc+OaVs+ftkOuoYGq2xPREqt48ePk5KSQlBQULrtQUFB/PXXX5k+5p9//mHt2rXce++9fPPNN+zZs4dHHnmE5ORkxo4dm+ljJk6cyPjx450ev4iIFJxi1Rxi48aN6b4FBOjWrVuW3wKCvtVLtTn6ZIaRpivZgdj4RDZHnyy8oERESgCbzUZgYCAffvghLVq0oG/fvjzzzDNMmzYty8eMGTOG+Pj4tMuBAwcKMWIREcmLYtUcIi4uLtNvARMSEjh//jw+Pj4ZHqNv9Yyjp7NOmvKyn4hISVSpUiXc3d05cuRIuu1HjhwhODg408eEhITg4eGBu7t72raGDRsSFxfHhQsX8PT0zPAYLy8vvLy8nBu8iIgUqGI14pQX+lbPCPTzznmnXOwnIlISeXp60qJFC9asWZO2zWazsWbNGtq2bZvpY9q3b8+ePXuw2Wxp23bv3k1ISEimSZOIiBRPxSpxCg4OzvRbQH9//0xHm8B8q+fv75/uUhq1qh1AiNWbrGYvWYAQq2lNLiJSmo0aNYqPPvqITz75hJ07dzJ06FDOnj3LkCFDABg4cCBjxoxJ23/o0KGcPHmSESNGsHv3bpYvX87LL7/MsGHDXPUURESkABSrUr22bdtmWFBw1apVWX4LKJe5u1kYGxHK0NmRWCBdk4jUZGpsRKgaQ4hIqde3b1+OHTvG888/T1xcHE2bNmXFihVppeIxMTG4uV3+3rF69eqsXLmSxx9/nMaNG1O1alVGjBjB6NGjXfUURESkAFjsdntmjdYKxZkzZ9izZw8AzZo146233uKmm24iICCAGjVqMGbMGA4dOsSnn34KmHbkYWFhDBs2jPvvv5+1a9fy2GOPsXz5coe76iUkJGC1WomPjy+Vo08romIZv2xHukYRIVZvxkaEEh4WAmiBXBEpOKX9PTgrel1ERFwjN++/Lh1x2rp1KzfddFPa7VGjRgEwaNAgZs2aRWxsLDExMWn3165dm+XLl/P4448zefJkqlWrxscff6xW5LkQHhZC19DgLBMjRxIrEREREZHSxqUjTq6gb/WylrpA7tW/EKljTVMHNFfyJCL5ovfgzOl1ERFxjdy8/xar5hBScHJaIBfMArkptlKVZ4uIiIiIAEqc5BItkCsiIiIikjUlTgJogVwRERERkewocRJAC+SKiIiIiGSnWK3jJAUndYHcuPjETOc5WYDgSwvkql25iIiIiJQ2SpwEcHyB3FU74tSuXERERERKHZXqSZrwsBCmDmhOsDV9OV6w1ZupA5oDMHR2ZIYmEnHxiQydHcmKqNhCi1VEREREpDBpxEnSyWqBXIAOr67Nsl25BdOuvGtosMr2RERERKTEUeIkGbi7WWhbp2K6bRv3nnC4XfnVjxURERERKe5UqicOUbtyERERESnNNOIkDslNu3J13RMRERGRkkaJkzjE0Xbl/569QIdX16rrnoiIiIiUKCrVE4ektiuHy+3JU6Xevr1JCMPmquueiIiIiJQ8SpzEYdm1K3/vnmYs3R6bZdc9MF33UmyZ7SEiIiIiUrSpVE9yJat25ZujT6rrnoiIiIiUWEqcJNcya1eem657ah4hIiIiIsWNEidxCke77u07fk7NI0RERESk2NEcJ3GK1K57WY0bWYDyvh5MWr1bzSNEREREpNhR4iROkVPXvdSWEGoeISIiIiLFkRIncZrsuu493qUep84lZ/nYK5tHiIiIiIgUNZrjJE6VVde9r38/7NDjU5tMqIGEiIiIiBQlSpzE6TLruudo84hAP29WRMUyftkONZAQERERkSJDpXpSKBxpHhFi9ebfsxcYOjtSDSREREREpEhR4iSFIqfmEQDP9WjIC8t3ONRAIsVmZ+PeE3y17RAb955QUwkRERERKVAq1ZNCk9o84uoyvOBLZXhWH88MI01XSm0gMWXtHuZtiVEpn4iIiIgUGiVOUqiyah7h7mbhq22HHDrG26t3Z9iWWso3dUBzJU8iIiK5ZLPZuHDhgqvDECkQnp6euLnlv9BOiZMUusyaR4DjDSQyY8eU/I1ftoOuocEA6sonIiLigAsXLhAdHY3NZnN1KCIFws3Njdq1a+Pp6Zmv4yhxkiIjtYFEXHxipvOccqJSPhERkdyx2+3Exsbi7u5O9erVnfKtvEhRYrPZOHz4MLGxsdSoUQOLJe9fpCtxkiIjtYHE0NmRWCBd8nT17eyolE9ERMQxFy9e5Ny5c1SpUgVfX19XhyNSICpXrszhw4e5ePEiHh4eeT6OvlaQIiW1gUSwNX3ZXrDVm8e71MvzcdWVT0REJKOUlBSAfJcwiRRlqb/fqb/veaURJylysmogATBvywGV8omIiDhZfsqXRIo6Z/1+a8RJiqTUBhI9m1albZ2KuLtZHFoLyhFvr96d4wK7GpESERERkSspcZJipTBK+b75PZYOr66l/0ebGDFvG/0/2kSHV9emJVUiIiKSXkn4wrFWrVpMmjTJ4f3XrVuHxWLh1KlTBRaTFC0q1ZNip6BL+R6ZG5nhPjWXEBERydyKqNgMi9sXZAl8TmVXY8eOZdy4cbk+7pYtWyhbtqzD+7dr147Y2FisVmuuz5VXDRo0IDo6mv379xMcHFxo5xVDI05SLBVkKV9mrm4uASXj2zUREZH8WBEVy9DZkTmWwDtTbGxs2mXSpEn4+/un2/bkk0+m7Wu327l48aJDx61cuXKuOgt6enoSHBxcaPPD1q9fz/nz57nrrrv45JNPCuWc2UlOTnZ1CIVOiZOUKAVVygeXR6Q2R59kRZTK+UREpHRLsdkZv2xHplUemX3h6CzBwcFpF6vVisViSbv9119/4efnx7fffkuLFi3w8vJi/fr17N27l549exIUFES5cuVo2bIlq1evTnfcq0v1LBYLH3/8Mb1798bX15d69eqxdOnStPuvLtWbNWsW5cuXZ+XKlTRs2JBy5coRHh5ObOzlzwYXL17kscceo3z58lSsWJHRo0czaNAgevXqlePznj59Ovfccw/33XcfM2bMyHD/wYMH6d+/PwEBAZQtW5brr7+eX375Je3+ZcuW0bJlS7y9valUqRK9e/dO91yXLFmS7njly5dn1qxZAOzbtw+LxcL8+fO58cYb8fb2Zs6cOZw4cYL+/ftTtWpVfH19adSoEZ9//nm649hsNl577TXq1q2Ll5cXNWrU4KWXXgKgc+fODB8+PN3+x44dw9PTkzVr1uT4mhQ2JU5S4oSHhbB+dGc+f6gNk/s15fOH2rB+dGeGd65HiNU736NPq3bEOfTtmkakRESkJNscfTLD38IrXfmFY2F7+umneeWVV9i5cyeNGzfmzJkzdO/enTVr1vDbb78RHh5OREQEMTEx2R5n/Pjx9OnTh99//53u3btz7733cvJk1s/n3LlzvPHGG3z22Wf8+OOPxMTEpBsBe/XVV5kzZw4zZ85kw4YNJCQkZEhYMnP69GkWLFjAgAED6Nq1K/Hx8fz0009p9585c4Ybb7yRQ4cOsXTpUrZv385TTz2FzWYDYPny5fTu3Zvu3bvz22+/sWbNGlq1apXjea/29NNPM2LECHbu3Em3bt1ITEykRYsWLF++nKioKB5++GHuu+8+Nm/enPaYMWPG8Morr/Dcc8+xY8cO5s6dS1BQEAAPPvggc+fOJSkpKW3/2bNnU7VqVTp37pzr+Aqa5jhJiZRaync1Zyywu2Tb4Sy/XbNgvl2z2eCF5YVX7y0iIlLYjp7OOmnKy37ONGHCBLp27Zp2OyAggCZNmqTdfuGFF1i8eDFLly7NMOJxpcGDB9O/f38AXn75Zd555x02b95MeHh4pvsnJyczbdo06tSpA8Dw4cOZMGFC2v3vvvsuY8aMSRvtmTJlCt98802Oz2fevHnUq1eP6667DoB+/foxffp0brjhBgDmzp3LsWPH2LJlCwEBZt533bp10x7/0ksv0a9fP8aPH5+27crXw1EjR47kjjvuSLftysTw0UcfZeXKlXzxxRe0atWK06dPM3nyZKZMmcKgQYMAqFOnDh06dADgjjvuYPjw4Xz11Vf06dMHMCN3gwcPLpIt8jXiJKVKdqV879/TLNsRKQsQUNaDk2cvZHn8KxtMaERKRERKskA/75x3ysV+znT99denu33mzBmefPJJGjZsSPny5SlXrhw7d+7MccSpcePGadfLli2Lv78/R48ezXJ/X1/ftKQJICQkJG3/+Ph4jhw5km6kx93dnRYtWuT4fGbMmMGAAQPSbg8YMIAFCxZw+vRpALZt20azZs3Skqarbdu2jZtvvjnH8+Tk6tc1JSWFF154gUaNGhEQEEC5cuVYuXJl2uu6c+dOkpKSsjy3t7d3utLDyMhIoqKiGDx4cL5jLQgacZJSJ6uufO5uFtzcLFmOSAH0blqV6Rv25em8uR2RSrHZM41RRESkKGhVO4AQq3eW3WwtmC8mUzvfFqaru+M9+eSTrFq1ijfeeIO6devi4+PDXXfdxYULWX8ZCuDh4ZHutsViSSt/c3R/uz1/X4zu2LGDTZs2sXnzZkaPHp22PSUlhXnz5vHQQw/h4+OT7TFyuj+zODNr/nD16/r6668zefJkJk2aRKNGjShbtiwjR45Me11zOi+Ycr2mTZty8OBBZs6cSefOnalZs2aOj3MFjThJqZRZVz7IfkRq6oDmdAnNX+tPR0ekHG0+oVErERFxFUe62Y6NCC0SX/pt2LCBwYMH07t3bxo1akRwcDD79u0r1BisVitBQUFs2bIlbVtKSgqRkRmXQbnS9OnT6dixI9u3b2fbtm1pl1GjRjF9+nTAjIxt27Yty/lXjRs3zrbZQuXKldM1sfj77785d+5cjs9pw4YN9OzZkwEDBtCkSROuueYadu/enXZ/vXr18PHxyfbcjRo14vrrr+ejjz5i7ty53H///Tme11U04iRylexGpFJs9my/XcuP1BGppxf9Qfy55AzHv3otqcJeN0NERORqqV84Xv33KLiI/T2qV68eixYtIiIiAovFwnPPPZftyFFBefTRR5k4cSJ169alQYMGvPvuu/z7779ZzudJTk7ms88+Y8KECYSFhaW778EHH+Stt97izz//pH///rz88sv06tWLiRMnEhISwm+//UaVKlVo27YtY8eO5eabb6ZOnTr069ePixcv8s0336SNYHXu3JkpU6bQtm1bUlJSGD16dIbRs8zUq1ePhQsX8vPPP1OhQgXeeustjhw5QmioSai9vb0ZPXo0Tz31FJ6enrRv355jx47x559/8sADD6R7LsOHD6ds2bLpuv0VNRpxEslEViNSBblWFJjk6VQmSVPqfWBK/b753bF1MzQiJSIiBS2rbrZFJWkCeOutt6hQoQLt2rUjIiKCbt260bx580KPY/To0fTv35+BAwfStm1bypUrR7du3fD2znwe2NKlSzlx4kSmyUTDhg1p2LAh06dPx9PTk++++47AwEC6d+9Oo0aNeOWVV3B3dwegU6dOLFiwgKVLl9K0aVM6d+6crvPdm2++SfXq1bnhhhu45557ePLJJx1a0+rZZ5+lefPmdOvWjU6dOhEcHJyhtfpzzz3HE088wfPPP0/Dhg3p27dvhnli/fv3p0yZMvTv3z/L16IosNjzW3hZzCQkJGC1WomPj8ff39/V4UgxldVoz3M9GvLC8p0FMiJ1pYCynlk2qUitKX+uR6g6+0mRo/fgzOl1EVdJTEwkOjqa2rVrF+kPrCWVzWajYcOG9OnThxdeeMHV4bjMvn37qFOnDlu2bCmQhDa73/PcvP+qVE8kD/LaYMJZyZSjnf2udnW5X04NKNSgQkRExHn279/Pd999x4033khSUhJTpkwhOjqae+65x9WhuURycjInTpzg2WefpU2bNi4ZBcwNJU4ieZTVWlHZ1XsX1ohUVnLT2c+ROVRKrERERBzn5ubGrFmzePLJJ7Hb7YSFhbF69WoaNmzo6tBcYsOGDdx0001ce+21LFy40NXh5EileiIFJKukYkWUmZ8EmY9Ilff1yLQ5ROo+Fcp6cPJsxhahzpCa8jzcsTYf/hidIYbU+6cOMN8IqTmF5JbegzOn10VcRaV6Uho4q1RPzSFECkheWp5PG9CcV+5oBGTdfOLFnmHZLtSbH/ZLl49+ypg0pd4PpvOfI80pQA0qREREpGRQqZ6IC2Q3RwrIsbVrQc+jyi63Se38l9V9qaWAXUODWbUjTuV+IiIiUiIocRJxkazmSEHOiVVRn0cVG5/IlLV7mLR6d7brUUHO5X5KrERERKQoUOIkUkRll1iB6zv75WTmhqzL/Rxd6Bfyn1gp8RIRERFnUOIkUowVVGc/NwvY7flLsk6dz7qBhSPlfs5IrBzpDAj5T66UnImIiJR8SpxESqi8jkgBPHSD6aqX1YhVTp3/rD4e2SZOOXFGYpVVZ8Cr17LKb9t1R5MzERERKd7UVU+kBMtLZ7+pA5ozpntovjr/DWlfqwCezWWpiVVWpYCOdAYcv2wH3/wem2N3wBVRsXR4dS39P9rEiHnb6P/RJjq8ujbtPke6C6qzoIhI0dOpUydGjhyZdrtWrVpMmjQp28dYLBaWLFmS73M76zhSuDTiJFJKOdKAIq+d/7qGBjNvywGXNaiAnDsDxsYn8uxXUXmeh/V/syMp7+uR7eMdWWgYHCv1UzmgiIgRERFBcnIyK1asyHDfTz/9RMeOHdm+fTv/3969R9Wc7/8Df+5uW6US0sUtpoSQkTJxXGMSOpjcZoUaYaFaLsca+rllZsg5dYzriTlHNZZD5MhlHJyEZibXQTSGBitpKOWg21HRfv/+aPp8bV32rr1rF8/HWnut9ufz3p/Pe7/2Xr169X5/3p8+ffrU6bhXrlyBqamptroJAAgLC8Phw4eRmpqqtD07OxuWlpZaPVdNXr58ifbt20NPTw+PHj2CXC5vlPO+i1g4Eb3HVC1AocnKf2t8eta6QEVt0/0ay7Pishr3qZouiFr2V7bJzi/Bgr3Xquyr6wIYmk4nVGc/EVFzERgYCF9fX/z222/o0KGD0r6YmBj079+/zkUTAFhZWWmriyrZ2Ng02rn+9a9/wdnZGUIIHD58GFOnTm20c79NCIHy8nIYGDTPEoRT9Yio3mqaCghofqPfViaGDXKT36agLjcSVmc6YG3TCQGo3A+oN52QUw6bJn4upFVCAGXFunkI9b6748aNg5WVFWJjY5W2FxUVIT4+HoGBgfjvf/+LTz/9FO3bt4eJiQl69+6Nffv21Xrct6fq3b17F0OGDEGLFi3Qs2dPJCYmVnnNsmXL0K1bN5iYmKBr165YtWoVXr2q+KdabGws1q5dixs3bkAmk0Emk0l9fnuqXlpaGkaMGAFjY2O0adMGc+fORVFRkbQ/ICAAEyZMQGRkJGxtbdGmTRsEBQVJ56rNrl27MH36dEyfPh27du2qsv/WrVsYN24czM3NYWZmhsGDB+P+/fvS/ujoaDg7O0Mul8PW1hbBwcEAgAcPHkAmkymNpr148QIymQznzp0DAJw7dw4ymQwnTpyAq6sr5HI5fvzxR9y/fx/jx4+HtbU1WrZsCTc3N5w+fVqpX6WlpVi2bBk6duwIuVwOBwcH7Nq1C0IIODg4IDIyUql9amoqZDIZ7t27pzIm9dU8yz0iahY0me4HQKMRq9pWBpQBsDQ1xLPi+i9goSl1FsAIO3oLgEyjZd3VWSQD0M6oFzU+fi6kda/+B6y30825/99jwEj1VDkDAwPMnDkTsbGxWLFiBWSyipwSHx+P8vJyfPrppygqKoKrqyuWLVsGc3NzHD9+HDNmzMAHH3wAd3d3ledQKBT45JNPYG1tjUuXLiE/P1/peqhKZmZmiI2NhZ2dHdLS0jBnzhyYmZnh888/x9SpU/Hzzz/j5MmTUlFgYWFR5RjFxcXw8vKCh4cHrly5gtzcXMyePRvBwcFKxeHZs2dha2uLs2fP4t69e5g6dSr69u2LOXPm1Pg+7t+/jwsXLuDQoUMQQmDx4sXIzMxE586dAQCPHj3CkCFDMGzYMJw5cwbm5uZISUnB69evAQBRUVFYsmQJNmzYAG9vb+Tn5yMlJUVl/N62fPlyREZGomvXrrC0tERWVhbGjBmDdevWQS6XY/fu3fDx8UF6ejo6deoEAJg5cyYuXLiALVu2wMXFBRkZGXj69ClkMhlmzZqFmJgYLF26VDpHTEwMhgwZAgcHhzr3T11NonDavn07IiIikJOTAxcXF2zdurXGL3VsbCw+++wzpW1yuRwlJSXVtici3dJkul99Cyug9pUBAeCr8b10eqNgVQSAnIJSlW1UTSesbZGMuiz7Pn/PNZUrFFLjqhyN5OdC76NZs2YhIiICycnJGDZsGICKP5x9fX1hYWEBCwsLpT+qQ0JCcOrUKRw4cECtwun06dO4c+cOTp06BTu7ikJy/fr18Pb2Vmq3cuVK6Wd7e3ssXboUcXFx+Pzzz2FsbIyWLVvCwMCg1ql5e/fuRUlJCXbv3i1dY7Vt2zb4+Pjgz3/+M6ytrQEAlpaW2LZtG/T19dG9e3eMHTsWSUlJtRZO0dHR8Pb2lq6n8vLyQkxMDMLCwgBU/A1uYWGBuLg4GBoaAgC6desmvf6rr77Cn/70JyxcuFDa5ubmpjJ+b/viiy8watQo6Xnr1q3h4uIiPf/yyy+RkJCAo0ePIjg4GL/++isOHDiAxMREjBw5EgDQtWtXqX1AQABWr16Ny5cvw93dHa9evcLevXurjEJpm84Lp/3792PJkiXYsWMHBgwYgE2bNsHLywvp6elo165dta8xNzdHenq69LzyPw1E1Pw0VGE1upctPuxkWet+VTcKVrns+u/7UcPrmwJVi2RoY9Rr7bFfMKqnDa+ZakTlCoG1x37h50LaZ2hSMfKjq3OrqXv37hg4cCCio6MxbNgw3Lt3Dz/88AO++OILAEB5eTnWr1+PAwcO4NGjRygrK0NpaSlMTNQ7x+3bt9GxY0epaAIADw+PKu3279+PLVu24P79+ygqKsLr169hbm6u9vuoPJeLi4vSwhSDBg2CQqFAenq6VDg5OztDX19famNra4u0tLQaj1teXo5vv/0WmzdvlrZNnz4dS5cuxerVq6Gnp4fU1FQMHjxYKprelJubi8ePH8PT07NO76c6/fv3V3peVFSEsLAwHD9+HNnZ2Xj9+jVevnyJhw8fAqiYdqevr4+hQ4dWezw7OzuMHTsW0dHRcHd3x7Fjx1BaWorJkydr3Nfa6Lxw2rhxI+bMmSONIu3YsQPHjx9HdHQ0li9fXu1rZDJZo15UR0S6o0lhpc5+TUa1Kq/Tqu+Nhps6dUe9svNLcDnjWa0LjZB2Xc54VuW6tzfxc6F6k8nUmi7XFAQGBiIkJATbt29HTEwMPvjgA+kP7YiICGzevBmbNm1C7969YWpqikWLFqGsrOZFgerqwoUL8PPzw9q1a+Hl5SWN3Pz1r3/V2jne9HZxI5PJoFAoamx/6tQpPHr0qMpiEOXl5UhKSsKoUaNgbGxc4+tr2wcAenoVSyWIN65Nq+maq7dXK1y6dCkSExMRGRkJBwcHGBsbY9KkSdLno+rcADB79mzMmDEDX3/9NWJiYjB16lS1C+P60mnhVFZWhqtXryI0NFTapqenh5EjR+LChQs1vq6oqAidO3eGQqFAv379sH79ejg7O1fbtrS0FKWl/5f4CwoKtPcGiEjnNFkZENB8VAtAvW40rM6IlrW5HIAMTwqafvGVW8jp0o1J3Xjzc6F32ZQpU7Bw4ULs3bsXu3fvxvz586VZSCkpKRg/fjymT58OoOKapV9//RU9e/ZU69g9evRAVlYWsrOzYWtb8bv+4sWLSm3Onz+Pzp07Y8WKFdK2zMxMpTZGRkYoLy9Xea7Y2FgUFxdLBUZKSgr09PTg5OSkVn+rs2vXLkybNk2pfwCwbt067Nq1C6NGjUKfPn3w7bff4tWrV1UKMzMzM9jb2yMpKQnDhw+vcvzKVQizs7Px4YcfAkCVZddrkpKSgoCAAEycOBFAxd/2Dx48kPb37t0bCoUCycnJ0lS9t40ZMwampqaIiorCyZMn8f3336t1bk3otHB6+vQpysvLpSHIStbW1rhz5061r3FyckJ0dDT69OmD/Px8REZGYuDAgbh161aVJSkBIDw8HGvXrm2Q/hPRu0GTUa3aXq/piFbYH51rbaPpIhna1M6shepGpDXqxpufC73LWrZsialTpyI0NBQFBQUICAiQ9jk6OuLgwYM4f/48LC0tsXHjRjx58kTtwmnkyJHo1q0b/P39ERERgYKCgioFiKOjIx4+fIi4uDi4ubnh+PHjSEhIUGpjb2+PjIwMpKamokOHDjAzM6tyHyU/Pz+sWbMG/v7+CAsLQ15eHkJCQjBjxowqfyOrKy8vD8eOHcPRo0fRq1cvpX0zZ87ExIkT8ezZMwQHB2Pr1q2YNm0aQkNDYWFhgYsXL8Ld3R1OTk4ICwvDvHnz0K5dO3h7e6OwsBApKSkICQmBsbExPvroI2zYsAFdunRBbm6u0jVftXF0dMShQ4fg4+MDmUyGVatWKY2e2dvbw9/fH7NmzZIWh8jMzERubi6mTJkCANDX10dAQABCQ0Ph6OhY7VRKbWt2y5F7eHhg5syZ6Nu3L4YOHYpDhw7BysoKO3furLZ9aGgo8vPzpUdWVlYj95iImrvall1XZXQvW/y4bAT2zfkIm6f1xb45H+HHZSMwupdtrUu2V17Yr8my7jJULJJR036g9mXfZQBszOWwMW9Raxtbi4pikhqPe5fWsLXg50IUGBiI58+fw8vLS+l6pJUrV6Jfv37w8vLCsGHDYGNjgwkTJqh9XD09PSQkJODly5dwd3fH7NmzsW7dOqU2f/zjH7F48WIEBwejb9++OH/+PFatWqXUxtfXF6NHj8bw4cNhZWVV7ZLoJiYmOHXqFJ49ewY3NzdMmjQJnp6e2LZtW92C8YbKhSaquz7J09MTxsbG2LNnD9q0aYMzZ86gqKgIQ4cOhaurK/7+979Lo0/+/v7YtGkT/va3v8HZ2Rnjxo3D3bt3pWNFR0fj9evXcHV1xaJFi/DVV1+p1b+NGzfC0tISAwcOhI+PD7y8vNCvXz+lNlFRUZg0aRIWLFiA7t27Y86cOSguLlZqExgYiLKysioLxzUUmRBqLprfAMrKymBiYoKDBw8qfZn9/f3x4sULHDlyRK3jTJ48GQYGBirX5wcqpupZWFggPz+/zhfvERE1BHVuTltbG1VLUte2H6gY0QKqH/V6c1W92tqou3obfwdXrz5xqVxVD9D8c6H3V0lJCTIyMtClSxe0aMERSmpefvjhB3h6eiIrK6vW0bnavud1+f2r06l6RkZGcHV1RVJSklQ4KRQKJCUlSTfXUqW8vBxpaWkYM2ZMA/aUiKjhqLoOS1UbTRfJUOc6LnXaUONSNRWUnwsRvatKS0uRl5eHsLAwTJ48ud5TGutK56vqLVmyBP7+/ujfvz/c3d2xadMmFBcXS0NuM2fORPv27REeHg6gYh34jz76CA4ODnjx4gUiIiKQmZmJ2bNn6/JtEBHplCaLZKhzHZc6bd4lzeX+gu/b50JEBAD79u1DYGAg+vbti927dzfaeXVeOE2dOhV5eXlYvXo1cnJy0LdvX5w8eVKqHB8+fCgtdwgAz58/x5w5c5CTkwNLS0u4urri/Pnzal/sR0REVWk66vUuaW73F3xfPhciokoBAQFKi4E0Fp1e46QLnF9PRKQ7zeF38IABA+Dm5iZdmK1QKNCxY0eEhIRUe3/B2NhYLFq0CC9evKj3OZtDXOjdxGuc6H2grWucmt2qekRERA2l8v6Cb943pC73F+zYsSPGjx+PW7du1Xqe0tJSFBQUKD2IdOk9+z86vWe09f1m4URERPS72u4vmJOTU+1rKu8veOTIEezZswcKhQIDBw7Eb7/9VuN5wsPDYWFhIT06duyo1fdBpC59fX0AFf80IHpXVX6/K7/v9aXza5yIiIiaMw8PD6UbLw4cOBA9evTAzp078eWXX1b7mtDQUCxZskR6XlBQwOKJdMLAwAAmJibIy8uDoaGh0nXlRO8ChUKBvLw8mJiYwMBAs9KHhRMREdHv2rZtC319fTx58kRp+5MnT2BjY6PWMQwNDfHhhx/i3r17NbaRy+WQy+Ua9ZVIG2QyGWxtbZGRkYHMzExdd4eoQejp6aFTp04aL9zDwomIiOh3vL8gvY+MjIzg6OjI6Xr0zjIyMtLKaCoLJyIiojfw/oL0PtLT0+OqekQqsHAiIiJ6A+8vSERE1eF9nIiIqNHwd3D1GBciIt3gfZyIiIiIiIi06L2bqlc5wMabDRIRNb7K373v2WQHlZibiIh0oy556b0rnAoLCwGA98sgItKhwsJCWFhY6LobTQZzExGRbqmTl967a5wUCgUeP34MMzOzatdyr7wJYVZWFueZa4Bx1A7GUTsYR+3RNJZCCBQWFsLOzo432nwDc1PjYBy1g3HUDsZROxozL713I056enro0KGDynbm5ub8EmsB46gdjKN2MI7ao0ksOdJUFXNT42IctYNx1A7GUTsaIy/x331EREREREQqsHAiIiIiIiJSgYXTW+RyOdasWQO5XK7rrjRrjKN2MI7awThqD2OpG4y7djCO2sE4agfjqB2NGcf3bnEIIiIiIiKiuuKIExERERERkQosnIiIiIiIiFRg4URERERERKQCCyciIiIiIiIVWDi9Zfv27bC3t0eLFi0wYMAAXL58WdddatK+//57+Pj4wM7ODjKZDIcPH1baL4TA6tWrYWtrC2NjY4wcORJ3797VTWebsPDwcLi5ucHMzAzt2rXDhAkTkJ6ertSmpKQEQUFBaNOmDVq2bAlfX188efJERz1umqKiotCnTx/pJngeHh44ceKEtJ8xrLsNGzZAJpNh0aJF0jbGsXExL9Udc5PmmJe0g3mpYegqN7FwesP+/fuxZMkSrFmzBteuXYOLiwu8vLyQm5ur6641WcXFxXBxccH27dur3f+Xv/wFW7ZswY4dO3Dp0iWYmprCy8sLJSUljdzTpi05ORlBQUG4ePEiEhMT8erVK3z88ccoLi6W2ixevBjHjh1DfHw8kpOT8fjxY3zyySc67HXT06FDB2zYsAFXr17FTz/9hBEjRmD8+PG4desWAMawrq5cuYKdO3eiT58+StsZx8bDvFQ/zE2aY17SDuYl7dNpbhIkcXd3F0FBQdLz8vJyYWdnJ8LDw3XYq+YDgEhISJCeKxQKYWNjIyIiIqRtL168EHK5XOzbt08HPWw+cnNzBQCRnJwshKiIm6GhoYiPj5fa3L59WwAQFy5c0FU3mwVLS0vxj3/8gzGso8LCQuHo6CgSExPF0KFDxcKFC4UQ/C42NuYlzTE3aQfzkvYwL9WfrnMTR5x+V1ZWhqtXr2LkyJHSNj09PYwcORIXLlzQYc+ar4yMDOTk5CjF1MLCAgMGDGBMVcjPzwcAtG7dGgBw9epVvHr1SimW3bt3R6dOnRjLGpSXlyMuLg7FxcXw8PBgDOsoKCgIY8eOVYoXwO9iY2JeahjMTfXDvKQ55iXN6To3GWjtSM3c06dPUV5eDmtra6Xt1tbWuHPnjo561bzl5OQAQLUxrdxHVSkUCixatAiDBg1Cr169AFTE0sjICK1atVJqy1hWlZaWBg8PD5SUlKBly5ZISEhAz549kZqayhiqKS4uDteuXcOVK1eq7ON3sfEwLzUM5qa6Y17SDPOSdjSF3MTCiaiJCQoKws8//4wff/xR111plpycnJCamor8/HwcPHgQ/v7+SE5O1nW3mo2srCwsXLgQiYmJaNGiha67Q0RNAPOSZpiXNNdUchOn6v2ubdu20NfXr7L6xpMnT2BjY6OjXjVvlXFjTNUXHByM7777DmfPnkWHDh2k7TY2NigrK8OLFy+U2jOWVRkZGcHBwQGurq4IDw+Hi4sLNm/ezBiq6erVq8jNzUW/fv1gYGAAAwMDJCcnY8uWLTAwMIC1tTXj2EiYlxoGc1PdMC9pjnlJc00lN7Fw+p2RkRFcXV2RlJQkbVMoFEhKSoKHh4cOe9Z8denSBTY2NkoxLSgowKVLlxjTtwghEBwcjISEBJw5cwZdunRR2u/q6gpDQ0OlWKanp+Phw4eMpQoKhQKlpaWMoZo8PT2RlpaG1NRU6dG/f3/4+flJPzOOjYN5qWEwN6mHeanhMC/VXZPJTVpbZuIdEBcXJ+RyuYiNjRW//PKLmDt3rmjVqpXIycnRddearMLCQnH9+nVx/fp1AUBs3LhRXL9+XWRmZgohhNiwYYNo1aqVOHLkiLh586YYP3686NKli3j58qWOe960zJ8/X1hYWIhz586J7Oxs6fG///1PajNv3jzRqVMncebMGfHTTz8JDw8P4eHhocNeNz3Lly8XycnJIiMjQ9y8eVMsX75cyGQy8Z///EcIwRjW15srFwnBODYm5qX6YW7SHPOSdjAvNRxd5CYWTm/ZunWr6NSpkzAyMhLu7u7i4sWLuu5Sk3b27FkBoMrD399fCFGx7OuqVauEtbW1kMvlwtPTU6Snp+u2001QdTEEIGJiYqQ2L1++FAsWLBCWlpbCxMRETJw4UWRnZ+uu003QrFmzROfOnYWRkZGwsrISnp6eUnISgjGsr7eTE+PYuJiX6o65SXPMS9rBvNRwdJGbZEIIob3xKyIiIiIioncPr3EiIiIiIiJSgYUTERERERGRCiyciIiIiIiIVGDhREREREREpAILJyIiIiIiIhVYOBEREREREanAwomIiIiIiEgFFk5EREREREQqsHAiekfJZDIcPnxY190gIiKSMDdRc8bCiagBBAQEQCaTVXmMHj1a110jIqL3FHMTkWYMdN0BonfV6NGjERMTo7RNLpfrqDdERETMTUSa4IgTUQORy+WwsbFRelhaWgKomKoQFRUFb29vGBsbo2vXrjh48KDS69PS0jBixAgYGxujTZs2mDt3LoqKipTaREdHw9nZGXK5HLa2tggODlba//TpU0ycOBEmJiZwdHTE0aNHpX3Pnz+Hn58frKysYGxsDEdHxyrJlIiI3i3MTUT1x8KJSEdWrVoFX19f3LhxA35+fpg2bRpu374NACguLoaXlxcsLS1x5coVxMfH4/Tp00rJJyoqCkFBQZg7dy7S0tJw9OhRODg4KJ1j7dq1mDJlCm7evIkxY8bAz88Pz549k87/yy+/4MSJE7h9+zaioqLQtm3bxgsAERE1OcxNRLUQRKR1/v7+Ql9fX5iamio91q1bJ4QQAoCYN2+e0msGDBgg5s+fL4QQ4ptvvhGWlpaiqKhI2n/8+HGhp6cncnJyhBBC2NnZiRUrVtTYBwBi5cqV0vOioiIBQJw4cUIIIYSPj4/47LPPtPOGiYioyWNuItIMr3EiaiDDhw9HVFSU0rbWrVtLP3t4eCjt8/DwQGpqKgDg9u3bcHFxgampqbR/0KBBUCgUSE9Ph0wmw+PHj+Hp6VlrH/r06SP9bGpqCnNzc+Tm5gIA5s+fD19fX1y7dg0ff/wxJkyYgIEDB9brvRIRUfPA3ERUfyyciBqIqalplekJ2mJsbKxWO0NDQ6XnMpkMCoUCAODt7Y3MzEz8+9//RmJiIjw9PREUFITIyEit95eIiJoG5iai+uM1TkQ6cvHixSrPe/ToAQDo0aMHbty4geLiYml/SkoK9PT04OTkBDMzM9jb2yMpKUmjPlhZWcHf3x979uzBpk2b8M0332h0PCIiat6Ym4hqxhEnogZSWlqKnJwcpW0GBgbSRa7x8fHo378//vCHP+Cf//wnLl++jF27dgEA/Pz8sGbNGvj7+yMsLAx5eXkICQnBjBkzYG1tDQAICwvDvHnz0K5dO3h7e6OwsBApKSkICQlRq3+rV6+Gq6srnJ2dUVpaiu+++05KjkRE9G5ibiKqPxZORA3k5MmTsLW1Vdrm5OSEO3fuAKhYVSguLg4LFiyAra0t9u3bh549ewIATExMcOrUKSxcuBBubm4wMTGBr68vNm7cKB3L398fJSUl+Prrr7F06VK0bdsWkyZNUrt/RkZGCA0NxYMHD2BsbIzBgwcjLi5OC++ciIiaKuYmovqTCSGErjtB9L6RyWRISEjAhAkTdN0VIiIiAMxNRKrwGiciIiIiIiIVWDgRERERERGpwKl6REREREREKnDEiYiIiIiISAUWTkRERERERCqwcCIiIiIiIlKBhRMREREREZEKLJyIiIiIiIhUYOFERERERESkAgsnIiIiIiIiFVg4ERERERERqfD/AUMh+XiX0xwTAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. 11번의 에포크에 대해 재훈련 및 결과 출력\n",
        "\n"
      ],
      "metadata": {
        "id": "SGa2DRqImOa8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history_weight_reg = model.fit(x_train,\n",
        "                              y_train,\n",
        "                              epochs=11,\n",
        "                              batch_size=512,)\n",
        "\n",
        "model.evaluate(x_test, y_test)"
      ],
      "metadata": {
        "id": "pd4jDX8imXtV",
        "outputId": "29e19f8c-4520-4ca1-d736-cee196554db5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/11\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 141ms/step - accuracy: 0.9363 - loss: 0.5729\n",
            "Epoch 2/11\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.9449 - loss: 0.5331\n",
            "Epoch 3/11\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9512 - loss: 0.5178 \n",
            "Epoch 4/11\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9527 - loss: 0.5081\n",
            "Epoch 5/11\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9504 - loss: 0.5154\n",
            "Epoch 6/11\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9495 - loss: 0.5104\n",
            "Epoch 7/11\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9548 - loss: 0.4865\n",
            "Epoch 8/11\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9515 - loss: 0.4962\n",
            "Epoch 9/11\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9568 - loss: 0.4738\n",
            "Epoch 10/11\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9531 - loss: 0.4954\n",
            "Epoch 11/11\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9565 - loss: 0.4686\n",
            "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - accuracy: 0.7928 - loss: 1.2169\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.2265625, 0.78759765625]"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 드랍아웃을 적용한 경우\n",
        "1. 과대적합 시점을 알기 위한 탐색적 훈련"
      ],
      "metadata": {
        "id": "n4SJWYdZyUaX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 정의\n",
        "\n",
        "model = keras.Sequential([\n",
        "    layers.Dense(256, activation=\"relu\"),\n",
        "    layers.Dropout(0.4),\n",
        "    layers.Dense(256, activation=\"relu\"),\n",
        "    layers.Dropout(0.4),\n",
        "    layers.Dense(46, activation=\"softmax\")\n",
        "])\n",
        "\n",
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "nOHB_KFizAyd"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 탐색적 훈련\n",
        "\n",
        "history_dropout = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=20,\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val))"
      ],
      "metadata": {
        "id": "JF6nvIKHAwlF",
        "outputId": "da73290b-9c59-462e-95cd-ad4ee45bda41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 305ms/step - accuracy: 0.3774 - loss: 2.7929 - val_accuracy: 0.6758 - val_loss: 1.4043\n",
            "Epoch 2/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 9ms/step - accuracy: 0.6884 - loss: 1.3551 - val_accuracy: 0.7402 - val_loss: 1.1523\n",
            "Epoch 3/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7565 - loss: 1.0881 - val_accuracy: 0.7612 - val_loss: 1.0479\n",
            "Epoch 4/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7935 - loss: 0.8732 - val_accuracy: 0.8071 - val_loss: 0.9341\n",
            "Epoch 5/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8346 - loss: 0.7216 - val_accuracy: 0.8062 - val_loss: 0.9043\n",
            "Epoch 6/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8496 - loss: 0.6216 - val_accuracy: 0.8149 - val_loss: 0.8496\n",
            "Epoch 7/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8815 - loss: 0.4954 - val_accuracy: 0.8208 - val_loss: 0.8521\n",
            "Epoch 8/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.8971 - loss: 0.4387 - val_accuracy: 0.8198 - val_loss: 0.8560\n",
            "Epoch 9/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.9201 - loss: 0.3535 - val_accuracy: 0.8311 - val_loss: 0.8359\n",
            "Epoch 10/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9247 - loss: 0.3316 - val_accuracy: 0.8198 - val_loss: 0.8623\n",
            "Epoch 11/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9353 - loss: 0.2712 - val_accuracy: 0.8232 - val_loss: 0.8730\n",
            "Epoch 12/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9380 - loss: 0.2416 - val_accuracy: 0.8218 - val_loss: 0.8999\n",
            "Epoch 13/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9441 - loss: 0.2239 - val_accuracy: 0.8340 - val_loss: 0.8672\n",
            "Epoch 14/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9490 - loss: 0.1977 - val_accuracy: 0.8271 - val_loss: 0.8784\n",
            "Epoch 15/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9512 - loss: 0.1838 - val_accuracy: 0.8252 - val_loss: 0.8911\n",
            "Epoch 16/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9536 - loss: 0.1811 - val_accuracy: 0.8262 - val_loss: 0.8848\n",
            "Epoch 17/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9549 - loss: 0.1613 - val_accuracy: 0.8281 - val_loss: 0.8950\n",
            "Epoch 18/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9562 - loss: 0.1665 - val_accuracy: 0.8320 - val_loss: 0.9111\n",
            "Epoch 19/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9620 - loss: 0.1454 - val_accuracy: 0.8149 - val_loss: 0.9536\n",
            "Epoch 20/20\n",
            "\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.9583 - loss: 0.1463 - val_accuracy: 0.8232 - val_loss: 0.9482\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 시각화\n",
        "\n",
        "loss = history_dropout.history[\"loss\"]\n",
        "val_loss = history_dropout.history[\"val_loss\"]\n",
        "acc = history_dropout.history[\"accuracy\"]\n",
        "val_acc = history_dropout.history[\"val_accuracy\"]\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "fig = plt.figure(figsize = (10, 5))\n",
        "\n",
        "train_val_loss = fig.add_subplot(1, 2, 1)\n",
        "train_val_loss.plot(epochs, loss, 'o', label = \"Training loss\")\n",
        "train_val_loss.plot(epochs, val_loss, '', label = \"Validation loss\")\n",
        "train_val_loss.set_title(\"Loss\")\n",
        "train_val_loss.set_xlabel(\"Epochs\")\n",
        "train_val_loss.set_ylabel(\"Loss\")\n",
        "train_val_loss.legend()\n",
        "\n",
        "train_val_acc = fig.add_subplot(1, 2, 2)\n",
        "train_val_acc.plot(epochs, acc, 'o', label = \"Training Accuracy\")\n",
        "train_val_acc.plot(epochs, val_acc, '', label = \"Validation Accuracy\")\n",
        "train_val_acc.set_title(\"Accuracy\")\n",
        "train_val_acc.set_xlabel(\"Epochs\")\n",
        "train_val_acc.set_ylabel(\"Accuracy\")\n",
        "train_val_acc.legend()\n",
        "\n",
        "train_val_loss.set_ylim([0.5, 1.5])\n",
        "train_val_acc.set_ylim([0.7, 0.9])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "iVjnBL9Yzi2E",
        "outputId": "e9596627-b03a-4b4f-fcdf-5f10179c540a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. 7번의 에포크에 대해 재훈련 및 결과 출력"
      ],
      "metadata": {
        "id": "CKUJrABO0IA6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history_weight_reg = model.fit(x_train,\n",
        "                    y_train,\n",
        "                    epochs=7,\n",
        "                    batch_size=512,)\n",
        "\n",
        "model.evaluate(x_test, y_test)"
      ],
      "metadata": {
        "id": "SEt_yuS80Hd2",
        "outputId": "5822bfe0-2889-4d11-f065-a0ee4d07d5dc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/7\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9087 - loss: 0.3928\n",
            "Epoch 2/7\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9222 - loss: 0.3460\n",
            "Epoch 3/7\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9247 - loss: 0.3165\n",
            "Epoch 4/7\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9328 - loss: 0.2757\n",
            "Epoch 5/7\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9373 - loss: 0.2468\n",
            "Epoch 6/7\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9463 - loss: 0.2098\n",
            "Epoch 7/7\n",
            "\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9469 - loss: 0.1941\n",
            "\u001b[1m71/71\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8219 - loss: 0.9439\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.98046875, 0.8095703125]"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFtnILDjRyej"
      },
      "source": [
        "### 정리"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VOxWp69JziI8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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"
      },
      "outputs": [],
      "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": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}