Model_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "from keras.preprocessing.image import ImageDataGenerator, load_img\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "FAST_RUN = False\n",
    "IMAGE_WIDTH=128\n",
    "IMAGE_HEIGHT=128\n",
    "IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)\n",
    "IMAGE_CHANNELS=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = os.listdir(r\"C:\\Users\\nguyent2\\Desktop\\Training_1\")\n",
    "categories = []\n",
    "for filename in filenames:\n",
    "    category = filename[0]\n",
    "    if category == \"3\":\n",
    "        categories.append(\"triangle\")\n",
    "    elif category == \"4\":\n",
    "        categories.append(\"square\")\n",
    "    elif category == \"5\":\n",
    "        categories.append(\"pentagon\")\n",
    "    elif category == \"6\":\n",
    "        categories.append(\"hexagon\")\n",
    "    elif category == \"7\":\n",
    "        categories.append(\"heptagon\")\n",
    "    elif category == \"8\":\n",
    "        categories.append(\"octagon\")\n",
    "    elif category == \"9\":\n",
    "        categories.append(\"nonagon\")\n",
    "    else:\n",
    "        categories.append(\"decagon\")\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'filename': filenames,\n",
    "    'category': categories\n",
    "})\n",
    "\n",
    "#This line used to shuffle the dataset\n",
    "df = df.sample(frac=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "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>filename</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5561</th>\n",
       "      <td>7_sides_603.png</td>\n",
       "      <td>heptagon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5537</th>\n",
       "      <td>7_sides_582.png</td>\n",
       "      <td>heptagon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6366</th>\n",
       "      <td>8_sides_428.png</td>\n",
       "      <td>octagon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5627</th>\n",
       "      <td>7_sides_663.png</td>\n",
       "      <td>heptagon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3913</th>\n",
       "      <td>5_sides_920.png</td>\n",
       "      <td>pentagon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             filename  category\n",
       "5561  7_sides_603.png  heptagon\n",
       "5537  7_sides_582.png  heptagon\n",
       "6366  8_sides_428.png   octagon\n",
       "5627  7_sides_663.png  heptagon\n",
       "3913  5_sides_920.png  pentagon"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv(r\"C:\\Users\\nguyent2\\Desktop\\New.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4332</th>\n",
       "      <td>6_sides_398.png</td>\n",
       "      <td>hexagon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>3_sides_322.png</td>\n",
       "      <td>triangle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1363</th>\n",
       "      <td>3_sides_425.png</td>\n",
       "      <td>triangle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2341</th>\n",
       "      <td>4_sides_405.png</td>\n",
       "      <td>square</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4734</th>\n",
       "      <td>6_sides_76.png</td>\n",
       "      <td>hexagon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             filename  category\n",
       "4332  6_sides_398.png   hexagon\n",
       "1249  3_sides_322.png  triangle\n",
       "1363  3_sides_425.png  triangle\n",
       "2341  4_sides_405.png    square\n",
       "4734   6_sides_76.png   hexagon"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['category'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x26832ec2370>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = random.choice(filenames)\n",
    "image = load_img(r\"C:\\Users\\nguyent2\\Desktop\\Training_1\\\\\" + sample)\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, validate_df = train_test_split(df, test_size=0.20, random_state=42)\n",
    "train_df = train_df.reset_index(drop=True)\n",
    "validate_df = validate_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eWNHcXwGs6ylfLukQSQuBRcDGgwsxIiL219zpdpD0UeBFwJGStgFvAS4C1ko6F7gdOBvA9mZJa4EbgF3AebZ3Dyn2iIiYwrTJ3fY5Uzx0+hT7rwJWHUxQERFxcDJCNSKiQknuEREVSnKPiKhQkntERIWS3CMiKpTkHhFRoST3iIgKJblHRFQoyT0iokJJ7hERFUpyj4ioUJJ7RESFktwjIiqU5B4RUaEk94iICiW5R0RUaGjJXdKZkrZI2irpgmG9TkRETDaU5C5pDvD/gJcCi4FzJC0exmtFRMRkwzpzXwpstX2L7e8DlwDLhvRaERExgWzP/JNKrwTOtP36Zvu1wHNsv6Fnn5XAymbzRGDLDIdxJPCtGX7OYUicMytxzqxRiHMUYoThxPlk22P9Hph2gewDpD5le32K2F4NrB7S6yNpk+0lw3r+mZI4Z1binFmjEOcoxAizH+ewmmW2AQt6tucD24f0WhERMcGwkvvXgEWSFkp6BLAcWD+k14qIiAmG0ixje5ekNwCfAeYAH7S9eRivtQ9Da/KZYYlzZiXOmTUKcY5CjDDLcQ7lgmpERLQrI1QjIiqU5B4RUaEk94iICiW5t0DSIyWd2HYcEVGvYQ1iao2k5wHH0fO72f7r1gKaQNLLgT8BHgEslHQK8Hbbr2g1sBEmaQz4JSbX+y+2FdNUJB0DPJm94/xCexHtTZKAVwPH2367pGOBJ9re2HJoexmVOpd0AvAmJtf5i4f92lUld0kfAp4CXAPsbooNdCa5A2+lzL1zOYDtayQd12I8fUn6aeAPgaMoI44F2PZjWw2sv3XAvwCfY0+9d46kPwReBdzA3n+fnUnuwMXAg8CLgbcD9wIfB57dZlB9jESdA38L/DnwF8xynFUld2AJsNjd7t+5y/Y95QSp0/4IeLntG9sOZACH2f6ttoMYwFnAibYfaDuQfXiO7WdJuhrA9rebgYhdMyp1vsv2e9t44dra3K8Hnth2ENO4XtLPAXMkLZL0HuDLbQfVx10jktgBPinpJ9sOYgC3AA9vO4hp/HczZbfhoeaPB9sNqa9RqfO/l/SrkuZJOmL8ZzZeuKpBTJI+D5wCbAQeOjvqUnu2pMOA3wF+gtLU8RngHbbvbzWwCSS9m/JB+Xfs/V5e2lZMU5F0L/Ao4PvAfzfFnWtCkvRx4GRgA3u/p7/WWlATSHo1penoWcAa4JXAm23/bauBTTBCdX5rn2LbPn7or11Zcv+xfuW2/3m2Yxl1kv6yT7G7dsFqlEha0a/c9prZjqUfSQ8DTgX+AzidcvKxYYS+wUWPqpI7gKSj2XPxZ6PtHW3GM07S3zNh2uNeXfp2MYokvQJ4YbN5ue1PthnPVJr26xOazS22/3tf+882SV+x/dy24xjEKNS5pIcDv0JPnMD7ZqPeq0rukn4W+GPKGyjgR4E32f5Ym3HB1N8qxnXt24Wk+cB7gOdTPpS+CLzR9rZWA+tD0kWUD/QPN0XnAFfa7tTavZJeRGnq+Abl73MBsKJjXSHfBlwLXNrljgkjVOfvp1xnGf929lpg9/hCRkN97Q7X336T9HXgJeNn683FoM/ZPrndyEaPpMuAjwAfaopeA7za9kvai6o/SdcCp9h+sNmeA1xt+xntRrY3SVcCP2d7S7N9AvBR2z/SbmR79LRl7wLup6NdYEeozr8+Mf/0KxuG2rpCPmxCM8zddKxHkKTrmNw8cw+wCfg923fPflR9jdnubXf/K0nntxXMAB5PaSsGeFyLcezLw8cTO4Dtf22+tneG7ce0HcN+eDzdr/Pdkp5i+98AJB3PLPV3ry25f1rSZ4CPNtuvAv6xxXj6+RSlcj/SbC+nnB3dA/wV8PJ2wprkW5Jew5738hzKh2UX/QFwddNbSpT2zQvbDamvTZI+wJ5vQ68Grmwxnr4kHQ4sAg4dL+tS01FjVOr8TcDnJd1CifPJwOtm44WrapYBkPQzlHZiAV+w/YmWQ9qLpC/Zfn6/MknX2X56W7H1aoad/1/guZRvGl+mtLnf1mpgU5A0j9IGK+AK2//eckiTSDoEOA94Ac3fJ3BxlwY1SXo98EbK0pjXUHrPfGU2hsvvr1Goc3io3k+kxHnTbNV3dcm965rrAittX9FsLwX+wvbJkq62/cx2Ixw9kp7Vp/ge4Dbbu2Y7nlHWNBs+G/iq7VMkPRV4m+1XtRzaXkalzptpPCa6B7hu2D35qmqWaS4GTdWe/Zu2b5n9qCZ5PfBBSY+mfJL/J/B6SY+ifNXsBEl/1qf4HmCT7XWzHc80LqYMurmW8p4+rbn/BEn/0/Zn2wxu3Ihcb7nf9v2SkHSI7Zs6OoPpSNQ5cC7l2+8/UeJ8EfBV4ARJb7f9oX0ce1CqSu7Au4DtlPZsUdqznwhsAT5IeWNbZftrwNMlPY7yzek7PQ+vbSeqvg4FnkqZ+AjgZ4DNwLmSTrN9fluB9fEN4NzxdXolLaa0db4DuBToyj/6KFxv2Sbp8ZSRyZdJ+jblf6prvsFo1PmDwA/bvgseGofzXuA5lGa5oSX3qpplJF1h+zkTyr5q+9TZ6n40nab97WeYPFXp29uKqR9J/wT8xPhXXElzKf8wL6F8pVzcZny9JF1j+5R+Zf0ea8uoXG8Z14zNeBzwadvfbzueXiNU53vVq8qMgdfZftqwm2FrO3N/sBnIND5o6ZU9j3XlU2wd5UztSnrmF+mgYyj9ne9pth8FPMn2bkldi3uLpPcClzTbrwL+tfkg7dII0EdLes6E6y2Pbh7rRDtxcyF93Pi8KE8Ebm8hnH0ZlTr/F0mfZO9vwF9ommG/M8wXru3M/Xjg3ezp4fFV4NeBbwI/YvuLLYYHgKTrbT+t7TimI+lc4M3sGe37QuD3KV0j32r7Te1FtzdJjwR+lT29UL5IaZO9nzI17H+1GN5DJD2b0jy41/UWSnPXy2y33izXc11AlKa5hZRpEk5qNbAJRqjORUno4z34vgh8fDZG/1aV3EeBpNXAe2xf13Ys02m6mi2l/FFutN3FtteRM8X1lk5qeqX8su1fbjuW2D9VJXdJh1KuTp/E3gMwOjOToaQbgB+ifOV9gD3Duzs1bBpGZjALkhZRehotZu9Yhz6t6v6S9DIm/3126nrLRJKust2v62FrRqXOJZ1KmaPphylLa84B7puN6Rxqa3P/EHATcAZlibBXA12brvSlbQcwiKkGs1CWX+uavwTeAvwpcBplBGDnlrqS9OfAYZQY30+5JtS1tUl/o2fzYZTuhjtbCmdfRqLOKQMBl1Pa3JcAP085uRu6Ts27MgN+yPbvUj4Z1wAvAzrVA8H2bc0oz+9R2jbHf7rmjZTBLLfZPg14Jt38Jwd4pO0NlG+it9l+K938EHqe7Z8Hvm37bZRrQwtajmmix/T8HAL8A7Cs1Yj6G5U6x/ZWYI7t3c18TafNxuvWduY+fpX8O5KeBvw7pcthZzRzUL8TeBKwgzLXxI2Ur+pdMiqDWQDuV1lo4mZJb6BcQD+q5Zj6+V5z+11JT6LM1bOwxXgmaT50RsGo1Pl3Vebwv0bSHwF3UnqeDV1tyX110078ZmA9pVfC77Yb0iTvoDRxfM72MyWdRpmUq2tGZTALwPmU5o5fo7y/p1G+/nbNJ5v39I+Bqyjf2N7fakQTSFq/r8fdnUVlzmc06vy1lBaSN1B67i2g9J4ZutouqC60fet0ZW2StMn2kmaOmWfaflDSRttL245tKl0ezAIg6WxPWOOzX1mXNP2xD7V9z7Q7zyLtWTv3b5qicyijQT8D3VlUZhTrfLbVltwnXdWXdKW7tRjC54CzKFf6j6Q0zTzb9vPajGui5ir/Ztv3NtuPARaPD8DpkinqvYs9PN5BmYRrfNTvY4F3256VKWAHIekLtl84XVnbRqjOnw+8ldL82jsifei9eqpolmlmrjsJeNyEWdgeS083qY5YRhlo8euU3jyPo/Ts6Zr3UnpKjLuvT1mrJL0U+EngmAkTnT2Wjoz4nGAucIWk11HOjt/T/HTJmKTjxyfZawYGjrUc00NGsM4/QPlfv5JZWqRjXBXJnTJX8k9RVmbpnXzpXuCX2ghoKrbv69nsxKr3U1DvKLqm+ahrfy/bKTMqvoK9F724l/IP1Sm2L5S0AbgC+DbwwqYnRZecD1yusriEKRd8V7Ya0d5Gqs6Be2x/qo0Xrq1Z5rm2v9J2HP1I+qLtF2jytMRdXaPyUsrUA+9tin4VOM32WW3FNBWVpepEmcXSlOHyXbw28ELK+/k3lC66RwC/2KWRv5LOprSvL6Qk0OcBv2P7qlYDm0DSw213aQ6ZvlQW8p5DmanyoTmZZuP9rC25r6GsFvSdZvtw4J1dGqE6KiQdBfwZpe+wgQ3A+R7yAgMHQtJPAu8D/o2S5BdShsy3csY0FUkbgV+wfUOz/dPA79t+aruR7SHpWtvPkPQCylxC7wR+e+Jsq23r05Y9fpLUtRGqn+9TbM/Cyla1JfdJU2gOe1rN/dH0y712FCYOGyWSbgJ+aryJQ9JTgH/oUtIEkDTH9u4JZU9wNxbpAPb8v0j6A8rUtB/p0v/QuKbOJ7Vld+m9bFttI1Qf1pytAyDpCDp0XcH2g8DXJ0yr2kmSTpC0QdL1zfYzJL257bimsGNC2/UtlF5IXfOUie8p8CstxzTRNyW9D/hZ4B+bLptdzBP32P6U7R227x7/aTuoiSQ9TtK7JG1qft7ZTBw3/Neu7Mz954HfZs/cyWcDqzzEpaz2l8oiGM+mzCny0MXVDg0OAUDSP1NWtnnf+FmbOjpdscq83k+mrGRlSr1vAb4EYPvS9qLbYxTeU0mHAWdSztpvVpkZ9OnuzrJ1QLtt2ftD0seB69nTeeK1wMm2+62tOqM6c1Y7E2z/taStlAl6HgRe18ELrI+m9OwZJ+APW4plXw6zvVHaay6mLnY1g9Ld9S7gx5rtnZSLlS+nJPtOJHdG4D21/V163i/bd1KGzHfN+DWAJT1lpnvzyzzFdu+I1LdJumY2Xriq5C7pjZTFDy6lJM33SfoL213qSzx34ig/lYUHuuZbTdu1ASS9km7+k9OlQUDTGJn3tOuayexGwfckvcDNQkHNheDvTXPMjKitWeZa4LnjfclVlrL6ijswV7qkX6F0Jzye0qtj3GOAL9l+TSuBTaEZvLKa0hXu25T551/tMqNlp0g6gdLF8GiXtSmfAbzC9u+1HNpeRuk97TqVhaZ/n7L040tVFsh+ru0PtBzaXiSdQmmSGW9n/zawwva1Q3/typL7dZSh/Pc324cCX3MHFh5uLqIcTpl24IKeh+61/R/tRDWZ9p7PG+CRlAtq9wHYftesBzWNUWjLhofmk3klZabSIyjL7NkdX6yjiyR9ijKn++/YPrkZYHd1F/7Xe/XU+VMogyzvYZbqvKpmGUplXyHpE832WZThv61rJoi6h27OANnrMc3tiZQLv+soTVyvBTq3ClOj823ZjXWURZGvorszbI6KI22vlXQhgO1dkmZ1eP+Aeuv8m7P5wlUld9vvknQ5exbNfZ3tq9uNarS4mc9b0meBZ/VMHPZW9vRC6ppRacueb/vMtoOoxH2SnsCeOj+VcvLUNa3VeVXJHR7qCtWp7lAj6ligdwj/9+nYwic9zqO0ZT9V0jdp2rLbDamvL0t6ukdgcfQR8BuUNRuOl/QlyuRmr2w3pL5aq/PqknvMmA8BG5smLgP/g+5OdPZNSpPc59nTlr2Cjsy22VwLMuX/7XXNpFydXhx9BNwAfAL4LmXSsL8D/rXNgKbwAuAXJN3KLNd5VRdUY2ZJehbwo83mF7raxCXp0+xp1+wdiv7OtmLqJenJ+3o8vWX2n6S1lA/xDzdF5wCH2z67vagmm6ruZ6POk9xj5HWxZ0wMl6Sv2z55urIfZF2cMyJif31ZUqe6wMXQXd1cRAVA0nNoppuIImfuMbImtGUvokwYlrbsHwCSbqR01729KToWuJEy7UjqniT3GGFpy/7BlbqfXpJ7RESF0uYeEVGhJPeIiAoluUdEVCjJPSKiQknuEREV+v/CkSKuqm8MdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df['category'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "validate_df['category'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_train = train_df.shape[0]\n",
    "total_validate = validate_df.shape[0]\n",
    "batch_size=15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 6400 validated image filenames belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "    rotation_range=15,\n",
    "    rescale=1./255,\n",
    "    shear_range=0.1,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1\n",
    ")\n",
    "\n",
    "train_generator = train_datagen.flow_from_dataframe(\n",
    "    train_df, r\"C:\\Users\\nguyent2\\Desktop\\Training_1\", \n",
    "    x_col='filename',\n",
    "    y_col='category',\n",
    "    target_size=IMAGE_SIZE,\n",
    "    class_mode='categorical',\n",
    "    batch_size= batch_size\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1600 validated image filenames belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    "validation_generator = validation_datagen.flow_from_dataframe(\n",
    "    validate_df, \n",
    "    r\"C:\\Users\\nguyent2\\Desktop\\Training_1\", \n",
    "    x_col='filename',\n",
    "    y_col='category',\n",
    "    target_size=IMAGE_SIZE,\n",
    "    class_mode='categorical',\n",
    "    batch_size=batch_size\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1 validated image filenames belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "example_df = train_df.sample(n=1).reset_index(drop=True)\n",
    "example_generator = train_datagen.flow_from_dataframe(\n",
    "    example_df,   r\"C:\\Users\\nguyent2\\Desktop\\Training_1\", \n",
    "    x_col='filename',\n",
    "    y_col='category',\n",
    "    target_size=IMAGE_SIZE,\n",
    "    class_mode='categorical'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 9216x9216 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(128, 128))\n",
    "for i in range(0, 15):\n",
    "    plt.subplot(5, 3, i+1)\n",
    "    for X_batch, Y_batch in example_generator:\n",
    "        image = X_batch[0]\n",
    "        plt.imshow(image)\n",
    "        break\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 126, 126, 32)      896       \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 126, 126, 32)      128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 63, 63, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 63, 63, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 61, 61, 64)        18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 61, 61, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 30, 30, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 30, 30, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 28, 28, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 28, 28, 128)       512       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               12845568  \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 8)                 4104      \n",
      "=================================================================\n",
      "Total params: 12,945,864\n",
      "Trainable params: 12,944,392\n",
      "Non-trainable params: 1,472\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(8, activation='softmax')) # 8 because we have 8 classes, or categories\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystop = EarlyStopping(patience=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy', \n",
    "                                            patience=2, \n",
    "                                            verbose=1, \n",
    "                                            factor=0.5, \n",
    "                                            min_lr=0.00001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [earlystop, learning_rate_reduction]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-23-63cb34b6e89a>:2: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use Model.fit, which supports generators.\n",
      "Epoch 1/50\n",
      "426/426 [==============================] - 260s 610ms/step - loss: 2.8506 - accuracy: 0.1648 - val_loss: 3.7227 - val_accuracy: 0.1390\n",
      "Epoch 2/50\n",
      "396/426 [==========================>...] - ETA: 19s - loss: 2.2014 - accuracy: 0.2403"
     ]
    }
   ],
   "source": [
    "epochs=3 if FAST_RUN else 50\n",
    "history = model.fit_generator(\n",
    "    train_generator, \n",
    "    epochs=epochs,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=total_validate//batch_size,\n",
    "    steps_per_epoch=total_train//batch_size,\n",
    "    callbacks=callbacks\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights(\"model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 12))\n",
    "ax1.plot(history.history['loss'], color='b', label=\"Training loss\")\n",
    "ax1.plot(history.history['val_loss'], color='r', label=\"validation loss\")\n",
    "ax1.set_xticks(np.arange(1, epochs, 1))\n",
    "ax1.set_yticks(np.arange(0, 1, 0.1))\n",
    "\n",
    "ax2.plot(history.history['accuracy'], color='b', label=\"Training accuracy\")\n",
    "ax2.plot(history.history['val_accuracy'], color='r',label=\"Validation accuracy\")\n",
    "ax2.set_xticks(np.arange(1, epochs, 1))\n",
    "\n",
    "legend = plt.legend(loc='best', shadow=True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_filenames = os.listdir(r\"C:\\Users\\nguyent2\\Desktop\\Testing_1\")\n",
    "\n",
    "test_df = pd.DataFrame({\n",
    "    'filename': test_filenames\n",
    "})\n",
    "nb_samples = test_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = ImageDataGenerator(rescale=1./255)\n",
    "test_generator = test_gen.flow_from_dataframe(\n",
    "    test_df, \n",
    "    r\"C:\\Users\\nguyent2\\Desktop\\Testing_1\", \n",
    "    x_col='filename',\n",
    "    y_col=None,\n",
    "    class_mode=None,\n",
    "    target_size=IMAGE_SIZE,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = model.predict_generator(test_generator, steps=np.ceil(nb_samples/batch_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['category'] = np.argmax(predict, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_map = dict((v,k) for k,v in train_generator.class_indices.items())\n",
    "test_df['category'] = test_df['category'].replace(label_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['category'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_test = test_df.head(18)\n",
    "sample_test.head()\n",
    "plt.figure(figsize=(12, 24))\n",
    "for index, row in sample_test.iterrows():\n",
    "    filename = row['filename']\n",
    "    category = row['category']\n",
    "    img = load_img(r\"C:\\Users\\nguyent2\\Desktop\\Training\\\\\"+filename, target_size=IMAGE_SIZE)\n",
    "    plt.subplot(6, 3, index+1)\n",
    "    plt.imshow(img)\n",
    "    plt.xlabel(filename + '(' + \"{}\".format(category) + ')' )\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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