ml_kfood150_prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Tensorflow 설치\n"
      ],
      "metadata": {
        "id": "ZnkyNxfCOwp0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "1oc6fhEvARZX"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "wSvGPuQjO_er"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uYHLTmXAzR8B",
        "outputId": "ffbaf3c6-b71f-45fc-ff2b-d8465641e1c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.9.16\n"
          ]
        }
      ],
      "source": [
        "!python --version"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "YWQg5jmSzj_p",
        "outputId": "d308ccba-60f1-444a-d878-7b9ea35b31b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.11.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "tf.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCZ1QDbXFO4S"
      },
      "source": [
        "## 2. Load Saved Model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load saved model (버킷에서 모델 로드)"
      ],
      "metadata": {
        "id": "uhinSk_vTXE1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Z_NkiB7VU7HZ"
      },
      "outputs": [],
      "source": [
        "# 계정 연결\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBJsiJS_Uzjd",
        "outputId": "4384ec80-c01e-4e2f-ba1c-d061a76b38bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "gs://kfood150_all_images/kfood150.zip\n",
            "gs://kfood150_all_images/kfood150/\n",
            "gs://kfood150_all_images/model/\n"
          ]
        }
      ],
      "source": [
        "!gsutil ls gs://kfood150_all_images"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.getcwd()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "PYDzlGz9RBOS",
        "outputId": "f506aaaa-c3fc-4794-995e-1c5ce9f23ef3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDZUdhBYZam2",
        "outputId": "eaafb518-6314-4825-e948-182e630d7c28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Copying gs://kfood150_all_images/model/fingerprint.pb...\n",
            "/ [0 files][    0.0 B/   55.0 B]                                                \r/ [1 files][   55.0 B/   55.0 B]                                                \rCopying gs://kfood150_all_images/model/keras_metadata.pb...\n",
            "Copying gs://kfood150_all_images/model/saved_model.pb...\n",
            "Copying gs://kfood150_all_images/model/variables/variables.data-00000-of-00001...\n",
            "- [4 files][ 13.8 MiB/ 13.8 MiB]                                                \n",
            "==> NOTE: You are performing a sequence of gsutil operations that may\n",
            "run significantly faster if you instead use gsutil -m cp ... Please\n",
            "see the -m section under \"gsutil help options\" for further information\n",
            "about when gsutil -m can be advantageous.\n",
            "\n",
            "Copying gs://kfood150_all_images/model/variables/variables.index...\n",
            "- [5 files][ 13.9 MiB/ 13.9 MiB]                                                \n",
            "Operation completed over 5 objects/13.9 MiB.                                     \n"
          ]
        }
      ],
      "source": [
        "# 저장된 모델 로컬에 다운로드\n",
        "!gsutil cp -r gs://kfood150_all_images/model/ ."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction에 쓸 샘플 이미지 로컬에 다운로드\n",
        "!gsutil cp -r gs://kfood150_all_images/kfood150/ ."
      ],
      "metadata": {
        "id": "fzyhImCgSwRn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_nFzoT-0byl"
      },
      "source": [
        "## 3. Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Our model\n"
      ],
      "metadata": {
        "id": "lPs_xqMI5xRS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "j55ZzwWhtj-5",
        "outputId": "bfe6d938-d9e0-4a6c-cd8f-4e2c97035088"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " Conv1 (Conv2D)                 (None, 112, 112, 32  864         ['input_1[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " bn_Conv1 (BatchNormalization)  (None, 112, 112, 32  128         ['Conv1[0][0]']                  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " Conv1_relu (ReLU)              (None, 112, 112, 32  0           ['bn_Conv1[0][0]']               \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise (Depth  (None, 112, 112, 32  288        ['Conv1_relu[0][0]']             \n",
            " wiseConv2D)                    )                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise_BN (Ba  (None, 112, 112, 32  128        ['expanded_conv_depthwise[0][0]']\n",
            " tchNormalization)              )                                                                 \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise_relu (  (None, 112, 112, 32  0          ['expanded_conv_depthwise_BN[0][0\n",
            " ReLU)                          )                                ]']                              \n",
            "                                                                                                  \n",
            " expanded_conv_project (Conv2D)  (None, 112, 112, 16  512        ['expanded_conv_depthwise_relu[0]\n",
            "                                )                                [0]']                            \n",
            "                                                                                                  \n",
            " expanded_conv_project_BN (Batc  (None, 112, 112, 16  64         ['expanded_conv_project[0][0]']  \n",
            " hNormalization)                )                                                                 \n",
            "                                                                                                  \n",
            " block_1_expand (Conv2D)        (None, 112, 112, 96  1536        ['expanded_conv_project_BN[0][0]'\n",
            "                                )                                ]                                \n",
            "                                                                                                  \n",
            " block_1_expand_BN (BatchNormal  (None, 112, 112, 96  384        ['block_1_expand[0][0]']         \n",
            " ization)                       )                                                                 \n",
            "                                                                                                  \n",
            " block_1_expand_relu (ReLU)     (None, 112, 112, 96  0           ['block_1_expand_BN[0][0]']      \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block_1_pad (ZeroPadding2D)    (None, 113, 113, 96  0           ['block_1_expand_relu[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " block_1_depthwise (DepthwiseCo  (None, 56, 56, 96)  864         ['block_1_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_1_depthwise_BN (BatchNor  (None, 56, 56, 96)  384         ['block_1_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_1_depthwise_relu (ReLU)  (None, 56, 56, 96)   0           ['block_1_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_1_project (Conv2D)       (None, 56, 56, 24)   2304        ['block_1_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_1_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_1_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_2_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_1_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_2_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_2_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_2_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_2_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_2_depthwise (DepthwiseCo  (None, 56, 56, 144)  1296       ['block_2_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_2_depthwise_BN (BatchNor  (None, 56, 56, 144)  576        ['block_2_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_2_depthwise_relu (ReLU)  (None, 56, 56, 144)  0           ['block_2_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_2_project (Conv2D)       (None, 56, 56, 24)   3456        ['block_2_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_2_project_BN (BatchNorma  (None, 56, 56, 24)  96          ['block_2_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_2_add (Add)              (None, 56, 56, 24)   0           ['block_1_project_BN[0][0]',     \n",
            "                                                                  'block_2_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_3_expand (Conv2D)        (None, 56, 56, 144)  3456        ['block_2_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_3_expand_BN (BatchNormal  (None, 56, 56, 144)  576        ['block_3_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_3_expand_relu (ReLU)     (None, 56, 56, 144)  0           ['block_3_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_3_pad (ZeroPadding2D)    (None, 57, 57, 144)  0           ['block_3_expand_relu[0][0]']    \n",
            "                                                                                                  \n",
            " block_3_depthwise (DepthwiseCo  (None, 28, 28, 144)  1296       ['block_3_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_3_depthwise_BN (BatchNor  (None, 28, 28, 144)  576        ['block_3_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_3_depthwise_relu (ReLU)  (None, 28, 28, 144)  0           ['block_3_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_3_project (Conv2D)       (None, 28, 28, 32)   4608        ['block_3_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_3_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_3_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_4_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_3_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_4_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_4_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_4_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_4_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_4_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_4_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_4_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_4_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_4_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_4_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_4_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_4_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_4_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_4_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_4_add (Add)              (None, 28, 28, 32)   0           ['block_3_project_BN[0][0]',     \n",
            "                                                                  'block_4_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_5_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_4_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_5_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_5_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_5_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_5_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_5_depthwise (DepthwiseCo  (None, 28, 28, 192)  1728       ['block_5_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_5_depthwise_BN (BatchNor  (None, 28, 28, 192)  768        ['block_5_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_5_depthwise_relu (ReLU)  (None, 28, 28, 192)  0           ['block_5_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_5_project (Conv2D)       (None, 28, 28, 32)   6144        ['block_5_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_5_project_BN (BatchNorma  (None, 28, 28, 32)  128         ['block_5_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_5_add (Add)              (None, 28, 28, 32)   0           ['block_4_add[0][0]',            \n",
            "                                                                  'block_5_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_6_expand (Conv2D)        (None, 28, 28, 192)  6144        ['block_5_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_6_expand_BN (BatchNormal  (None, 28, 28, 192)  768        ['block_6_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_6_expand_relu (ReLU)     (None, 28, 28, 192)  0           ['block_6_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_6_pad (ZeroPadding2D)    (None, 29, 29, 192)  0           ['block_6_expand_relu[0][0]']    \n",
            "                                                                                                  \n",
            " block_6_depthwise (DepthwiseCo  (None, 14, 14, 192)  1728       ['block_6_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_6_depthwise_BN (BatchNor  (None, 14, 14, 192)  768        ['block_6_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_6_depthwise_relu (ReLU)  (None, 14, 14, 192)  0           ['block_6_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_6_project (Conv2D)       (None, 14, 14, 64)   12288       ['block_6_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_6_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_6_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_7_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_6_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_7_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_7_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_7_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_7_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_7_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_7_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_7_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_7_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_7_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_7_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_7_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_7_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_7_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_7_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_7_add (Add)              (None, 14, 14, 64)   0           ['block_6_project_BN[0][0]',     \n",
            "                                                                  'block_7_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_8_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_7_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_8_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_8_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_8_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_8_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_8_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_8_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_8_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_8_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_8_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_8_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_8_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_8_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_8_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_8_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_8_add (Add)              (None, 14, 14, 64)   0           ['block_7_add[0][0]',            \n",
            "                                                                  'block_8_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_9_expand (Conv2D)        (None, 14, 14, 384)  24576       ['block_8_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_9_expand_BN (BatchNormal  (None, 14, 14, 384)  1536       ['block_9_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_9_expand_relu (ReLU)     (None, 14, 14, 384)  0           ['block_9_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_9_depthwise (DepthwiseCo  (None, 14, 14, 384)  3456       ['block_9_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_9_depthwise_BN (BatchNor  (None, 14, 14, 384)  1536       ['block_9_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_9_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           ['block_9_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_9_project (Conv2D)       (None, 14, 14, 64)   24576       ['block_9_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_9_project_BN (BatchNorma  (None, 14, 14, 64)  256         ['block_9_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_9_add (Add)              (None, 14, 14, 64)   0           ['block_8_add[0][0]',            \n",
            "                                                                  'block_9_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_10_expand (Conv2D)       (None, 14, 14, 384)  24576       ['block_9_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_10_expand_BN (BatchNorma  (None, 14, 14, 384)  1536       ['block_10_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_10_expand_relu (ReLU)    (None, 14, 14, 384)  0           ['block_10_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_10_depthwise (DepthwiseC  (None, 14, 14, 384)  3456       ['block_10_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_10_depthwise_BN (BatchNo  (None, 14, 14, 384)  1536       ['block_10_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0          ['block_10_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_10_project (Conv2D)      (None, 14, 14, 96)   36864       ['block_10_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_10_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_10_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_11_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_10_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_11_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_11_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_11_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_11_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_11_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_11_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_11_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_11_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_11_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_11_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_11_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_11_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_11_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_11_add (Add)             (None, 14, 14, 96)   0           ['block_10_project_BN[0][0]',    \n",
            "                                                                  'block_11_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_12_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_11_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_12_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_12_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_12_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_12_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_12_depthwise (DepthwiseC  (None, 14, 14, 576)  5184       ['block_12_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_12_depthwise_BN (BatchNo  (None, 14, 14, 576)  2304       ['block_12_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0          ['block_12_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_12_project (Conv2D)      (None, 14, 14, 96)   55296       ['block_12_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_12_project_BN (BatchNorm  (None, 14, 14, 96)  384         ['block_12_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_12_add (Add)             (None, 14, 14, 96)   0           ['block_11_add[0][0]',           \n",
            "                                                                  'block_12_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_13_expand (Conv2D)       (None, 14, 14, 576)  55296       ['block_12_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_13_expand_BN (BatchNorma  (None, 14, 14, 576)  2304       ['block_13_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_13_expand_relu (ReLU)    (None, 14, 14, 576)  0           ['block_13_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_13_pad (ZeroPadding2D)   (None, 15, 15, 576)  0           ['block_13_expand_relu[0][0]']   \n",
            "                                                                                                  \n",
            " block_13_depthwise (DepthwiseC  (None, 7, 7, 576)   5184        ['block_13_pad[0][0]']           \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_13_depthwise_BN (BatchNo  (None, 7, 7, 576)   2304        ['block_13_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)   0           ['block_13_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_13_project (Conv2D)      (None, 7, 7, 160)    92160       ['block_13_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_13_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_13_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_14_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_13_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_14_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_14_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_14_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_14_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_14_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_14_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_14_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_14_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_14_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_14_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_14_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_14_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_14_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_14_add (Add)             (None, 7, 7, 160)    0           ['block_13_project_BN[0][0]',    \n",
            "                                                                  'block_14_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_15_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_14_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_15_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_15_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_15_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_15_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_15_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_15_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_15_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_15_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_15_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_15_project (Conv2D)      (None, 7, 7, 160)    153600      ['block_15_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_15_project_BN (BatchNorm  (None, 7, 7, 160)   640         ['block_15_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_15_add (Add)             (None, 7, 7, 160)    0           ['block_14_add[0][0]',           \n",
            "                                                                  'block_15_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_16_expand (Conv2D)       (None, 7, 7, 960)    153600      ['block_15_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_16_expand_BN (BatchNorma  (None, 7, 7, 960)   3840        ['block_16_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_16_expand_relu (ReLU)    (None, 7, 7, 960)    0           ['block_16_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_16_depthwise (DepthwiseC  (None, 7, 7, 960)   8640        ['block_16_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_16_depthwise_BN (BatchNo  (None, 7, 7, 960)   3840        ['block_16_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)   0           ['block_16_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_16_project (Conv2D)      (None, 7, 7, 320)    307200      ['block_16_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_16_project_BN (BatchNorm  (None, 7, 7, 320)   1280        ['block_16_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " Conv_1 (Conv2D)                (None, 7, 7, 1280)   409600      ['block_16_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)  5120        ['Conv_1[0][0]']                 \n",
            "                                                                                                  \n",
            " out_relu (ReLU)                (None, 7, 7, 1280)   0           ['Conv_1_bn[0][0]']              \n",
            "                                                                                                  \n",
            " global_average_pooling2d_1 (Gl  (None, 1280)        0           ['out_relu[0][0]']               \n",
            " obalAveragePooling2D)                                                                            \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 150)          192150      ['global_average_pooling2d_1[0][0\n",
            "                                                                 ]']                              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,450,134\n",
            "Trainable params: 192,150\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n input: (batch_size, 224, 224, 3)\\n output: (batch_size, 150)\\n num_classes: 150개\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "new_model = tf.keras.models.load_model('/content/model')  # 예측 모델 저장\n",
        "\n",
        "# Check its architecture 모델 구조 확인\n",
        "new_model.summary()\n",
        "'''\n",
        " input: (batch_size, 224, 224, 3)\n",
        " output: (batch_size, 150)\n",
        " num_classes: 150개\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "3xB-hybdvIP5"
      },
      "outputs": [],
      "source": [
        "# 전처리 함수\n",
        "preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input\n",
        "\n",
        "def proprecessing(test_image_path, IMAGE_SIZE):\n",
        "  test_image = cv2.imread(test_image_path)\n",
        "  test_image = cv2.resize(test_image, (IMAGE_SIZE, IMAGE_SIZE))\n",
        "  test_image = cv2.cvtColor(test_image, cv2.COLOR_BGR2RGB)\n",
        "  test_image = preprocess_input(test_image)\n",
        "  test_image = tf.expand_dims(test_image, 0)\n",
        "\n",
        "  return test_image"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Image for prediction"
      ],
      "metadata": {
        "id": "2cq2D_K65-II"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 예측할 이미지 파일 경로; jpg, JPG 파일 권장\n",
        "test_image_path = \"/content/kfood150/Img_001_0020.jpg\"   "
      ],
      "metadata": {
        "id": "XEW3Y277Wjp2"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "6BZjPxjjwIwz",
        "outputId": "57003d15-3747-4071-df17-5084e0b78e6b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# test 이미지 원본 보기\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as img\n",
        "\n",
        "fig = plt.figure(figsize=(5,5))\n",
        "\n",
        "image = img.imread(test_image_path)\n",
        "plt.imshow(image)\n",
        "plt.title(test_image_path.split('/')[3])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# image preprocessing\n",
        "IMAGE_SIZE = 224\n",
        "test_image = proprecessing(test_image_path,IMAGE_SIZE)\n",
        "\n",
        "print('test image shape:',test_image.shape)   # (1,224,224,3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MVsM743-XULa",
        "outputId": "f65def71-03d9-42f7-d2d5-9df3e14b73b6"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test image shape: (1, 224, 224, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AqkPt04Yugdf",
        "outputId": "9ac3fcb7-0244-423f-cd62-1c23c3656fdc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 97ms/step\n",
            "prediction: 1\n"
          ]
        }
      ],
      "source": [
        "# predict label\n",
        "pred = np.argmax(new_model.predict(test_image))\n",
        "print('prediction:',pred)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "tFs8QNDz6LjR"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels = pd.read_csv('/content/Food150 Korean Classes.CSV', encoding='cp949')"
      ],
      "metadata": {
        "id": "UCfIlzTB7Hu2"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels.drop(columns=labels.columns[0],inplace=True)\n",
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Csa6awA-7TDL",
        "outputId": "7f42e611-3025-44ad-f2a3-df355d19187d"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    대분류    소분류\n",
              "0    구이   갈비구이\n",
              "1    구이   갈치구이\n",
              "2    구이  고등어구이\n",
              "3    구이   곱창구이\n",
              "4    구이    닭갈비\n",
              "..   ..    ...\n",
              "145  한과     한과\n",
              "146  해물     멍게\n",
              "147  해물    산낙지\n",
              "148   회     물회\n",
              "149   회     육회\n",
              "\n",
              "[150 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f6b30ca6-0b93-447a-8d88-4abb941a0b34\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>대분류</th>\n",
              "      <th>소분류</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>구이</td>\n",
              "      <td>갈비구이</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>구이</td>\n",
              "      <td>갈치구이</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>구이</td>\n",
              "      <td>고등어구이</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>구이</td>\n",
              "      <td>곱창구이</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>구이</td>\n",
              "      <td>닭갈비</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>한과</td>\n",
              "      <td>한과</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>해물</td>\n",
              "      <td>멍게</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>해물</td>\n",
              "      <td>산낙지</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>회</td>\n",
              "      <td>물회</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>회</td>\n",
              "      <td>육회</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f6b30ca6-0b93-447a-8d88-4abb941a0b34')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f6b30ca6-0b93-447a-8d88-4abb941a0b34 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f6b30ca6-0b93-447a-8d88-4abb941a0b34');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_label = labels['소분류'][pred]\n",
        "print(pred_label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0UCNDudG9Mx1",
        "outputId": "24bfbf92-43c5-447b-b0f8-2298872533df"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "갈치구이\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}