{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = torch.tensor([[1],[2],[3]], dtype=torch.float)\n",
    "y_data = torch.tensor([[2],[4],[6]], dtype = torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LinearRegression(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LinearRegression,self).__init__()\n",
    "        self.linear = torch.nn.Linear(1,1)\n",
    "    def forward(self,x):\n",
    "        y_pred = self.linear(x)\n",
    "        return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = torch.nn.MSELoss(size_average=False)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_list = []\n",
    "loss_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Training  3.9182913303375244\n"
     ]
    }
   ],
   "source": [
    "print(\"Before Training \", model(torch.tensor([[4]], dtype = torch.float)).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for epoch in range(500):\n",
    "    y_pred = model(x_data)\n",
    "    loss = criterion(y_pred,y_data)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    epoch_list.append(epoch)\n",
    "    loss_list.append(loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x118035978>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dWWZtnRjV1onRM3rf/EJTLxxv6PnjDR0+Pq/Dx+fz9WP58vnjDR2da+jo3IKOzi3o2HxDR04s6NjcgmaeP66jJxaK1/J9GmX/TDhNo62wLz4IRmpWvZapnlkjtUz1mjWS5ct6LdNI5mXrxXta+2RLX2uVZbZqWf6TubWtxfLMVpZZtbbyxdeK8sz5773WdrzMWnbsfNu2LMnFtop1F8exnG8rL2tfb70un3x/61iL72tbX/I6H5BY4/oJ9Isl/bRt+6CkX+mvOmkbqWX5UMzGM/sgKDPXaOrY3IKOzjd0Yr6pE42m5hpNnWgs6ERrOd/U3EKzeL1V3lzyemu9sRBqNJuaXwg1FppqNEPzC/kxj8wt5GULoflmvmwsNDXfjFPL+/ygSdmSDwoVHyiLZSc/AE7uf3JryceBO64u3f909llWt07v6HScpWVnds4lZ3HJeh/HLHPGH6dn+IZePq7PpA1/9buv1usvObeHs5y+fgK9U0tO+T/d9h5JeyTpJS95SR+nW39G65lG65nO0chqV2WJiNBCMw/2RjNfbzZDCxFqRqjZVL7ezLcXFpdaXG8vb4aWHGOhGYqi7ORxTh4zlL8ekf/BNYuVVnmzbT2K+ub7R7F/vp63JX9/qHW8tn3bjt9aV1Hf9uOfPH8+XLf4e1ryO2tbb3ulvXz577jX45Tt33plyb59HK99/5LV02rH6TjTLkSc4Ql66qKc4Zs2jtV6OcsZ6SfQD0p6cdv2DklPLN8pIvZK2itJU1NT67drN0Ts1tDMatcEQLt+plx8X9Iu25fYHpX0Hkm3DqZaAIAz1XMPPSIatv9E0jeUT1u8KSIeHFjNAABnpK956BHxNUlfG1BdAAB94C4XABgSBDoADAkCHQCGBIEOAEOCQAeAIeEzvaOqr5PZs5L+r8e3b5f09ACrkwLavD7Q5vWhnza/NCImq3Za0UDvh+3piJha7XqsJNq8PtDm9WEl2syQCwAMCQIdAIZESoG+d7UrsApo8/pAm9eHs97mZMbQAQDdpdRDBwB0kUSg295t+2HbB2xft9r1GRTbN9mesf1AW9m5tm+3/Uix3FaU2/bfFb+DH9p+3erVvDe2X2z7W7b3237Q9rVF+dC2WZJsj9v+nu0fFO3+y6L8Ett3F+3+t+JrqGV7rNg+ULy+czXr3yvbNdv32r6t2B7q9kqS7cdt32/7PtvTRdmK/X2v+UBvexj1b0p6laSrbL9qdWs1MJ+TtHtZ2XWS7oiIXZLuKLalvP27ip89kj67QnUcpIakD0XEKyVdJumPi/+Ww9xmSToh6YqI+GVJr5G02/Zlkj4h6VNFuw9JuqbY/xpJhyLi5ZI+VeyXomsl7W/bHvb2tvxGRLymbYriyv1954/aWrs/kt4g6Rtt29dLun616zXA9u2U9EDb9sOSLizWL5T0cLH+z5Ku6rRfqj+SvirpynXW5glJ9yh//u7TkupF+eLfufJnDLyhWK8X+3m1636G7dxRhNcVkm5T/sjKoW1vW7sfl7R9WdmK/X2v+R66Oj+M+uJVqstKuCAinpSkYnl+UT5Uv4fin9WvlXS31kGbi+GH+yTNSLpd0qOSno2IRrFLe9sW2128/pyk81a2xn37tKQPS2oW2+dpuNvbEpK+aXtf8TxlaQX/vvt6wMUKOa2HUa8DQ/N7sL1J0pclfTAiDnd5cvrQtDkiFiS9xvZWSf8h6ZWddiuWSbfb9jskzUTEPttvbhV32HUo2rvM5RHxhO3zJd1u+0dd9h14u1PooZ/Ww6iHyFO2L5SkYjlTlA/F78H2iPIwvzkivlIUD3Wb20XEs5LuUn4NYavtVqeqvW2L7S5eP0fSMytb075cLumdth+X9EXlwy6f1vC2d1FEPFEsZ5R/cL9eK/j3nUKgr7eHUd8q6epi/Wrl48yt8t8vroxfJum51j/jUuG8K36jpP0R8cm2l4a2zZJke7Lomcv2BklvVX6x8FuS3l3strzdrd/HuyXdGcUgawoi4vqI2BERO5X//3pnRLxXQ9reFtsbbW9urUt6m6QHtJJ/36t9EeE0LzS8XdL/Kh93/Ohq12eA7bpF0pOS5pV/Wl+jfOzwDkmPFMtzi32tfLbPo5LulzS12vXvob2/pvyflD+UdF/x8/ZhbnPRjl+SdG/R7gck/UVR/jJJ35N0QNK/SxoryseL7QPF6y9b7Tb00fY3S7ptPbS3aN8Pip8HW1m1kn/f3CkKAEMihSEXAMBpINABYEgQ6AAwJAh0ABgSBDoADAkCHQCGBIEOAEOCQAeAIfH/ry1sfMzof9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114d1d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epoch_list,loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After Training  7.980043411254883\n"
     ]
    }
   ],
   "source": [
    "print(\"After Training \", model(torch.tensor([[4]], dtype = torch.float)).item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Parameter containing:\n",
       " tensor([[ 1.9884]]), Parameter containing:\n",
       " tensor(1.00000e-02 *\n",
       "        [ 2.6272])]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(model.parameters())"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}