base3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from keras.preprocessing import image\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "#from keras.utils import to_categorical\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Flatten\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def end_to_end_data_prep():\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    import matplotlib.pyplot as plt\n",
    "    import os\n",
    "    from glob import glob\n",
    "    from PIL import Image\n",
    "    %matplotlib inline\n",
    "    base = '/cxldata/skin_disease_1/'\n",
    "    metadata = pd.read_csv(os.path.join(base,'HAM10000_metadata_orig.csv'))\n",
    "    #metadata.info()\n",
    "    #if we combine data into one directory then no need to mention *\n",
    "    image_path = {os.path.splitext(os.path.basename(x))[0]: x\n",
    "                  for x in glob(os.path.join('/cxldata/skin_disease_1/HAM10000_images_draftv1/orig/','*.jpg'))}\n",
    "    metadata['path'] = metadata['image_id'].map(image_path.get)\n",
    "    #upload data into dataset with resize 254,254\n",
    "    #metadata['image'] = metadata['path'].map(lambda x: np.asarray(Image.open(x).resize((224,224))))\n",
    "    \n",
    "    return metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata_all = end_to_end_data_prep()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first import all data into dataframe then run train_test_split function to split data\n",
    "\n",
    "# total samples are 42650\n",
    "# split with train_test_split (stratify = y,test 0.2 size)\n",
    "# train  - 34120\n",
    "#test - 8530"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save train data into csv to overcome processing again n again( 34120 records)\n",
    "train.to_csv('/cxldata/skin_disease_1/sudeep/sudeep_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save test data into csv to overcome processing again n again ( 8530 records)\n",
    "\n",
    "test.to_csv('/cxldata/skin_disease_1/sudeep/sudeep_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import train data (34120 records)\n",
    "import pandas as pd\n",
    "train_df = pd.read_csv('/cxldata/skin_disease_1/sudeep/sudeep_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import test data ( 8530 records)\n",
    "import pandas as pd \n",
    "test_df = pd.read_csv('/cxldata/skin_disease_1/sudeep/sudeep_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# created another df because we are not able to run fit model on 34120 records so created another df with 12000 recods\n",
    "train_df1 = train_df[0:12000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12000, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mel      1830\n",
       "nv       1820\n",
       "bkl      1770\n",
       "df       1766\n",
       "vasc     1764\n",
       "akiec    1592\n",
       "bcc      1458\n",
       "Name: dx, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data is stratified in new data frame\n",
    "train_df1['dx'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8530 entries, 0 to 8529\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Unnamed: 0    8530 non-null   int64  \n",
      " 1   Unnamed: 0.1  8530 non-null   int64  \n",
      " 2   lesion_id     1996 non-null   object \n",
      " 3   image_id      8530 non-null   object \n",
      " 4   dx            8530 non-null   object \n",
      " 5   dx_type       8530 non-null   object \n",
      " 6   age           1985 non-null   float64\n",
      " 7   sex           8530 non-null   object \n",
      " 8   localization  8530 non-null   object \n",
      " 9   dataset       8530 non-null   object \n",
      " 10  path          8530 non-null   object \n",
      "dtypes: float64(1), int64(2), object(8)\n",
      "memory usage: 733.2+ KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8530"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/cxldata/skin_disease_1/HAM10000_images_draftv1/akiec1/akiec_0_6806.jpg'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df1['path'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/cxldata/skin_disease_1/HAM10000_images_draftv1/bkl2/bkl_0_7689.jpg'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['path'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</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>dataset</th>\n",
       "      <th>path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13481</td>\n",
       "      <td>13481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>akiec_0_6806</td>\n",
       "      <td>akiec</td>\n",
       "      <td>histo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>scalp</td>\n",
       "      <td>vidir_modern</td>\n",
       "      <td>/cxldata/skin_disease_1/HAM10000_images_draftv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14323</td>\n",
       "      <td>14323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>akiec_0_7555</td>\n",
       "      <td>akiec</td>\n",
       "      <td>histo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>back</td>\n",
       "      <td>rosendahl</td>\n",
       "      <td>/cxldata/skin_disease_1/HAM10000_images_draftv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41377</td>\n",
       "      <td>41377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vasc_0_1955</td>\n",
       "      <td>vasc</td>\n",
       "      <td>histo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>lower extremity</td>\n",
       "      <td>vidir_modern</td>\n",
       "      <td>/cxldata/skin_disease_1/HAM10000_images_draftv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38669</td>\n",
       "      <td>38669</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vasc_0_6309</td>\n",
       "      <td>vasc</td>\n",
       "      <td>histo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>back</td>\n",
       "      <td>vidir_modern</td>\n",
       "      <td>/cxldata/skin_disease_1/HAM10000_images_draftv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37655</td>\n",
       "      <td>37655</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vasc_0_3636</td>\n",
       "      <td>vasc</td>\n",
       "      <td>histo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>back</td>\n",
       "      <td>vidir_modern</td>\n",
       "      <td>/cxldata/skin_disease_1/HAM10000_images_draftv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11995</th>\n",
       "      <td>1572</td>\n",
       "      <td>1572</td>\n",
       "      <td>HAM_0005010</td>\n",
       "      <td>ISIC_0033603</td>\n",
       "      <td>mel</td>\n",
       "      <td>histo</td>\n",
       "      <td>30.0</td>\n",
       "      <td>female</td>\n",
       "      <td>upper extremity</td>\n",
       "      <td>vidir_modern</td>\n",
       "      <td>/cxldata/skin_disease_1/HAM10000_images_draftv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11996</th>\n",
       "      <td>41111</td>\n",
       "      <td>41111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vasc_0_7716</td>\n",
       "      <td>vasc</td>\n",
       "      <td>histo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>trunk</td>\n",
       "      <td>vidir_modern</td>\n",
       "      <td>/cxldata/skin_disease_1/HAM10000_images_draftv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11997</th>\n",
       "      <td>7964</td>\n",
       "      <td>7964</td>\n",
       "      <td>HAM_0004367</td>\n",
       "      <td>ISIC_0033396</td>\n",
       "      <td>nv</td>\n",
       "      <td>histo</td>\n",
       "      <td>80.0</td>\n",
       "      <td>male</td>\n",
       "      <td>back</td>\n",
       "      <td>vidir_modern</td>\n",
       "      <td>/cxldata/skin_disease_1/HAM10000_images_draftv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11998</th>\n",
       "      <td>34470</td>\n",
       "      <td>34470</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mel_0_7760</td>\n",
       "      <td>mel</td>\n",
       "      <td>histo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>female</td>\n",
       "      <td>foot</td>\n",
       "      <td>rosendahl</td>\n",
       "      <td>/cxldata/skin_disease_1/HAM10000_images_draftv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11999</th>\n",
       "      <td>5397</td>\n",
       "      <td>5397</td>\n",
       "      <td>HAM_0005452</td>\n",
       "      <td>ISIC_0024722</td>\n",
       "      <td>nv</td>\n",
       "      <td>follow_up</td>\n",
       "      <td>65.0</td>\n",
       "      <td>female</td>\n",
       "      <td>upper extremity</td>\n",
       "      <td>vidir_molemax</td>\n",
       "      <td>/cxldata/skin_disease_1/HAM10000_images_draftv...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  Unnamed: 0.1    lesion_id      image_id     dx    dx_type  \\\n",
       "0           13481         13481          NaN  akiec_0_6806  akiec      histo   \n",
       "1           14323         14323          NaN  akiec_0_7555  akiec      histo   \n",
       "2           41377         41377          NaN   vasc_0_1955   vasc      histo   \n",
       "3           38669         38669          NaN   vasc_0_6309   vasc      histo   \n",
       "4           37655         37655          NaN   vasc_0_3636   vasc      histo   \n",
       "...           ...           ...          ...           ...    ...        ...   \n",
       "11995        1572          1572  HAM_0005010  ISIC_0033603    mel      histo   \n",
       "11996       41111         41111          NaN   vasc_0_7716   vasc      histo   \n",
       "11997        7964          7964  HAM_0004367  ISIC_0033396     nv      histo   \n",
       "11998       34470         34470          NaN    mel_0_7760    mel      histo   \n",
       "11999        5397          5397  HAM_0005452  ISIC_0024722     nv  follow_up   \n",
       "\n",
       "        age     sex     localization        dataset  \\\n",
       "0       NaN    male            scalp   vidir_modern   \n",
       "1       NaN  female             back      rosendahl   \n",
       "2       NaN    male  lower extremity   vidir_modern   \n",
       "3       NaN    male             back   vidir_modern   \n",
       "4       NaN    male             back   vidir_modern   \n",
       "...     ...     ...              ...            ...   \n",
       "11995  30.0  female  upper extremity   vidir_modern   \n",
       "11996   NaN    male            trunk   vidir_modern   \n",
       "11997  80.0    male             back   vidir_modern   \n",
       "11998   NaN  female             foot      rosendahl   \n",
       "11999  65.0  female  upper extremity  vidir_molemax   \n",
       "\n",
       "                                                    path  \n",
       "0      /cxldata/skin_disease_1/HAM10000_images_draftv...  \n",
       "1      /cxldata/skin_disease_1/HAM10000_images_draftv...  \n",
       "2      /cxldata/skin_disease_1/HAM10000_images_draftv...  \n",
       "3      /cxldata/skin_disease_1/HAM10000_images_draftv...  \n",
       "4      /cxldata/skin_disease_1/HAM10000_images_draftv...  \n",
       "...                                                  ...  \n",
       "11995  /cxldata/skin_disease_1/HAM10000_images_draftv...  \n",
       "11996  /cxldata/skin_disease_1/HAM10000_images_draftv...  \n",
       "11997  /cxldata/skin_disease_1/HAM10000_images_draftv...  \n",
       "11998  /cxldata/skin_disease_1/HAM10000_images_draftv...  \n",
       "11999  /cxldata/skin_disease_1/HAM10000_images_draftv...  \n",
       "\n",
       "[12000 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12000/12000 [00:59<00:00, 201.66it/s]\n"
     ]
    }
   ],
   "source": [
    "train_image = []\n",
    "for i in tqdm(range(train_df1.shape[0])):\n",
    "    img = image.load_img(train_df1['path'][i],target_size=[64,64,3])\n",
    "    img = image.img_to_array(img)\n",
    "    img = img/255\n",
    "    train_image.append(img)\n",
    "\n",
    "X = np.array(train_image)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "y = train_df1['dx'].values\n",
    "onehotencoder = OneHotEncoder(categories='auto',sparse=False)\n",
    "y = onehotencoder.fit_transform(y.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12000, 64, 64, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12000, 7)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_valid,y_train,y_valid = train_test_split(X,y,stratify=y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9600, 64, 64, 3)\n",
      "(2400, 64, 64, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9600, 7)\n",
      "(2400, 7)\n"
     ]
    }
   ],
   "source": [
    "print(y_train.shape)\n",
    "print(y_valid.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Flatten\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "base_model = keras.applications.vgg16.VGG16(weights=\"imagenet\",\n",
    "                                                  include_top=False,input_shape = (64,64,3))\n",
    "model_t1 = Sequential()\n",
    "model_t1.add(base_model)\n",
    "model_t1.add(Flatten())\n",
    "model_t1.add(Dense(64,activation=\"relu\"))\n",
    "model_t1.add(Dense(7,activation=\"softmax\"))\n",
    "#avg = keras.layers.GlobalAveragePooling2D()(base_model.output)\n",
    "#output = keras.layers.Dense(7, activation=\"softmax\")(avg)\n",
    "#model = keras.models.Model(inputs=base_model.input, outputs=output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in base_model.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend as K \n",
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "300/300 [==============================] - 201s 668ms/step - loss: 0.8168 - accuracy: 0.7405 - val_loss: 0.5015 - val_accuracy: 0.8571\n",
      "Epoch 2/3\n",
      "300/300 [==============================] - 203s 679ms/step - loss: 0.4337 - accuracy: 0.8783 - val_loss: 0.3899 - val_accuracy: 0.8871\n",
      "Epoch 3/3\n",
      "300/300 [==============================] - 214s 714ms/step - loss: 0.3437 - accuracy: 0.9035 - val_loss: 0.3476 - val_accuracy: 0.9125\n"
     ]
    }
   ],
   "source": [
    "#optimizer = keras.optimizers.SGD(lr=0.2)\n",
    "model_t1.compile(loss=\"categorical_crossentropy\", optimizer=\"nadam\",\n",
    "              metrics=[\"accuracy\"])\n",
    "history = model_t1.fit(X_train,y_train,epochs=3,validation_data=(X_valid,y_valid)) #changed from 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_path = '/cxldata/skin_disease_1/HAM10000_images_draftv1/akiec/akiec_0_100.jpg'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=64x64 at 0x7FDC8BDE3A90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = image.load_img(img_path,color_mode='rgb', target_size=(64, 64))\n",
    "display(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = image.img_to_array(img)\n",
    "x.shape\n",
    "# Adding the fouth dimension, for number of images\n",
    "x = np.expand_dims(x, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.vgg16 import preprocess_input,decode_predictions\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.9952668 , 0.        , 0.        , 0.        , 0.00473323,\n",
       "        0.        , 0.        ]], dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = preprocess_input(x)\n",
    "features = model_t1.predict(x)\n",
    "features\n",
    "#p = decode_predictions(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_t1.save('/cxldata/skin_disease_1/sudeep/1/4_nadam.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = ['akiec', 'bcc', 'bkl', 'df', 'mel', 'nv', 'vasc']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "akiec\n"
     ]
    }
   ],
   "source": [
    "#return position of max\n",
    "MaxPosition=np.argmax(features)  \n",
    "prediction_label=classes[MaxPosition]\n",
    "print(prediction_label) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=64x64 at 0x7FDC9135C6D8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "akiec\n"
     ]
    }
   ],
   "source": [
    "classes = ['akiec', 'bcc', 'bkl', 'df', 'mel', 'nv', 'vasc']\n",
    "img_path1 = '/cxldata/skin_disease_1/HAM10000_images_draftv1/akiec2/akiec_0_9864.jpg'\n",
    "img1 = image.load_img(img_path1,color_mode='rgb', target_size=(64, 64))\n",
    "display(img1)\n",
    "x1 = image.img_to_array(img1)\n",
    "x1.shape\n",
    "# Adding the fouth dimension, for number of images\n",
    "x1 = np.expand_dims(x1, axis=0)\n",
    "\n",
    "x1 = preprocess_input(x1)\n",
    "features1 = model_t1.predict(x1)\n",
    "features1\n",
    "#p = decode_predictions(features)\n",
    "MaxPosition=np.argmax(features1)  \n",
    "prediction_label=classes[MaxPosition]\n",
    "print(prediction_label) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0000000e+00, 3.0424418e-28, 0.0000000e+00, 0.0000000e+00,\n",
       "        2.4646536e-20, 0.0000000e+00, 0.0000000e+00]], dtype=float32)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MaxPosition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model('/cxldata/skin_disease_1/sudeep/1/4_nadam.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8530/8530 [00:44<00:00, 192.01it/s]\n"
     ]
    }
   ],
   "source": [
    "# for test dataset\n",
    "train_image = []\n",
    "for i in tqdm(range(test_df.shape[0])):\n",
    "    img = image.load_img(test_df['path'][i],target_size=[64,64,3])\n",
    "    img = image.img_to_array(img)\n",
    "    img = img/255\n",
    "    train_image.append(img)\n",
    "\n",
    "X_test = np.array(train_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "y_test = test_df['dx'].values\n",
    "onehotencoder = OneHotEncoder(categories='auto',sparse=False)\n",
    "y_test = onehotencoder.fit_transform(y_test.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8530, 64, 64, 3)\n",
      "(8530, 7)\n"
     ]
    }
   ],
   "source": [
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nv       1341\n",
       "mel      1302\n",
       "df       1256\n",
       "bkl      1253\n",
       "vasc     1226\n",
       "akiec    1121\n",
       "bcc      1031\n",
       "Name: dx, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['dx'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate on test data\n",
      "67/67 [==============================] - 157s 2s/step - loss: 0.3456 - accuracy: 0.9089\n",
      "test loss, test acc: [0.3455950915813446, 0.9089097380638123]\n"
     ]
    }
   ],
   "source": [
    "print(\"Evaluate on test data\")\n",
    "results = model.evaluate(X_test, y_test, batch_size=128)\n",
    "print(\"test loss, test acc:\", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "tensorflow2",
   "language": "python",
   "name": "tensorflow2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
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   "title_cell": "Table of Contents",
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