CNN_Arabic_Handwritten_Characters.ipynb

{
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   "id": "5354e59c",
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   "source": [
    "## Most Important\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from pathlib import Path\n",
    "from PIL import Image\n",
    "\n",
    "## less Important\n",
    "from functools import partial\n",
    "import os\n",
    "from scipy import stats\n",
    "import missingno as msno\n",
    "import joblib\n",
    "import tarfile\n",
    "import shutil\n",
    "import urllib\n",
    "\n",
    "## Sklearn\n",
    "from sklearn import datasets\n",
    "## Preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "## Metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "## tensorflow & Keras\n",
    "import tensorflow as tf    ## i will use tf for every thing and for keras using tf.keras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4155dbd",
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     "start_time": "2021-11-07T17:42:14.201284",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Loading the Data and Look at the Big Picture"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba51284",
   "metadata": {
    "papermill": {
     "duration": 0.022177,
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     "start_time": "2021-11-07T17:42:14.246547",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "`Only for training here`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dffeaefb",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    ../input/arabic-hwr-ai-pro-intake1/train/00000...\n",
       "1    ../input/arabic-hwr-ai-pro-intake1/train/00001...\n",
       "2    ../input/arabic-hwr-ai-pro-intake1/train/00002...\n",
       "3    ../input/arabic-hwr-ai-pro-intake1/train/00003...\n",
       "4    ../input/arabic-hwr-ai-pro-intake1/train/00004...\n",
       "Name: Filepath, dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels = pd.read_csv('../input/arabic-hwr-ai-pro-intake1/train.csv')\n",
    "train_images = Path(r'../input/arabic-hwr-ai-pro-intake1/train')\n",
    "\n",
    "## read these all training images paths as Series\n",
    "train_images_paths = pd.Series(sorted(list(train_images.glob(r'*.png'))), name='Filepath').astype(str)\n",
    "\n",
    "train_images_paths.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "082d5270",
   "metadata": {
    "papermill": {
     "duration": 0.023018,
     "end_time": "2021-11-07T17:42:14.960491",
     "exception": false,
     "start_time": "2021-11-07T17:42:14.937473",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Explore the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69839f50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T17:42:15.017536Z",
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     "iopub.status.idle": "2021-11-07T17:42:16.513262Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 28 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_key_value = {}\n",
    "for value in train_labels['label'].unique():\n",
    "    img_key_value[value] = train_labels[train_labels['label']==value].index[0]\n",
    "    \n",
    "img_index = list(img_key_value.values())\n",
    "img_label = list(img_key_value.keys())\n",
    "\n",
    "fig, ax = plt.subplots(4, 7, figsize=(12, 8))\n",
    "\n",
    "i = 0\n",
    "for row in range(4):\n",
    "    for col in range(7):\n",
    "        plt.sca(ax[row, col])\n",
    "        plt.title(f'label = {img_label[i]}')\n",
    "        img = plt.imread(train_images_paths.iloc[img_index[i]])\n",
    "        plt.imshow(img)\n",
    "        plt.axis('off')\n",
    "        i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c88fd2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T17:42:16.568984Z",
     "iopub.status.busy": "2021-11-07T17:42:16.568308Z",
     "iopub.status.idle": "2021-11-07T17:42:16.582195Z",
     "shell.execute_reply": "2021-11-07T17:42:16.582764Z",
     "shell.execute_reply.started": "2021-11-07T16:58:42.243336Z"
    },
    "papermill": {
     "duration": 0.04299,
     "end_time": "2021-11-07T17:42:16.582940",
     "exception": false,
     "start_time": "2021-11-07T17:42:16.539950",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Instances in train_set => 13440\n",
      "Number of Instances in train_labels => 13440\n",
      "\n",
      "shape of each Image is => (32, 32, 4)\n"
     ]
    }
   ],
   "source": [
    "print('Number of Instances in train_set =>', len(train_images_paths))\n",
    "print('Number of Instances in train_labels =>', len(train_labels))\n",
    "\n",
    "print()\n",
    "\n",
    "img = plt.imread(train_images_paths.iloc[img_index[0]])\n",
    "print('shape of each Image is =>', img.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75314e4",
   "metadata": {
    "papermill": {
     "duration": 0.025585,
     "end_time": "2021-11-07T17:42:16.634698",
     "exception": false,
     "start_time": "2021-11-07T17:42:16.609113",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23a56f4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T17:42:16.689482Z",
     "iopub.status.busy": "2021-11-07T17:42:16.688853Z",
     "iopub.status.idle": "2021-11-07T17:42:58.166385Z",
     "shell.execute_reply": "2021-11-07T17:42:58.166957Z",
     "shell.execute_reply.started": "2021-11-07T16:58:42.253239Z"
    },
    "papermill": {
     "duration": 41.506526,
     "end_time": "2021-11-07T17:42:58.167146",
     "exception": false,
     "start_time": "2021-11-07T17:42:16.660620",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_full_set.shape => (13440, 32, 32, 3)\n",
      "train_full_labels.shape => (13440,)\n"
     ]
    }
   ],
   "source": [
    "train_full_labels = train_labels['label'].values\n",
    "train_full_set = np.empty((13440, 32, 32, 3), dtype=np.float32)  #take only the first 3 channels\n",
    "\n",
    "for idx, path in enumerate(train_images_paths):\n",
    "    img = plt.imread(path)\n",
    "    img = img[:,:,:3]\n",
    "    train_full_set[idx] = img\n",
    "    \n",
    "print('train_full_set.shape =>', train_full_set.shape)\n",
    "print('train_full_labels.shape =>', train_full_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e131819",
   "metadata": {
    "papermill": {
     "duration": 0.026227,
     "end_time": "2021-11-07T17:42:58.220142",
     "exception": false,
     "start_time": "2021-11-07T17:42:58.193915",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Split the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "968905a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T17:42:58.276156Z",
     "iopub.status.busy": "2021-11-07T17:42:58.275432Z",
     "iopub.status.idle": "2021-11-07T17:42:58.352063Z",
     "shell.execute_reply": "2021-11-07T17:42:58.352523Z",
     "shell.execute_reply.started": "2021-11-07T16:59:23.670269Z"
    },
    "papermill": {
     "duration": 0.106269,
     "end_time": "2021-11-07T17:42:58.352713",
     "exception": false,
     "start_time": "2021-11-07T17:42:58.246444",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape => (10752, 32, 32, 3)\n",
      "X_valid.shape => (2688, 32, 32, 3)\n",
      "y_train.shape => (10752,)\n",
      "y_valid.shape => (2688,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(train_full_set, train_full_labels, \n",
    "                                                      test_size=0.2, shuffle=True, random_state=42)\n",
    "\n",
    "print('X_train.shape =>', X_train.shape)\n",
    "print('X_valid.shape =>', X_valid.shape)\n",
    "print('y_train.shape =>', y_train.shape)\n",
    "print('y_valid.shape =>', y_valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b7a223",
   "metadata": {
    "papermill": {
     "duration": 0.026175,
     "end_time": "2021-11-07T17:42:58.406020",
     "exception": false,
     "start_time": "2021-11-07T17:42:58.379845",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07878ec0",
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2021-11-07T17:42:58.471417Z",
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     "shell.execute_reply": "2021-11-07T17:42:58.473229Z",
     "shell.execute_reply.started": "2021-11-07T16:59:23.749645Z"
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     "exception": false,
     "start_time": "2021-11-07T17:42:58.432523",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_model2(activation='relu', optimizer='adam', kernel_initializer='uniform'):\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', \n",
    "                     activation ='relu', input_shape = (32,32,3)))\n",
    "    \n",
    "    model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))\n",
    "    model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))\n",
    "    model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(256, activation = \"relu\"))\n",
    "    model.add(Dense(256, activation = 'relu'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(29, activation = \"softmax\"))\n",
    "    \n",
    "#     model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
    "    model.compile(loss='sparse_categorical_crossentropy', \n",
    "                      metrics=['accuracy'], \n",
    "                      optimizer=tf.keras.optimizers.Nadam(learning_rate=0.01))\n",
    "        \n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "21b47cba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T17:42:58.532822Z",
     "iopub.status.busy": "2021-11-07T17:42:58.532131Z",
     "iopub.status.idle": "2021-11-07T17:42:59.221353Z",
     "shell.execute_reply": "2021-11-07T17:42:59.220756Z",
     "shell.execute_reply.started": "2021-11-07T16:59:23.763520Z"
    },
    "papermill": {
     "duration": 0.720446,
     "end_time": "2021-11-07T17:42:59.221495",
     "exception": false,
     "start_time": "2021-11-07T17:42:58.501049",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from keras import Sequential\n",
    "from keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "808d2706",
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2021-11-07T17:42:59.280389Z",
     "iopub.status.busy": "2021-11-07T17:42:59.279739Z",
     "iopub.status.idle": "2021-11-07T17:43:00.082281Z",
     "shell.execute_reply": "2021-11-07T17:43:00.082825Z",
     "shell.execute_reply.started": "2021-11-07T16:59:24.426811Z"
    },
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     "duration": 0.834794,
     "end_time": "2021-11-07T17:43:00.083012",
     "exception": false,
     "start_time": "2021-11-07T17:42:59.248218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "User settings:\n",
      "\n",
      "   KMP_AFFINITY=granularity=fine,verbose,compact,1,0\n",
      "   KMP_BLOCKTIME=0\n",
      "   KMP_DUPLICATE_LIB_OK=True\n",
      "   KMP_INIT_AT_FORK=FALSE\n",
      "   KMP_SETTINGS=1\n",
      "   KMP_WARNINGS=0\n",
      "\n",
      "Effective settings:\n",
      "\n",
      "   KMP_ABORT_DELAY=0\n",
      "   KMP_ADAPTIVE_LOCK_PROPS='1,1024'\n",
      "   KMP_ALIGN_ALLOC=64\n",
      "   KMP_ALL_THREADPRIVATE=128\n",
      "   KMP_ATOMIC_MODE=2\n",
      "   KMP_BLOCKTIME=0\n",
      "   KMP_CPUINFO_FILE: value is not defined\n",
      "   KMP_DETERMINISTIC_REDUCTION=false\n",
      "   KMP_DEVICE_THREAD_LIMIT=2147483647\n",
      "   KMP_DISP_NUM_BUFFERS=7\n",
      "   KMP_DUPLICATE_LIB_OK=true\n",
      "   KMP_ENABLE_TASK_THROTTLING=true\n",
      "   KMP_FORCE_REDUCTION: value is not defined\n",
      "   KMP_FOREIGN_THREADS_THREADPRIVATE=true\n",
      "   KMP_FORKJOIN_BARRIER='2,2'\n",
      "   KMP_FORKJOIN_BARRIER_PATTERN='hyper,hyper'\n",
      "   KMP_GTID_MODE=3\n",
      "   KMP_HANDLE_SIGNALS=false\n",
      "   KMP_HOT_TEAMS_MAX_LEVEL=1\n",
      "   KMP_HOT_TEAMS_MODE=0\n",
      "   KMP_INIT_AT_FORK=true\n",
      "   KMP_LIBRARY=throughput\n",
      "   KMP_LOCK_KIND=queuing\n",
      "   KMP_MALLOC_POOL_INCR=1M\n",
      "   KMP_NUM_LOCKS_IN_BLOCK=1\n",
      "   KMP_PLAIN_BARRIER='2,2'\n",
      "   KMP_PLAIN_BARRIER_PATTERN='hyper,hyper'\n",
      "   KMP_REDUCTION_BARRIER='1,1'\n",
      "   KMP_REDUCTION_BARRIER_PATTERN='hyper,hyper'\n",
      "   KMP_SCHEDULE='static,balanced;guided,iterative'\n",
      "   KMP_SETTINGS=true\n",
      "   KMP_SPIN_BACKOFF_PARAMS='4096,100'\n",
      "   KMP_STACKOFFSET=64\n",
      "   KMP_STACKPAD=0\n",
      "   KMP_STACKSIZE=8M\n",
      "   KMP_STORAGE_MAP=false\n",
      "   KMP_TASKING=2\n",
      "   KMP_TASKLOOP_MIN_TASKS=0\n",
      "   KMP_TASK_STEALING_CONSTRAINT=1\n",
      "   KMP_TEAMS_THREAD_LIMIT=4\n",
      "   KMP_TOPOLOGY_METHOD=all\n",
      "   KMP_USE_YIELD=1\n",
      "   KMP_VERSION=false\n",
      "   KMP_WARNINGS=false\n",
      "   OMP_AFFINITY_FORMAT='OMP: pid %P tid %i thread %n bound to OS proc set {%A}'\n",
      "   OMP_ALLOCATOR=omp_default_mem_alloc\n",
      "   OMP_CANCELLATION=false\n",
      "   OMP_DEFAULT_DEVICE=0\n",
      "   OMP_DISPLAY_AFFINITY=false\n",
      "   OMP_DISPLAY_ENV=false\n",
      "   OMP_DYNAMIC=false\n",
      "   OMP_MAX_ACTIVE_LEVELS=1\n",
      "   OMP_MAX_TASK_PRIORITY=0\n",
      "   OMP_NESTED: deprecated; max-active-levels-var=1\n",
      "   OMP_NUM_THREADS: value is not defined\n",
      "   OMP_PLACES: value is not defined\n",
      "   OMP_PROC_BIND='intel'\n",
      "   OMP_SCHEDULE='static'\n",
      "   OMP_STACKSIZE=8M\n",
      "   OMP_TARGET_OFFLOAD=DEFAULT\n",
      "   OMP_THREAD_LIMIT=2147483647\n",
      "   OMP_WAIT_POLICY=PASSIVE\n",
      "   KMP_AFFINITY='verbose,warnings,respect,granularity=fine,compact,1,0'\n",
      "\n",
      "2021-11-07 17:42:59.319635: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    }
   ],
   "source": [
    "model = create_model2(kernel_initializer='he_uniform', activation='relu', optimizer='Nadam');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "efddc8a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T17:43:00.141153Z",
     "iopub.status.busy": "2021-11-07T17:43:00.140479Z",
     "iopub.status.idle": "2021-11-07T17:43:00.152442Z",
     "shell.execute_reply": "2021-11-07T17:43:00.151499Z",
     "shell.execute_reply.started": "2021-11-07T16:59:25.131155Z"
    },
    "papermill": {
     "duration": 0.042027,
     "end_time": "2021-11-07T17:43:00.152611",
     "exception": false,
     "start_time": "2021-11-07T17:43:00.110584",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 32, 32, 32)        2432      \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        25632     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               1048832   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 29)                7453      \n",
      "=================================================================\n",
      "Total params: 1,205,565\n",
      "Trainable params: 1,205,565\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7bd0b9c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T17:43:00.220048Z",
     "iopub.status.busy": "2021-11-07T17:43:00.219291Z",
     "iopub.status.idle": "2021-11-07T17:43:00.224643Z",
     "shell.execute_reply": "2021-11-07T17:43:00.225094Z",
     "shell.execute_reply.started": "2021-11-07T16:59:25.145831Z"
    },
    "papermill": {
     "duration": 0.044193,
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     "exception": false,
     "start_time": "2021-11-07T17:43:00.181072",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "early_stopp = tf.keras.callbacks.EarlyStopping(patience=5, restore_best_weights=True)\n",
    "\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(\"weights.hdf5\",\n",
    "                                                 monitor='val_accuracy',\n",
    "                                                 verbose=1,\n",
    "                                                 save_best_only=True)\n",
    "\n",
    "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n",
    "                                                  factor=0.5,\n",
    "                                                  patience=4,\n",
    "                                                  min_lr=0.00005,\n",
    "                                                  verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc3e4e46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T17:43:00.287336Z",
     "iopub.status.busy": "2021-11-07T17:43:00.286648Z",
     "iopub.status.idle": "2021-11-07T17:43:00.292042Z",
     "shell.execute_reply": "2021-11-07T17:43:00.291372Z",
     "shell.execute_reply.started": "2021-11-07T16:59:25.165072Z"
    },
    "papermill": {
     "duration": 0.039085,
     "end_time": "2021-11-07T17:43:00.292179",
     "exception": false,
     "start_time": "2021-11-07T17:43:00.253094",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint\n",
    "aug = ImageDataGenerator(\n",
    "        rotation_range=10,  \n",
    "        zoom_range = 0.1, \n",
    "        width_shift_range=0.1,  \n",
    "        height_shift_range=0.1)\n",
    "\n",
    "gen_train = aug.flow(X_train, y_train, batch_size=64)\n",
    "\n",
    "gen_val = aug.flow(X_valid, y_valid, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "57a5de08",
   "metadata": {
    "_kg_hide-output": true,
    "execution": {
     "iopub.execute_input": "2021-11-07T17:43:00.353209Z",
     "iopub.status.busy": "2021-11-07T17:43:00.352559Z",
     "iopub.status.idle": "2021-11-07T18:49:07.200045Z",
     "shell.execute_reply": "2021-11-07T18:49:07.199301Z"
    },
    "papermill": {
     "duration": 3966.880378,
     "end_time": "2021-11-07T18:49:07.200207",
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     "start_time": "2021-11-07T17:43:00.319829",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-07 17:43:00.475148: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "168/168 [==============================] - 36s 208ms/step - loss: 2.7808 - accuracy: 0.1578 - val_loss: 1.7206 - val_accuracy: 0.4416\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.44159, saving model to weights.hdf5\n",
      "Epoch 2/100\n",
      "168/168 [==============================] - 35s 205ms/step - loss: 1.4877 - accuracy: 0.4928 - val_loss: 0.8957 - val_accuracy: 0.6957\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.44159 to 0.69568, saving model to weights.hdf5\n",
      "Epoch 3/100\n",
      "168/168 [==============================] - 34s 205ms/step - loss: 0.9513 - accuracy: 0.6728 - val_loss: 0.5665 - val_accuracy: 0.8103\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.69568 to 0.81027, saving model to weights.hdf5\n",
      "Epoch 4/100\n",
      "168/168 [==============================] - 34s 205ms/step - loss: 0.7119 - accuracy: 0.7546 - val_loss: 0.4227 - val_accuracy: 0.8650\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.81027 to 0.86496, saving model to weights.hdf5\n",
      "Epoch 5/100\n",
      "168/168 [==============================] - 35s 205ms/step - loss: 0.5582 - accuracy: 0.8158 - val_loss: 0.3417 - val_accuracy: 0.8929\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.86496 to 0.89286, saving model to weights.hdf5\n",
      "Epoch 6/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.4814 - accuracy: 0.8419 - val_loss: 0.3114 - val_accuracy: 0.9062\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.89286 to 0.90625, saving model to weights.hdf5\n",
      "Epoch 7/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.4047 - accuracy: 0.8682 - val_loss: 0.2782 - val_accuracy: 0.9182\n",
      "\n",
      "Epoch 00007: val_accuracy improved from 0.90625 to 0.91815, saving model to weights.hdf5\n",
      "Epoch 8/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.3754 - accuracy: 0.8763 - val_loss: 0.2578 - val_accuracy: 0.9211\n",
      "\n",
      "Epoch 00008: val_accuracy improved from 0.91815 to 0.92113, saving model to weights.hdf5\n",
      "Epoch 9/100\n",
      "168/168 [==============================] - 35s 209ms/step - loss: 0.3419 - accuracy: 0.8914 - val_loss: 0.2295 - val_accuracy: 0.9241\n",
      "\n",
      "Epoch 00009: val_accuracy improved from 0.92113 to 0.92411, saving model to weights.hdf5\n",
      "Epoch 10/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.3182 - accuracy: 0.8951 - val_loss: 0.1879 - val_accuracy: 0.9386\n",
      "\n",
      "Epoch 00010: val_accuracy improved from 0.92411 to 0.93862, saving model to weights.hdf5\n",
      "Epoch 11/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.2875 - accuracy: 0.9067 - val_loss: 0.1971 - val_accuracy: 0.9423\n",
      "\n",
      "Epoch 00011: val_accuracy improved from 0.93862 to 0.94234, saving model to weights.hdf5\n",
      "Epoch 12/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.2671 - accuracy: 0.9165 - val_loss: 0.1990 - val_accuracy: 0.9334\n",
      "\n",
      "Epoch 00012: val_accuracy did not improve from 0.94234\n",
      "Epoch 13/100\n",
      "168/168 [==============================] - 35s 207ms/step - loss: 0.2422 - accuracy: 0.9206 - val_loss: 0.1683 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00013: val_accuracy improved from 0.94234 to 0.94940, saving model to weights.hdf5\n",
      "Epoch 14/100\n",
      "168/168 [==============================] - 35s 208ms/step - loss: 0.2348 - accuracy: 0.9262 - val_loss: 0.1843 - val_accuracy: 0.9464\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.94940\n",
      "Epoch 15/100\n",
      "168/168 [==============================] - 35s 207ms/step - loss: 0.2191 - accuracy: 0.9322 - val_loss: 0.1582 - val_accuracy: 0.9494\n",
      "\n",
      "Epoch 00015: val_accuracy did not improve from 0.94940\n",
      "Epoch 16/100\n",
      "168/168 [==============================] - 35s 211ms/step - loss: 0.2083 - accuracy: 0.9349 - val_loss: 0.1530 - val_accuracy: 0.9557\n",
      "\n",
      "Epoch 00016: val_accuracy improved from 0.94940 to 0.95573, saving model to weights.hdf5\n",
      "Epoch 17/100\n",
      "168/168 [==============================] - 35s 208ms/step - loss: 0.2101 - accuracy: 0.9327 - val_loss: 0.1475 - val_accuracy: 0.9583\n",
      "\n",
      "Epoch 00017: val_accuracy improved from 0.95573 to 0.95833, saving model to weights.hdf5\n",
      "Epoch 18/100\n",
      "168/168 [==============================] - 35s 207ms/step - loss: 0.1938 - accuracy: 0.9381 - val_loss: 0.1473 - val_accuracy: 0.9591\n",
      "\n",
      "Epoch 00018: val_accuracy improved from 0.95833 to 0.95908, saving model to weights.hdf5\n",
      "Epoch 19/100\n",
      "168/168 [==============================] - 35s 208ms/step - loss: 0.1802 - accuracy: 0.9423 - val_loss: 0.1504 - val_accuracy: 0.9591\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.95908\n",
      "Epoch 20/100\n",
      "168/168 [==============================] - 35s 208ms/step - loss: 0.1787 - accuracy: 0.9423 - val_loss: 0.1457 - val_accuracy: 0.9550\n",
      "\n",
      "Epoch 00020: val_accuracy did not improve from 0.95908\n",
      "Epoch 21/100\n",
      "168/168 [==============================] - 35s 207ms/step - loss: 0.1775 - accuracy: 0.9429 - val_loss: 0.1556 - val_accuracy: 0.9513\n",
      "\n",
      "Epoch 00021: val_accuracy did not improve from 0.95908\n",
      "Epoch 22/100\n",
      "168/168 [==============================] - 36s 211ms/step - loss: 0.1776 - accuracy: 0.9461 - val_loss: 0.1283 - val_accuracy: 0.9624\n",
      "\n",
      "Epoch 00022: val_accuracy improved from 0.95908 to 0.96243, saving model to weights.hdf5\n",
      "Epoch 23/100\n",
      "168/168 [==============================] - 35s 208ms/step - loss: 0.1567 - accuracy: 0.9497 - val_loss: 0.1392 - val_accuracy: 0.9598\n",
      "\n",
      "Epoch 00023: val_accuracy did not improve from 0.96243\n",
      "Epoch 24/100\n",
      "168/168 [==============================] - 35s 208ms/step - loss: 0.1672 - accuracy: 0.9479 - val_loss: 0.1337 - val_accuracy: 0.9632\n",
      "\n",
      "Epoch 00024: val_accuracy improved from 0.96243 to 0.96317, saving model to weights.hdf5\n",
      "Epoch 25/100\n",
      "168/168 [==============================] - 35s 210ms/step - loss: 0.1578 - accuracy: 0.9477 - val_loss: 0.1678 - val_accuracy: 0.9501\n",
      "\n",
      "Epoch 00025: val_accuracy did not improve from 0.96317\n",
      "Epoch 26/100\n",
      "168/168 [==============================] - 36s 213ms/step - loss: 0.1498 - accuracy: 0.9519 - val_loss: 0.1184 - val_accuracy: 0.9650\n",
      "\n",
      "Epoch 00026: val_accuracy improved from 0.96317 to 0.96503, saving model to weights.hdf5\n",
      "Epoch 27/100\n",
      "168/168 [==============================] - 35s 210ms/step - loss: 0.1412 - accuracy: 0.9553 - val_loss: 0.1185 - val_accuracy: 0.9647\n",
      "\n",
      "Epoch 00027: val_accuracy did not improve from 0.96503\n",
      "Epoch 28/100\n",
      "168/168 [==============================] - 35s 209ms/step - loss: 0.1405 - accuracy: 0.9571 - val_loss: 0.1356 - val_accuracy: 0.9609\n",
      "\n",
      "Epoch 00028: val_accuracy did not improve from 0.96503\n",
      "Epoch 29/100\n",
      "168/168 [==============================] - 35s 209ms/step - loss: 0.1379 - accuracy: 0.9590 - val_loss: 0.1312 - val_accuracy: 0.9650\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.96503\n",
      "Epoch 30/100\n",
      "168/168 [==============================] - 35s 209ms/step - loss: 0.1351 - accuracy: 0.9575 - val_loss: 0.1228 - val_accuracy: 0.9609\n",
      "\n",
      "Epoch 00030: val_accuracy did not improve from 0.96503\n",
      "\n",
      "Epoch 00030: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
      "Epoch 31/100\n",
      "168/168 [==============================] - 35s 209ms/step - loss: 0.0980 - accuracy: 0.9674 - val_loss: 0.1097 - val_accuracy: 0.9665\n",
      "\n",
      "Epoch 00031: val_accuracy improved from 0.96503 to 0.96652, saving model to weights.hdf5\n",
      "Epoch 32/100\n",
      "168/168 [==============================] - 35s 211ms/step - loss: 0.1045 - accuracy: 0.9660 - val_loss: 0.1043 - val_accuracy: 0.9710\n",
      "\n",
      "Epoch 00032: val_accuracy improved from 0.96652 to 0.97098, saving model to weights.hdf5\n",
      "Epoch 33/100\n",
      "168/168 [==============================] - 35s 210ms/step - loss: 0.0886 - accuracy: 0.9724 - val_loss: 0.1219 - val_accuracy: 0.9658\n",
      "\n",
      "Epoch 00033: val_accuracy did not improve from 0.97098\n",
      "Epoch 34/100\n",
      "168/168 [==============================] - 35s 210ms/step - loss: 0.0894 - accuracy: 0.9739 - val_loss: 0.1059 - val_accuracy: 0.9725\n",
      "\n",
      "Epoch 00034: val_accuracy improved from 0.97098 to 0.97247, saving model to weights.hdf5\n",
      "Epoch 35/100\n",
      "168/168 [==============================] - 36s 212ms/step - loss: 0.0895 - accuracy: 0.9712 - val_loss: 0.1087 - val_accuracy: 0.9702\n",
      "\n",
      "Epoch 00035: val_accuracy did not improve from 0.97247\n",
      "Epoch 36/100\n",
      "168/168 [==============================] - 36s 212ms/step - loss: 0.0839 - accuracy: 0.9754 - val_loss: 0.1030 - val_accuracy: 0.9695\n",
      "\n",
      "Epoch 00036: val_accuracy did not improve from 0.97247\n",
      "Epoch 37/100\n",
      "168/168 [==============================] - 36s 214ms/step - loss: 0.0903 - accuracy: 0.9722 - val_loss: 0.1167 - val_accuracy: 0.9673\n",
      "\n",
      "Epoch 00037: val_accuracy did not improve from 0.97247\n",
      "Epoch 38/100\n",
      "168/168 [==============================] - 36s 212ms/step - loss: 0.0887 - accuracy: 0.9723 - val_loss: 0.0998 - val_accuracy: 0.9688\n",
      "\n",
      "Epoch 00038: val_accuracy did not improve from 0.97247\n",
      "Epoch 39/100\n",
      "168/168 [==============================] - 35s 211ms/step - loss: 0.0826 - accuracy: 0.9750 - val_loss: 0.1170 - val_accuracy: 0.9688\n",
      "\n",
      "Epoch 00039: val_accuracy did not improve from 0.97247\n",
      "Epoch 40/100\n",
      "168/168 [==============================] - 36s 212ms/step - loss: 0.0829 - accuracy: 0.9738 - val_loss: 0.1024 - val_accuracy: 0.9676\n",
      "\n",
      "Epoch 00040: val_accuracy did not improve from 0.97247\n",
      "Epoch 41/100\n",
      "168/168 [==============================] - 36s 213ms/step - loss: 0.0916 - accuracy: 0.9726 - val_loss: 0.0941 - val_accuracy: 0.9728\n",
      "\n",
      "Epoch 00041: val_accuracy improved from 0.97247 to 0.97284, saving model to weights.hdf5\n",
      "Epoch 42/100\n",
      "168/168 [==============================] - 35s 210ms/step - loss: 0.0801 - accuracy: 0.9732 - val_loss: 0.1055 - val_accuracy: 0.9721\n",
      "\n",
      "Epoch 00042: val_accuracy did not improve from 0.97284\n",
      "Epoch 43/100\n",
      "168/168 [==============================] - 36s 211ms/step - loss: 0.0805 - accuracy: 0.9750 - val_loss: 0.1166 - val_accuracy: 0.9684\n",
      "\n",
      "Epoch 00043: val_accuracy did not improve from 0.97284\n",
      "Epoch 44/100\n",
      "168/168 [==============================] - 36s 212ms/step - loss: 0.0788 - accuracy: 0.9731 - val_loss: 0.1192 - val_accuracy: 0.9661\n",
      "\n",
      "Epoch 00044: val_accuracy did not improve from 0.97284\n",
      "Epoch 45/100\n",
      "168/168 [==============================] - 36s 212ms/step - loss: 0.0778 - accuracy: 0.9754 - val_loss: 0.1114 - val_accuracy: 0.9684\n",
      "\n",
      "Epoch 00045: val_accuracy did not improve from 0.97284\n",
      "\n",
      "Epoch 00045: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "Epoch 46/100\n",
      "168/168 [==============================] - 36s 213ms/step - loss: 0.0726 - accuracy: 0.9769 - val_loss: 0.0887 - val_accuracy: 0.9777\n",
      "\n",
      "Epoch 00046: val_accuracy improved from 0.97284 to 0.97768, saving model to weights.hdf5\n",
      "Epoch 47/100\n",
      "168/168 [==============================] - 36s 213ms/step - loss: 0.0650 - accuracy: 0.9796 - val_loss: 0.1006 - val_accuracy: 0.9732\n",
      "\n",
      "Epoch 00047: val_accuracy did not improve from 0.97768\n",
      "Epoch 48/100\n",
      "168/168 [==============================] - 36s 215ms/step - loss: 0.0620 - accuracy: 0.9795 - val_loss: 0.1071 - val_accuracy: 0.9725\n",
      "\n",
      "Epoch 00048: val_accuracy did not improve from 0.97768\n",
      "Epoch 49/100\n",
      "168/168 [==============================] - 36s 213ms/step - loss: 0.0651 - accuracy: 0.9801 - val_loss: 0.0932 - val_accuracy: 0.9769\n",
      "\n",
      "Epoch 00049: val_accuracy did not improve from 0.97768\n",
      "Epoch 50/100\n",
      "168/168 [==============================] - 36s 214ms/step - loss: 0.0603 - accuracy: 0.9802 - val_loss: 0.1069 - val_accuracy: 0.9725\n",
      "\n",
      "Epoch 00050: val_accuracy did not improve from 0.97768\n",
      "\n",
      "Epoch 00050: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "Epoch 51/100\n",
      "168/168 [==============================] - 36s 215ms/step - loss: 0.0612 - accuracy: 0.9799 - val_loss: 0.0985 - val_accuracy: 0.9743\n",
      "\n",
      "Epoch 00051: val_accuracy did not improve from 0.97768\n",
      "Epoch 52/100\n",
      "168/168 [==============================] - 36s 213ms/step - loss: 0.0553 - accuracy: 0.9817 - val_loss: 0.0957 - val_accuracy: 0.9732\n",
      "\n",
      "Epoch 00052: val_accuracy did not improve from 0.97768\n",
      "Epoch 53/100\n",
      "168/168 [==============================] - 36s 212ms/step - loss: 0.0544 - accuracy: 0.9824 - val_loss: 0.0908 - val_accuracy: 0.9740\n",
      "\n",
      "Epoch 00053: val_accuracy did not improve from 0.97768\n",
      "Epoch 54/100\n",
      "168/168 [==============================] - 36s 215ms/step - loss: 0.0493 - accuracy: 0.9844 - val_loss: 0.0926 - val_accuracy: 0.9762\n",
      "\n",
      "Epoch 00054: val_accuracy did not improve from 0.97768\n",
      "\n",
      "Epoch 00054: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "Epoch 55/100\n",
      "168/168 [==============================] - 36s 212ms/step - loss: 0.0530 - accuracy: 0.9834 - val_loss: 0.0865 - val_accuracy: 0.9754\n",
      "\n",
      "Epoch 00055: val_accuracy did not improve from 0.97768\n",
      "Epoch 56/100\n",
      "168/168 [==============================] - 36s 213ms/step - loss: 0.0507 - accuracy: 0.9843 - val_loss: 0.0953 - val_accuracy: 0.9784\n",
      "\n",
      "Epoch 00056: val_accuracy improved from 0.97768 to 0.97842, saving model to weights.hdf5\n",
      "Epoch 57/100\n",
      "168/168 [==============================] - 34s 204ms/step - loss: 0.0486 - accuracy: 0.9849 - val_loss: 0.0874 - val_accuracy: 0.9762\n",
      "\n",
      "Epoch 00057: val_accuracy did not improve from 0.97842\n",
      "Epoch 58/100\n",
      "168/168 [==============================] - 36s 217ms/step - loss: 0.0480 - accuracy: 0.9850 - val_loss: 0.0879 - val_accuracy: 0.9777\n",
      "\n",
      "Epoch 00058: val_accuracy did not improve from 0.97842\n",
      "Epoch 59/100\n",
      "168/168 [==============================] - 35s 205ms/step - loss: 0.0490 - accuracy: 0.9856 - val_loss: 0.0821 - val_accuracy: 0.9792\n",
      "\n",
      "Epoch 00059: val_accuracy improved from 0.97842 to 0.97917, saving model to weights.hdf5\n",
      "Epoch 60/100\n",
      "168/168 [==============================] - 36s 213ms/step - loss: 0.0472 - accuracy: 0.9854 - val_loss: 0.0942 - val_accuracy: 0.9781\n",
      "\n",
      "Epoch 00060: val_accuracy did not improve from 0.97917\n",
      "Epoch 61/100\n",
      "168/168 [==============================] - 34s 205ms/step - loss: 0.0490 - accuracy: 0.9844 - val_loss: 0.0986 - val_accuracy: 0.9740\n",
      "\n",
      "Epoch 00061: val_accuracy did not improve from 0.97917\n",
      "Epoch 62/100\n",
      "168/168 [==============================] - 36s 213ms/step - loss: 0.0495 - accuracy: 0.9836 - val_loss: 0.0984 - val_accuracy: 0.9766\n",
      "\n",
      "Epoch 00062: val_accuracy did not improve from 0.97917\n",
      "Epoch 63/100\n",
      "168/168 [==============================] - 36s 213ms/step - loss: 0.0515 - accuracy: 0.9839 - val_loss: 0.0854 - val_accuracy: 0.9732\n",
      "\n",
      "Epoch 00063: val_accuracy did not improve from 0.97917\n",
      "\n",
      "Epoch 00063: ReduceLROnPlateau reducing learning rate to 5e-05.\n",
      "Epoch 64/100\n",
      "168/168 [==============================] - 35s 205ms/step - loss: 0.0439 - accuracy: 0.9862 - val_loss: 0.0949 - val_accuracy: 0.9769\n",
      "\n",
      "Epoch 00064: val_accuracy did not improve from 0.97917\n",
      "Epoch 65/100\n",
      "168/168 [==============================] - 36s 214ms/step - loss: 0.0456 - accuracy: 0.9852 - val_loss: 0.0875 - val_accuracy: 0.9769\n",
      "\n",
      "Epoch 00065: val_accuracy did not improve from 0.97917\n",
      "Epoch 66/100\n",
      "168/168 [==============================] - 34s 204ms/step - loss: 0.0417 - accuracy: 0.9862 - val_loss: 0.0891 - val_accuracy: 0.9758\n",
      "\n",
      "Epoch 00066: val_accuracy did not improve from 0.97917\n",
      "Epoch 67/100\n",
      "168/168 [==============================] - 36s 216ms/step - loss: 0.0447 - accuracy: 0.9853 - val_loss: 0.1097 - val_accuracy: 0.9754\n",
      "\n",
      "Epoch 00067: val_accuracy did not improve from 0.97917\n",
      "Epoch 68/100\n",
      "168/168 [==============================] - 34s 205ms/step - loss: 0.0465 - accuracy: 0.9857 - val_loss: 0.0980 - val_accuracy: 0.9740\n",
      "\n",
      "Epoch 00068: val_accuracy did not improve from 0.97917\n",
      "Epoch 69/100\n",
      "168/168 [==============================] - 34s 205ms/step - loss: 0.0421 - accuracy: 0.9855 - val_loss: 0.1005 - val_accuracy: 0.9736\n",
      "\n",
      "Epoch 00069: val_accuracy did not improve from 0.97917\n",
      "Epoch 70/100\n",
      "168/168 [==============================] - 36s 214ms/step - loss: 0.0414 - accuracy: 0.9872 - val_loss: 0.0990 - val_accuracy: 0.9751\n",
      "\n",
      "Epoch 00070: val_accuracy did not improve from 0.97917\n",
      "Epoch 71/100\n",
      "168/168 [==============================] - 34s 204ms/step - loss: 0.0462 - accuracy: 0.9856 - val_loss: 0.0991 - val_accuracy: 0.9762\n",
      "\n",
      "Epoch 00071: val_accuracy did not improve from 0.97917\n",
      "Epoch 72/100\n",
      "168/168 [==============================] - 36s 217ms/step - loss: 0.0430 - accuracy: 0.9864 - val_loss: 0.1030 - val_accuracy: 0.9743\n",
      "\n",
      "Epoch 00072: val_accuracy did not improve from 0.97917\n",
      "Epoch 73/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.0465 - accuracy: 0.9860 - val_loss: 0.0841 - val_accuracy: 0.9766\n",
      "\n",
      "Epoch 00073: val_accuracy did not improve from 0.97917\n",
      "Epoch 74/100\n",
      "168/168 [==============================] - 37s 217ms/step - loss: 0.0445 - accuracy: 0.9863 - val_loss: 0.0842 - val_accuracy: 0.9769\n",
      "\n",
      "Epoch 00074: val_accuracy did not improve from 0.97917\n",
      "Epoch 75/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.0413 - accuracy: 0.9866 - val_loss: 0.0921 - val_accuracy: 0.9773\n",
      "\n",
      "Epoch 00075: val_accuracy did not improve from 0.97917\n",
      "Epoch 76/100\n",
      "168/168 [==============================] - 35s 205ms/step - loss: 0.0414 - accuracy: 0.9858 - val_loss: 0.0916 - val_accuracy: 0.9769\n",
      "\n",
      "Epoch 00076: val_accuracy did not improve from 0.97917\n",
      "Epoch 77/100\n",
      "168/168 [==============================] - 36s 216ms/step - loss: 0.0438 - accuracy: 0.9847 - val_loss: 0.0935 - val_accuracy: 0.9773\n",
      "\n",
      "Epoch 00077: val_accuracy did not improve from 0.97917\n",
      "Epoch 78/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.0391 - accuracy: 0.9866 - val_loss: 0.0968 - val_accuracy: 0.9795\n",
      "\n",
      "Epoch 00078: val_accuracy improved from 0.97917 to 0.97954, saving model to weights.hdf5\n",
      "Epoch 79/100\n",
      "168/168 [==============================] - 37s 221ms/step - loss: 0.0394 - accuracy: 0.9888 - val_loss: 0.1043 - val_accuracy: 0.9751\n",
      "\n",
      "Epoch 00079: val_accuracy did not improve from 0.97954\n",
      "Epoch 80/100\n",
      "168/168 [==============================] - 34s 205ms/step - loss: 0.0440 - accuracy: 0.9857 - val_loss: 0.1037 - val_accuracy: 0.9736\n",
      "\n",
      "Epoch 00080: val_accuracy did not improve from 0.97954\n",
      "Epoch 81/100\n",
      "168/168 [==============================] - 36s 217ms/step - loss: 0.0452 - accuracy: 0.9853 - val_loss: 0.1057 - val_accuracy: 0.9732\n",
      "\n",
      "Epoch 00081: val_accuracy did not improve from 0.97954\n",
      "Epoch 82/100\n",
      "168/168 [==============================] - 35s 210ms/step - loss: 0.0443 - accuracy: 0.9854 - val_loss: 0.0929 - val_accuracy: 0.9758\n",
      "\n",
      "Epoch 00082: val_accuracy did not improve from 0.97954\n",
      "Epoch 83/100\n",
      "168/168 [==============================] - 35s 208ms/step - loss: 0.0407 - accuracy: 0.9871 - val_loss: 0.0965 - val_accuracy: 0.9773\n",
      "\n",
      "Epoch 00083: val_accuracy did not improve from 0.97954\n",
      "Epoch 84/100\n",
      "168/168 [==============================] - 37s 217ms/step - loss: 0.0439 - accuracy: 0.9843 - val_loss: 0.0810 - val_accuracy: 0.9792\n",
      "\n",
      "Epoch 00084: val_accuracy did not improve from 0.97954\n",
      "Epoch 85/100\n",
      "168/168 [==============================] - 34s 204ms/step - loss: 0.0430 - accuracy: 0.9862 - val_loss: 0.1030 - val_accuracy: 0.9762\n",
      "\n",
      "Epoch 00085: val_accuracy did not improve from 0.97954\n",
      "Epoch 86/100\n",
      "168/168 [==============================] - 37s 218ms/step - loss: 0.0419 - accuracy: 0.9862 - val_loss: 0.0936 - val_accuracy: 0.9773\n",
      "\n",
      "Epoch 00086: val_accuracy did not improve from 0.97954\n",
      "Epoch 87/100\n",
      "168/168 [==============================] - 34s 204ms/step - loss: 0.0408 - accuracy: 0.9874 - val_loss: 0.0956 - val_accuracy: 0.9773\n",
      "\n",
      "Epoch 00087: val_accuracy did not improve from 0.97954\n",
      "Epoch 88/100\n",
      "168/168 [==============================] - 37s 221ms/step - loss: 0.0456 - accuracy: 0.9856 - val_loss: 0.0899 - val_accuracy: 0.9803\n",
      "\n",
      "Epoch 00088: val_accuracy improved from 0.97954 to 0.98028, saving model to weights.hdf5\n",
      "Epoch 89/100\n",
      "168/168 [==============================] - 34s 205ms/step - loss: 0.0369 - accuracy: 0.9876 - val_loss: 0.0995 - val_accuracy: 0.9792\n",
      "\n",
      "Epoch 00089: val_accuracy did not improve from 0.98028\n",
      "Epoch 90/100\n",
      "168/168 [==============================] - 37s 218ms/step - loss: 0.0424 - accuracy: 0.9867 - val_loss: 0.0831 - val_accuracy: 0.9781\n",
      "\n",
      "Epoch 00090: val_accuracy did not improve from 0.98028\n",
      "Epoch 91/100\n",
      "168/168 [==============================] - 34s 205ms/step - loss: 0.0442 - accuracy: 0.9863 - val_loss: 0.0940 - val_accuracy: 0.9762\n",
      "\n",
      "Epoch 00091: val_accuracy did not improve from 0.98028\n",
      "Epoch 92/100\n",
      "168/168 [==============================] - 37s 222ms/step - loss: 0.0424 - accuracy: 0.9876 - val_loss: 0.0933 - val_accuracy: 0.9769\n",
      "\n",
      "Epoch 00092: val_accuracy did not improve from 0.98028\n",
      "Epoch 93/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.0393 - accuracy: 0.9875 - val_loss: 0.0919 - val_accuracy: 0.9766\n",
      "\n",
      "Epoch 00093: val_accuracy did not improve from 0.98028\n",
      "Epoch 94/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.0424 - accuracy: 0.9860 - val_loss: 0.0943 - val_accuracy: 0.9751\n",
      "\n",
      "Epoch 00094: val_accuracy did not improve from 0.98028\n",
      "Epoch 95/100\n",
      "168/168 [==============================] - 37s 219ms/step - loss: 0.0377 - accuracy: 0.9872 - val_loss: 0.0948 - val_accuracy: 0.9751\n",
      "\n",
      "Epoch 00095: val_accuracy did not improve from 0.98028\n",
      "Epoch 96/100\n",
      "168/168 [==============================] - 35s 207ms/step - loss: 0.0426 - accuracy: 0.9873 - val_loss: 0.0843 - val_accuracy: 0.9792\n",
      "\n",
      "Epoch 00096: val_accuracy did not improve from 0.98028\n",
      "Epoch 97/100\n",
      "168/168 [==============================] - 37s 219ms/step - loss: 0.0426 - accuracy: 0.9878 - val_loss: 0.0916 - val_accuracy: 0.9792\n",
      "\n",
      "Epoch 00097: val_accuracy did not improve from 0.98028\n",
      "Epoch 98/100\n",
      "168/168 [==============================] - 34s 204ms/step - loss: 0.0433 - accuracy: 0.9863 - val_loss: 0.0924 - val_accuracy: 0.9751\n",
      "\n",
      "Epoch 00098: val_accuracy did not improve from 0.98028\n",
      "Epoch 99/100\n",
      "168/168 [==============================] - 37s 219ms/step - loss: 0.0361 - accuracy: 0.9884 - val_loss: 0.1000 - val_accuracy: 0.9762\n",
      "\n",
      "Epoch 00099: val_accuracy did not improve from 0.98028\n",
      "Epoch 100/100\n",
      "168/168 [==============================] - 35s 206ms/step - loss: 0.0423 - accuracy: 0.9865 - val_loss: 0.0883 - val_accuracy: 0.9766\n",
      "\n",
      "Epoch 00100: val_accuracy did not improve from 0.98028\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(gen_train, validation_data=gen_val, \n",
    "                    epochs=100, batch_size=512, callbacks=[checkpoint, reduce_lr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "159f2434",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:49:17.733880Z",
     "iopub.status.busy": "2021-11-07T18:49:17.732829Z",
     "iopub.status.idle": "2021-11-07T18:49:17.751646Z",
     "shell.execute_reply": "2021-11-07T18:49:17.751080Z"
    },
    "papermill": {
     "duration": 5.254034,
     "end_time": "2021-11-07T18:49:17.751821",
     "exception": false,
     "start_time": "2021-11-07T18:49:12.497787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.load_weights(\"weights.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee7fe4c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:49:28.473096Z",
     "iopub.status.busy": "2021-11-07T18:49:28.472053Z",
     "iopub.status.idle": "2021-11-07T18:49:28.744941Z",
     "shell.execute_reply": "2021-11-07T18:49:28.745435Z"
    },
    "papermill": {
     "duration": 5.653529,
     "end_time": "2021-11-07T18:49:28.745616",
     "exception": false,
     "start_time": "2021-11-07T18:49:23.092087",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# plot loss function for train and validation.\n",
    "sns.set()\n",
    "plt.title('loss and validation loss with epochs', \n",
    "          fontsize=16)\n",
    "\n",
    "plt.plot(history.history['loss'],\n",
    "         marker='o',\n",
    "         color=\"green\",\n",
    "         label=\"loss\")\n",
    "\n",
    "plt.plot(history.history['val_loss'],\n",
    "         marker='o',\n",
    "         color=\"orange\", \n",
    "         label=\"val_loss\")\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6120e4a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:49:39.261476Z",
     "iopub.status.busy": "2021-11-07T18:49:39.260439Z",
     "iopub.status.idle": "2021-11-07T18:49:39.518127Z",
     "shell.execute_reply": "2021-11-07T18:49:39.518643Z"
    },
    "papermill": {
     "duration": 5.56991,
     "end_time": "2021-11-07T18:49:39.518817",
     "exception": false,
     "start_time": "2021-11-07T18:49:33.948907",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot accuracy for train and validation data.\n",
    "plt.title('accuracy and validation accuracy with epochs',\n",
    "          fontsize=16)\n",
    "\n",
    "plt.plot(history.history['accuracy'],\n",
    "         marker='o',\n",
    "         color=\"green\",\n",
    "         label=\"accuracy\")\n",
    "\n",
    "plt.plot(history.history['val_accuracy'],\n",
    "         marker='o',\n",
    "         color=\"orange\",\n",
    "         label=\"val_accuracy\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c3c2bb59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:49:50.020481Z",
     "iopub.status.busy": "2021-11-07T18:49:50.019460Z",
     "iopub.status.idle": "2021-11-07T18:50:01.781609Z",
     "shell.execute_reply": "2021-11-07T18:50:01.781093Z"
    },
    "papermill": {
     "duration": 17.059809,
     "end_time": "2021-11-07T18:50:01.781754",
     "exception": false,
     "start_time": "2021-11-07T18:49:44.721945",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss_all_data => 0.01967890001833439\n",
      "acc_all_data => 0.9947916865348816\n"
     ]
    }
   ],
   "source": [
    "loss_all_data, acc_all_data = model.evaluate(train_full_set, train_full_labels, verbose=0)\n",
    "print('loss_all_data =>', loss_all_data)\n",
    "print('acc_all_data =>', acc_all_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1c959177",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:50:12.377688Z",
     "iopub.status.busy": "2021-11-07T18:50:12.376710Z",
     "iopub.status.idle": "2021-11-07T18:50:25.592084Z",
     "shell.execute_reply": "2021-11-07T18:50:25.591521Z"
    },
    "papermill": {
     "duration": 18.51366,
     "end_time": "2021-11-07T18:50:25.592223",
     "exception": false,
     "start_time": "2021-11-07T18:50:07.078563",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "classes = np.argmax(model.predict(train_full_set), axis=-1)\n",
    "cm = confusion_matrix(train_full_labels, classes)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(cm, fmt='d', cmap='viridis', annot = True, square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b99faf",
   "metadata": {
    "papermill": {
     "duration": 5.425843,
     "end_time": "2021-11-07T18:50:36.238529",
     "exception": false,
     "start_time": "2021-11-07T18:50:30.812686",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Evaluation on Testing DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d7268091",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:50:46.926238Z",
     "iopub.status.busy": "2021-11-07T18:50:46.925529Z",
     "iopub.status.idle": "2021-11-07T18:50:47.140280Z",
     "shell.execute_reply": "2021-11-07T18:50:47.139749Z"
    },
    "papermill": {
     "duration": 5.67939,
     "end_time": "2021-11-07T18:50:47.140420",
     "exception": false,
     "start_time": "2021-11-07T18:50:41.461030",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    ../input/arabic-hwr-ai-pro-intake1/test/00000.png\n",
       "1    ../input/arabic-hwr-ai-pro-intake1/test/00001.png\n",
       "2    ../input/arabic-hwr-ai-pro-intake1/test/00002.png\n",
       "3    ../input/arabic-hwr-ai-pro-intake1/test/00003.png\n",
       "4    ../input/arabic-hwr-ai-pro-intake1/test/00004.png\n",
       "Name: Filepath, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels = pd.read_csv('../input/arabic-hwr-ai-pro-intake1/test.csv')\n",
    "test_images = Path(r'../input/arabic-hwr-ai-pro-intake1/test')\n",
    "\n",
    "## read these all training images paths as Series\n",
    "test_images_paths = pd.Series(sorted(list(test_images.glob(r'*.png'))), name='Filepath').astype(str)\n",
    "\n",
    "test_images_paths.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "70fec3dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:50:57.697506Z",
     "iopub.status.busy": "2021-11-07T18:50:57.696750Z",
     "iopub.status.idle": "2021-11-07T18:50:57.700212Z",
     "shell.execute_reply": "2021-11-07T18:50:57.700804Z"
    },
    "papermill": {
     "duration": 5.325434,
     "end_time": "2021-11-07T18:50:57.700988",
     "exception": false,
     "start_time": "2021-11-07T18:50:52.375554",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Instances in test_set is 3360\n"
     ]
    }
   ],
   "source": [
    "print('Number of Instances in test_set is', len(test_images_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3025fce5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:51:08.325966Z",
     "iopub.status.busy": "2021-11-07T18:51:08.324897Z",
     "iopub.status.idle": "2021-11-07T18:51:18.354446Z",
     "shell.execute_reply": "2021-11-07T18:51:18.355059Z"
    },
    "papermill": {
     "duration": 15.30735,
     "end_time": "2021-11-07T18:51:18.355239",
     "exception": false,
     "start_time": "2021-11-07T18:51:03.047889",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_full_set.shape => (3360, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "test_full_set = np.empty((3360, 32, 32, 3), dtype=np.float32)  #take only the first 3 channels\n",
    "\n",
    "for idx, path in enumerate(test_images_paths):\n",
    "    img = plt.imread(path)\n",
    "    img = img[:,:,:3]\n",
    "    test_full_set[idx] = img\n",
    "    \n",
    "print('test_full_set.shape =>', test_full_set.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6a7d2d96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:51:29.048425Z",
     "iopub.status.busy": "2021-11-07T18:51:29.047180Z",
     "iopub.status.idle": "2021-11-07T18:51:31.637431Z",
     "shell.execute_reply": "2021-11-07T18:51:31.636725Z"
    },
    "papermill": {
     "duration": 7.951049,
     "end_time": "2021-11-07T18:51:31.637571",
     "exception": false,
     "start_time": "2021-11-07T18:51:23.686522",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_preds_classes = np.argmax(model.predict(test_full_set), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "61da6f89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:51:42.244870Z",
     "iopub.status.busy": "2021-11-07T18:51:42.244149Z",
     "iopub.status.idle": "2021-11-07T18:51:42.248848Z",
     "shell.execute_reply": "2021-11-07T18:51:42.249360Z"
    },
    "papermill": {
     "duration": 5.237753,
     "end_time": "2021-11-07T18:51:42.249527",
     "exception": false,
     "start_time": "2021-11-07T18:51:37.011774",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_labels['label'] = y_preds_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6f930dbf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:51:52.793354Z",
     "iopub.status.busy": "2021-11-07T18:51:52.792671Z",
     "iopub.status.idle": "2021-11-07T18:51:52.809523Z",
     "shell.execute_reply": "2021-11-07T18:51:52.810074Z"
    },
    "papermill": {
     "duration": 5.256824,
     "end_time": "2021-11-07T18:51:52.810243",
     "exception": false,
     "start_time": "2021-11-07T18:51:47.553419",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\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>id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3355</th>\n",
       "      <td>3355</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>3356</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>3357</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>3358</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3359</th>\n",
       "      <td>3359</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3360 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  label\n",
       "0        0     26\n",
       "1        1     10\n",
       "2        2      1\n",
       "3        3     10\n",
       "4        4     15\n",
       "...    ...    ...\n",
       "3355  3355     16\n",
       "3356  3356      6\n",
       "3357  3357      4\n",
       "3358  3358     11\n",
       "3359  3359     20\n",
       "\n",
       "[3360 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6b0fb581",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-07T18:52:03.314981Z",
     "iopub.status.busy": "2021-11-07T18:52:03.314229Z",
     "iopub.status.idle": "2021-11-07T18:52:03.327882Z",
     "shell.execute_reply": "2021-11-07T18:52:03.328413Z"
    },
    "papermill": {
     "duration": 5.226223,
     "end_time": "2021-11-07T18:52:03.328579",
     "exception": false,
     "start_time": "2021-11-07T18:51:58.102356",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_labels[['id', 'label']].to_csv('/kaggle/working/submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef186d73",
   "metadata": {
    "papermill": {
     "duration": 5.304445,
     "end_time": "2021-11-07T18:52:14.007902",
     "exception": false,
     "start_time": "2021-11-07T18:52:08.703457",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Done :D"
   ]
  }
 ],
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