skindata_efficientNet.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"AUCrD1mP_xJg"},"source":["# 초기설정"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":31644,"status":"ok","timestamp":1700637654978,"user":{"displayName":"권재성","userId":"17009958908754473775"},"user_tz":-540},"id":"iwe4tekCyPMY","outputId":"49ff8253-25df-402e-a317-5abace8074c8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["import sys\n","from google.colab import drive\n","drive.mount('/content/drive')\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":40290,"status":"ok","timestamp":1700637695266,"user":{"displayName":"권재성","userId":"17009958908754473775"},"user_tz":-540},"id":"R3njEoNXExFp","outputId":"2c6e873a-c926-44f7-beb2-1d6ea2313921"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["        lesion_id      image_id     dx    dx_type   age     sex  \\\n","1713  HAM_0002342  ISIC_0032925    mel      histo  40.0    male   \n","9998  HAM_0004282  ISIC_0033358  akiec      histo  65.0  female   \n","6717  HAM_0000892  ISIC_0029845     nv      histo  40.0    male   \n","5173  HAM_0003492  ISIC_0029228     nv  follow_up  35.0    male   \n","2150  HAM_0004507  ISIC_0029958    mel      histo  65.0  female   \n","6146  HAM_0005934  ISIC_0031740     nv  follow_up  45.0    male   \n","4275  HAM_0002407  ISIC_0028731     nv  follow_up  75.0  female   \n","3711  HAM_0006659  ISIC_0028160     nv  follow_up  55.0    male   \n","3433  HAM_0005797  ISIC_0027220     nv  follow_up  60.0    male   \n","5772  HAM_0001124  ISIC_0026587     nv  follow_up  50.0  female   \n","9466  HAM_0004387  ISIC_0033821     nv  consensus  55.0  female   \n","3504  HAM_0001431  ISIC_0028142     nv  follow_up  35.0    male   \n","9965  HAM_0001302  ISIC_0032206  akiec      histo  50.0    male   \n","2037  HAM_0003608  ISIC_0028228    mel      histo  75.0  female   \n","4469  HAM_0004822  ISIC_0029788     nv  follow_up  45.0  female   \n","8543  HAM_0001154  ISIC_0025013     nv      histo  45.0    male   \n","5208  HAM_0006752  ISIC_0031842     nv  follow_up  45.0    male   \n","706   HAM_0004660  ISIC_0027504    bkl      histo  80.0    male   \n","7257  HAM_0000249  ISIC_0027847     nv      histo  65.0  female   \n","5214  HAM_0001351  ISIC_0031684     nv  follow_up  50.0  female   \n","8301  HAM_0001525  ISIC_0024355     nv      histo  35.0    male   \n","8567  HAM_0001941  ISIC_0024790     nv      histo  75.0    male   \n","4178  HAM_0005493  ISIC_0028535     nv  follow_up  40.0  female   \n","5476  HAM_0007613  ISIC_0024912     nv  follow_up  45.0    male   \n","2402  HAM_0001249  ISIC_0033817   vasc  consensus  85.0    male   \n","9651  HAM_0002566  ISIC_0024437     nv  consensus  55.0  female   \n","3075  HAM_0000576  ISIC_0025585     nv  follow_up  55.0    male   \n","3367  HAM_0000713  ISIC_0031174     nv  follow_up  55.0    male   \n","921   HAM_0000208  ISIC_0032694    bkl  consensus  70.0    male   \n","4689  HAM_0004969  ISIC_0025521     nv  follow_up  55.0  female   \n","\n","         localization                                               path  \\\n","1713             back  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","9998             face  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","6717            trunk  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","5173  lower extremity  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","2150             face  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","6146             back  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","4275  lower extremity  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","3711            trunk  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","3433          abdomen  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","5772          abdomen  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","9466            trunk  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","3504  lower extremity  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","9965             neck  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","2037             back  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","4469  lower extremity  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","8543             back  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","5208             back  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","706             scalp  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","7257             back  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","5214             back  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","8301  upper extremity  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","8567             back  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","4178             back  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","5476            trunk  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","2402             back  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","9651            chest  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","3075             foot  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","3367          abdomen  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","921             scalp  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","4689  lower extremity  /content/drive/MyDrive/skin_data/HAM10000_imag...   \n","\n","                          cell_type  cell_type_idx  \n","1713                       Melanoma              5  \n","9998              Actinic keratoses              0  \n","6717               Melanocytic nevi              4  \n","5173               Melanocytic nevi              4  \n","2150                       Melanoma              5  \n","6146               Melanocytic nevi              4  \n","4275               Melanocytic nevi              4  \n","3711               Melanocytic nevi              4  \n","3433               Melanocytic nevi              4  \n","5772               Melanocytic nevi              4  \n","9466               Melanocytic nevi              4  \n","3504               Melanocytic nevi              4  \n","9965              Actinic keratoses              0  \n","2037                       Melanoma              5  \n","4469               Melanocytic nevi              4  \n","8543               Melanocytic nevi              4  \n","5208               Melanocytic nevi              4  \n","706   Benign keratosis like lesions              2  \n","7257               Melanocytic nevi              4  \n","5214               Melanocytic nevi              4  \n","8301               Melanocytic nevi              4  \n","8567               Melanocytic nevi              4  \n","4178               Melanocytic nevi              4  \n","5476               Melanocytic nevi              4  \n","2402               Vascular lesions              6  \n","9651               Melanocytic nevi              4  \n","3075               Melanocytic nevi              4  \n","3367               Melanocytic nevi              4  \n","921   Benign keratosis like lesions              2  \n","4689               Melanocytic nevi              4  "],"text/html":["\n","  <div id=\"df-70a973de-b359-4d08-8ac3-5d2e7eae1052\" 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>lesion_id</th>\n","      <th>image_id</th>\n","      <th>dx</th>\n","      <th>dx_type</th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>localization</th>\n","      <th>path</th>\n","      <th>cell_type</th>\n","      <th>cell_type_idx</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1713</th>\n","      <td>HAM_0002342</td>\n","      <td>ISIC_0032925</td>\n","      <td>mel</td>\n","      <td>histo</td>\n","      <td>40.0</td>\n","      <td>male</td>\n","      <td>back</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanoma</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>9998</th>\n","      <td>HAM_0004282</td>\n","      <td>ISIC_0033358</td>\n","      <td>akiec</td>\n","      <td>histo</td>\n","      <td>65.0</td>\n","      <td>female</td>\n","      <td>face</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Actinic keratoses</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6717</th>\n","      <td>HAM_0000892</td>\n","      <td>ISIC_0029845</td>\n","      <td>nv</td>\n","      <td>histo</td>\n","      <td>40.0</td>\n","      <td>male</td>\n","      <td>trunk</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>5173</th>\n","      <td>HAM_0003492</td>\n","      <td>ISIC_0029228</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>35.0</td>\n","      <td>male</td>\n","      <td>lower extremity</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2150</th>\n","      <td>HAM_0004507</td>\n","      <td>ISIC_0029958</td>\n","      <td>mel</td>\n","      <td>histo</td>\n","      <td>65.0</td>\n","      <td>female</td>\n","      <td>face</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanoma</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>6146</th>\n","      <td>HAM_0005934</td>\n","      <td>ISIC_0031740</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>45.0</td>\n","      <td>male</td>\n","      <td>back</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4275</th>\n","      <td>HAM_0002407</td>\n","      <td>ISIC_0028731</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>75.0</td>\n","      <td>female</td>\n","      <td>lower extremity</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3711</th>\n","      <td>HAM_0006659</td>\n","      <td>ISIC_0028160</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>55.0</td>\n","      <td>male</td>\n","      <td>trunk</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3433</th>\n","      <td>HAM_0005797</td>\n","      <td>ISIC_0027220</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>60.0</td>\n","      <td>male</td>\n","      <td>abdomen</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>5772</th>\n","      <td>HAM_0001124</td>\n","      <td>ISIC_0026587</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>50.0</td>\n","      <td>female</td>\n","      <td>abdomen</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>9466</th>\n","      <td>HAM_0004387</td>\n","      <td>ISIC_0033821</td>\n","      <td>nv</td>\n","      <td>consensus</td>\n","      <td>55.0</td>\n","      <td>female</td>\n","      <td>trunk</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3504</th>\n","      <td>HAM_0001431</td>\n","      <td>ISIC_0028142</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>35.0</td>\n","      <td>male</td>\n","      <td>lower extremity</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>9965</th>\n","      <td>HAM_0001302</td>\n","      <td>ISIC_0032206</td>\n","      <td>akiec</td>\n","      <td>histo</td>\n","      <td>50.0</td>\n","      <td>male</td>\n","      <td>neck</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Actinic keratoses</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2037</th>\n","      <td>HAM_0003608</td>\n","      <td>ISIC_0028228</td>\n","      <td>mel</td>\n","      <td>histo</td>\n","      <td>75.0</td>\n","      <td>female</td>\n","      <td>back</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanoma</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>4469</th>\n","      <td>HAM_0004822</td>\n","      <td>ISIC_0029788</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>45.0</td>\n","      <td>female</td>\n","      <td>lower extremity</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>8543</th>\n","      <td>HAM_0001154</td>\n","      <td>ISIC_0025013</td>\n","      <td>nv</td>\n","      <td>histo</td>\n","      <td>45.0</td>\n","      <td>male</td>\n","      <td>back</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>5208</th>\n","      <td>HAM_0006752</td>\n","      <td>ISIC_0031842</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>45.0</td>\n","      <td>male</td>\n","      <td>back</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>706</th>\n","      <td>HAM_0004660</td>\n","      <td>ISIC_0027504</td>\n","      <td>bkl</td>\n","      <td>histo</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","      <td>scalp</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Benign keratosis like lesions</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>7257</th>\n","      <td>HAM_0000249</td>\n","      <td>ISIC_0027847</td>\n","      <td>nv</td>\n","      <td>histo</td>\n","      <td>65.0</td>\n","      <td>female</td>\n","      <td>back</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>5214</th>\n","      <td>HAM_0001351</td>\n","      <td>ISIC_0031684</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>50.0</td>\n","      <td>female</td>\n","      <td>back</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>8301</th>\n","      <td>HAM_0001525</td>\n","      <td>ISIC_0024355</td>\n","      <td>nv</td>\n","      <td>histo</td>\n","      <td>35.0</td>\n","      <td>male</td>\n","      <td>upper extremity</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>8567</th>\n","      <td>HAM_0001941</td>\n","      <td>ISIC_0024790</td>\n","      <td>nv</td>\n","      <td>histo</td>\n","      <td>75.0</td>\n","      <td>male</td>\n","      <td>back</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4178</th>\n","      <td>HAM_0005493</td>\n","      <td>ISIC_0028535</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>40.0</td>\n","      <td>female</td>\n","      <td>back</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>5476</th>\n","      <td>HAM_0007613</td>\n","      <td>ISIC_0024912</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>45.0</td>\n","      <td>male</td>\n","      <td>trunk</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2402</th>\n","      <td>HAM_0001249</td>\n","      <td>ISIC_0033817</td>\n","      <td>vasc</td>\n","      <td>consensus</td>\n","      <td>85.0</td>\n","      <td>male</td>\n","      <td>back</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Vascular lesions</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>9651</th>\n","      <td>HAM_0002566</td>\n","      <td>ISIC_0024437</td>\n","      <td>nv</td>\n","      <td>consensus</td>\n","      <td>55.0</td>\n","      <td>female</td>\n","      <td>chest</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3075</th>\n","      <td>HAM_0000576</td>\n","      <td>ISIC_0025585</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>55.0</td>\n","      <td>male</td>\n","      <td>foot</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3367</th>\n","      <td>HAM_0000713</td>\n","      <td>ISIC_0031174</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>55.0</td>\n","      <td>male</td>\n","      <td>abdomen</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>921</th>\n","      <td>HAM_0000208</td>\n","      <td>ISIC_0032694</td>\n","      <td>bkl</td>\n","      <td>consensus</td>\n","      <td>70.0</td>\n","      <td>male</td>\n","      <td>scalp</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Benign keratosis like lesions</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4689</th>\n","      <td>HAM_0004969</td>\n","      <td>ISIC_0025521</td>\n","      <td>nv</td>\n","      <td>follow_up</td>\n","      <td>55.0</td>\n","      <td>female</td>\n","      <td>lower extremity</td>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>Melanocytic nevi</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div 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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","\n","\n","<div id=\"df-f3e1bba2-7a72-441b-9ac2-63eb3f2a5207\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3e1bba2-7a72-441b-9ac2-63eb3f2a5207')\"\n","            title=\"Suggest charts\"\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","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-f3e1bba2-7a72-441b-9ac2-63eb3f2a5207 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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tensorflow.keras import backend as K\n","from sklearn.metrics import confusion_matrix\n","\n","base_dir = '/content/drive/MyDrive/skin_data/'\n","\n","# ex) {'ISIC_0032711': '/content/drive/MyDrive/skin_data/HAM10000_images_part_2/ISIC_0032711.jpg'}\n","imageid_path_dict = {os.path.splitext(os.path.basename(x))[0]: x for x in glob(os.path.join(base_dir, '*', '*.jpg'))}\n","\n","\n","lesion_type_dict = {\n","    'nv': 'Melanocytic nevi',\n","    'mel': 'Melanoma',\n","    'bkl': 'Benign keratosis like lesions',\n","    'bcc': 'Basal cell carcinoma',\n","    'akiec': 'Actinic keratoses',\n","    'vasc': 'Vascular lesions',\n","    'df': 'Dermatofibroma'\n","}\n","\n","\n","# 이미지 파일(.jpg) : 600x450\n","# path : 이미지 파일 경로, cell_type : 병명, cell_type_idx\n","tile_df = pd.read_csv(os.path.join(base_dir, 'HAM10000_metadata.csv'))\n","tile_df['path'] = tile_df['image_id'].map(imageid_path_dict.get)\n","tile_df['cell_type'] = tile_df['dx'].map(lesion_type_dict.get)\n","tile_df['cell_type_idx'] = pd.Categorical(tile_df['cell_type']).codes\n","\n","tile_df.sample(30)\n","#tile_df[tile_df['path'].isna()]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":224},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1700637695266,"user":{"displayName":"권재성","userId":"17009958908754473775"},"user_tz":-540},"id":"wlGLIN5KUgX1","outputId":"59bcc8e1-0313-44bb-82d8-d2730ec1e628"},"outputs":[{"output_type":"stream","name":"stdout","text":["10015\n"]},{"output_type":"execute_result","data":{"text/plain":["                                           filepaths labels\n","0  /content/drive/MyDrive/skin_data/HAM10000_imag...      2\n","1  /content/drive/MyDrive/skin_data/HAM10000_imag...      2\n","2  /content/drive/MyDrive/skin_data/HAM10000_imag...      2\n","3  /content/drive/MyDrive/skin_data/HAM10000_imag...      2\n","4  /content/drive/MyDrive/skin_data/HAM10000_imag...      2"],"text/html":["\n","  <div id=\"df-1b2b4fdd-abbf-431a-b593-64653606ca85\" 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>filepaths</th>\n","      <th>labels</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>2</td>\n","    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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":3}],"source":["df = pd.DataFrame()\n","\n","df['filepaths'] = tile_df['path']\n","df['labels'] = tile_df['cell_type_idx']\n","df['labels'] = df['labels'].apply(lambda x: str(x))\n","print(len(df))\n","df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1700637695266,"user":{"displayName":"권재성","userId":"17009958908754473775"},"user_tz":-540},"id":"U0MTRz-yXqMi","outputId":"99a2329b-8c21-4e16-a777-693295c4c5de"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                            filepath labels   age   sex\n","0  /content/drive/MyDrive/skin_data/HAM10000_imag...      2  80.0  male\n","1  /content/drive/MyDrive/skin_data/HAM10000_imag...      2  80.0  male\n","2  /content/drive/MyDrive/skin_data/HAM10000_imag...      2  80.0  male\n","3  /content/drive/MyDrive/skin_data/HAM10000_imag...      2  80.0  male\n","4  /content/drive/MyDrive/skin_data/HAM10000_imag...      2  75.0  male"],"text/html":["\n","  <div id=\"df-e54ebe33-76e7-432c-9ba5-f2ebace4d8ee\" 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>filepath</th>\n","      <th>labels</th>\n","      <th>age</th>\n","      <th>sex</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>2</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>2</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>2</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>2</td>\n","      <td>80.0</td>\n","      <td>male</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>2</td>\n","      <td>75.0</td>\n","      <td>male</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e54ebe33-76e7-432c-9ba5-f2ebace4d8ee')\"\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 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\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","    .colab-df-buttons div {\n","      margin-bottom: 4px;\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-e54ebe33-76e7-432c-9ba5-f2ebace4d8ee 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-e54ebe33-76e7-432c-9ba5-f2ebace4d8ee');\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","\n","\n","<div id=\"df-b8374898-77e4-4498-b4ed-7bc72afa2062\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b8374898-77e4-4498-b4ed-7bc72afa2062')\"\n","            title=\"Suggest charts\"\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","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </g>\n","</svg>\n","  </button>\n","\n","<style>\n","  .colab-df-quickchart {\n","      --bg-color: #E8F0FE;\n","      --fill-color: #1967D2;\n","      --hover-bg-color: #E2EBFA;\n","      --hover-fill-color: #174EA6;\n","      --disabled-fill-color: #AAA;\n","      --disabled-bg-color: #DDD;\n","  }\n","\n","  [theme=dark] .colab-df-quickchart {\n","      --bg-color: #3B4455;\n","      --fill-color: #D2E3FC;\n","      --hover-bg-color: #434B5C;\n","      --hover-fill-color: #FFFFFF;\n","      --disabled-bg-color: #3B4455;\n","      --disabled-fill-color: #666;\n","  }\n","\n","  .colab-df-quickchart {\n","    background-color: var(--bg-color);\n","    border: none;\n","    border-radius: 50%;\n","    cursor: pointer;\n","    display: none;\n","    fill: var(--fill-color);\n","    height: 32px;\n","    padding: 0;\n","    width: 32px;\n","  }\n","\n","  .colab-df-quickchart:hover {\n","    background-color: var(--hover-bg-color);\n","    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n","    fill: var(--button-hover-fill-color);\n","  }\n","\n","  .colab-df-quickchart-complete:disabled,\n","  .colab-df-quickchart-complete:disabled:hover {\n","    background-color: var(--disabled-bg-color);\n","    fill: var(--disabled-fill-color);\n","    box-shadow: none;\n","  }\n","\n","  .colab-df-spinner {\n","    border: 2px solid var(--fill-color);\n","    border-color: transparent;\n","    border-bottom-color: var(--fill-color);\n","    animation:\n","      spin 1s steps(1) infinite;\n","  }\n","\n","  @keyframes spin {\n","    0% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","      border-left-color: var(--fill-color);\n","    }\n","    20% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    30% {\n","      border-color: transparent;\n","      border-left-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","      border-right-color: var(--fill-color);\n","    }\n","    40% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-top-color: var(--fill-color);\n","    }\n","    60% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","    }\n","    80% {\n","      border-color: transparent;\n","      border-right-color: var(--fill-color);\n","      border-bottom-color: var(--fill-color);\n","    }\n","    90% {\n","      border-color: transparent;\n","      border-bottom-color: var(--fill-color);\n","    }\n","  }\n","</style>\n","\n","  <script>\n","    async function quickchart(key) {\n","      const quickchartButtonEl =\n","        document.querySelector('#' + key + ' button');\n","      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n","      quickchartButtonEl.classList.add('colab-df-spinner');\n","      try {\n","        const charts = await google.colab.kernel.invokeFunction(\n","            'suggestCharts', [key], {});\n","      } catch (error) {\n","        console.error('Error during call to suggestCharts:', error);\n","      }\n","      quickchartButtonEl.classList.remove('colab-df-spinner');\n","      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n","    }\n","    (() => {\n","      let quickchartButtonEl =\n","        document.querySelector('#df-b8374898-77e4-4498-b4ed-7bc72afa2062 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":4}],"source":["# for Fairness Evaluation\n","df2 = pd.DataFrame()\n","\n","df2['filepath'] = tile_df['path']\n","df2['labels'] = tile_df['cell_type_idx']\n","df2['labels'] = df['labels'].apply(lambda x: str(x))\n","df2['age'] = tile_df['age']\n","df2['sex'] = tile_df['sex']\n","df2.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1700637695266,"user":{"displayName":"권재성","userId":"17009958908754473775"},"user_tz":-540},"id":"Qa6cVIiHYItT","outputId":"e9139f67-e57b-45d2-d583-71ba67595de8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['2', '4', '3', '5', '6', '1', '0'], dtype=object)"]},"metadata":{},"execution_count":5}],"source":["df.labels.unique()"]},{"cell_type":"markdown","metadata":{"id":"LjfIJXlIFO5p"},"source":["# Split(df1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1700637695266,"user":{"displayName":"권재성","userId":"17009958908754473775"},"user_tz":-540},"id":"9KndZ5mGZVr0","outputId":"39854722-a574-43e8-84ec-1e190855aadc"},"outputs":[{"output_type":"stream","name":"stdout","text":["split 이후 train data --> length : 8012, class_count : 7\n","split 이후 train data 의 class 별 data 개수 --> \n"," 4    5364\n","5     890\n","2     879\n","1     411\n","0     262\n","6     114\n","3      92\n","Name: labels, dtype: int64\n"]}],"source":["def split_df(df, trsize, column):\n","  train_df, dummy_df = train_test_split(df, train_size=trsize, random_state=42, stratify=df[column])\n","  valid_df, test_df = train_test_split(dummy_df, train_size=trsize, random_state=42, stratify=dummy_df[column])\n","  return train_df, valid_df, test_df\n","\n","train_df, valid_df, test_df = split_df(df, 0.8, 'labels')\n","\n","\n","print(f\"split 이후 train data --> length : {len(train_df)}, class_count : {len(train_df['labels'].unique())}\")\n","print(f\"split 이후 train data 의 class 별 data 개수 --> \\n {train_df['labels'].value_counts()}\")"]},{"cell_type":"markdown","metadata":{"id":"5QKOAQuKFd8S"},"source":["# Split(df2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LVvM-Ul0PVz8"},"outputs":[],"source":["train_df2, valid_df2, test_df2 = split_df(df2, 0.8, 'labels')"]},{"cell_type":"markdown","metadata":{"id":"b6szEAABFUNh"},"source":["# Trim(데이터 균형)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1700637695267,"user":{"displayName":"권재성","userId":"17009958908754473775"},"user_tz":-540},"id":"8kuarFQpPgbn","outputId":"8c05636d-50be-4e64-8cb5-18161aa53ad4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Classes -> ['4' '2' '5' '3' '1' '0' '6']\n","Before trim -> length : 8012, class_count : 7\n","After trim -> sample max : 1000, sample min : 0\n","After trim -> length : 3648, class_count : 7\n","trim 이후 train data 길이 --> 3648\n","\n","trim 이후 train data 의 class 별 data 개수 --> \n"," 4    1000\n","5     890\n","2     879\n","1     411\n","0     262\n","6     114\n","3      92\n","Name: labels, dtype: int64\n"]}],"source":["def trim(df, max_samples, min_samples, column):\n","  df = df.copy()\n","  classes = df[column].unique()\n","  class_count = len(classes)\n","  length = len(df)\n","\n","  print(f'Classes -> {classes}')\n","  print(f'Before trim -> length : {length}, class_count : {class_count}')\n","\n","  groups = df.groupby(column)\n","  trimmed_df = pd.DataFrame(columns = df.columns)\n","  groups = df.groupby(column)\n","\n","  for label in df[column].unique():\n","    group = groups.get_group(label)\n","    count = len(group)\n","\n","    if count > max_samples:\n","      sampled_group = group.sample(n=max_samples, random_state=123, axis=0)\n","      trimmed_df = pd.concat([trimmed_df, sampled_group], axis=0)\n","\n","    else:\n","      if count >= min_samples:\n","        sampled_group = group\n","        trimmed_df = pd.concat([trimmed_df, sampled_group], axis=0)\n","\n","  print(f'After trim -> sample max : {max_samples}, sample min : {min_samples}')\n","\n","  classes = trimmed_df[column].unique()\n","  class_count = len(classes)\n","  length = len(trimmed_df)\n","\n","  print(f'After trim -> length : {length}, class_count : {class_count}')\n","\n","  return trimmed_df, classes, class_count\n","\n","max_samples, min_samples = 1000, 0\n","column = 'labels'\n","\n","train_df, classes, class_count = trim(train_df, max_samples, min_samples, column)\n","print(f'trim 이후 train data 길이 --> {len(train_df)}')\n","print(f\"\\ntrim 이후 train data 의 class 별 data 개수 --> \\n {train_df['labels'].value_counts()}\")"]},{"cell_type":"markdown","metadata":{"id":"TXKEGwmrFWqx"},"source":["# Balance(데이터 증강)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["e877efea9935459eb3591471496d074f","779a534be54644f2a57ab8301ecd16a5","db7ef870c56e492db912be271776d8ee","a9afb8433c2640859ee9edcd119fe978","f7aa1f3d7d1846c49827a770f78f5b44","0e3b082d277948bdb4e7fd8fe792824c","99ebb17d8dca4630922efbdb0e5a7db0","79fbac4efdd04196a3ebc568416b3449","fe968203cf7641448998a38663a8bd37","09e6cf3e3ece40a48a0328f409722fd8","506254eb62a646ee845c79ec50edd02b","c4c6c9dc624246edb23c41b033fee90d","bac886326c534b8d8c44209d111627fb","5a6666c4eaf746e0ab92a2d41b84d272","10916ada677c443dba040a0924ca48a5","00a83304227c4e339d0563b2fd195290","22f643c743ed404fbb83decfae8cbe43","681e22f7123b4304a51e3fd3369d9923","466d68282f10416e9f2bbd6d846318dd","0eaca7b499d84216893aa3162455a346","3b0ed5b2c2c44d3ba76ee13f9f1c466f","1f3dfea35d474c1aa1abad28748e8a74","33822d6dde2849a6a4a0bc59b406fb3c","ba3d390679184393906846f035019450","2cad81a793e94bbebc28810f5b0008fa","1b3dee2df44d4a97a36a236b35a8c4c4","c42feb1488fe4e3e8b3decca0efdc037","90c8e1a4479943ac8075d21914f444e2","a9a82eb4ed094f40a7abd3c9a99fc4b4","6439def8e07c41838575d6cf4245ad57","fffd486023164de9b8ae429933fb6859","6c5be9a8884c453ebeebf7a9869e1d18","cffbcf6b28d34683a3590c5e9105870a","b74e83b5e0e14fb4bee33ec49550b40e","d75f2f42ece64a04ae2bec28910dd672","f6b486825d3d4c6c825de4bdb1924d0f","5585b17266ba4c65b93fcc7f1484ab01","739c794dd69446748efc2e54637acb52","6fef7a23c0b1432c8e4ddc2fd33122b5","f1db019acaeb44db806c7c8180d5b600","e60169b5667649158f6d959ee7812ad6","b0717fcae5cf4663bfa9f50fbfae2b5c","2cdb2dc752a9486298b728d0d8f27f22","1e25f14927ce435fb6fd40420f539fcf","9fc62586b70540b59c740a793c8b5bc6","f5687cf08a314f5ba063139d8a41bd19","4b154f79b0d64716b5c3bad41753e657","37aa0650540e4b7cb37dc494599bf4cf","e46f17d96e4a485c8598be9e4381da03","e1edb066c2e34ceb98d1100ff5e3b353","3e2bdba8e2f04e1191056bcb152b2ace","21be7200dfb84c90b16f80381f36313b","269e9d555ca4477695aeadd495a5952e","b9218a0fff604bab959acfc86cc7c94b","93243a35bd484441a96c7501d5a321c9","745c56ada6dc49fba833a41fa569f320","7a0e694dfaea4d148fbec41962dd2e53","715dc21801ca4668bd2c27abb6d8c401","bf4d141301be466b9d8db8efd08f39a8","9bfaf18c288b4190a2a5ac913211df3f","3b363cec5282423fa0e827f62437d3b7","449eebdc6c94479ca174faf02e19eace","ef561c776a2e49bb8bdda950f6540295","c2ebebbcf2a84a83b75d78a3c7c80313","9b68506404a840a99a6068bb789871d1","e53d9533404946518b5488247a129bc0"]},"id":"XxE9UKehciBf","executionInfo":{"status":"ok","timestamp":1700638248775,"user_tz":-540,"elapsed":553516,"user":{"displayName":"권재성","userId":"17009958908754473775"}},"outputId":"a4f990cc-a02a-4f4d-a74d-8fb5690a0029"},"outputs":[{"output_type":"stream","name":"stdout","text":["처음 df 길이 --> 3648\n"]},{"output_type":"display_data","data":{"text/plain":["augmenting class 2:   0%|                                                                    | 0/121 [00:00<?,…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e877efea9935459eb3591471496d074f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["augmenting class 5:   0%|                                                                    | 0/110 [00:00<?,…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c4c6c9dc624246edb23c41b033fee90d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["augmenting class 3:   0%|                                                                    | 0/908 [00:00<?,…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"33822d6dde2849a6a4a0bc59b406fb3c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["augmenting class 1:   0%|                                                                    | 0/589 [00:00<?,…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b74e83b5e0e14fb4bee33ec49550b40e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["augmenting class 0:   0%|                                                                    | 0/738 [00:00<?,…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9fc62586b70540b59c740a793c8b5bc6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["augmenting class 6:   0%|                                                                    | 0/886 [00:00<?,…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"745c56ada6dc49fba833a41fa569f320"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["이미지 증강 수 --> 3352\n","증강된 df 길이 --> 7000\n","\n"," balance 이후 train data 의 class 별 data 개수 --> \n"," 4    1000\n","2    1000\n","5    1000\n","3    1000\n","1    1000\n","0    1000\n","6    1000\n","Name: labels, dtype: int64\n"]}],"source":["def balance(df, n,column, working_dir, img_size):\n","\n","    def get_augmented_image(image):\n","        width = int(image.shape[1] * 0.8)\n","        height = int(image.shape[0] * 0.8)\n","        transform = A.Compose([\n","            A.HorizontalFlip(p=0.5),\n","            A.Rotate(limit=30, p=0.25),\n","            A.RandomBrightnessContrast(p=0.5),\n","            A.RandomGamma(p=0.5),\n","            A.RandomCrop(width=width, height=height, p=0.25)\n","            ])\n","\n","        return transform(image=image)['image']\n","\n","    def dummy(image):\n","        return image\n","\n","\n","    df=df.copy()\n","    print(f'처음 df 길이 --> {len(df)}')\n","\n","    aug_dir = os.path.join(working_dir, 'aug') # 증강된 이미지가 저장될 주소\n","\n","    if os.path.isdir(aug_dir):\n","        shutil.rmtree(aug_dir)\n","\n","    os.mkdir(aug_dir)\n","\n","    for label in df[column].unique():\n","        dir_path = os.path.join(aug_dir, label) # label 별로 저장하기 위함\n","        os.mkdir(dir_path)\n","\n","    total = 0\n","    groups = df.groupby(column) # group by class\n","\n","    for label in df[column].unique(): # Class 별로 수행\n","        group = groups.get_group(label)  # a dataframe holding only rows with the specified label\n","        sample_count = len(group)   # determine how many samples there are in this class\n","\n","        if sample_count < n: # if the class has less than target number of images\n","            aug_img_count = 0\n","            delta = n - sample_count  # number of augmented images to create\n","            target_dir = os.path.join(aug_dir, label)  # define where to write the images\n","            desc = f'augmenting class {label}'\n","\n","            for i in tqdm(range(delta), ncols=120, unit='files', colour='blue',desc=desc):\n","                j = i % sample_count\n","                img_path = group['filepaths'].iloc[j]\n","                img = cv2.imread(img_path)\n","                img = get_augmented_image(img)\n","                fname = os.path.basename(img_path)\n","                fname = 'aug' + str(i) + '-' + fname\n","                dest_path = os.path.join(target_dir, fname)\n","                cv2.imwrite(dest_path, img) # 이미지 저장\n","                aug_img_count += 1\n","\n","            total += aug_img_count\n","\n","    print(f'이미지 증강 수 --> {total}')\n","\n","    # create aug_df and merge with train_df to create composite training set ndf\n","    aug_fpaths = []\n","    aug_labels = []\n","    classlist = sorted(os.listdir(aug_dir))\n","\n","    for klass in classlist:\n","        classpath = os.path.join(aug_dir, klass)\n","        flist = sorted(os.listdir(classpath))\n","\n","        for f in flist:\n","            fpath = os.path.join(classpath, f)\n","            aug_fpaths.append(fpath)\n","            aug_labels.append(klass)\n","\n","    Fseries = pd.Series(aug_fpaths, name='filepaths')\n","    Lseries = pd.Series(aug_labels, name='labels')\n","    aug_df = pd.concat([Fseries, Lseries], axis=1)\n","    df = pd.concat([df, aug_df], axis=0).reset_index(drop=True)\n","\n","    print(f'증강된 df 길이 --> {len(df)}')\n","    return df\n","\n","n = 1000\n","working_dir = r'/content/drive/MyDrive/'\n","img_size = (310, 640)\n","column = 'labels'\n","\n","train_df = balance(train_df, n, column, working_dir, img_size)\n","print(f\"\\n balance 이후 train data 의 class 별 data 개수 --> \\n {train_df['labels'].value_counts()}\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"jzdNoHqpNa4q","executionInfo":{"status":"ok","timestamp":1700638248776,"user_tz":-540,"elapsed":24,"user":{"displayName":"권재성","userId":"17009958908754473775"}},"outputId":"5f7f1379-0d57-4399-82ac-371e3cb0ea63"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                              filepaths labels\n","0     /content/drive/MyDrive/skin_data/HAM10000_imag...      4\n","1     /content/drive/MyDrive/skin_data/HAM10000_imag...      4\n","2     /content/drive/MyDrive/skin_data/HAM10000_imag...      4\n","3     /content/drive/MyDrive/skin_data/HAM10000_imag...      4\n","4     /content/drive/MyDrive/skin_data/HAM10000_imag...      4\n","...                                                 ...    ...\n","6995  /content/drive/MyDrive/aug/6/aug95-ISIC_003121...      6\n","6996  /content/drive/MyDrive/aug/6/aug96-ISIC_003399...      6\n","6997  /content/drive/MyDrive/aug/6/aug97-ISIC_002587...      6\n","6998  /content/drive/MyDrive/aug/6/aug98-ISIC_002871...      6\n","6999  /content/drive/MyDrive/aug/6/aug99-ISIC_002987...      6\n","\n","[7000 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-fed1d40f-f091-4582-b848-a5eb68894271\" 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>filepaths</th>\n","      <th>labels</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>/content/drive/MyDrive/skin_data/HAM10000_imag...</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>6995</th>\n","      <td>/content/drive/MyDrive/aug/6/aug95-ISIC_003121...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>6996</th>\n","      <td>/content/drive/MyDrive/aug/6/aug96-ISIC_003399...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>6997</th>\n","      <td>/content/drive/MyDrive/aug/6/aug97-ISIC_002587...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>6998</th>\n","      <td>/content/drive/MyDrive/aug/6/aug98-ISIC_002871...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>6999</th>\n","      <td>/content/drive/MyDrive/aug/6/aug99-ISIC_002987...</td>\n","      <td>6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>7000 rows × 2 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fed1d40f-f091-4582-b848-a5eb68894271')\"\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 -960 960 960\">\n","    <path 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ImageDataGenerator(horizontal_flip=True)\n","   t_and_v_gen = ImageDataGenerator()\n","   msg = '{0:70s} for train generator'.format(' ')\n","\n","   print(msg, '\\r', end='')\n","\n","   # Train Generator\n","   train_gen = trgen.flow_from_dataframe(\n","      train_df, x_col='filepaths',\n","      y_col=ycol, target_size=img_size,\n","      class_mode='categorical',\n","      color_mode='rgb',\n","      shuffle=True,\n","      batch_size=batch_size)\n","\n","   print(msg, '\\r', end='')\n","\n","   # Valid Generator\n","   valid_gen = t_and_v_gen.flow_from_dataframe(\n","      valid_df, x_col='filepaths',\n","      y_col=ycol, target_size=img_size,\n","      class_mode='categorical',\n","      color_mode='rgb',\n","      shuffle=False,\n","      batch_size=batch_size\n","   )\n","\n","   length = len(test_df)\n","   test_batch_size = sorted([int(length/n) for n in range(1, length+1) if length % n == 0 and length/n <= 80], reverse=True)[0]\n","   test_steps = int(length/test_batch_size)\n","\n","   msg = '{0:70s} for test generator'.format(' ')\n","\n","   print(msg, '\\r', end='')\n","\n","   # Test Generator\n","   test_gen = t_and_v_gen.flow_from_dataframe(\n","      test_df, x_col='filepaths',\n","      y_col=ycol, target_size=img_size,\n","      class_mode='categorical',\n","      color_mode='rgb',\n","      shuffle=False,\n","      batch_size=test_batch_size\n","   )\n","\n","   classes = list(train_gen.class_indices.keys())\n","   class_indices = list(train_gen.class_indices.values())\n","   class_count = len(classes)\n","   labels = test_gen.labels\n","   print(f'test batch size : {test_batch_size}, test steps : {test_steps}, number of classes : {class_count}')\n","\n","   return train_gen, test_gen, valid_gen, test_steps\n","\n","bs = 16\n","ycol = 'labels'\n","train_gen, test_gen, valid_gen, test_steps = make_gens(bs, ycol, train_df, test_df, valid_df, img_size)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oowrcT_nSYva"},"outputs":[],"source":["def F1_score(y_true, y_pred):\n","  true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n","  possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n","  predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n","  precision = true_positives / (predicted_positives + K.epsilon())\n","  recall = true_positives / (possible_positives + K.epsilon())\n","  f1_val = 2 * (precision * recall) / (precision + recall + K.epsilon())\n","  return f1_val"]},{"cell_type":"markdown","metadata":{"id":"LewFFnh4kBM6"},"source":["# Model 생성 및 훈련"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BVtOH6imhlA1","executionInfo":{"status":"ok","timestamp":1700638258039,"user_tz":-540,"elapsed":8576,"user":{"displayName":"권재성","userId":"17009958908754473775"}},"outputId":"2c79e680-4f85-4d0f-c176-f64a4a41884e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb3_notop.h5\n","43941136/43941136 [==============================] - 2s 0us/step\n"]}],"source":["def make_model(img_size, lr, mod_num=3):\n","  img_shape = (img_size[0], img_size[1], 3)\n","\n","  if mod_num == 0:\n","    base_model = tf.keras.applications.efficientnet.EfficientNetB0(\n","        include_top=False, weights=\"imagenet\", input_shape=img_shape, pooling='max'\n","    )\n","\n","  elif mod_num == 3:\n","    base_model = tf.keras.applications.efficientnet.EfficientNetB3(\n","        include_top=False, weights=\"imagenet\", input_shape=img_shape, pooling='max'\n","    )\n","\n","  elif mod_num == 5:\n","    base_model = tf.keras.applications.efficientnet.EfficientNetB5(\n","        include_top=False, weights=\"imagenet\", input_shape=img_shape, pooling='max'\n","    )\n","\n","  else:\n","    base_model = tf.keras.applications.efficientnet.EfficientNetB7(\n","        include_top=False, weigths=\"imagenet\", input_shape=img_shape, pooling='max'\n","    )\n","\n","  base_model.trainable = True\n","\n","  x = base_model.output\n","  x = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(x)\n","  x = Dense(\n","            256,\n","            kernel_regularizer=regularizers.l2(l=0.016),\n","            activity_regularizer=regularizers.l1(0.006),\n","            bias_regularizer=regularizers.l1(0.006),\n","            activation='relu'\n","            )(x)\n","\n","  x = Dropout(rate=0.4, seed=123)(x)\n","  output = Dense(class_count, activation='softmax')(x)\n","  model = Model(inputs=base_model.input, outputs=output)\n","\n","  model.compile(Adamax(learning_rate=lr), loss='categorical_crossentropy', metrics=['accuracy', F1_score, 'AUC'])\n","  return model\n","\n","\n","lr = 0.001\n","model = make_model(img_size, lr)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BDlGvl_R3FhK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700646068514,"user_tz":-540,"elapsed":7810495,"user":{"displayName":"권재성","userId":"17009958908754473775"}},"outputId":"bd792d36-5cb0-4955-b9e3-443d53c81a57"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/40\n","438/438 [==============================] - 976s 2s/step - loss: 6.8604 - accuracy: 0.6381 - F1_score: 0.6144 - auc: 0.9096 - val_loss: 5.2976 - val_accuracy: 0.6536 - val_F1_score: 0.6589 - val_auc: 0.9450\n","Epoch 2/40\n","438/438 [==============================] - 176s 402ms/step - loss: 3.6457 - accuracy: 0.8117 - F1_score: 0.7987 - auc: 0.9757 - val_loss: 2.7729 - val_accuracy: 0.7903 - val_F1_score: 0.7674 - val_auc: 0.9712\n","Epoch 3/40\n","438/438 [==============================] - 175s 400ms/step - loss: 1.9813 - accuracy: 0.8744 - F1_score: 0.8652 - auc: 0.9872 - val_loss: 1.6073 - val_accuracy: 0.8059 - val_F1_score: 0.7966 - val_auc: 0.9788\n","Epoch 4/40\n","438/438 [==============================] - 175s 399ms/step - loss: 1.1637 - accuracy: 0.9031 - F1_score: 0.8934 - auc: 0.9912 - val_loss: 1.2546 - val_accuracy: 0.7809 - val_F1_score: 0.7730 - val_auc: 0.9694\n","Epoch 5/40\n","438/438 [==============================] - 174s 397ms/step - loss: 0.7502 - accuracy: 0.9263 - F1_score: 0.9205 - auc: 0.9945 - val_loss: 0.8804 - val_accuracy: 0.8340 - val_F1_score: 0.8348 - val_auc: 0.9782\n","Epoch 6/40\n","438/438 [==============================] - 176s 400ms/step - loss: 0.5340 - accuracy: 0.9436 - F1_score: 0.9415 - auc: 0.9964 - val_loss: 0.7359 - val_accuracy: 0.8371 - val_F1_score: 0.8314 - val_auc: 0.9817\n","Epoch 7/40\n","438/438 [==============================] - 175s 400ms/step - loss: 0.4257 - accuracy: 0.9564 - F1_score: 0.9530 - auc: 0.9972 - val_loss: 0.7100 - val_accuracy: 0.8408 - val_F1_score: 0.8382 - val_auc: 0.9785\n","Epoch 8/40\n","438/438 [==============================] - 176s 401ms/step - loss: 0.3579 - accuracy: 0.9633 - F1_score: 0.9611 - auc: 0.9983 - val_loss: 0.6254 - val_accuracy: 0.8502 - val_F1_score: 0.8469 - val_auc: 0.9834\n","Epoch 9/40\n","438/438 [==============================] - 175s 399ms/step - loss: 0.3319 - accuracy: 0.9640 - F1_score: 0.9626 - auc: 0.9982 - val_loss: 0.7411 - val_accuracy: 0.8321 - val_F1_score: 0.8302 - val_auc: 0.9698\n","Epoch 10/40\n","438/438 [==============================] - 176s 401ms/step - loss: 0.2905 - accuracy: 0.9740 - F1_score: 0.9714 - auc: 0.9985 - val_loss: 0.6620 - val_accuracy: 0.8477 - val_F1_score: 0.8425 - val_auc: 0.9742\n","Epoch 11/40\n","438/438 [==============================] - 174s 397ms/step - loss: 0.2565 - accuracy: 0.9811 - F1_score: 0.9791 - auc: 0.9992 - val_loss: 0.5835 - val_accuracy: 0.8808 - val_F1_score: 0.8789 - val_auc: 0.9805\n","Epoch 12/40\n","438/438 [==============================] - 175s 400ms/step - loss: 0.2494 - accuracy: 0.9769 - F1_score: 0.9764 - auc: 0.9989 - val_loss: 0.6457 - val_accuracy: 0.8452 - val_F1_score: 0.8400 - val_auc: 0.9777\n","Epoch 13/40\n","438/438 [==============================] - 175s 399ms/step - loss: 0.2294 - accuracy: 0.9836 - F1_score: 0.9820 - auc: 0.9992 - val_loss: 0.6441 - val_accuracy: 0.8508 - val_F1_score: 0.8506 - val_auc: 0.9758\n","Epoch 14/40\n","438/438 [==============================] - 173s 396ms/step - loss: 0.2102 - accuracy: 0.9871 - F1_score: 0.9861 - auc: 0.9994 - val_loss: 0.6192 - val_accuracy: 0.8608 - val_F1_score: 0.8578 - val_auc: 0.9778\n","Epoch 15/40\n","438/438 [==============================] - 175s 399ms/step - loss: 0.2072 - accuracy: 0.9864 - F1_score: 0.9852 - auc: 0.9993 - val_loss: 0.6902 - val_accuracy: 0.8396 - val_F1_score: 0.8360 - val_auc: 0.9663\n","Epoch 16/40\n","438/438 [==============================] - 175s 399ms/step - loss: 0.1934 - accuracy: 0.9877 - F1_score: 0.9871 - auc: 0.9995 - val_loss: 0.9489 - val_accuracy: 0.7884 - val_F1_score: 0.7862 - val_auc: 0.9369\n","Epoch 17/40\n","438/438 [==============================] - 176s 402ms/step - loss: 0.1841 - accuracy: 0.9899 - F1_score: 0.9884 - auc: 0.9993 - val_loss: 0.5884 - val_accuracy: 0.8845 - val_F1_score: 0.8831 - val_auc: 0.9758\n","Epoch 18/40\n","438/438 [==============================] - 176s 402ms/step - loss: 0.1665 - accuracy: 0.9916 - F1_score: 0.9909 - auc: 0.9997 - val_loss: 0.7080 - val_accuracy: 0.8527 - val_F1_score: 0.8510 - val_auc: 0.9635\n","Epoch 19/40\n","438/438 [==============================] - 175s 400ms/step - loss: 0.1769 - accuracy: 0.9879 - F1_score: 0.9871 - auc: 0.9995 - val_loss: 0.6870 - val_accuracy: 0.8458 - val_F1_score: 0.8456 - val_auc: 0.9722\n","Epoch 20/40\n","438/438 [==============================] - 175s 400ms/step - loss: 0.1749 - accuracy: 0.9864 - F1_score: 0.9856 - auc: 0.9993 - val_loss: 0.6147 - val_accuracy: 0.8583 - val_F1_score: 0.8571 - val_auc: 0.9761\n","Epoch 21/40\n","438/438 [==============================] - 175s 400ms/step - loss: 0.1615 - accuracy: 0.9891 - F1_score: 0.9885 - auc: 0.9995 - val_loss: 0.9075 - val_accuracy: 0.7940 - val_F1_score: 0.7886 - val_auc: 0.9471\n","Epoch 22/40\n","438/438 [==============================] - 175s 400ms/step - loss: 0.1515 - accuracy: 0.9929 - F1_score: 0.9922 - auc: 0.9996 - val_loss: 0.7597 - val_accuracy: 0.8265 - val_F1_score: 0.8225 - val_auc: 0.9642\n","Epoch 23/40\n","438/438 [==============================] - 175s 399ms/step - loss: 0.1465 - accuracy: 0.9936 - F1_score: 0.9924 - auc: 0.9997 - val_loss: 0.7767 - val_accuracy: 0.8227 - val_F1_score: 0.8176 - val_auc: 0.9616\n","Epoch 24/40\n","438/438 [==============================] - 176s 401ms/step - loss: 0.1420 - accuracy: 0.9940 - F1_score: 0.9929 - auc: 0.9998 - val_loss: 0.8038 - val_accuracy: 0.8390 - val_F1_score: 0.8383 - val_auc: 0.9582\n","Epoch 25/40\n","438/438 [==============================] - 176s 402ms/step - loss: 0.1470 - accuracy: 0.9921 - F1_score: 0.9914 - auc: 0.9996 - val_loss: 0.6138 - val_accuracy: 0.8733 - val_F1_score: 0.8687 - val_auc: 0.9736\n","Epoch 26/40\n","438/438 [==============================] - 177s 403ms/step - loss: 0.1278 - accuracy: 0.9967 - F1_score: 0.9954 - auc: 0.9998 - val_loss: 0.6495 - val_accuracy: 0.8602 - val_F1_score: 0.8592 - val_auc: 0.9701\n","Epoch 27/40\n","438/438 [==============================] - 179s 408ms/step - loss: 0.1463 - accuracy: 0.9904 - F1_score: 0.9899 - auc: 0.9992 - val_loss: 1.0919 - val_accuracy: 0.7665 - val_F1_score: 0.7641 - val_auc: 0.9306\n","Epoch 28/40\n","438/438 [==============================] - 176s 403ms/step - loss: 0.1335 - accuracy: 0.9923 - F1_score: 0.9916 - auc: 0.9998 - val_loss: 0.7245 - val_accuracy: 0.8458 - val_F1_score: 0.8425 - val_auc: 0.9639\n","Epoch 29/40\n","438/438 [==============================] - 176s 401ms/step - loss: 0.1258 - accuracy: 0.9939 - F1_score: 0.9934 - auc: 0.9997 - val_loss: 0.7854 - val_accuracy: 0.8283 - val_F1_score: 0.8235 - val_auc: 0.9613\n","Epoch 30/40\n","438/438 [==============================] - 177s 403ms/step - loss: 0.1287 - accuracy: 0.9943 - F1_score: 0.9933 - auc: 0.9998 - val_loss: 0.7826 - val_accuracy: 0.8271 - val_F1_score: 0.8283 - val_auc: 0.9493\n","Epoch 31/40\n","438/438 [==============================] - 175s 400ms/step - loss: 0.1149 - accuracy: 0.9966 - F1_score: 0.9962 - auc: 0.9997 - val_loss: 0.7803 - val_accuracy: 0.8258 - val_F1_score: 0.8257 - val_auc: 0.9624\n","Epoch 32/40\n","438/438 [==============================] - 175s 398ms/step - loss: 0.1301 - accuracy: 0.9917 - F1_score: 0.9911 - auc: 0.9996 - val_loss: 0.7982 - val_accuracy: 0.8290 - val_F1_score: 0.8243 - val_auc: 0.9567\n","Epoch 33/40\n","438/438 [==============================] - 175s 399ms/step - loss: 0.1219 - accuracy: 0.9944 - F1_score: 0.9935 - auc: 0.9997 - val_loss: 0.8247 - val_accuracy: 0.8208 - val_F1_score: 0.8184 - val_auc: 0.9538\n","Epoch 34/40\n","438/438 [==============================] - 174s 397ms/step - loss: 0.1139 - accuracy: 0.9950 - F1_score: 0.9943 - auc: 0.9998 - val_loss: 0.6332 - val_accuracy: 0.8683 - val_F1_score: 0.8639 - val_auc: 0.9700\n","Epoch 35/40\n","438/438 [==============================] - 174s 397ms/step - loss: 0.1141 - accuracy: 0.9939 - F1_score: 0.9932 - auc: 0.9998 - val_loss: 0.7556 - val_accuracy: 0.8458 - val_F1_score: 0.8454 - val_auc: 0.9565\n","Epoch 36/40\n","438/438 [==============================] - 175s 399ms/step - loss: 0.1076 - accuracy: 0.9971 - F1_score: 0.9962 - auc: 0.9998 - val_loss: 0.7507 - val_accuracy: 0.8577 - val_F1_score: 0.8544 - val_auc: 0.9568\n","Epoch 37/40\n","438/438 [==============================] - 175s 398ms/step - loss: 0.1107 - accuracy: 0.9951 - F1_score: 0.9944 - auc: 0.9998 - val_loss: 0.6900 - val_accuracy: 0.8614 - val_F1_score: 0.8607 - val_auc: 0.9655\n","Epoch 38/40\n","438/438 [==============================] - 173s 395ms/step - loss: 0.1095 - accuracy: 0.9959 - F1_score: 0.9949 - auc: 0.9996 - val_loss: 0.7794 - val_accuracy: 0.8452 - val_F1_score: 0.8418 - val_auc: 0.9572\n","Epoch 39/40\n","438/438 [==============================] - 173s 395ms/step - loss: 0.1058 - accuracy: 0.9953 - F1_score: 0.9954 - auc: 0.9998 - val_loss: 0.8133 - val_accuracy: 0.8383 - val_F1_score: 0.8378 - val_auc: 0.9496\n","Epoch 40/40\n","438/438 [==============================] - 176s 402ms/step - loss: 0.1091 - accuracy: 0.9949 - F1_score: 0.9938 - auc: 0.9998 - val_loss: 0.7958 - val_accuracy: 0.8427 - val_F1_score: 0.8369 - val_auc: 0.9537\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.src.callbacks.History at 0x786bbc23c7f0>"]},"metadata":{},"execution_count":14}],"source":["epochs = 40\n","\n","model.fit(x=train_gen, epochs=epochs, verbose=1, validation_data=valid_gen, validation_steps=None, shuffle=False, initial_epoch=0)"]},{"cell_type":"code","source":["# 모델 저장\n","\n","model.save('/content/drive/MyDrive')\n","print('모델 저장 완료!')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"670Uasj4_FAn","executionInfo":{"status":"ok","timestamp":1700646119436,"user_tz":-540,"elapsed":50939,"user":{"displayName":"권재성","userId":"17009958908754473775"}},"outputId":"f635538e-ed3f-4610-9ce7-04d0df348555"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["모델 저장 완료!\n"]}]},{"cell_type":"markdown","metadata":{"id":"zn4X5kTv7ut9"},"source":["# Plot"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Nrv2jnZ73mZw","colab":{"base_uri":"https://localhost:8080/","height":251},"executionInfo":{"status":"error","timestamp":1700646119436,"user_tz":-540,"elapsed":34,"user":{"displayName":"권재성","userId":"17009958908754473775"}},"outputId":"30be1795-7c20-40c5-c3c2-dfccc325d78f"},"outputs":[{"output_type":"error","ename":"SyntaxError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mSyntaxError\u001b[0m                               Traceback (most recent call last)","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/compilerop.py\u001b[0m in \u001b[0;36mast_parse\u001b[0;34m(self, source, filename, symbol)\u001b[0m\n\u001b[1;32m     99\u001b[0m         \u001b[0mArguments\u001b[0m \u001b[0mare\u001b[0m \u001b[0mexactly\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msame\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mstandard\u001b[0m \u001b[0mlibrary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m         and are passed to the built-in compile function.\"\"\"\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msymbol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mPyCF_ONLY_AST\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreset_compiler_flags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mSyntaxError\u001b[0m: f-string: unmatched ')' (<ipython-input-16-2bed2be56332>, line 28)"]}],"source":["def tr_plot(tr_data):\n","  start_epochs = 0\n","\n","  tacc = tr_data.history['accuracy']\n","  tlose = tr_data.history['lose']\n","  vacc = tr_data.history['val_accuracy']\n","  vloss = tr_data.history['val_loss']\n","  tf1 = tr_data.history['F1_Score']\n","  vf1 = tr_data.history['val_F1_score']\n","  tauc = tr_data.history['auc']\n","  vauc = tr_data.history['val_auc']\n","  epoch_count = len(tacc) + start_epochs\n","  epochs = []\n","\n","  for i in range(start_epochs, epoch_counts):\n","    epochs.append(i+1)\n","\n","  index_loss = np.argmin(vloss)\n","  val_lowest = vloss[index_loss]\n","  index_acc = np.argmax(vacc)\n","  acc_highest = vacc[index_acc]\n","  auc_index = np.argmax(vauc)\n","  val_highst_auc = vauc[auc_index]\n","  plt.style.use('fivethirtyeight')\n","\n","  sc_label = f'best epochs = {str(index_loss + 1 + start_epochs)}'\n","  vc_label = f'best epochs = {str(index_acc + 1 + start_epochs)}'\n","  auc_label = f'best epochs = {str(auc_index) + 1 + start_epochs)}'\n","  fig.axes = plt.subplots(nrows=1, ncols=4, figsize=(25,10))\n","  axes[0].plot(Epochs,tloss, 'r', label='Training loss')\n","  axes[0].plot(Epochs,vloss,'g',label='Validation loss' )\n","  axes[0].scatter(index_loss+1 +start_epoch,val_lowest, s=150, c= 'blue', label=sc_label)\n","  axes[0].scatter(Epochs, tloss, s=100, c='red')\n","  axes[0].set_title('Training and Validation Loss')\n","  axes[0].set_xlabel('Epochs', fontsize=18)\n","  axes[0].set_ylabel('Loss', fontsize=18)\n","  axes[0].legend()\n","\n","  axes[1].plot(Epochs, tacc, 'r', label='Training Accuracy')\n","  axes[1].scatter(Epochs, tacc, s=100, c='red')\n","  axes[1].plot (Epochs, vacc, 'g', label='Validation Accuracy')\n","  axes[1].scatter(index_acc + 1 + start_epoch, acc_highest, s=150, c='blue', label=vc_label)\n","  axes[1].set_title('Training and Validation Accuracy')\n","  axes[1].set_xlabel('Epochs', fontsize=18)\n","  axes[1].set_ylabel('Accuracy', fontsize=18)\n","  axes[1].legend()\n","\n","  axes[2].plot(Epochs, tf1, 'r', label='Training F1 score')\n","  axes[2].plot(Epochs, vf1, 'g', label='Validation F1 score')\n","  index_tf1 = np.argmax(tf1)#  this is the epoch with the highest training F1 score\n","  tf1max = tf1[index_tf1]\n","  index_vf1 = np.argmax(vf1)# thisiis the epoch with the highest validation F1 score\n","  vf1max = vf1[index_vf1]\n","  axes[2].scatter(index_vf1 + 1 + start_epoch, vf1max, s=150, c='blue', label=vc_label)\n","  axes[2].scatter(Epochs, tf1, s=100, c='red')\n","  axes[2].set_title('Training and Validation F1 score')\n","  axes[2].set_xlabel('Epochs', fontsize=18)\n","  axes[2].set_ylabel('F1 score', fontsize=18)\n","  axes[2].legend()\n","  axes[3].plot(Epochs, tauc, 'r', label='Training AUC')\n","  axes[3].plot(Epochs, vauc,'g', label='Validation AUC' )\n","  axes[3].scatter(auc_index + 1 + start_epoch, val_highest_auc, s=150, c= 'blue', label=sc_label)\n","  axes[3].scatter(Epochs, tauc, s=100, c='red')\n","  axes[3].set_title('Training and Validation AUC')\n","  axes[3].set_xlabel('Epochs', fontsize=18)\n","  axes[3].set_ylabel('AUC', fontsize=18)\n","  axes[3].legend()\n","  plt.tight_layout\n","  plt.show()\n","  return\n","\n","tr_plot(history)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"tNWYZXNJ708F"},"outputs":[],"source":[]},{"cell_type":"markdown","metadata":{"id":"wxG4cl1672zX"},"source":["#(예정) Evaluation(Confusion Matrix -> accuracy, precision, recall, F1-Score)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RcVmhbsp8Bvd","colab":{"base_uri":"https://localhost:8080/","height":898},"executionInfo":{"status":"ok","timestamp":1700648895839,"user_tz":-540,"elapsed":7983,"user":{"displayName":"권재성","userId":"17009958908754473775"}},"outputId":"f5c6d519-6f87-4cf7-be1a-d7dd04337b2c"},"outputs":[{"output_type":"stream","name":"stdout","text":["401/401 [==============================] - 7s 17ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x800 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Classification Report:\n","----------------------\n","               precision    recall  f1-score   support\n","\n","           0     0.8462    0.8462    0.8462        13\n","           1     0.8571    0.8571    0.8571        21\n","           2     0.6964    0.8864    0.7800        44\n","           3     0.6000    0.7500    0.6667         4\n","           4     0.9749    0.8694    0.9191       268\n","           5     0.6441    0.8444    0.7308        45\n","           6     0.6250    0.8333    0.7143         6\n","\n","    accuracy                         0.8653       401\n","   macro avg     0.7491    0.8410    0.7877       401\n","weighted avg     0.8879    0.8653    0.8715       401\n","\n"]}],"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","def predictor(test_gen):\n","    y_pred = []\n","    error_list = []\n","    error_pred_list = []\n","    y_true = test_gen.labels\n","    classes = list(test_gen.class_indices.keys())\n","    class_count = len(classes)\n","    errors = 0\n","    preds = model.predict(test_gen, verbose=1)\n","    tests = len(preds)\n","\n","    for i, p in enumerate(preds):\n","        pred_index = np.argmax(p)\n","        true_index = test_gen.labels[i]  # labels are integer values\n","        if pred_index != true_index: # a misclassification has occurred\n","            errors += 1\n","            file = test_gen.filenames[i]\n","            error_list.append(file)\n","            error_class = classes[pred_index]\n","            error_pred_list.append(error_class)\n","        y_pred.append(pred_index)\n","\n","    acc= ( 1-errors/tests ) * 100\n","    msg=f'there were {errors} errors in {tests} tests for an accuracy of {acc:6.2f}'\n","    ypred = np.array(y_pred)\n","    ytrue = np.array(y_true)\n","\n","    f1score = f1_score(ytrue, ypred, average='weighted')* 100\n","    if class_count <= 30:\n","        cm = confusion_matrix(ytrue, ypred)\n","        # plot the confusion matrix\n","        plt.figure(figsize=(12, 8))\n","        sns.heatmap(cm, annot=True, vmin=0, fmt='g', cmap='Blues', cbar=False)\n","        plt.xticks(np.arange(class_count)+.5, classes, rotation=90)\n","        plt.yticks(np.arange(class_count)+.5, classes, rotation=0)\n","        plt.xlabel(\"Predicted\")\n","        plt.ylabel(\"Actual\")\n","        plt.title(\"Confusion Matrix\")\n","        plt.show()\n","\n","    clr = classification_report(y_true, y_pred, target_names=classes, digits= 4) # create classification report\n","    print(\"Classification Report:\\n----------------------\\n\", clr)\n","    return errors, tests, error_list, error_pred_list, f1score, y_pred, y_true\n","\n","errors, tests, error_list, error_pred_list, f1score, y_pred, y_true = predictor(test_gen)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HrZusw7SZPxu","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1700649091091,"user_tz":-540,"elapsed":2428,"user":{"displayName":"권재성","userId":"17009958908754473775"}},"outputId":"d99927aa-cf72-4790-b01c-255ca2f39bbb"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n","  saving_api.save_model(\n"]},{"output_type":"stream","name":"stdout","text":["모델 저장 완료!\n"]}],"source":["# 모델 저장\n","\n","model.save('/content/drive/MyDrive/efficientnet.h5')\n","print('모델 저장 완료!')"]},{"cell_type":"code","source":["import cv2\n","import numpy as np\n","import tensorflow\n","from tensorflow.keras.preprocessing import image\n","import tensorflow as tf\n","from tensorflow.keras.models import load_model\n","from tensorflow.keras.preprocessing import image\n","from tensorflow.keras.applications.efficientnet import preprocess_input\n","from tensorflow.keras import backend as K\n","import numpy as np\n","\n","def F1_score(y_true, y_pred):\n","  true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n","  possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n","  predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n","  precision = true_positives / (predicted_positives + K.epsilon())\n","  recall = true_positives / (possible_positives + K.epsilon())\n","  f1_val = 2 * (precision * recall) / (precision + recall + K.epsilon())\n","  return f1_val\n","\n","tf.keras.utils.get_custom_objects()['F1_score'] = F1_score\n","\n","loaded_model = load_model(\"/content/drive/MyDrive/efficientnet.h5\")\n","\n","image_path = '/content/drive/MyDrive/aug/6/aug0-ISIC_0026336.jpg'\n","\n","img_size = (224, 224)\n","img = image.load_img(image_path, target_size=img_size)\n","img_array = image.img_to_array(img)\n","img_array = np.expand_dims(img_array, axis=0)\n","img_array = preprocess_input(img_array)\n","\n","\n","result = model.predict(img_array)\n","\n","print(result.astype(float))\n","\n","# top-1\n","class_index = np.argmax(result)\n","\n","print(\"Predicted class index:\", class_index)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8NHEgnahoQQ9","executionInfo":{"status":"ok","timestamp":1700655416830,"user_tz":-540,"elapsed":5359,"user":{"displayName":"권재성","userId":"17009958908754473775"}},"outputId":"256d8ffe-f704-4f0d-cddb-8559e55e6432"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 36ms/step\n","[[7.84766235e-05 1.35920418e-04 2.20677219e-04 1.44202873e-04\n","  2.32578212e-04 2.19093781e-04 9.98969078e-01]]\n","Predicted class index: 6\n"]}]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["zn4X5kTv7ut9"],"provenance":[],"machine_shape":"hm","gpuType":"V100","authorship_tag":"ABX9TyPVgyuoyFMVxU/wTIzCtNSc"},"kernelspec":{"display_name":"Python 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