{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 301 Regression\n",
    "\n",
    "View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n",
    "My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n",
    "\n",
    "Dependencies:\n",
    "* torch: 0.1.11\n",
    "* matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.autograd import Variable\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x1074602a0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(1)    # reproducible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XQ6/d7cW+bmb/Dfh3adqUV7vcfS749wUz+zpwJfBHDHis8mqXmf0k8AidH/Wj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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10961dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)\n",
    "y = x.pow(2) + 0.2*torch.rand(x.size())                 # noisy y data (tensor), shape=(100, 1)\n",
    "\n",
    "# torch can only train on Variable, so convert them to Variable\n",
    "x, y = Variable(x), Variable(y)\n",
    "\n",
    "plt.scatter(x.data.numpy(), y.data.numpy())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Net(torch.nn.Module):\n",
    "    def __init__(self, n_feature, n_hidden, n_output):\n",
    "        super(Net, self).__init__()\n",
    "        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer\n",
    "        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.hidden(x))      # activation function for hidden layer\n",
    "        x = self.predict(x)             # linear output\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net (\n",
      "  (hidden): Linear (1 -> 10)\n",
      "  (predict): Linear (10 -> 1)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "net = Net(n_feature=1, n_hidden=10, n_output=1)     # define the network\n",
    "print(net)  # net architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.SGD(net.parameters(), lr=0.2)\n",
    "loss_func = torch.nn.MSELoss()  # this is for regression mean squared loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.ion()   # something about plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2e8z/ZxoXz5LlcvdXIr9+j9Afl33tsXmvfWAwyhQxB/hsdEMNz1Up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\niIhITf1/NeO2zE0A7TgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10971fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9Qy1ekEHAyfbJ+ObEFBQLNgUuW3ee2awhVbPLLnD99Tp2Z8YMHQUfdDqjjz/W\n9e5bt9bxI88+C7/+mnmZwrR0qfavRFdFLfe5CO+wA/z1rzpn3X33aXCqAUEGASfaB6jd5luTcyxB\noEBFkwKikyZ6/8rpZAt1HPEG8T4pAiwaeVyGpY1jwwb4z380seC111KbeaBxY52x4A9/0CWLc2WZ\n4lmzdHzM6NGwcaP//l276viYU0/1X/4hx1jiQHgsQcDkjDAmTazxtvniYp0i57nnNLHgqad0FuIg\nfRNr1mjn+vHHayrweedpRlyQrK5sW7NGp4bp3VuX5H72Wf9Ac/DB8PrrOlbp3HPzLtCAJQ6Y6gKk\nBJl84zdpYir8akg1Mq38ttvCaafp1/LlWjN47jltQvPz00/w8MP6teOO2rE+ZIiu65LilC2BOad9\nUU89panLq1b5v6ZePZ1z7uqrYf/9wylXDYh+XhK1l1jiQN1lNZsCFEZSAOTItPI77aR9NGVleuf/\nl79A+/bBXvvttzoC/8ADoWNHXeJ65szspVIvXqzrAXXurFlzjzziH2hKSjRRYt48TfPO80Dj/bzE\nsjVv6jbrsylAmbaXe2sz8eRcu/vmzVqTePZZrUmsXJna67t0gZNP1hrPbrul9tqff9bBqk89ldrc\ncC1bambZhRdqZl6aUh2wG+YA30SfO9DPjE1jkx352mdjwaYAZTKFSLzXxgotKSAb1q3T5Yyff17H\n86S6pMEee2jQOflkzZKLZ9MmHQvz9NPw6qupHWOvvXRw6mmnaa0mA6n+ncOeWqbGk0jqqHwNNtaM\nVoDSXreG+P09sXK63b1hQxgwQPt1VqzQRIGBA4NPdfP55zpgtHNnHcdy5526/PWmTTp7wZ/+BO3a\naZbbc88FCzRNmsCZZ+paQDNnasJChoEGUk9HDzV9HRvgaZKzBIECNahn67TuVv36dfKq3X2bbbSW\nMmQIVFTA2LEahCZODJaZVlamX1ddpevwpLJujwj07auDNQcP1oCTZan2zYWdIXZ1v65xa05583kx\nobKajaki2V1oXq81X1oKZ5yhc48tXw4PPgiHHRY8Iy1ooOnaFf7f/9PZmidO1DEyIQQaSL0mEXbN\nI5MatSl81mdTh8XrLAZqZMr4nFkDpbxcl6sePVpnL0hH8+YwdKjWYvbdN7yU6hi51mdjaka+9tlY\nsKmjkl14gFADQc5e9BYt0my20aN1tH8y9evroNPTT9eJMLM423IqcikbzdQMCzYpsmBTu2pzOpG8\nmMrkyy+10gL/AAAVVUlEQVR1sssXXtA520CnwTnqKPjtbzXpIOj8bcZkUb4GG0sQqKNqczqRvJjK\nZNdd4YYbdILQr7/WaWc6dszLqWOCsBqPCZsFmzqqVWlJ3NpFTaSp1uaxUyYSfIaCPBXbrBmdoRmw\ngGOyxrLR6qgga5UU4rFNdWGPvzEGrGZTZ0XvWGuj6STTY1uTj78g58hvWqLyikr6jJxs59dkhSUI\nmLQkupiFHQhyNpOtBvmd4yDnKMi0RIlea2pXviYIBAo2ItIfuAcoAh51zo2Mef4K4BxgI/A9cJZz\nbkmy97Rgk7/iXaiiyw9kulCb34U0LzLZAkg3KAcJJEHOUbJJM+PJt/NbyPI12Pj22YhIEXA/cAyw\nGzBURGKnxp0J9HLO7QmMAW7PdkFN7ki0Xo73e1Qqbf/eKeqjSwlf/uIsOnjWsM+LTDYf8X7PoEsm\nB+lfCXKOUj1f+XR+TW4KkiCwHzDfObfQObceGA0M9O7gnJvinFsTeTgdaJPdYpqwjZ1ZTp+Rk+no\nubAnEtaFKlkQi16QSxvHX+I5JzPZEsikQz5IIAkyLU2ifYoSzH6QT+fX5KYgwaY1sNTzeFlkWyJn\nA29lUihTs1K90071whN0f7+gVLlhE86R95lsmdTOggSSINl+ifYZun/bvD+/JjdlNfVZRIYBvYA7\nEjw/XETKRKTs+++/z+ahTQZSvdOOd6FKJJULVZCgtKpyQ5XJHktLimlUXI/LX5zlWyMLKpVaXjoy\nmRAzWSCJlvvyF2fRsH49mjcuTjghZqJJM/82qLtNpmlC4ZsgICK9gRudc/0ij68BcM7dGrPfkcB9\nwKHOuRV+B7YEgdyRzqJX3rTZ2KSA6OPSkmJEoGLNhsDzdgXJkIqu+giZTRrq7aRvFinryjUbAiU5\nZJJ1lyzBonWAzL7anEDV1L58TRAIEmzqA/OAI4By4CPgD8652Z59eqKJAf2dc18FObAFm9yRrWWk\ns3HxSxbEvEqKi2hUXI+VazakVe5UUn+975utC3uy37OkuIgT9mnNKx+XBz5GoWTpGX8FG2wARORY\n4G409flx59wtInIzUOacGyciE4HuwPLIS752zg1I9p4WbHJHGGNXsnHx8xt0mEiQZYhTTf2NyjTI\nBS1HkQib4vxvJjpGstrpXSf3sEGwBSRfg02gGQScc28Cb8Zsu97z85FZLpepQWHMJpCNFOXoaqOJ\nLqSJBOn7SDeVt3LDpoS1oXTeM9Fr4gWaZPsnmm+uWUmxzXtmcoLNjWYAvfBMG9GXRSOPY9qIvhlf\niLK5KmSi15SWFKedORVGKm82f7dEKcgO4iYtJEocEMHmPTM5wYKNCUU2J9tM9F43Dtg9UOZUvOwy\nv4y6ZGttZhLkYqWSghwVLzU9UXZZRZzmPrBBmqbm2dxoJjTZnCctjOldgGrZaN7MOUicCOB9bVi/\nm1+fVZA+IkscKDz52mdjwcYUtDAy7Wq6ryOd1PQom7i08ORrsLElBkxByzRRIZqkUJsyWWyuNpeS\nMMbLgo0paHm1KmgCV/frGrd2ErSPKBcCpjGWIGAKWiGsCpqo898CiMkn1mdjCl68aWmCTqFjTK6x\nPhtjkqjNjvZoM1JsZ7kNcDSm5lgzmgldJouFZVMm68gYYzJjNRsTumQX+WSzG2dbIazyaUy+spqN\nCV2yi3xN1nqyOYWOMSY1FmxM6JJd5GuyaasQMtOMyVcWbEzokl3ks9m05bfCpqUQG1N7rM/GhC7Z\nKPZEc3+l2rQVNNMs7AGOmfY/5cL0OMaEwWo2pkYkWsIgW01buZBplmn/U65k7aVNRL/qmjlz4KST\nYIcdoFEj6NoVbrgBKtNIPFm2DM46C1q1goYNoUMHuOwyWLmy+r4iTRG5G5F3EfkGkbWIrEDkQ0Qu\nQ6RJ3GPo6/6CyCxEKhBZhchniPwVkZZx9u+GyE2IvI7I14i4yFdKlRULNqZWZatpKxcyzTINeLkQ\nME2KZsyAffeFsWPhyCPh0kuhaVO4+WY46ihYty74ey1YAPvsA088AfvtB5dfDp06wT33QO/e8OOP\nsa/YDhgObALeAP4BvAxsC9wFfIhI0yqvEGkGfATcAmwAngAeB9YD1wH/Q+Q3McfpB1wPHAesBtYG\n/6W2smY0U+uy0bSVC3OgJQps5RWV9Bk52bdJLBcCpknBpk1w5pmwZg28/joMGKDbN2/Wms4rr8Bd\nd8GIEcHe78ILYcUKuPdeuOSSrduvuELf59prYdQo7yuWAs1wrvqiRSLPAqcA5wO3e54ZDnQBnsC5\ns2Je8yRwOnAecLPnmbeAD4BPca4SkcVA+2C/1FZWszEFIRcyzZIFtiBNYnUuNXvSJOjfH7bbTpuM\nunTRC/OqVdX3XbgQhg+HXXaBkhJ9TffucP75Ve/416/Xi/Xee0Pz5tC4sTZFDRwIEydmt/z//S98\n8QUccsjWQANQrx7cHrm+jxoFQaYEW7AAJkzQsl50UdXnbroJmjSBZ56B1au3bnduU9xAo16OfO8c\ns71T5Pu/4rxmXOR71aY05+bi3Aycy+iux4KNKQi5kGnmt/pn5YZNXPbirLiZcoleX7Cp2Q89pM1M\n06bBoEHaZLTddnDbbXDggVBRsXXf5cu1qeqJJ2D33eGPf4RTT4WOHfUCvHz51n3POEObsjZsgNNO\n030POQQ++wzefju7v8Pkyfq9f//qz3XqpMFzyRINlH6mTNHvRx+twcpr222hTx+tQU2fHrR0x0e+\nfxqzfXbke7yFkH4b+Z7lqKysGc0UjNqeSt+bdZdodU1InikXfX1BZ6MtWaJBYJtt4MMPYdddtz53\n4YXw4IPwpz/Bww/rtjFj4Kef4O67NZB4rV699eK8ahWMHq39HjNmQFFM4I/t83jySVi8OHi5O3TQ\nYBY1N9KX1qVL/P07d4Z58/Rr552Tv3eQ95owQd8rlnbUXxd5tB1wMNADmAI8ErP3o8BQ4GxEugPT\nItsPBnYDrsW515MXNj0WbExBqq0U4mjAS7RCaJR3up54ry9ozz6rzV1XXlk10ADccos+/8wzcN99\n2rwWVRKnObGJJ+FKRJusGjasXjsA2H77qo+ffFKbwoI69NCqwSba3NesWfz9o9u9tbREMnuv+sAN\nMdueAS7Euaqd+c6tRaQvcA/aN7Of59kxwFj/wqbHmtFMwQkrhdhv0KiXX5Ma1OGO///9T7/3jbMs\nd/Pm0LMnrF0LX36p2wYM0FrQRRfBCSdojWf27Op9IU2bwvHHw/vvQ48emhE2ZYo2P8Uzdaq+R9Cv\nqVOzdQayy7m1OCfo9bwNcAZwJFCGSIcq+4psD4wHBgFDgBaRryFo7WYGIvsRAqvZmILjN/FnOpIN\nGo0eM14tKlmTWsF2/PuJ3sXvtFP856Pbo3fx7dtrc9uNN2q/y6uv6va2beGqq7RJLurFF7Xf5/nn\ndawL6NiXE0+Ev/8dfhOb1ZuBaG0jXkKDd3tpac28ly5OVg48hchcNIPsn2ztiwG4EzgUGIhz4zzb\nX0RkLVqzuR04zL/QqbFgYwpOGCnEiQLYjeNms27j5oQzF8RbRwcKuOM/iOiF9dtvtcM/VrTD39uk\n1K2bBpKNG+GTTzSz7L77tA+nSRM4+2zdr6REg9KNN8LSpfDOO9pc9uyz2j/z7rtb3zPTPpuukb9f\nvH4UgK++0u+J+mG8svleAM5NR6SC6kEjGnimxHlVdNs+wQ6SmkDBRkT6o218RcCjzrmRMc83BJ5G\nC/kjcLJzbnF2i2pMMGGMuUkUqCoqq2eextai6kzHf1A9e2rtZOpUOOKIqs9VVMCsWVob6dat+mvr\n19cEgH320ay1Qw7RAZXRYOPVti2ccgoMHaoX8/fe0ySBaN9Npn02fftqH9Pbb8M111Tdd+FCDRzt\n22tmmp/DD9fvEyboOB1vn9Mvv2jWXuPGcMABwcoqsi3QFPgl5ploJ1jLOM9FU57XBztIanz7bESk\nCLgfOAbNVhgqIrvF7HY2sNI5tws6cvW2bBfUmKDCSCFONVDFBqdE0/XUScOGQXGx1kzmz6/63P/9\nH/z8s+4TTQ74+OP4zUvffaffGzfW799/rynOsVavhl9/1UDVoMHW7Zn22Rx6qAbEd96BcZ4Wqc2b\n4c9/1p/PP7/qFD4bNmhf1IIFVd9r55017XnxYrj//qrP3XCD/g6nnhqbENEdkUbVfl+RBmjzWT10\nZgGvaNXuBkTqeV5TBNwUeTSp2ntmgTifAUci0hu40TnXL/L4GgDn3K2efcZH9vlANA3vW6ClS/Lm\nvXr1cmVlZVn4FYypLtvZaImawhoV12Plmuq1m9alJUwbEacDvJBFL6qnn554nwce0ODwwAPa4b/t\ntjravmVLrWV88IFmqE2bpuNuQOcGe+ghOOggvSg3b64X63/9S4PAlCk6ncusWVpr6t4d9txTazY/\n/wz//jd8/bX27dxzT3Z/5xkztIazYYP2C7Vrp4NVy8p0bMykSVUz6hYv1vFB7dtXb8JbsEBraytW\n6CDUbt30/adM0eaz99+H7bdHRD52zvVC5G7gTDR9eQlQAbQCjgZ2BOYCh+Pc1oFImu78LtAMHXMT\nGSzEEWhl4gegN87N97ymBfB3T0lPBJqgrVnRa/xInPsy2akKEmxOBPo7586JPD4V2N85d7Fnn88j\n+yyLPF4Q2eeHmPcajk6XQLt27fZZsmRJ0mMbk0viBTAgbhCqk0sXBJmEc+XKrZ3cEyZop/1HH2nG\nWNu2MHgw/OUvVTvCZ8zQJq/339d+mMpKaN0aDj5Y06f32EP3q6jQ2QOmTtVxKz/8oAGra1c47zwY\nMiSciULnzNHax5Qp2uTVvr023Y0YUT1dO1mwAf39rr9em+Z+/FGTJX73O33/5s0BvMGmDxpseqNB\nZlvgZ2AO2tH/AM5VT8UT6Qj8GTgKzV5z6NQ3b6NBozxm/w7AIp+zcDjOTU22Q40GGy+r2ZhCYcsC\nmJq0JdjkmSAJAuVAW8/jNpFt8fZZFmlGa4YmChhT8OrEQExjMhRkUOdHQGcR6Sja8TSErRO2RY1D\nZwsFbc+bnKy/xhhjTN3iW7Nxzm0UkYvRUadFwOPOudkicjNQ5nRg0GPAMyIyH/gJDUjGGGMMEHCc\njXPuTeDNmG3Xe35eC/w+u0UzxhhTKGxuNGOMMaGzYGOMMSZ0FmyMMcaEzoKNMcaY0PkO6gztwCLf\no1MsZKIFOr1CLsnFMoGVK1W5WK5cLBNYuVKRjTK1d8619N8tt9RasMkGESnLtZG0uVgmsHKlKhfL\nlYtlAitXKnKxTDXFmtGMMcaEzoKNMcaY0OV7sHm4tgsQRy6WCaxcqcrFcuVimcDKlYpcLFONyOs+\nG2OMMfkh32s2xhhj8kDOBxsR+b2IzBaRzSKSMItDRPqLyFwRmS8iIzzbO4rIjMj2FyMzV2dapu1E\n5D8i8lXke/M4+xwuIrM8X2tFZFDkuSdFZJHnuR6ZlilouSL7bfIce5xne9bPVdByiUgPEfkg8rf+\nVERO9jyXtfOV6HPieb5h5HefHzkXHTzPXRPZPldE+qVbhjTLdYWIzImcm0ki0t7zXNy/Zw2V6wwR\n+d5z/HM8z50e+Zt/JSJJlu/Mepnu8pRnnohUeJ4L5VyJyOMiskJ0ba94z4uI3Bsp86cisrfnuVDO\nU85xzuX0F9AN6ApMBXol2KcIWAB0AhoAnwC7RZ57CRgS+XkUcEEWynQ7MCLy8wjgNp/9t0Nnw24c\nefwkcGII5ypQuYBfE2zP+rkKWi6gC9A58nMrYDlQms3zlexz4tnnQmBU5OchwIuRn3eL7N8Q6Bh5\nn6IsnZ8g5Trc8/m5IFquZH/PGirXGcA/47x2O2Bh5HvzyM/Na6JMMftfgs5UH/a5OgTYG/g8wfPH\nAm8BAhwAzAjzPOXiV87XbJxzXzjn5vrsth8w3zm30Dm3HhgNDBQRAfoCYyL7PQUMykKxBkbeK+h7\nngi85eIt0ZpdqZZrixDPVaByOefmOee+ivz8DbACyPbAtbifkyRlHQMcETk3A4HRzrl1zrlFwPzI\n+9VIuZxzUzyfn+noIoZhC3K+EukH/Mc595NzbiXwH6B/LZRpKPBCFo6blHPuHfSGMpGBwNNOTQdK\nRWQnwjtPOSfng01ArdE1tKOWRbZtD1Q45zbGbM/Ub5xzyyM/fwv8xmf/IVT/wN8SqU7fJSINs1Cm\nVMrVSETKRGR6tGmP8M5VKuUCQET2Q+9aF3g2Z+N8JfqcxN0nci5WoecmyGvTlep7n43eJUfF+3vW\nZLlOiPxtxohIdFXfsM5X4PeNNDV2BCZ7Nod1rvwkKneYn6ucEmg9m7CJyERgxzhPXeuce72mywPJ\ny+R94JxzIpIwpS9y99IdXXwu6hr0otsATYX8M3BzDZarvXOuXEQ6AZNF5DP0opq2LJ+vZ4DTnXOb\nI5vTPl+FRkSGAb2AQz2bq/09nXML4r9D1v0LeME5t05EzkNrhX1r6Nh+hgBjnHObPNtq81zVaTkR\nbJxzR2b4FuVAW8/jNpFtP6LV1fqRu9To9ozKJCLfichOzrnlkYvjiiRvdRLwmnNug+e9o3f560Tk\nCeCqIGXKVrmcc+WR7wtFZCrQE3iFNM9VtsolIk2BN9CbjOme9077fMVI9DmJt88yEakPNEM/R0Fe\nm65A7y0iR6LB+1Dn3Lro9gR/z2xcQH3L5Zz70fPwUbR/Lvraw2JeO7UmyuQxBLjIuyHEc+UnUbnD\nOk85p1Ca0T4COotmUzVAP2TjnHMOmIL2mQCcDmSjpjQu8l5B3rNam3HkghvtJxkExM1gCaNcItI8\n2gwlIi2APsCcEM9V0HI1AF5D27XHxDyXrfMV93OSpKwnApMj52YcMEQ0W60j0Bn4MM1ypFwuEekJ\nPAQMcM6t8GyP+/eswXLt5Hk4APgi8vN44OhI+ZoDR1O1dh9amSLl2hXtcP/Asy3Mc+VnHHBaJCvt\nAGBV5CYqrPOUe2o7Q8HvC/gd2o65DvgOGB/Z3gp407PfscA89C7lWs/2TuhFYT7wMtAwC2XaHpgE\nfAVMBLaLbO8FPOrZrwN651Iv5vWTgc/Qi+azwDZZOle+5QIOjBz7k8j3s8M8VymUaxiwAZjl+eqR\n7fMV73OCNskNiPzcKPK7z4+ci06e114bed1c4Jgsf879yjUx8vmPnptxfn/PGirXrcDsyPGnALt6\nXntW5DzOB86sqTJFHt8IjIx5XWjnCr2hXB75DC9D+9XOB86PPC/A/ZEyf4Ynszas85RrXzaDgDHG\nmNAVSjOaMcaYHGbBxhhjTOgs2BhjjAmdBRtjjDGhs2BjjDEmdBZsjDHGhM6CjTHGmNBZsDHGGBO6\n/w+pufCHdmrqwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109703a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LbeR1DQFcg5AGKKXCy/t/Ki09gzMP7OG5T/5Dq23fuT/g73+3qzSXLx+9SuZRvG6epsEm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\no5RSKuI02CillIo4DTZKKaUi7v8BpcLlW1efGAQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1097d8828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XkxPYlpYGr74K11wT826pKFq3Djp2tMOmTh59FO65h+wlGx0rj+u1mthL1GCj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\nlIo6DTZKKaWi7v8Be5B264H163QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109a19a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IbALOAeaIiKNGtjFmgjGmtTGmde3atYvvdRkm0FDFkQDlBHzb101L5VByCv2v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\n5DmbdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109b2acf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5ig8llWFg13tZVreZ6pspQQnF2WQA9X1e1/OUeakMnAwsFJGNwL+BWSLi2OFq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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109a048d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UUplKqcz9Ns06wj9vY24+/HaX7fbxCzeYb268kfT2Q23POXTuG/ZZRUIA/PWX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\nKXWZS1lIrlPE0VpH9AfQAKgPLAOaetk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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1099f8198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KBpJTUhk2dzMvJG62LU/845hZ1qhZ09JXw4PbGDdvIq8tdr9vEAQbvv3WxN2z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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109b434a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3oywfpdRFQE2t9YIgyibY4PS5ycjMQgMZmVmkz9rAU3M22JbPueQGeOwxSz+l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\nBEEQhJDz//n8Xll7wex0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1099f85f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RNsEG5xRGWnoGGnIE45nUDZbyJxansXDsu7ZpB+7++Wvu/PkrMjKzGTt/S9jf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\nA9eQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109dd6da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(100):\n",
    "    prediction = net(x)     # input x and predict based on x\n",
    "\n",
    "    loss = loss_func(prediction, y)     # must be (1. nn output, 2. target)\n",
    "\n",
    "    optimizer.zero_grad()   # clear gradients for next train\n",
    "    loss.backward()         # backpropagation, compute gradients\n",
    "    optimizer.step()        # apply gradients\n",
    "\n",
    "    if t % 10 == 0:\n",
    "        # plot and show learning process\n",
    "        plt.cla()\n",
    "        plt.scatter(x.data.numpy(), y.data.numpy())\n",
    "        plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)\n",
    "        plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color':  'red'})\n",
    "        plt.show()\n",
    "        plt.pause(0.1)\n",
    "\n",
    "plt.ioff()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
