chapter08_intro-to-dl-for-computer-vision.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RopAYL6Wp5sn"
      },
      "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/chapter08_intro-to-dl-for-computer-vision.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": "markdown",
      "metadata": {
        "id": "elnAFHXqp5sq"
      },
      "source": [
        "# 컴퓨터 비전을 위한 딥러닝"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bzu9mBB_p5sq"
      },
      "source": [
        "## 합성곱 신경망 소개"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5mI5o81Gp5sq"
      },
      "source": [
        "**간단한 컨브넷 만들기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "1NSiHAoHp5sr"
      },
      "outputs": [],
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "inputs = keras.Input(shape=(28, 28, 1))\n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(inputs)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.Flatten()(x)\n",
        "outputs = layers.Dense(10, activation=\"softmax\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zL63Dp7Jp5ss"
      },
      "source": [
        "**모델의 `summary()` 메서드 출력**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHuRv7xqp5ss",
        "outputId": "6dd8221a-bfeb-4906-dac8-c94d69d5bcc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 3, 3, 128)         73856     \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1152)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 10)                11530     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 104,202\n",
            "Trainable params: 104,202\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLeAgy4mp5st"
      },
      "source": [
        "**MNIST 이미지에서 컨브넷 훈련하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-yvpFfip5st",
        "outputId": "f50c4f76-65ab-4292-89fc-42ec2ada7ab1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n",
            "Epoch 1/5\n",
            "938/938 [==============================] - 12s 4ms/step - loss: 0.1536 - accuracy: 0.9517\n",
            "Epoch 2/5\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 0.0420 - accuracy: 0.9875\n",
            "Epoch 3/5\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 0.0303 - accuracy: 0.9905\n",
            "Epoch 4/5\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 0.0224 - accuracy: 0.9930\n",
            "Epoch 5/5\n",
            "938/938 [==============================] - 4s 4ms/step - loss: 0.0178 - accuracy: 0.9946\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f42d013bf50>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
        "train_images = train_images.reshape((60000, 28, 28, 1))\n",
        "train_images = train_images.astype(\"float32\") / 255\n",
        "test_images = test_images.reshape((10000, 28, 28, 1))\n",
        "test_images = test_images.astype(\"float32\") / 255\n",
        "model.compile(optimizer=\"rmsprop\",\n",
        "    loss=\"sparse_categorical_crossentropy\",\n",
        "    metrics=[\"accuracy\"])\n",
        "model.fit(train_images, train_labels, epochs=5, batch_size=64)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZqvCHmcp5su"
      },
      "source": [
        "**컨브넷 평가하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GCGUDKzhp5su",
        "outputId": "cb016320-0624-441b-e685-44903a012c18"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 0.0361 - accuracy: 0.9883\n",
            "테스트 정확도: 0.988\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
        "print(f\"테스트 정확도: {test_acc:.3f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6p9ZmaOMp5su"
      },
      "source": [
        "### 합성곱 연산"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W-AdUTy6p5sv"
      },
      "source": [
        "#### 경계 문제와 패딩 이해하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WuFrnVi4p5sv"
      },
      "source": [
        "#### 합성곱 스트라이등 이해하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zejCs68fp5sw"
      },
      "source": [
        "### 최대 풀링 연산"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ZZPbYcMp5sx"
      },
      "source": [
        "**최대 풀링 층이 빠진 잘못된 구조의 컨브넷**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "7nWoPj-6p5sx"
      },
      "outputs": [],
      "source": [
        "inputs = keras.Input(shape=(28, 28, 1))\n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(inputs)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.Flatten()(x)\n",
        "outputs = layers.Dense(10, activation=\"softmax\")(x)\n",
        "model_no_max_pool = keras.Model(inputs=inputs, outputs=outputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NnakXWDsp5sx",
        "outputId": "1f1892d3-c141-48f1-8364-272e3de1114f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 24, 24, 64)        18496     \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 22, 22, 128)       73856     \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 61952)             0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                619530    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 712,202\n",
            "Trainable params: 712,202\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model_no_max_pool.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZSqIwnWp5sx"
      },
      "source": [
        "## 소규모 데이터셋에서 밑바닥부터 컨브넷 훈련하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95ToQXTSp5sx"
      },
      "source": [
        "### 작은 데이터셋 문제에서 딥러닝의 타당성"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DAO-qtfHp5sy"
      },
      "source": [
        "### 데이터 내려받기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2LNJ4FNQRcY"
      },
      "source": [
        "캐글에서 dogs-vs-cats 데이터셋을 다운로드하려면 캐글에 가입해야 한 후 생성한 API 키를 사용해야 합니다. 이런 과정이 번거롭다면 다음 명령으로 구글 드라이브에서 직접 다운로드할 수 있습니다.\n",
        "\n",
        "```python\n",
        "import gdown\n",
        "gdown.download(id='18uC7WTuEXKJDDxbj-Jq6EjzpFrgE7IAd', output='dogs-vs-cats.zip')\n",
        "```\n",
        "\n",
        "그다음 아래 3개의 코드 셀을 건너 뛰고 바로 `unzip` 명령을 실행하세요."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "3IHkXG1_p5sy",
        "outputId": "1bfc0919-e6c3-4939-c57e-8bc9a065f2e3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7ffe74d6-532c-4456-867e-f850bb441c92\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7ffe74d6-532c-4456-867e-f850bb441c92\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"haesunpark\",\"key\":\"08f234c75bf1f9cff6991a8a6d5d49ac\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# kaggle.json 파일을 업로드하세요.\n",
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "oBM0hEv5p5sy"
      },
      "outputs": [],
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cmu5iOmqp5sy",
        "outputId": "5829a8ba-166e-4e45-f4cb-b9754fcadcf6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading dogs-vs-cats.zip to /content\n",
            " 98% 798M/812M [00:04<00:00, 186MB/s]\n",
            "100% 812M/812M [00:04<00:00, 174MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle competitions download -c dogs-vs-cats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "JGgUS6Egp5sy"
      },
      "outputs": [],
      "source": [
        "!unzip -qq dogs-vs-cats.zip\n",
        "!unzip -qq train.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0gMB5twp5sz"
      },
      "source": [
        "**이미지를 훈련, 검증, 테스트 디렉토리로 복사하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "VimZpH8Lp5sz"
      },
      "outputs": [],
      "source": [
        "import os, shutil, pathlib\n",
        "\n",
        "original_dir = pathlib.Path(\"train\")\n",
        "new_base_dir = pathlib.Path(\"cats_vs_dogs_small\")\n",
        "\n",
        "def make_subset(subset_name, start_index, end_index):\n",
        "    for category in (\"cat\", \"dog\"):\n",
        "        dir = new_base_dir / subset_name / category\n",
        "        os.makedirs(dir)\n",
        "        fnames = [f\"{category}.{i}.jpg\" for i in range(start_index, end_index)]\n",
        "        for fname in fnames:\n",
        "            shutil.copyfile(src=original_dir / fname,\n",
        "                            dst=dir / fname)\n",
        "\n",
        "make_subset(\"train\", start_index=0, end_index=1000)\n",
        "make_subset(\"validation\", start_index=1000, end_index=1500)\n",
        "make_subset(\"test\", start_index=1500, end_index=2500)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhxZvOJ0p5sz"
      },
      "source": [
        "### 모델 만들기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lf3Z-OWyp5sz"
      },
      "source": [
        "**강아지 vs. 고양이 분류를 위한 소규모 컨브넷 만들기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "RNeJQPUTp5sz"
      },
      "outputs": [],
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "inputs = keras.Input(shape=(180, 180, 3))\n",
        "x = layers.Rescaling(1./255)(inputs)\n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.Flatten()(x)\n",
        "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H8izud1Ip5sz",
        "outputId": "4fcd9717-7d68-4b8d-96e8-de9ff49370c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 180, 180, 3)]     0         \n",
            "                                                                 \n",
            " rescaling (Rescaling)       (None, 180, 180, 3)       0         \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 178, 178, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 89, 89, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 87, 87, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 43, 43, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_8 (Conv2D)           (None, 41, 41, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 20, 20, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 18, 18, 256)       295168    \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 9, 9, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 7, 7, 256)         590080    \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 12544)             0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 12545     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 991,041\n",
            "Trainable params: 991,041\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VTbpjhPvp5s0"
      },
      "source": [
        "**모델 훈련 설정하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "eLERLmImp5s0"
      },
      "outputs": [],
      "source": [
        "model.compile(loss=\"binary_crossentropy\",\n",
        "              optimizer=\"rmsprop\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4Jt9cQnp5s0"
      },
      "source": [
        "### 데이터 전처리"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ZAQ7vhLp5s0"
      },
      "source": [
        "**`image_dataset_from_directory`를 사용하여 이미지 읽기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0hCBQvamp5s0",
        "outputId": "c6010e04-f706-47a1-f23f-79b4a4668f58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2000 files belonging to 2 classes.\n",
            "Found 1000 files belonging to 2 classes.\n",
            "Found 2000 files belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.utils import image_dataset_from_directory\n",
        "\n",
        "train_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"train\",\n",
        "    image_size=(180, 180),\n",
        "    batch_size=32)\n",
        "validation_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"validation\",\n",
        "    image_size=(180, 180),\n",
        "    batch_size=32)\n",
        "test_dataset = image_dataset_from_directory(\n",
        "    new_base_dir / \"test\",\n",
        "    image_size=(180, 180),\n",
        "    batch_size=32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "AJsyXCcEp5s0"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "random_numbers = np.random.normal(size=(1000, 16))\n",
        "dataset = tf.data.Dataset.from_tensor_slices(random_numbers)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ek_A52WEp5s1",
        "outputId": "0caa1ff6-56d5-44d9-cdef-cb4d91d5a1bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(16,)\n",
            "(16,)\n",
            "(16,)\n"
          ]
        }
      ],
      "source": [
        "for i, element in enumerate(dataset):\n",
        "    print(element.shape)\n",
        "    if i >= 2:\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fgDNrmhp5s1",
        "outputId": "97b7fe48-6f1c-4bad-8706-c668a794e27e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(32, 16)\n",
            "(32, 16)\n",
            "(32, 16)\n"
          ]
        }
      ],
      "source": [
        "batched_dataset = dataset.batch(32)\n",
        "for i, element in enumerate(batched_dataset):\n",
        "    print(element.shape)\n",
        "    if i >= 2:\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "htBoTe6hp5s1",
        "outputId": "53f8a6ea-c1fe-4bbe-fd02-3e3759a3639b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4, 4)\n",
            "(4, 4)\n",
            "(4, 4)\n"
          ]
        }
      ],
      "source": [
        "reshaped_dataset = dataset.map(lambda x: tf.reshape(x, (4, 4)))\n",
        "for i, element in enumerate(reshaped_dataset):\n",
        "    print(element.shape)\n",
        "    if i >= 2:\n",
        "        break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBS9KohQp5s1"
      },
      "source": [
        "**`Dataset`이 반환하는 데이터와 레이블 크기 확인하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yF-t9Pshp5s1",
        "outputId": "5a993028-9753-4b2a-ff8f-1e4dcb8557e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "데이터 배치 크기: (32, 180, 180, 3)\n",
            "레이블 배치 크기: (32,)\n"
          ]
        }
      ],
      "source": [
        "for data_batch, labels_batch in train_dataset:\n",
        "    print(\"데이터 배치 크기:\", data_batch.shape)\n",
        "    print(\"레이블 배치 크기:\", labels_batch.shape)\n",
        "    break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Ev6xpa1p5s1"
      },
      "source": [
        "**`Dataset`을 사용해 모델 훈련하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_GKAiwKp5s2",
        "outputId": "999ef540-04ba-4ede-e4a0-583e7b6bb613"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "63/63 [==============================] - 7s 80ms/step - loss: 0.7116 - accuracy: 0.5185 - val_loss: 0.6898 - val_accuracy: 0.5050\n",
            "Epoch 2/30\n",
            "63/63 [==============================] - 5s 73ms/step - loss: 0.6980 - accuracy: 0.5365 - val_loss: 0.6799 - val_accuracy: 0.5570\n",
            "Epoch 3/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.6939 - accuracy: 0.5820 - val_loss: 0.6553 - val_accuracy: 0.6380\n",
            "Epoch 4/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.6491 - accuracy: 0.6505 - val_loss: 0.6172 - val_accuracy: 0.6560\n",
            "Epoch 5/30\n",
            "63/63 [==============================] - 6s 87ms/step - loss: 0.6282 - accuracy: 0.6725 - val_loss: 0.6125 - val_accuracy: 0.6620\n",
            "Epoch 6/30\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.5698 - accuracy: 0.7065 - val_loss: 0.5989 - val_accuracy: 0.7000\n",
            "Epoch 7/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.5225 - accuracy: 0.7435 - val_loss: 0.6065 - val_accuracy: 0.7150\n",
            "Epoch 8/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.5036 - accuracy: 0.7660 - val_loss: 0.5817 - val_accuracy: 0.7000\n",
            "Epoch 9/30\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.4622 - accuracy: 0.7850 - val_loss: 0.6566 - val_accuracy: 0.7160\n",
            "Epoch 10/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.4155 - accuracy: 0.8105 - val_loss: 0.5993 - val_accuracy: 0.7150\n",
            "Epoch 11/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.3636 - accuracy: 0.8340 - val_loss: 0.6831 - val_accuracy: 0.7040\n",
            "Epoch 12/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.3103 - accuracy: 0.8730 - val_loss: 0.7105 - val_accuracy: 0.7310\n",
            "Epoch 13/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.2691 - accuracy: 0.8835 - val_loss: 0.7672 - val_accuracy: 0.7360\n",
            "Epoch 14/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.1941 - accuracy: 0.9245 - val_loss: 1.0161 - val_accuracy: 0.7140\n",
            "Epoch 15/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.1798 - accuracy: 0.9270 - val_loss: 0.9981 - val_accuracy: 0.7290\n",
            "Epoch 16/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.1592 - accuracy: 0.9405 - val_loss: 1.0061 - val_accuracy: 0.7240\n",
            "Epoch 17/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.1107 - accuracy: 0.9610 - val_loss: 1.1547 - val_accuracy: 0.7120\n",
            "Epoch 18/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.0954 - accuracy: 0.9625 - val_loss: 1.4231 - val_accuracy: 0.7220\n",
            "Epoch 19/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.0978 - accuracy: 0.9700 - val_loss: 1.3668 - val_accuracy: 0.7230\n",
            "Epoch 20/30\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.0541 - accuracy: 0.9820 - val_loss: 1.5036 - val_accuracy: 0.7300\n",
            "Epoch 21/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.0889 - accuracy: 0.9715 - val_loss: 1.5047 - val_accuracy: 0.7260\n",
            "Epoch 22/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.0920 - accuracy: 0.9765 - val_loss: 1.6676 - val_accuracy: 0.7100\n",
            "Epoch 23/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.0674 - accuracy: 0.9790 - val_loss: 1.8342 - val_accuracy: 0.6880\n",
            "Epoch 24/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.0631 - accuracy: 0.9850 - val_loss: 1.9400 - val_accuracy: 0.7260\n",
            "Epoch 25/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.0490 - accuracy: 0.9800 - val_loss: 2.0786 - val_accuracy: 0.6920\n",
            "Epoch 26/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.0630 - accuracy: 0.9830 - val_loss: 2.0640 - val_accuracy: 0.7200\n",
            "Epoch 27/30\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.0645 - accuracy: 0.9825 - val_loss: 1.8796 - val_accuracy: 0.7380\n",
            "Epoch 28/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.0835 - accuracy: 0.9775 - val_loss: 1.9789 - val_accuracy: 0.7170\n",
            "Epoch 29/30\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.0471 - accuracy: 0.9835 - val_loss: 2.4441 - val_accuracy: 0.7030\n",
            "Epoch 30/30\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.0335 - accuracy: 0.9865 - val_loss: 3.4569 - val_accuracy: 0.6820\n"
          ]
        }
      ],
      "source": [
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\n",
        "        filepath=\"convnet_from_scratch.keras\",\n",
        "        save_best_only=True,\n",
        "        monitor=\"val_loss\")\n",
        "]\n",
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    epochs=30,\n",
        "    validation_data=validation_dataset,\n",
        "    callbacks=callbacks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpSmsYkap5s2"
      },
      "source": [
        "**훈련 정확도와 손실 그래프 그리기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "PgxLrhrcp5s2",
        "outputId": "ca0ac594-be6a-40f6-9215-791afbd58d78"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "accuracy = history.history[\"accuracy\"]\n",
        "val_accuracy = history.history[\"val_accuracy\"]\n",
        "loss = history.history[\"loss\"]\n",
        "val_loss = history.history[\"val_loss\"]\n",
        "epochs = range(1, len(accuracy) + 1)\n",
        "plt.plot(epochs, accuracy, \"bo\", label=\"Training accuracy\")\n",
        "plt.plot(epochs, val_accuracy, \"b\", label=\"Validation accuracy\")\n",
        "plt.title(\"Training and validation accuracy\")\n",
        "plt.legend()\n",
        "plt.figure()\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.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lfF3GHBCp5s2"
      },
      "source": [
        "**테스트 세트에서 모델 평가하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXLkHc3ep5s2",
        "outputId": "32c944c8-38c9-42da-d30a-c3568a025c0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 3s 37ms/step - loss: 0.6083 - accuracy: 0.6985\n",
            "테스트 정확도: 0.698\n"
          ]
        }
      ],
      "source": [
        "test_model = keras.models.load_model(\"convnet_from_scratch.keras\")\n",
        "test_loss, test_acc = test_model.evaluate(test_dataset)\n",
        "print(f\"테스트 정확도: {test_acc:.3f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhfKL0AAp5s2"
      },
      "source": [
        "### 데이터 증식 사용하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qHOF4kzp5s2"
      },
      "source": [
        "**컨브넷에 추가할 데이터 증식 단계 정의하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "BqR3LP78p5s2"
      },
      "outputs": [],
      "source": [
        "data_augmentation = keras.Sequential(\n",
        "    [\n",
        "        layers.RandomFlip(\"horizontal\"),\n",
        "        layers.RandomRotation(0.1),\n",
        "        layers.RandomZoom(0.2),\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NrNZDyszp5s2"
      },
      "source": [
        "**랜덤하게 증식된 훈련 이미지 출력하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "bBYg_zb3p5s3",
        "outputId": "5be6b855-6db1-442e-fe19-3bc0bd18e95a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "for images, _ in train_dataset.take(1):\n",
        "    for i in range(9):\n",
        "        augmented_images = data_augmentation(images)\n",
        "        ax = plt.subplot(3, 3, i + 1)\n",
        "        plt.imshow(augmented_images[0].numpy().astype(\"uint8\"))\n",
        "        plt.axis(\"off\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "68Rb2Ipip5s3"
      },
      "source": [
        "**이미지 증식과 드롭아웃을 포함한 컨브넷 만들기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "MP8o8Xvgp5s3"
      },
      "outputs": [],
      "source": [
        "inputs = keras.Input(shape=(180, 180, 3))\n",
        "x = data_augmentation(inputs)\n",
        "x = layers.Rescaling(1./255)(x)\n",
        "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling2D(pool_size=2)(x)\n",
        "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.Dropout(0.5)(x)\n",
        "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "\n",
        "model.compile(loss=\"binary_crossentropy\",\n",
        "              optimizer=\"rmsprop\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_BIxwHSp5s3"
      },
      "source": [
        "**규제를 추가한 컨브넷 훈련하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j9T_WPzXp5s3",
        "outputId": "2390039d-3c70-4d81-fc89-01a7a96adb9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "63/63 [==============================] - 8s 97ms/step - loss: 0.7120 - accuracy: 0.5035 - val_loss: 0.6927 - val_accuracy: 0.5150\n",
            "Epoch 2/100\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.7008 - accuracy: 0.5245 - val_loss: 0.6906 - val_accuracy: 0.5560\n",
            "Epoch 3/100\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.6944 - accuracy: 0.5610 - val_loss: 0.6631 - val_accuracy: 0.6530\n",
            "Epoch 4/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.6679 - accuracy: 0.6115 - val_loss: 0.6150 - val_accuracy: 0.6570\n",
            "Epoch 5/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.6475 - accuracy: 0.6370 - val_loss: 0.9533 - val_accuracy: 0.5450\n",
            "Epoch 6/100\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.6420 - accuracy: 0.6355 - val_loss: 0.6699 - val_accuracy: 0.5900\n",
            "Epoch 7/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.6149 - accuracy: 0.6615 - val_loss: 0.6679 - val_accuracy: 0.5920\n",
            "Epoch 8/100\n",
            "63/63 [==============================] - 7s 110ms/step - loss: 0.5971 - accuracy: 0.6795 - val_loss: 0.6729 - val_accuracy: 0.6720\n",
            "Epoch 9/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.5920 - accuracy: 0.6990 - val_loss: 0.5696 - val_accuracy: 0.6940\n",
            "Epoch 10/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.5812 - accuracy: 0.7100 - val_loss: 0.5772 - val_accuracy: 0.7060\n",
            "Epoch 11/100\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.5687 - accuracy: 0.7055 - val_loss: 0.5652 - val_accuracy: 0.7180\n",
            "Epoch 12/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.5402 - accuracy: 0.7270 - val_loss: 0.6682 - val_accuracy: 0.6620\n",
            "Epoch 13/100\n",
            "63/63 [==============================] - 6s 99ms/step - loss: 0.5364 - accuracy: 0.7430 - val_loss: 0.5412 - val_accuracy: 0.7270\n",
            "Epoch 14/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.5115 - accuracy: 0.7550 - val_loss: 0.5856 - val_accuracy: 0.7180\n",
            "Epoch 15/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.5152 - accuracy: 0.7580 - val_loss: 0.5920 - val_accuracy: 0.7240\n",
            "Epoch 16/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.5169 - accuracy: 0.7545 - val_loss: 0.5242 - val_accuracy: 0.7440\n",
            "Epoch 17/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.4923 - accuracy: 0.7590 - val_loss: 0.6905 - val_accuracy: 0.7290\n",
            "Epoch 18/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.4679 - accuracy: 0.7765 - val_loss: 0.9012 - val_accuracy: 0.6670\n",
            "Epoch 19/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.4864 - accuracy: 0.7770 - val_loss: 0.5243 - val_accuracy: 0.7590\n",
            "Epoch 20/100\n",
            "63/63 [==============================] - 6s 98ms/step - loss: 0.4814 - accuracy: 0.7845 - val_loss: 0.4824 - val_accuracy: 0.7660\n",
            "Epoch 21/100\n",
            "63/63 [==============================] - 6s 98ms/step - loss: 0.4570 - accuracy: 0.7775 - val_loss: 0.5423 - val_accuracy: 0.7300\n",
            "Epoch 22/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.4520 - accuracy: 0.7850 - val_loss: 0.5505 - val_accuracy: 0.7200\n",
            "Epoch 23/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.4414 - accuracy: 0.7995 - val_loss: 0.6805 - val_accuracy: 0.6960\n",
            "Epoch 24/100\n",
            "63/63 [==============================] - 6s 97ms/step - loss: 0.4194 - accuracy: 0.8015 - val_loss: 0.4646 - val_accuracy: 0.7890\n",
            "Epoch 25/100\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.4470 - accuracy: 0.7800 - val_loss: 0.4248 - val_accuracy: 0.8170\n",
            "Epoch 26/100\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.4351 - accuracy: 0.8025 - val_loss: 0.5014 - val_accuracy: 0.7750\n",
            "Epoch 27/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.4309 - accuracy: 0.8120 - val_loss: 0.5139 - val_accuracy: 0.7830\n",
            "Epoch 28/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.4198 - accuracy: 0.8140 - val_loss: 0.5043 - val_accuracy: 0.7890\n",
            "Epoch 29/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.3826 - accuracy: 0.8295 - val_loss: 0.4724 - val_accuracy: 0.7900\n",
            "Epoch 30/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.3993 - accuracy: 0.8185 - val_loss: 0.5099 - val_accuracy: 0.7930\n",
            "Epoch 31/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.3849 - accuracy: 0.8310 - val_loss: 0.7302 - val_accuracy: 0.7110\n",
            "Epoch 32/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.3922 - accuracy: 0.8235 - val_loss: 0.4474 - val_accuracy: 0.7910\n",
            "Epoch 33/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.3776 - accuracy: 0.8325 - val_loss: 0.5518 - val_accuracy: 0.7680\n",
            "Epoch 34/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.3763 - accuracy: 0.8385 - val_loss: 0.4503 - val_accuracy: 0.8100\n",
            "Epoch 35/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.3600 - accuracy: 0.8435 - val_loss: 0.6959 - val_accuracy: 0.7440\n",
            "Epoch 36/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.3435 - accuracy: 0.8490 - val_loss: 0.5079 - val_accuracy: 0.8020\n",
            "Epoch 37/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.3519 - accuracy: 0.8455 - val_loss: 0.4568 - val_accuracy: 0.8100\n",
            "Epoch 38/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.3352 - accuracy: 0.8675 - val_loss: 0.4670 - val_accuracy: 0.8170\n",
            "Epoch 39/100\n",
            "63/63 [==============================] - 6s 92ms/step - loss: 0.3515 - accuracy: 0.8555 - val_loss: 0.7215 - val_accuracy: 0.7620\n",
            "Epoch 40/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.3477 - accuracy: 0.8500 - val_loss: 0.4837 - val_accuracy: 0.8020\n",
            "Epoch 41/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.3264 - accuracy: 0.8630 - val_loss: 0.4613 - val_accuracy: 0.8240\n",
            "Epoch 42/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.3288 - accuracy: 0.8675 - val_loss: 0.4428 - val_accuracy: 0.8230\n",
            "Epoch 43/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.3146 - accuracy: 0.8680 - val_loss: 0.4594 - val_accuracy: 0.8230\n",
            "Epoch 44/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.4171 - accuracy: 0.8670 - val_loss: 0.4384 - val_accuracy: 0.8330\n",
            "Epoch 45/100\n",
            "63/63 [==============================] - 6s 92ms/step - loss: 0.2867 - accuracy: 0.8745 - val_loss: 0.5113 - val_accuracy: 0.8080\n",
            "Epoch 46/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.3089 - accuracy: 0.8845 - val_loss: 0.4364 - val_accuracy: 0.8110\n",
            "Epoch 47/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2987 - accuracy: 0.8885 - val_loss: 0.6028 - val_accuracy: 0.7680\n",
            "Epoch 48/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.3032 - accuracy: 0.8820 - val_loss: 0.4755 - val_accuracy: 0.8210\n",
            "Epoch 49/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2849 - accuracy: 0.8900 - val_loss: 0.4489 - val_accuracy: 0.8490\n",
            "Epoch 50/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2919 - accuracy: 0.8835 - val_loss: 0.5726 - val_accuracy: 0.8210\n",
            "Epoch 51/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2916 - accuracy: 0.8865 - val_loss: 0.9006 - val_accuracy: 0.7520\n",
            "Epoch 52/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2711 - accuracy: 0.8910 - val_loss: 0.4047 - val_accuracy: 0.8470\n",
            "Epoch 53/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.2726 - accuracy: 0.8925 - val_loss: 0.4674 - val_accuracy: 0.8390\n",
            "Epoch 54/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.2582 - accuracy: 0.8985 - val_loss: 0.8012 - val_accuracy: 0.8090\n",
            "Epoch 55/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.3061 - accuracy: 0.8735 - val_loss: 0.4262 - val_accuracy: 0.8310\n",
            "Epoch 56/100\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.2519 - accuracy: 0.8930 - val_loss: 0.5092 - val_accuracy: 0.8400\n",
            "Epoch 57/100\n",
            "63/63 [==============================] - 7s 111ms/step - loss: 0.2557 - accuracy: 0.8930 - val_loss: 0.4639 - val_accuracy: 0.8250\n",
            "Epoch 58/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2283 - accuracy: 0.9080 - val_loss: 0.6309 - val_accuracy: 0.8250\n",
            "Epoch 59/100\n",
            "63/63 [==============================] - 6s 97ms/step - loss: 0.2467 - accuracy: 0.9020 - val_loss: 0.5023 - val_accuracy: 0.8400\n",
            "Epoch 60/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2575 - accuracy: 0.9030 - val_loss: 0.4087 - val_accuracy: 0.8370\n",
            "Epoch 61/100\n",
            "63/63 [==============================] - 6s 97ms/step - loss: 0.2276 - accuracy: 0.9125 - val_loss: 0.5281 - val_accuracy: 0.8320\n",
            "Epoch 62/100\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.2260 - accuracy: 0.9110 - val_loss: 0.4949 - val_accuracy: 0.8430\n",
            "Epoch 63/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2444 - accuracy: 0.9050 - val_loss: 0.6676 - val_accuracy: 0.8050\n",
            "Epoch 64/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2575 - accuracy: 0.8970 - val_loss: 0.5447 - val_accuracy: 0.8190\n",
            "Epoch 65/100\n",
            "63/63 [==============================] - 6s 97ms/step - loss: 0.2164 - accuracy: 0.9200 - val_loss: 0.5274 - val_accuracy: 0.8570\n",
            "Epoch 66/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2452 - accuracy: 0.9115 - val_loss: 0.5202 - val_accuracy: 0.8340\n",
            "Epoch 67/100\n",
            "63/63 [==============================] - 6s 92ms/step - loss: 0.2079 - accuracy: 0.9185 - val_loss: 0.9648 - val_accuracy: 0.7450\n",
            "Epoch 68/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.2233 - accuracy: 0.9105 - val_loss: 0.6750 - val_accuracy: 0.7930\n",
            "Epoch 69/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2289 - accuracy: 0.9135 - val_loss: 0.6380 - val_accuracy: 0.8340\n",
            "Epoch 70/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2129 - accuracy: 0.9200 - val_loss: 0.5994 - val_accuracy: 0.8300\n",
            "Epoch 71/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.2228 - accuracy: 0.9120 - val_loss: 0.5149 - val_accuracy: 0.8460\n",
            "Epoch 72/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.2297 - accuracy: 0.9175 - val_loss: 0.5051 - val_accuracy: 0.8390\n",
            "Epoch 73/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2070 - accuracy: 0.9215 - val_loss: 0.6239 - val_accuracy: 0.8300\n",
            "Epoch 74/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.1996 - accuracy: 0.9240 - val_loss: 0.5030 - val_accuracy: 0.8390\n",
            "Epoch 75/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2622 - accuracy: 0.8950 - val_loss: 0.6147 - val_accuracy: 0.8350\n",
            "Epoch 76/100\n",
            "63/63 [==============================] - 6s 97ms/step - loss: 0.2061 - accuracy: 0.9225 - val_loss: 0.5921 - val_accuracy: 0.8490\n",
            "Epoch 77/100\n",
            "63/63 [==============================] - 6s 92ms/step - loss: 0.2362 - accuracy: 0.9095 - val_loss: 0.9938 - val_accuracy: 0.7800\n",
            "Epoch 78/100\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.2158 - accuracy: 0.9165 - val_loss: 0.4853 - val_accuracy: 0.8450\n",
            "Epoch 79/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.2104 - accuracy: 0.9135 - val_loss: 0.7247 - val_accuracy: 0.8490\n",
            "Epoch 80/100\n",
            "63/63 [==============================] - 7s 112ms/step - loss: 0.2111 - accuracy: 0.9260 - val_loss: 0.6608 - val_accuracy: 0.8330\n",
            "Epoch 81/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2234 - accuracy: 0.9135 - val_loss: 0.4389 - val_accuracy: 0.8500\n",
            "Epoch 82/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2085 - accuracy: 0.9245 - val_loss: 0.6066 - val_accuracy: 0.8550\n",
            "Epoch 83/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2164 - accuracy: 0.9210 - val_loss: 0.4747 - val_accuracy: 0.8610\n",
            "Epoch 84/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.2071 - accuracy: 0.9245 - val_loss: 0.5296 - val_accuracy: 0.8470\n",
            "Epoch 85/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2043 - accuracy: 0.9245 - val_loss: 0.6336 - val_accuracy: 0.8340\n",
            "Epoch 86/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2064 - accuracy: 0.9270 - val_loss: 0.6598 - val_accuracy: 0.8410\n",
            "Epoch 87/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.2070 - accuracy: 0.9190 - val_loss: 0.5339 - val_accuracy: 0.8450\n",
            "Epoch 88/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2005 - accuracy: 0.9285 - val_loss: 0.6922 - val_accuracy: 0.8550\n",
            "Epoch 89/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.1993 - accuracy: 0.9255 - val_loss: 0.8001 - val_accuracy: 0.8330\n",
            "Epoch 90/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2208 - accuracy: 0.9260 - val_loss: 1.0678 - val_accuracy: 0.8230\n",
            "Epoch 91/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2003 - accuracy: 0.9350 - val_loss: 0.7915 - val_accuracy: 0.8480\n",
            "Epoch 92/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2073 - accuracy: 0.9270 - val_loss: 0.8633 - val_accuracy: 0.8060\n",
            "Epoch 93/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.1877 - accuracy: 0.9285 - val_loss: 0.7296 - val_accuracy: 0.8350\n",
            "Epoch 94/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2226 - accuracy: 0.9280 - val_loss: 0.6650 - val_accuracy: 0.8370\n",
            "Epoch 95/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.1916 - accuracy: 0.9300 - val_loss: 0.6808 - val_accuracy: 0.8330\n",
            "Epoch 96/100\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.2047 - accuracy: 0.9270 - val_loss: 0.7522 - val_accuracy: 0.8360\n",
            "Epoch 97/100\n",
            "63/63 [==============================] - 6s 95ms/step - loss: 0.2070 - accuracy: 0.9400 - val_loss: 0.9681 - val_accuracy: 0.8110\n",
            "Epoch 98/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.2132 - accuracy: 0.9270 - val_loss: 0.7383 - val_accuracy: 0.8380\n",
            "Epoch 99/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2233 - accuracy: 0.9275 - val_loss: 1.0556 - val_accuracy: 0.8360\n",
            "Epoch 100/100\n",
            "63/63 [==============================] - 6s 96ms/step - loss: 0.2172 - accuracy: 0.9250 - val_loss: 0.5616 - val_accuracy: 0.8670\n"
          ]
        }
      ],
      "source": [
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\n",
        "        filepath=\"convnet_from_scratch_with_augmentation.keras\",\n",
        "        save_best_only=True,\n",
        "        monitor=\"val_loss\")\n",
        "]\n",
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    epochs=100,\n",
        "    validation_data=validation_dataset,\n",
        "    callbacks=callbacks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FRiGQC66p5s3"
      },
      "source": [
        "**테스트 세트에서 모델 훈련하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ER4799PVp5s3",
        "outputId": "f895e5b7-4af4-49ce-e055-5ee3e583ebcf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 3s 36ms/step - loss: 0.4682 - accuracy: 0.8315\n",
            "테스트 정확도: 0.831\n"
          ]
        }
      ],
      "source": [
        "test_model = keras.models.load_model(\n",
        "    \"convnet_from_scratch_with_augmentation.keras\")\n",
        "test_loss, test_acc = test_model.evaluate(test_dataset)\n",
        "print(f\"테스트 정확도: {test_acc:.3f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5XD3IoVp5s3"
      },
      "source": [
        "## 사전 훈련된 모델 활용하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9shmAU2p5s4"
      },
      "source": [
        "### 사전 훈련된 모델을 사용한 특성 추출"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lKBrqX6jp5s4"
      },
      "source": [
        "**VGG16 합성곱 기반 층 만들기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7xnU3alhp5s4",
        "outputId": "7ef61079-96e7-4a63-cbba-4d57fb8dd7b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58889256/58889256 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "conv_base = keras.applications.vgg16.VGG16(\n",
        "    weights=\"imagenet\",\n",
        "    include_top=False,\n",
        "    input_shape=(180, 180, 3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aUi5BiVgp5s4",
        "outputId": "f82aa114-7d9a-4a07-b964-bde4cddaf5a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_5 (InputLayer)        [(None, 180, 180, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 180, 180, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 180, 180, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 90, 90, 64)        0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 90, 90, 128)       73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 90, 90, 128)       147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 45, 45, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 45, 45, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 45, 45, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 45, 45, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 22, 22, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 22, 22, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 22, 22, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 22, 22, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 11, 11, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 11, 11, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 11, 11, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 11, 11, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 5, 5, 512)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "conv_base.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SfZKWT24p5s4"
      },
      "source": [
        "#### 데이터 증식을 사용하지 않는 빠른 특성 추출"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5Rqof2Gp5s4"
      },
      "source": [
        "**VGG16 특성과 해당 레이블 추출하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "g0wJ9bo_p5s4",
        "outputId": "f0263f41-2851-4bf1-a405-178a5eb1876a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
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            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 1s 1s/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
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          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "def get_features_and_labels(dataset):\n",
        "    all_features = []\n",
        "    all_labels = []\n",
        "    for images, labels in dataset:\n",
        "        preprocessed_images = keras.applications.vgg16.preprocess_input(images)\n",
        "        features = conv_base.predict(preprocessed_images)\n",
        "        all_features.append(features)\n",
        "        all_labels.append(labels)\n",
        "    return np.concatenate(all_features), np.concatenate(all_labels)\n",
        "\n",
        "train_features, train_labels =  get_features_and_labels(train_dataset)\n",
        "val_features, val_labels =  get_features_and_labels(validation_dataset)\n",
        "test_features, test_labels =  get_features_and_labels(test_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zXiJ_T_2p5s4",
        "outputId": "0d966375-3c29-43ba-add4-5fe5046dc61b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 5, 5, 512)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "train_features.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VJ7crjq4p5s4"
      },
      "source": [
        "**밀집 연결 분류기 정의하고 훈련하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eq-_7ez4p5s4",
        "outputId": "5a7a43a1-a115-46e0-d47d-4f964338acdf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "63/63 [==============================] - 1s 9ms/step - loss: 18.8357 - accuracy: 0.9310 - val_loss: 12.4233 - val_accuracy: 0.9160\n",
            "Epoch 2/20\n",
            "63/63 [==============================] - 1s 8ms/step - loss: 3.5305 - accuracy: 0.9745 - val_loss: 3.3123 - val_accuracy: 0.9720\n",
            "Epoch 3/20\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 2.0476 - accuracy: 0.9845 - val_loss: 4.9244 - val_accuracy: 0.9720\n",
            "Epoch 4/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.9414 - accuracy: 0.9900 - val_loss: 6.7497 - val_accuracy: 0.9710\n",
            "Epoch 5/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 1.0989 - accuracy: 0.9900 - val_loss: 10.1257 - val_accuracy: 0.9590\n",
            "Epoch 6/20\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 1.7547 - accuracy: 0.9900 - val_loss: 5.1521 - val_accuracy: 0.9790\n",
            "Epoch 7/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.6967 - accuracy: 0.9950 - val_loss: 16.6897 - val_accuracy: 0.9400\n",
            "Epoch 8/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.7474 - accuracy: 0.9980 - val_loss: 5.0486 - val_accuracy: 0.9740\n",
            "Epoch 9/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.4305 - accuracy: 0.9980 - val_loss: 4.8480 - val_accuracy: 0.9770\n",
            "Epoch 10/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.3970 - accuracy: 0.9980 - val_loss: 5.6260 - val_accuracy: 0.9740\n",
            "Epoch 11/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.2060 - accuracy: 0.9980 - val_loss: 5.1332 - val_accuracy: 0.9760\n",
            "Epoch 12/20\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 0.0196 - accuracy: 0.9985 - val_loss: 4.1417 - val_accuracy: 0.9750\n",
            "Epoch 13/20\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 0.1138 - accuracy: 0.9990 - val_loss: 4.6580 - val_accuracy: 0.9770\n",
            "Epoch 14/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 8.2792e-28 - accuracy: 1.0000 - val_loss: 4.6580 - val_accuracy: 0.9770\n",
            "Epoch 15/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.0285 - accuracy: 0.9995 - val_loss: 5.6987 - val_accuracy: 0.9740\n",
            "Epoch 16/20\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 0.3203 - accuracy: 0.9970 - val_loss: 5.9561 - val_accuracy: 0.9740\n",
            "Epoch 17/20\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 0.4344 - accuracy: 0.9990 - val_loss: 5.0790 - val_accuracy: 0.9780\n",
            "Epoch 18/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.0656 - accuracy: 0.9990 - val_loss: 5.8511 - val_accuracy: 0.9770\n",
            "Epoch 19/20\n",
            "63/63 [==============================] - 0s 6ms/step - loss: 0.1062 - accuracy: 0.9985 - val_loss: 5.2108 - val_accuracy: 0.9800\n",
            "Epoch 20/20\n",
            "63/63 [==============================] - 0s 7ms/step - loss: 0.0743 - accuracy: 0.9990 - val_loss: 5.1213 - val_accuracy: 0.9810\n"
          ]
        }
      ],
      "source": [
        "inputs = keras.Input(shape=(5, 5, 512))\n",
        "x = layers.Flatten()(inputs)\n",
        "x = layers.Dense(256)(x)\n",
        "x = layers.Dropout(0.5)(x)\n",
        "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "model.compile(loss=\"binary_crossentropy\",\n",
        "              optimizer=\"rmsprop\",\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\n",
        "      filepath=\"feature_extraction.keras\",\n",
        "      save_best_only=True,\n",
        "      monitor=\"val_loss\")\n",
        "]\n",
        "history = model.fit(\n",
        "    train_features, train_labels,\n",
        "    epochs=20,\n",
        "    validation_data=(val_features, val_labels),\n",
        "    callbacks=callbacks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6WfdBvJUp5s5"
      },
      "source": [
        "**결과를 그래프로 나타내기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "p2VT5RPyp5s5",
        "outputId": "2a4126c5-ea04-4002-f464-be19b515bb33"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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/5oYbbgCgefPmLF26lIceeohp06bx6KOPRvz7Uj2ccaUtelUdpaqtVDVLVdt4QR5VHaOqD5db9msvyKOqAVXt4j3yVdVuA2KMiSi0+ya02+Zvf/sb3bp1o2vXrqxYseKwbpbyFi1axLnnnkuDBg04+uijGT58+MF5y5cvp1+/fpx66qnMnDmTFStWVFifzz//nNzcXE488UQALr30UhYuXHhw/siRIwHo3r37wYHQIlm8eDEXX3wxEH444/vuu49t27ZRp04devTowRNPPMHUqVP55JNPaNSoUYVlR8OujDXGHKailncijRgxgokTJ7J06VLKysro3r07X375JdOmTeODDz6gSZMmjBkzJuLwxJUZM2YMc+bMoUuXLsyYMYMFCxb4qm9wqGM/wxxPmjSJs88+m7lz59KnTx/mz59/cDjjV155hTFjxnDjjTdyySWX+KqrjXVjjEkL2dnZDBw4kLFjxx5sze/YsYOGDRtyzDHHsHHjRubNm1dhGf3792fOnDl8//33lJaW8tJLLx2cV1paSqtWrdi7d+/BoYUBGjVqRGlp6RFldezYkTVr1rB69WoAnn76aX74wx/G9Lelejhja9GbuPnZzyA/391SzphYjBo1inPPPfdgF05wWN9OnTrRtm1b+vTpU+H63bp148ILL6RLly60aNGCHj16HJx355130qtXL3JycujVq9fB4H7RRRdx5ZVXct999x08CQtQv359nnjiCc4//3z27dtHjx49GD9+fEx/V/Betp07d6ZBgwaHDWf81ltvUatWLfLz8xkyZAizZs3i7rvvJisri+zs7LjcoMSGKTZxoQpHH+1OuiXxnscmTmyY4urFhik2KbFlC+zcCV9+meqaGGPKs0Bv4iIY4NevhxjPlRljEsQCvYmLQODQ67VrU1cPE7t07MY1R4rl/2SB3sRFaKAPfW2qh/r161NSUmLBPs2pKiUlJdSvX79K61nWjYmLQADq1YPdu62fvjpq06YNxcXF2Mix6a9+/fq0adOmSutYoDdxEQhA167u0nlr0Vc/WVlZ5ObmproaJkGs68bERSAAxx8PubkW6I1JN9aiN77t2QNffQV5ebBtm3XdGJNurEVvfFu3Dg4ccIE+2KK3c3rGpA8L9Ma3YAs+L889duyAb79NbZ2MMYdYoDe+Bfvkc3PdI3SaMSb1LNAb3wIBqFsXjjvOtejB+umNSScW6I1vgQB06AC1a1uL3ph0VGmgF5HHRWSTiCwPmTZVRNaLyDLvMTTCuoNF5HMRWS0ik+JZcZM+AoFDLflGjaB5cwv0xqSTaFr0M4DBYab/SVULvMfc8jNFpDbwIDAEOBkYJSIn+6msSU+hgR7ca+u6MSZ9RHPP2IXA1hjK7gms9u4duweYBYyIoRyTxr791uXOhwZ6u2jKmPTip4/+WhH52OvaaRJmfmvgq5D3xd60sERknIgUiUiRjbdRfYSmVgbl5bkRLPfvT02djDGHizXQ/xk4HigANgD3+K2Iqk5X1UJVLczJyfFbnEmS0NTKoNxc2LcPiotTUydjzOFiCvSqulFV96vqAeARXDdNeeuBtiHv23jTTAYJF+gtxdKY9BJToBeRViFvzwWWh1nsA+AEEckVkbrARcCLsWzPpK9AAJo1g2OOOTTNUiyNSS+VDmomIs8CA4DmIlIMTAEGiEgBoMAa4Cpv2eOAR1V1qKruE5FrgflAbeBxVV2RkL/CpEz5jBuAtm1dTr0FemPSQ6WBXlVHhZn8WIRlvwaGhryfCxyRemkyRyAAheXuO5+VBe3aWdeNMenCrow1Mdu3z2XXlG/Rg6VYGpNOLNCbmBUXu2AfLtDbRVPGpA8L9CZm4XLog3JzYeNG+O675NbJGHMkC/QmZuFSK4OCwX/NmqRVxxgTgQV6E7NAwGXXtG175DxLsTQmfVigNzELBKB9e6gTJncr2KK3QG9M6lmgNzELl0Mf1Lw5ZGfbCVlj0oEFehOzigK9iKVYGpMuLNCbmJSWwpYtkQM9WIqlMenCAr2JSUWplUHBFr1qcupkjAnPAr2JSbBLprIWfVkZ2O0FjEktC/QmJhXl0AdZiqUx6cECvYlJIOCGJm4S7t5iHkuxNCY9WKA3MQlm3IhEXqZDB/dsJ2SNSS0L9CYmFaVWBjVoAC1bWovemFSzQG+q7MAB10qvLNCDpVgakw4s0Jsq27AB9uyJLtDbRVPGpJ4FelNl0aRWBuXlwVdfwd69ia2TMSaySgO9iDwuIptEZHnItLtFZKWIfCwiL4hI4wjrrhGRT0RkmYgUxbPiJnWqEuhzc11Xz7p1ia2TMSayaFr0M4DB5aa9Bpyiqp2B/wK/rGD9gapaoKqFFSxjqpFAwGXbtGtX+bKWYmlM6lUa6FV1IbC13LRXVXWf9/Y/QJsE1K1KDhyATz6xE3/JEAi4Mejr1q182eBFU/Z/MSZ14tFHPxaYF2GeAq+KyBIRGVdRISIyTkSKRKRocwzXzKtCz57w4INVXtVUUTSplUGtW0NWlrXojUklX4FeRCYD+4CZERbpq6rdgCHABBHpH6ksVZ2uqoWqWpiTk1PlutSuDZ06wYoVVV7VVFFVAn3t2u7CKWvRG5M6MQd6ERkDDANGq4Yfn1BV13vPm4AXgJ6xbi8a+fkW6BOtrAy++Sb6QA+WYmlMqsUU6EVkMHArMFxVyyIs01BEGgVfA2cCy8MtGy/5+S6Vb8eORG6lZgve7Lsqgd4umjImtaJJr3wWeBfoKCLFInI58ADQCHjNS5182Fv2OBGZ6616LLBYRD4C3gdeUdV/JeSv8Jx8snv+9NNEbqVmq0pqZVBuLpSUwPbtiamTMaZiYW7rfDhVHRVm8mMRlv0aGOq9DgBdfNWuivLz3fOKFdC7dzK3XHPEEuiDy375JRQUxL9OxpiKZdSVsbm5UL++9dMnUiAADRu6m39Hy1IsjUmtjAr0tWvDSSdZoE+kaIYnLs8umjImtTIq0IPrvrE++sSpSmplUJMm0LixteiNSZWMDPTFxXbiLxFUox+euDxLsTQmdTIy0IO16hNh0yaXRx9LoLcUS2NSJ2MDvfXTx18sGTdBubku0B84EN86GWMql3GBvkMHOOooC/SJ4CfQ5+XB7t3upiXGmOTKuEBfq5Zl3iRKMNAHb/pdFZZiaUzqZFygBxvzJlECATjuOHetQlVZiqUxqZOxgf7rr2HbtlTXJLPEkloZ1L69y723Fr0xyZexgR6sVR9vfgJ9vXpubHpr0RuTfBkd6C3FMn5274b162MP9GAplsakSkYG+vbtoUEDa9HH09q17oIpP4HeLpoyJjUyMtDXquWGLLZAHz9+UiuD8vLcUcGuXfGpkzEmOhkZ6MEyb+ItHoE+mGK5dq3/+hhjopfRgX7DBvj221TXJDMEAi6tsmXL2MuwFEtjUiOjAz1Yqz5eAgHXIq/K8MTl2UVTxqRGVIFeRB4XkU0isjxkWlMReU1EVnnPTSKse6m3zCoRuTReFa9M8LaCNSnQ792buLFk/KRWBrVs6Y4KrEVvTHJF26KfAQwuN20S8IaqngC84b0/jIg0BaYAvYCewJRIPwjx1q6duxNSTQn0+/ZBly5w003xL1s1PoG+Vq1Dg5sZY5InqkCvqguBreUmjwCe9F4/CZwTZtWzgNdUdauqfgu8xpE/GAlR0zJv/vEP+OwzePbZ+Lfqt26F0lL/gR4sxdKYVPDTR3+sqgbHIvwGODbMMq2Br0LeF3vTjiAi40SkSESKNm/e7KNah9SUu02pwrRpUKcObNwI778f3/LjkXETlJfnylP1X5YxJjpxORmrqgr4+uqq6nRVLVTVwpycnHhUi/x8+OYb1yLNZIsWwQcfwJ13umD/z3/Gt/x4BvrcXNixw7KhjEkmP4F+o4i0AvCeN4VZZj3QNuR9G29aUtSUzJtp06B5c7j+eujfH158Mb7lBwN9MGvGD0uxNCb5/AT6F4FgFs2lQLh25HzgTBFp4p2EPdOblhQ1IdB//jm89BJMmOBuuDJihOuuWr06ftsIBKBFC3dy2y9LsTQm+aJNr3wWeBfoKCLFInI58L/Aj0VkFXCG9x4RKRSRRwFUdStwJ/CB97jDm5YUbdtCo0aZHej/+EeXsnjNNe798OHuOZ6t+nhk3AQFA7216I1JnjrRLKSqoyLMGhRm2SLgipD3jwOPx1Q7n0QyO/Nm0yZ48kkYM8a1uMHd/alzZ9dPf+ON8dlOIACnnx6fso4+2nUzWYvemOTJ2CtjgzI50D/4IOzZAxMnHj59+HBYvBi2bPG/jb17Yd26+LXowVIsjUm2jA/0+fmu5RuPoJdOysrgoYfgJz+Bjh0PnzdihMulnzvX/3a++sqVFc9AH0yxNMYkR40I9JB5rfqnnnI/XjfffOS87t3dvV3jkWYZz9TKoNxcN4Ll/v3xK9MYE1mNCfSZdOHU/v3uJGzPntC375HzRVz3zfz5/sd+T0Sgz8tzQzYUF8evTGNMZBkf6Nu0cScAM6lF/9JLsGqVG9cm0miSI0bAd9/Bm2/621YgAHXruiOEeLEUS2OSK+MDfSZm3txzj8uuGTky8jIDB0J2tv/um0DA3Zqxdm1/5YSyi6aMSa6MD/SQWXeb+s9/XEbNxIluuINI6tWDwYNd69/PIGfxzKEPatvWDTpnLXpjkqPGBPrNm92jurvnHmjcGMaOrXzZ4cPdXbaKimLfXiICfVaWG0baWvTGJEeNCfRQ/Vv1gYAbjnj8eNctU5mzz3ZdLrFeJbttmxt8LN6BHizF0phkqhGBPlPuNnXvvS5w//zn0S3ftCn06xd7P32wayURgd5uQGJM8tSIQN+6dfXPvNm6FR57DEaPrloGzPDhsHx5bK3nRKRWBuXlubHzv/su/mUbYw5XIwK9SPU/Ifvww+5q2KreKtDPIGfxHJ64vGCZa9bEv2xjzOFqRKCH6n23qd274f774ayz4JRTqrbu8ce7vz2W7ptAwHX/HHNM1detjKVYGpM8NSrQb9nixr2pbv76V3enrHDDHURjxAh3F6qq3mkrERk3QXbRlDHJU6MCPVS/7pvg/WC7dIFBRwwKHZ0RI9ywCVUd5CyRgT4nx93IxFr0xiSeBfo0969/uS6nm2+OPNxBZQoLoVWrqvXT79/vBh5LVKAXcWVbi96YxKsxgb5VK3ehUXUL9NOmuayhCy+MvYxatdxwxvPmuf7+aKxf78aiT1SgBxuX3phkiTnQi0hHEVkW8tghIjeUW2aAiGwPWeZ2/1WOtb7VL/Pmww/doGTXX++uJvVj+HDYuRMWLIhu+USmVgYFL5pSTdw2jDE+Ar2qfq6qBapaAHQHyoAXwiy6KLicqt4R6/biIRjoq0tgueced8/bceP8lzVoEDRoEH32TTICfW6uSxnNhKEpjEln8eq6GQR8oapr41ReQpx8sss82bgx1TWp3FdfwaxZcMUV8UlvrF/fpWe++GJ0P3SBgLsKt21b/9uOxFIsjUmOeAX6i4BnI8w7TUQ+EpF5IpIfqQARGSciRSJStDlBTbzqdEL2//7PPV9/ffzKHDHC9b0vXVr5soGAG3isohEy/bIUS2OSw3egF5G6wHDguTCzlwLtVbULcD8wJ1I5qjpdVQtVtTAnJ8dvtcKqLneb2r4dpk+HCy5wY8HHy9lnuxOz0XTfJDK1MigY6K1Fb0xixaNFPwRYqqpHdIio6g5V3em9ngtkiUjzOGwzJi1bQpMm6d+if/RRKC2t+nAHlWneHPr0iS7NMhmBvkEDOPZYa9Ebk2jxCPSjiNBtIyItRVz2t4j09LZXEodtxqQ6ZN7s3etGqRwwwN3kO95GjICPPqp4jJmdO90J0kQHerDhio1JBl+BXkQaAj8G/hEybbyIjPfengcsF5GPgPuAi1RTm/OS7pk3f/ubu2l2rMMdVCaaQc4SOTxxeRbojUk8X4FeVb9T1Waquj1k2sOq+rD3+gFVzVfVLqraW1Xf8Vthv/Lz3c00vvkm1TU5kqpLqTzpJBgyJDHbOOEEV35FgT4ZqZVBubkuw2jv3sRvy5iaqsZcGRuUzpk3b73lLpK66SZ30jRRhg+Ht992d5AKJ5mBPi/P3dN23brEb8uYmsoCfZpQhd/9Dlq0cDcXSaQRI2DfPjckQjiBgNMS8NYAABXbSURBVMvdb9IksfUAS7E0JhlqXKBv0cKNsZ5ugf53v4PXX4fbbnMXNyVSr15uP0RKswwEXACOdRC1qrCLpoxJvBoX6NMx8+Yf/4DJk11L/rrrEr+90EHO9uw5cn4yUiuDWrd24/hYi96YxKlxgR4O3W0qHTJvli6Fiy+G3r1d/nwyWtHgum927HB99aEOHHCpl8kK9LVru4vCrEVvTOLU2EC/bRts2JDaenz9tTsx2rw5zJmT+C6bUIMGwVFHHdl98803sGtX8gI9WIqlMYlWYwM9pLb7pqzMtaq3bYOXXnJXiCZTgwZw5plHDnKWzIyboNxc67oxJpEs0KeAKlx2GSxZ4u4H27lzauoxfLjLYV+27NC0VAT6vDwoKXFdScaY+KuRgb5FC9ddkqpAf8cd7grY3//+0JWqqTBsmDsnEHrxVCDgpsVzMLXKWIqlMYlVIwM9pC7zZvZsmDoVxoxJ3DAH0WrRAk4//fB++kDAjUFft27y6mEpljWbqrtHxJtvunGYTPzV+ECfzMybDz5wAb5vX3j44eRl2FRk+HB3Ne5XX7n3wRz6ZLIWfc2xZYvL9HroIZgwAX74Q8jJcSPLDhoEBQUuzbi4OPl127bt0Pcg09TYQH/yya5PeP365GyvuNidfG3Z0uXN16uXnO1WZsQI9xzsvklmDn1QkybuSlxr0WeOb7+FxYvhL39x14YMGuQSDnJy3MisEybA00+7MY7OPRf+9CeYP99dT/L883Diia6Ls6wsOXX99a/djXbatTuUpLB/f+K3nSwJvH9Qegs9IdumTWK39d13h27O/eqr7sOeLjp2dF+qf/4Txo51KafJDvQilmJZ3R044K4D+fvf3Xfq668PzcvOdg2rs89237vgo02bI49qzzwTLr8cfvELmDLFlfmHP8CFF8b/CHjbNjck+J/+5Bp955/v6vXII64B1L49jB/vbufZPGV30YgTVU27R/fu3TXRNm1SBdU//jGx29m/X3XkSNVatVRfeSWx24rVLbeoZmWpvvuu2yczZya/DiNHqnbqlPztBr3zjurZZ6v+6Eeq99+vWlycurpUN2vXqp5xhvvs5OerXnKJ6u9/r/ryy6pffum+A7F4+23VggJXbp8+qh98EJ/6btum+j//o9q4sSt75EjVjz46NH/vXtXnn1cdONDNr1fP/U3vvx+f7ScKUKQRYmrKg3q4RzICvapqTo7q5ZcndhuTJyfnB8WPRYtcHS++2D2/+27y63Dzze4LFWtQiNX776sOHuz+7pwc1ZNOcq9B9bTTVO++WzUQSG6dqosDB1SfeEL16KNVGzZU/ctf3LR42rdP9ZFHVFu0UBVRHTNG9euvYytrxw7V3/xGtUkT9/8dMUL1ww8rXmf5ctVrrlHNznbr9OihOmOG6vffx1aHRLJAH8GAAaq9eyeu/GeecXv4yivj/wWIp337XJDLynL13bgx+XV48EG37fXrk7O9Dz9U/clP3DabNXMt0J073bxPP3UBoWvXQ0G/a1fVO+9084zqhg2qw4e7fdOvn+oXXyR2e9u3HzryzM5W/e1vow+2paWqv/ud+z+D6rBhqkVFVd/+/fe7o87gZ+YXv3BHLOnCAn0EEya41kgigvA777gW6oABqrt3x7/8eLvsMvdpaNgwNT9K8+a57S9alNjtfPKJO1QHd+j+m9+4ll4kgYDqtGmudR8M+p06uSO1pUvT+wc8UZ57zgW6evVU77knuUdhq1a5ljio5ua6LpZI/4OdO1X/8AfV5s3d8kOGqL73nr/tHzig+sYbquee67pja9VyP3jz5yf/aLS8hAZ6YA3wCbAs3IYAwd1GcDXwMdCtsjKTFegfesjtgXXr4lvumjXuUPP441W3bIlv2YnywgtuX5xySmq2v3Kl2/5TTyWm/M8+U73wQnf4f/TRqlOmqH77bdXKWL9e9YEHXN9trVqHgs1NN7kf9lR/0ROtpET1Zz9zf3dhYWqPbl5/3X1WwTWmli07NO+779wPUIsWbv6ZZyamO3LdOveDn5PjtnPiiaq33ea2/eij7gfx1Vfdj8vKle4oqKwscY2DZAT65hXMHwrM8wJ+b+C9yspMVqB/+223B+bNi1+ZpaWqnTurHnOMCy7Vxc6dqvXru9ZJKnz/vQvCU6fGt9xVq1T/3/9zgblhQ/dFLCnxX+7mze7LPGTIoS6v445zPyDp2H/r19y5qq1aqdapo3rHHap79qS6Ru6k6YMPuqOLWrVUx41z58JatnT/j0GDVBcvTnw9du1y3bS9ex866qvokZXljjKOP161WzfXcDjnHNVLL3XdQbGqKNCLmx87EVkDFKrqlgjz/wIsUNVnvfefAwNUNeLYkYWFhVpUVOSrXtEoKXFpU9Omudv3+XXggMsJfuUVmDvXpYpVJ7NmQYcObsjkVGjTxg1bPGiQu4gqL889cnNdDnZV0uu+/BLuvBOeespd5XvttXDLLYlJbd2+HV5+2e2/l1+GTp3gscfcVcfVXWmp+2488ohLPXzqKejWLdW1Oty338L//A88+KC7c9qAAe59//7Jr8v+/W6fbd9e9ce2bS4V9bPPYtu2iCxR1cKwMyP9AkT7AL4ElgJLgHFh5r8M9A15/wbuh6H8cuOAIqCoXbt2sf+sVdGxx7r+6Xi45Rb3i33//fEpr6Z54AHVvn1dy7h8K6hBA5e6N2yY6nXXqd57r+o//+n63IMnUVVdqt+4ca7lWa+e6g03uEPmZPnXv1TbtXNHJ9dd547wqqsFC1Q7dHB/y623upZrOlu9On4pmNURCe66ae09twA+AvqXmx9VoA99JKvrRtUdNvXs6b+cP/zB7c1rrqmZJ+jirazMdX298or74bzxRnd427nzoVS30EeLFi71rW5d95gwIXkZPOXt2KF67bWuXh06uH7a6qSsTHXiRBfgjz8+Od0fxr+KAr3vK2NVdb33vElEXgB6AgtDFlkPtA1538ablhby82HGDBcuYr3y7uGH4dZb3dV7992XHmPYVHdHHeW6QDp1OnKequt2+/JLdzVtIHDo9eWXw6RJ7lL2VGnUCO6/330errjCdeFddhncc09ybrjuxwcfwCWXwMqVcM017qrUhg1TXSvjl69ALyINgVqqWuq9PhO4o9xiLwLXisgsoBewXSvon0+2/Hw3NMFXX8UWHJ55xn0hhg1zY3fUrh3/OprDibhzK82bQ48eqa5NZH37urH+77jDBcx581w/8siR8d3O/v1uXJnnnoPVq2MvZ98+WLAAWrVyQ3X8+Mdxq6JJMb8t+mOBF8Q1YesAf1XVf4nIeABVfRiYi8u8WQ2UAZf53GZchY55U9VAP2eOG41y4ED3JcvKinv1TDVXvz789rduHJWxY+GnP3WPBx5wA9zF6sABePddN+z13//uxihq0ABOOcXfEeWVV8LvfgeNG8dehklDkfp0UvlIZh99SYnrS7377qqt9+qrri+4d+/qfcLNJM+ePe6Kznr13GX4M2ZU7XzOgQMuH3ziRNU2bdzntn59dwHY7NmHn5Q2NQ8V9NHX2GGKg5o2dS2rqtyEZPFiN7rdSSe5NMrs7MTVz2SOrCz45S9dd87JJ7ujwSFDYO3ayOuoQlGROweUmwunnea6f7p3h5kzYdMmN6zvBRdYX7qJrMYHeqja3aaWLnXDrbZr5/ox0/3kmkk/nTrBwoXuhO3ixe7z98ADrjsGXHBftsz9KPzgB+48xL33um6Zp55ywX3OHPjZz9yJX2MqU2PHow+Vn+8ucDlwAGpV8NP36acug6JJE3jtNXcrPmNiUauWu4jrJz+Bq66Cn//cXXA1YIA73/Pf/7oT+2ecAb/6FZxzjjUqTOws0OMC/Xffwbp17srQcAIB96XLyoLXX3f3VTXGr/btXTbO00/DDTe4E6wDB7r7CZ97bgbc8MKkBQv0uP5ScN034QL9+vXusvzdu90h9w9+kNTqmQwn4nLXR450n7FmzVJdI5NprI+eQymWn3565LxNm1xLvqTE3dMyuKwx8ZadbUHeJIa16HF9n61aHXlCdts2OOsslxUxfz4Uhh8uyBhj0pq16D3NmsGzz7qTZB06uJOzQ4e64P/CC9CvX6praIwxsbEWPS4feeVKdwk4uBb8uHHu9XPPuVa9McZUV9aiByZPPhTkgw4ccF068R6XxBhjks0CPS6tMpytW5NbD2OMSQQL9EQezCyVQ90aY0y8WKAH7rrLjfwXqkEDN90YY6o7C/TA6NEwfbq7SlHEPU+f7qYbY0x1Z1k3ntGjLbAbYzKTteiNMSbDWaA3xpgMZ4HeGGMyXMyBXkTaishbIvKpiKwQkevDLDNARLaLyDLvcbu/6hpjjKkqPydj9wE3qepSEWkELBGR11S1/BiQi1R1mI/tGGOM8SHmFr2qblDVpd7rUuAzoHW8KmaMMSY+4tJHLyIdgK7Ae2FmnyYiH4nIPBGJOJq7iIwTkSIRKdq8eXM8qmWMMYY4BHoRyQaeB25Q1R3lZi8F2qtqF+B+YE6kclR1uqoWqmphTk6O32ol3cyZbnjj4DDHM2emukbGGOP4CvQikoUL8jNV9R/l56vqDlXd6b2eC2SJSMbdBXPmTDes8dq1oHpomGML9saYdOAn60aAx4DPVPWPEZZp6S2HiPT0tlcS6zbT1eTJUFZ2+LSyMjfdGGNSzU/WTR/gYuATEVnmTbsNaAegqg8D5wFXi8g+4HvgIlVVH9tMS5GGOY403RhjkinmQK+qiwGpZJkHgAdi3UZ10a6d664JN726mDnTHYGsW+fqfdddNvaPMZnCroyNg+o+zLGdYzAms1mgj4PqPsyxnWMwJrNJOnaZFxYWalFRUaqrUWPUquVa8uWJuHvnGmPSn4gsUdXCcPOsRW/sVorGZDgL9Kban2MwxlTMAn2G8HNlbnU/x2CMqZjdSjADBLNmgidUg1kzEH2wtlspGpO5rEWfJvy0yC1rxhhTEWvRpwG/LXK7MtcYUxFr0acBvy3yTMiasdE/jUkcC/RpwG+LPB2yZvwEarsy15jEskCfBvy2yFOdNeM3UNs5BmMSywJ9GohHi3z0aFizxl3JumZNcjNo/AbqeJxjsK6fms3+/xWzQJ8GUt0i98tvoPZ7RBOPrh8LFNWXdf1FQVXT7tG9e3c11Uf79qruK3b4o3376NZ/5hnVBg0OX7dBAze9Omzf+PfMM+7/JeKeq7Lv/f7/MwVQpBFiasqDeriHBfrqJR6B0s8XXST8F10kuvXjESj81L+m8/v58fv/zxQW6E3CpTLQ+Q3UfgNFOhwRVOcfGr//v3T4oU71+qoJDPTAYOBzYDUwKcz8esBsb/57QIdoyrVAb6oi1V0/qQ40qT6i8rt+qn9oq/v6QQkJ9EBt4AsgD6gLfAScXG6Za4CHvdcXAbOjKdsCvamqVAbKVAeqVJ+jSHX9g3VIVR9/qtcPSlSgPw2YH/L+l8Avyy0zHzjNe10H2IJ3s5OKHhboTbJV50CR6nMUqf6h8cvv/kv1+kEVBXo/6ZWtga9C3hd708Iuo6r7gO1As3CFicg4ESkSkaLNmzf7qJYxVefnOgS/10GkOj3V7/b9rp/q9GK/+y/V60cjbfLoVXW6qhaqamFOTk6qq2NM1PwGKr9fdL8/NOkQqFJ5wZ/f/Zfq9aMSqalf2QPrujEmLlJ9MjXVffTpINVZM2mbdeMF7gCQy6GTsfnllpnA4Sdj/xZN2RboTU2T6vTIdAhUxp+KAr24+bERkaHAvbgMnMdV9S4RucPb4IsiUh94GugKbAUuUtVAZeUWFhZqUVFRzPUyxpiaRkSWqGphuHm+bjyiqnOBueWm3R7yehdwvp9tGGOM8SdtTsYaY4xJDAv0xhiT4SzQG2NMhrNAb4wxGc5X1k2iiMhmYG2q6xFBc9z1AOnK6ueP1c8fq58/furXXlXDXm2aloE+nYlIUaQUpnRg9fPH6ueP1c+fRNXPum6MMSbDWaA3xpgMZ4G+6qanugKVsPr5Y/Xzx+rnT0LqZ330xhiT4axFb4wxGc4CvTHGZDgL9GGISFsReUtEPhWRFSJyfZhlBojIdhFZ5j1uD1dWAuu4RkQ+8bZ9xFCf4twnIqtF5GMR6ZbEunUM2S/LRGSHiNxQbpmk7j8ReVxENonI8pBpTUXkNRFZ5T03ibDupd4yq0Tk0iTW724RWen9/14QkcYR1q3ws5DA+k0VkfUh/8OhEdYdLCKfe5/FSUms3+yQuq0RkWUR1k3G/gsbU5L2GYw0fnFNfgCtgG7e60bAfznyxucDgJdTWMc1QPMK5g8F5gEC9AbeS1E9awPf4C7mSNn+A/oD3YDlIdP+AEzyXk8Cfh9mvaa4+y40BZp4r5skqX5nAnW8178PV79oPgsJrN9U4OYo/v9fAHkcum/FycmoX7n59wC3p3D/hY0pyfoMWos+DFXdoKpLvdelwGcceT/cdDcCeEqd/wCNRaRVCuoxCPhCVVN6pbOqLsTdEyHUCOBJ7/WTwDlhVj0LeE1Vt6rqt8BrwOBk1E9VX1V3r2WA/wBt4r3daEXYf9HoCaxW1YCq7gFm4fZ7XFVUPxER4ALg2XhvN1oVxJSkfAYt0FdCRDrgbpzyXpjZp4nIRyIyT0Tyk1oxUOBVEVkiIuPCzI/m5u3JcBGRv2Cp3H8Ax6rqBu/1N8CxYZZJl/04FneEFk5ln4VEutbrWno8QrdDOuy/fsBGVV0VYX5S91+5mJKUz6AF+gqISDbwPHCDqu4oN3sprjuiC3A/MCfJ1eurqt2AIcAEEemf5O1XSkTqAsOB58LMTvX+O4y6Y+S0zDUWkcnAPmBmhEVS9Vn4M3A8UABswHWPpKNRVNyaT9r+qyimJPIzaIE+AhHJwv1DZqrqP8rPV9UdqrrTez0XyBKR5smqn6qu9543AS/gDpFDrQfahrxv401LpiHAUlXdWH5GqvefZ2OwO8t73hRmmZTuRxEZAwwDRnuB4AhRfBYSQlU3qup+VT0APBJhu6nef3WAkcDsSMska/9FiClJ+QxaoA/D69N7DPhMVf8YYZmW3nKISE/cvixJUv0aikij4GvcSbvl5RZ7EbjEy77pDWwPOURMlogtqVTuvxAvAsEMhkuBf4ZZZj5wpog08bomzvSmJZyIDAZuBYaralmEZaL5LCSqfqHnfM6NsN0PgBNEJNc7wrsIt9+T5QxgpaoWh5uZrP1XQUxJzmcwkWeaq+sD6Is7hPoYWOY9hgLjgfHeMtcCK3BZBP8BTk9i/fK87X7k1WGyNz20fgI8iMt4+AQoTPI+bIgL3MeETEvZ/sP94GwA9uL6OC8HmgFvAKuA14Gm3rKFwKMh644FVnuPy5JYv9W4vtngZ/Bhb9njgLkVfRaSVL+nvc/Wx7iA1ap8/bz3Q3FZJl8ks37e9BnBz1zIsqnYf5FiSlI+gzYEgjHGZDjrujHGmAxngd4YYzKcBXpjjMlwFuiNMSbDWaA3xpgMZ4HeGGMynAV6Y4zJcP8fK6q6XJGwMc4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history[\"accuracy\"]\n",
        "val_acc = history.history[\"val_accuracy\"]\n",
        "loss = history.history[\"loss\"]\n",
        "val_loss = history.history[\"val_loss\"]\n",
        "epochs = range(1, len(acc) + 1)\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.legend()\n",
        "plt.figure()\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.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDtmFDk67KR3",
        "outputId": "73fdd119-418f-4190-acc5-1e889b5592f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 3ms/step - loss: 5.3572 - accuracy: 0.9705\n",
            "테스트 정확도: 0.970\n"
          ]
        }
      ],
      "source": [
        "test_model = keras.models.load_model(\n",
        "    \"feature_extraction.keras\")\n",
        "test_loss, test_acc = test_model.evaluate(test_features,test_labels)\n",
        "print(f\"테스트 정확도: {test_acc:.3f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kyA3FT-Wp5s5"
      },
      "source": [
        "#### 데이터 증식을 사용한 특성 추출"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jjk9ahEKp5s5"
      },
      "source": [
        "**VGG16 합성곱 기반 층을 만들고 동결하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "a1kKhLZNp5s5"
      },
      "outputs": [],
      "source": [
        "conv_base  = keras.applications.vgg16.VGG16(\n",
        "    weights=\"imagenet\",\n",
        "    include_top=False)\n",
        "conv_base.trainable = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0N9SdDVEp5s5"
      },
      "source": [
        "**동결하기 전과 후에 훈련 가능한 가중치 리스트를 출력하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oxj_iAKPp5s5",
        "outputId": "4800500d-9d83-4e44-87b8-c230b1248e96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "합성곱 기반 층을 동결하기 전의 훈련 가능한 가중치 개수: 26\n"
          ]
        }
      ],
      "source": [
        "conv_base.trainable = True\n",
        "print(\"합성곱 기반 층을 동결하기 전의 훈련 가능한 가중치 개수:\",\n",
        "      len(conv_base.trainable_weights))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgdkdXBBp5s5",
        "outputId": "9547cdaa-9a2f-4e0f-b160-408e2eac0a1f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "합성곱 기반 층을 동결한 후의 훈련 가능한 가중치 개수: 0\n"
          ]
        }
      ],
      "source": [
        "conv_base.trainable = False\n",
        "print(\"합성곱 기반 층을 동결한 후의 훈련 가능한 가중치 개수:\",\n",
        "      len(conv_base.trainable_weights))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "REqowwk8p5s5"
      },
      "source": [
        "**데이터 증식 단계와 밀집 분류기를 합성곱 기반 층에 추가하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "G5OTDK3Tp5s5"
      },
      "outputs": [],
      "source": [
        "data_augmentation = keras.Sequential(\n",
        "    [\n",
        "        layers.RandomFlip(\"horizontal\"),\n",
        "        layers.RandomRotation(0.1),\n",
        "        layers.RandomZoom(0.2),\n",
        "    ]\n",
        ")\n",
        "\n",
        "inputs = keras.Input(shape=(180, 180, 3))\n",
        "x = data_augmentation(inputs)\n",
        "x = keras.applications.vgg16.preprocess_input(x)\n",
        "x = conv_base(x)\n",
        "x = layers.Flatten()(x)\n",
        "x = layers.Dense(256)(x)\n",
        "x = layers.Dropout(0.5)(x)\n",
        "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "model.compile(loss=\"binary_crossentropy\",\n",
        "              optimizer=\"rmsprop\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bZhwtNjWp5s6",
        "outputId": "1fd5dd7c-3ad6-437d-898b-4cb0dd98bf63"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "63/63 [==============================] - 14s 202ms/step - loss: 16.8770 - accuracy: 0.8920 - val_loss: 3.6804 - val_accuracy: 0.9720\n",
            "Epoch 2/50\n",
            "63/63 [==============================] - 13s 205ms/step - loss: 4.7586 - accuracy: 0.9575 - val_loss: 3.6411 - val_accuracy: 0.9720\n",
            "Epoch 3/50\n",
            "63/63 [==============================] - 13s 202ms/step - loss: 6.9902 - accuracy: 0.9475 - val_loss: 7.1186 - val_accuracy: 0.9570\n",
            "Epoch 4/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 4.1640 - accuracy: 0.9665 - val_loss: 5.9981 - val_accuracy: 0.9630\n",
            "Epoch 5/50\n",
            "63/63 [==============================] - 13s 201ms/step - loss: 4.0658 - accuracy: 0.9595 - val_loss: 2.9750 - val_accuracy: 0.9810\n",
            "Epoch 6/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 3.3065 - accuracy: 0.9645 - val_loss: 3.9064 - val_accuracy: 0.9730\n",
            "Epoch 7/50\n",
            "63/63 [==============================] - 13s 197ms/step - loss: 2.9704 - accuracy: 0.9715 - val_loss: 6.0621 - val_accuracy: 0.9600\n",
            "Epoch 8/50\n",
            "63/63 [==============================] - 13s 199ms/step - loss: 3.8426 - accuracy: 0.9675 - val_loss: 3.4599 - val_accuracy: 0.9750\n",
            "Epoch 9/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 2.6351 - accuracy: 0.9770 - val_loss: 3.1015 - val_accuracy: 0.9780\n",
            "Epoch 10/50\n",
            "63/63 [==============================] - 13s 202ms/step - loss: 2.3347 - accuracy: 0.9785 - val_loss: 2.3854 - val_accuracy: 0.9810\n",
            "Epoch 11/50\n",
            "63/63 [==============================] - 13s 201ms/step - loss: 1.7206 - accuracy: 0.9810 - val_loss: 2.7103 - val_accuracy: 0.9820\n",
            "Epoch 12/50\n",
            "63/63 [==============================] - 13s 197ms/step - loss: 1.9742 - accuracy: 0.9815 - val_loss: 5.0254 - val_accuracy: 0.9690\n",
            "Epoch 13/50\n",
            "63/63 [==============================] - 13s 199ms/step - loss: 2.0595 - accuracy: 0.9750 - val_loss: 3.1573 - val_accuracy: 0.9780\n",
            "Epoch 14/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 1.4813 - accuracy: 0.9765 - val_loss: 2.6392 - val_accuracy: 0.9800\n",
            "Epoch 15/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 1.9960 - accuracy: 0.9800 - val_loss: 2.7955 - val_accuracy: 0.9810\n",
            "Epoch 16/50\n",
            "63/63 [==============================] - 13s 200ms/step - loss: 1.5733 - accuracy: 0.9805 - val_loss: 3.0166 - val_accuracy: 0.9800\n",
            "Epoch 17/50\n",
            "63/63 [==============================] - 13s 201ms/step - loss: 1.2019 - accuracy: 0.9790 - val_loss: 2.5891 - val_accuracy: 0.9810\n",
            "Epoch 18/50\n",
            "63/63 [==============================] - 13s 199ms/step - loss: 1.6640 - accuracy: 0.9785 - val_loss: 3.2121 - val_accuracy: 0.9760\n",
            "Epoch 19/50\n",
            "63/63 [==============================] - 13s 196ms/step - loss: 1.1636 - accuracy: 0.9840 - val_loss: 2.5425 - val_accuracy: 0.9830\n",
            "Epoch 20/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 1.2614 - accuracy: 0.9805 - val_loss: 2.4340 - val_accuracy: 0.9770\n",
            "Epoch 21/50\n",
            "63/63 [==============================] - 13s 200ms/step - loss: 1.0670 - accuracy: 0.9820 - val_loss: 3.1440 - val_accuracy: 0.9770\n",
            "Epoch 22/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 1.0809 - accuracy: 0.9815 - val_loss: 2.5745 - val_accuracy: 0.9810\n",
            "Epoch 23/50\n",
            "63/63 [==============================] - 13s 202ms/step - loss: 0.7431 - accuracy: 0.9840 - val_loss: 2.2952 - val_accuracy: 0.9800\n",
            "Epoch 24/50\n",
            "63/63 [==============================] - 13s 196ms/step - loss: 1.0184 - accuracy: 0.9840 - val_loss: 3.1077 - val_accuracy: 0.9850\n",
            "Epoch 25/50\n",
            "63/63 [==============================] - 13s 203ms/step - loss: 1.0024 - accuracy: 0.9820 - val_loss: 2.2130 - val_accuracy: 0.9860\n",
            "Epoch 26/50\n",
            "63/63 [==============================] - 13s 204ms/step - loss: 1.1670 - accuracy: 0.9825 - val_loss: 2.8822 - val_accuracy: 0.9790\n",
            "Epoch 27/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 0.8764 - accuracy: 0.9870 - val_loss: 2.2132 - val_accuracy: 0.9840\n",
            "Epoch 28/50\n",
            "63/63 [==============================] - 13s 202ms/step - loss: 1.2564 - accuracy: 0.9795 - val_loss: 1.9942 - val_accuracy: 0.9820\n",
            "Epoch 29/50\n",
            "63/63 [==============================] - 13s 200ms/step - loss: 0.7393 - accuracy: 0.9835 - val_loss: 2.8839 - val_accuracy: 0.9790\n",
            "Epoch 30/50\n",
            "63/63 [==============================] - 13s 201ms/step - loss: 0.8807 - accuracy: 0.9865 - val_loss: 2.0409 - val_accuracy: 0.9810\n",
            "Epoch 31/50\n",
            "63/63 [==============================] - 13s 203ms/step - loss: 0.5883 - accuracy: 0.9895 - val_loss: 1.9543 - val_accuracy: 0.9830\n",
            "Epoch 32/50\n",
            "63/63 [==============================] - 13s 200ms/step - loss: 0.3291 - accuracy: 0.9915 - val_loss: 2.6365 - val_accuracy: 0.9780\n",
            "Epoch 33/50\n",
            "63/63 [==============================] - 14s 212ms/step - loss: 0.4712 - accuracy: 0.9880 - val_loss: 1.8256 - val_accuracy: 0.9790\n",
            "Epoch 34/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 0.7222 - accuracy: 0.9870 - val_loss: 3.7551 - val_accuracy: 0.9710\n",
            "Epoch 35/50\n",
            "63/63 [==============================] - 13s 202ms/step - loss: 0.5662 - accuracy: 0.9865 - val_loss: 2.1721 - val_accuracy: 0.9810\n",
            "Epoch 36/50\n",
            "63/63 [==============================] - 13s 199ms/step - loss: 0.3337 - accuracy: 0.9910 - val_loss: 2.5927 - val_accuracy: 0.9810\n",
            "Epoch 37/50\n",
            "63/63 [==============================] - 13s 201ms/step - loss: 0.6519 - accuracy: 0.9885 - val_loss: 2.2788 - val_accuracy: 0.9790\n",
            "Epoch 38/50\n",
            "63/63 [==============================] - 13s 200ms/step - loss: 1.1290 - accuracy: 0.9780 - val_loss: 2.3941 - val_accuracy: 0.9800\n",
            "Epoch 39/50\n",
            "63/63 [==============================] - 13s 196ms/step - loss: 0.4251 - accuracy: 0.9865 - val_loss: 3.1618 - val_accuracy: 0.9770\n",
            "Epoch 40/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 0.7107 - accuracy: 0.9875 - val_loss: 2.7791 - val_accuracy: 0.9770\n",
            "Epoch 41/50\n",
            "63/63 [==============================] - 13s 203ms/step - loss: 0.4502 - accuracy: 0.9890 - val_loss: 2.4707 - val_accuracy: 0.9780\n",
            "Epoch 42/50\n",
            "63/63 [==============================] - 13s 201ms/step - loss: 0.4987 - accuracy: 0.9900 - val_loss: 2.2871 - val_accuracy: 0.9780\n",
            "Epoch 43/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 0.4816 - accuracy: 0.9895 - val_loss: 1.8420 - val_accuracy: 0.9810\n",
            "Epoch 44/50\n",
            "63/63 [==============================] - 13s 202ms/step - loss: 0.3120 - accuracy: 0.9920 - val_loss: 1.5404 - val_accuracy: 0.9820\n",
            "Epoch 45/50\n",
            "63/63 [==============================] - 13s 200ms/step - loss: 0.5460 - accuracy: 0.9885 - val_loss: 1.6243 - val_accuracy: 0.9830\n",
            "Epoch 46/50\n",
            "63/63 [==============================] - 13s 197ms/step - loss: 0.5239 - accuracy: 0.9870 - val_loss: 1.5500 - val_accuracy: 0.9810\n",
            "Epoch 47/50\n",
            "63/63 [==============================] - 13s 198ms/step - loss: 0.5616 - accuracy: 0.9880 - val_loss: 1.8141 - val_accuracy: 0.9790\n",
            "Epoch 48/50\n",
            "63/63 [==============================] - 13s 200ms/step - loss: 0.6100 - accuracy: 0.9875 - val_loss: 2.7472 - val_accuracy: 0.9760\n",
            "Epoch 49/50\n",
            "63/63 [==============================] - 13s 199ms/step - loss: 0.6906 - accuracy: 0.9820 - val_loss: 2.6316 - val_accuracy: 0.9760\n",
            "Epoch 50/50\n",
            "63/63 [==============================] - 13s 199ms/step - loss: 0.7553 - accuracy: 0.9855 - val_loss: 1.8617 - val_accuracy: 0.9770\n"
          ]
        }
      ],
      "source": [
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\n",
        "        filepath=\"feature_extraction_with_data_augmentation.keras\",\n",
        "        save_best_only=True,\n",
        "        monitor=\"val_loss\")\n",
        "]\n",
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    epochs=50,\n",
        "    validation_data=validation_dataset,\n",
        "    callbacks=callbacks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "id": "4WkHfAyu7AL2",
        "outputId": "e0684b30-f583-44e7-e5f2-0a4ad5f2e54e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history[\"accuracy\"]\n",
        "val_acc = history.history[\"val_accuracy\"]\n",
        "loss = history.history[\"loss\"]\n",
        "val_loss = history.history[\"val_loss\"]\n",
        "epochs = range(1, len(acc) + 1)\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.legend()\n",
        "plt.figure()\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.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DaDTEVE0p5s6"
      },
      "source": [
        "**테스트 세트에서 모델 평가하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NKN8cDg7p5s6",
        "outputId": "47c437b1-e756-4321-afad-63a983ea1093"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 8s 114ms/step - loss: 2.4575 - accuracy: 0.9765\n",
            "테스트 정확도: 0.976\n"
          ]
        }
      ],
      "source": [
        "test_model = keras.models.load_model(\n",
        "    \"feature_extraction_with_data_augmentation.keras\")\n",
        "test_loss, test_acc = test_model.evaluate(test_dataset)\n",
        "print(f\"테스트 정확도: {test_acc:.3f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bs8AubRdp5s6"
      },
      "source": [
        "### 사전 훈련된 모델 미세 조정하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qnLFq4-3p5s6",
        "outputId": "9db94faf-3683-4bd3-a91e-b7df7dd31b6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_7 (InputLayer)        [(None, None, None, 3)]   0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, None, None, 64)    1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, None, None, 64)    36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, None, None, 64)    0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, None, None, 128)   73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, None, None, 128)   147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, None, None, 128)   0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, None, None, 256)   295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, None, None, 256)   590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, None, None, 256)   590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, None, None, 256)   0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, None, None, 512)   1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, None, None, 512)   0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, None, None, 512)   2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, None, None, 512)   0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 0\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "conv_base.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zoFdLqsnp5s6"
      },
      "source": [
        "**마지막에서 네 번째 층까지 모든 층 동결하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "MeFPGch_p5s6"
      },
      "outputs": [],
      "source": [
        "conv_base.trainable = True\n",
        "for layer in conv_base.layers[:-4]:\n",
        "    layer.trainable = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3e4jweClp5s6"
      },
      "source": [
        "**모델 미세 조정하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yy4iqjaDp5s6",
        "outputId": "52c81639-3b70-4bc8-a2e6-280f758b38c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "63/63 [==============================] - 16s 228ms/step - loss: 0.4330 - accuracy: 0.9905 - val_loss: 1.6960 - val_accuracy: 0.9820\n",
            "Epoch 2/30\n",
            "63/63 [==============================] - 14s 221ms/step - loss: 0.3498 - accuracy: 0.9915 - val_loss: 1.5210 - val_accuracy: 0.9820\n",
            "Epoch 3/30\n",
            "63/63 [==============================] - 14s 221ms/step - loss: 0.4870 - accuracy: 0.9925 - val_loss: 1.1473 - val_accuracy: 0.9850\n",
            "Epoch 4/30\n",
            "63/63 [==============================] - 14s 219ms/step - loss: 0.3000 - accuracy: 0.9940 - val_loss: 1.3070 - val_accuracy: 0.9820\n",
            "Epoch 5/30\n",
            "63/63 [==============================] - 14s 218ms/step - loss: 0.4618 - accuracy: 0.9900 - val_loss: 1.3919 - val_accuracy: 0.9820\n",
            "Epoch 6/30\n",
            "63/63 [==============================] - 14s 220ms/step - loss: 0.2962 - accuracy: 0.9940 - val_loss: 1.7083 - val_accuracy: 0.9810\n",
            "Epoch 7/30\n",
            "63/63 [==============================] - 14s 216ms/step - loss: 0.2111 - accuracy: 0.9935 - val_loss: 1.5046 - val_accuracy: 0.9840\n",
            "Epoch 8/30\n",
            "63/63 [==============================] - 14s 218ms/step - loss: 0.1273 - accuracy: 0.9955 - val_loss: 1.6466 - val_accuracy: 0.9780\n",
            "Epoch 9/30\n",
            "63/63 [==============================] - 14s 219ms/step - loss: 0.2551 - accuracy: 0.9900 - val_loss: 1.4026 - val_accuracy: 0.9850\n",
            "Epoch 10/30\n",
            "63/63 [==============================] - 14s 218ms/step - loss: 0.0952 - accuracy: 0.9955 - val_loss: 1.5540 - val_accuracy: 0.9820\n",
            "Epoch 11/30\n",
            "63/63 [==============================] - 14s 219ms/step - loss: 0.2567 - accuracy: 0.9925 - val_loss: 1.6762 - val_accuracy: 0.9780\n",
            "Epoch 12/30\n",
            "63/63 [==============================] - 14s 218ms/step - loss: 0.2041 - accuracy: 0.9960 - val_loss: 1.2485 - val_accuracy: 0.9830\n",
            "Epoch 13/30\n",
            "63/63 [==============================] - 14s 218ms/step - loss: 0.1681 - accuracy: 0.9950 - val_loss: 1.5728 - val_accuracy: 0.9790\n",
            "Epoch 14/30\n",
            "63/63 [==============================] - 14s 218ms/step - loss: 0.2032 - accuracy: 0.9940 - val_loss: 1.3675 - val_accuracy: 0.9790\n",
            "Epoch 15/30\n",
            "63/63 [==============================] - 14s 217ms/step - loss: 0.0438 - accuracy: 0.9965 - val_loss: 1.2873 - val_accuracy: 0.9820\n",
            "Epoch 16/30\n",
            "63/63 [==============================] - 14s 219ms/step - loss: 0.1112 - accuracy: 0.9955 - val_loss: 1.6322 - val_accuracy: 0.9810\n",
            "Epoch 17/30\n",
            "63/63 [==============================] - 14s 217ms/step - loss: 0.2179 - accuracy: 0.9935 - val_loss: 1.5322 - val_accuracy: 0.9800\n",
            "Epoch 18/30\n",
            "63/63 [==============================] - 14s 217ms/step - loss: 0.1121 - accuracy: 0.9945 - val_loss: 1.4209 - val_accuracy: 0.9830\n",
            "Epoch 19/30\n",
            "63/63 [==============================] - 14s 219ms/step - loss: 0.1317 - accuracy: 0.9960 - val_loss: 2.0430 - val_accuracy: 0.9780\n",
            "Epoch 20/30\n",
            "63/63 [==============================] - 14s 220ms/step - loss: 0.2262 - accuracy: 0.9900 - val_loss: 1.3365 - val_accuracy: 0.9840\n",
            "Epoch 21/30\n",
            "63/63 [==============================] - 14s 217ms/step - loss: 0.0620 - accuracy: 0.9960 - val_loss: 1.7376 - val_accuracy: 0.9760\n",
            "Epoch 22/30\n",
            "63/63 [==============================] - 14s 218ms/step - loss: 0.1875 - accuracy: 0.9950 - val_loss: 1.7963 - val_accuracy: 0.9770\n",
            "Epoch 23/30\n",
            "63/63 [==============================] - 14s 221ms/step - loss: 0.0360 - accuracy: 0.9955 - val_loss: 1.3428 - val_accuracy: 0.9810\n",
            "Epoch 24/30\n",
            "63/63 [==============================] - 14s 217ms/step - loss: 0.1134 - accuracy: 0.9980 - val_loss: 1.7994 - val_accuracy: 0.9790\n",
            "Epoch 25/30\n",
            "63/63 [==============================] - 14s 218ms/step - loss: 0.0548 - accuracy: 0.9960 - val_loss: 1.4874 - val_accuracy: 0.9800\n",
            "Epoch 26/30\n",
            "63/63 [==============================] - 14s 220ms/step - loss: 0.0488 - accuracy: 0.9970 - val_loss: 1.4191 - val_accuracy: 0.9820\n",
            "Epoch 27/30\n",
            "63/63 [==============================] - 14s 216ms/step - loss: 0.1403 - accuracy: 0.9940 - val_loss: 1.3545 - val_accuracy: 0.9830\n",
            "Epoch 28/30\n",
            "63/63 [==============================] - 14s 218ms/step - loss: 0.0975 - accuracy: 0.9960 - val_loss: 1.2643 - val_accuracy: 0.9830\n",
            "Epoch 29/30\n",
            "63/63 [==============================] - 14s 219ms/step - loss: 0.0853 - accuracy: 0.9970 - val_loss: 1.7219 - val_accuracy: 0.9800\n",
            "Epoch 30/30\n",
            "63/63 [==============================] - 14s 220ms/step - loss: 0.0679 - accuracy: 0.9970 - val_loss: 1.5242 - val_accuracy: 0.9810\n"
          ]
        }
      ],
      "source": [
        "model.compile(loss=\"binary_crossentropy\",\n",
        "              optimizer=keras.optimizers.RMSprop(learning_rate=1e-5),\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\n",
        "        filepath=\"fine_tuning.keras\",\n",
        "        save_best_only=True,\n",
        "        monitor=\"val_loss\")\n",
        "]\n",
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    epochs=30,\n",
        "    validation_data=validation_dataset,\n",
        "    callbacks=callbacks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "beQr-ZI3p5s6",
        "outputId": "aa2918da-00c8-49b1-f2c7-8489f28e685e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 8s 115ms/step - loss: 1.4822 - accuracy: 0.9795\n",
            "테스트 정확도: 0.979\n"
          ]
        }
      ],
      "source": [
        "model = keras.models.load_model(\"fine_tuning.keras\")\n",
        "test_loss, test_acc = model.evaluate(test_dataset)\n",
        "print(f\"테스트 정확도: {test_acc:.3f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NchoBPrip5s6"
      },
      "source": [
        "## 요약"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "chapter08_intro-to-dl-for-computer-vision.i",
      "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
}