Sign_recognisation demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "import sys,os\n",
    "sys.path.append(os.pardir)\n",
    "import glob as glob\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_last_line(l=[\"\"]):\n",
    "    while l[-1]==\"\":\n",
    "        l.remove(l[-1])\n",
    "        \n",
    "    return l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def txt_to_array(xn=\"\"):\n",
    "    x1=open(xn,\"r\")\n",
    "    lx = x1.read().splitlines()\n",
    "    lx=remove_last_line(lx)\n",
    "    lx_=np.float_(lx)\n",
    "    return(lx_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "path=\"C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\"\n",
    "file_names=glob.glob(os.path.join(path,'one_pack\\\\one'+str(2)+'\\\\*.txt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    }
   ],
   "source": [
    "#print(file_names)\n",
    "print(len(file_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "xn=file_names[2]\n",
    "yn=file_names[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.TextIOWrapper name='C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\one_pack\\\\one2\\\\little-one-x-2.txt' mode='r' encoding='cp936'>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116\n",
      "100 100\n"
     ]
    }
   ],
   "source": [
    "x1=open(xn,\"r\")\n",
    "y1=open(yn,\"r\")\n",
    "lx = x1.read().splitlines()\n",
    "print(len(lx))\n",
    "lx=remove_last_line(lx)\n",
    "ly = y1.read().splitlines()\n",
    "ly=remove_last_line(ly)\n",
    "print(len(lx),len(ly))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "lx_=np.float_(lx)\n",
    "ly_=np.float_(ly)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20db220edc8>]"
      ]
     },
     "execution_count": 132,
     "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": [
    "\n",
    "plt.plot(lx_,ly_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 2]\n"
     ]
    }
   ],
   "source": [
    "lis=np.array([1,1,2])\n",
    "np.delete(lis,[1])\n",
    "print(lis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creation of the directory C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0 failed\n"
     ]
    }
   ],
   "source": [
    "#fingers=[\"fore\",\"little\",\"middle\",\"palm\",\"ring\",\"thumb\"]\n",
    "path=\"C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\"\n",
    "file_names=glob.glob(os.path.join(path,'figs\\\\one\\\\*.txt'))\n",
    "#print(file_names)\n",
    "\n",
    "try:\n",
    "    os.mkdir(os.path.join(path,\"one_figs\"))\n",
    "except OSError:\n",
    "    print (\"Creation of the directory %s failed\" % path)\n",
    "else:\n",
    "    for i in range(6):\n",
    "        xn=file_names[2*i]\n",
    "        yn=file_names[2*i+1]\n",
    "\n",
    "        lx_=txt_to_array(xn)\n",
    "        ly_=txt_to_array(yn)\n",
    "\n",
    "        if len(lx_)==len(ly_):\n",
    "            plt.plot(lx_,ly_)\n",
    "        else:\n",
    "            print(len(lx_),len(ly_))\n",
    "\n",
    "\n",
    "        fig_path=os.path.join(path,\"one_figs\\\\hand1.jpg\")\n",
    "        plt.savefig(fig_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "path=\"C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\"\n",
    "\n",
    "# def gen_one_hand(st=\"\"):    \n",
    "#     file_names=glob.glob(os.path.join(path,'one_pack\\\\one'+str(st)+'\\\\*.txt'))\n",
    "#     for i in range(6):\n",
    "#         xn=file_names[2*i]\n",
    "#         yn=file_names[2*i+1]\n",
    "\n",
    "#         lx_=txt_to_array(xn)\n",
    "#         ly_=txt_to_array(yn)\n",
    "\n",
    "#         if len(lx_)==len(ly_):\n",
    "#             plt.plot(lx_,ly_)\n",
    "\n",
    "# gen_one_hand(\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": [
    "gen_one_hand(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "gen_one_hand(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\one_pack'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_one=os.path.join(path,'one_pack')\n",
    "#p=os.walk(path_one)\n",
    "path_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['one',\n",
       " 'one10',\n",
       " 'one11',\n",
       " 'one12',\n",
       " 'one13',\n",
       " 'one14',\n",
       " 'one15',\n",
       " 'one16',\n",
       " 'one17',\n",
       " 'one18',\n",
       " 'one19',\n",
       " 'one2',\n",
       " 'one20',\n",
       " 'one21',\n",
       " 'one22',\n",
       " 'one3',\n",
       " 'one4',\n",
       " 'one5',\n",
       " 'one6',\n",
       " 'one7',\n",
       " 'one8',\n",
       " 'one9']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_one=os.listdir(path_one)\n",
    "list_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['eight_pack', 'five_pack', 'four_pack', 'nine_pack', 'one_pack', 'seven_pack', 'six_pack', 'ten_pack', 'three_pack', 'two_pack']\n"
     ]
    }
   ],
   "source": [
    "path=\"C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\"\n",
    "\n",
    "signList=os.listdir(path)\n",
    "print(signList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\eight_pack\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\five_pack\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\four_pack\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\nine_pack\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\one_figs\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\one_pack\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\seven_pack\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\six_pack\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\ten_pack\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\three_pack\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for sign in signList:\n",
    "    sign_path=os.path.join(path,sign)\n",
    "    print(sign_path)\n",
    "    \n",
    "sign_path\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two10\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two11\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two12\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two13\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two14\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two15\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two16\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two17\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two18\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two19\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two2\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two20\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two21\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two3\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two4\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two5\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two6\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two7\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two8\n",
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two9\n"
     ]
    }
   ],
   "source": [
    "packs=os.listdir(sign_path)\n",
    "\n",
    "for pack in packs:\n",
    "    pack_path=os.path.join(sign_path,pack)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\\two_pack\\two9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\fore-two-x-1.txt',\n",
       " 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\fore-two-y-1.txt',\n",
       " 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\little-two-x-1.txt',\n",
       " 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\little-two-y-1.txt',\n",
       " 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\middle-two-x-1.txt',\n",
       " 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\middle-two-y-1.txt',\n",
       " 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\palm-two-x-1.txt',\n",
       " 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\palm-two-y-1.txt',\n",
       " 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\ring-two-x-1.txt',\n",
       " 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\ring-two-y-1.txt',\n",
       " 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\thumb-two-x-1.txt',\n",
       " 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\two_pack\\\\two9\\\\thumb-two-y-1.txt']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_names=glob.glob(os.path.join(pack_path,\"*.txt\"))\n",
    "file_names\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yexue\\Gesture-Recognition-based-on-IMU\\data\\rawdata_0\n"
     ]
    },
    {
     "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": [
    "print(path)\n",
    "def gen_one_hand(st=\"\"):    \n",
    "    file_names=glob.glob(os.path.join(path,'one_pack\\\\one'+str(st)+'\\\\*.txt'))\n",
    "    for i in range(6):\n",
    "        xn=file_names[2*i]\n",
    "        yn=file_names[2*i+1]\n",
    "\n",
    "        lx_=txt_to_array(xn)\n",
    "        ly_=txt_to_array(yn)\n",
    "\n",
    "        if len(lx_)==len(ly_):\n",
    "            plt.plot(lx_,ly_)\n",
    "\n",
    "gen_one_hand(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[WinError 3] The system cannot find the path specified: 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\hand_figs\\\\one'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-216-b9cfbc00bee3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mfig_path\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\hand_figs\\\\one\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_one\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] The system cannot find the path specified: 'C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\hand_figs\\\\one'"
     ]
    }
   ],
   "source": [
    "#\"one_pack\"\n",
    "\n",
    "fig_path=\"C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\hand_figs\\\\one\"\n",
    "\n",
    "os.mkdir(fig_path)\n",
    "\n",
    "for i in range(len(path_one)):\n",
    "    path_i=path_one[i]\n",
    "    file_names=glob.glob(os.path.join(path_i,'\\\\*.txt'))\n",
    "    for i in range(6):\n",
    "        xn=file_names[2*i]\n",
    "        yn=file_names[2*i+1]\n",
    "\n",
    "        lx_=txt_to_array(xn)\n",
    "        ly_=txt_to_array(yn)\n",
    "\n",
    "        if len(lx_)==len(ly_):\n",
    "            plt.plot(lx_,ly_)\n",
    "            \n",
    "    plt.savefig(os.path.join(fig_path,\"\\\\hand\"+str(i)+\".jpg\"))\n",
    "    plt.clf()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[b'\\xef\\xbb\\xbf177.79', b'177.88', b'177.82', b'177.97', b'177.85', b'179.51', b'178.36', b'174.42', b'178.91', b'179.37', b'178.47', b'177.46', b'176.73', b'179.15', b'180.09', b'177.32', b'176.23', b'177.2', b'175.36', b'174.82', b'175.39', b'174.78', b'167.54', b'164.56', b'172.84', b'160.5', b'208.08', b'126.85', b'146.43', b'160.21', b'157.88', b'156.56', b'99.54', b'105.36', b'136.14', b'136.74', b'113.57', b'95.34', b'104.18', b'98.13', b'75.6', b'72.76', b'65.23', b'39.53', b'34.72', b'38.71', b'46.59', b'14.49', b'18.51', b'41.32', b'24.29', b'8.05', b'27.98', b'15.62', b'12.63', b'22.36', b'25.84', b'19.49', b'15.13', b'18.82', b'19.45', b'12.64', b'14.61', b'21.98', b'24.29', b'27.37', b'34.3', b'4.98', b'16.77', b'37.09', b'53.81', b'13.61', b'16.77', b'58.22', b'76.77', b'61.72', b'35.7', b'79.75', b'111.01', b'143.29', b'128.32', b'136.08', b'131.35', b'138.99', b'138.25', b'152.02', b'154.39', b'146.28', b'146.33', b'164.94', b'159.78', b'148.19', b'161.51', b'163.42', b'164.75', b'157.83', b'161.05', b'160.6', b'159.93', b'180']\n",
      "['177.79', '177.88', '177.82', '177.97', '177.85', '179.51', '178.36', '174.42', '178.91', '179.37', '178.47', '177.46', '176.73', '179.15', '180.09', '177.32', '176.23', '177.2', '175.36', '174.82', '175.39', '174.78', '167.54', '164.56', '172.84', '160.5', '208.08', '126.85', '146.43', '160.21', '157.88', '156.56', '99.54', '105.36', '136.14', '136.74', '113.57', '95.34', '104.18', '98.13', '75.6', '72.76', '65.23', '39.53', '34.72', '38.71', '46.59', '14.49', '18.51', '41.32', '24.29', '8.05', '27.98', '15.62', '12.63', '22.36', '25.84', '19.49', '15.13', '18.82', '19.45', '12.64', '14.61', '21.98', '24.29', '27.37', '34.3', '4.98', '16.77', '37.09', '53.81', '13.61', '16.77', '58.22', '76.77', '61.72', '35.7', '79.75', '111.01', '143.29', '128.32', '136.08', '131.35', '138.99', '138.25', '152.02', '154.39', '146.28', '146.33', '164.94', '159.78', '148.19', '161.51', '163.42', '164.75', '157.83', '161.05', '160.6', '159.93', '180']\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'remove_last_line' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-c1fe2f9d8ff9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mstr_lx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'utf-8-sig'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr_lx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mstr_lx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mremove_last_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr_lx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr_lx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'remove_last_line' is not defined"
     ]
    }
   ],
   "source": [
    "x1=open(\"C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\rawdata_0\\\\nine_pack\\\\nine2\\\\little-nine-x-1.txt\",\"rb\")\n",
    "lx=x1.read().splitlines()\n",
    "#type(lx)\n",
    "print(lx)\n",
    "str_lx=[x.decode('utf-8-sig') for x in lx]\n",
    "print(str_lx)\n",
    "str_lx=remove_last_line(str_lx)\n",
    "np.float_(str_lx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pytorch\n",
    "\n",
    "from glob import glob\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import shutil\n",
    "from torchvision import transforms\n",
    "from torchvision import models\n",
    "import torch\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.optim import lr_scheduler\n",
    "from torch import optim\n",
    "from torchvision.datasets import ImageFolder\n",
    "from torchvision.utils import make_grid\n",
    "from torch.utils.data import Dataset,DataLoader\n",
    "import time\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['eight', '1']\n",
      "['eight', '1']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'eight.1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data renaming\n",
    "import re\n",
    "lis=re.findall(r'[0-9]+|[a-z]+',\"eight1\")\n",
    "print(lis)\n",
    "print(lis)\n",
    "\".\".join(lis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dog-vs-cat\n",
    "\n",
    "def imshow(inp):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['eight_pack',\n",
       " 'five_pack',\n",
       " 'four_pack',\n",
       " 'nine_pack',\n",
       " 'one_pack',\n",
       " 'seven_pack',\n",
       " 'six_pack',\n",
       " 'ten_pack',\n",
       " 'three_pack',\n",
       " 'two_pack']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\figs_0\\\\\"\n",
    "train_path=os.path.join(path,\"train\")\n",
    "packs=os.listdir(train_path)\n",
    "packs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.mkdir(os.path.join(path,'valid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eight\n",
      "eight.21.jpg\n",
      "eight\n",
      "eight.8.jpg\n",
      "eight\n",
      "eight.20.jpg\n",
      "eight\n",
      "eight.11.jpg\n",
      "eight\n",
      "eight.7.jpg\n",
      "eight\n",
      "eight.2.jpg\n",
      "eight\n",
      "eight.14.jpg\n",
      "five\n",
      "five.17.jpg\n",
      "five\n",
      "five.5.jpg\n",
      "five\n",
      "five.4.jpg\n",
      "five\n",
      "five.9.jpg\n",
      "five\n",
      "five.1.jpg\n",
      "five\n",
      "five.13.jpg\n",
      "five\n",
      "five.6.jpg\n",
      "four\n",
      "four.20.jpg\n",
      "four\n",
      "four.1.jpg\n",
      "four\n",
      "four.16.jpg\n",
      "four\n",
      "four.9.jpg\n",
      "four\n",
      "four.21.jpg\n",
      "four\n",
      "four.12.jpg\n",
      "four\n",
      "four.18.jpg\n",
      "nine\n",
      "nine.17.jpg\n",
      "nine\n",
      "nine.22.jpg\n",
      "nine\n",
      "nine.21.jpg\n",
      "nine\n",
      "nine.7.jpg\n",
      "nine\n",
      "nine.15.jpg\n",
      "nine\n",
      "nine.1.jpg\n",
      "nine\n",
      "nine.19.jpg\n",
      "one\n",
      "one.20.jpg\n",
      "one\n",
      "one.17.jpg\n",
      "one\n",
      "one.6.jpg\n",
      "one\n",
      "one.22.jpg\n",
      "one\n",
      "one.14.jpg\n",
      "one\n",
      "one.2.jpg\n",
      "one\n",
      "one.15.jpg\n",
      "seven\n",
      "seven.13.jpg\n",
      "seven\n",
      "seven.7.jpg\n",
      "seven\n",
      "seven.22.jpg\n",
      "seven\n",
      "seven.11.jpg\n",
      "seven\n",
      "seven.16.jpg\n",
      "seven\n",
      "seven.1.jpg\n",
      "seven\n",
      "seven.18.jpg\n",
      "six\n",
      "six.14.jpg\n",
      "six\n",
      "six.4.jpg\n",
      "six\n",
      "six.12.jpg\n",
      "six\n",
      "six.15.jpg\n",
      "six\n",
      "six.10.jpg\n",
      "six\n",
      "six.11.jpg\n",
      "six\n",
      "six.13.jpg\n",
      "ten\n",
      "ten.26.jpg\n",
      "ten\n",
      "ten.15.jpg\n",
      "ten\n",
      "ten.14.jpg\n",
      "ten\n",
      "ten.13.jpg\n",
      "ten\n",
      "ten.3.jpg\n",
      "ten\n",
      "ten.11.jpg\n",
      "ten\n",
      "ten.24.jpg\n",
      "three\n",
      "three.18.jpg\n",
      "three\n",
      "three.15.jpg\n",
      "three\n",
      "three.7.jpg\n",
      "three\n",
      "three.20.jpg\n",
      "three\n",
      "three.17.jpg\n",
      "three\n",
      "three.1.jpg\n",
      "three\n",
      "three.6.jpg\n",
      "two\n",
      "two.2.jpg\n",
      "two\n",
      "two.9.jpg\n",
      "two\n",
      "two.16.jpg\n",
      "two\n",
      "two.12.jpg\n",
      "two\n",
      "two.4.jpg\n",
      "two\n",
      "two.3.jpg\n",
      "two\n",
      "two.17.jpg\n"
     ]
    }
   ],
   "source": [
    "for pack in packs:\n",
    "    #print(pack)\n",
    "    files=glob.glob(os.path.join(train_path,pack,\"*.jpg\"))\n",
    "    #print(os.path.join(train_path,pack,\"*.jpg\"))\n",
    "    num = len(files)\n",
    "    valid_size = math.floor(num/3)\n",
    "    shuffle = np.random.permutation(num)\n",
    "    #print(shuffle)\n",
    "    \n",
    "    valid_path=os.path.join(path,'valid')\n",
    "    if not os.path.isdir(valid_path):\n",
    "        os.mkdir(os.path.join(valid_path,pack))\n",
    "    \n",
    "    for i in shuffle[0:valid_size]:\n",
    "        folder = files[i].split('\\\\')[-1].split('.')[0]\n",
    "        image = files[i].split('\\\\')[-1]\n",
    "        os.rename(files[i],os.path.join(path,'valid',folder+\"_pack\",image))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(280, 300, 3)\n",
      "(288, 432, 3)\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "\n",
    "image = cv2.imread(\"C:\\\\Users\\\\yexue\\\\Desktop\\\\cat.1.jpg\", flags=cv2.IMREAD_COLOR)\n",
    "print(image.shape)\n",
    "image2 = cv2.imread(\"C:\\\\Users\\\\yexue\\\\Gesture-Recognition-based-on-IMU\\\\data\\\\figs_0\\\\train\\\\eight_pack\\\\eight.1.jpg\")\n",
    "print(image2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_transform = transforms.Compose([transforms.Resize((224,224))\n",
    "                                       ,transforms.ToTensor()\n",
    "                                       ,transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n",
    "train = ImageFolder(train_path,simple_transform)\n",
    "valid = ImageFolder(valid_path,simple_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "149"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 224, 224)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-220-0e5464edbd21>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0ma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "for i in range(len(train)):\n",
    "    a=np.array(train[i][0])\n",
    "    a=a.reshape([1,3,224,224])\n",
    "    print(a.shape)\n",
    "    a=torch.from_numpy(a)\n",
    "    train[i][0]=a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-221-35fb60f9f6a8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "train[50][0]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Tensor' object has no attribute 'expand_dims'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-144-5f7558a291b4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'Tensor' object has no attribute 'expand_dims'"
     ]
    }
   ],
   "source": [
    "for i in range(len(train)):\n",
    "    train[i][0].expand_dims()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "torch.reshape(train[50][0],([1,3,224,224]))\n",
    "print(train[50][0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 224, 224)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 224, 224])"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.array(train[50][0])\n",
    "a=a.reshape([1,3,224,224])\n",
    "#print(a.shape)\n",
    "a=torch.from_numpy(a)\n",
    "a\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.]])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.zeros((1,2))\n",
    "a.reshape(1,1,2)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(valid)):\n",
    "    valid[i][0].reshape([1,3,224,224])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'eight_pack': 0, 'five_pack': 1, 'four_pack': 2, 'nine_pack': 3, 'one_pack': 4, 'seven_pack': 5, 'six_pack': 6, 'ten_pack': 7, 'three_pack': 8, 'two_pack': 9}\n",
      "['eight_pack', 'five_pack', 'four_pack', 'nine_pack', 'one_pack', 'seven_pack', 'six_pack', 'ten_pack', 'three_pack', 'two_pack']\n"
     ]
    }
   ],
   "source": [
    "print(train.class_to_idx)\n",
    "print(train.classes) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_loader = torch.utils.data.DataLoader(train,batch_size=1,num_workers=3,shuffle=True)\n",
    "valid_data_loader = torch.utils.data.DataLoader(valid,batch_size=1,num_workers=3,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_sizes = {'train':len(train_data_gen.dataset),'valid':len(valid_data_gen.dataset)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataloaders = {'train':train_data_gen,'valid':valid_data_gen}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 10, kernel_size=5)\n",
    "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
    "        self.conv2_drop = nn.Dropout2d()\n",
    "        self.fc1 = nn.Linear(56180, 500)\n",
    "        self.fc2 = nn.Linear(500,50)\n",
    "        self.fc3 = nn.Linear(50, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n",
    "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.dropout(x, training=self.training)\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = F.dropout(x,training=self.training)\n",
    "        x = self.fc3(x)\n",
    "        return F.log_softmax(x,dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = optim.SGD(model.parameters(),lr=0.01,momentum=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(epoch,model,data_loader,phase='training',volatile=False):\n",
    "    if phase == 'training':\n",
    "        model.train()\n",
    "    if phase == 'validation':\n",
    "        model.eval()\n",
    "        volatile=True\n",
    "    running_loss = 0.0\n",
    "    running_correct = 0\n",
    "    for batch_idx , (data,target) in enumerate(data_loader):\n",
    "#         if is_cuda:\n",
    "#             data,target = data.cuda(),target.cuda()\n",
    "        data , target = Variable(data,volatile),Variable(target)\n",
    "        if phase == 'training':\n",
    "            optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.nll_loss(output,target)\n",
    "        \n",
    "        running_loss += F.nll_loss(output,target,size_average=False).data[0]\n",
    "        preds = output.data.max(dim=1,keepdim=True)[1]\n",
    "        running_correct += preds.eq(target.data.view_as(preds)).cpu().sum()\n",
    "        if phase == 'training':\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "    \n",
    "    loss = running_loss/len(data_loader.dataset)\n",
    "    accuracy = 100. * running_correct/len(data_loader.dataset)\n",
    "    \n",
    "    print(f'{phase} loss is {loss:{5}.{2}} and {phase} accuracy is {running_correct}/{len(data_loader.dataset)}{accuracy:{10}.{4}}')\n",
    "    return loss,accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Given groups=1, weight of size 10 1 5 5, expected input[1, 3, 224, 224] to have 1 channels, but got 3 channels instead",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-138-ad34324b511a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mval_losses\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mval_accuracy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mepoch_loss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_accuracy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtrain_data_loader\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mphase\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'training'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mval_epoch_loss\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mval_epoch_accuracy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalid_data_loader\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mphase\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'validation'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mtrain_losses\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch_loss\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-137-f6f522d8a934>\u001b[0m in \u001b[0;36mfit\u001b[1;34m(epoch, model, data_loader, phase, volatile)\u001b[0m\n\u001b[0;32m     13\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mphase\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'training'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m         \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m         \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnll_loss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 541\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    542\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-60-cfc48233a6a7>\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     13\u001b[0m         \u001b[0min_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# one batch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[1;31m# x: 64*10*12*12\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m         \u001b[1;31m# x: 64*20*4*4\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 541\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    542\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2d_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    346\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    347\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mconv2d_forward\u001b[1;34m(self, input, weight)\u001b[0m\n\u001b[0;32m    340\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[0;32m    341\u001b[0m         return F.conv2d(input, weight, self.bias, self.stride,\n\u001b[1;32m--> 342\u001b[1;33m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Given groups=1, weight of size 10 1 5 5, expected input[1, 3, 224, 224] to have 1 channels, but got 3 channels instead"
     ]
    }
   ],
   "source": [
    "train_losses , train_accuracy = [],[]\n",
    "val_losses , val_accuracy = [],[]\n",
    "for epoch in range(1,20):\n",
    "    epoch_loss, epoch_accuracy = fit(epoch,model,train_data_loader,phase='training')\n",
    "    val_epoch_loss , val_epoch_accuracy = fit(epoch,model,valid_data_loader,phase='validation')\n",
    "    train_losses.append(epoch_loss)\n",
    "    train_accuracy.append(epoch_accuracy)\n",
    "    val_losses.append(val_epoch_loss)\n",
    "    val_accuracy.append(val_epoch_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader=dataloaders[\"train\"]\n",
    "test_loader=dataloaders[\"valid\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Given groups=1, weight of size 10 1 5 5, expected input[1, 3, 224, 224] to have 1 channels, but got 3 channels instead",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-60-cfc48233a6a7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 62\u001b[1;33m     \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     63\u001b[0m     \u001b[0mtest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-60-cfc48233a6a7>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(epoch)\u001b[0m\n\u001b[0;32m     31\u001b[0m         \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mVariable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mVariable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m         \u001b[0moutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m         \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnll_loss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m         \u001b[0mloss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 541\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    542\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-60-cfc48233a6a7>\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     13\u001b[0m         \u001b[0min_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# one batch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[1;31m# x: 64*10*12*12\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m         \u001b[1;31m# x: 64*20*4*4\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 541\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    542\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconv2d_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    346\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    347\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001b[0m in \u001b[0;36mconv2d_forward\u001b[1;34m(self, input, weight)\u001b[0m\n\u001b[0;32m    340\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[0;32m    341\u001b[0m         return F.conv2d(input, weight, self.bias, self.stride,\n\u001b[1;32m--> 342\u001b[1;33m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[0;32m    343\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Given groups=1, weight of size 10 1 5 5, expected input[1, 3, 224, 224] to have 1 channels, but got 3 channels instead"
     ]
    }
   ],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        # 输入1通道，输出10通道，kernel 5*5\n",
    "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
    "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
    "        self.mp = nn.MaxPool2d(2)\n",
    "        # fully connect\n",
    "        self.fc = nn.Linear(320, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # in_size = 64\n",
    "        in_size = x.size(0) # one batch\n",
    "        # x: 64*10*12*12\n",
    "        x = F.relu(self.mp(self.conv1(x)))\n",
    "        # x: 64*20*4*4\n",
    "        x = F.relu(self.mp(self.conv2(x)))\n",
    "        # x: 64*320\n",
    "        x = x.view(in_size, -1) # flatten the tensor\n",
    "        # x: 64*10\n",
    "        x = self.fc(x)\n",
    "        return F.log_softmax(x)\n",
    "\n",
    "\n",
    "model = Net()\n",
    "\n",
    "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)\n",
    "\n",
    "def train(epoch):\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = Variable(data), Variable(target)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.nll_loss(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if batch_idx % 200 == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "                100. * batch_idx / len(train_loader), loss.data[0]))\n",
    "\n",
    "\n",
    "def test():\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    for data, target in test_loader:\n",
    "        data, target = Variable(data, volatile=True), Variable(target)\n",
    "        output = model(data)\n",
    "        # sum up batch loss\n",
    "        test_loss += F.nll_loss(output, target, size_average=False).data[0]\n",
    "        # get the index of the max log-probability\n",
    "        pred = output.data.max(1, keepdim=True)[1]\n",
    "        correct += pred.eq(target.data.view_as(pred)).cpu().sum()\n",
    "\n",
    "    test_loss /= len(test_loader.dataset)\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct / len(test_loader.dataset)))\n",
    "\n",
    "\n",
    "for epoch in range(1, 10):\n",
    "    train(epoch)\n",
    "    test()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}