cnn_pytorch-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x232d855c2b0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seed = 42\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "#The compose function allows for multiple transforms\n",
    "#transforms.ToTensor() converts our PILImage to a tensor of shape (C x H x W) in the range [0,1]\n",
    "#transforms.Normalize(mean,std) normalizes a tensor to a (mean, std) for (R, G, B)\n",
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "\n",
    "train_set = torchvision.datasets.CIFAR10(root='./cifardata', train=True, download=True, transform=transform)\n",
    "\n",
    "test_set = torchvision.datasets.CIFAR10(root='./cifardata', train=False, download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(train_set, batch_size=4,\n",
    "                                          shuffle=True, num_workers=2)\n",
    "testloader = torch.utils.data.DataLoader(test_set, batch_size=4,\n",
    "                                         shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = ('plane', 'car', 'bird', 'cat',\n",
    "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ship  ship  bird horse\n"
     ]
    },
    {
     "data": {
      "image/png": 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nAQD33qrKQgkPu//QwVDW4v6ff/IrAIDT3/s94Vq5QgrprnFxLXESi47NwRCO\n9MV9ERN7DUawfdT2Q0YHpWPHbjpe7z5M5ojlHRrHw0vMn5Yq9ZZZob68qAq5Z7/2dQDAKGd/v+Oe\nO8O1bsSbNXixRiRGksgjZ7YoFQc8Z7EyK7bzQdxZFA1aMUw/ek69Jl25SFslk+fc5tst5z5z8Yi6\ntO6Tr6oI76WnXqS2zN4d20tKxclZMpt99YKa0p5+6AMAgHsePKHNNjnMb0dFRMfZk/TFs3TvC1/7\nWrh26lZa9+6yru3EfiqrDquIbYEVsM7t4U+tL3usVFaDC/ii6Ph6sWahjXNuFMBfAvh57/38avXN\nfY845844587UTZCrhISEhIS3F2vi0J1zFdDL/E+893/FxZedc4eYOz8E4ErsXu/9owAeBYDDhw8X\nWIJY7BLhSGMclXBbVRMPI5ZCLcaV9XPE+TRl/IvprYkY1asYh5usJP0W2xAlatUm1YiYJ/XHxrAK\n0EHJJqzzUD9iilXbhiSFaDVVaSlOXb0WKZYas5qQYOo8xSC5fH4ylIkpno1302Gz0+o8/8bbeBgS\ng8TEFmmxgqgzo9HrFhaJ41piU7LXRpTLmeGIhkdvV8712J3kmNO2ayuMKIpYayo+RSTBWlBQKqqj\n5DBV57kv102kRI4ueO2azmmPFc1o6Rp0SjSGb5whDnD3XlWq7Zqg9rvQ+hoJskitFOVMMsOesQ52\nPJaYQjNiNhs1pQ1zatvIz36ro84+EtmznIsSyabIOTNVjtnE+6hjHNvqDdqvn/28poprsVL+B37g\n/lBWm6CT0lSPntV3nLw9XDv6jlNE/rBJgDJEHP1Q7m1I43r3/SR4WNipp42pL30DANA11pPXpigJ\nTvUtTYYzdCfFkmmVOJqjU+ekUoigaRT7PVm39cd0WZVDd7SKvwvgBe/9r5tLjwN4mL8/DOAz66Yi\nISEhIWHDWAuH/t0AfhrAs845+Wn8DwB+FcCfO+c+BuAtAD/xrSExISEhIWEtWPWF7r3/B6wca/T7\nN0pAyC5vjs8iLhkkQonl6LQYHJsiljyClTaRJA8Vo4zshdgsVGYTJcRs6oMdsBWJoCgSGUTjWkKV\nLi6qoiiMxdAhYpIlo5DzbIffYGVd/apKza6++TrVn76mnUheUqMUZb0WdnUk36iu4+xrbwAApo2N\nfJfHcNF6LnLZEIeBferzX1Q6Fonu762pGObkXfdw5xH7bK8lgth+GgQxB855QQbzbG1jeAcfoXmt\nXnnxxXAtY2VX24hXqp6+V+wyst35Iodk/ebT3wiXTn8XedBaj2YVddj5W/maj3hRBw9NUyZxkETx\n6TIrBpRYJ7ZPFpfklKL5/blHcIp9AAAWjUlEQVT/oIo61BfAihJZZmFsvGW+MjY+GBnWsc+yzf7U\ntO7197//PgDAHe87FcpmrtFcHj9KopQDB1T0M8QuDplRgEoPNhmOIGMR4fit6r05c4HEYpPn3wxl\nh1n8dv6510JZrXcMALD7DhK9VEomIQvv3Z7X/WHjPa0XyVM0ISEhYZtg02O5xEz3ljkN18yMOp6K\nElTSu9mUbv1t2fayXPS6fk9RE1uB77WZybrMjXcMFyJmd+JRmuX0ZhGFpnAktu9enkOPKX9jsUYG\nKUVnjddaLL1agyMDzs6pkm5uhmNMsOKxOa/GS3PM0TcaqvnxS1Svl/MG5c+IJ2XXyaddF4lZUjxt\nzC1TbJRlwwm2uN2ZRR1fm03UrMmcTK+LHmLWxpmH2lFXW1Ekmtg2XO87HngAAHDAKDRF0S1JQYg2\nPuGY1Ifnz1NEwlme+9dffTlcO3iIolDedpua2OkBwcZa6S/J8d7SeeGq3WOZpGzsSRwg48EoY8hx\n16ubgE5f1RNL8F61sVxE4Wn2tUYU5dPxkjEBXqJ733vfLaHsHXfTHHWbepKcv0bf52bIK/TIYVVG\nZo6jpNrELZJkxHgjh3hIHD+mZ7jrW+8ljn9u+i4d6yLRWTGK8fbTlGbu8mU6XR5915FwbcdOnu9c\nEhr5YlIIXicSh56QkJCwTZBe6AkJCQnbBJsucokFnBKb6YsX1UtRlHqnT5+mAlc8YMZsty0GiTjU\n1tZ6m+bbB9QbL7jSRcQ8HXOElJOu9RQN+SkjysuYDfla7KiHhtTWW+hoG9GIjLlaU/v9sV1kr+s4\nuYfbr86+e/bS99mrmrl9kUU0dRN6tNls8CetWbtjRQyswDbJLIS2/JhYnMHT0DZrIMkxRndpwK5r\n00RT2x7fWaEVc1zU+bXzPEBkELSiRdvt2Lrs4dyVtSEVDyxcY1tiZ23lWRlpPC53HaBwq0sscnn1\nVc1B+sqLdGQ/dFDXZXgHJwHJ0SE+EcXEMLFgW8VryIVJ7q89aE8O2psOundKIXiaXg+rbHMBZ/ky\nq4gd4mfu5HENUTu+k30ATEjd2SlSil67RnuzbJJT+DaLcY1HseQ+zbKiaE7muWneGROjJFqbg4p9\nv/QiKbP/2XtPhrL9QyRqeX2S5qG0V/s8eQ95Srfa6uGqSCKXhISEhG97bDqHLqFBSyZThGQ2P3Lr\nraFsnjkYMWfqxcKHlvX3SZQw7YhCU37087FOuH7XciFMW8XEWxAOE6LMtaZtbIKZU4pyfWveKCZi\n4rFqODYE80nTrHArA3R7VhEq46rVTLZ44eAzk3yjR1yNmC+6juFaDtP3xpJy44vs6dgyiTBkXUSB\nnQtRyx6obU4EAQAtPjVYzk6Ui5JCrWXmtMypwoaN9+gwh6m1WdpFmed5GmLcpKWt0xnAWToxP7UJ\nQoqewaFv3lAjJuxvj83dpi5rGrZOi+ahbNhU2c9jfBI5eVL3/KXLFEdkakrNSW8ZIVO4fAKPPBee\nGe5T9vz1etDGPZtzfHukZr69WmZP30Vz466EzzW1gsc2m/BlNpUgz+nIiJpUdtj0sddRTnf3XgrN\n/PSXKRWdcdbFPp7nVk+VqB1WVttnKKRnlK3eNd7Ls9TnlauamvKe08cBACe/Q2MUdafo5jpvgcqs\neljPLtNz02ioErUX3gPHsF4kDj0hISFhmyC90BMSEhK2CTZd5CK5DtvGw1DsgMfGVfkxykclqd9s\nqxIkHM8i9sODQqba0JwhYFEuzyKVLdcbhb60feM1mcnnYDp6A4Ijxe3PiaYB0XOjogDbltjx93LK\nLxYtsOtiz+QDdXyMH6qoErXsaD1K5mg6wUfGcfZ0zNHPIpduXY+aMuVlE8BsmD1EK1zWNiKoVpvm\nTZS01EYxd2Y5iOzWGiaWRUpzVwvXNEBVrpCbN6KOPlFYZh6n3XvI5rg6tDOUTZ59AQCwMKeByRyL\nXHaM0DxXTPtjoyQy8yYKVE/EFLFAWRHREoJosDhMGw5XpDYhd6/Z590gqbTZlHhPlu0kmWhVAFpt\nfW5ELGRDQHeEKOMJm7HfpoRLdl6vNdoydqVjucN9eBVdjO8nkdwptvt+bfLVcG3XIfYorWgbLQ4e\n27ZiONHQ16n/+oxRup4n0dltd6qI7eA7yR6+UzWe2JzrdV7y7V7V52Bvk64tNbTs+oPIFZE49ISE\nhIRtgk3n0MW0brVwneLJFuO4Y6ZwouCIeU3232fbtX0KV2v7FA40Hva3GItB7hVTTHtvzGwxFto0\neLGWYqqtItYas0TyNwYrQatUlhyrRjFY5oqZXasSz/0QmYbl5psVmd2dajYmp6/MmCYOD0uu0lqh\nT+HsylVjlimKa2fpZU404k0bm49Bc9RhTVjPmDZ2Q8whe4LLf+kZZWSHeaUdo8qh7x6heSgZRd/o\nfuLsqpLD1cTTaTeovQuTqlgd23+c7tupSjrxehSO12XWk1f2qxkg/2PDDzs2r+xG2hAzzpwiVg4F\nvZV5woW2KsNDEou2PRXQiTAzp4ESh6f20qfX/XRphuq1TQjo2/eKKbI5BYLm8o73Eqd+4ayeiC69\nxd6jt6nyss6mt+WyOW13aH4vvEbrUTKvnYnDrJTfY8JCl6n/jlV4V/l0eYg4+e4OpUPmvm2NCAa8\nq9aKxKEnJCQkbBNsOoceDZ7PyHHogTstygkHcbWxeC3R9iOya7k3noAC3Kcr1LdDkXbzJlErOzjF\naNP7VqwenY8Y52/jcQROO8iATdyboi9OiEqXGUIq3EbGZpF2nJJFvQs1nxQ67HzXanJvMSqiOJaU\nSyrLd0Hmaujtc3SxbcjcWBPTaLgWRp3j3thoi3Ji6RrTzv7oKL2yWc8WyVIXL5lkBcwJHj6mkQEr\nu8jEDleJC6+ZFGYYoQh///jkk6GoXX4WAPDggw/oWCohPCR/WvNdrhM5AVt5vcxoh08llZ6eKGMR\nTj3rsDptKzcfhUWja3RPYf8ZXZIktrDhTDhujKxVr6t0N5q0B+anzcnCc/wcp3GfWsL9csyfiSO6\ndxav0HpMqs9ikMlXq8Oh7NWzxMlfu0bt3nnnrnBteZROHo2WSWHZzXJ9A0C1Q7M6upcToVTVtHKB\n9VVLZuyNNp0sri/yUB6JQ09ISEjYJkgv9ISEhIRtglVFLs65IQB/B6DG9T/tvf8V59wJAJ8CsBvA\n1wD8tA8R69eOWJb7IDKwZoVZ/rOPxkJZv/LSfo+JJGJ5RoUmK84o5iq1HqsiSinWj4XxjZkpST3b\nZ/g+4Cy21hC8NjZGqW+OojkmDTwr/Ww4XNlBGZujZUaxKiZnmaku4iurYAsZ3lmU4nLKX1krY9om\ncUGsg+2A5B+hzhrPsiVRilrPUlZqd0wcG1EgiuVed1GP/d2r5OWZM2W87TYAQLumyrQOhwXuXn6d\n2tqvyRj2H6Zj/tGDY6GsPUOxXlozasZZYrPPaMwVEXFY8ZGILe2e6TPVVGGJ7q2OjckjZsZGrIK9\n74XFkjXp5c9K2T4HQprxHm3XuUxitKi4c98Riplz4KB6UrZ7JP5YbGno5yb322TxTZapyMXzXL3+\nqipsh8oktjl85LZQ9vdff4au7aO9MGG9rrnPnjGp9B1SlHaNSKnUIhHLyBiJzromScu1Ool0OuYZ\nbfKE5AVX14e1cOhNAB/w3r8LwLsBPOScOw3g1wD8hvf+FIAZAB/bAB0JCQkJCRvEWlLQeQCS86nC\nfx7ABwD8Sy5/DMAnAPz2egmJKgELaieTIMH+qkdMDmMJLvq5dttGTHkpsM5EUk+495LhJmMKv1jk\nw/60e7Z9qWfbkOs+FkqQYaO2BX2maSOYWRpGVjh0YcuikTqs/0op3xbV43kuFblr4aRLhm8oS+IE\n224vf3LKsogC2VtzN7GZ00aEM+pFlKKx9R5ktjh/5Ry1aRXq7Nwj8UQsPHNiyxc0UmKpQu0PH9Kk\nBvMLrImbU0WpmydOvv36KwCAmleONxsnDv197zyhZTwPvq2xSHqtfCTIknXUycRBxyhASxIPyZxA\nuvmop7n0e6wIznLrzpdMG/27880ZNdPLJMKoWcca01EzRgeNBnHo4rBUMcrwSjbL41NueeYalbW6\nmgAlnDL4y3JbT1UZ2Hlth0ZKrFVonl+/di6UdTjgYYs/X1/U+a7NU3uZOcGNDA0zbTr31Tbvf1ae\nuh06lkZbUlnqWGo3ymzROVfiBNFXAPwtgFcBzHrvZRdMAjiywr2POOfOOOfO1Ov1WJWEhISEhLcB\na3qhe++73vt3AzgK4EEAd8eqrXDvo977B7z3D8TSxiUkJCQkvD24Lh7fez/rnPsigNMAJpxzZebS\njwK4MPDmFRDzFI0p8+QEKLEgrGhE7YyLikQrzpCyZrMp4wnXwjHUtCF0WC9PgXg15o/x+X7s9ZgI\nZVCftn6wfR+g+IvFHbH5LCWbuj1aB4VtTNMcGtavUt+ulaxHiNHhinTbxAFhWDkfgyz36ePBXsM3\nCZ2cD8XKZWK/HI1nYuZvgIK0OfMG1zGex3yUHjIxaAK9nvZTbZ9mhh8aJ9vjhvE2LbOqMTPCiV6J\n1qU7QaqwyrCJv1NmL1kjghKyu7k9A6YjIm7SDKLFgRrxB6fODKKrjlHsZ+VqoV3ZM1XTbuEpMYpE\nUXK2mypSCuMyYpWsxnPOssGOab/JYpX5WSOyYqVpzcybKKlb7EzRjmRbzcbUs3Sx8yYAYKGjbQwd\nJsXnEuhzuq6K7P0jpKQeHrZ7XURyKvrMMlrTuXnqqzZi3k/smWvD/o6ysrwo1Fs7VuXQnXP7nKOo\nTM65YQAfBPACgC8A+HGu9jCAz2yAjoSEhISEDWItHPohAI85shvLAPy59/6zzrnnAXzKOfcfAXwd\nwO9uhJCY8tKin/uVLPaAmsJZj84YZyxlEjvEcu9yTbh321esDTW3tDFaiiaYcq9NNiGni5j5YoiK\nGOHaYcyk+hVQbeOxF2tXuKuY12vMtDLK7UXmtN9L1xnPPmEXckpiSRxgOMBw2hL3VOvhyvfGFLxd\nw/2KJ6TPioppVT4raYM49GqIxGfMT5kzz0rmkZE5GuIYNJmapXW5/0ruEeNTo+G4OxVWru8i7Vt5\nyKRL47k04U/C/NmzmoSQkWr5oUkUUVsmJxwtEU/HoGg25oLCJbucJjuviI1hR9kmFKH9OT5hjPJE\nGd7SZ67MZo0NMXQwp0zPp6Qdu5RbXubUh7MtfR9I0MQK+78OmzhA4rHaMJzxAojz75nEGcfGhplE\nNrM11gQl3ndNa2rNe6ZSMt6jzGpPN8iksjqvSuLymJhlmsQ3kThE14u1WLk8A+D+SPlrIHl6QkJC\nQsJNgOQpmpCQkLBNsOnBueSUbW1cxYMtM3lGRSEox/OYB6hVXsY8RTV4lsu1ZcuyiBI1s5klJCgX\nf6kar7/MFRVyMfGHiHXEprli7U/5OGftdcVLcZAnp7Vzj9ldy1hjyud+Ja3tK2ZTH4OINWL29jFx\nk0WIERYUqsWgWHnRDwplLhPRT1EEEJS5Vjk2IBRxl22VrVK5ywqzsivSL2KejrFBRrc4V4Fuaz/P\nYpou2zG3NE8DHCsofS5HaMQblOdNkl6UIslnI9FzkQtu1s3fY0VL3hWV4SH37QDZ1YJJDCP7Y2lZ\nlZE9Hl/V7iveK3N1moiq7VJElVDRiCjDOyZIXi8EE+P9VzLJVMLzat4LPF82l+0w31vlPpsmyUhT\nFLs2VK6ITmzSFe72wFGy7isNq5iny6LVpkkq04vsmetF4tATEhIStgk2nUMfaS2tfLFZLIqpYCKR\nXgPsb17/r9fKfpcrt9FPx3UHrzEQeq2PXidWcQ0Y7kuNtyJs/A7+3mWueq3zsVbI+FqGa9/IfN0o\ndHYVVEZhD6x3fb6V6N//q0W1iT0ng3bPesO5Zq/9C/1+nfeOr15lXYi8UrCj79NC5rJqyqqRejFc\n717fiLmiIHHoCQkJCdsE6YWekJCQsE2QXugJCQkJ2wTphZ6QkJCwTeAGeXq97Z05NwVgCcDVG9bp\ntwZ7sbXHsNXpB7b+GLY6/cDWH8NWov+Y937fapVu6AsdAJxzZ7z3D6xe8+bFVh/DVqcf2Ppj2Or0\nA1t/DFud/hiSyCUhISFhmyC90BMSEhK2CTbjhf7oJvT5dmOrj2Gr0w9s/TFsdfqBrT+GrU5/ATdc\nhp6QkJCQ8K1BErkkJCQkbBPc0Be6c+4h59xLzrmzzrmP38i+1wPn3C3OuS84515wzj3nnPs5Lt/t\nnPtb59wr/Llrs2kdBE7y/XXn3Gf5/xPOuSeZ/j9zzq01PMWmwDk34Zz7tHPuRV6L79yCa/DveA99\n0zn3p865oZt5HZxzv+ecu+Kc+6Ypi865I/xXfq6fcc69Z/MoV6wwhv/E++gZ59z/kGxsfO2XeAwv\nOed+cHOo3hhu2AudMx79FoAPAbgHwEedc/fcqP7XiQ6AX/De3w3Ko/qzTPPHATzhvT8F4An+/2bG\nz4HSBgp+DcBvMP0zAD62KVStHb8J4P947+8C8C7QWLbMGjjnjgD4twAe8N7fC4qF9RHc3OvwBwAe\n6itbac4/BOAU/z0C4LdvEI2r4Q9QHMPfArjXe38fgJcB/BIA8HP9EQDv4Hv+G7+zthRuJIf+IICz\n3vvXvPctAJ8C8OEb2P91w3t/0Xv/Nf6+AHqRHAHR/RhXewzAj20OhavDOXcUwA8D+B3+3wH4AIBP\nc5Wbnf4xAO8Hpzj03re897PYQmvAKAMYds6VAYwAuIibeB28938HYLqveKU5/zCAP/SEL4MSyB+6\nMZSujNgYvPf/lxPbA8CXQQnuARrDp7z3Te/96wDOYgtmZLuRL/QjAM6Z/ye5bEvAOXcclIrvSQAH\nvPcXAXrpA9i/eZStiv8C4N9DI8DuATBrNvXNvg4nAUwB+H0WG/2Oc24HttAaeO/PA/jPAN4Cvcjn\nAHwVW2sdgJXnfKs+2z8D4H/z9606hhxu5As9FlZ5S5jYOOdGAfwlgJ/33s9vNj1rhXPuRwBc8d5/\n1RZHqt7M61AG8B4Av+29vx8UOuKmFa/EwLLmDwM4AeAwKPT2hyJVb+Z1GISttqfgnPtlkEj1T6Qo\nUu2mHkMMN/KFPgngFvP/UQAXbmD/64JzrgJ6mf+J9/6vuPiyHCn588pm0bcKvhvAjzrn3gCJuD4A\n4tgn+OgP3PzrMAlg0nv/JP//adALfqusAQB8EMDr3vsp730bwF8B+C5srXUAVp7zLfVsO+ceBvAj\nAH7Kq932lhrDSriRL/SnAJxizX4VpIB4/Ab2f91gefPvAnjBe//r5tLjAB7m7w8D+MyNpm0t8N7/\nkvf+qPf+OGi+P++9/ykAXwDw41ztpqUfALz3lwCcc87dyUXfD+B5bJE1YLwF4LRzboT3lIxhy6wD\nY6U5fxzAv2Zrl9MA5kQ0c7PBOfcQgF8E8KPe+7q59DiAjzjnas65EyAF71c2g8YNwXt/w/4A/BBI\ns/wqgF++kX2vk97vAR27ngHwNP/9EEgO/QSAV/hz92bTuoaxfB+Az/L3k6DNehbAXwCobTZ9q9D+\nbgBneB3+J4BdW20NAHwSwIsAvgngjwDUbuZ1APCnIHl/G8S9fmylOQeJK36Ln+tnQdY8N+sYzoJk\n5fI8/3dT/5d5DC8B+NBm07+ev+QpmpCQkLBNkDxFExISErYJ0gs9ISEhYZsgvdATEhIStgnSCz0h\nISFhmyC90BMSEhK2CdILPSEhIWGbIL3QExISErYJ0gs9ISEhYZvg/wNWNQiJ/dYFLgAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "\n",
    "#Training\n",
    "n_training_samples = 20000\n",
    "train_sampler = SubsetRandomSampler(np.arange(n_training_samples, dtype=np.int64))\n",
    "\n",
    "#Validation\n",
    "n_val_samples = 5000\n",
    "val_sampler = SubsetRandomSampler(np.arange(n_training_samples, n_training_samples + n_val_samples, dtype=np.int64))\n",
    "\n",
    "#Test\n",
    "n_test_samples = 5000\n",
    "test_sampler = SubsetRandomSampler(np.arange(n_test_samples, dtype=np.int64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.autograd import Variable\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class SimpleCNN(torch.nn.Module):\n",
    "    \n",
    "    #Our batch shape for input x is (3, 32, 32)\n",
    "    \n",
    "    def __init__(self):\n",
    "        super(SimpleCNN, self).__init__()\n",
    "        \n",
    "        #Input channels = 3, output channels = 18\n",
    "        self.conv1 = torch.nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)\n",
    "        self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
    "        \n",
    "        #4608 input features, 64 output features (see sizing flow below)\n",
    "        self.fc1 = torch.nn.Linear(18 * 16 * 16, 64)\n",
    "        \n",
    "        #64 input features, 10 output features for our 10 defined classes\n",
    "        self.fc2 = torch.nn.Linear(64, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        #Computes the activation of the first convolution\n",
    "        #Size changes from (3, 32, 32) to (18, 32, 32)\n",
    "        x = F.relu(self.conv1(x))\n",
    "        \n",
    "        #Size changes from (18, 32, 32) to (18, 16, 16)\n",
    "        x = self.pool(x)\n",
    "        \n",
    "        #Reshape data to input to the input layer of the neural net\n",
    "        #Size changes from (18, 16, 16) to (1, 4608)\n",
    "        #Recall that the -1 infers this dimension from the other given dimension\n",
    "        x = x.view(-1, 18 * 16 *16)\n",
    "        \n",
    "        #Computes the activation of the first fully connected layer\n",
    "        #Size changes from (1, 4608) to (1, 64)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        \n",
    "        #Computes the second fully connected layer (activation applied later)\n",
    "        #Size changes from (1, 64) to (1, 10)\n",
    "        x = self.fc2(x)\n",
    "        return(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outputSize(in_size, kernel_size, stride, padding):\n",
    "\n",
    "    output = int((in_size - kernel_size + 2*(padding)) / stride) + 1\n",
    "\n",
    "    return(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DataLoader takes in a dataset and a sampler for loading (num_workers deals with system level memory) \n",
    "def get_train_loader(batch_size):\n",
    "    train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size,\n",
    "                                           sampler=train_sampler, num_workers=2)\n",
    "    return(train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Test and validation loaders have constant batch sizes, so we can define them directly\n",
    "test_loader = torch.utils.data.DataLoader(test_set, batch_size=4, sampler=test_sampler, num_workers=2)\n",
    "val_loader = torch.utils.data.DataLoader(train_set, batch_size=128, sampler=val_sampler, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "def createLossAndOptimizer(net, learning_rate=0.001):\n",
    "    \n",
    "    #Loss function\n",
    "    loss = torch.nn.CrossEntropyLoss()\n",
    "    \n",
    "    #Optimizer\n",
    "    optimizer = optim.Adam(net.parameters(), lr=learning_rate)\n",
    "    \n",
    "    return(loss, optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "def trainNet(net, batch_size, n_epochs, learning_rate):\n",
    "    \n",
    "    #Print all of the hyperparameters of the training iteration:\n",
    "    print(\"===== HYPERPARAMETERS =====\")\n",
    "    print(\"batch_size=\", batch_size)\n",
    "    print(\"epochs=\", n_epochs)\n",
    "    print(\"learning_rate=\", learning_rate)\n",
    "    print(\"=\" * 30)\n",
    "    \n",
    "    #Get training data\n",
    "    train_loader = get_train_loader(batch_size)\n",
    "    n_batches = len(train_loader)\n",
    "    \n",
    "    #Create our loss and optimizer functions\n",
    "    loss, optimizer = createLossAndOptimizer(net, learning_rate)\n",
    "    \n",
    "    #Time for printing\n",
    "    training_start_time = time.time()\n",
    "    \n",
    "    #Loop for n_epochs\n",
    "    for epoch in range(n_epochs):\n",
    "        \n",
    "        running_loss = 0.0\n",
    "        print_every = n_batches // 10\n",
    "        start_time = time.time()\n",
    "        total_train_loss = 0\n",
    "        \n",
    "        for i, data in enumerate(train_loader, 0):\n",
    "            \n",
    "            #Get inputs\n",
    "            inputs, labels = data\n",
    "            \n",
    "            #Wrap them in a Variable object\n",
    "            inputs, labels = Variable(inputs), Variable(labels)\n",
    "            \n",
    "            #Set the parameter gradients to zero\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            #Forward pass, backward pass, optimize\n",
    "            outputs = net(inputs)\n",
    "            loss_size = loss(outputs, labels)\n",
    "            loss_size.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            #Print statistics\n",
    "            running_loss += loss_size.data[0]\n",
    "            total_train_loss += loss_size.data[0]\n",
    "            \n",
    "            #Print every 10th batch of an epoch\n",
    "            if (i + 1) % (print_every + 1) == 0:\n",
    "                print(\"Epoch {}, {:d}% \\t train_loss: {:.2f} took: {:.2f}s\".format(\n",
    "                        epoch+1, int(100 * (i+1) / n_batches), running_loss / print_every, time.time() - start_time))\n",
    "                #Reset running loss and time\n",
    "                running_loss = 0.0\n",
    "                start_time = time.time()\n",
    "            \n",
    "        #At the end of the epoch, do a pass on the validation set\n",
    "        total_val_loss = 0\n",
    "        for inputs, labels in val_loader:\n",
    "            \n",
    "            #Wrap tensors in Variables\n",
    "            inputs, labels = Variable(inputs), Variable(labels)\n",
    "            \n",
    "            #Forward pass\n",
    "            val_outputs = net(inputs)\n",
    "            val_loss_size = loss(val_outputs, labels)\n",
    "            total_val_loss += val_loss_size.data[0]\n",
    "            \n",
    "        print(\"Validation loss = {:.2f}\".format(total_val_loss / len(val_loader)))\n",
    "        \n",
    "    print(\"Training finished, took {:.2f}s\".format(time.time() - training_start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== HYPERPARAMETERS =====\n",
      "batch_size= 32\n",
      "epochs= 5\n",
      "learning_rate= 0.001\n",
      "==============================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayank\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:48: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n",
      "C:\\Users\\Mayank\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, 10% \t train_loss: 2.13 took: 8.44s\n",
      "Epoch 1, 20% \t train_loss: 1.90 took: 3.09s\n",
      "Epoch 1, 30% \t train_loss: 1.71 took: 2.90s\n",
      "Epoch 1, 40% \t train_loss: 1.65 took: 2.96s\n",
      "Epoch 1, 50% \t train_loss: 1.56 took: 2.96s\n",
      "Epoch 1, 60% \t train_loss: 1.54 took: 2.79s\n",
      "Epoch 1, 70% \t train_loss: 1.46 took: 2.99s\n",
      "Epoch 1, 80% \t train_loss: 1.45 took: 3.02s\n",
      "Epoch 1, 90% \t train_loss: 1.39 took: 2.74s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mayank\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:69: UserWarning: invalid index of a 0-dim tensor. This will be an error in PyTorch 0.5. Use tensor.item() to convert a 0-dim tensor to a Python number\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation loss = 1.33\n",
      "Epoch 2, 10% \t train_loss: 1.32 took: 8.16s\n",
      "Epoch 2, 20% \t train_loss: 1.33 took: 3.91s\n",
      "Epoch 2, 30% \t train_loss: 1.27 took: 4.15s\n",
      "Epoch 2, 40% \t train_loss: 1.30 took: 3.61s\n",
      "Epoch 2, 50% \t train_loss: 1.25 took: 3.31s\n",
      "Epoch 2, 60% \t train_loss: 1.27 took: 3.34s\n",
      "Epoch 2, 70% \t train_loss: 1.30 took: 3.37s\n",
      "Epoch 2, 80% \t train_loss: 1.25 took: 3.44s\n",
      "Epoch 2, 90% \t train_loss: 1.25 took: 3.30s\n",
      "Validation loss = 1.27\n",
      "Epoch 3, 10% \t train_loss: 1.12 took: 8.23s\n",
      "Epoch 3, 20% \t train_loss: 1.13 took: 3.28s\n",
      "Epoch 3, 30% \t train_loss: 1.13 took: 3.41s\n",
      "Epoch 3, 40% \t train_loss: 1.13 took: 3.39s\n",
      "Epoch 3, 50% \t train_loss: 1.16 took: 3.33s\n",
      "Epoch 3, 60% \t train_loss: 1.13 took: 3.34s\n",
      "Epoch 3, 70% \t train_loss: 1.15 took: 3.36s\n",
      "Epoch 3, 80% \t train_loss: 1.18 took: 3.50s\n",
      "Epoch 3, 90% \t train_loss: 1.14 took: 3.37s\n",
      "Validation loss = 1.18\n",
      "Epoch 4, 10% \t train_loss: 1.02 took: 8.36s\n",
      "Epoch 4, 20% \t train_loss: 1.02 took: 3.33s\n",
      "Epoch 4, 30% \t train_loss: 1.07 took: 3.33s\n",
      "Epoch 4, 40% \t train_loss: 1.02 took: 3.55s\n",
      "Epoch 4, 50% \t train_loss: 1.05 took: 3.33s\n",
      "Epoch 4, 60% \t train_loss: 1.03 took: 3.31s\n",
      "Epoch 4, 70% \t train_loss: 1.06 took: 3.31s\n",
      "Epoch 4, 80% \t train_loss: 1.06 took: 3.43s\n",
      "Epoch 4, 90% \t train_loss: 1.09 took: 3.45s\n",
      "Validation loss = 1.18\n",
      "Epoch 5, 10% \t train_loss: 0.90 took: 8.92s\n",
      "Epoch 5, 20% \t train_loss: 0.96 took: 3.30s\n",
      "Epoch 5, 30% \t train_loss: 0.98 took: 3.28s\n",
      "Epoch 5, 40% \t train_loss: 0.95 took: 3.42s\n",
      "Epoch 5, 50% \t train_loss: 0.96 took: 3.37s\n",
      "Epoch 5, 60% \t train_loss: 0.95 took: 3.29s\n",
      "Epoch 5, 70% \t train_loss: 0.92 took: 3.27s\n",
      "Epoch 5, 80% \t train_loss: 0.97 took: 3.33s\n",
      "Epoch 5, 90% \t train_loss: 0.99 took: 3.53s\n",
      "Validation loss = 1.21\n",
      "Training finished, took 230.77s\n"
     ]
    }
   ],
   "source": [
    "CNN = SimpleCNN()\n",
    "trainNet(CNN, batch_size=32, n_epochs=5, learning_rate=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleCNN(\n",
       "  (conv1): Conv2d(3, 18, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  (fc1): Linear(in_features=4608, out_features=64, bias=True)\n",
       "  (fc2): Linear(in_features=64, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GroundTruth:    cat  ship  ship plane\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tra5kXmd12UcXPRorEjVJ3SfXx7L01MaVzZQ1rFNs3yy8hO7h4eGxQ+Bf6B4e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\ngv8P8QITwTAXGKoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# print images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputss=CNN(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.2995, -2.9127, -1.9791,  3.8283, -1.1985,  1.4808,  0.3422, -1.9791,\n",
       "          0.9524, -0.9883],\n",
       "        [ 5.0634,  5.8446, -2.0959, -2.8254, -3.3452, -6.2351, -6.5705, -7.3737,\n",
       "          7.0277,  2.2735],\n",
       "        [ 2.2431,  3.2895, -1.1895, -0.4353, -2.4037, -2.7561, -5.0246, -2.2916,\n",
       "          3.5242,  1.8942],\n",
       "        [ 3.4626,  0.3240,  1.7702, -1.7610, -1.1258, -4.2789, -3.0989, -4.6687,\n",
       "          4.8347, -1.4147]], grad_fn=<ThAddmmBackward>)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outputss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted:    cat  ship plane  ship\n"
     ]
    }
   ],
   "source": [
    "_, predicted = torch.max(outputss, 1)\n",
    "\n",
    "print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]\n",
    "                              for j in range(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 58 %\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs = CNN(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
    "    100 * correct / total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of plane : 64 %\n",
      "Accuracy of   car : 58 %\n",
      "Accuracy of  bird : 49 %\n",
      "Accuracy of   cat : 28 %\n",
      "Accuracy of  deer : 60 %\n",
      "Accuracy of   dog : 48 %\n",
      "Accuracy of  frog : 71 %\n",
      "Accuracy of horse : 69 %\n",
      "Accuracy of  ship : 67 %\n",
      "Accuracy of truck : 62 %\n"
     ]
    }
   ],
   "source": [
    "class_correct = list(0. for i in range(10))\n",
    "class_total = list(0. for i in range(10))\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        outputs =CNN(images)\n",
    "        _, predicted = torch.max(outputs, 1)\n",
    "        c = (predicted == labels).squeeze()\n",
    "        for i in range(4):\n",
    "            label = labels[i]\n",
    "            class_correct[label] += c[i].item()\n",
    "            class_total[label] += 1\n",
    "\n",
    "\n",
    "for i in range(10):\n",
    "    print('Accuracy of %5s : %2d %%' % (\n",
    "        classes[i], 100 * class_correct[i] / class_total[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 3, 32, 32])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images.shape"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}