8-2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VsZV03UD5Qb5"
      },
      "source": [
        "# 합성곱 신경망을 사용한 이미지 분류"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MiG7uEQF5QcA"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/8-2.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pS9ZuQWfCeHe"
      },
      "outputs": [],
      "source": [
        "# 실행마다 동일한 결과를 얻기 위해 케라스에 랜덤 시드를 사용하고 텐서플로 연산을 결정적으로 만듭니다.\n",
        "import tensorflow as tf\n",
        "\n",
        "tf.keras.utils.set_random_seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQSb0wK85QcA"
      },
      "source": [
        "## 패션 MNIST 데이터 불러오기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "BGJBttQs5QcA",
        "outputId": "69d38c54-30bd-420f-c453-f1851fb80e02",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "(train_input, train_target), (test_input, test_target) = \\\n",
        "    keras.datasets.fashion_mnist.load_data()\n",
        "\n",
        "train_scaled = train_input.reshape(-1, 28, 28, 1) / 255.0\n",
        "\n",
        "train_scaled, val_scaled, train_target, val_target = train_test_split(\n",
        "    train_scaled, train_target, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4m7l2115QcB"
      },
      "source": [
        "## 합성곱 신경망 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "wubmkM6q5QcB"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "fcuXinil5QcB",
        "outputId": "cf0f095d-2419-4f9f-af33-8ff98f4029a5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "model.add(keras.layers.Conv2D(32, kernel_size=3, activation='relu',\n",
        "                              padding='same', input_shape=(28,28,1)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "ddZAIUwJ5QcB"
      },
      "outputs": [],
      "source": [
        "model.add(keras.layers.MaxPooling2D(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "6NbO08605QcC"
      },
      "outputs": [],
      "source": [
        "model.add(keras.layers.Conv2D(64, kernel_size=(3,3), activation='relu',\n",
        "                              padding='same'))\n",
        "model.add(keras.layers.MaxPooling2D(2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "sxlx0yyS5QcC"
      },
      "outputs": [],
      "source": [
        "model.add(keras.layers.Flatten())\n",
        "model.add(keras.layers.Dense(100, activation='relu'))\n",
        "model.add(keras.layers.Dropout(0.4))\n",
        "model.add(keras.layers.Dense(10, activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "1CFKlE-B5QcC",
        "outputId": "61fc192c-b323-446b-d1d7-9a810d205158"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │             \u001b[38;5;34m320\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │          \u001b[38;5;34m18,496\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m64\u001b[0m)            │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3136\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)                 │         \u001b[38;5;34m313,700\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │           \u001b[38;5;34m1,010\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │          <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)            │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3136</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">313,700</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,010</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m333,526\u001b[0m (1.27 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">333,526</span> (1.27 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m333,526\u001b[0m (1.27 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">333,526</span> (1.27 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uO2xZfrF5QcD",
        "outputId": "d3d7b9c0-35b3-4b79-b447-e18af64cf4c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "keras.utils.plot_model(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FQTWlPbY5QcD",
        "outputId": "d8d62d3d-c8c8-4208-adca-66e8bdcda178"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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Pb/M+ffq0bNy40ap+6NCh8o9//ENq1KghPj4+UqNGDXnvvffk008/Nexn06ZNmf/OyMgw/N1Ru3Zt2bx5s4SHh4ufn5+EhobKwIEDZenSpVZtMzIy5Pvvv7eqN/r988UXX7BMzwHHjh2TESNGZL728vKSRYsWybRp08Tf3z/b/T09PWXQoEGyY8cOixsrr1mzRubMmaNlzFkpbDFPYZsPXIMYzj0Rw7lXDCcicvHiRcPYbN68eTJp0iSpVKmSlChRQiIjIyUqKkqKFi1q0e7atWsWMZwtzpi7O8Rwbpl8W7BgQeYTNx577LHMeqWUfPfdd9K2bVspU6aMFClSRAICAiQsLEzeeOMNOXXqlM0+p02bltlntWrVMuvj4uLkb3/7mzRq1EjKly8vPj4+Ur58eWnWrJnMmjUryyU3H3/8cWafXl5eds1t9uzZhvs8/BTYh59gMnToUIsnkOT0L6pms1m++eYb6dOnj4SFhUlgYKAUKVJEihUrJuXKlZNmzZrJmDFj5Ndff7W7zwfZ8vT0dPnqq6+kbdu2Uq1aNSlatKiUKlVKateuLSNHjpQzZ87Y1d+9e/fkhx9+kEGDBkndunUlKChIvL29xdfXV0JDQ6Vdu3YydepUiY2NzbIfHef6UZcvX5bJkydL69atJTQ0VIoVKyYlS5aUGjVqSIcOHeSf//yn1T06HvXxr6inAAAgAElEQVTwZ8FkMhn+Bzo31qxZY7WePyAgQLp27Wp3H02aNLF4nZ6eLqNHj3bK+B4VHR0tY8eOlfDwcKlcubIUL15cSpQoIVWqVJHw8HAZO3asXU9H/uqrr6ye3NO2bdvM7UopWblypXTo0CHzCULBwcHy7LPPyscffyy3bt2ya7xJSUkyb948efHFFzOvMClatKhUqVJFWrVqJZ9++mm2n1XdAgICZOnSpbJy5UoJDAx0at9z5861uGw9MDBQ22cjJ3TNPb/N+8cff7S634enp6fh8gARkf79+4uPj49V/cNPKj148KBcuXLFqs24ceMMv+vatGkjFStWtKr/7bffrOq6d+8uJUqUsKi7efOmYaIOxiZOnGjxV+6//e1v8vLLLzvcT506dWTFihXi4fG/UHPixIkWy1gf5uqYx9nf7a6ej6PcJY4rKDGciN44jhiOGI4YrvDHcCIiK1eutLpyLTw8XF577TWrtjVr1pQ///nPUr16dWnbtq0MHz5cZs+ebXhF3KOcMXe3iOHseSpDYXva6dKlSzO3ly1bVimlVEJCQrZPXPP29lZLly41PO7cuXMtns6hlFJ79uxRZcqUybLPihUrqujoaMM+P/roo8x2np6eds1/1qxZhvs8+r7YKjExMXYd52F79+5VNWrUsKt/EVHdu3c3fALeo0/JunDhgrpy5Uq2T9fz9va2eirTo44cOaLq1q1r1/h8fX3V/Pnzbfal41w/cPfuXfXuu+8qb2/vbMcZFBSkFixYYLOvhz8LIqKioqKyPLaj2rZtazWmwYMH22xv9KSsTz75RFWqVMmqfvPmzTb7cfRJWT///LNq3ry53Z/Ppk2bZvmEvuXLl1vt07hxY6XU/aeGtWzZMsv+K1SooA4dOmSz/4yMDDV9+nTl5+eX7VhLliyZ5WdVh1WrVikRUREREerChQsW2ypUqGA1xpw8Lers2bPK19fXop8FCxaoAwcOWPWfl0+M0j33/DjvhQsXqs6dO6umTZuqJ554QgUHB6sGDRpkuU/16tWtxvvOO+9kbt+6datq1aqVqlevnqpRo4YKDg5WPj4+6urVqzb7NPoZfuGFFwzbvvLKK1Ztu3TpkrM3wMny+9NOz549qzw9PTPH89RTT6n09PRc9Tl06FCLOdp6QqerYx5nf7e7ej6OcKc4rqDEcErpjeOI4f5XiOHuI4bL3dzz67wbN25sdfxFixY59RjOnHtBjeF42mkWvL29M/99+/ZtMZvNEhERke0T18xmswwcOFCOHz9ute3hv1AmJyfLxYsXpX379tn+VePChQvSsWNHOXnypIOzyB9OnjwpERERcvr0abv3Wb16tXTt2jXbp6eYTCZp165dtn/JNZvN0rdvX6t7Cj1w6tQpadGihRw8eNCu8aWkpMjgwYPlX//6l+F2Xec6PT1dOnbsKFOnThWz2ZztOG/cuCEDBgyQjz/+ONu2zpaamirbt2+3qm/fvr1D/dy6dUsmT55sVT9q1CjJyMjI8fgeWLx4sTRv3lx27txp9z7R0dHSokULWbRokeF2o6t6kpKSMs/ftm3bsuz/0qVL0rp1a7lx44bVtoyMDOnRo4eMHj3arr+uJiUlyeDBg+WDDz7Itq2zFC9eXD799FP5z3/+I6GhoU7vX/3/pyQ9fI+xDh06SP/+/S3u0eEKOueeX+fdt29fWbt2rezatUuOHz8usbGxFlfePCo1NdXwhr4P38ejZcuW8tNPP8n+/fvl1KlTEhsbK6mpqRISEmKz3+vXr1vV2fprtdHvoS1btrj0iZsFxbfffmvxeXvjjTfE09MzV32++eabFvd9WblyZa7604U4zj3iOGI4YrhHEcM5T36NZUTcM4a7c+eOYcwWERHhtGM4e+6FPYZzy+RbkSJFMv+dmpoqU6ZMkf3798uTTz4pS5culStXrsjdu3clLi5O1q9fL08//XRm+7S0NPnkk0+s+nw4OE1LS5N3331XEhISpEmTJvLdd9/J1atXxWw2y9WrV2X58uVSo0aNzPYJCQkycuRITbO9b8iQIaKUslozPW/ePFFKZZYGDRo41O+4ceMyL1v39vaW999/X2JiYiQhIUHS09Pl1q1bcvr0aVm2bJnF5enbtm2TVatWZdn3tGnT5NChQ/L444/LwoUL5fLly2I2m+X69evy7bffSq1atTLbpqeny/Tp0w37GTZsmMXl/R06dJB169bJpUuXJC0tTVJSUuTAgQMycuRIi+Uxo0aNMlxmoOtcv//++xZr6mvWrClffPGFHDt2TFJSUiQ5OVkOHz4sH330kQQFBVnst2XLlqzeSqeLjo62Wjrk6ekprVq1cqifhIQE6dOnj9Xn7vDhw4b3InHEhg0bpF+/fnYFwY+6e/eu9O/fX3788UerbQ8n7x9ISkqSadOmyZ49e+zqPzY2Vj788EOr+nfeeUdWr17t8HgnTJgg//73vx3eLyfat28vI0aM0HYD2Tlz5lgEv4GBgTJ//nwtx3KUzrnn53k7YubMmVYPZyhVqpR07tw5x33++uuv8vvvv1vV16xZ07B9RESE1TlKTk62utcLrD38GTSZTPLSSy/lus/HHnvM4nf83r177b55c07lJObJz3GcrhhOxP3iOGI4YrhHEcM5T36OZdwxhjt+/LhVIrxMmTJSrlw5px3D2XMv9DGcPZfHFbZlpw9fFm8ymVTRokVVmzZt1O3btw37jIuLU4GBgZn7VK5c2arNggULrN6Prl27qrt37xr2mZiYqB577DGL9ocPH7Zo48wlCw/cuXPH4pi2ln/YIyMjQxUvXjyzr+nTp2e7z8svv6xCQkJUgwYN1MyZMy22PbpcwcfHR0VERKiUlBTDvm7cuKFKly5tcSn4o86cOWN1TrLy8ccfW7Q3Wgah41yfPXtWeXl5ZW6PjIy0+XlUSqmLFy+qKlWqZLavXbt2lvNytoc/mw9KrVq1stzHaMnCsGHDlFJKbd++3fCy5KSkJKt+7FmyEB8fb/HZeLj06dNH7dmzR926dUslJyer3bt3q+7duxu2LVeunNXnb8OGDVbtihcvrvz9/ZWHh4d666231OnTp1Vqaqo6ePCg6tSpk2HfQUFBFp+Z3377TXl4eFi1e+aZZ9SGDRvUlStXVGJiooqOjlaRkZFW7apVq6bS0tJyekqdIreX7Rtdtv7wz2BMTIzDl6/nldzMvSDP+969eyo2NlZt3rxZ9ezZ02qcHh4eavXq1Tnu32w2q0aNGhn+DJ0+fdrmfkZLX2fNmpXjcThLfl92GhQUlDmWp556ymn9vvXWWxbz1L1M8wFHYh5nf7e7ej72cMc4jhiOGO7RdsRw9xHDFb4YbvHixVbHbtSokVJKqdTUVDV//nwVERGhKlSooLy9vVVwcLBq2rSpmjRpkoqLi8u2f11zL4gxHMtO7aSUkqJFi8rSpUulWLFihm2CgoKkR48ema//+OMPq5uUPqpEiRLy5Zdf2rzJrr+/v0ydOtWibv369Q6O3rUSExPl9u3bma8ffTywkcWLF8vVq1clJiZG3nrrrSzbFi9eXJYvXy7Fixc33B4YGCg9e/bMfH3p0iWr83Lp0iVp3ry5PPbYY1KyZEkZPnx4lsccMWKExZWR9jxVzBnnetasWZKeni4iIsHBwbJs2TKbn0cRkQoVKsjnn3+e+fq3337L1Y2WHXXo0CGrOnvO/6MezLlFixZWT8S5du2afPTRRzka3+effy5xcXFW9R988IEsWbJEnn32WSlRooT4+vpKeHi4rFq1yvCzceXKFVm2bJlFndFfzG7fvi03b96UTz75RGbOnCnVq1cXHx8fqVOnjvz73/+2uimxyP0lJw9fzTN58mSrv05VqVJFtm3bJpGRkVK2bFnx9/eXJk2ayIYNG6RDhw4Wbc+ePZtnfznVQRlctt6tWzfp1auXC0elX0Gd9969e8VkMomnp6eUKVNGIiIiZMWKFRZtypcvL99//71069YtR8fIyMiQAQMGyL59+6y2PbghsC0PX7H+gNHvLfxPenq6xVKqJ5980ml9165d2+K10QM38hviOGOFLY4jhruPGI4YLjcKaiyTW/l93ka3ASlVqpQcPXpU6tevL4MHD5bNmzfLpUuXMq9Kjo6Olr/85S9StWpVWbJkic2+dc69MMdwbp98E7n/dLbSpUtn2aZu3boWr7N7StGLL75ocVm5kQ4dOlg80SM6OjqbkeYvJUuWtLh8/4cffnBq/wMHDsz2vISFhVm8jo+Pt3jdvHlz2bFjh5w4cUJu3rwpzz//fJb9FS9e3OKpekZf/o9yxrmOiorK/HefPn3seuR127ZtLca6bt3/Y+/Ow6Kq/geOf4ZdEWSRwN00y7LMXQslt0xNkxJNsVyyUr+Wlmku2WOL1VdTrG9upV/NNMvSylzQ1My9xC37SkaGC26gIIrKzv394QM/hrmzMndmYN6v5znPA+fee5aZYebwmXPOXW/2GntR2xvmnnvuKVeZs2bN0hswi9we0J45c8bqstSmOzdp0kSmTZtm9JqZM2eq7iG1YsUKi+ps3bq16uDP09PT6N1+iu+gXFhYqPcaKPbKK69IYGCg0faWZctyB1dRdtp6WFiYLFy40HkNcpDK1m9PT0+Jjo6WZcuWycmTJw3+wbBUfn6+DBkyRL788kuDY9WqVTO6PK2Y2vuRpXdUdFdl9zCy5x3gypaltl+Sq2EcV34VYRzHGO42xnCM4cqjso1lLOXq/VabLJSVlSU9e/aU48ePm7w2KytLnn32WVmyZInqcS37XpnHcATfRMx+kIuIweCh9DeFaizZN8HLy0tatGhR8nvxm3hF4enpKZ06dSr5/aOPPpKXX35Zzp8/b5fyLdkMsuzzUnY/FFuU/ray+Fs9U8r7XF+8eFFvIFT6PHPat29f8vOxY8csvq68Lly4YJBX3v0D7r77bhk1apReXk5OjkyePNmqcs6ePSunTp0yyI+NjdXbC6asqlWrSu/evQ3yExISLHodDBs2zOgxtW9NRW7POhC5vadV8c+ltW3b1miZ9913nwQHB+vllb7Fd0WSnJxs8DwvWrRIwsLCnNQix6iM/S4sLJQtW7bIf//7X1m6dKnBvkKWuHr1qvTq1Us18KbT6WTZsmXSsGFDk2XUrl3bIO/cuXNWt8WdlB2kG5utZIvSgQu1ulwR47jyqwjjOMZwtzGGG2b0GGM40yrjWMYSFaHfajf+2Ldvn6SkpFhcxksvvSTJycl6eVr3vTKP4Qi+ye1pweaUvTuOYuYOT2W/yTOmfv36JT9b84fgKj788EO9Qc68efOkXr16EhkZKW+++aZs377dpn++RETq1atn9pyyG6eael5SU1Nl6dKl8txzz0mHDh2kcePGEh4eLsHBwVKtWjXx8/MTLy8vs98ElFXe5/rs2bN65w0dOlR0Op1FqfRmx46805ranQcjIiLKXe706dOlevXqenlff/21VZtsGltiYslG1GqD5uzsbIvuAld6EF1WjRo1VAeNxZuOqw00RW4P+Iw99x4eHgYzcNPT0yU1NdVsW12J2rT1wYMHy1NPPeXEVmmvMvc7Oztb9uzZIy+99JLce++9cvjwYYuvPXnypLRv3162bdumevzjjz+WmJgYs+Wo/SNZ0f42HK3sbB21Gw7ZqmxZZf/pdEWM49xjHMcY7jbGcIzhbFGZxzKmVJR+m7rrcMeOHWXbtm2Snp4uWVlZEh8fb7DST+T26/zDDz8s+d0Rfa/MYziCb2L4jaw9WLpco/SHVHZ2tl1uze1ILVq0kK1bt8qdd95ZkldUVCT79u2TGTNmSLdu3SQ4OFh69OghS5YssWowb69v3XNzc+XVV1+V+vXry4gRI2TZsmWyd+9eOXnypKSlpUlmZqbcvHlTcnNzbbolcnmf67JLLGyl9q2bFvLz81Vv92yP5ys0NFTeeOMNg/zS+8qYu0uR2qBS5Pb+U+YYG3xa8hyZGrh6enoaDEitLd9SlgwyXcm8efNk586dJb/XrFlTPvnkEye2yDEqer/bt28viqJIUVGRpKeny5EjR2TGjBkGQZXTp09Lly5dLFousHfvXnnooYdU/wn18vKSTz/9VF5++WWL2qf2fmSPGTWVWXBwsN77qyXbLliq7HucuWV+roBxnHuM4xjD3cYYjjGcLSr6WMZWFaXfAQEBqvkPP/ywbNu2Tbp27SohISFSrVo16dGjh+zevVv1b630foSO6HtlHsMRfNOIv7+/ReeV/cbPlltqO1tkZKT8/fffsnLlSmnXrp3BB2tOTo5s2bJFXnjhBWnQoIF88MEHDhuc5ubmSpcuXeSjjz4q+YbK3sr7XJf+5qA8HLWMx9jj6OfnZ5fyx44dazAb9ddff5WvvvpKRMTopsjF1KZYi4jJzY/NnWOszNLKzo4ty9RyCXs+d9evX7dbWVpLTk6WKVOm6OUtXry4QsyKKY/K1G+dTichISHSvHlzeeONN+TgwYMGyw6uXbsmEydONFnON998I127dlUN+AQHB8uGDRvkxRdftLhdan/LiqJo9jlQGXh4eOjtQXXkyBG7lV12o+TSs4hcFeO4/1eZx3GM4W5jDMcYzlqVaSxjjYrUb2N7Dr711lsG72kityckqS0VT01NlX/++cdhfa/MYziCbxqx9MVReiq/Tqcz++bvqjw9PWXw4MHy66+/ysWLF2XZsmUycOBAg3/CMjMzZerUqfLUU0/ZNMvMWm+++abs27ev5Hdvb28ZOnSofP3113Lw4EFJTk6WjIwMycrKkuzsbCkoKJCmTZtaVUd5n+uy30ps2bJFFEWxOtlziZAtzC3FtpSvr6/qHbImT54sOTk5ZgeIxj5oLBkgGzvH1Dee9mDsmylbWDLIdBU//fSTwWPeu3dvo8s02rRpY1BGamqq3jkzZsxwVPNtVpn73bBhQ9VA2/r1643+U/HFF1/IoEGDVN9L77//fklISJDHHnvMqnbY6/3I3URGRpb8fP78eTl9+rRdyi297CwkJMTipX7OxDjOPcZxjOFuYwxnO8Zw/6+yjGVMqUj9Vts7TcT03pTGlninpqY6rO+VeQxH8E0jln6Ilp5mHhAQYHY6tjmu8I1JeHi4DBs2TL766itJTU2VQ4cOyeTJk/X2k1m3bp3md4PJycnRu2NScHCw/Pbbb/L555/L008/La1atZI777xTb68QT09PqweT5X2uy+6z4+p3gTP2zaKte8KoGThwoLRr104v7+zZsxIXF2f2LmLGNvu0ZKNOY5tMa715qrFvjA4fPmz1AH7AgAGathUwR22wVVBQoLoP0zfffCPDhw9XnUUTHR0t+/fvl0aNGlndBrX3o4ocGHGUqKgovd+XLVtW7jL/+usvvX2cHnnkEZOzSKyh5ZjHGeM4VxjDibjXOI4x3G2M4WzHGA6u6sEHH1TNNzVz2VjAzpGzuivzGI7gm0ZOnDhh0Xmlv1Uuuwyj9ACusLDQosGEvb6lthedTictW7aUDz74QI4fPy6NGzcuOTZr1ixN6/7jjz/0BktTp041exeqvLw8qzdMLu9zfc899+g91//73/+sqt/RPD09DW4nL2L+DsDWmjNnjkHev//9b7N/By1btlTNP3DggNk61c4JDg42e2fF8rr33ntV8yvi5t2oPHJycmTMmDHSv39/eeSRR+Tee++V0NBQmTlzpsnrjAVWyg7c9uzZI0OGDFEdBP7rX/+StWvX2rwnq9r7kT3v3llZ9e/fX+9xWrRoUbkDQmX3ghk6dKjqea425invZ7ur9cdWlX0cxxjuNsZwtmMMB1d1zz33qI59/vrrL6PXlL0RSDFH7tVamcdwBN80snv3brPn5OXlydGjR0t+v+eee/SOl/12yty3aUVFRfLzzz9b0UrHqlWrlt5GrCkpKZpOr7548aLe76buZFTsxx9/tHr/jvI+10FBQXqD2Q0bNlhVvzPccccdBnlpaWl2rSMyMlL69eunl5eVlSXz5883eV29evVU72C8atUqk7ebz8jIkE2bNhnkR0VFlXtGqjlNmzZV/TbYktcWoBU/Pz/57rvvZM2aNbJr1y45ceKEZGRkyMaNG01eV3Z/r2Lh4eElP6enp8vTTz+tuuTr3Xfflfnz55drdlTZ938R+9zNr7ILDQ2VYcOGlfyelpYmr7zyis3l/frrr3qzo5o2bSpPPPGE6rmuNuYp72e7q/XHHirjOI4x3G2M4WzHGA6uytPTU3r37m2Qb2pWu9rr1tvb26ZVCLaqzGM4gm8aWbVqldkNOL///nu9O3d06tRJ73jZOzCV/uBXs3btWjlz5oxV7SzPfh3z58+XmJgYadCggaxatcqia8reOtheS0/UlC3b3AAxMzPTYJNJS6bh2+O5Lv3PyLFjxyQ+Pt5svbm5udK8eXPp37+/fP755w67U5aI+l2nLly4YPd6Zs6cabAhaOm9X4xR25g9OTlZ3n33XdXzi4qK5F//+pfqNy0jR460sLW20+l0Eh0dbZC/aNEio3e+2rRpk1SrVk0aNmwo7du3lyeeeELvjmIiIps3b1bdg2HPnj2a9MNao0aNsmo5RkJCgkEZ4eHheudMmzZNRFy77xWp348//rhB3u7du2XFihWq59+8eVPmzZtnkB8SEqL3D+qYMWNU3zNGjhxZ0pfyUCvb2FIK6JsyZYre+GPZsmXyzjvvWF1OYmKi9OvXr2Rmo06nk5kzZxr9R9jVxjzl/Wx3tf6oYRzHGK4YYzjbMYZz/bGMPVW0fj/zzDMGeStWrJBjx44Z5GdlZUlcXJxBfvv27aVq1aqa9r20yjyGI/imkbS0NHn55ZeNbhh45coVmTRpUsnvapHp++67T+/3RYsWGa0vMTFRxowZY3YjU09PT73fyzMl+tdffy0ZLL7xxhuSnJxs9ppvv/225Oc6depYfJcpW9x55516v69Zs8bouRcuXJAePXpIRkaGtG3btiTfkiUg9niuR44cqTfIfO6550xOCc7Ly5MRI0bI77//LmvWrJEXX3zRoZv1lv4nupip9tqqUaNGMmbMGKuvGz16tNSoUcMg/5133pHnn39efv/9d8nNzZXMzEzZunWrPProo7J69WqD81u3bi09evSwqe3WGj9+vME/pTdu3JAOHTrI0qVLJTU1VfLz8yUlJUXmzZsnAwcOlJs3b8qpU6fkt99+k/Xr11eKvRDgWkaPHq0aLBk+fLhMmDBB/vnnH8nPz5dz587Jd999J61bt5akpCSD8/v161fy+XPgwAHVv7eIiAjVpUq2UHs/uuuuu+xSdmVXp04dWbp0qV7e9OnTJTY21uieSqUpiiLLly+XqKgovQH0xIkTVYO5xVx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bWTZ79mzNjQ+CgoKkTZs2aY/ffPNNGTp0qMN3yHUWe2I0Oo8h78jhXFPze3bklCPf0n+DaDKZVO7cudUbb7yhHj16pDtnVFSUCg4OTlumdOnSmjFLlizRbI927dqppKQk3TljY2PVc889Zzb++PHjZmOc+c3uU48fPzZbp7XTVGxJTU1VefPmTZtr1qxZNpfp2bOnCg0NVbVq1VKzZ882ey7jN7t+fn6qadOmKj4+Xneu6OhoVbBgwbTxxYsX14y5ePGiZp9Y89lnn5mN1ztdw4h9fenSJZUrV66051u2bGnx91Eppa5fv67KlCmTNv6FF16w+rqcLf3v5tNWtWpVq8vofbs7ePBgpZRSP//8s+a50NBQ9eDBA8089ny7GxMTY/a7q9HJDAAAIABJREFUkb716NFDHThwQMXFxamHDx+q/fv3W/w2tWjRoprfv61bt2rG5c2bVwUEBCgvLy81YsQIdeHCBZWQkKB+//131bp1a925Q0JCNL8zf/zxh+43iC+99JLaunWrunXrloqNjVX79u1TLVu21IwrV66cevLkSWZ2qdP8/vvvZqfXiYgKCAiw+G22q7hDnHpHNtSpU0cppVRCQoJatGiRatq0qSpevLjy9fVVhQoVUg0bNlRTpkyxGafe6abp388yc+SbUvqnoMyZMydrG8JJrOUnIq498i0kJCQtjipVqjht3hEjRpi9xuw4TfMpe/MJ8iPyI0/Jj8iNyI0sSUlJUZGRkWrnzp2qa9eumhi9vLzU2rVr7Z7PiCPfMhOjkXnMU+6ad3DkW9axhWzIicU3EVGBgYHq7t27VucdOHCg2TJxcXFmz2dMOPLnz2/zTWXDhg1my3z66admz7t7chkTE2M2144dOzI9l1La/RIUFGRzvwwZMsTqftmzZ49q3Lixeu6555S/v7/V62UopVR8fLzy8fFJm2/UqFGaMUbs66FDh6Y9V6hQIXXv3j2r8yml1LZt28zmzMz1aDJL70O5e/fuVpfRSzAHDBiQ9nzbtm01z48fP14zjz0J5qeffqr7HvWPf/zDYnwZf5eetozXDNG7RtjT9p///Eczb3JyssXrSZ44ccJsbLdu3TRjypQpo+7fv68bc6tWrTTjV69ebfE1ZodXXnlFE5O1U39cxR3i1Ptdbt68ufrjjz9U1apVrX7eFihQQC1fvlx3Xr3TTTt06GA2JrPFt7fffluzXJ8+fZyyPbLK2vYScV3xLSkpyeq+yIqvvvrKbG5L/7y5U/GN/Mhx5EfPRn5EbkRulNGBAwdsfjYVK1ZMbd682aF5nVl8y0qMRuUx6blr3kHxLes47dRD9enTRwoWLGh1TI0aNcwe27qTUqdOncwOfdfTqlUrs7u47Nu3z0ak7sXf39/sFIMtW7Y4df5+/frZ3C/VqlUzexwTE2P2uHHjxrJnzx45e/as3L9/X15//XWr8+XNm1dKliyZ9jgqKspmnM7Y1+Hh4Wk/9+jRw667LDZv3tws1k2bNtlcxlkuXLig6atUqVKW5pwxY4bZKeEiInPmzJErV644PJfeqXXPP/+8/O1vf7O4zPTp03XvILV8+XK71lmrVi0ZMmSIpt/b29viXcrS30E5JSXF7PfgqeHDh4u/v7/FmDPKzGkZzvL999/Lnj17zPpCQ0MNP23MUe4SZ8Y74omIxMXFScuWLeXkyZNWl42Li5N33nlHvvzyS81zGU83LVSokISFhWU5XhH9v3N776joqaKjo80eZ/ZOdXoyzpVxXe6I/CjryI+sc1V+RG6kRW6kz9vbW9q1aydLliyRCxcuSKtWrVwdkoY9MRqVx6RH3pFzUXzzULYSDhHRJDmPHj2yOt6e6zvkypVLXnrppbTH6T9sngXe3t7SpEmTtMdz586VoUOHyo0bN5wyf9OmTW2OybhfMl6zJTPy5MmT9nNycrLN8Vnd17du3TJL2NKPs6VevXppPx8/ftzu5bLq5s2bmr6sXt/hueeek4EDB5r1JSQkyLhx4xya5+rVq3L58mVNf/fu3c2uWZNR3rx55a233tL0R0RE2PV70KdPH4vPpb+eT3qxsbFpP//2229mj5+qU6eOxXmrVKkiQUFBZn27d++2EakxlFIyadIkTf+IESPM/qZczZ3ijIuL0/Tt379frl27ZvccQ4YMkUuXLqU9vnTpkuZvZsGCBVKoUKHMB5pO8eLFNX3Xr193ytw5VcZ/TvLmzeu0udMXLfTW5Y7Ij7KO/Mg2V+RH5EZanp4bWZKSkiLbt2+Xr776ShYvXqy5VqA7sCdGI/KYjMg7ci6Kbx6qTJkyNsf4+fmZPVZKWR2f8RtHS0qXLp32syNvVO5i5syZZsnYvHnzpFSpUtKwYUP56KOPZNeuXZn+QClVqpTNMRkv8Gptv9y5c0cWL14s/fr1k0aNGknFihUlNDRUgoKCJH/+/JI7d27JlSuXzW9qMsrqvs54wffevXuLyWSyq6W/KPO5c+ccijsr7t69q+krUqRIluf9+OOPJSAgwKxv9erVDl1Y9ciRI7r99lwsWy+xf/z4se632RmlT/QzKliwoG5ym/7i6HpJscifyaml/e/l5aU5Cjc6Olru3LljM15n++677+SPP/4w6wsICJBBgwZleyzWuFOcGS9SnF7jxo1l586dEh0dLXFxcRIeHq45Alvkz9+hmTNnisif73/9+vUzu0Byjx49pH379k6LWe8fSVf8vj1LMh6po3eh+szKOFfGfzjdEfkR+VFGOSU/IjfS8vTcyJrHjx/LL7/8IkOGDJHKlSvL0aNHXR2Shq0YnZ3H6CHvyLkovnmojN8cO4O9p5Wk/zB9/Pix1Tcxd/TSSy/Jjh07pGzZsml9qampsn//fpkyZYo0bdpUgoKCpEWLFvLll1869E+Hs44OePLkiYwYMUJKly4t/fv3lyVLlsi+ffvkwoULEhkZKbGxsRIfHy9PnjyRlJQUh+fP6r7OeCpIZul9M2iEpKQkSUpK0vQ7Y3+FhITIxIkTNf0jRoxI+9nWXZ/0kl8RkWLFitlcv6Uk2Z59ZC3B9vb21iTOmVmHvexJiJ1txowZmr7evXtbPC3EVdwpzgIFCuj2N2jQQHbu3Cmvv/66BAcHS/78+aVFixayd+9e3d/jp3dymzdvnvz8889p/UWLFpX//Oc/To1Z7+/cGUfU5GRBQUFm71v2nK5nr4zvG7ZO8XMH5EfkRxnlhPyI3MixZUU8IzeqV6+eKKUkNTVVoqOj5bfffpMpU6Zovij573//K6+99ppLTqfMSozOzmP0kHfkXBTf4DT58uWza1zGbyYzc+tvV2vYsKGcP39eVqxYIXXr1tUkAAkJCbJ9+3Z57733pEyZMjJt2rRsS6KfPHkir732msydO9fsmzRnyuq+zngb78zKrtONLG3H3LlzO2X+Dz74QHM06sGDB+Wbb74RkT9PUbFG7xB4EbHrlEJLYyzNmV7Go2MzsnZah4hz99+DBw+cNpc9Dh8+rPutet++fbM1DlvcLU5LBb9JkyZp3i9E/vyiSO9Uozt37siOHTtk/PjxZv2LFi1y+pFQen8jSinD3l9zAi8vL7PrT/32229Om/vYsWNmj9MfQeSuyI/+h/zoTzkhPyI30ufJuVF6JpNJgoODpUaNGjJx4kT59ddfNZeDuH//vnz44YcuijBzMTozj7FUeCTvyLkovsFp7H1DSH/Kgclksvkh5a68vb2lR48ecvDgQbl165YsWbJEunbtqnnTjo2NlQkTJkj79u0z9S2qoz766CPZv39/2mMfHx/p3bu3rF69Wn799Ve5dOmSxMTESFxcnDx+/FiSk5OlatWqDq0jq/s647dG27dvF/Xn3Zcdas48lSkzbJ2KbS8/Pz+ZNm2apn/cuHGSkJBgM5G1lAjYk8RbGmPrm1lnsPTtYWbYkxA7k97F/KtXr657eoEruVucetcxEbF+XSNLpwgtX75c8/v71ltvWTwtp3bt2po57ty5YzZmypQpmjHO+jv3NA0bNkz7+caNG/Lf//7XKfOmP+0sODjY7tP8XIn8iPwoo5ycH5EbZc2znBtZU65cOd1C26ZNm1xaJEzPnhidmcdYOpWUvCPnovgGp7H3gz79ofAFChSwedi4Le7whh0aGip9+vSRb775Ru7cuSNHjhyRcePGmV335ocffnDa3fcsSUhIMLuzU1BQkBw6dEi+/vpr6dKli9SsWVPKli1rdk0Tb29vh5PerO7rjNcDcve71Vn6BtSZF4vt2rWr1K1b16zv6tWrMnv2bJt3OrN0YXl7Ls5q6WLYzrpYvTWWjlA6evSow/9odO7c2fB4n0pMTJR169Zp+jt06JBtMdjDHeOsXr26br+1I18sJbrZdVSQ3t/5s1wYyS6vvPKK2eMlS5Zkec6zZ8+aHcn5l7/8xeZRJI4wKp8gPyI/yign5EfkRsZ4VnMje+h9CZacnOzwtRWNZCvG7MhjyDtyLopvcJozZ87YNS79t98ZTxdJn2impKTYlfQ469t0ZzGZTPLyyy/LtGnT5OTJk1KxYsW05/SuveRMJ06cMEvoJkyYYPNOWYmJiQ5f2Dmr+7pSpUpm+zrjxeDdjbe3t+a29yK27wDsqH/+85+avs8++8zm38HLL7+s23/48GGb69QbExQUJOXKlbO5bFZVrlxZt9/dLzS+e/du3X9qW7Zs6YJoLHPHOCtVqqR7LZOzZ89aXCbjRaSfyq5rfen9nTvz7p05VadOncy204IFC7JcDMp4Pb/evXtbHOtO+QT50Z/Ij/4nJ+RH5EbGeBZyo4SEBBk8eLB06tRJ/vKXv0jlypUlJCREpk+fbnU5S1+WGPFlmlExZkceQ96Rc1F8g9Ps3bvX5pjExET5/fff0x5XqlTJ7PmM36LZ+sYvNTVVfvzxRweizF7FihUzu2DstWvXDD0E/NatW2aPrd1x6amNGzc6fI2RrO7rwMBAs6R78+bNDq3fFQoXLqzpi4yMdOo6GjZsqDkqKS4uTubPn291uVKlSunewXjVqlWSnJx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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "keras.utils.plot_model(model, show_shapes=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7sO3by845QcE"
      },
      "source": [
        "## 모델 컴파일과 훈련"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Msys0HQp5QcE",
        "outputId": "8344a1f4-2096-4c86-ab9c-489d8a5c587d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 3ms/step - accuracy: 0.7493 - loss: 0.7043 - val_accuracy: 0.8833 - val_loss: 0.3211\n",
            "Epoch 2/20\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8726 - loss: 0.3552 - val_accuracy: 0.8975 - val_loss: 0.2714\n",
            "Epoch 3/20\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8905 - loss: 0.2989 - val_accuracy: 0.9087 - val_loss: 0.2455\n",
            "Epoch 4/20\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9043 - loss: 0.2608 - val_accuracy: 0.9100 - val_loss: 0.2380\n",
            "Epoch 5/20\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9113 - loss: 0.2400 - val_accuracy: 0.9136 - val_loss: 0.2326\n",
            "Epoch 6/20\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9184 - loss: 0.2174 - val_accuracy: 0.9201 - val_loss: 0.2193\n",
            "Epoch 7/20\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9263 - loss: 0.2022 - val_accuracy: 0.9151 - val_loss: 0.2330\n",
            "Epoch 8/20\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9314 - loss: 0.1847 - val_accuracy: 0.9208 - val_loss: 0.2206\n"
          ]
        }
      ],
      "source": [
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint('best-cnn-model.keras',\n",
        "                                                save_best_only=True)\n",
        "early_stopping_cb = keras.callbacks.EarlyStopping(patience=2,\n",
        "                                                  restore_best_weights=True)\n",
        "\n",
        "history = model.fit(train_scaled, train_target, epochs=20,\n",
        "                    validation_data=(val_scaled, val_target),\n",
        "                    callbacks=[checkpoint_cb, early_stopping_cb])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Q9ZA46lb5QcE"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "1JaC-KDn5QcF",
        "outputId": "dcd2d05a-727b-4f2d-d521-c310dce4988d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W3pWBHLK5QcF",
        "outputId": "81cda7fa-5098-41b6-9c12-226bb7532bf5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9231 - loss: 0.2125\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.21925583481788635, 0.9200833439826965]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "model.evaluate(val_scaled, val_target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "a9wUbDQL5QcG",
        "outputId": "eb7dd23a-e32a-4dd2-a5ff-1d64a9b79b92"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.imshow(val_scaled[0].reshape(28, 28), cmap='gray_r')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRe6TDpw5QcG",
        "outputId": "48744b9b-934f-4840-a076-56c0d82e7d37",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 411ms/step\n",
            "[[8.7171188e-19 1.7660993e-26 4.9542759e-21 1.7801612e-19 6.3786056e-18\n",
            "  4.7823439e-21 2.4926043e-19 3.2195997e-17 1.0000000e+00 4.5894585e-22]]\n"
          ]
        }
      ],
      "source": [
        "preds = model.predict(val_scaled[0:1])\n",
        "print(preds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "bOBfv6De5QcG",
        "outputId": "67c6ff90-01d3-46b0-fcfa-a417bcf2d66b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.bar(range(1, 11), preds[0])\n",
        "plt.xlabel('class')\n",
        "plt.ylabel('prob.')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "917fZGHu5QcG"
      },
      "outputs": [],
      "source": [
        "classes = ['티셔츠', '바지', '스웨터', '드레스', '코트',\n",
        "           '샌달', '셔츠', '스니커즈', '가방', '앵클 부츠']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1JqgKgk5QcH",
        "outputId": "13cc7914-d4ac-4017-fa53-6e46f4daf936"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "가방\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "print(classes[np.argmax(preds)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "G2ihSV-l5QcH"
      },
      "outputs": [],
      "source": [
        "test_scaled = test_input.reshape(-1, 28, 28, 1) / 255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1AHKDd2U5QcH",
        "outputId": "ec7f48be-b9fb-425d-8384-be05d3c6801a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.9114 - loss: 0.2617\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.2516588270664215, 0.9125000238418579]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "model.evaluate(test_scaled, test_target)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "8-2.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}