10 Digit Recognizer.ipynb

{
 "cells": [
  {
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    }
   },
   "cell_type": "markdown",
   "id": "illegal-february",
   "metadata": {
    "papermill": {
     "duration": 0.044706,
     "end_time": "2021-06-26T05:54:30.349951",
     "exception": false,
     "start_time": "2021-06-26T05:54:30.305245",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "![68222222101258.jpg](attachment:033ef31e-d3ff-4c86-9408-39d539b36845.jpg)\n",
    "# Digit Recognizer\n",
    "\n",
    "## Overview\n",
    "This competition uses the most basic mnist data in deep learning. We need to create a model to classify the numbers in the image. <br/>\n",
    "You can use other techniques instead of deep learning. However, this notebook will be written based on deep learning. <br/>\n",
    "In addition, we will use \"Pytorch\" among the frameworks that enable deep learning.\n",
    "\n",
    "#### My opinion :\n",
    "* 1) I think it is important to use various techniques and get used to deep learning through this competition.\n",
    "* 2) This book will model **Basic CNN / ResNet / DenseNet** :)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unusual-general",
   "metadata": {
    "papermill": {
     "duration": 0.042501,
     "end_time": "2021-06-26T05:54:30.435378",
     "exception": false,
     "start_time": "2021-06-26T05:54:30.392877",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "***\n",
    "## My Workflow\n",
    "\n",
    "#### 1. Import & Install libray\n",
    "* Import Basic libray\n",
    "* Import Enginnering libray\n",
    "\n",
    "#### 2. Check out my data\n",
    "* Check Shape / Info / Describe\n",
    "\n",
    "#### 3. Exploratory Data Analysis(EDA) with Visualization [Before Preprocessing]\n",
    "* Plot the null values\n",
    "* Plot the label Percent\n",
    "* Plot the image Data\n",
    "\n",
    "#### 4. Feature Enginnering\n",
    "* Split Train set / Validation set\n",
    "* Convert to data suitable for CNN model (Dataset / Dataloader)\n",
    "\n",
    "#### 5.Modeling\n",
    "* CNN Modeling\n",
    "* ResNet Modeling\n",
    "* DenseNet Modeling\n",
    "* Training\n",
    "* Evaluating\n",
    "\n",
    "#### 6. Submission\n",
    "* Submit the predictions.\n",
    "<br/><br/>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "immune-extra",
   "metadata": {
    "papermill": {
     "duration": 0.042553,
     "end_time": "2021-06-26T05:54:30.520606",
     "exception": false,
     "start_time": "2021-06-26T05:54:30.478053",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 1. Import & Install libray\n",
    "* Import Basic libray\n",
    "* Import Enginnering libray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "attended-channel",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:30.619133Z",
     "iopub.status.busy": "2021-06-26T05:54:30.618628Z",
     "iopub.status.idle": "2021-06-26T05:54:31.306402Z",
     "shell.execute_reply": "2021-06-26T05:54:31.305816Z",
     "shell.execute_reply.started": "2021-06-26T05:11:20.089307Z"
    },
    "papermill": {
     "duration": 0.742661,
     "end_time": "2021-06-26T05:54:31.306546",
     "exception": false,
     "start_time": "2021-06-26T05:54:30.563885",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import OrderedDict\n",
    "\n",
    "from PIL import Image\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import missingno as msno\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "economic-category",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:31.397117Z",
     "iopub.status.busy": "2021-06-26T05:54:31.396609Z",
     "iopub.status.idle": "2021-06-26T05:54:32.815307Z",
     "shell.execute_reply": "2021-06-26T05:54:32.814501Z",
     "shell.execute_reply.started": "2021-06-26T05:11:20.813239Z"
    },
    "papermill": {
     "duration": 1.465799,
     "end_time": "2021-06-26T05:54:32.815497",
     "exception": false,
     "start_time": "2021-06-26T05:54:31.349698",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader, Dataset, TensorDataset\n",
    "\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "higher-charge",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:32.921600Z",
     "iopub.status.busy": "2021-06-26T05:54:32.921087Z",
     "iopub.status.idle": "2021-06-26T05:54:38.092876Z",
     "shell.execute_reply": "2021-06-26T05:54:38.092387Z",
     "shell.execute_reply.started": "2021-06-26T05:11:22.374082Z"
    },
    "papermill": {
     "duration": 5.220074,
     "end_time": "2021-06-26T05:54:38.093006",
     "exception": false,
     "start_time": "2021-06-26T05:54:32.872932",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0      1       0       0       0       0       0       0       0       0   \n",
       "1      0       0       0       0       0       0       0       0       0   \n",
       "2      1       0       0       0       0       0       0       0       0   \n",
       "3      4       0       0       0       0       0       0       0       0   \n",
       "4      0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\n",
    "test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "heavy-heath",
   "metadata": {
    "papermill": {
     "duration": 0.043582,
     "end_time": "2021-06-26T05:54:38.180822",
     "exception": false,
     "start_time": "2021-06-26T05:54:38.137240",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 2. Check out my data\n",
    "* Check Shape / Info / Describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "gorgeous-scout",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:38.271287Z",
     "iopub.status.busy": "2021-06-26T05:54:38.270695Z",
     "iopub.status.idle": "2021-06-26T05:54:38.275921Z",
     "shell.execute_reply": "2021-06-26T05:54:38.275480Z",
     "shell.execute_reply.started": "2021-06-26T05:11:27.633759Z"
    },
    "papermill": {
     "duration": 0.052116,
     "end_time": "2021-06-26T05:54:38.276031",
     "exception": false,
     "start_time": "2021-06-26T05:54:38.223915",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data shape : (42000, 785)\n",
      "test data shape : (28000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(\"train data shape : {}\".format(train_df.shape))\n",
    "print(\"test data shape : {}\".format(test_df.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "connected-bearing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:38.445496Z",
     "iopub.status.busy": "2021-06-26T05:54:38.367737Z",
     "iopub.status.idle": "2021-06-26T05:54:40.368054Z",
     "shell.execute_reply": "2021-06-26T05:54:40.367637Z",
     "shell.execute_reply.started": "2021-06-26T05:11:27.641256Z"
    },
    "papermill": {
     "duration": 2.04805,
     "end_time": "2021-06-26T05:54:40.368168",
     "exception": false,
     "start_time": "2021-06-26T05:54:38.320118",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>pixel0</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel774</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.00000</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.000000</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>42000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.456643</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219286</td>\n",
       "      <td>0.117095</td>\n",
       "      <td>0.059024</td>\n",
       "      <td>0.02019</td>\n",
       "      <td>0.017238</td>\n",
       "      <td>0.002857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.887730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.312890</td>\n",
       "      <td>4.633819</td>\n",
       "      <td>3.274488</td>\n",
       "      <td>1.75987</td>\n",
       "      <td>1.894498</td>\n",
       "      <td>0.414264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>253.00000</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              label   pixel0   pixel1   pixel2   pixel3   pixel4   pixel5  \\\n",
       "count  42000.000000  42000.0  42000.0  42000.0  42000.0  42000.0  42000.0   \n",
       "mean       4.456643      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "std        2.887730      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "min        0.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "25%        2.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "50%        4.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "75%        7.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "max        9.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "\n",
       "        pixel6   pixel7   pixel8  ...      pixel774      pixel775  \\\n",
       "count  42000.0  42000.0  42000.0  ...  42000.000000  42000.000000   \n",
       "mean       0.0      0.0      0.0  ...      0.219286      0.117095   \n",
       "std        0.0      0.0      0.0  ...      6.312890      4.633819   \n",
       "min        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "25%        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "50%        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "75%        0.0      0.0      0.0  ...      0.000000      0.000000   \n",
       "max        0.0      0.0      0.0  ...    254.000000    254.000000   \n",
       "\n",
       "           pixel776     pixel777      pixel778      pixel779  pixel780  \\\n",
       "count  42000.000000  42000.00000  42000.000000  42000.000000   42000.0   \n",
       "mean       0.059024      0.02019      0.017238      0.002857       0.0   \n",
       "std        3.274488      1.75987      1.894498      0.414264       0.0   \n",
       "min        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "25%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "50%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "75%        0.000000      0.00000      0.000000      0.000000       0.0   \n",
       "max      253.000000    253.00000    254.000000     62.000000       0.0   \n",
       "\n",
       "       pixel781  pixel782  pixel783  \n",
       "count   42000.0   42000.0   42000.0  \n",
       "mean        0.0       0.0       0.0  \n",
       "std         0.0       0.0       0.0  \n",
       "min         0.0       0.0       0.0  \n",
       "25%         0.0       0.0       0.0  \n",
       "50%         0.0       0.0       0.0  \n",
       "75%         0.0       0.0       0.0  \n",
       "max         0.0       0.0       0.0  \n",
       "\n",
       "[8 rows x 785 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dirty-director",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:40.463700Z",
     "iopub.status.busy": "2021-06-26T05:54:40.462713Z",
     "iopub.status.idle": "2021-06-26T05:54:41.234982Z",
     "shell.execute_reply": "2021-06-26T05:54:41.235360Z",
     "shell.execute_reply.started": "2021-06-26T05:11:29.915938Z"
    },
    "papermill": {
     "duration": 0.820955,
     "end_time": "2021-06-26T05:54:41.235504",
     "exception": false,
     "start_time": "2021-06-26T05:54:40.414549",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check train data values : \n",
      "[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255]\n"
     ]
    }
   ],
   "source": [
    "print(\"Check train data values : \\n{}\".format(np.unique(train_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ahead-jonathan",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:41.332294Z",
     "iopub.status.busy": "2021-06-26T05:54:41.331416Z",
     "iopub.status.idle": "2021-06-26T05:54:41.350330Z",
     "shell.execute_reply": "2021-06-26T05:54:41.349835Z",
     "shell.execute_reply.started": "2021-06-26T05:11:30.951614Z"
    },
    "papermill": {
     "duration": 0.070039,
     "end_time": "2021-06-26T05:54:41.350431",
     "exception": false,
     "start_time": "2021-06-26T05:54:41.280392",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>Greys</strong> </div><div class=\"cmap\"><img alt=\"Greys colormap\" title=\"Greys\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAgAAAABACAYAAABsv8+/AAAAFHRFWHRUaXRsZQBHcmV5cyBjb2xvcm1hcORtd/YAAAAadEVYdERlc2NyaXB0aW9uAEdyZXlzIGNvbG9ybWFwGjLeEAAAADB0RVh0QXV0aG9yAE1hdHBsb3RsaWIgdjMuNC4xLCBodHRwczovL21hdHBsb3RsaWIub3JnmTvVjgAAADJ0RVh0U29mdHdhcmUATWF0cGxvdGxpYiB2My40LjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcme3nUqpAAABaklEQVR4nO3WSwrCMABAwej9z9y6UReCKPYrb2YTmjRpoJt3med5HmOM+/C09/NR3333/Gl+6fre+9Za/3Zces7R+6dpWrT+r+esvW+v87Y+91/v8es9X+e3fu8s99jq/57t/cd4HQBAjgAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAEHQDHfmnnRvzoAgAAAAASUVORK5CYII=\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#ffffffff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #ffffffff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#000000ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #000000ff;\"></div></div>"
      ],
      "text/plain": [
       "<matplotlib.colors.LinearSegmentedColormap at 0x7fcda669fed0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(\"Greys\", as_cmap=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "emerging-investigator",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:41.449935Z",
     "iopub.status.busy": "2021-06-26T05:54:41.448921Z",
     "iopub.status.idle": "2021-06-26T05:54:41.511763Z",
     "shell.execute_reply": "2021-06-26T05:54:41.511366Z",
     "shell.execute_reply.started": "2021-06-26T05:11:30.977500Z"
    },
    "papermill": {
     "duration": 0.11658,
     "end_time": "2021-06-26T05:54:41.511884",
     "exception": false,
     "start_time": "2021-06-26T05:54:41.395304",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAABECAYAAACF4e8fAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAACH0lEQVR4nO3Yoa2iURSF0QsZCAmGkCAQKAwSQwUkVAFNkNAETWBogSqoAYFAUQCOfyqY5+67yZ617DFbfjm9rusKAECyfusBAAC1CR4AIJ7gAQDiCR4AIJ7gAQDi/fnp+P1+u8/n81tbft1wOCzv97v1jGqm02l5PB6tZ1Txfr/LZrMp9/u99ZQq1ut1ud1urWdUs9vtyuVyaT2jmsPhUM7nc+sZ1RyPx3I6nVrPqOL5fJbr9Vr2+33rKVVcLpey3W5bz6jm9Xr1/nX78cPT72c/gAaDQesJVY1Go9YTqhqPx60nVDOZTFpPqGo2m7WeUNV8Pm89oarFYtF6QlXL5bL1hGpWq1XrCc1kFw0AQBE8AMB/QPAAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPF6Xde13gAAUJUPDwAQT/AAAPEEDwAQT/AAAPEEDwAQT/AAAPH+Ais8MMPMyHJtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Greys_palette = sns.color_palette(\"Greys\",10)\n",
    "sns.palplot(Greys_palette)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "southwest-vaccine",
   "metadata": {
    "papermill": {
     "duration": 0.045167,
     "end_time": "2021-06-26T05:54:41.603341",
     "exception": false,
     "start_time": "2021-06-26T05:54:41.558174",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 3. Exploratory Data Analysis(EDA) with Visualization [Before Preprocessing]\n",
    "* Plot the null values\n",
    "* Plot the label Percent\n",
    "* Plot the image Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "valid-skill",
   "metadata": {
    "papermill": {
     "duration": 0.045008,
     "end_time": "2021-06-26T05:54:41.694077",
     "exception": false,
     "start_time": "2021-06-26T05:54:41.649069",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3-1) Plot the null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "optical-stick",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:41.790463Z",
     "iopub.status.busy": "2021-06-26T05:54:41.789112Z",
     "iopub.status.idle": "2021-06-26T05:54:41.926848Z",
     "shell.execute_reply": "2021-06-26T05:54:41.927223Z",
     "shell.execute_reply.started": "2021-06-26T05:11:31.061430Z"
    },
    "papermill": {
     "duration": 0.187966,
     "end_time": "2021-06-26T05:54:41.927368",
     "exception": false,
     "start_time": "2021-06-26T05:54:41.739402",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_002f4e8a_d643_11eb_9636_0242ac130202row0_col0,#T_002f4e8a_d643_11eb_9636_0242ac130202row1_col0,#T_002f4e8a_d643_11eb_9636_0242ac130202row2_col0,#T_002f4e8a_d643_11eb_9636_0242ac130202row3_col0,#T_002f4e8a_d643_11eb_9636_0242ac130202row4_col0{\n",
       "            background-color:  #ffffff;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_002f4e8a_d643_11eb_9636_0242ac130202\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Null Count</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_002f4e8a_d643_11eb_9636_0242ac130202level0_row0\" class=\"row_heading level0 row0\" >pixel783</th>\n",
       "                        <td id=\"T_002f4e8a_d643_11eb_9636_0242ac130202row0_col0\" class=\"data row0 col0\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_002f4e8a_d643_11eb_9636_0242ac130202level0_row1\" class=\"row_heading level0 row1\" >pixel267</th>\n",
       "                        <td id=\"T_002f4e8a_d643_11eb_9636_0242ac130202row1_col0\" class=\"data row1 col0\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_002f4e8a_d643_11eb_9636_0242ac130202level0_row2\" class=\"row_heading level0 row2\" >pixel265</th>\n",
       "                        <td id=\"T_002f4e8a_d643_11eb_9636_0242ac130202row2_col0\" class=\"data row2 col0\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_002f4e8a_d643_11eb_9636_0242ac130202level0_row3\" class=\"row_heading level0 row3\" >pixel264</th>\n",
       "                        <td id=\"T_002f4e8a_d643_11eb_9636_0242ac130202row3_col0\" class=\"data row3 col0\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_002f4e8a_d643_11eb_9636_0242ac130202level0_row4\" class=\"row_heading level0 row4\" >pixel263</th>\n",
       "                        <td id=\"T_002f4e8a_d643_11eb_9636_0242ac130202row4_col0\" class=\"data row4 col0\" >0</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcce73e0a90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(train_df.isnull().sum().sort_values(ascending=False), columns=[\"Null Count\"]).head().style.background_gradient(cmap='Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "opposite-azerbaijan",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:42.023772Z",
     "iopub.status.busy": "2021-06-26T05:54:42.022953Z",
     "iopub.status.idle": "2021-06-26T05:54:48.873113Z",
     "shell.execute_reply": "2021-06-26T05:54:48.872354Z",
     "shell.execute_reply.started": "2021-06-26T05:11:31.217967Z"
    },
    "papermill": {
     "duration": 6.900032,
     "end_time": "2021-06-26T05:54:48.873249",
     "exception": false,
     "start_time": "2021-06-26T05:54:41.973217",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zUyz7oUwry1aLZUlya6YSuO8YY89V9jHbf37se4/V/Lit/caeqDEAAADA+vbyTM3g67aESvLzSX41yYVJnpMp0GxKct78+oNJDsq059a3zK9fm2m7qi8tXoyHvTxLvOePuYfZGGOvJO/exssvzlTxrs8Uo66pqivmcc/K9IsEh2Qbe4YtvM91mcLa4VV17cJr703yuiRvqKpL5mO7Jbk90y9l7rv4S5mrXW8ujHcleSjJc/tNGmPsnincPS/JflX1+UebLwAAAAC7pu3e9H8129r0f4zxrZk23P++JOdW1XmrX+ER13ptksuSbE5yRFXdPx9/SaYg98UkL6iqe9qYtyZ5R5L3Jzm2qh6ajx+Z5KokNyc5aOX4/NrvJvmZTF8PPb0dPzXJ+Uk+UlWv2uGbAQAAAMAuYbs2/X8crswUy25Nsvsc1hZdVVWfac8vT3J0kmOS/MMY4+oke2f6qdA9MkW5exau8a4kPzqPuXGM8deZVpz9eJIvZ1qR9tDCmLMyLes7bYxxcJJPJnlhkiMzrT47ecc/LgAAAAC7ip0VzJ4/P+6XaQXaam5P8pmVJ1W1dV5ltjnJGzJ9H/X+JNcleVtVbV68QFU9MMbYkOTMJK9N8uYk92RaXXZuVd28ypi7xxgvned1VKavbd6d5JIk51TVHTv2UQEAAADYlfy/vpIJAAAAALua3Zc9AQAAAABYSwQzAAAAAGgEMwAAAABoBDMAAAAAaAQzAAAAAGgEMwAAAABoBDMAAAAAaAQzAAAAAGgEMwAAAABoBDMAAAAAaP4Xfx6UED8CEjoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df=train_df.iloc[:,:],figsize=(20,5),color=Greys_palette[8])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gothic-capitol",
   "metadata": {
    "papermill": {
     "duration": 0.046353,
     "end_time": "2021-06-26T05:54:48.966741",
     "exception": false,
     "start_time": "2021-06-26T05:54:48.920388",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3-2) Plot the label Percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "analyzed-organizer",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:49.070189Z",
     "iopub.status.busy": "2021-06-26T05:54:49.069396Z",
     "iopub.status.idle": "2021-06-26T05:54:49.216795Z",
     "shell.execute_reply": "2021-06-26T05:54:49.216384Z",
     "shell.execute_reply.started": "2021-06-26T05:11:38.538751Z"
    },
    "papermill": {
     "duration": 0.203596,
     "end_time": "2021-06-26T05:54:49.216911",
     "exception": false,
     "start_time": "2021-06-26T05:54:49.013315",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.1, 0.92, 'distribution by Label in Mnist dataset')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(10,7))\n",
    "sns.countplot(x='label', data=train_df, palette=Greys_palette[::-1], ax=ax)\n",
    "ax.patch.set_alpha(0)\n",
    "\n",
    "fig.text(0.1,0.92,\"distribution by Label in Mnist dataset\", fontweight=\"bold\", fontfamily='serif', fontsize=17)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pleasant-process",
   "metadata": {
    "papermill": {
     "duration": 0.047497,
     "end_time": "2021-06-26T05:54:49.311717",
     "exception": false,
     "start_time": "2021-06-26T05:54:49.264220",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3-3) * Plot the image Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "determined-bridges",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:49.410312Z",
     "iopub.status.busy": "2021-06-26T05:54:49.409592Z",
     "iopub.status.idle": "2021-06-26T05:54:49.412292Z",
     "shell.execute_reply": "2021-06-26T05:54:49.411886Z",
     "shell.execute_reply.started": "2021-06-26T05:11:38.720375Z"
    },
    "papermill": {
     "duration": 0.053107,
     "end_time": "2021-06-26T05:54:49.412386",
     "exception": false,
     "start_time": "2021-06-26T05:54:49.359279",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = train_df.iloc[:,1:].values\n",
    "y = train_df.iloc[:,0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "gothic-building",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:49.510280Z",
     "iopub.status.busy": "2021-06-26T05:54:49.509672Z",
     "iopub.status.idle": "2021-06-26T05:54:49.513158Z",
     "shell.execute_reply": "2021-06-26T05:54:49.513676Z",
     "shell.execute_reply.started": "2021-06-26T05:11:38.727540Z"
    },
    "papermill": {
     "duration": 0.054512,
     "end_time": "2021-06-26T05:54:49.513846",
     "exception": false,
     "start_time": "2021-06-26T05:54:49.459334",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape : (42000, 784)\n",
      "Y shape : (42000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X shape : {}\".format(x.shape))\n",
    "print(\"Y shape : {}\".format(y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "invisible-skirt",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:49.628036Z",
     "iopub.status.busy": "2021-06-26T05:54:49.627256Z",
     "iopub.status.idle": "2021-06-26T05:54:50.693692Z",
     "shell.execute_reply": "2021-06-26T05:54:50.694082Z",
     "shell.execute_reply.started": "2021-06-26T05:11:38.738287Z"
    },
    "papermill": {
     "duration": 1.132447,
     "end_time": "2021-06-26T05:54:50.694236",
     "exception": false,
     "start_time": "2021-06-26T05:54:49.561789",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig , axes = plt.subplots(5, 5, figsize=(15,15))\n",
    "\n",
    "x_idx = 0\n",
    "y_idx = 0\n",
    "\n",
    "for i in range(5*5):\n",
    "    if x_idx == 5:\n",
    "        x_idx = 0\n",
    "        y_idx += 1\n",
    "        \n",
    "    axes[y_idx][x_idx].imshow(x[i].reshape(28,28), 'gray')\n",
    "    axes[y_idx][x_idx].axis(\"off\")\n",
    "    axes[y_idx][x_idx].set_title(\"Target : \" + str(y[i]))\n",
    "    x_idx += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interstate-robertson",
   "metadata": {
    "papermill": {
     "duration": 0.048516,
     "end_time": "2021-06-26T05:54:50.791809",
     "exception": false,
     "start_time": "2021-06-26T05:54:50.743293",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 4. Feature Enginnering\n",
    "* Split Train set / Validation set\n",
    "* Convert to data suitable for CNN model (Dataset / Dataloader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intermediate-fraud",
   "metadata": {
    "papermill": {
     "duration": 0.048151,
     "end_time": "2021-06-26T05:54:50.888371",
     "exception": false,
     "start_time": "2021-06-26T05:54:50.840220",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4-1) Split Train set / Validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "manual-partner",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:50.992031Z",
     "iopub.status.busy": "2021-06-26T05:54:50.990988Z",
     "iopub.status.idle": "2021-06-26T05:54:51.359961Z",
     "shell.execute_reply": "2021-06-26T05:54:51.359501Z",
     "shell.execute_reply.started": "2021-06-26T05:11:39.932294Z"
    },
    "papermill": {
     "duration": 0.422656,
     "end_time": "2021-06-26T05:54:51.360091",
     "exception": false,
     "start_time": "2021-06-26T05:54:50.937435",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "streaming-parish",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:51.462552Z",
     "iopub.status.busy": "2021-06-26T05:54:51.461911Z",
     "iopub.status.idle": "2021-06-26T05:54:51.465412Z",
     "shell.execute_reply": "2021-06-26T05:54:51.465982Z",
     "shell.execute_reply.started": "2021-06-26T05:11:40.312282Z"
    },
    "papermill": {
     "duration": 0.057154,
     "end_time": "2021-06-26T05:54:51.466144",
     "exception": false,
     "start_time": "2021-06-26T05:54:51.408990",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape : (35700, 784)\n",
      "y_train shape : (35700,)\n",
      "x_val shape : (6300, 784)\n",
      "y_val shape : (6300,)\n"
     ]
    }
   ],
   "source": [
    "print(\"x_train shape : {}\".format(x_train.shape))\n",
    "print(\"y_train shape : {}\".format(y_train.shape))\n",
    "print(\"x_val shape : {}\".format(x_val.shape))\n",
    "print(\"y_val shape : {}\".format(y_val.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "narrow-difference",
   "metadata": {
    "papermill": {
     "duration": 0.048737,
     "end_time": "2021-06-26T05:54:51.564530",
     "exception": false,
     "start_time": "2021-06-26T05:54:51.515793",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4-2) Convert to data suitable for CNN model (Dataset / Dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "retired-isolation",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:51.670046Z",
     "iopub.status.busy": "2021-06-26T05:54:51.669379Z",
     "iopub.status.idle": "2021-06-26T05:54:51.672085Z",
     "shell.execute_reply": "2021-06-26T05:54:51.671677Z",
     "shell.execute_reply.started": "2021-06-26T05:11:40.320772Z"
    },
    "papermill": {
     "duration": 0.058906,
     "end_time": "2021-06-26T05:54:51.672187",
     "exception": false,
     "start_time": "2021-06-26T05:54:51.613281",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class BasicDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self,x_data, y_data, is_labeled=False):\n",
    "        self.x = self.get_float_type(x_data)\n",
    "        self.is_labeled = is_labeled\n",
    "        \n",
    "        if self.is_labeled:\n",
    "            self.y = self.get_long_type(y_data)\n",
    "        else:\n",
    "            self.y = None\n",
    "        \n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.x)\n",
    "    \n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        global data\n",
    "        data = {}\n",
    "        \n",
    "        x_row = self.x[index]\n",
    "        data['data'] = x_row\n",
    "    \n",
    "        if self.is_labeled == True: \n",
    "            y_row = self.y[index]\n",
    "            data['target'] = y_row\n",
    "        return data\n",
    "    \n",
    "        \n",
    "    def get_float_type(self, data):\n",
    "        data = data.astype('float32')\n",
    "        data /= 255\n",
    "        data = data.reshape(-1, 1, 28, 28)\n",
    "        return data\n",
    "    \n",
    "    \n",
    "    def get_long_type(self, target):\n",
    "        target = target.astype(torch.LongTensor)\n",
    "        return target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "regional-wheel",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:51.774045Z",
     "iopub.status.busy": "2021-06-26T05:54:51.773152Z",
     "iopub.status.idle": "2021-06-26T05:54:51.846076Z",
     "shell.execute_reply": "2021-06-26T05:54:51.846481Z",
     "shell.execute_reply.started": "2021-06-26T05:11:40.333608Z"
    },
    "papermill": {
     "duration": 0.125452,
     "end_time": "2021-06-26T05:54:51.846643",
     "exception": false,
     "start_time": "2021-06-26T05:54:51.721191",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset = BasicDataset(x_train, y_train, is_labeled=True)\n",
    "val_dataset = BasicDataset(x_val, y_val, is_labeled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "prepared-brooklyn",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:51.959381Z",
     "iopub.status.busy": "2021-06-26T05:54:51.958762Z",
     "iopub.status.idle": "2021-06-26T05:54:51.963332Z",
     "shell.execute_reply": "2021-06-26T05:54:51.963948Z",
     "shell.execute_reply.started": "2021-06-26T05:11:40.414882Z"
    },
    "papermill": {
     "duration": 0.06621,
     "end_time": "2021-06-26T05:54:51.964134",
     "exception": false,
     "start_time": "2021-06-26T05:54:51.897924",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data of train dataset : [[[0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.21568628 0.92941177\n",
      "   0.27058825 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.80784315 0.99215686\n",
      "   0.40392157 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.03529412\n",
      "   0.         0.         0.         0.         0.8392157  0.99215686\n",
      "   0.27058825 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.13333334 0.8235294\n",
      "   0.654902   0.06666667 0.         0.         0.9019608  0.99215686\n",
      "   0.27058825 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.46666667 1.\n",
      "   0.8784314  0.06666667 0.         0.         0.90588236 0.99607843\n",
      "   0.27058825 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.11764706 0.87058824 0.99607843\n",
      "   0.74509805 0.         0.         0.         0.9019608  0.99215686\n",
      "   0.27058825 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.4862745  0.99215686 0.96862745\n",
      "   0.2784314  0.         0.         0.         0.9019608  0.99215686\n",
      "   0.27058825 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.26666668 0.9411765  0.99215686 0.72156864\n",
      "   0.         0.         0.         0.2        0.96862745 0.99215686\n",
      "   0.27058825 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.6431373  0.99607843 0.99607843 0.13333334\n",
      "   0.         0.         0.         0.27450982 0.99607843 0.99607843\n",
      "   0.27058825 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.21568628 0.9529412  0.99215686 0.65882355 0.\n",
      "   0.         0.         0.         0.27450982 0.99215686 0.99215686\n",
      "   0.27058825 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.6313726  0.99215686 0.99215686 0.9254902  0.8156863\n",
      "   0.4862745  0.45490196 0.45490196 0.73333335 0.99215686 0.99215686\n",
      "   0.27058825 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.6313726  0.99215686 0.99215686 0.99215686 0.99607843\n",
      "   0.99215686 0.99215686 0.99215686 0.99607843 0.99215686 0.99215686\n",
      "   0.5294118  0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.33333334 0.3647059  0.3647059  0.3647059\n",
      "   0.3647059  0.3647059  0.3647059  0.53333336 0.99607843 0.9372549\n",
      "   0.09803922 0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.47058824 0.99215686 0.9019608\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.6313726  0.99215686 0.9019608\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.6313726  0.99215686 0.9019608\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.73333335 0.99607843 0.5411765\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.99607843 0.99215686 0.5372549\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.99607843 0.99215686 0.5058824\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.8627451  0.99215686 0.18039216\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]\n",
      "  [0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.         0.         0.\n",
      "   0.         0.         0.         0.        ]]]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Target of train dataset : 4\n"
     ]
    }
   ],
   "source": [
    "print(\"Data of train dataset : {}\\n\\n\\n\\n\".format(train_dataset[0]['data']))\n",
    "print(\"Target of train dataset : {}\".format(train_dataset[0]['target']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "found-retail",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:52.068428Z",
     "iopub.status.busy": "2021-06-26T05:54:52.067709Z",
     "iopub.status.idle": "2021-06-26T05:54:52.070342Z",
     "shell.execute_reply": "2021-06-26T05:54:52.069853Z",
     "shell.execute_reply.started": "2021-06-26T05:11:40.434126Z"
    },
    "papermill": {
     "duration": 0.055617,
     "end_time": "2021-06-26T05:54:52.070442",
     "exception": false,
     "start_time": "2021-06-26T05:54:52.014825",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "consecutive-capacity",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:52.173813Z",
     "iopub.status.busy": "2021-06-26T05:54:52.173100Z",
     "iopub.status.idle": "2021-06-26T05:54:52.175759Z",
     "shell.execute_reply": "2021-06-26T05:54:52.175298Z",
     "shell.execute_reply.started": "2021-06-26T05:11:40.440354Z"
    },
    "papermill": {
     "duration": 0.055943,
     "end_time": "2021-06-26T05:54:52.175873",
     "exception": false,
     "start_time": "2021-06-26T05:54:52.119930",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle = True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "popular-bookmark",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:52.277940Z",
     "iopub.status.busy": "2021-06-26T05:54:52.277313Z",
     "iopub.status.idle": "2021-06-26T05:54:52.279981Z",
     "shell.execute_reply": "2021-06-26T05:54:52.279585Z",
     "shell.execute_reply.started": "2021-06-26T05:11:40.450970Z"
    },
    "papermill": {
     "duration": 0.054886,
     "end_time": "2021-06-26T05:54:52.280081",
     "exception": false,
     "start_time": "2021-06-26T05:54:52.225195",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataloaders_dict = {\"train\": train_loader, \"val\": val_loader}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "animated-feedback",
   "metadata": {
    "papermill": {
     "duration": 0.049534,
     "end_time": "2021-06-26T05:54:52.379637",
     "exception": false,
     "start_time": "2021-06-26T05:54:52.330103",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 5.Modeling\n",
    "* CNN Modeling\n",
    "* ResNet Modeling\n",
    "* DenseNet Modeling\n",
    "* Training\n",
    "* Evaluating"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electronic-boost",
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   },
   "source": [
    "### 5-1) CNN Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "effective-class",
   "metadata": {
    "execution": {
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    "tags": []
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   "outputs": [],
   "source": [
    "INPUT_DIM = 1\n",
    "HIDDEN_DIM_1 = 32\n",
    "HIDDEN_DIM_2 = 64\n",
    "OUTPUT = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "private-while",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "class BasicCNN(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim_1, hidden_dim_2, output):\n",
    "        super(BasicCNN, self).__init__()\n",
    "        \n",
    "        self.convolution_layer = nn.Sequential(OrderedDict([\n",
    "            ('conv1', nn.Conv2d(input_dim, hidden_dim_1, 3, 1, 0)),\n",
    "            ('relu1', nn.ReLU()),\n",
    "            ('pool1', nn.MaxPool2d(2,2)),\n",
    "            ('conv2', nn.Conv2d(hidden_dim_1, hidden_dim_2, 3, 1, 0)),\n",
    "            ('relu2', nn.ReLU()),\n",
    "            ('pool2', nn.MaxPool2d(2,2))    \n",
    "        ]))\n",
    "        self.fc_layer = nn.Sequential(OrderedDict([\n",
    "            ('Dense1', nn.Linear(1600, output)),\n",
    "            ('softmax', nn.Softmax())\n",
    "        ]))\n",
    "    \n",
    "    def forward(self,x):\n",
    "        out = self.convolution_layer(x)\n",
    "        out = out.view(out.size(0),-1)\n",
    "        out = self.fc_layer(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "mounted-length",
   "metadata": {
    "execution": {
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    },
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     "end_time": "2021-06-26T05:54:52.838234",
     "exception": false,
     "start_time": "2021-06-26T05:54:52.748297",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BasicCNN(\n",
      "  (convolution_layer): Sequential(\n",
      "    (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (relu1): ReLU()\n",
      "    (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
      "    (relu2): ReLU()\n",
      "    (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (fc_layer): Sequential(\n",
      "    (Dense1): Linear(in_features=1600, out_features=10, bias=True)\n",
      "    (softmax): Softmax(dim=None)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "cnn_model = BasicCNN(INPUT_DIM, HIDDEN_DIM_1, HIDDEN_DIM_2, OUTPUT)\n",
    "print(cnn_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "civil-passage",
   "metadata": {
    "papermill": {
     "duration": 0.049518,
     "end_time": "2021-06-26T05:54:52.937153",
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     "start_time": "2021-06-26T05:54:52.887635",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5-2) ResNet Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "million-tonight",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ResidualBlock(nn.Module):\n",
    "    def __init__(self, data_dim, layer_name, is_last_layer=False):\n",
    "        super(ResidualBlock, self).__init__()\n",
    "        self.is_last_layer = is_last_layer\n",
    "        \n",
    "        self.resblock_1 = nn.Sequential(OrderedDict([\n",
    "            ('{}_conv1'.format(layer_name), nn.Conv2d(data_dim, data_dim, kernel_size=3, stride=1, padding=1, bias=False)),\n",
    "            ('{}_batchnorm1'.format(layer_name), nn.BatchNorm2d(data_dim)),\n",
    "            ('{}_relu1'.format(layer_name), nn.ReLU()),\n",
    "            \n",
    "            ('{}_conv2'.format(layer_name), nn.Conv2d(data_dim, data_dim, kernel_size=3, stride=1, padding=1, bias=False)),\n",
    "            ('{}_batchnorm2'.format(layer_name), nn.BatchNorm2d(data_dim)),\n",
    "            ('{}_relu2'.format(layer_name), nn.ReLU())\n",
    "        ]))\n",
    "        self.resblock_2 = nn.Sequential(OrderedDict([\n",
    "            ('{}_conv1'.format(layer_name), nn.Conv2d(data_dim, data_dim, kernel_size=3, stride=1, padding=1, bias=False)),\n",
    "            ('{}_batchnorm1'.format(layer_name), nn.BatchNorm2d(data_dim)),\n",
    "            ('{}_relu1'.format(layer_name), nn.ReLU()),\n",
    "            \n",
    "            ('{}_conv2'.format(layer_name), nn.Conv2d(data_dim, data_dim, kernel_size=3, stride=1, padding=0, bias=False)),\n",
    "            ('{}_batchnorm2'.format(layer_name), nn.BatchNorm2d(data_dim)),\n",
    "            ('{}_relu2'.format(layer_name), nn.ReLU())\n",
    "        ]))\n",
    "        \n",
    "        # Adjustment layer because height and width values are different\n",
    "        self.set_shortcut = nn.Conv2d(data_dim, data_dim, kernel_size=3, stride=1, padding=0, bias=False)\n",
    "        \n",
    "        \n",
    "    def forward(self, x):\n",
    "        shortcut = x\n",
    "        out = self.resblock_1(x)\n",
    "        out = out + shortcut\n",
    "        \n",
    "        shortcut = self.set_shortcut(out)\n",
    "        out = self.resblock_2(out)\n",
    "        out = out + shortcut\n",
    "\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "explicit-animal",
   "metadata": {
    "execution": {
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   "source": [
    "class ResNet_mini(nn.Module):\n",
    "    def __init__(self, input_dim, output):\n",
    "        super(ResNet_mini, self).__init__()\n",
    "        self.FirstBlock = nn.Sequential(OrderedDict([\n",
    "            ('FirstBlock_conv1', nn.Conv2d(input_dim, 64, kernel_size=4, stride=2, padding=4, bias=False)),\n",
    "            ('FirstBlock_batchnorm1', nn.BatchNorm2d(64)),\n",
    "            ('FirstBlock_relu1', nn.ReLU()),\n",
    "            ('FirstBlock_maxpool2d1', nn.MaxPool2d(kernel_size=2, stride=2, padding=1)),\n",
    "        ]))\n",
    "        self.SecondBlock = ResidualBlock(64,'SecondBlock')\n",
    "        self.ChangeChannel_1 = nn.Conv2d(64, 128, kernel_size=1, stride=1, padding=0)\n",
    "        \n",
    "        self.ThridBlock = ResidualBlock(128,'ThridBlock')\n",
    "        self.ChangeChannel_2 = nn.Conv2d(128, 256, kernel_size=1, stride=1, padding=0)\n",
    "        \n",
    "        self.FourthBlock = ResidualBlock(256,'FourthBlock')  \n",
    "        \n",
    "        self.AveragePool2d = nn.AdaptiveAvgPool2d((1,1))\n",
    "        \n",
    "        self.FC = nn.Sequential(OrderedDict([\n",
    "            ('FC_linear', nn.Linear(256, output)),\n",
    "            ('FC_softmax', nn.Softmax(dim=1))\n",
    "        ]))\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = self.FirstBlock(x)\n",
    "\n",
    "        out = self.SecondBlock(out)\n",
    "        out = self.ChangeChannel_1(out)\n",
    "\n",
    "        out = self.ThridBlock(out)\n",
    "        out = self.ChangeChannel_2(out)\n",
    "\n",
    "        out = self.FourthBlock(out)\n",
    "\n",
    "        out = self.AveragePool2d(out)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.FC(out)\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "prompt-state",
   "metadata": {
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet_mini(\n",
      "  (FirstBlock): Sequential(\n",
      "    (FirstBlock_conv1): Conv2d(1, 64, kernel_size=(4, 4), stride=(2, 2), padding=(4, 4), bias=False)\n",
      "    (FirstBlock_batchnorm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (FirstBlock_relu1): ReLU()\n",
      "    (FirstBlock_maxpool2d1): MaxPool2d(kernel_size=2, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (SecondBlock): ResidualBlock(\n",
      "    (resblock_1): Sequential(\n",
      "      (SecondBlock_conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (SecondBlock_batchnorm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (SecondBlock_relu1): ReLU()\n",
      "      (SecondBlock_conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (SecondBlock_batchnorm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (SecondBlock_relu2): ReLU()\n",
      "    )\n",
      "    (resblock_2): Sequential(\n",
      "      (SecondBlock_conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (SecondBlock_batchnorm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (SecondBlock_relu1): ReLU()\n",
      "      (SecondBlock_conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
      "      (SecondBlock_batchnorm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (SecondBlock_relu2): ReLU()\n",
      "    )\n",
      "    (set_shortcut): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
      "  )\n",
      "  (ChangeChannel_1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1))\n",
      "  (ThridBlock): ResidualBlock(\n",
      "    (resblock_1): Sequential(\n",
      "      (ThridBlock_conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (ThridBlock_batchnorm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (ThridBlock_relu1): ReLU()\n",
      "      (ThridBlock_conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (ThridBlock_batchnorm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (ThridBlock_relu2): ReLU()\n",
      "    )\n",
      "    (resblock_2): Sequential(\n",
      "      (ThridBlock_conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (ThridBlock_batchnorm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (ThridBlock_relu1): ReLU()\n",
      "      (ThridBlock_conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
      "      (ThridBlock_batchnorm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (ThridBlock_relu2): ReLU()\n",
      "    )\n",
      "    (set_shortcut): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
      "  )\n",
      "  (ChangeChannel_2): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))\n",
      "  (FourthBlock): ResidualBlock(\n",
      "    (resblock_1): Sequential(\n",
      "      (FourthBlock_conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (FourthBlock_batchnorm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (FourthBlock_relu1): ReLU()\n",
      "      (FourthBlock_conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (FourthBlock_batchnorm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (FourthBlock_relu2): ReLU()\n",
      "    )\n",
      "    (resblock_2): Sequential(\n",
      "      (FourthBlock_conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (FourthBlock_batchnorm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (FourthBlock_relu1): ReLU()\n",
      "      (FourthBlock_conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
      "      (FourthBlock_batchnorm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (FourthBlock_relu2): ReLU()\n",
      "    )\n",
      "    (set_shortcut): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
      "  )\n",
      "  (AveragePool2d): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (FC): Sequential(\n",
      "    (FC_linear): Linear(in_features=256, out_features=10, bias=True)\n",
      "    (FC_softmax): Softmax(dim=1)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "resnet_model = ResNet_mini(INPUT_DIM, OUTPUT)\n",
    "print(resnet_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceramic-junction",
   "metadata": {
    "papermill": {
     "duration": 0.050034,
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    },
    "tags": []
   },
   "source": [
    "### 5-3) DenseNet Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aerial-chamber",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [],
   "source": [
    "class DenseBlock(nn.Module):\n",
    "    def __init__(self,input_dim, layer_name):\n",
    "        super(DenseBlock, self).__init__()\n",
    "        \n",
    "        hidden_dim = input_dim * 2\n",
    "        \n",
    "        self.denseblock1 = nn.Sequential(OrderedDict([\n",
    "            ('{}_batchnorm1'.format(layer_name), nn.BatchNorm2d(input_dim)),\n",
    "            ('{}_relu1'.format(layer_name), nn.ReLU()),\n",
    "            ('{}_conv1'.format(layer_name), nn.Conv2d(input_dim, hidden_dim, kernel_size=3, stride=1, padding=1, bias=False)),\n",
    "        ]))\n",
    "        \n",
    "        self.denseblock2 = nn.Sequential(OrderedDict([\n",
    "            ('{}_batchnorm2'.format(layer_name), nn.BatchNorm2d(hidden_dim)),\n",
    "            ('{}_relu2'.format(layer_name), nn.ReLU()),\n",
    "            ('{}_conv2'.format(layer_name), nn.Conv2d(hidden_dim, 32, kernel_size=3, stride=1, padding=1, bias=False)),\n",
    "        ]))\n",
    "        \n",
    "    def forward(self, x):\n",
    "        shortcut = x\n",
    "        out = self.denseblock1(x)\n",
    "        out = self.denseblock2(out)\n",
    "        out = torch.cat([out, shortcut], 1)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cleared-hypothetical",
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   "outputs": [],
   "source": [
    "class DenseNet_mini(nn.Module):\n",
    "    def __init__(self, input_dim, output):\n",
    "        super(DenseNet_mini, self).__init__()\n",
    "        self.FirstBlock = nn.Sequential(OrderedDict([\n",
    "            ('FirstBlock_conv1', nn.Conv2d(input_dim, 32, kernel_size=4, stride=2, padding=4, bias=False)),\n",
    "            ('FirstBlock_batchnorm1', nn.BatchNorm2d(32)),\n",
    "            ('FirstBlock_relu1', nn.ReLU()),\n",
    "            ('FirstBlock_maxpool2d1', nn.MaxPool2d(kernel_size=2, stride=2, padding=1)),\n",
    "        ]))\n",
    "        self.SecondBlock = DenseBlock(32,'SecondBlock')\n",
    "        self.ThridBlock = DenseBlock(32+32,'ThridBlock')\n",
    "        self.FourthBlock = DenseBlock(64+32,'FourthBlock')\n",
    "        self.FifthBlock = DenseBlock(96+32,'FifthBlock')\n",
    "        \n",
    "        self.AveragePool2d = nn.AdaptiveAvgPool2d((1,1))\n",
    "        \n",
    "        self.FC = nn.Sequential(OrderedDict([\n",
    "            ('FC_linear', nn.Linear(128, output)),\n",
    "            ('FC_softmax', nn.Softmax(dim=1))\n",
    "        ]))\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = self.FirstBlock(x)\n",
    "        out = self.SecondBlock(out)\n",
    "        out = self.ThridBlock(out)\n",
    "        out = self.FourthBlock(out)\n",
    "\n",
    "        out = self.AveragePool2d(out)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.FC(out)\n",
    "        \n",
    "        return out"
   ]
  },
  {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DenseNet_mini(\n",
      "  (FirstBlock): Sequential(\n",
      "    (FirstBlock_conv1): Conv2d(1, 32, kernel_size=(4, 4), stride=(2, 2), padding=(4, 4), bias=False)\n",
      "    (FirstBlock_batchnorm1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (FirstBlock_relu1): ReLU()\n",
      "    (FirstBlock_maxpool2d1): MaxPool2d(kernel_size=2, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (SecondBlock): DenseBlock(\n",
      "    (denseblock1): Sequential(\n",
      "      (SecondBlock_batchnorm1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (SecondBlock_relu1): ReLU()\n",
      "      (SecondBlock_conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    )\n",
      "    (denseblock2): Sequential(\n",
      "      (SecondBlock_batchnorm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (SecondBlock_relu2): ReLU()\n",
      "      (SecondBlock_conv2): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    )\n",
      "  )\n",
      "  (ThridBlock): DenseBlock(\n",
      "    (denseblock1): Sequential(\n",
      "      (ThridBlock_batchnorm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (ThridBlock_relu1): ReLU()\n",
      "      (ThridBlock_conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    )\n",
      "    (denseblock2): Sequential(\n",
      "      (ThridBlock_batchnorm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (ThridBlock_relu2): ReLU()\n",
      "      (ThridBlock_conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    )\n",
      "  )\n",
      "  (FourthBlock): DenseBlock(\n",
      "    (denseblock1): Sequential(\n",
      "      (FourthBlock_batchnorm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (FourthBlock_relu1): ReLU()\n",
      "      (FourthBlock_conv1): Conv2d(96, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    )\n",
      "    (denseblock2): Sequential(\n",
      "      (FourthBlock_batchnorm2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (FourthBlock_relu2): ReLU()\n",
      "      (FourthBlock_conv2): Conv2d(192, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    )\n",
      "  )\n",
      "  (FifthBlock): DenseBlock(\n",
      "    (denseblock1): Sequential(\n",
      "      (FifthBlock_batchnorm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (FifthBlock_relu1): ReLU()\n",
      "      (FifthBlock_conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    )\n",
      "    (denseblock2): Sequential(\n",
      "      (FifthBlock_batchnorm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (FifthBlock_relu2): ReLU()\n",
      "      (FifthBlock_conv2): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "    )\n",
      "  )\n",
      "  (AveragePool2d): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (FC): Sequential(\n",
      "    (FC_linear): Linear(in_features=128, out_features=10, bias=True)\n",
      "    (FC_softmax): Softmax(dim=1)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "densenet_model = DenseNet_mini(INPUT_DIM, OUTPUT)\n",
    "print(densenet_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cloudy-liverpool",
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     "duration": 0.05035,
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5-4) Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "palestinian-scholar",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "def train_model(model, dataloaders_dict, loss_fn, optimizer, num_epochs, filename):\n",
    "    model.to(device)\n",
    "\n",
    "    history ={'train_loss' : [],\n",
    "             'train_acc' : [],\n",
    "              'val_loss' : [],\n",
    "              'val_acc' : []\n",
    "             }\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        for i, phase in enumerate(['train','val']):\n",
    "            if phase=='train':\n",
    "                model.train()\n",
    "            else:\n",
    "                model.eval()\n",
    "                \n",
    "            correct = 0\n",
    "            epoch_loss = 0\n",
    "            epoch_acc = 0 \n",
    "\n",
    "            for data in dataloaders_dict[phase]:\n",
    "                x_data = data['data'].to(device)\n",
    "                y_data = data['target'].to(device)\n",
    "\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                predict = model(x_data)\n",
    "                target = y_data\n",
    "                loss = loss_fn(predict, target)\n",
    "\n",
    "                if phase == 'train':\n",
    "                    loss.backward()\n",
    "                    optimizer.step()\n",
    "\n",
    "                epoch_loss += loss.item() * len(x_data)\n",
    "                correct += (target==predict.argmax(axis=1)).sum()\n",
    "                    \n",
    "            epoch_loss = epoch_loss / len(dataloaders_dict[phase].dataset)\n",
    "            epoch_acc = 100 * (correct / len(dataloaders_dict[phase].dataset))\n",
    "            print(\"{} : [{} / {}] Loss : {:.4f}  | Accuracy : {:.2f}%\".format(phase.upper(), epoch+1, num_epochs, epoch_loss, epoch_acc))\n",
    "            \n",
    "            history['{}_loss'.format(phase)].append(epoch_loss)\n",
    "            history['{}_acc'.format(phase)].append(epoch_acc)\n",
    "            \n",
    "        torch.save(model.state_dict(), filename)\n",
    "        print(\" \")\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "noted-assembly",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "A device that proceeds with :  cuda:0\n"
     ]
    }
   ],
   "source": [
    "USE_CUDA = torch.cuda.is_available()\n",
    "print(USE_CUDA)\n",
    "device = torch.device('cuda:0' if USE_CUDA else 'cpu')\n",
    "print('A device that proceeds with : ',device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-forty",
   "metadata": {
    "papermill": {
     "duration": 0.050553,
     "end_time": "2021-06-26T05:54:54.223004",
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* CNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "adult-worker",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:54:54.329193Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(cnn_model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "brief-wages",
   "metadata": {
    "execution": {
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     "status": "completed"
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py:117: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  input = module(input)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN : [1 / 30] Loss : 1.7795  | Accuracy : 67.94%\n",
      "VAL : [1 / 30] Loss : 1.6091  | Accuracy : 85.21%\n",
      " \n",
      "TRAIN : [2 / 30] Loss : 1.5851  | Accuracy : 87.55%\n",
      "VAL : [2 / 30] Loss : 1.5880  | Accuracy : 87.29%\n",
      " \n",
      "TRAIN : [3 / 30] Loss : 1.5745  | Accuracy : 88.64%\n",
      "VAL : [3 / 30] Loss : 1.5891  | Accuracy : 87.16%\n",
      " \n",
      "TRAIN : [4 / 30] Loss : 1.5716  | Accuracy : 88.92%\n",
      "VAL : [4 / 30] Loss : 1.5820  | Accuracy : 87.87%\n",
      " \n",
      "TRAIN : [5 / 30] Loss : 1.5705  | Accuracy : 89.02%\n",
      "VAL : [5 / 30] Loss : 1.5805  | Accuracy : 87.95%\n",
      " \n",
      "TRAIN : [6 / 30] Loss : 1.5692  | Accuracy : 89.15%\n",
      "VAL : [6 / 30] Loss : 1.5871  | Accuracy : 87.33%\n",
      " \n",
      "TRAIN : [7 / 30] Loss : 1.5694  | Accuracy : 89.13%\n",
      "VAL : [7 / 30] Loss : 1.5857  | Accuracy : 87.44%\n",
      " \n",
      "TRAIN : [8 / 30] Loss : 1.5684  | Accuracy : 89.24%\n",
      "VAL : [8 / 30] Loss : 1.5901  | Accuracy : 87.06%\n",
      " \n",
      "TRAIN : [9 / 30] Loss : 1.5695  | Accuracy : 89.11%\n",
      "VAL : [9 / 30] Loss : 1.5867  | Accuracy : 87.38%\n",
      " \n",
      "TRAIN : [10 / 30] Loss : 1.5686  | Accuracy : 89.22%\n",
      "VAL : [10 / 30] Loss : 1.5836  | Accuracy : 87.75%\n",
      " \n",
      "TRAIN : [11 / 30] Loss : 1.5691  | Accuracy : 89.17%\n",
      "VAL : [11 / 30] Loss : 1.5861  | Accuracy : 87.46%\n",
      " \n",
      "TRAIN : [12 / 30] Loss : 1.5693  | Accuracy : 89.15%\n",
      "VAL : [12 / 30] Loss : 1.5879  | Accuracy : 87.24%\n",
      " \n",
      "TRAIN : [13 / 30] Loss : 1.5688  | Accuracy : 89.18%\n",
      "VAL : [13 / 30] Loss : 1.5881  | Accuracy : 87.24%\n",
      " \n",
      "TRAIN : [14 / 30] Loss : 1.5707  | Accuracy : 89.01%\n",
      "VAL : [14 / 30] Loss : 1.5947  | Accuracy : 86.57%\n",
      " \n",
      "TRAIN : [15 / 30] Loss : 1.5692  | Accuracy : 89.17%\n",
      "VAL : [15 / 30] Loss : 1.5918  | Accuracy : 86.89%\n",
      " \n",
      "TRAIN : [16 / 30] Loss : 1.5699  | Accuracy : 89.08%\n",
      "VAL : [16 / 30] Loss : 1.5832  | Accuracy : 87.76%\n",
      " \n",
      "TRAIN : [17 / 30] Loss : 1.5690  | Accuracy : 89.20%\n",
      "VAL : [17 / 30] Loss : 1.5825  | Accuracy : 87.83%\n",
      " \n",
      "TRAIN : [18 / 30] Loss : 1.5697  | Accuracy : 89.13%\n",
      "VAL : [18 / 30] Loss : 1.5866  | Accuracy : 87.44%\n",
      " \n",
      "TRAIN : [19 / 30] Loss : 1.5698  | Accuracy : 89.11%\n",
      "VAL : [19 / 30] Loss : 1.5880  | Accuracy : 87.32%\n",
      " \n",
      "TRAIN : [20 / 30] Loss : 1.4991  | Accuracy : 96.18%\n",
      "VAL : [20 / 30] Loss : 1.4835  | Accuracy : 97.76%\n",
      " \n",
      "TRAIN : [21 / 30] Loss : 1.4799  | Accuracy : 98.13%\n",
      "VAL : [21 / 30] Loss : 1.4808  | Accuracy : 98.03%\n",
      " \n",
      "TRAIN : [22 / 30] Loss : 1.4797  | Accuracy : 98.15%\n",
      "VAL : [22 / 30] Loss : 1.4825  | Accuracy : 97.89%\n",
      " \n",
      "TRAIN : [23 / 30] Loss : 1.4763  | Accuracy : 98.47%\n",
      "VAL : [23 / 30] Loss : 1.4799  | Accuracy : 98.11%\n",
      " \n",
      "TRAIN : [24 / 30] Loss : 1.4772  | Accuracy : 98.39%\n",
      "VAL : [24 / 30] Loss : 1.4814  | Accuracy : 97.95%\n",
      " \n",
      "TRAIN : [25 / 30] Loss : 1.4755  | Accuracy : 98.55%\n",
      "VAL : [25 / 30] Loss : 1.4921  | Accuracy : 96.89%\n",
      " \n",
      "TRAIN : [26 / 30] Loss : 1.4765  | Accuracy : 98.46%\n",
      "VAL : [26 / 30] Loss : 1.4832  | Accuracy : 97.79%\n",
      " \n",
      "TRAIN : [27 / 30] Loss : 1.4755  | Accuracy : 98.56%\n",
      "VAL : [27 / 30] Loss : 1.4809  | Accuracy : 98.02%\n",
      " \n",
      "TRAIN : [28 / 30] Loss : 1.4741  | Accuracy : 98.70%\n",
      "VAL : [28 / 30] Loss : 1.4799  | Accuracy : 98.11%\n",
      " \n",
      "TRAIN : [29 / 30] Loss : 1.4771  | Accuracy : 98.40%\n",
      "VAL : [29 / 30] Loss : 1.4770  | Accuracy : 98.41%\n",
      " \n",
      "TRAIN : [30 / 30] Loss : 1.4757  | Accuracy : 98.54%\n",
      "VAL : [30 / 30] Loss : 1.4827  | Accuracy : 97.83%\n",
      " \n"
     ]
    }
   ],
   "source": [
    "NUM_EPOCHS = 30\n",
    "SAVE_MODEL_PATH = f'cnn.pth'\n",
    "\n",
    "history = train_model(\n",
    "    cnn_model,\n",
    "    dataloaders_dict,\n",
    "    loss_fn, \n",
    "    optimizer,\n",
    "    NUM_EPOCHS,\n",
    "    SAVE_MODEL_PATH\n",
    "    )"
   ]
  },
  {
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   "id": "external-delta",
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   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(18,8))\n",
    "axes[0].plot(history['train_loss'],label='train_loss')\n",
    "axes[0].plot(history['val_loss'], label='val_loss')\n",
    "axes[0].set_xticks(np.arange(0,NUM_EPOCHS,5))\n",
    "axes[0].legend()\n",
    "\n",
    "\n",
    "axes[1].plot(history['train_acc'], label='train_acc')\n",
    "axes[1].plot(history['val_acc'], label='val_acc')\n",
    "axes[1].set_xticks(np.arange(0,NUM_EPOCHS,5))\n",
    "axes[1].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accepted-proposition",
   "metadata": {
    "papermill": {
     "duration": 0.059261,
     "end_time": "2021-06-26T05:55:32.908214",
     "exception": false,
     "start_time": "2021-06-26T05:55:32.848953",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Mini ResNet Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fallen-applicant",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:55:33.176986Z",
     "iopub.status.busy": "2021-06-26T05:55:33.175445Z",
     "iopub.status.idle": "2021-06-26T05:55:33.177651Z",
     "shell.execute_reply": "2021-06-26T05:55:33.178076Z",
     "shell.execute_reply.started": "2021-06-26T05:12:41.948235Z"
    },
    "papermill": {
     "duration": 0.210635,
     "end_time": "2021-06-26T05:55:33.178219",
     "exception": false,
     "start_time": "2021-06-26T05:55:32.967584",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Clear cache\n",
    "import gc\n",
    "gc.collect()\n",
    "\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "quiet-vegetarian",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:55:33.303382Z",
     "iopub.status.busy": "2021-06-26T05:55:33.302872Z",
     "iopub.status.idle": "2021-06-26T05:55:33.337769Z",
     "shell.execute_reply": "2021-06-26T05:55:33.338331Z",
     "shell.execute_reply.started": "2021-06-26T05:12:42.103552Z"
    },
    "papermill": {
     "duration": 0.098957,
     "end_time": "2021-06-26T05:55:33.338472",
     "exception": false,
     "start_time": "2021-06-26T05:55:33.239515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "resnet_model = ResNet_mini(INPUT_DIM, OUTPUT)\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(resnet_model.parameters(), lr=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "roman-rouge",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:55:33.461002Z",
     "iopub.status.busy": "2021-06-26T05:55:33.460492Z",
     "iopub.status.idle": "2021-06-26T05:57:02.823013Z",
     "shell.execute_reply": "2021-06-26T05:57:02.823544Z",
     "shell.execute_reply.started": "2021-06-26T05:12:42.152711Z"
    },
    "papermill": {
     "duration": 89.425692,
     "end_time": "2021-06-26T05:57:02.823748",
     "exception": false,
     "start_time": "2021-06-26T05:55:33.398056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN : [1 / 15] Loss : 1.5797  | Accuracy : 89.67%\n",
      "VAL : [1 / 15] Loss : 1.5132  | Accuracy : 95.32%\n",
      " \n",
      "TRAIN : [2 / 15] Loss : 1.4816  | Accuracy : 98.20%\n",
      "VAL : [2 / 15] Loss : 1.4978  | Accuracy : 96.62%\n",
      " \n",
      "TRAIN : [3 / 15] Loss : 1.4746  | Accuracy : 98.83%\n",
      "VAL : [3 / 15] Loss : 1.4950  | Accuracy : 96.76%\n",
      " \n",
      "TRAIN : [4 / 15] Loss : 1.4726  | Accuracy : 98.97%\n",
      "VAL : [4 / 15] Loss : 1.5059  | Accuracy : 95.75%\n",
      " \n",
      "TRAIN : [5 / 15] Loss : 1.4705  | Accuracy : 99.17%\n",
      "VAL : [5 / 15] Loss : 1.4880  | Accuracy : 97.43%\n",
      " \n",
      "TRAIN : [6 / 15] Loss : 1.4692  | Accuracy : 99.26%\n",
      "VAL : [6 / 15] Loss : 1.4793  | Accuracy : 98.21%\n",
      " \n",
      "TRAIN : [7 / 15] Loss : 1.4682  | Accuracy : 99.35%\n",
      "VAL : [7 / 15] Loss : 1.4856  | Accuracy : 97.68%\n",
      " \n",
      "TRAIN : [8 / 15] Loss : 1.4674  | Accuracy : 99.44%\n",
      "VAL : [8 / 15] Loss : 1.4772  | Accuracy : 98.46%\n",
      " \n",
      "TRAIN : [9 / 15] Loss : 1.4666  | Accuracy : 99.52%\n",
      "VAL : [9 / 15] Loss : 1.4929  | Accuracy : 96.94%\n",
      " \n",
      "TRAIN : [10 / 15] Loss : 1.4656  | Accuracy : 99.60%\n",
      "VAL : [10 / 15] Loss : 1.4824  | Accuracy : 98.02%\n",
      " \n",
      "TRAIN : [11 / 15] Loss : 1.4665  | Accuracy : 99.50%\n",
      "VAL : [11 / 15] Loss : 1.4793  | Accuracy : 98.16%\n",
      " \n",
      "TRAIN : [12 / 15] Loss : 1.4662  | Accuracy : 99.52%\n",
      "VAL : [12 / 15] Loss : 1.4889  | Accuracy : 97.22%\n",
      " \n",
      "TRAIN : [13 / 15] Loss : 1.4673  | Accuracy : 99.45%\n",
      "VAL : [13 / 15] Loss : 1.4802  | Accuracy : 98.08%\n",
      " \n",
      "TRAIN : [14 / 15] Loss : 1.4651  | Accuracy : 99.63%\n",
      "VAL : [14 / 15] Loss : 1.4733  | Accuracy : 98.84%\n",
      " \n",
      "TRAIN : [15 / 15] Loss : 1.4649  | Accuracy : 99.64%\n",
      "VAL : [15 / 15] Loss : 1.4775  | Accuracy : 98.44%\n",
      " \n"
     ]
    }
   ],
   "source": [
    "NUM_EPOCHS = 15\n",
    "SAVE_MODEL_PATH = f'resnet.pth'\n",
    "\n",
    "history = train_model(\n",
    "    resnet_model,\n",
    "    dataloaders_dict,\n",
    "    loss_fn, \n",
    "    optimizer,\n",
    "    NUM_EPOCHS,\n",
    "    SAVE_MODEL_PATH\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cathedral-practitioner",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:57:03.025515Z",
     "iopub.status.busy": "2021-06-26T05:57:03.024724Z",
     "iopub.status.idle": "2021-06-26T05:57:03.278418Z",
     "shell.execute_reply": "2021-06-26T05:57:03.278856Z",
     "shell.execute_reply.started": "2021-06-26T05:13:42.461313Z"
    },
    "papermill": {
     "duration": 0.386539,
     "end_time": "2021-06-26T05:57:03.279006",
     "exception": false,
     "start_time": "2021-06-26T05:57:02.892467",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(18,8))\n",
    "axes[0].plot(history['train_loss'],label='train_loss')\n",
    "axes[0].plot(history['val_loss'], label='val_loss')\n",
    "axes[0].set_xticks(np.arange(0,NUM_EPOCHS))\n",
    "axes[0].legend()\n",
    "\n",
    "\n",
    "axes[1].plot(history['train_acc'], label='train_acc')\n",
    "axes[1].plot(history['val_acc'], label='val_acc')\n",
    "axes[1].set_xticks(np.arange(0,NUM_EPOCHS))\n",
    "axes[1].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "linear-circus",
   "metadata": {
    "papermill": {
     "duration": 0.06959,
     "end_time": "2021-06-26T05:57:03.418088",
     "exception": false,
     "start_time": "2021-06-26T05:57:03.348498",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "* Mini DenseNet Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "independent-offer",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:57:03.694318Z",
     "iopub.status.busy": "2021-06-26T05:57:03.693595Z",
     "iopub.status.idle": "2021-06-26T05:57:03.697146Z",
     "shell.execute_reply": "2021-06-26T05:57:03.696695Z"
    },
    "papermill": {
     "duration": 0.209913,
     "end_time": "2021-06-26T05:57:03.697261",
     "exception": false,
     "start_time": "2021-06-26T05:57:03.487348",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Clear cache\n",
    "import gc\n",
    "gc.collect()\n",
    "\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "toxic-sound",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:57:03.841119Z",
     "iopub.status.busy": "2021-06-26T05:57:03.840376Z",
     "iopub.status.idle": "2021-06-26T05:57:03.851829Z",
     "shell.execute_reply": "2021-06-26T05:57:03.851424Z"
    },
    "papermill": {
     "duration": 0.084349,
     "end_time": "2021-06-26T05:57:03.851937",
     "exception": false,
     "start_time": "2021-06-26T05:57:03.767588",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "densenet_model = DenseNet_mini(INPUT_DIM, OUTPUT)\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(densenet_model.parameters(), lr=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "textile-massachusetts",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:57:03.994891Z",
     "iopub.status.busy": "2021-06-26T05:57:03.994027Z",
     "iopub.status.idle": "2021-06-26T05:57:58.397943Z",
     "shell.execute_reply": "2021-06-26T05:57:58.397294Z"
    },
    "papermill": {
     "duration": 54.476646,
     "end_time": "2021-06-26T05:57:58.398109",
     "exception": false,
     "start_time": "2021-06-26T05:57:03.921463",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN : [1 / 20] Loss : 2.1098  | Accuracy : 35.01%\n",
      "VAL : [1 / 20] Loss : 1.9172  | Accuracy : 61.44%\n",
      " \n",
      "TRAIN : [2 / 20] Loss : 1.7157  | Accuracy : 80.52%\n",
      "VAL : [2 / 20] Loss : 1.5919  | Accuracy : 92.65%\n",
      " \n",
      "TRAIN : [3 / 20] Loss : 1.5349  | Accuracy : 95.51%\n",
      "VAL : [3 / 20] Loss : 1.5324  | Accuracy : 94.73%\n",
      " \n",
      "TRAIN : [4 / 20] Loss : 1.5038  | Accuracy : 96.96%\n",
      "VAL : [4 / 20] Loss : 1.5137  | Accuracy : 95.86%\n",
      " \n",
      "TRAIN : [5 / 20] Loss : 1.4928  | Accuracy : 97.64%\n",
      "VAL : [5 / 20] Loss : 1.5018  | Accuracy : 96.62%\n",
      " \n",
      "TRAIN : [6 / 20] Loss : 1.4863  | Accuracy : 98.08%\n",
      "VAL : [6 / 20] Loss : 1.4979  | Accuracy : 96.83%\n",
      " \n",
      "TRAIN : [7 / 20] Loss : 1.4828  | Accuracy : 98.34%\n",
      "VAL : [7 / 20] Loss : 1.4963  | Accuracy : 96.95%\n",
      " \n",
      "TRAIN : [8 / 20] Loss : 1.4788  | Accuracy : 98.63%\n",
      "VAL : [8 / 20] Loss : 1.5045  | Accuracy : 96.30%\n",
      " \n",
      "TRAIN : [9 / 20] Loss : 1.4767  | Accuracy : 98.80%\n",
      "VAL : [9 / 20] Loss : 1.4976  | Accuracy : 96.78%\n",
      " \n",
      "TRAIN : [10 / 20] Loss : 1.4742  | Accuracy : 99.01%\n",
      "VAL : [10 / 20] Loss : 1.4902  | Accuracy : 97.43%\n",
      " \n",
      "TRAIN : [11 / 20] Loss : 1.4731  | Accuracy : 99.07%\n",
      "VAL : [11 / 20] Loss : 1.5135  | Accuracy : 95.19%\n",
      " \n",
      "TRAIN : [12 / 20] Loss : 1.4718  | Accuracy : 99.15%\n",
      "VAL : [12 / 20] Loss : 1.4929  | Accuracy : 97.19%\n",
      " \n",
      "TRAIN : [13 / 20] Loss : 1.4704  | Accuracy : 99.26%\n",
      "VAL : [13 / 20] Loss : 1.4930  | Accuracy : 97.08%\n",
      " \n",
      "TRAIN : [14 / 20] Loss : 1.4690  | Accuracy : 99.39%\n",
      "VAL : [14 / 20] Loss : 1.4869  | Accuracy : 97.70%\n",
      " \n",
      "TRAIN : [15 / 20] Loss : 1.4693  | Accuracy : 99.37%\n",
      "VAL : [15 / 20] Loss : 1.4873  | Accuracy : 97.65%\n",
      " \n",
      "TRAIN : [16 / 20] Loss : 1.4684  | Accuracy : 99.42%\n",
      "VAL : [16 / 20] Loss : 1.4973  | Accuracy : 96.89%\n",
      " \n",
      "TRAIN : [17 / 20] Loss : 1.4676  | Accuracy : 99.48%\n",
      "VAL : [17 / 20] Loss : 1.4896  | Accuracy : 97.40%\n",
      " \n",
      "TRAIN : [18 / 20] Loss : 1.4673  | Accuracy : 99.52%\n",
      "VAL : [18 / 20] Loss : 1.4884  | Accuracy : 97.57%\n",
      " \n",
      "TRAIN : [19 / 20] Loss : 1.4672  | Accuracy : 99.54%\n",
      "VAL : [19 / 20] Loss : 1.4892  | Accuracy : 97.46%\n",
      " \n",
      "TRAIN : [20 / 20] Loss : 1.4668  | Accuracy : 99.56%\n",
      "VAL : [20 / 20] Loss : 1.4823  | Accuracy : 97.94%\n",
      " \n"
     ]
    }
   ],
   "source": [
    "NUM_EPOCHS = 20\n",
    "SAVE_MODEL_PATH = f'densenet.pth'\n",
    "\n",
    "history = train_model(\n",
    "    densenet_model,\n",
    "    dataloaders_dict,\n",
    "    loss_fn, \n",
    "    optimizer,\n",
    "    NUM_EPOCHS,\n",
    "    SAVE_MODEL_PATH\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "charitable-springfield",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:57:58.573880Z",
     "iopub.status.busy": "2021-06-26T05:57:58.572765Z",
     "iopub.status.idle": "2021-06-26T05:57:58.796300Z",
     "shell.execute_reply": "2021-06-26T05:57:58.796695Z"
    },
    "papermill": {
     "duration": 0.320869,
     "end_time": "2021-06-26T05:57:58.796847",
     "exception": false,
     "start_time": "2021-06-26T05:57:58.475978",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize=(18,8))\n",
    "axes[0].plot(history['train_loss'],label='train_loss')\n",
    "axes[0].plot(history['val_loss'], label='val_loss')\n",
    "axes[0].set_xticks(np.arange(0,NUM_EPOCHS,5))\n",
    "axes[0].legend()\n",
    "\n",
    "\n",
    "axes[1].plot(history['train_acc'], label='train_acc')\n",
    "axes[1].plot(history['val_acc'], label='val_acc')\n",
    "axes[1].set_xticks(np.arange(0,NUM_EPOCHS,5))\n",
    "axes[1].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unknown-relaxation",
   "metadata": {
    "papermill": {
     "duration": 0.076903,
     "end_time": "2021-06-26T05:57:58.951385",
     "exception": false,
     "start_time": "2021-06-26T05:57:58.874482",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 5-5) Evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "auburn-tuning",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:57:59.112207Z",
     "iopub.status.busy": "2021-06-26T05:57:59.111480Z",
     "iopub.status.idle": "2021-06-26T05:57:59.113730Z",
     "shell.execute_reply": "2021-06-26T05:57:59.114181Z",
     "shell.execute_reply.started": "2021-06-26T05:13:42.716461Z"
    },
    "papermill": {
     "duration": 0.085572,
     "end_time": "2021-06-26T05:57:59.114309",
     "exception": false,
     "start_time": "2021-06-26T05:57:59.028737",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def evalueate_model(model, test_loader):\n",
    "    predictions = []\n",
    "\n",
    "    #model = BasicCNN()\n",
    "    #model.to(device)\n",
    "    #model.load_state_dict(torch.load(f'cnn.pth'))\n",
    "    \n",
    "    model.eval()\n",
    "\n",
    "    for data in test_loader:\n",
    "        x_data = data['data'].to(device)\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            predicts = model(x_data)\n",
    "            predicts = predicts.argmax(axis=1)\n",
    "            predicts = predicts.cpu().numpy()\n",
    "            \n",
    "            # Put the batch size data into the list one by one.\n",
    "            for pred in predicts:\n",
    "                predictions.append(pred)\n",
    "        \n",
    "    return(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "polished-tribute",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:57:59.284299Z",
     "iopub.status.busy": "2021-06-26T05:57:59.283524Z",
     "iopub.status.idle": "2021-06-26T05:57:59.286896Z",
     "shell.execute_reply": "2021-06-26T05:57:59.287293Z",
     "shell.execute_reply.started": "2021-06-26T05:13:42.724719Z"
    },
    "papermill": {
     "duration": 0.095661,
     "end_time": "2021-06-26T05:57:59.287417",
     "exception": false,
     "start_time": "2021-06-26T05:57:59.191756",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 784 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0       0       0       0       0   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 784 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "affected-taylor",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:57:59.446181Z",
     "iopub.status.busy": "2021-06-26T05:57:59.445334Z",
     "iopub.status.idle": "2021-06-26T05:57:59.493384Z",
     "shell.execute_reply": "2021-06-26T05:57:59.492921Z",
     "shell.execute_reply.started": "2021-06-26T05:13:42.746335Z"
    },
    "papermill": {
     "duration": 0.128807,
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     "exception": false,
     "start_time": "2021-06-26T05:57:59.364704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_dataset = BasicDataset(test_df.values, y_data=None, is_labeled=False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "parallel-squad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:57:59.653758Z",
     "iopub.status.busy": "2021-06-26T05:57:59.653253Z",
     "iopub.status.idle": "2021-06-26T05:58:08.928421Z",
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     "shell.execute_reply.started": "2021-06-26T05:13:42.801968Z"
    },
    "papermill": {
     "duration": 9.356275,
     "end_time": "2021-06-26T05:58:08.928538",
     "exception": false,
     "start_time": "2021-06-26T05:57:59.572263",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py:117: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "  input = module(input)\n"
     ]
    }
   ],
   "source": [
    "cnn_pred = evalueate_model(cnn_model, test_loader)\n",
    "resnet_pred = evalueate_model(resnet_model, test_loader)\n",
    "densenet_pred = evalueate_model(densenet_model, test_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "black-complement",
   "metadata": {
    "papermill": {
     "duration": 0.077975,
     "end_time": "2021-06-26T05:58:09.085587",
     "exception": false,
     "start_time": "2021-06-26T05:58:09.007612",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 6. Submission\n",
    "* Submit the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "alpha-found",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:58:09.251340Z",
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     "shell.execute_reply.started": "2021-06-26T05:13:49.717980Z"
    },
    "papermill": {
     "duration": 0.155816,
     "end_time": "2021-06-26T05:58:09.320496",
     "exception": false,
     "start_time": "2021-06-26T05:58:09.164680",
     "status": "completed"
    },
    "tags": []
   },
   "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>ImageId</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ImageId  Label\n",
       "0        1      2\n",
       "1        2      0\n",
       "2        3      9\n",
       "3        4      9\n",
       "4        5      3"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_submission = pd.read_csv('../input/digit-recognizer/sample_submission.csv')\n",
    "cnn_submission['Label'] = cnn_pred\n",
    "\n",
    "resnet_submission = pd.read_csv('../input/digit-recognizer/sample_submission.csv')\n",
    "resnet_submission['Label'] = resnet_pred\n",
    "\n",
    "\n",
    "densenet_submission = pd.read_csv('../input/digit-recognizer/sample_submission.csv')\n",
    "densenet_submission['Label'] = densenet_pred\n",
    "cnn_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "material-costume",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:58:09.494174Z",
     "iopub.status.busy": "2021-06-26T05:58:09.493373Z",
     "iopub.status.idle": "2021-06-26T05:58:09.720529Z",
     "shell.execute_reply": "2021-06-26T05:58:09.720121Z",
     "shell.execute_reply.started": "2021-06-26T05:13:49.784667Z"
    },
    "papermill": {
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     "start_time": "2021-06-26T05:58:09.400928",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,5, figsize=(20,5))\n",
    "\n",
    "for i in range(5):\n",
    "    axes[i].imshow(test_df.iloc[i].values.reshape(28,28), 'gray')\n",
    "    axes[i].axis('off')\n",
    "    axes[i].set_title(\"CNN Predict Label :\" + str(cnn_pred[i]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "acceptable-moore",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:58:09.899527Z",
     "iopub.status.busy": "2021-06-26T05:58:09.898724Z",
     "iopub.status.idle": "2021-06-26T05:58:10.121261Z",
     "shell.execute_reply": "2021-06-26T05:58:10.120814Z",
     "shell.execute_reply.started": "2021-06-26T05:13:50.051857Z"
    },
    "papermill": {
     "duration": 0.320977,
     "end_time": "2021-06-26T05:58:10.121371",
     "exception": false,
     "start_time": "2021-06-26T05:58:09.800394",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,5, figsize=(20,5))\n",
    "\n",
    "for i in range(5):\n",
    "    axes[i].imshow(test_df.iloc[i].values.reshape(28,28), 'gray')\n",
    "    axes[i].axis('off')\n",
    "    axes[i].set_title(\"RESNET Predict Label :\" + str(resnet_pred[i]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "tribal-cambodia",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:58:10.294265Z",
     "iopub.status.busy": "2021-06-26T05:58:10.293465Z",
     "iopub.status.idle": "2021-06-26T05:58:10.524016Z",
     "shell.execute_reply": "2021-06-26T05:58:10.523495Z"
    },
    "papermill": {
     "duration": 0.321563,
     "end_time": "2021-06-26T05:58:10.524131",
     "exception": false,
     "start_time": "2021-06-26T05:58:10.202568",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,5, figsize=(20,5))\n",
    "\n",
    "for i in range(5):\n",
    "    axes[i].imshow(test_df.iloc[i].values.reshape(28,28), 'gray')\n",
    "    axes[i].axis('off')\n",
    "    axes[i].set_title(\"DENSENET Predict Label :\" + str(densenet_pred[i]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "representative-target",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-26T05:58:10.689302Z",
     "iopub.status.busy": "2021-06-26T05:58:10.688812Z",
     "iopub.status.idle": "2021-06-26T05:58:10.939181Z",
     "shell.execute_reply": "2021-06-26T05:58:10.938710Z",
     "shell.execute_reply.started": "2021-06-26T05:13:50.323208Z"
    },
    "papermill": {
     "duration": 0.334024,
     "end_time": "2021-06-26T05:58:10.939303",
     "exception": false,
     "start_time": "2021-06-26T05:58:10.605279",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cnn_submission.to_csv('cnn_submission.csv', index=False)\n",
    "resnet_submission.to_csv('resnet_submission.csv', index=False)\n",
    "densenet_submission.to_csv('densenet_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "widespread-daniel",
   "metadata": {
    "papermill": {
     "duration": 0.0802,
     "end_time": "2021-06-26T05:58:11.100352",
     "exception": false,
     "start_time": "2021-06-26T05:58:11.020152",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "##### reference \n",
    "* https://www.kaggle.com/kanncaa1/pytorch-tutorial-for-deep-learning-lovers\n",
    "\n",
    "###  If this notebook is useful for your kaggling, \"UPVOTE\" for it 👀\n",
    "#### THX to Reading My Notebook🌈"
   ]
  }
 ],
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