chapter09_part02_modern-convnet-architecture-patterns.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6fcNJ9ngWdao"
      },
      "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/chapter09_part02_modern-convnet-architecture-patterns.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": "V6vyo9pMWdar"
      },
      "source": [
        "## 최신 컨브넷 아키텍처 패턴"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRLUswhpWdar"
      },
      "source": [
        "### 모듈화, 계층화 그리고 재사용"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DrVGd0xfWdas"
      },
      "source": [
        "### 잔차 연결"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3p_4BtQWdas"
      },
      "source": [
        "**필터 개수가 변경되는 잔차 블록**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "_sMjx-EsWdas"
      },
      "outputs": [],
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "inputs = keras.Input(shape=(32, 32, 3))\n",
        "x = layers.Conv2D(32, 3, activation=\"relu\")(inputs)\n",
        "residual = x\n",
        "x = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(x)\n",
        "residual = layers.Conv2D(64, 1)(residual)\n",
        "x = layers.add([x, residual])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0qicvVeLWdav"
      },
      "source": [
        "**최대 풀링 층을 가진 잔차 블록**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "VujtXbBHWdav"
      },
      "outputs": [],
      "source": [
        "inputs = keras.Input(shape=(32, 32, 3))\n",
        "x = layers.Conv2D(32, 3, activation=\"relu\")(inputs)\n",
        "residual = x\n",
        "x = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(x)\n",
        "x = layers.MaxPooling2D(2, padding=\"same\")(x)\n",
        "residual = layers.Conv2D(64, 1, strides=2)(residual)\n",
        "x = layers.add([x, residual])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqRZwb1eWdaw",
        "outputId": "3be96d04-52a2-42e8-e88c-343f15ded1a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_3 (InputLayer)        [(None, 32, 32, 3)]          0         []                            \n",
            "                                                                                                  \n",
            " rescaling (Rescaling)       (None, 32, 32, 3)            0         ['input_3[0][0]']             \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)           (None, 32, 32, 32)           896       ['rescaling[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)           (None, 32, 32, 32)           9248      ['conv2d_6[0][0]']            \n",
            "                                                                                                  \n",
            " max_pooling2d_1 (MaxPoolin  (None, 16, 16, 32)           0         ['conv2d_7[0][0]']            \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)           (None, 16, 16, 32)           128       ['rescaling[0][0]']           \n",
            "                                                                                                  \n",
            " add_2 (Add)                 (None, 16, 16, 32)           0         ['max_pooling2d_1[0][0]',     \n",
            "                                                                     'conv2d_8[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)           (None, 16, 16, 64)           18496     ['add_2[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)          (None, 16, 16, 64)           36928     ['conv2d_9[0][0]']            \n",
            "                                                                                                  \n",
            " max_pooling2d_2 (MaxPoolin  (None, 8, 8, 64)             0         ['conv2d_10[0][0]']           \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)          (None, 8, 8, 64)             2112      ['add_2[0][0]']               \n",
            "                                                                                                  \n",
            " add_3 (Add)                 (None, 8, 8, 64)             0         ['max_pooling2d_2[0][0]',     \n",
            "                                                                     'conv2d_11[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)          (None, 8, 8, 128)            73856     ['add_3[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)          (None, 8, 8, 128)            147584    ['conv2d_12[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)          (None, 8, 8, 128)            8320      ['add_3[0][0]']               \n",
            "                                                                                                  \n",
            " add_4 (Add)                 (None, 8, 8, 128)            0         ['conv2d_13[0][0]',           \n",
            "                                                                     'conv2d_14[0][0]']           \n",
            "                                                                                                  \n",
            " global_average_pooling2d (  (None, 128)                  0         ['add_4[0][0]']               \n",
            " GlobalAveragePooling2D)                                                                          \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, 1)                    129       ['global_average_pooling2d[0][\n",
            "                                                                    0]']                          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 297697 (1.14 MB)\n",
            "Trainable params: 297697 (1.14 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "inputs = keras.Input(shape=(32, 32, 3))\n",
        "x = layers.Rescaling(1./255)(inputs)\n",
        "\n",
        "def residual_block(x, filters, pooling=False):\n",
        "    residual = x\n",
        "    x = layers.Conv2D(filters, 3, activation=\"relu\", padding=\"same\")(x)\n",
        "    x = layers.Conv2D(filters, 3, activation=\"relu\", padding=\"same\")(x)\n",
        "    if pooling:\n",
        "        x = layers.MaxPooling2D(2, padding=\"same\")(x)\n",
        "        residual = layers.Conv2D(filters, 1, strides=2)(residual)\n",
        "    elif filters != residual.shape[-1]:\n",
        "        residual = layers.Conv2D(filters, 1)(residual)\n",
        "    x = layers.add([x, residual])\n",
        "    return x\n",
        "\n",
        "x = residual_block(x, filters=32, pooling=True)\n",
        "x = residual_block(x, filters=64, pooling=True)\n",
        "x = residual_block(x, filters=128, pooling=False)\n",
        "\n",
        "x = layers.GlobalAveragePooling2D()(x)\n",
        "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2yTgvoeKWdaw"
      },
      "source": [
        "### 배치 정규화"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPKA2OB7Wdax"
      },
      "source": [
        "### 깊이별 분리 합성곱"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RkVCpdH6Wdax"
      },
      "source": [
        "### Xception 유사 모델에 모두 적용하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJP0kj1tRe_e"
      },
      "source": [
        "캐글에서 dogs-vs-cats 데이터셋을 다운로드하려면 캐글에 가입해야 한 후 생성한 API 키를 사용해야 합니다. 다운로드에 문제가 있다면 다음 명령으로 구글 드라이브에서 직접 다운로드할 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gdown\n",
        "gdown.download(id='18uC7WTuEXKJDDxbj-Jq6EjzpFrgE7IAd', output='dogs-vs-cats.zip')"
      ],
      "metadata": {
        "id": "R_H-D_xsuK4h",
        "outputId": "6a28a448-4aa8-431b-e703-c6c7e1e9ad24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=18uC7WTuEXKJDDxbj-Jq6EjzpFrgE7IAd\n",
            "To: /content/dogs-vs-cats.zip\n",
            "100%|██████████| 852M/852M [00:17<00:00, 49.2MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'dogs-vs-cats.zip'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "코랩의 Secrets 탭에 캐글 키를 저장해 놓았다면 다음처럼 노트북에서 불러올 수 있습니다. Secrets에 저장한 키 이름이 `'kaggle'`이라 가정합니다.\n",
        "\n",
        "```python\n",
        "from google.colab import userdata\n",
        "key = userdata.get('kaggle')\n",
        "with open('kaggle.json', 'w') as f:\n",
        "    f.write(f'{{\"username\":\"haesunpark\",\"key\":\"{key}\"}}')\n",
        "```"
      ],
      "metadata": {
        "id": "9kwJ5oLAWdax"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "또는 로컬 컴퓨터에 저장되어 있는 `'kaggle.json'` 파일을 직접 코랩에 업로드할 수 있습니다.\n",
        "```python\n",
        "# kaggle.json 파일을 업로드하세요.\n",
        "from google.colab import files\n",
        "files.upload()\n",
        "```"
      ],
      "metadata": {
        "id": "UMf82mhduZV4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`'kaggle.json'` 파일이 생성되었다면 적절한 위치로 이동시키고 파일 권한을 변경한 다음 캐글에서 데이터를 다운로드합니다.\n",
        "\n",
        "```bash\n",
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle competitions download -c dogs-vs-cats\n",
        "```"
      ],
      "metadata": {
        "id": "ZSOaTdZ7Re_f"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "HQRYmlcoWdax"
      },
      "outputs": [],
      "source": [
        "!unzip -qq dogs-vs-cats.zip\n",
        "!unzip -qq train.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1TQ1jwjAWday",
        "outputId": "9d774aa4-50f9-41c1-9c32-3a1a7871f967"
      },
      "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": [
        "import os, shutil, pathlib\n",
        "from tensorflow.keras.utils import image_dataset_from_directory\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)\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": 7,
      "metadata": {
        "id": "ocyuOyHbWday"
      },
      "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": "code",
      "execution_count": 8,
      "metadata": {
        "id": "-qBdzynbWday"
      },
      "outputs": [],
      "source": [
        "inputs = keras.Input(shape=(180, 180, 3))\n",
        "x = data_augmentation(inputs)\n",
        "\n",
        "x = layers.Rescaling(1./255)(x)\n",
        "x = layers.Conv2D(filters=32, kernel_size=5, use_bias=False)(x)\n",
        "\n",
        "for size in [32, 64, 128, 256, 512]:\n",
        "    residual = x\n",
        "\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.Activation(\"relu\")(x)\n",
        "    x = layers.SeparableConv2D(size, 3, padding=\"same\", use_bias=False)(x)\n",
        "\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.Activation(\"relu\")(x)\n",
        "    x = layers.SeparableConv2D(size, 3, padding=\"same\", use_bias=False)(x)\n",
        "\n",
        "    x = layers.MaxPooling2D(3, strides=2, padding=\"same\")(x)\n",
        "\n",
        "    residual = layers.Conv2D(\n",
        "        size, 1, strides=2, padding=\"same\", use_bias=False)(residual)\n",
        "    x = layers.add([x, residual])\n",
        "\n",
        "x = layers.GlobalAveragePooling2D()(x)\n",
        "x = layers.Dropout(0.5)(x)\n",
        "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
        "model = keras.Model(inputs=inputs, outputs=outputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p4rMrBCmWdaz",
        "outputId": "5240a373-04c9-415b-9dc8-59ef1147d202"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "63/63 [==============================] - 23s 125ms/step - loss: 0.6946 - accuracy: 0.5690 - val_loss: 0.6927 - val_accuracy: 0.5510\n",
            "Epoch 2/100\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.6635 - accuracy: 0.6055 - val_loss: 0.6943 - val_accuracy: 0.5000\n",
            "Epoch 3/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.6491 - accuracy: 0.6175 - val_loss: 0.6975 - val_accuracy: 0.5000\n",
            "Epoch 4/100\n",
            "63/63 [==============================] - 6s 86ms/step - loss: 0.6202 - accuracy: 0.6715 - val_loss: 0.7487 - val_accuracy: 0.5000\n",
            "Epoch 5/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.6157 - accuracy: 0.6560 - val_loss: 0.7567 - val_accuracy: 0.5000\n",
            "Epoch 6/100\n",
            "63/63 [==============================] - 6s 84ms/step - loss: 0.5925 - accuracy: 0.6940 - val_loss: 0.7327 - val_accuracy: 0.5000\n",
            "Epoch 7/100\n",
            "63/63 [==============================] - 4s 68ms/step - loss: 0.5812 - accuracy: 0.7070 - val_loss: 0.9060 - val_accuracy: 0.5100\n",
            "Epoch 8/100\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.5744 - accuracy: 0.7050 - val_loss: 0.8647 - val_accuracy: 0.5160\n",
            "Epoch 9/100\n",
            "63/63 [==============================] - 6s 87ms/step - loss: 0.5369 - accuracy: 0.7350 - val_loss: 0.5823 - val_accuracy: 0.6710\n",
            "Epoch 10/100\n",
            "63/63 [==============================] - 4s 68ms/step - loss: 0.5316 - accuracy: 0.7510 - val_loss: 0.6305 - val_accuracy: 0.6440\n",
            "Epoch 11/100\n",
            "63/63 [==============================] - 5s 81ms/step - loss: 0.5202 - accuracy: 0.7485 - val_loss: 0.6611 - val_accuracy: 0.6500\n",
            "Epoch 12/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.5060 - accuracy: 0.7560 - val_loss: 0.6355 - val_accuracy: 0.6720\n",
            "Epoch 13/100\n",
            "63/63 [==============================] - 6s 88ms/step - loss: 0.4900 - accuracy: 0.7660 - val_loss: 0.7268 - val_accuracy: 0.6680\n",
            "Epoch 14/100\n",
            "63/63 [==============================] - 6s 91ms/step - loss: 0.4908 - accuracy: 0.7620 - val_loss: 0.5663 - val_accuracy: 0.7200\n",
            "Epoch 15/100\n",
            "63/63 [==============================] - 4s 68ms/step - loss: 0.4757 - accuracy: 0.7850 - val_loss: 0.5607 - val_accuracy: 0.7070\n",
            "Epoch 16/100\n",
            "63/63 [==============================] - 6s 86ms/step - loss: 0.4706 - accuracy: 0.7850 - val_loss: 0.9293 - val_accuracy: 0.6130\n",
            "Epoch 17/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.4435 - accuracy: 0.8030 - val_loss: 1.2253 - val_accuracy: 0.5940\n",
            "Epoch 18/100\n",
            "63/63 [==============================] - 5s 81ms/step - loss: 0.4367 - accuracy: 0.8055 - val_loss: 1.1940 - val_accuracy: 0.6060\n",
            "Epoch 19/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.4222 - accuracy: 0.8025 - val_loss: 0.6517 - val_accuracy: 0.7190\n",
            "Epoch 20/100\n",
            "63/63 [==============================] - 6s 88ms/step - loss: 0.4150 - accuracy: 0.8135 - val_loss: 0.4697 - val_accuracy: 0.7840\n",
            "Epoch 21/100\n",
            "63/63 [==============================] - 5s 72ms/step - loss: 0.4136 - accuracy: 0.8140 - val_loss: 0.7576 - val_accuracy: 0.6760\n",
            "Epoch 22/100\n",
            "63/63 [==============================] - 5s 84ms/step - loss: 0.4223 - accuracy: 0.8105 - val_loss: 0.8984 - val_accuracy: 0.6450\n",
            "Epoch 23/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.3857 - accuracy: 0.8310 - val_loss: 0.5109 - val_accuracy: 0.7510\n",
            "Epoch 24/100\n",
            "63/63 [==============================] - 6s 88ms/step - loss: 0.4005 - accuracy: 0.8160 - val_loss: 0.4694 - val_accuracy: 0.7980\n",
            "Epoch 25/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.3755 - accuracy: 0.8355 - val_loss: 0.9264 - val_accuracy: 0.6810\n",
            "Epoch 26/100\n",
            "63/63 [==============================] - 6s 92ms/step - loss: 0.3570 - accuracy: 0.8415 - val_loss: 0.5405 - val_accuracy: 0.7570\n",
            "Epoch 27/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.3534 - accuracy: 0.8445 - val_loss: 0.6165 - val_accuracy: 0.7450\n",
            "Epoch 28/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.3674 - accuracy: 0.8390 - val_loss: 0.8126 - val_accuracy: 0.7080\n",
            "Epoch 29/100\n",
            "63/63 [==============================] - 6s 85ms/step - loss: 0.3666 - accuracy: 0.8395 - val_loss: 0.4645 - val_accuracy: 0.7840\n",
            "Epoch 30/100\n",
            "63/63 [==============================] - 6s 84ms/step - loss: 0.3490 - accuracy: 0.8510 - val_loss: 0.5679 - val_accuracy: 0.7600\n",
            "Epoch 31/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.3476 - accuracy: 0.8365 - val_loss: 0.6251 - val_accuracy: 0.7630\n",
            "Epoch 32/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.3386 - accuracy: 0.8490 - val_loss: 0.6847 - val_accuracy: 0.7470\n",
            "Epoch 33/100\n",
            "63/63 [==============================] - 6s 84ms/step - loss: 0.3223 - accuracy: 0.8655 - val_loss: 0.4961 - val_accuracy: 0.7820\n",
            "Epoch 34/100\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.3352 - accuracy: 0.8610 - val_loss: 0.4015 - val_accuracy: 0.8160\n",
            "Epoch 35/100\n",
            "63/63 [==============================] - 5s 83ms/step - loss: 0.3191 - accuracy: 0.8665 - val_loss: 0.4262 - val_accuracy: 0.8110\n",
            "Epoch 36/100\n",
            "63/63 [==============================] - 6s 88ms/step - loss: 0.3035 - accuracy: 0.8690 - val_loss: 0.4562 - val_accuracy: 0.8100\n",
            "Epoch 37/100\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.2938 - accuracy: 0.8820 - val_loss: 0.5622 - val_accuracy: 0.7790\n",
            "Epoch 38/100\n",
            "63/63 [==============================] - 5s 80ms/step - loss: 0.2929 - accuracy: 0.8690 - val_loss: 0.4843 - val_accuracy: 0.7970\n",
            "Epoch 39/100\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.2701 - accuracy: 0.8850 - val_loss: 0.4847 - val_accuracy: 0.7940\n",
            "Epoch 40/100\n",
            "63/63 [==============================] - 6s 87ms/step - loss: 0.2817 - accuracy: 0.8850 - val_loss: 0.4864 - val_accuracy: 0.7940\n",
            "Epoch 41/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.2638 - accuracy: 0.8865 - val_loss: 0.8037 - val_accuracy: 0.7640\n",
            "Epoch 42/100\n",
            "63/63 [==============================] - 6s 92ms/step - loss: 0.2883 - accuracy: 0.8760 - val_loss: 0.5113 - val_accuracy: 0.8210\n",
            "Epoch 43/100\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.2479 - accuracy: 0.8855 - val_loss: 0.8061 - val_accuracy: 0.7730\n",
            "Epoch 44/100\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.2762 - accuracy: 0.8870 - val_loss: 0.5445 - val_accuracy: 0.8030\n",
            "Epoch 45/100\n",
            "63/63 [==============================] - 6s 86ms/step - loss: 0.2887 - accuracy: 0.8820 - val_loss: 0.3525 - val_accuracy: 0.8460\n",
            "Epoch 46/100\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.2555 - accuracy: 0.8875 - val_loss: 0.5561 - val_accuracy: 0.8100\n",
            "Epoch 47/100\n",
            "63/63 [==============================] - 6s 93ms/step - loss: 0.2547 - accuracy: 0.8885 - val_loss: 0.4853 - val_accuracy: 0.8290\n",
            "Epoch 48/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.2458 - accuracy: 0.8995 - val_loss: 0.5512 - val_accuracy: 0.8320\n",
            "Epoch 49/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.2370 - accuracy: 0.8950 - val_loss: 1.6119 - val_accuracy: 0.7070\n",
            "Epoch 50/100\n",
            "63/63 [==============================] - 6s 87ms/step - loss: 0.2314 - accuracy: 0.9005 - val_loss: 0.6155 - val_accuracy: 0.7930\n",
            "Epoch 51/100\n",
            "63/63 [==============================] - 4s 68ms/step - loss: 0.2322 - accuracy: 0.9055 - val_loss: 0.3525 - val_accuracy: 0.8460\n",
            "Epoch 52/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.2346 - accuracy: 0.9015 - val_loss: 0.6413 - val_accuracy: 0.7970\n",
            "Epoch 53/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.2138 - accuracy: 0.9145 - val_loss: 0.4114 - val_accuracy: 0.8290\n",
            "Epoch 54/100\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.2118 - accuracy: 0.9095 - val_loss: 0.7264 - val_accuracy: 0.7990\n",
            "Epoch 55/100\n",
            "63/63 [==============================] - 5s 80ms/step - loss: 0.2071 - accuracy: 0.9245 - val_loss: 1.0334 - val_accuracy: 0.7220\n",
            "Epoch 56/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.1964 - accuracy: 0.9190 - val_loss: 0.4229 - val_accuracy: 0.8500\n",
            "Epoch 57/100\n",
            "63/63 [==============================] - 5s 82ms/step - loss: 0.2009 - accuracy: 0.9175 - val_loss: 0.5565 - val_accuracy: 0.8280\n",
            "Epoch 58/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.1967 - accuracy: 0.9140 - val_loss: 0.8254 - val_accuracy: 0.7590\n",
            "Epoch 59/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.1864 - accuracy: 0.9255 - val_loss: 0.4731 - val_accuracy: 0.8420\n",
            "Epoch 60/100\n",
            "63/63 [==============================] - 6s 85ms/step - loss: 0.2083 - accuracy: 0.9145 - val_loss: 0.4107 - val_accuracy: 0.8470\n",
            "Epoch 61/100\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.1688 - accuracy: 0.9320 - val_loss: 0.4085 - val_accuracy: 0.8540\n",
            "Epoch 62/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.1834 - accuracy: 0.9275 - val_loss: 0.6121 - val_accuracy: 0.8330\n",
            "Epoch 63/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.1764 - accuracy: 0.9295 - val_loss: 0.4571 - val_accuracy: 0.8460\n",
            "Epoch 64/100\n",
            "63/63 [==============================] - 6s 87ms/step - loss: 0.1812 - accuracy: 0.9255 - val_loss: 0.7324 - val_accuracy: 0.8110\n",
            "Epoch 65/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.2059 - accuracy: 0.9140 - val_loss: 0.5492 - val_accuracy: 0.8280\n",
            "Epoch 66/100\n",
            "63/63 [==============================] - 6s 84ms/step - loss: 0.1871 - accuracy: 0.9200 - val_loss: 0.3938 - val_accuracy: 0.8370\n",
            "Epoch 67/100\n",
            "63/63 [==============================] - 4s 68ms/step - loss: 0.1646 - accuracy: 0.9340 - val_loss: 0.3870 - val_accuracy: 0.8640\n",
            "Epoch 68/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.1634 - accuracy: 0.9355 - val_loss: 1.0535 - val_accuracy: 0.7710\n",
            "Epoch 69/100\n",
            "63/63 [==============================] - 6s 88ms/step - loss: 0.1567 - accuracy: 0.9380 - val_loss: 0.5314 - val_accuracy: 0.8140\n",
            "Epoch 70/100\n",
            "63/63 [==============================] - 5s 71ms/step - loss: 0.1693 - accuracy: 0.9315 - val_loss: 0.2984 - val_accuracy: 0.8880\n",
            "Epoch 71/100\n",
            "63/63 [==============================] - 5s 83ms/step - loss: 0.1662 - accuracy: 0.9350 - val_loss: 0.4728 - val_accuracy: 0.8440\n",
            "Epoch 72/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.1758 - accuracy: 0.9280 - val_loss: 0.3569 - val_accuracy: 0.8750\n",
            "Epoch 73/100\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.1512 - accuracy: 0.9390 - val_loss: 0.5620 - val_accuracy: 0.8260\n",
            "Epoch 74/100\n",
            "63/63 [==============================] - 6s 86ms/step - loss: 0.1623 - accuracy: 0.9330 - val_loss: 0.9826 - val_accuracy: 0.7460\n",
            "Epoch 75/100\n",
            "63/63 [==============================] - 4s 67ms/step - loss: 0.1324 - accuracy: 0.9470 - val_loss: 0.8841 - val_accuracy: 0.7930\n",
            "Epoch 76/100\n",
            "63/63 [==============================] - 5s 74ms/step - loss: 0.1651 - accuracy: 0.9395 - val_loss: 0.5022 - val_accuracy: 0.8400\n",
            "Epoch 77/100\n",
            "63/63 [==============================] - 5s 76ms/step - loss: 0.1502 - accuracy: 0.9375 - val_loss: 0.4829 - val_accuracy: 0.8490\n",
            "Epoch 78/100\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.1401 - accuracy: 0.9420 - val_loss: 0.5237 - val_accuracy: 0.8340\n",
            "Epoch 79/100\n",
            "63/63 [==============================] - 5s 84ms/step - loss: 0.1472 - accuracy: 0.9430 - val_loss: 0.6644 - val_accuracy: 0.8260\n",
            "Epoch 80/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.1382 - accuracy: 0.9430 - val_loss: 0.9668 - val_accuracy: 0.7670\n",
            "Epoch 81/100\n",
            "63/63 [==============================] - 4s 68ms/step - loss: 0.1360 - accuracy: 0.9505 - val_loss: 1.0781 - val_accuracy: 0.7360\n",
            "Epoch 82/100\n",
            "63/63 [==============================] - 5s 77ms/step - loss: 0.1228 - accuracy: 0.9495 - val_loss: 0.4107 - val_accuracy: 0.8690\n",
            "Epoch 83/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.1380 - accuracy: 0.9420 - val_loss: 0.7775 - val_accuracy: 0.7700\n",
            "Epoch 84/100\n",
            "63/63 [==============================] - 7s 102ms/step - loss: 0.1392 - accuracy: 0.9425 - val_loss: 0.3761 - val_accuracy: 0.8620\n",
            "Epoch 85/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.1331 - accuracy: 0.9475 - val_loss: 0.3088 - val_accuracy: 0.8790\n",
            "Epoch 86/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.1366 - accuracy: 0.9475 - val_loss: 0.4692 - val_accuracy: 0.8560\n",
            "Epoch 87/100\n",
            "63/63 [==============================] - 5s 81ms/step - loss: 0.1337 - accuracy: 0.9450 - val_loss: 0.3389 - val_accuracy: 0.8810\n",
            "Epoch 88/100\n",
            "63/63 [==============================] - 4s 67ms/step - loss: 0.1375 - accuracy: 0.9510 - val_loss: 0.4493 - val_accuracy: 0.8380\n",
            "Epoch 89/100\n",
            "63/63 [==============================] - 5s 83ms/step - loss: 0.1207 - accuracy: 0.9570 - val_loss: 0.4013 - val_accuracy: 0.8790\n",
            "Epoch 90/100\n",
            "63/63 [==============================] - 5s 68ms/step - loss: 0.1190 - accuracy: 0.9585 - val_loss: 0.3564 - val_accuracy: 0.8790\n",
            "Epoch 91/100\n",
            "63/63 [==============================] - 6s 86ms/step - loss: 0.1170 - accuracy: 0.9535 - val_loss: 0.4972 - val_accuracy: 0.8510\n",
            "Epoch 92/100\n",
            "63/63 [==============================] - 5s 82ms/step - loss: 0.1267 - accuracy: 0.9490 - val_loss: 0.2866 - val_accuracy: 0.9020\n",
            "Epoch 93/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.1238 - accuracy: 0.9455 - val_loss: 0.3289 - val_accuracy: 0.8970\n",
            "Epoch 94/100\n",
            "63/63 [==============================] - 6s 87ms/step - loss: 0.1274 - accuracy: 0.9490 - val_loss: 0.6969 - val_accuracy: 0.8230\n",
            "Epoch 95/100\n",
            "63/63 [==============================] - 6s 85ms/step - loss: 0.1194 - accuracy: 0.9520 - val_loss: 0.8178 - val_accuracy: 0.8200\n",
            "Epoch 96/100\n",
            "63/63 [==============================] - 5s 69ms/step - loss: 0.0903 - accuracy: 0.9605 - val_loss: 0.5123 - val_accuracy: 0.8520\n",
            "Epoch 97/100\n",
            "63/63 [==============================] - 6s 89ms/step - loss: 0.1066 - accuracy: 0.9580 - val_loss: 0.3932 - val_accuracy: 0.8790\n",
            "Epoch 98/100\n",
            "63/63 [==============================] - 5s 70ms/step - loss: 0.1199 - accuracy: 0.9530 - val_loss: 0.4456 - val_accuracy: 0.8860\n",
            "Epoch 99/100\n",
            "63/63 [==============================] - 6s 94ms/step - loss: 0.0939 - accuracy: 0.9680 - val_loss: 0.3387 - val_accuracy: 0.9040\n",
            "Epoch 100/100\n",
            "63/63 [==============================] - 4s 68ms/step - loss: 0.0981 - accuracy: 0.9630 - val_loss: 0.5832 - val_accuracy: 0.8460\n"
          ]
        }
      ],
      "source": [
        "model.compile(loss=\"binary_crossentropy\",\n",
        "              optimizer=\"rmsprop\",\n",
        "              metrics=[\"accuracy\"])\n",
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    epochs=100,\n",
        "    validation_data=validation_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "EF7VzM_58F_-",
        "outputId": "2981ef48-a359-4aa6-8d3a-6bc189bee491"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "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()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "chapter09_part02_modern-convnet-architecture-patterns.i",
      "provenance": [],
      "gpuType": "V100"
    },
    "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",
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