02_HodaDataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><div style=\"direction:rtl;font-family:B Lotus, B Nazanin, Tahoma\">به نام خدا</div></center>\n",
    "<h1><center><div style=\"direction:rtl;font-family:B Lotus, B Nazanin, Tahoma\">معرفی مجموعه داده هدی و تغییر اندازه و شکل برای استفاده در الگوریتم‌های یادگیری ماشین</div></center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">معرفی مجموعه داده ارقام دستنویس هدی</div>\n",
    "\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "مجموعه ارقام دستنویس هدی که اولین مجموعه‌ی بزرگ ارقام دستنویس فارسی است، مشتمل بر ۱۰۲۳۵۳ نمونه دستنوشته سیاه سفید است. این مجموعه طی انجام یک پروژه‏ی کارشناسی ارشد درباره بازشناسی فرمهای دستنویس تهیه شده است. داده های این مجموعه از حدود ۱۲۰۰۰ فرم ثبت نام آزمون سراسری کارشناسی ارشد سال ۱۳۸۴ و آزمون کاردانی پیوسته‏ی دانشگاه جامع علمی کاربردی سال ۱۳۸۳ استخراج شده است[1].\n",
    "</div>\n",
    "<p>\n",
    "<a href=\"https://www.sciencedirect.com/science/article/pii/S0167865507000037\" target=\"_blank\">\n",
    "H. Khosravi and E. Kabir, \"Introducing a very large dataset of handwritten Farsi digits and a study on their varieties,\" Pattern Recognition Letters, vol. 28, pp. 1133-1141, 2007. ]\n",
    "</a>    \n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">لود کتابخانه‌های مورد استفاده</div>\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "برای اجرای این نوت‌بوک باید کتابخانه‌های  numpy و scipy و matplotlib و scikit-image را نصب کنید.\n",
    "بدین منظور به اینترنت متصل شود و در cmd  دستورات زیر را بنویسید:\n",
    "</div>\n",
    "<p>pip install numpy</p>\n",
    "<p>pip install scipy</p>\n",
    "<p>pip install matplotlib</p>\n",
    "<p>pip install scikit-image</p>\n",
    "\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "میتوانید به جای pip از کلمه ی conda استفاده کنید. (اگر از آناکوندا استفاده میکنید.)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbpresent": {
     "id": "de829a92-1fb6-44ad-a2c6-fc1001e1f6e1"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import io\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage.transform import resize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">لود مجموعه داده (dataset)</div>\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "این مجموعه داده در قالب فایل mat متلب منتشر شده است. در پایتون میتوانیم آن را با کتابخانه scipy لود کنیم.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load dataset\n",
    "dataset = io.loadmat('./dataset/Data_hoda_full.mat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">انتخاب مجموعه داده آموزش و مجموعه داده آزمون(تست)</div>\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "در این مجموعه داده 60000 داده تصادفی وجود دارد.\n",
    "<br>\n",
    "برای آزمایشاتمان 1000 داده‌ی اول را برای آموزش و 200 داده‌ی بعدی را برای آزمون انتخاب می‌کنیم.\n",
    "<br>\n",
    "**داده های آزمون نباید قبلا در داده های آموزشی دیده شده باشند.**\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test and training set\n",
    "X_train_orginal = np.squeeze(dataset['Data'][:1000])\n",
    "y_train = np.squeeze(dataset['labels'][:1000])\n",
    "X_test_original = np.squeeze(dataset['Data'][1000:1200])\n",
    "y_test = np.squeeze(dataset['labels'][1000:1200])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"squeeze\": Remove single-dimensional entries from the shape of an array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "نگاهی به مجموعه داده بیندازیم!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.imshow(X_train_orginal[999], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 70th image is: 8\n"
     ]
    }
   ],
   "source": [
    "i=70\n",
    "image = X_train_orginal[i]\n",
    "label = y_train[i]\n",
    "plt.imshow(image, cmap='gray')\n",
    "plt.show()\n",
    "print(\"Label {}th image is: {}\".format(i, label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "در کد بالا مقدار i را تغییر دهید و تصاویر را ببینید</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">تغییر اندازه تصاویر مجموعه داده</div>\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "اندازه تصاویر مجموعه داده متفاوت است. <br>\n",
    "تمام تصاویر را به یک اندازه مشخص (5*5) تغییر اندازه می‌دهیم.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alire\\Miniconda3\\envs\\tensorflow\\lib\\site-packages\\skimage\\transform\\_warps.py:105: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n",
      "C:\\Users\\alire\\Miniconda3\\envs\\tensorflow\\lib\\site-packages\\skimage\\transform\\_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
     ]
    }
   ],
   "source": [
    "#resize\n",
    "X_train_5by5 = [resize(img, (5, 5)) for img in X_train_orginal]\n",
    "X_test_5by_5 = [resize(img, (5, 5)) for img in X_test_original]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "image = X_train_5by5[1]\n",
    "plt.imshow(image, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\">تغییر شکل مجموعه داده</div>\n",
    "<div style=\"direction:rtl;text-align:right;font-family:Tahoma\">\n",
    "حالا داده های تصویری در مقیاس 5x5 است. برای استفاده در الگوریتم‌های خود آن ها را به یک بردار 25 تایی تغییر شکل میدهیم.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reshape\n",
    "X_train = [x.reshape(25) for x in X_train_5by5]\n",
    "X_test = [x.reshape(25) for x in X_test_5by_5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<div style=\"direction:rtl;text-align:right;font-family:B Lotus, B Nazanin, Tahoma\"> دوره مقدماتی یادگیری عمیق<br>علیرضا اخوان پور<br>پنج شنبه، ۱۸ بهمن ۱۳۹۷<br>\n",
    "</div>\n",
    "<a href=\"http://class.vision\">Class.Vision</a> - <a href=\"http://AkhavanPour.ir\">AkhavanPour.ir</a> - <a href=\"https://github.com/Alireza-Akhavan/\">GitHub</a>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "tensorflow",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "nbpresent": {
   "slides": {
    "300ee14f-a043-486e-b274-7ff253907cd7": {
     "id": "300ee14f-a043-486e-b274-7ff253907cd7",
     "prev": "cb74e0bc-4513-4d13-b7f1-14c3078a7927",
     "regions": {
      "26dc3f39-a230-447c-af4c-f5e5b2fb7835": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "c58440a5-3f8f-4f37-9c79-6bf766209406",
        "part": "whole"
       },
       "id": "26dc3f39-a230-447c-af4c-f5e5b2fb7835"
      }
     }
    },
    "878aa53a-1444-4100-8f50-7a408191c579": {
     "id": "878aa53a-1444-4100-8f50-7a408191c579",
     "prev": null,
     "regions": {
      "a6c6843a-5ea6-4fbc-b890-3b4b8ae475b3": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "588ee1fa-64b5-453b-ade7-8e6b2515821c",
        "part": "whole"
       },
       "id": "a6c6843a-5ea6-4fbc-b890-3b4b8ae475b3"
      }
     }
    },
    "96ffe88e-7b50-43de-afdd-942e564f4e3e": {
     "id": "96ffe88e-7b50-43de-afdd-942e564f4e3e",
     "prev": "878aa53a-1444-4100-8f50-7a408191c579",
     "regions": {
      "b7e52e12-489a-468d-b10c-af2024fd2856": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "de829a92-1fb6-44ad-a2c6-fc1001e1f6e1",
        "part": "whole"
       },
       "id": "b7e52e12-489a-468d-b10c-af2024fd2856"
      }
     }
    },
    "cb74e0bc-4513-4d13-b7f1-14c3078a7927": {
     "id": "cb74e0bc-4513-4d13-b7f1-14c3078a7927",
     "prev": "96ffe88e-7b50-43de-afdd-942e564f4e3e",
     "regions": {
      "444878ee-68f3-4abb-acff-a7079b21e86d": {
       "attrs": {
        "height": 0.8,
        "width": 0.8,
        "x": 0.1,
        "y": 0.1
       },
       "content": {
        "cell": "25f3f538-1ee8-4d98-a6bb-14cbeb7a702d",
        "part": "whole"
       },
       "id": "444878ee-68f3-4abb-acff-a7079b21e86d"
      }
     }
    }
   },
   "themes": {}
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}