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  "cells": [
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GrainSack/Assignments/blob/main/Ablation_study.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-NCXFwfe-yj",
        "outputId": "00b0b296-1248-4adb-d28e-96e94f2fb8e5"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "# TensorFlow and tf.keras\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "# Helper libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "# Tensorflow가 활용할 GPU가 장착되어 있는지 확인해 봅니다.\n",
        "tf.config.list_physical_devices('GPU')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import urllib3\n",
        "urllib3.disable_warnings()\n",
        "\n",
        "#tfds.disable_progress_bar()   # 이 주석을 풀면 데이터셋 다운로드과정의 프로그레스바가 나타나지 않습니다.\n",
        "\n",
        "(ds_train, ds_test), ds_info = tfds.load(\n",
        "    'cifar10',\n",
        "    split=['train', 'test'],\n",
        "    shuffle_files=True,\n",
        "    with_info=True,\n",
        ")"
      ],
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        "id": "ZYkeJZErfIPS",
        "outputId": "3d39fac7-78e3-440f-f0a8-91443a175ce3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 162.17 MiB (download: 162.17 MiB, generated: 132.40 MiB, total: 294.58 MiB) to /root/tensorflow_datasets/cifar10/3.0.2...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "265b42c5613a469f9a22703410fc06c6"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f3bfb64866c24a2c88caed533c2fe471"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4ca270411ba436db213c980ee10ce6a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/2 [00:00<?, ? splits/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d779e37a5544f37a40d031af2340c30"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/50000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29e8c9bbda4e4572afeaf6c23e1d66bc"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/cifar10/3.0.2.incomplete0NDTJA/cifar10-train.tfrecord*...:   0%|          …"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "7407e061e8ab46a881a417c89f9693db"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test examples...:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e7a991492f44dd580044365af956179"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/cifar10/3.0.2.incomplete0NDTJA/cifar10-test.tfrecord*...:   0%|          |…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8a83b932a4e4c0a8e2e5e5e9c207aae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset cifar10 downloaded and prepared to /root/tensorflow_datasets/cifar10/3.0.2. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tensorflow 데이터셋을 로드하면 꼭 feature 정보를 확인해 보세요.\n",
        "print(ds_info.features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aBG0mJAPfNkN",
        "outputId": "0ed471ee-f451-4330-c90e-f87f030b3520"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FeaturesDict({\n",
            "    'id': Text(shape=(), dtype=string),\n",
            "    'image': Image(shape=(32, 32, 3), dtype=uint8),\n",
            "    'label': ClassLabel(shape=(), dtype=int64, num_classes=10),\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터의 개수도 확인해 봅시다.\n",
        "print(tf.data.experimental.cardinality(ds_train))\n",
        "print(tf.data.experimental.cardinality(ds_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-nemz4iOfRBX",
        "outputId": "702b915c-e10a-4f61-cd53-9697b0e2d710"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(50000, shape=(), dtype=int64)\n",
            "tf.Tensor(10000, shape=(), dtype=int64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Normalization"
      ],
      "metadata": {
        "id": "HVw9pAiBfabC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def normalize_and_resize_img(image, label):\n",
        "    \"\"\"Normalizes images: `uint8` -> `float32`.\"\"\"\n",
        "    image = tf.cast(image, tf.float32) / 255.0\n",
        "    return image, label"
      ],
      "metadata": {
        "id": "wo6t8TVnfUL8"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def apply_normalize_on_dataset(ds, is_test=False, batch_size=16):\n",
        "    ds = ds.map(\n",
        "        normalize_and_resize_img,\n",
        "        num_parallel_calls=1\n",
        "    )\n",
        "    ds = ds.batch(batch_size)\n",
        "    if not is_test:\n",
        "        ds = ds.repeat()\n",
        "        ds = ds.shuffle(200)\n",
        "    ds = ds.prefetch(tf.data.experimental.AUTOTUNE)\n",
        "    return ds"
      ],
      "metadata": {
        "id": "lOHbPJAlfxhG"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ds_info.features[\"label\"].num_classes"
      ],
      "metadata": {
        "id": "WdFIVgB1f55T",
        "outputId": "3fd51ecf-d366-4f4e-8568-0d6e09fc2aef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_info.features[\"label\"].names"
      ],
      "metadata": {
        "id": "EZiz6_THgFGZ",
        "outputId": "6bc1dee0-3e08-45c8-dd79-cf32cc12b68a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['airplane',\n",
              " 'automobile',\n",
              " 'bird',\n",
              " 'cat',\n",
              " 'deer',\n",
              " 'dog',\n",
              " 'frog',\n",
              " 'horse',\n",
              " 'ship',\n",
              " 'truck']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### train and test datasets"
      ],
      "metadata": {
        "id": "gu9GBucCgUHs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = tfds.show_examples(ds_train, ds_info)"
      ],
      "metadata": {
        "id": "TFO2ehB_gHRZ",
        "outputId": "93e93ce6-e31c-4ed5-c171-b9b8a6a450b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = tfds.show_examples(ds_test, ds_info)"
      ],
      "metadata": {
        "id": "ULqUYDFTgJ0H",
        "outputId": "8c9f0c16-2032-40db-da4e-675dedaec109",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Build Resnet"
      ],
      "metadata": {
        "id": "WBNaXMKwggbD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# function for building VGG Block\n",
        "\n",
        "def build_vgg_block(input_layer,\n",
        "                    num_cnn=3,\n",
        "                    channel=64,\n",
        "                    block_num=1,\n",
        "                   ):\n",
        "    # 입력 레이어\n",
        "    x = input_layer\n",
        "\n",
        "    # CNN 레이어\n",
        "    for cnn_num in range(num_cnn):\n",
        "        x = keras.layers.Conv2D(\n",
        "            filters=channel,\n",
        "            kernel_size=(3,3),\n",
        "            activation='relu',\n",
        "            kernel_initializer='he_normal',\n",
        "            padding='same',\n",
        "            name=f'block{block_num}_conv{cnn_num}'\n",
        "        )(x)\n",
        "\n",
        "    # Max Pooling 레이어\n",
        "    x = keras.layers.MaxPooling2D(\n",
        "        pool_size=(2, 2),\n",
        "        strides=2,\n",
        "        name=f'block{block_num}_pooling'\n",
        "    )(x)\n",
        "\n",
        "    return x"
      ],
      "metadata": {
        "id": "Am2orlapgQN6"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vgg_input_layer = keras.layers.Input(shape=(32,32,3))   # 입력 레이어 생성\n",
        "vgg_block_output = build_vgg_block(vgg_input_layer)    # VGG 블록 생성"
      ],
      "metadata": {
        "id": "CsRjFW-sgkt1"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 블록 1개짜리 model 생성\n",
        "model = keras.Model(inputs=vgg_input_layer, outputs=vgg_block_output)\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "Qug4Aw7VglYO",
        "outputId": "d53c7d8b-e70a-445f-bedf-399cd3ae3e52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
            "                                                                 \n",
            " block1_conv0 (Conv2D)       (None, 32, 32, 64)        1792      \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 32, 32, 64)        36928     \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 32, 32, 64)        36928     \n",
            "                                                                 \n",
            " block1_pooling (MaxPooling2  (None, 16, 16, 64)       0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 75,648\n",
            "Trainable params: 75,648\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### VGG-16"
      ],
      "metadata": {
        "id": "CiwAolR6gtpg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# VGG 모델 자체를 생성하는 함수입니다.\n",
        "def build_vgg(input_shape=(32,32,3),\n",
        "              num_cnn_list=[2,2,3,3,3],\n",
        "              channel_list=[64,128,256,512,512],\n",
        "              num_classes=10):\n",
        "\n",
        "    assert len(num_cnn_list) == len(channel_list) #모델을 만들기 전에 config list들이 같은 길이인지 확인합니다.\n",
        "\n",
        "    input_layer = keras.layers.Input(shape=input_shape)  # input layer를 만들어둡니다.\n",
        "    output = input_layer\n",
        "\n",
        "    # config list들의 길이만큼 반복해서 블록을 생성합니다.\n",
        "    for i, (num_cnn, channel) in enumerate(zip(num_cnn_list, channel_list)):\n",
        "        output = build_vgg_block(\n",
        "            output,\n",
        "            num_cnn=num_cnn,\n",
        "            channel=channel,\n",
        "            block_num=i\n",
        "        )\n",
        "\n",
        "    output = keras.layers.Flatten(name='flatten')(output)\n",
        "    output = keras.layers.Dense(4096, activation='relu', name='fc1')(output)\n",
        "    output = keras.layers.Dense(4096, activation='relu', name='fc2')(output)\n",
        "    output = keras.layers.Dense(num_classes, activation='softmax', name='predictions')(output)\n",
        "\n",
        "    model = keras.Model(\n",
        "        inputs=input_layer,\n",
        "        outputs=output\n",
        "    )\n",
        "    return model"
      ],
      "metadata": {
        "id": "_oGGl2lngnI_"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 기본값을 그대로 사용해서 VGG 모델을 만들면 VGG-16이 됩니다.\n",
        "vgg_16 = build_vgg()\n",
        "\n",
        "vgg_16.summary()"
      ],
      "metadata": {
        "id": "IE19Xm9Vgw5b",
        "outputId": "1d28145e-fa9c-4bfb-cfb6-510b35be2d60",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
            "                                                                 \n",
            " block0_conv0 (Conv2D)       (None, 32, 32, 64)        1792      \n",
            "                                                                 \n",
            " block0_conv1 (Conv2D)       (None, 32, 32, 64)        36928     \n",
            "                                                                 \n",
            " block0_pooling (MaxPooling2  (None, 16, 16, 64)       0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " block1_conv0 (Conv2D)       (None, 16, 16, 128)       73856     \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 16, 16, 128)       147584    \n",
            "                                                                 \n",
            " block1_pooling (MaxPooling2  (None, 8, 8, 128)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " block2_conv0 (Conv2D)       (None, 8, 8, 256)         295168    \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 8, 8, 256)         590080    \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 8, 8, 256)         590080    \n",
            "                                                                 \n",
            " block2_pooling (MaxPooling2  (None, 4, 4, 256)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " block3_conv0 (Conv2D)       (None, 4, 4, 512)         1180160   \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block3_pooling (MaxPooling2  (None, 2, 2, 512)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " block4_conv0 (Conv2D)       (None, 2, 2, 512)         2359808   \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 2, 2, 512)         2359808   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 2, 2, 512)         2359808   \n",
            "                                                                 \n",
            " block4_pooling (MaxPooling2  (None, 1, 1, 512)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 512)               0         \n",
            "                                                                 \n",
            " fc1 (Dense)                 (None, 4096)              2101248   \n",
            "                                                                 \n",
            " fc2 (Dense)                 (None, 4096)              16781312  \n",
            "                                                                 \n",
            " predictions (Dense)         (None, 10)                40970     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 33,638,218\n",
            "Trainable params: 33,638,218\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### VGG-19"
      ],
      "metadata": {
        "id": "YH-LIMSYg5ZH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 원하는 블록의 설계에 따라 매개변수로 리스트를 전달해 줍니다.\n",
        "vgg_19 = build_vgg(\n",
        "    num_cnn_list=[2,2,4,4,4],\n",
        "    channel_list=[64,128,256,512,512]\n",
        ")\n",
        "\n",
        "vgg_19.summary()"
      ],
      "metadata": {
        "id": "Lw6iVLamg0XY",
        "outputId": "4c706bcf-bef2-4f3f-e9c0-bcba73fb7b92",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
            "                                                                 \n",
            " block0_conv0 (Conv2D)       (None, 32, 32, 64)        1792      \n",
            "                                                                 \n",
            " block0_conv1 (Conv2D)       (None, 32, 32, 64)        36928     \n",
            "                                                                 \n",
            " block0_pooling (MaxPooling2  (None, 16, 16, 64)       0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " block1_conv0 (Conv2D)       (None, 16, 16, 128)       73856     \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 16, 16, 128)       147584    \n",
            "                                                                 \n",
            " block1_pooling (MaxPooling2  (None, 8, 8, 128)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " block2_conv0 (Conv2D)       (None, 8, 8, 256)         295168    \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 8, 8, 256)         590080    \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 8, 8, 256)         590080    \n",
            "                                                                 \n",
            " block2_conv3 (Conv2D)       (None, 8, 8, 256)         590080    \n",
            "                                                                 \n",
            " block2_pooling (MaxPooling2  (None, 4, 4, 256)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " block3_conv0 (Conv2D)       (None, 4, 4, 512)         1180160   \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block3_pooling (MaxPooling2  (None, 2, 2, 512)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " block4_conv0 (Conv2D)       (None, 2, 2, 512)         2359808   \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 2, 2, 512)         2359808   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 2, 2, 512)         2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 2, 2, 512)         2359808   \n",
            "                                                                 \n",
            " block4_pooling (MaxPooling2  (None, 1, 1, 512)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 512)               0         \n",
            "                                                                 \n",
            " fc1 (Dense)                 (None, 4096)              2101248   \n",
            "                                                                 \n",
            " fc2 (Dense)                 (None, 4096)              16781312  \n",
            "                                                                 \n",
            " predictions (Dense)         (None, 10)                40970     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 38,947,914\n",
            "Trainable params: 38,947,914\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### VGG-16"
      ],
      "metadata": {
        "id": "BDSb0SAfhds3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Q. VGG-16보다 작은 네트워크인 VGG-13을 만들어 보세요.\n",
        "vgg_13 = build_vgg(\n",
        "    num_cnn_list=[2, 2, 2, 2, 2],\n",
        "    channel_list=[64, 128, 256, 512, 512]\n",
        ")\n",
        "vgg_13.summary()"
      ],
      "metadata": {
        "id": "uyjIjecTg6w9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model compare"
      ],
      "metadata": {
        "id": "uVVw12TJhpcS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 256\n",
        "EPOCH = 15\n",
        "\n",
        "(ds_train, ds_test), ds_info = tfds.load(\n",
        "    'cifar10',\n",
        "    split=['train', 'test'],\n",
        "    as_supervised=True,\n",
        "    shuffle_files=True,\n",
        "    with_info=True,\n",
        ")\n",
        "ds_train = apply_normalize_on_dataset(ds_train, batch_size=BATCH_SIZE)\n",
        "ds_test = apply_normalize_on_dataset(ds_test, batch_size=BATCH_SIZE)"
      ],
      "metadata": {
        "id": "qZCVQXoBhrDr"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vgg_16.compile(\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer=tf.keras.optimizers.SGD(lr=0.01, clipnorm=1.),\n",
        "    metrics=['accuracy'],\n",
        ")\n",
        "\n",
        "history_16 = vgg_16.fit(\n",
        "    ds_train,\n",
        "    steps_per_epoch=int(ds_info.splits['train'].num_examples/BATCH_SIZE),\n",
        "    validation_steps=int(ds_info.splits['test'].num_examples/BATCH_SIZE),\n",
        "    epochs=EPOCH,\n",
        "    validation_data=ds_test,\n",
        "    verbose=1,\n",
        "    use_multiprocessing=True,\n",
        ")"
      ],
      "metadata": {
        "id": "KuuzSAhnhrKV",
        "outputId": "5f8e57d6-e5f6-4a46-e9c6-ebfc8ffe591c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "195/195 [==============================] - 25s 41ms/step - loss: 2.2038 - accuracy: 0.1832 - val_loss: 2.0444 - val_accuracy: 0.2689\n",
            "Epoch 2/15\n",
            "195/195 [==============================] - 6s 33ms/step - loss: 2.0150 - accuracy: 0.2696 - val_loss: 1.9792 - val_accuracy: 0.2922\n",
            "Epoch 3/15\n",
            "195/195 [==============================] - 7s 34ms/step - loss: 1.8833 - accuracy: 0.3248 - val_loss: 1.8425 - val_accuracy: 0.3326\n",
            "Epoch 4/15\n",
            "195/195 [==============================] - 6s 33ms/step - loss: 1.7710 - accuracy: 0.3632 - val_loss: 1.6508 - val_accuracy: 0.4138\n",
            "Epoch 5/15\n",
            "195/195 [==============================] - 7s 34ms/step - loss: 1.6837 - accuracy: 0.3947 - val_loss: 1.6907 - val_accuracy: 0.3723\n",
            "Epoch 6/15\n",
            "195/195 [==============================] - 7s 34ms/step - loss: 1.6180 - accuracy: 0.4166 - val_loss: 1.5848 - val_accuracy: 0.4301\n",
            "Epoch 7/15\n",
            "195/195 [==============================] - 7s 34ms/step - loss: 1.5576 - accuracy: 0.4392 - val_loss: 1.5349 - val_accuracy: 0.4370\n",
            "Epoch 8/15\n",
            "195/195 [==============================] - 6s 33ms/step - loss: 1.5075 - accuracy: 0.4605 - val_loss: 1.4906 - val_accuracy: 0.4633\n",
            "Epoch 9/15\n",
            "195/195 [==============================] - 6s 33ms/step - loss: 1.4690 - accuracy: 0.4724 - val_loss: 1.4637 - val_accuracy: 0.4699\n",
            "Epoch 10/15\n",
            "195/195 [==============================] - 6s 33ms/step - loss: 1.4248 - accuracy: 0.4894 - val_loss: 1.3671 - val_accuracy: 0.5121\n",
            "Epoch 11/15\n",
            "195/195 [==============================] - 6s 33ms/step - loss: 1.3861 - accuracy: 0.5044 - val_loss: 1.3637 - val_accuracy: 0.5010\n",
            "Epoch 12/15\n",
            "195/195 [==============================] - 7s 34ms/step - loss: 1.3389 - accuracy: 0.5189 - val_loss: 1.3655 - val_accuracy: 0.5014\n",
            "Epoch 13/15\n",
            "195/195 [==============================] - 6s 33ms/step - loss: 1.3116 - accuracy: 0.5315 - val_loss: 1.3088 - val_accuracy: 0.5364\n",
            "Epoch 14/15\n",
            "195/195 [==============================] - 6s 33ms/step - loss: 1.2755 - accuracy: 0.5450 - val_loss: 1.3475 - val_accuracy: 0.5203\n",
            "Epoch 15/15\n",
            "195/195 [==============================] - 6s 33ms/step - loss: 1.2499 - accuracy: 0.5512 - val_loss: 1.2687 - val_accuracy: 0.5476\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vgg_19.compile(\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    optimizer=tf.keras.optimizers.SGD(lr=0.01, clipnorm=1.),\n",
        "    metrics=['accuracy'],\n",
        ")\n",
        "\n",
        "history_19 = vgg_19.fit(\n",
        "    ds_train,\n",
        "    steps_per_epoch=int(ds_info.splits['train'].num_examples/BATCH_SIZE),\n",
        "    validation_steps=int(ds_info.splits['test'].num_examples/BATCH_SIZE),\n",
        "    epochs=EPOCH,\n",
        "    validation_data=ds_test,\n",
        "    verbose=1,\n",
        "    use_multiprocessing=True,\n",
        ")"
      ],
      "metadata": {
        "id": "y5Euga7dhx-i",
        "outputId": "3dd46f10-9006-4754-c55e-028bb22b439a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.SGD.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "195/195 [==============================] - 13s 37ms/step - loss: 2.2205 - accuracy: 0.1634 - val_loss: 2.1674 - val_accuracy: 0.1906\n",
            "Epoch 2/15\n",
            "195/195 [==============================] - 7s 35ms/step - loss: 2.0649 - accuracy: 0.2418 - val_loss: 1.9840 - val_accuracy: 0.2826\n",
            "Epoch 3/15\n",
            "195/195 [==============================] - 7s 35ms/step - loss: 1.9386 - accuracy: 0.2992 - val_loss: 1.8712 - val_accuracy: 0.3375\n",
            "Epoch 4/15\n",
            "195/195 [==============================] - 7s 36ms/step - loss: 1.8223 - accuracy: 0.3437 - val_loss: 1.7508 - val_accuracy: 0.3825\n",
            "Epoch 5/15\n",
            "195/195 [==============================] - 7s 36ms/step - loss: 1.7291 - accuracy: 0.3770 - val_loss: 1.6922 - val_accuracy: 0.3898\n",
            "Epoch 6/15\n",
            "195/195 [==============================] - 7s 36ms/step - loss: 1.6515 - accuracy: 0.4037 - val_loss: 1.5952 - val_accuracy: 0.4297\n",
            "Epoch 7/15\n",
            "195/195 [==============================] - 7s 36ms/step - loss: 1.5905 - accuracy: 0.4267 - val_loss: 1.5664 - val_accuracy: 0.4457\n",
            "Epoch 8/15\n",
            "195/195 [==============================] - 7s 35ms/step - loss: 1.5389 - accuracy: 0.4450 - val_loss: 1.5061 - val_accuracy: 0.4742\n",
            "Epoch 9/15\n",
            "195/195 [==============================] - 7s 36ms/step - loss: 1.4844 - accuracy: 0.4673 - val_loss: 1.4777 - val_accuracy: 0.4857\n",
            "Epoch 10/15\n",
            "195/195 [==============================] - 7s 36ms/step - loss: 1.4540 - accuracy: 0.4792 - val_loss: 1.4374 - val_accuracy: 0.4726\n",
            "Epoch 11/15\n",
            "195/195 [==============================] - 7s 36ms/step - loss: 1.4088 - accuracy: 0.4938 - val_loss: 1.3618 - val_accuracy: 0.5153\n",
            "Epoch 12/15\n",
            "195/195 [==============================] - 7s 36ms/step - loss: 1.3746 - accuracy: 0.5078 - val_loss: 1.3770 - val_accuracy: 0.5034\n",
            "Epoch 13/15\n",
            "195/195 [==============================] - 7s 36ms/step - loss: 1.3307 - accuracy: 0.5266 - val_loss: 1.3528 - val_accuracy: 0.5186\n",
            "Epoch 14/15\n",
            "195/195 [==============================] - 7s 35ms/step - loss: 1.2946 - accuracy: 0.5348 - val_loss: 1.3100 - val_accuracy: 0.5220\n",
            "Epoch 15/15\n",
            "195/195 [==============================] - 7s 36ms/step - loss: 1.2673 - accuracy: 0.5483 - val_loss: 1.3051 - val_accuracy: 0.5259\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history_16.history['loss'], 'r')\n",
        "plt.plot(history_19.history['loss'], 'b')\n",
        "plt.title('Model training loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['vgg_16', 'vgg_19'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "VwJVA7b-h_zo",
        "outputId": "0b597f8c-6195-4d72-8c66-6037232bd61c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history_16.history['val_accuracy'], 'r')\n",
        "plt.plot(history_19.history['val_accuracy'], 'b')\n",
        "plt.title('Model validation accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['vgg_16', 'vgg_19'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "L9tqcQyviA0H",
        "outputId": "78b587ea-502e-465d-c0dc-28bab6f09436",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3OKC1Ls8iPnQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}