04.1 - Loading Data.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Lab 4.1: Load Data\r\n",
    "\r\n",
    "Edited By Steve Ive\r\n",
    "\r\n",
    "Reference from Seungjae Lee\r\n",
    "\r\n",
    "https://github.com/deeplearningzerotoall/PyTorch/blob/master/lab-04_2_load_data.ipynb"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Slicing 1D Array"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "nums = [0, 1, 2, 3, 4]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "source": [
    "print(nums)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0, 1, 2, 3, 4]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "contain index 2 before 4"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "source": [
    "print(nums[2:4])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[2, 3]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "bring all from index 2"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "source": [
    "print(nums[2:])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[2, 3, 4]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "bring all before index 2"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "source": [
    "print(nums[:2])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0, 1]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "bring all"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "source": [
    "print(nums[:])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0, 1, 2, 3, 4]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "bring all before the last index"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "print(nums[:-1])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can assign too!"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "nums[2:4] = [8, 9]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "source": [
    "print(nums)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0, 1, 8, 9, 4]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Slicing 2D Array"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "source": [
    "import numpy as np"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "b = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "source": [
    "print(b)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "source": [
    "b[:, 1]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 2,  6, 10])"
      ]
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "source": [
    "b[-1]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 9, 10, 11, 12])"
      ]
     },
     "metadata": {},
     "execution_count": 47
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "source": [
    "b[-1, :]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 9, 10, 11, 12])"
      ]
     },
     "metadata": {},
     "execution_count": 48
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "source": [
    "b[-1, ...]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([ 9, 10, 11, 12])"
      ]
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "source": [
    "b[0:2, :]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8]])"
      ]
     },
     "metadata": {},
     "execution_count": 50
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Loading Data from .csv file"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "source": [
    "import numpy as np"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "source": [
    "xy = np.loadtxt('data-01-test-score.csv', delimiter=',', dtype=np.float32)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Take a Moment!\n",
    "\n",
    "```y_data = xy[:, [-1]]```\n",
    "\n",
    "brings the all rows of last column with [] array wrapped"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "source": [
    "x_data = xy[:, 0: -1]\r\n",
    "y_data = xy[:, [-1]]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "source": [
    "print(x_data.shape)\r\n",
    "print(len(x_data))\r\n",
    "print(x_data[:5])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(25, 3)\n",
      "25\n",
      "[[ 73.  80.  75.]\n",
      " [ 93.  88.  93.]\n",
      " [ 89.  91.  90.]\n",
      " [ 96.  98. 100.]\n",
      " [ 73.  66.  70.]]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "source": [
    "print(y_data.shape)\r\n",
    "print(len(y_data))\r\n",
    "print(y_data[:5])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(25, 1)\n",
      "25\n",
      "[[152.]\n",
      " [185.]\n",
      " [180.]\n",
      " [196.]\n",
      " [142.]]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Imports"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "source": [
    "import torch\r\n",
    "import torch.nn as nn\r\n",
    "import torch.nn.functional as F\r\n",
    "import torch.optim as optim"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "source": [
    "torch.manual_seed(1)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x2055c8eb8f0>"
      ]
     },
     "metadata": {},
     "execution_count": 57
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Low-level Implementation"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "source": [
    "#Data\r\n",
    "x_train = torch.FloatTensor(x_data)\r\n",
    "y_train = torch.FloatTensor(y_data)\r\n",
    "\r\n",
    "#Model Initialize\r\n",
    "W = torch.zeros((3, 1), requires_grad=True)\r\n",
    "b = torch.zeros(1, requires_grad=True)\r\n",
    "\r\n",
    "#Set optimizer\r\n",
    "optimizer = optim.SGD([W, b], lr=1e-5)\r\n",
    "\r\n",
    "nb_epochs = 20\r\n",
    "\r\n",
    "for epoch in range(nb_epochs + 1):\r\n",
    "\r\n",
    "    #Hypothesis\r\n",
    "    pred = x_train.matmul(W) + b\r\n",
    "\r\n",
    "    #Cost\r\n",
    "    cost = torch.mean((pred - y_train)**2)\r\n",
    "\r\n",
    "    #Reduce cost\r\n",
    "    optimizer.zero_grad()\r\n",
    "    cost.backward()\r\n",
    "    optimizer.step()\r\n",
    "\r\n",
    "    print('Epoch {:4d}/{} Hypotheis: {} Cost: {:.6f}'.format(epoch, nb_epochs, pred.squeeze().detach(), cost.item()))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch    0/20 Hypotheis: tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0.]) Cost: 26811.960938\n",
      "Epoch    1/20 Hypotheis: tensor([60.3300, 72.5121, 71.4468, 77.8114, 55.3021, 40.7728, 58.2450, 43.1799,\n",
      "        67.7685, 62.7711, 56.1159, 55.3320, 73.8140, 61.3605, 58.5129, 73.5830,\n",
      "        58.4375, 69.8998, 70.3709, 62.9651, 68.3015, 68.0264, 65.1199, 60.8261,\n",
      "        75.1500]) Cost: 9920.530273\n",
      "Epoch    2/20 Hypotheis: tensor([ 97.0136, 116.6032, 114.8901, 125.1249,  88.9286,  65.5651,  93.6612,\n",
      "         69.4359, 108.9755, 100.9401,  90.2373,  88.9771, 118.6964,  98.6703,\n",
      "         94.0921, 118.3256,  93.9699, 112.4028, 113.1596, 101.2509, 109.8326,\n",
      "        109.3903, 104.7163,  97.8108, 120.8450]) Cost: 3675.298828\n",
      "Epoch    3/20 Hypotheis: tensor([119.3189, 143.4130, 141.3056, 153.8940, 109.3752,  80.6404, 115.1964,\n",
      "         85.4014, 134.0320, 124.1496, 110.9851, 109.4354, 145.9869, 121.3560,\n",
      "        115.7265, 145.5315, 115.5747, 138.2472, 139.1770, 124.5302, 135.0859,\n",
      "        134.5418, 128.7932, 120.2987, 148.6299]) Cost: 1366.260986\n",
      "Epoch    4/20 Hypotheis: tensor([132.8815, 159.7147, 157.3676, 171.3871, 121.8078,  89.8073, 128.2911,\n",
      "         95.1098, 149.2679, 138.2630, 123.6010, 121.8754, 162.5806, 135.1496,\n",
      "        128.8816, 162.0744, 128.7110, 153.9623, 154.9965, 138.6849, 150.4415,\n",
      "        149.8355, 143.4333, 133.9718, 165.5244]) Cost: 512.542297\n",
      "Epoch    5/20 Hypotheis: tensor([141.1279, 169.6271, 167.1339, 182.0239, 129.3674,  95.3815, 136.2537,\n",
      "        101.0134, 158.5325, 146.8454, 131.2722, 129.4399, 172.6700, 143.5362,\n",
      "        136.8809, 172.1335, 136.6978, 163.5182, 164.6151, 147.2913, 159.7788,\n",
      "        159.1350, 152.3354, 142.2851, 175.7970]) Cost: 196.896500\n",
      "Epoch    6/20 Hypotheis: tensor([146.1420, 175.6543, 173.0722, 188.4916, 133.9639,  98.7712, 141.0955,\n",
      "        104.6036, 164.1662, 152.0648, 135.9367, 134.0399, 178.8046, 148.6351,\n",
      "        141.7451, 178.2501, 141.5535, 169.3291, 170.4633, 152.5240, 165.4566,\n",
      "        164.7898, 157.7485, 147.3392, 182.0432]) Cost: 80.190903\n",
      "Epoch    7/20 Hypotheis: tensor([149.1906, 179.3192, 176.6828, 192.4244, 136.7588, 100.8327, 144.0398,\n",
      "        106.7870, 167.5922, 155.2392, 138.7732, 136.8372, 182.5343, 151.7350,\n",
      "        144.7032, 181.9696, 144.5053, 172.8627, 174.0189, 155.7055, 168.9092,\n",
      "        168.2285, 161.0401, 150.4117, 185.8411]) Cost: 37.038647\n",
      "Epoch    8/20 Hypotheis: tensor([151.0440, 181.5477, 178.8781, 194.8158, 138.4581, 102.0864, 145.8304,\n",
      "        108.1151, 169.6756, 157.1702, 140.4980, 138.5385, 184.8019, 153.6193,\n",
      "        146.5022, 184.2314, 146.2995, 175.0116, 176.1805, 157.6395, 171.0088,\n",
      "        170.3195, 163.0418, 152.2793, 188.1503]) Cost: 21.081354\n",
      "Epoch    9/20 Hypotheis: tensor([152.1707, 182.9027, 180.2128, 196.2698, 139.4913, 102.8490, 146.9194,\n",
      "        108.9232, 170.9428, 158.3452, 141.5469, 139.5733, 186.1803, 154.7645,\n",
      "        147.5963, 185.6068, 147.3897, 176.3186, 177.4944, 158.8152, 172.2858,\n",
      "        171.5912, 164.2590, 153.4141, 189.5543]) Cost: 15.178741\n",
      "Epoch   10/20 Hypotheis: tensor([152.8556, 183.7267, 181.0242, 197.1540, 140.1195, 103.3131, 147.5817,\n",
      "        109.4149, 171.7137, 159.0604, 142.1848, 140.2028, 187.0181, 155.4602,\n",
      "        148.2619, 186.4433, 148.0520, 177.1137, 178.2929, 159.5296, 173.0624,\n",
      "        172.3646, 164.9993, 154.1034, 190.4078]) Cost: 12.993667\n",
      "Epoch   11/20 Hypotheis: tensor([153.2718, 184.2277, 181.5174, 197.6917, 140.5014, 103.5955, 147.9847,\n",
      "        109.7144, 172.1828, 159.4961, 142.5727, 140.5859, 187.5271, 155.8827,\n",
      "        148.6669, 186.9522, 148.4540, 177.5975, 178.7780, 159.9637, 173.5349,\n",
      "        172.8351, 165.4496, 154.5218, 190.9267]) Cost: 12.183028\n",
      "Epoch   12/20 Hypotheis: tensor([153.5246, 184.5324, 181.8171, 198.0186, 140.7336, 103.7675, 148.2300,\n",
      "        109.8970, 172.4683, 159.7618, 142.8088, 140.8192, 187.8363, 156.1390,\n",
      "        148.9135, 187.2618, 148.6977, 177.8920, 179.0725, 160.2272, 173.8225,\n",
      "        173.1214, 165.7235, 154.7755, 191.2421]) Cost: 11.880545\n",
      "Epoch   13/20 Hypotheis: tensor([153.6781, 184.7177, 181.9992, 198.2175, 140.8746, 103.8724, 148.3794,\n",
      "        110.0084, 172.6423, 159.9241, 142.9524, 140.9613, 188.0239, 156.2943,\n",
      "        149.0637, 187.4502, 148.8453, 178.0714, 179.2512, 160.3871, 173.9976,\n",
      "        173.2956, 165.8902, 154.9291, 191.4338]) Cost: 11.765953\n",
      "Epoch   14/20 Hypotheis: tensor([153.7712, 184.8304, 182.1098, 198.3384, 140.9604, 103.9365, 148.4704,\n",
      "        110.0766, 172.7484, 160.0236, 143.0398, 141.0481, 188.1376, 156.3882,\n",
      "        149.1554, 187.5650, 148.9343, 178.1808, 179.3594, 160.4839, 174.1043,\n",
      "        173.4018, 165.9918, 155.0218, 191.5502]) Cost: 11.720860\n",
      "Epoch   15/20 Hypotheis: tensor([153.8275, 184.8989, 182.1769, 198.4120, 141.0124, 103.9758, 148.5260,\n",
      "        110.1186, 172.8133, 160.0849, 143.0931, 141.1012, 188.2064, 156.4447,\n",
      "        149.2114, 187.6349, 148.9877, 178.2476, 179.4248, 160.5424, 174.1694,\n",
      "        173.4665, 166.0536, 155.0774, 191.6208]) Cost: 11.701425\n",
      "Epoch   16/20 Hypotheis: tensor([153.8615, 184.9406, 182.2175, 198.4567, 141.0440, 103.9999, 148.5600,\n",
      "        110.1445, 172.8530, 160.1229, 143.1256, 141.1338, 188.2479, 156.4785,\n",
      "        149.2458, 187.6777, 149.0195, 178.2886, 179.4641, 160.5776, 174.2093,\n",
      "        173.5061, 166.0914, 155.1105, 191.6637]) Cost: 11.691506\n",
      "Epoch   17/20 Hypotheis: tensor([153.8820, 184.9660, 182.2421, 198.4840, 141.0631, 104.0149, 148.5810,\n",
      "        110.1608, 172.8776, 160.1469, 143.1455, 141.1539, 188.2727, 156.4985,\n",
      "        149.2670, 187.7038, 149.0382, 178.3138, 179.4876, 160.5986, 174.2337,\n",
      "        173.5303, 166.1145, 155.1299, 191.6896]) Cost: 11.685120\n",
      "Epoch   18/20 Hypotheis: tensor([153.8941, 184.9814, 182.2568, 198.5006, 141.0747, 104.0243, 148.5939,\n",
      "        110.1711, 172.8928, 160.1622, 143.1577, 141.1665, 188.2875, 156.5100,\n",
      "        149.2802, 187.7199, 149.0488, 178.3295, 179.5014, 160.6110, 174.2489,\n",
      "        173.5453, 166.1287, 155.1410, 191.7052]) Cost: 11.680006\n",
      "Epoch   19/20 Hypotheis: tensor([153.9013, 184.9908, 182.2656, 198.5107, 141.0816, 104.0303, 148.6020,\n",
      "        110.1778, 172.9024, 160.1723, 143.1652, 141.1745, 188.2961, 156.5165,\n",
      "        149.2885, 187.7299, 149.0546, 178.3394, 179.5094, 160.6182, 174.2583,\n",
      "        173.5546, 166.1375, 155.1470, 191.7146]) Cost: 11.675381\n",
      "Epoch   20/20 Hypotheis: tensor([153.9054, 184.9966, 182.2708, 198.5169, 141.0858, 104.0343, 148.6072,\n",
      "        110.1824, 172.9086, 160.1792, 143.1699, 141.1797, 188.3009, 156.5199,\n",
      "        149.2939, 187.7361, 149.0575, 178.3457, 179.5139, 160.6222, 174.2643,\n",
      "        173.5604, 166.1430, 155.1500, 191.7202]) Cost: 11.670943\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## High-level Implementaion with ```nn.Module```"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "source": [
    "class MultivariateLinearRegressionModel(nn.Module):\r\n",
    "    def __init__(self):\r\n",
    "        super().__init__()\r\n",
    "        self.linear = nn.Linear(3, 1)\r\n",
    "\r\n",
    "    def forward(self, x):\r\n",
    "        return self.linear(x)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "source": [
    "x_train = torch.FloatTensor(x_data)\r\n",
    "y_train = torch.FloatTensor(y_data)\r\n",
    "\r\n",
    "model = MultivariateLinearRegressionModel()\r\n",
    "\r\n",
    "optimizer = optim.SGD(model.parameters(), lr=1e-5)\r\n",
    "\r\n",
    "nb_epochs = 20\r\n",
    "\r\n",
    "for epoch in range(nb_epochs + 1):\r\n",
    "\r\n",
    "    #Hypothesis\r\n",
    "    pred = model(x_train)\r\n",
    "\r\n",
    "    #cost\r\n",
    "    cost = F.mse_loss(pred, y_train)\r\n",
    "\r\n",
    "    #Reduce Cost\r\n",
    "    optimizer.zero_grad()\r\n",
    "    cost.backward()\r\n",
    "    optimizer.step()\r\n",
    "\r\n",
    "    print('Epoch {:4d}/{} Hypothesis: {} Cost: {:.6f}'.format(epoch, nb_epochs, pred.squeeze().detach(), cost.item()))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch    0/20 Hypothesis: tensor([-6.7933, -4.8968, -6.5155, -7.3361, -2.6660, -1.8403, -6.6781, -6.7331,\n",
      "        -4.0525, -3.9151, -5.2111, -3.7514, -6.4568, -4.7845, -6.2377, -5.4874,\n",
      "        -3.2482, -8.9763, -6.6201, -6.2942, -7.3238, -5.0026, -7.1896, -6.2176,\n",
      "        -5.5024]) Cost: 28693.490234\n",
      "Epoch    1/20 Hypothesis: tensor([55.6147, 70.1117, 67.3916, 73.1548, 54.5398, 40.3360, 53.5729, 37.9342,\n",
      "        66.0489, 61.0169, 52.8371, 53.4856, 69.8990, 58.6889, 54.2903, 70.6290,\n",
      "        57.2013, 63.3310, 66.1742, 58.8394, 63.3299, 65.3660, 60.1730, 56.7034,\n",
      "        72.2351]) Cost: 10618.750000\n",
      "Epoch    2/20 Hypothesis: tensor([ 93.5619, 115.7207, 112.3309, 122.0975,  89.3237,  65.9814,  90.2090,\n",
      "         65.0951, 108.6743, 100.4994,  88.1336,  88.2888, 116.3270,  97.2834,\n",
      "         91.0948, 116.9119,  93.9569, 107.2983, 110.4365,  98.4438, 106.2914,\n",
      "        108.1538, 101.1332,  94.9621, 119.5033]) Cost: 3936.015381\n",
      "Epoch    3/20 Hypothesis: tensor([116.6357, 143.4532, 139.6562, 151.8574, 110.4738,  81.5753, 112.4861,\n",
      "         81.6110, 134.5929, 124.5075, 109.5959, 109.4512, 144.5573, 120.7503,\n",
      "        113.4743, 145.0544, 116.3054, 134.0332, 137.3500, 122.5251, 132.4146,\n",
      "        134.1711, 126.0395, 118.2248, 148.2446]) Cost: 1465.219360\n",
      "Epoch    4/20 Hypothesis: tensor([130.6657, 160.3159, 156.2713, 169.9530, 123.3339,  91.0573, 126.0321,\n",
      "         91.6543, 150.3529, 139.1062, 122.6462, 122.3192, 161.7224, 135.0189,\n",
      "        127.0826, 162.1665, 129.8936, 150.2901, 153.7145, 137.1674, 148.2992,\n",
      "        149.9911, 141.1841, 132.3692, 165.7206]) Cost: 551.693909\n",
      "Epoch    5/20 Hypothesis: tensor([139.1967, 170.5691, 166.3740, 180.9561, 131.1532,  96.8230, 134.2691,\n",
      "         97.7619, 159.9359, 147.9835, 130.5816, 130.1438, 172.1594, 143.6943,\n",
      "        135.3576, 172.5717, 138.1552, 160.1758, 163.6646, 146.0705, 157.9583,\n",
      "        159.6105, 150.3931, 140.9692, 176.3467]) Cost: 213.934662\n",
      "Epoch    6/20 Hypothesis: tensor([144.3838, 176.8035, 172.5169, 187.6467, 135.9075, 100.3289, 139.2780,\n",
      "        101.4764, 165.7629, 153.3819, 135.4068, 134.9018, 178.5053, 148.9688,\n",
      "        140.3896, 178.8986, 143.1777, 166.1873, 169.7144, 151.4838, 163.8318,\n",
      "        165.4597, 155.9930, 146.1978, 182.8077]) Cost: 89.052254\n",
      "Epoch    7/20 Hypothesis: tensor([147.5378, 180.5942, 176.2519, 191.7150, 138.7980, 102.4608, 142.3241,\n",
      "        103.7357, 169.3062, 156.6650, 138.3410, 137.7950, 182.3636, 152.1754,\n",
      "        143.4497, 182.7458, 146.2309, 169.8433, 173.3927, 154.7751, 167.4036,\n",
      "        169.0164, 159.3982, 149.3766, 186.7361]) Cost: 42.876022\n",
      "Epoch    8/20 Hypothesis: tensor([149.4556, 182.8990, 178.5229, 194.1888, 140.5553, 103.7572, 144.1767,\n",
      "        105.1102, 171.4608, 158.6619, 140.1253, 139.5545, 184.7093, 154.1246,\n",
      "        145.3108, 185.0851, 148.0866, 172.0669, 175.6289, 156.7761, 169.5758,\n",
      "        171.1792, 161.4691, 151.3089, 189.1245]) Cost: 25.799620\n",
      "Epoch    9/20 Hypothesis: tensor([150.6216, 184.3003, 179.9037, 195.6931, 141.6236, 104.5455, 145.3035,\n",
      "        105.9468, 172.7711, 159.8766, 141.2103, 140.6245, 186.1352, 155.3092,\n",
      "        146.4428, 186.5076, 149.2141, 173.4196, 176.9883, 157.9925, 170.8970,\n",
      "        172.4943, 162.7286, 152.4832, 190.5766]) Cost: 19.482409\n",
      "Epoch   10/20 Hypothesis: tensor([151.3305, 185.1522, 180.7431, 196.6078, 142.2728, 105.0250, 145.9890,\n",
      "        106.4561, 173.5679, 160.6158, 141.8702, 141.2753, 187.0020, 156.0289,\n",
      "        147.1316, 187.3727, 149.8989, 174.2426, 177.8145, 158.7319, 171.7007,\n",
      "        173.2940, 163.4948, 153.1967, 191.4593]) Cost: 17.143106\n",
      "Epoch   11/20 Hypothesis: tensor([151.7615, 185.6701, 181.2534, 197.1641, 142.6673, 105.3166, 146.4063,\n",
      "        106.7666, 174.0525, 161.0658, 142.2716, 141.6713, 187.5286, 156.4660,\n",
      "        147.5508, 187.8987, 150.3144, 174.7437, 178.3166, 159.1812, 172.1898,\n",
      "        173.7804, 163.9609, 153.6300, 191.9958]) Cost: 16.274515\n",
      "Epoch   12/20 Hypothesis: tensor([152.0235, 185.9849, 181.5636, 197.5024, 142.9069, 105.4941, 146.6604,\n",
      "        106.9561, 174.3473, 161.3400, 142.5158, 141.9122, 187.8485, 156.7312,\n",
      "        147.8060, 188.2186, 150.5663, 175.0490, 178.6215, 159.4542, 172.4875,\n",
      "        174.0762, 164.2446, 153.8929, 192.3217]) Cost: 15.949698\n",
      "Epoch   13/20 Hypothesis: tensor([152.1828, 186.1761, 181.7521, 197.7082, 143.0523, 105.6021, 146.8152,\n",
      "        107.0721, 174.5266, 161.5073, 142.6644, 142.0589, 188.0427, 156.8918,\n",
      "        147.9616, 188.4132, 150.7186, 175.2352, 178.8065, 159.6198, 172.6689,\n",
      "        174.2561, 164.4174, 154.0522, 192.5197]) Cost: 15.825971\n",
      "Epoch   14/20 Hypothesis: tensor([152.2795, 186.2923, 181.8665, 197.8334, 143.1404, 105.6678, 146.9098,\n",
      "        107.1433, 174.6358, 161.6096, 142.7549, 142.1483, 188.1604, 156.9889,\n",
      "        148.0567, 188.5316, 150.8104, 175.3491, 178.9187, 159.7203, 172.7795,\n",
      "        174.3657, 164.5228, 154.1485, 192.6399]) Cost: 15.776586\n",
      "Epoch   15/20 Hypothesis: tensor([152.3383, 186.3628, 181.9360, 197.9096, 143.1937, 105.7079, 146.9676,\n",
      "        107.1874, 174.7023, 161.6723, 142.8101, 142.2028, 188.2317, 157.0474,\n",
      "        148.1148, 188.6036, 150.8654, 175.4189, 178.9865, 159.7812, 172.8472,\n",
      "        174.4323, 164.5872, 154.2065, 192.7127]) Cost: 15.754648\n",
      "Epoch   16/20 Hypothesis: tensor([152.3739, 186.4055, 181.9781, 197.9560, 143.2258, 105.7324, 147.0032,\n",
      "        107.2149, 174.7428, 161.7110, 142.8438, 142.2361, 188.2747, 157.0823,\n",
      "        148.1506, 188.6475, 150.8980, 175.4619, 179.0275, 159.8179, 172.8887,\n",
      "        174.4729, 164.6266, 154.2411, 192.7567]) Cost: 15.742906\n",
      "Epoch   17/20 Hypothesis: tensor([152.3956, 186.4313, 182.0036, 197.9843, 143.2451, 105.7474, 147.0252,\n",
      "        107.2324, 174.7675, 161.7351, 142.8644, 142.2566, 188.3004, 157.1030,\n",
      "        148.1727, 188.6743, 150.9170, 175.4887, 179.0520, 159.8399, 172.9143,\n",
      "        174.4977, 164.6508, 154.2617, 192.7833]) Cost: 15.734898\n",
      "Epoch   18/20 Hypothesis: tensor([152.4086, 186.4469, 182.0190, 198.0016, 143.2565, 105.7566, 147.0389,\n",
      "        107.2437, 174.7827, 161.7503, 142.8770, 142.2692, 188.3158, 157.1150,\n",
      "        148.1866, 188.6906, 150.9277, 175.5055, 179.0666, 159.8531, 172.9302,\n",
      "        174.5128, 164.6659, 154.2736, 192.7992]) Cost: 15.728318\n",
      "Epoch   19/20 Hypothesis: tensor([152.4165, 186.4562, 182.0282, 198.0122, 143.2631, 105.7623, 147.0477,\n",
      "        107.2514, 174.7921, 161.7601, 142.8849, 142.2771, 188.3248, 157.1217,\n",
      "        148.1954, 188.7006, 150.9334, 175.5164, 179.0751, 159.8608, 172.9402,\n",
      "        174.5221, 164.6753, 154.2803, 192.8087]) Cost: 15.722224\n",
      "Epoch   20/20 Hypothesis: tensor([152.4212, 186.4618, 182.0337, 198.0186, 143.2669, 105.7658, 147.0534,\n",
      "        107.2567, 174.7978, 161.7666, 142.8898, 142.2821, 188.3299, 157.1252,\n",
      "        148.2011, 188.7067, 150.9361, 175.5236, 179.0799, 159.8652, 172.9467,\n",
      "        174.5278, 164.6814, 154.2838, 192.8142]) Cost: 15.716328\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Dataset and DataLoader\n",
    "\n",
    "Let's create our custom dataset with ```Dataset``` from ```torch.utils.data```.\n",
    "\n",
    "We can create custom dataset class by inheritiing ```Dataset```.\n",
    "\n",
    "We should implement 2 magic method:\n",
    "\n",
    "- ```__len__()```: return the total length of dataset\n",
    "\n",
    "- ```__getitem()__```: return the data of corresponding index with converting torch.Tensor()."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "source": [
    "from torch.utils.data import Dataset\r\n",
    "\r\n",
    "class CustomDataset(Dataset):\r\n",
    "    def __init__(self, x_data, y_data):\r\n",
    "        super().__init__()\r\n",
    "        self.x_data = x_data\r\n",
    "        self.y_data = y_data\r\n",
    "\r\n",
    "    def __len__(self):\r\n",
    "        return len(self.x_data)\r\n",
    "\r\n",
    "    def __getitem__(self, idx):\r\n",
    "        x = torch.FloatTensor(self.x_data[idx])\r\n",
    "        y = torch.FloatTensor(self.y_data[idx])\r\n",
    "\r\n",
    "        return x, y\r\n",
    "\r\n",
    "dataset = CustomDataset(xy[:, :-1], xy[:, [-1]])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "- ```batch_size```: the size of the each minibatch. Conventionally, we set the number of squares of 2 (16, 32, 64, 128, 256, 512 ...)\n",
    "\n",
    "- ```shuffle=True```: Mix the dataset for every epochs to change the sequence of data."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "source": [
    "from torch.utils.data import DataLoader\r\n",
    "\r\n",
    "dataloader = DataLoader(\r\n",
    "    dataset,\r\n",
    "    batch_size = 2,\r\n",
    "    shuffle = True,\r\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "source": [
    "nb_epochs = 20\r\n",
    "\r\n",
    "for epoch in range(nb_epochs+1):\r\n",
    "    for batch_idx, samples in enumerate(dataloader):\r\n",
    "        x_train, y_train = samples\r\n",
    "\r\n",
    "        #Hypothesis\r\n",
    "        pred = model(x_train)\r\n",
    "\r\n",
    "        #cost Function\r\n",
    "        cost = F.mse_loss(pred, y_train)\r\n",
    "\r\n",
    "        #Reduce cost\r\n",
    "        optimizer.zero_grad()\r\n",
    "        cost.backward()\r\n",
    "        optimizer.step()\r\n",
    "        \r\n",
    "        print()\r\n",
    "        print('Epoch {:3d}/{} Batch {:3d}/{} \\n y: {} Hypothesis: {} \\n Cost: {:.6f}'.format(epoch, nb_epochs, batch_idx + 1, len(dataloader), y_train.squeeze(), pred.squeeze().detach(), cost.item()))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch   1/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.5893, 148.0238]) \n",
      " Cost: 44.757126\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch   2/13 \n",
      " y: tensor([141., 196.]) Hypothesis: tensor([144.7430, 201.2185]) \n",
      " Cost: 20.621504\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch   3/13 \n",
      " y: tensor([164., 185.]) Hypothesis: tensor([163.1870, 187.0544]) \n",
      " Cost: 2.440781\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch   4/13 \n",
      " y: tensor([115., 142.]) Hypothesis: tensor([108.5007, 143.2139]) \n",
      " Cost: 21.856876\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch   5/13 \n",
      " y: tensor([177., 184.]) Hypothesis: tensor([174.5395, 189.0653]) \n",
      " Cost: 15.855763\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch   6/13 \n",
      " y: tensor([175., 183.]) Hypothesis: tensor([175.0678, 176.7762]) \n",
      " Cost: 19.369955\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch   7/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([150.3080, 190.7705]) \n",
      " Cost: 3.419217\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch   8/13 \n",
      " y: tensor([152., 141.]) Hypothesis: tensor([157.8540, 144.4617]) \n",
      " Cost: 23.126225\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch   9/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([178.4932, 159.3994]) \n",
      " Cost: 1.194637\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch  10/13 \n",
      " y: tensor([101., 152.]) Hypothesis: tensor([105.5856, 151.8581]) \n",
      " Cost: 10.523733\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch  11/13 \n",
      " y: tensor([147., 180.]) Hypothesis: tensor([148.8653, 180.7034]) \n",
      " Cost: 1.987028\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch  12/13 \n",
      " y: tensor([149., 192.]) Hypothesis: tensor([152.0673, 190.7072]) \n",
      " Cost: 5.539771\n",
      "torch.float32\n",
      "\n",
      "Epoch   0/20 Batch  13/13 \n",
      " y: 175.0 Hypothesis: 173.0469512939453 \n",
      " Cost: 3.814399\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch   1/13 \n",
      " y: tensor([148., 164.]) Hypothesis: tensor([147.8298, 161.5555]) \n",
      " Cost: 3.002350\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch   2/13 \n",
      " y: tensor([149., 101.]) Hypothesis: tensor([153.0398, 105.5912]) \n",
      " Cost: 18.699759\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch   3/13 \n",
      " y: tensor([196., 147.]) Hypothesis: tensor([195.9116, 148.1528]) \n",
      " Cost: 0.668435\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch   4/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([140.8152, 141.3220]) \n",
      " Cost: 0.068914\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch   5/13 \n",
      " y: tensor([192., 159.]) Hypothesis: tensor([190.1254, 157.5042]) \n",
      " Cost: 2.875795\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch   6/13 \n",
      " y: tensor([152., 115.]) Hypothesis: tensor([155.0734, 107.3747]) \n",
      " Cost: 33.795311\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch   7/13 \n",
      " y: tensor([180., 149.]) Hypothesis: tensor([180.8835, 146.7724]) \n",
      " Cost: 2.871427\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch   8/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([172.7228, 174.2418]) \n",
      " Cost: 9.434676\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch   9/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([188.2878, 152.6751]) \n",
      " Cost: 9.420660\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch  10/13 \n",
      " y: tensor([183., 175.]) Hypothesis: tensor([175.5610, 164.3506]) \n",
      " Cost: 84.374390\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch  11/13 \n",
      " y: tensor([142., 175.]) Hypothesis: tensor([145.4425, 177.8037]) \n",
      " Cost: 9.855912\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch  12/13 \n",
      " y: tensor([177., 192.]) Hypothesis: tensor([180.5955, 190.8865]) \n",
      " Cost: 7.083564\n",
      "torch.float32\n",
      "\n",
      "Epoch   1/20 Batch  13/13 \n",
      " y: 185.0 Hypothesis: 187.7429656982422 \n",
      " Cost: 7.523861\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch   1/13 \n",
      " y: tensor([115., 149.]) Hypothesis: tensor([108.3347, 153.5833]) \n",
      " Cost: 32.716087\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch   2/13 \n",
      " y: tensor([101., 142.]) Hypothesis: tensor([105.9786, 142.9616]) \n",
      " Cost: 12.855739\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch   3/13 \n",
      " y: tensor([185., 183.]) Hypothesis: tensor([185.5141, 175.6308]) \n",
      " Cost: 27.284401\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch   4/13 \n",
      " y: tensor([175., 192.]) Hypothesis: tensor([165.8922, 189.7009]) \n",
      " Cost: 44.119362\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch   5/13 \n",
      " y: tensor([164., 177.]) Hypothesis: tensor([165.5939, 176.7755]) \n",
      " Cost: 1.295415\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch   6/13 \n",
      " y: tensor([177., 152.]) Hypothesis: tensor([181.7293, 155.0195]) \n",
      " Cost: 15.741822\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch   7/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([183.2944, 199.6823]) \n",
      " Cost: 12.206312\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch   8/13 \n",
      " y: tensor([175., 147.]) Hypothesis: tensor([174.3104, 149.4965]) \n",
      " Cost: 3.353972\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch   9/13 \n",
      " y: tensor([159., 148.]) Hypothesis: tensor([158.8897, 148.2016]) \n",
      " Cost: 0.026391\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch  10/13 \n",
      " y: tensor([192., 184.]) Hypothesis: tensor([191.7575, 187.1326]) \n",
      " Cost: 4.935945\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch  11/13 \n",
      " y: tensor([152., 175.]) Hypothesis: tensor([155.0835, 173.6088]) \n",
      " Cost: 5.721862\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch  12/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([141.7123, 141.1987]) \n",
      " Cost: 0.273423\n",
      "torch.float32\n",
      "\n",
      "Epoch   2/20 Batch  13/13 \n",
      " y: 149.0 Hypothesis: 146.02586364746094 \n",
      " Cost: 8.845487\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch   1/13 \n",
      " y: tensor([184., 175.]) Hypothesis: tensor([187.0864, 174.2568]) \n",
      " Cost: 5.039168\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch   2/13 \n",
      " y: tensor([147., 177.]) Hypothesis: tensor([148.6713, 177.2646]) \n",
      " Cost: 1.431668\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch   3/13 \n",
      " y: tensor([149., 180.]) Hypothesis: tensor([146.1739, 180.1228]) \n",
      " Cost: 4.000906\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch   4/13 \n",
      " y: tensor([192., 141.]) Hypothesis: tensor([187.5551, 142.1341]) \n",
      " Cost: 10.521824\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch   5/13 \n",
      " y: tensor([141., 192.]) Hypothesis: tensor([142.3096, 192.1604]) \n",
      " Cost: 0.870364\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch   6/13 \n",
      " y: tensor([152., 177.]) Hypothesis: tensor([151.8199, 172.8884]) \n",
      " Cost: 8.468917\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch   7/13 \n",
      " y: tensor([115., 183.]) Hypothesis: tensor([108.5378, 176.7579]) \n",
      " Cost: 40.361816\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch   8/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([177.3289, 165.0425]) \n",
      " Cost: 3.255448\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch   9/13 \n",
      " y: tensor([196., 148.]) Hypothesis: tensor([200.3729, 150.4567]) \n",
      " Cost: 12.578987\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch  10/13 \n",
      " y: tensor([159., 142.]) Hypothesis: tensor([159.8537, 143.1768]) \n",
      " Cost: 1.056878\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch  11/13 \n",
      " y: tensor([149., 101.]) Hypothesis: tensor([153.4971, 105.9142]) \n",
      " Cost: 22.186413\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch  12/13 \n",
      " y: tensor([175., 152.]) Hypothesis: tensor([163.6432, 154.9206]) \n",
      " Cost: 68.753654\n",
      "torch.float32\n",
      "\n",
      "Epoch   3/20 Batch  13/13 \n",
      " y: 185.0 Hypothesis: 186.5769805908203 \n",
      " Cost: 2.486868\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch   1/13 \n",
      " y: tensor([192., 115.]) Hypothesis: tensor([191.9885, 108.0312]) \n",
      " Cost: 24.282070\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch   2/13 \n",
      " y: tensor([141., 175.]) Hypothesis: tensor([142.9804, 175.1671]) \n",
      " Cost: 1.974963\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch   3/13 \n",
      " y: tensor([175., 184.]) Hypothesis: tensor([175.0910, 187.9929]) \n",
      " Cost: 7.975843\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch   4/13 \n",
      " y: tensor([147., 101.]) Hypothesis: tensor([149.0293, 105.4037]) \n",
      " Cost: 11.755579\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch   5/13 \n",
      " y: tensor([152., 183.]) Hypothesis: tensor([150.7337, 174.5421]) \n",
      " Cost: 36.569889\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch   6/13 \n",
      " y: tensor([148., 177.]) Hypothesis: tensor([149.0846, 173.8361]) \n",
      " Cost: 5.593336\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch   7/13 \n",
      " y: tensor([180., 175.]) Hypothesis: tensor([182.7567, 165.9258]) \n",
      " Cost: 44.970570\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch   8/13 \n",
      " y: tensor([149., 192.]) Hypothesis: tensor([149.4315, 191.0981]) \n",
      " Cost: 0.499789\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch   9/13 \n",
      " y: tensor([177., 141.]) Hypothesis: tensor([180.9225, 144.9543]) \n",
      " Cost: 15.511189\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch  10/13 \n",
      " y: tensor([164., 196.]) Hypothesis: tensor([163.1631, 198.8724]) \n",
      " Cost: 4.475545\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch  11/13 \n",
      " y: tensor([142., 185.]) Hypothesis: tensor([142.9201, 186.3627]) \n",
      " Cost: 1.351747\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch  12/13 \n",
      " y: tensor([149., 159.]) Hypothesis: tensor([153.1313, 159.1242]) \n",
      " Cost: 8.541348\n",
      "torch.float32\n",
      "\n",
      "Epoch   4/20 Batch  13/13 \n",
      " y: 152.0 Hypothesis: 155.17263793945312 \n",
      " Cost: 10.065632\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch   1/13 \n",
      " y: tensor([175., 115.]) Hypothesis: tensor([172.4785, 106.7570]) \n",
      " Cost: 37.153030\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch   2/13 \n",
      " y: tensor([152., 177.]) Hypothesis: tensor([151.6366, 177.7647]) \n",
      " Cost: 0.358456\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch   3/13 \n",
      " y: tensor([185., 148.]) Hypothesis: tensor([185.3152, 148.0219]) \n",
      " Cost: 0.049921\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch   4/13 \n",
      " y: tensor([101., 152.]) Hypothesis: tensor([105.3297, 155.4033]) \n",
      " Cost: 15.164431\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch   5/13 \n",
      " y: tensor([164., 149.]) Hypothesis: tensor([160.5349, 145.7210]) \n",
      " Cost: 11.379157\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch   6/13 \n",
      " y: tensor([177., 192.]) Hypothesis: tensor([172.5917, 191.4925]) \n",
      " Cost: 9.845321\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch   7/13 \n",
      " y: tensor([159., 141.]) Hypothesis: tensor([159.6694, 142.7271]) \n",
      " Cost: 1.715445\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch   8/13 \n",
      " y: tensor([142., 175.]) Hypothesis: tensor([142.6450, 174.4043]) \n",
      " Cost: 0.385456\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch   9/13 \n",
      " y: tensor([147., 149.]) Hypothesis: tensor([149.5312, 153.2657]) \n",
      " Cost: 12.301510\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch  10/13 \n",
      " y: tensor([141., 184.]) Hypothesis: tensor([141.8218, 186.1587]) \n",
      " Cost: 2.667638\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch  11/13 \n",
      " y: tensor([196., 180.]) Hypothesis: tensor([195.6079, 179.5390]) \n",
      " Cost: 0.183135\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch  12/13 \n",
      " y: tensor([175., 192.]) Hypothesis: tensor([163.2021, 186.6384]) \n",
      " Cost: 83.969124\n",
      "torch.float32\n",
      "\n",
      "Epoch   5/20 Batch  13/13 \n",
      " y: 183.0 Hypothesis: 178.23219299316406 \n",
      " Cost: 22.731983\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch   1/13 \n",
      " y: tensor([175., 115.]) Hypothesis: tensor([168.8763, 110.8496]) \n",
      " Cost: 27.362427\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch   2/13 \n",
      " y: tensor([192., 175.]) Hypothesis: tensor([198.8706, 180.7319]) \n",
      " Cost: 40.030037\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch   3/13 \n",
      " y: tensor([159., 180.]) Hypothesis: tensor([162.1258, 184.8647]) \n",
      " Cost: 16.717894\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch   4/13 \n",
      " y: tensor([183., 175.]) Hypothesis: tensor([177.5634, 175.7433]) \n",
      " Cost: 15.054274\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch   5/13 \n",
      " y: tensor([142., 185.]) Hypothesis: tensor([144.5893, 188.5643]) \n",
      " Cost: 9.704218\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch   6/13 \n",
      " y: tensor([147., 196.]) Hypothesis: tensor([150.4444, 199.1272]) \n",
      " Cost: 10.821516\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch   7/13 \n",
      " y: tensor([149., 141.]) Hypothesis: tensor([152.9420, 142.6248]) \n",
      " Cost: 9.089712\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch   8/13 \n",
      " y: tensor([177., 148.]) Hypothesis: tensor([171.8492, 147.3876]) \n",
      " Cost: 13.452672\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch   9/13 \n",
      " y: tensor([149., 184.]) Hypothesis: tensor([147.3001, 187.4016]) \n",
      " Cost: 7.230215\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch  10/13 \n",
      " y: tensor([101., 164.]) Hypothesis: tensor([105.3809, 161.7839]) \n",
      " Cost: 12.051603\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch  11/13 \n",
      " y: tensor([152., 152.]) Hypothesis: tensor([151.4373, 155.3386]) \n",
      " Cost: 5.731491\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch  12/13 \n",
      " y: tensor([177., 192.]) Hypothesis: tensor([176.9447, 187.1113]) \n",
      " Cost: 11.951162\n",
      "torch.float32\n",
      "\n",
      "Epoch   6/20 Batch  13/13 \n",
      " y: 141.0 Hypothesis: 142.2500457763672 \n",
      " Cost: 1.562614\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch   1/13 \n",
      " y: tensor([148., 152.]) Hypothesis: tensor([148.0878, 151.5926]) \n",
      " Cost: 0.086836\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch   2/13 \n",
      " y: tensor([177., 142.]) Hypothesis: tensor([172.7290, 142.1976]) \n",
      " Cost: 9.140096\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch   3/13 \n",
      " y: tensor([152., 180.]) Hypothesis: tensor([156.3720, 181.9999]) \n",
      " Cost: 11.556916\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch   4/13 \n",
      " y: tensor([149., 192.]) Hypothesis: tensor([146.6153, 187.5127]) \n",
      " Cost: 12.911391\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch   5/13 \n",
      " y: tensor([149., 192.]) Hypothesis: tensor([153.7322, 192.8248]) \n",
      " Cost: 11.536913\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch   6/13 \n",
      " y: tensor([164., 183.]) Hypothesis: tensor([161.8664, 175.6174]) \n",
      " Cost: 29.527554\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch   7/13 \n",
      " y: tensor([159., 175.]) Hypothesis: tensor([160.6639, 175.9448]) \n",
      " Cost: 1.830710\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch   8/13 \n",
      " y: tensor([184., 101.]) Hypothesis: tensor([188.6293, 106.3649]) \n",
      " Cost: 25.105946\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch   9/13 \n",
      " y: tensor([175., 115.]) Hypothesis: tensor([164.1164, 107.7171]) \n",
      " Cost: 85.746407\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch  10/13 \n",
      " y: tensor([177., 185.]) Hypothesis: tensor([180.8791, 188.6458]) \n",
      " Cost: 14.169703\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch  11/13 \n",
      " y: tensor([147., 141.]) Hypothesis: tensor([150.1023, 142.9820]) \n",
      " Cost: 6.776363\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch  12/13 \n",
      " y: tensor([175., 141.]) Hypothesis: tensor([174.5319, 142.7455]) \n",
      " Cost: 1.632919\n",
      "torch.float32\n",
      "\n",
      "Epoch   7/20 Batch  13/13 \n",
      " y: 196.0 Hypothesis: 197.39767456054688 \n",
      " Cost: 1.953494\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch   1/13 \n",
      " y: tensor([175., 148.]) Hypothesis: tensor([173.2939, 147.6542]) \n",
      " Cost: 1.515145\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch   2/13 \n",
      " y: tensor([184., 141.]) Hypothesis: tensor([186.8232, 142.3517]) \n",
      " Cost: 4.898832\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch   3/13 \n",
      " y: tensor([149., 183.]) Hypothesis: tensor([146.0793, 174.5805]) \n",
      " Cost: 39.709061\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch   4/13 \n",
      " y: tensor([196., 147.]) Hypothesis: tensor([198.8546, 150.1944]) \n",
      " Cost: 9.176312\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch   5/13 \n",
      " y: tensor([180., 101.]) Hypothesis: tensor([181.1165, 105.4941]) \n",
      " Cost: 10.721731\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch   6/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([171.9622, 158.0260]) \n",
      " Cost: 13.163910\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch   7/13 \n",
      " y: tensor([152., 142.]) Hypothesis: tensor([152.1221, 142.6162]) \n",
      " Cost: 0.197322\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch   8/13 \n",
      " y: tensor([185., 115.]) Hypothesis: tensor([185.8487, 108.1793]) \n",
      " Cost: 23.620958\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch   9/13 \n",
      " y: tensor([152., 175.]) Hypothesis: tensor([156.5675, 165.5138]) \n",
      " Cost: 55.424957\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch  10/13 \n",
      " y: tensor([177., 164.]) Hypothesis: tensor([180.0730, 163.9827]) \n",
      " Cost: 4.721855\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch  11/13 \n",
      " y: tensor([149., 192.]) Hypothesis: tensor([154.1171, 193.3119]) \n",
      " Cost: 13.952608\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch  12/13 \n",
      " y: tensor([192., 141.]) Hypothesis: tensor([188.2115, 142.1262]) \n",
      " Cost: 7.810528\n",
      "torch.float32\n",
      "\n",
      "Epoch   8/20 Batch  13/13 \n",
      " y: 175.0 Hypothesis: 175.1173095703125 \n",
      " Cost: 0.013762\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch   1/13 \n",
      " y: tensor([101., 175.]) Hypothesis: tensor([105.9744, 175.0659]) \n",
      " Cost: 12.374743\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch   2/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([164.6042, 162.1002]) \n",
      " Cost: 55.841232\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch   3/13 \n",
      " y: tensor([180., 115.]) Hypothesis: tensor([183.9572, 109.7052]) \n",
      " Cost: 21.847145\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch   4/13 \n",
      " y: tensor([183., 152.]) Hypothesis: tensor([178.3708, 157.8637]) \n",
      " Cost: 27.906517\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch   5/13 \n",
      " y: tensor([159., 184.]) Hypothesis: tensor([161.0409, 189.6601]) \n",
      " Cost: 18.101025\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch   6/13 \n",
      " y: tensor([177., 177.]) Hypothesis: tensor([173.4784, 178.4815]) \n",
      " Cost: 7.298102\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch   7/13 \n",
      " y: tensor([185., 152.]) Hypothesis: tensor([186.6534, 152.6736]) \n",
      " Cost: 1.593652\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch   8/13 \n",
      " y: tensor([148., 142.]) Hypothesis: tensor([148.7235, 142.6912]) \n",
      " Cost: 0.500630\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch   9/13 \n",
      " y: tensor([175., 141.]) Hypothesis: tensor([174.2552, 142.7856]) \n",
      " Cost: 1.871511\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch  10/13 \n",
      " y: tensor([141., 192.]) Hypothesis: tensor([142.1255, 188.2071]) \n",
      " Cost: 7.826511\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch  11/13 \n",
      " y: tensor([196., 149.]) Hypothesis: tensor([198.3129, 147.7940]) \n",
      " Cost: 3.401941\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch  12/13 \n",
      " y: tensor([147., 149.]) Hypothesis: tensor([149.4455, 153.2196]) \n",
      " Cost: 11.892809\n",
      "torch.float32\n",
      "\n",
      "Epoch   9/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 190.7884063720703 \n",
      " Cost: 1.467959\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch   1/13 \n",
      " y: tensor([148., 183.]) Hypothesis: tensor([148.0554, 175.5587]) \n",
      " Cost: 27.687664\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch   2/13 \n",
      " y: tensor([101., 177.]) Hypothesis: tensor([106.3743, 179.3212]) \n",
      " Cost: 17.135349\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch   3/13 \n",
      " y: tensor([142., 196.]) Hypothesis: tensor([142.4091, 197.5997]) \n",
      " Cost: 1.363167\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch   4/13 \n",
      " y: tensor([180., 175.]) Hypothesis: tensor([180.8451, 173.6971]) \n",
      " Cost: 1.205940\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch   5/13 \n",
      " y: tensor([177., 149.]) Hypothesis: tensor([172.6728, 146.9340]) \n",
      " Cost: 11.496565\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch   6/13 \n",
      " y: tensor([192., 152.]) Hypothesis: tensor([192.9807, 156.6342]) \n",
      " Cost: 11.219021\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch   7/13 \n",
      " y: tensor([141., 159.]) Hypothesis: tensor([142.0315, 158.8229]) \n",
      " Cost: 0.547674\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch   8/13 \n",
      " y: tensor([192., 175.]) Hypothesis: tensor([187.9178, 164.3738]) \n",
      " Cost: 64.789993\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch   9/13 \n",
      " y: tensor([152., 147.]) Hypothesis: tensor([154.4498, 151.6617]) \n",
      " Cost: 13.866438\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch  10/13 \n",
      " y: tensor([141., 149.]) Hypothesis: tensor([143.9633, 154.3123]) \n",
      " Cost: 18.500963\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch  11/13 \n",
      " y: tensor([184., 164.]) Hypothesis: tensor([187.1846, 162.1484]) \n",
      " Cost: 6.784943\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch  12/13 \n",
      " y: tensor([115., 185.]) Hypothesis: tensor([107.8654, 185.2381]) \n",
      " Cost: 25.479759\n",
      "torch.float32\n",
      "\n",
      "Epoch  10/20 Batch  13/13 \n",
      " y: 175.0 Hypothesis: 174.97694396972656 \n",
      " Cost: 0.000532\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch   1/13 \n",
      " y: tensor([196., 177.]) Hypothesis: tensor([198.1597, 173.5580]) \n",
      " Cost: 8.255697\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch   2/13 \n",
      " y: tensor([152., 177.]) Hypothesis: tensor([156.3873, 178.7590]) \n",
      " Cost: 11.171052\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch   3/13 \n",
      " y: tensor([141., 148.]) Hypothesis: tensor([141.7389, 147.9560]) \n",
      " Cost: 0.273961\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch   4/13 \n",
      " y: tensor([149., 149.]) Hypothesis: tensor([146.6817, 152.3340]) \n",
      " Cost: 8.244995\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch   5/13 \n",
      " y: tensor([115., 175.]) Hypothesis: tensor([107.5911, 173.2691]) \n",
      " Cost: 28.943846\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch   6/13 \n",
      " y: tensor([142., 141.]) Hypothesis: tensor([142.8130, 143.1421]) \n",
      " Cost: 2.624757\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch   7/13 \n",
      " y: tensor([192., 183.]) Hypothesis: tensor([191.8798, 176.0027]) \n",
      " Cost: 24.488323\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch   8/13 \n",
      " y: tensor([184., 159.]) Hypothesis: tensor([188.9804, 160.4607]) \n",
      " Cost: 13.468946\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch   9/13 \n",
      " y: tensor([164., 147.]) Hypothesis: tensor([162.3274, 149.2506]) \n",
      " Cost: 3.931252\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch  10/13 \n",
      " y: tensor([101., 180.]) Hypothesis: tensor([105.6473, 181.3489]) \n",
      " Cost: 11.708457\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch  11/13 \n",
      " y: tensor([192., 175.]) Hypothesis: tensor([187.3075, 173.5919]) \n",
      " Cost: 12.001068\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch  12/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.3536, 186.2739]) \n",
      " Cost: 0.873957\n",
      "torch.float32\n",
      "\n",
      "Epoch  11/20 Batch  13/13 \n",
      " y: 175.0 Hypothesis: 164.85400390625 \n",
      " Cost: 102.941238\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch   1/13 \n",
      " y: tensor([142., 159.]) Hypothesis: tensor([145.9900, 163.0843]) \n",
      " Cost: 16.300970\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch   2/13 \n",
      " y: tensor([141., 185.]) Hypothesis: tensor([144.5426, 188.7902]) \n",
      " Cost: 13.457901\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch   3/13 \n",
      " y: tensor([177., 177.]) Hypothesis: tensor([179.4171, 174.4364]) \n",
      " Cost: 6.207086\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch   4/13 \n",
      " y: tensor([152., 180.]) Hypothesis: tensor([153.1147, 182.7731]) \n",
      " Cost: 4.466376\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch   5/13 \n",
      " y: tensor([192., 175.]) Hypothesis: tensor([192.4660, 175.0083]) \n",
      " Cost: 0.108593\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch   6/13 \n",
      " y: tensor([141., 192.]) Hypothesis: tensor([143.0508, 188.7213]) \n",
      " Cost: 7.477826\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch   7/13 \n",
      " y: tensor([164., 175.]) Hypothesis: tensor([162.9833, 174.9807]) \n",
      " Cost: 0.517065\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch   8/13 \n",
      " y: tensor([147., 101.]) Hypothesis: tensor([150.0241, 106.2465]) \n",
      " Cost: 18.335781\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch   9/13 \n",
      " y: tensor([184., 115.]) Hypothesis: tensor([186.9799, 108.0336]) \n",
      " Cost: 28.705479\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch  10/13 \n",
      " y: tensor([148., 175.]) Hypothesis: tensor([148.4674, 164.7324]) \n",
      " Cost: 52.820827\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch  11/13 \n",
      " y: tensor([149., 149.]) Hypothesis: tensor([149.0770, 154.7982]) \n",
      " Cost: 16.812803\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch  12/13 \n",
      " y: tensor([183., 152.]) Hypothesis: tensor([177.0104, 156.5593]) \n",
      " Cost: 28.331320\n",
      "torch.float32\n",
      "\n",
      "Epoch  12/20 Batch  13/13 \n",
      " y: 196.0 Hypothesis: 199.18218994140625 \n",
      " Cost: 10.126333\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch   1/13 \n",
      " y: tensor([152., 180.]) Hypothesis: tensor([151.7006, 181.0979]) \n",
      " Cost: 0.647482\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch   2/13 \n",
      " y: tensor([185., 149.]) Hypothesis: tensor([185.2727, 152.5486]) \n",
      " Cost: 6.333354\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch   3/13 \n",
      " y: tensor([159., 152.]) Hypothesis: tensor([157.8863, 154.6481]) \n",
      " Cost: 4.126307\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch   4/13 \n",
      " y: tensor([115., 175.]) Hypothesis: tensor([107.2695, 173.0304]) \n",
      " Cost: 31.819803\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch   5/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.4083, 105.6611]) \n",
      " Cost: 10.946380\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch   6/13 \n",
      " y: tensor([192., 141.]) Hypothesis: tensor([187.5864, 142.1885]) \n",
      " Cost: 10.446198\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch   7/13 \n",
      " y: tensor([196., 177.]) Hypothesis: tensor([197.8091, 173.2710]) \n",
      " Cost: 8.589149\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch   8/13 \n",
      " y: tensor([183., 175.]) Hypothesis: tensor([176.6284, 174.7282]) \n",
      " Cost: 20.335358\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch   9/13 \n",
      " y: tensor([184., 164.]) Hypothesis: tensor([189.4653, 164.1523]) \n",
      " Cost: 14.946108\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch  10/13 \n",
      " y: tensor([175., 192.]) Hypothesis: tensor([165.4135, 192.6810]) \n",
      " Cost: 46.182423\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch  11/13 \n",
      " y: tensor([141., 177.]) Hypothesis: tensor([144.2980, 180.6592]) \n",
      " Cost: 12.133387\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch  12/13 \n",
      " y: tensor([148., 149.]) Hypothesis: tensor([149.4561, 148.2813]) \n",
      " Cost: 1.318349\n",
      "torch.float32\n",
      "\n",
      "Epoch  13/20 Batch  13/13 \n",
      " y: 147.0 Hypothesis: 149.97979736328125 \n",
      " Cost: 8.879192\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch   1/13 \n",
      " y: tensor([185., 142.]) Hypothesis: tensor([185.5822, 142.2746]) \n",
      " Cost: 0.207171\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch   2/13 \n",
      " y: tensor([175., 101.]) Hypothesis: tensor([174.1954, 105.4549]) \n",
      " Cost: 10.246802\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch   3/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([147.8535, 191.0935]) \n",
      " Cost: 0.421617\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch   4/13 \n",
      " y: tensor([149., 159.]) Hypothesis: tensor([152.4886, 158.5391]) \n",
      " Cost: 6.191388\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch   5/13 \n",
      " y: tensor([183., 175.]) Hypothesis: tensor([174.9806, 173.1140]) \n",
      " Cost: 33.933903\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch   6/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([188.4982, 152.9312]) \n",
      " Cost: 10.550259\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch   7/13 \n",
      " y: tensor([141., 115.]) Hypothesis: tensor([142.6567, 108.1761]) \n",
      " Cost: 24.655369\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch   8/13 \n",
      " y: tensor([192., 149.]) Hypothesis: tensor([188.9026, 147.7892]) \n",
      " Cost: 5.529755\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch   9/13 \n",
      " y: tensor([196., 141.]) Hypothesis: tensor([199.3527, 143.4536]) \n",
      " Cost: 8.630393\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch  10/13 \n",
      " y: tensor([177., 152.]) Hypothesis: tensor([173.3459, 155.9121]) \n",
      " Cost: 14.328577\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch  11/13 \n",
      " y: tensor([177., 147.]) Hypothesis: tensor([178.2765, 149.3326]) \n",
      " Cost: 3.535107\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch  12/13 \n",
      " y: tensor([175., 180.]) Hypothesis: tensor([164.3007, 180.8446]) \n",
      " Cost: 57.594326\n",
      "torch.float32\n",
      "\n",
      "Epoch  14/20 Batch  13/13 \n",
      " y: 164.0 Hypothesis: 163.7545623779297 \n",
      " Cost: 0.060240\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch   1/13 \n",
      " y: tensor([185., 148.]) Hypothesis: tensor([187.5233, 149.9472]) \n",
      " Cost: 5.079432\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch   2/13 \n",
      " y: tensor([147., 152.]) Hypothesis: tensor([149.7287, 156.3553]) \n",
      " Cost: 13.207355\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch   3/13 \n",
      " y: tensor([192., 164.]) Hypothesis: tensor([187.6036, 161.6888]) \n",
      " Cost: 12.334866\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch   4/13 \n",
      " y: tensor([115., 175.]) Hypothesis: tensor([108.8064, 165.5806]) \n",
      " Cost: 63.543465\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch   5/13 \n",
      " y: tensor([180., 159.]) Hypothesis: tensor([185.2397, 162.4163]) \n",
      " Cost: 19.562618\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch   6/13 \n",
      " y: tensor([141., 149.]) Hypothesis: tensor([144.2380, 154.5122]) \n",
      " Cost: 20.434206\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch   7/13 \n",
      " y: tensor([183., 177.]) Hypothesis: tensor([176.2487, 178.1317]) \n",
      " Cost: 23.430050\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch   8/13 \n",
      " y: tensor([152., 192.]) Hypothesis: tensor([153.1480, 193.4429]) \n",
      " Cost: 1.699898\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch   9/13 \n",
      " y: tensor([177., 196.]) Hypothesis: tensor([173.9578, 198.5689]) \n",
      " Cost: 7.927054\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch  10/13 \n",
      " y: tensor([141., 175.]) Hypothesis: tensor([142.9246, 175.0501]) \n",
      " Cost: 1.853282\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch  11/13 \n",
      " y: tensor([184., 142.]) Hypothesis: tensor([187.7714, 142.7755]) \n",
      " Cost: 7.412527\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch  12/13 \n",
      " y: tensor([149., 175.]) Hypothesis: tensor([146.8682, 173.9828]) \n",
      " Cost: 2.789674\n",
      "torch.float32\n",
      "\n",
      "Epoch  15/20 Batch  13/13 \n",
      " y: 101.0 Hypothesis: 105.70429992675781 \n",
      " Cost: 22.130438\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch   1/13 \n",
      " y: tensor([180., 149.]) Hypothesis: tensor([180.1176, 151.8559]) \n",
      " Cost: 4.084859\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch   2/13 \n",
      " y: tensor([141., 196.]) Hypothesis: tensor([140.8064, 195.6216]) \n",
      " Cost: 0.090327\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch   3/13 \n",
      " y: tensor([177., 148.]) Hypothesis: tensor([171.5027, 147.1126]) \n",
      " Cost: 15.504004\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch   4/13 \n",
      " y: tensor([183., 142.]) Hypothesis: tensor([175.8797, 142.1766]) \n",
      " Cost: 25.365145\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch   5/13 \n",
      " y: tensor([185., 192.]) Hypothesis: tensor([187.1501, 193.3742]) \n",
      " Cost: 3.255679\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch   6/13 \n",
      " y: tensor([177., 152.]) Hypothesis: tensor([178.5147, 152.3549]) \n",
      " Cost: 1.210192\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch   7/13 \n",
      " y: tensor([152., 147.]) Hypothesis: tensor([155.7581, 149.1495]) \n",
      " Cost: 9.371764\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch   8/13 \n",
      " y: tensor([184., 141.]) Hypothesis: tensor([186.0741, 141.8741]) \n",
      " Cost: 2.532909\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch   9/13 \n",
      " y: tensor([159., 192.]) Hypothesis: tensor([157.3798, 186.4555]) \n",
      " Cost: 16.683502\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch  10/13 \n",
      " y: tensor([101., 175.]) Hypothesis: tensor([105.6147, 174.4574]) \n",
      " Cost: 10.794770\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch  11/13 \n",
      " y: tensor([115., 149.]) Hypothesis: tensor([107.9333, 146.8637]) \n",
      " Cost: 27.251066\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch  12/13 \n",
      " y: tensor([175., 175.]) Hypothesis: tensor([175.0302, 165.5672]) \n",
      " Cost: 44.489460\n",
      "torch.float32\n",
      "\n",
      "Epoch  16/20 Batch  13/13 \n",
      " y: 164.0 Hypothesis: 164.9118194580078 \n",
      " Cost: 0.831415\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch   1/13 \n",
      " y: tensor([185., 141.]) Hypothesis: tensor([188.3208, 144.2189]) \n",
      " Cost: 10.694574\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch   2/13 \n",
      " y: tensor([196., 149.]) Hypothesis: tensor([198.8722, 153.8618]) \n",
      " Cost: 15.943461\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch   3/13 \n",
      " y: tensor([148., 147.]) Hypothesis: tensor([148.0069, 148.5511]) \n",
      " Cost: 1.203053\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch   4/13 \n",
      " y: tensor([175., 175.]) Hypothesis: tensor([173.3089, 173.6364]) \n",
      " Cost: 2.359647\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch   5/13 \n",
      " y: tensor([180., 149.]) Hypothesis: tensor([181.1079, 147.1619]) \n",
      " Cost: 2.302956\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch   6/13 \n",
      " y: tensor([192., 184.]) Hypothesis: tensor([188.1780, 187.0891]) \n",
      " Cost: 12.075054\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch   7/13 \n",
      " y: tensor([115., 177.]) Hypothesis: tensor([108.3288, 178.0348]) \n",
      " Cost: 22.787754\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch   8/13 \n",
      " y: tensor([192., 101.]) Hypothesis: tensor([192.7056, 106.0995]) \n",
      " Cost: 13.251397\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch   9/13 \n",
      " y: tensor([141., 164.]) Hypothesis: tensor([142.6714, 162.2250]) \n",
      " Cost: 2.972099\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch  10/13 \n",
      " y: tensor([175., 142.]) Hypothesis: tensor([164.7321, 142.3211]) \n",
      " Cost: 52.766880\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch  11/13 \n",
      " y: tensor([152., 177.]) Hypothesis: tensor([153.7391, 175.2170]) \n",
      " Cost: 3.101716\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch  12/13 \n",
      " y: tensor([183., 152.]) Hypothesis: tensor([178.3249, 157.5720]) \n",
      " Cost: 26.452181\n",
      "torch.float32\n",
      "\n",
      "Epoch  17/20 Batch  13/13 \n",
      " y: 159.0 Hypothesis: 160.8808135986328 \n",
      " Cost: 3.537460\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch   1/13 \n",
      " y: tensor([152., 184.]) Hypothesis: tensor([156.8308, 188.6316]) \n",
      " Cost: 22.394333\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch   2/13 \n",
      " y: tensor([159., 175.]) Hypothesis: tensor([158.2540, 164.0466]) \n",
      " Cost: 60.266766\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch   3/13 \n",
      " y: tensor([175., 177.]) Hypothesis: tensor([175.8959, 179.7338]) \n",
      " Cost: 4.138237\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch   4/13 \n",
      " y: tensor([141., 147.]) Hypothesis: tensor([142.9513, 149.7868]) \n",
      " Cost: 5.786998\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch   5/13 \n",
      " y: tensor([185., 149.]) Hypothesis: tensor([185.7101, 147.3589]) \n",
      " Cost: 1.598771\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch   6/13 \n",
      " y: tensor([180., 183.]) Hypothesis: tensor([181.4804, 176.3064]) \n",
      " Cost: 23.497593\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch   7/13 \n",
      " y: tensor([101., 142.]) Hypothesis: tensor([106.4425, 143.4165]) \n",
      " Cost: 15.813588\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch   8/13 \n",
      " y: tensor([177., 196.]) Hypothesis: tensor([173.4991, 198.0224]) \n",
      " Cost: 8.173188\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch   9/13 \n",
      " y: tensor([192., 149.]) Hypothesis: tensor([188.9705, 153.4085]) \n",
      " Cost: 14.306574\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch  10/13 \n",
      " y: tensor([152., 141.]) Hypothesis: tensor([152.3157, 143.1397]) \n",
      " Cost: 2.339078\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch  11/13 \n",
      " y: tensor([148., 175.]) Hypothesis: tensor([148.5430, 174.5348]) \n",
      " Cost: 0.255613\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch  12/13 \n",
      " y: tensor([164., 115.]) Hypothesis: tensor([162.3463, 108.3356]) \n",
      " Cost: 23.574238\n",
      "torch.float32\n",
      "\n",
      "Epoch  18/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 193.28561401367188 \n",
      " Cost: 1.652803\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch   1/13 \n",
      " y: tensor([196., 101.]) Hypothesis: tensor([198.3765, 106.0461]) \n",
      " Cost: 15.555332\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch   2/13 \n",
      " y: tensor([192., 164.]) Hypothesis: tensor([191.1965, 161.7714]) \n",
      " Cost: 2.806152\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch   3/13 \n",
      " y: tensor([180., 184.]) Hypothesis: tensor([181.3665, 187.2484]) \n",
      " Cost: 6.209785\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch   4/13 \n",
      " y: tensor([175., 177.]) Hypothesis: tensor([173.4722, 172.0977]) \n",
      " Cost: 13.183202\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch   5/13 \n",
      " y: tensor([183., 152.]) Hypothesis: tensor([176.5445, 155.9650]) \n",
      " Cost: 28.697594\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch   6/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.8244, 186.8095]) \n",
      " Cost: 1.976951\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch   7/13 \n",
      " y: tensor([148., 149.]) Hypothesis: tensor([148.9280, 153.2527]) \n",
      " Cost: 9.473434\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch   8/13 \n",
      " y: tensor([175., 192.]) Hypothesis: tensor([164.2501, 187.7077]) \n",
      " Cost: 66.991844\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch   9/13 \n",
      " y: tensor([141., 175.]) Hypothesis: tensor([145.0111, 176.9273]) \n",
      " Cost: 9.901875\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch  10/13 \n",
      " y: tensor([147., 149.]) Hypothesis: tensor([150.3557, 148.7180]) \n",
      " Cost: 5.670063\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch  11/13 \n",
      " y: tensor([141., 115.]) Hypothesis: tensor([143.0502, 108.9777]) \n",
      " Cost: 20.235561\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch  12/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.4804, 160.3085]) \n",
      " Cost: 3.932242\n",
      "torch.float32\n",
      "\n",
      "Epoch  19/20 Batch  13/13 \n",
      " y: 142.0 Hypothesis: 142.851806640625 \n",
      " Cost: 0.725575\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch   1/13 \n",
      " y: tensor([147., 142.]) Hypothesis: tensor([149.2791, 142.6033]) \n",
      " Cost: 2.779071\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch   2/13 \n",
      " y: tensor([159., 141.]) Hypothesis: tensor([158.7552, 142.0734]) \n",
      " Cost: 0.606014\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch   3/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.1750, 162.0290]) \n",
      " Cost: 2.282789\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch   4/13 \n",
      " y: tensor([177., 149.]) Hypothesis: tensor([178.1976, 147.5720]) \n",
      " Cost: 1.736718\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch   5/13 \n",
      " y: tensor([177., 192.]) Hypothesis: tensor([173.3948, 192.1152]) \n",
      " Cost: 6.505511\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch   6/13 \n",
      " y: tensor([184., 192.]) Hypothesis: tensor([188.2769, 189.3931]) \n",
      " Cost: 12.543888\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch   7/13 \n",
      " y: tensor([101., 141.]) Hypothesis: tensor([106.0162, 143.2539]) \n",
      " Cost: 15.121164\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch   8/13 \n",
      " y: tensor([185., 196.]) Hypothesis: tensor([185.1921, 197.0851]) \n",
      " Cost: 0.607196\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch   9/13 \n",
      " y: tensor([152., 183.]) Hypothesis: tensor([151.1967, 175.3702]) \n",
      " Cost: 29.429827\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch  10/13 \n",
      " y: tensor([152., 148.]) Hypothesis: tensor([156.5993, 149.4861]) \n",
      " Cost: 11.681000\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch  11/13 \n",
      " y: tensor([180., 149.]) Hypothesis: tensor([181.2097, 152.7373]) \n",
      " Cost: 7.715260\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch  12/13 \n",
      " y: tensor([115., 175.]) Hypothesis: tensor([107.6518, 163.7215]) \n",
      " Cost: 90.600044\n",
      "torch.float32\n",
      "\n",
      "Epoch  20/20 Batch  13/13 \n",
      " y: 175.0 Hypothesis: 176.4827423095703 \n",
      " Cost: 2.198525\n"
     ]
    }
   ],
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Or we can use ```TensorDataset``` from ```torch.utils.data```.\r\n",
    "\r\n",
    "```torch.utils.data.Dataset``` is more compatible for custom dataset, which should be set with various custom setting, otherwise, we can just use ```TensorDataset``` if we have x_train and y_train."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "source": [
    "from torch.utils.data import TensorDataset\r\n",
    "\r\n",
    "train_ds = TensorDataset(torch.FloatTensor(xy[:,:-1]), torch.FloatTensor(xy[:, [-1]]))\r\n",
    "\r\n",
    "#train_x, train_y = train_ds <--- Error\r\n",
    "#too many values to unpack (expected 2)\r\n",
    "# https://www.pythonpool.com/valueerror-too-many-values-to-unpack-expected-2-solved/\r\n",
    "\r\n",
    "train_x, train_y = zip(*train_ds)\r\n",
    "\r\n",
    "train_dl = DataLoader(train_ds, batch_size = 2)\r\n",
    "\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "source": [
    "nb_epochs = 20\r\n",
    "\r\n",
    "for epoch in range(nb_epochs+1):\r\n",
    "    for batch_idx, samples in enumerate(train_dl):\r\n",
    "        x_train, y_train = samples\r\n",
    "\r\n",
    "        #Hypothesis\r\n",
    "        pred = model(x_train)\r\n",
    "\r\n",
    "        #cost Function\r\n",
    "        cost = F.mse_loss(pred, y_train)\r\n",
    "\r\n",
    "        #Reduce cost\r\n",
    "        optimizer.zero_grad()\r\n",
    "        cost.backward()\r\n",
    "        optimizer.step()\r\n",
    "        \r\n",
    "        print()\r\n",
    "        print('Epoch {:3d}/{} Batch {:3d}/{} \\n y: {} Hypothesis: {} \\n Cost: {:.6f}'.format(epoch, nb_epochs, batch_idx + 1, len(dataloader), y_train.squeeze(), pred.squeeze().detach(), cost.item()))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Epoch   0/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([150.7479, 184.4272]) \n",
      " Cost: 0.947925\n",
      "\n",
      "Epoch   0/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([180.4276, 196.4123]) \n",
      " Cost: 0.176412\n",
      "\n",
      "Epoch   0/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([141.7346, 104.8539]) \n",
      " Cost: 7.461353\n",
      "\n",
      "Epoch   0/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([145.5017, 106.4138]) \n",
      " Cost: 42.980682\n",
      "\n",
      "Epoch   0/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.6315, 161.9280]) \n",
      " Cost: 2.214495\n",
      "\n",
      "Epoch   0/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.1907, 142.6220]) \n",
      " Cost: 3.714942\n",
      "\n",
      "Epoch   0/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.6170, 156.3239]) \n",
      " Cost: 15.889662\n",
      "\n",
      "Epoch   0/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.6165, 186.4181]) \n",
      " Cost: 16.535614\n",
      "\n",
      "Epoch   0/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.8710, 175.3498]) \n",
      " Cost: 33.384079\n",
      "\n",
      "Epoch   0/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.4694, 160.2435]) \n",
      " Cost: 3.822110\n",
      "\n",
      "Epoch   0/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.0028, 174.3907]) \n",
      " Cost: 8.174344\n",
      "\n",
      "Epoch   0/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.6702, 154.6098]) \n",
      " Cost: 59.257927\n",
      "\n",
      "Epoch   0/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.51177978515625 \n",
      " Cost: 6.309038\n",
      "\n",
      "Epoch   1/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7740, 186.8608]) \n",
      " Cost: 2.030874\n",
      "\n",
      "Epoch   1/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8098, 197.9245]) \n",
      " Cost: 3.489506\n",
      "\n",
      "Epoch   1/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.2288, 105.2287]) \n",
      " Cost: 8.967306\n",
      "\n",
      "Epoch   1/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([145.9262, 106.7434]) \n",
      " Cost: 38.809387\n",
      "\n",
      "Epoch   1/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9893, 162.2744]) \n",
      " Cost: 1.488986\n",
      "\n",
      "Epoch   1/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3651, 142.7957]) \n",
      " Cost: 4.409147\n",
      "\n",
      "Epoch   1/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.7616, 156.4343]) \n",
      " Cost: 16.906324\n",
      "\n",
      "Epoch   1/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.7041, 186.5141]) \n",
      " Cost: 15.887206\n",
      "\n",
      "Epoch   1/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.8849, 175.4179]) \n",
      " Cost: 32.905075\n",
      "\n",
      "Epoch   1/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.4885, 160.2619]) \n",
      " Cost: 3.892445\n",
      "\n",
      "Epoch   1/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.0359, 174.4144]) \n",
      " Cost: 8.028494\n",
      "\n",
      "Epoch   1/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.6879, 154.5994]) \n",
      " Cost: 59.033794\n",
      "\n",
      "Epoch   1/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.5120391845703 \n",
      " Cost: 6.310341\n",
      "\n",
      "Epoch   2/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7779, 186.8637]) \n",
      " Cost: 2.039242\n",
      "\n",
      "Epoch   2/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8115, 197.9331]) \n",
      " Cost: 3.509141\n",
      "\n",
      "Epoch   2/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.2217, 105.2337]) \n",
      " Cost: 8.986572\n",
      "\n",
      "Epoch   2/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([145.9415, 106.7695]) \n",
      " Cost: 38.547527\n",
      "\n",
      "Epoch   2/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9898, 162.2896]) \n",
      " Cost: 1.462870\n",
      "\n",
      "Epoch   2/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3643, 142.7967]) \n",
      " Cost: 4.409098\n",
      "\n",
      "Epoch   2/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.7437, 156.4098]) \n",
      " Cost: 16.730820\n",
      "\n",
      "Epoch   2/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.7193, 186.5192]) \n",
      " Cost: 15.839455\n",
      "\n",
      "Epoch   2/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.8571, 175.4361]) \n",
      " Cost: 32.688179\n",
      "\n",
      "Epoch   2/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.4771, 160.2532]) \n",
      " Cost: 3.853226\n",
      "\n",
      "Epoch   2/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.0519, 174.4211]) \n",
      " Cost: 7.961244\n",
      "\n",
      "Epoch   2/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.6966, 154.5806]) \n",
      " Cost: 58.847839\n",
      "\n",
      "Epoch   2/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.50576782226562 \n",
      " Cost: 6.278872\n",
      "\n",
      "Epoch   3/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7795, 186.8637]) \n",
      " Cost: 2.040438\n",
      "\n",
      "Epoch   3/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8116, 197.9398]) \n",
      " Cost: 3.522455\n",
      "\n",
      "Epoch   3/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.2141, 105.2381]) \n",
      " Cost: 9.003679\n",
      "\n",
      "Epoch   3/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([145.9562, 106.7951]) \n",
      " Cost: 38.292145\n",
      "\n",
      "Epoch   3/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9899, 162.3042]) \n",
      " Cost: 1.437869\n",
      "\n",
      "Epoch   3/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3634, 142.7974]) \n",
      " Cost: 4.408160\n",
      "\n",
      "Epoch   3/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.7258, 156.3852]) \n",
      " Cost: 16.555578\n",
      "\n",
      "Epoch   3/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.7344, 186.5242]) \n",
      " Cost: 15.792908\n",
      "\n",
      "Epoch   3/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.8294, 175.4541]) \n",
      " Cost: 32.473495\n",
      "\n",
      "Epoch   3/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.4658, 160.2445]) \n",
      " Cost: 3.814364\n",
      "\n",
      "Epoch   3/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.0678, 174.4278]) \n",
      " Cost: 7.894810\n",
      "\n",
      "Epoch   3/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7053, 154.5619]) \n",
      " Cost: 58.663078\n",
      "\n",
      "Epoch   3/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.49954223632812 \n",
      " Cost: 6.247711\n",
      "\n",
      "Epoch   4/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7811, 186.8636]) \n",
      " Cost: 2.041579\n",
      "\n",
      "Epoch   4/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8117, 197.9466]) \n",
      " Cost: 3.535785\n",
      "\n",
      "Epoch   4/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.2064, 105.2425]) \n",
      " Cost: 9.020738\n",
      "\n",
      "Epoch   4/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([145.9709, 106.8206]) \n",
      " Cost: 38.038616\n",
      "\n",
      "Epoch   4/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9901, 162.3188]) \n",
      " Cost: 1.413212\n",
      "\n",
      "Epoch   4/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3624, 142.7982]) \n",
      " Cost: 4.407233\n",
      "\n",
      "Epoch   4/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.7079, 156.3607]) \n",
      " Cost: 16.382240\n",
      "\n",
      "Epoch   4/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.7494, 186.5292]) \n",
      " Cost: 15.746753\n",
      "\n",
      "Epoch   4/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.8018, 175.4720]) \n",
      " Cost: 32.260872\n",
      "\n",
      "Epoch   4/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.4545, 160.2358]) \n",
      " Cost: 3.775874\n",
      "\n",
      "Epoch   4/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.0836, 174.4344]) \n",
      " Cost: 7.828888\n",
      "\n",
      "Epoch   4/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7139, 154.5433]) \n",
      " Cost: 58.479736\n",
      "\n",
      "Epoch   4/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.49330139160156 \n",
      " Cost: 6.216552\n",
      "\n",
      "Epoch   5/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7827, 186.8636]) \n",
      " Cost: 2.042734\n",
      "\n",
      "Epoch   5/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8118, 197.9533]) \n",
      " Cost: 3.549070\n",
      "\n",
      "Epoch   5/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1988, 105.2469]) \n",
      " Cost: 9.037687\n",
      "\n",
      "Epoch   5/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([145.9855, 106.8461]) \n",
      " Cost: 37.786728\n",
      "\n",
      "Epoch   5/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9901, 162.3333]) \n",
      " Cost: 1.388921\n",
      "\n",
      "Epoch   5/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3615, 142.7989]) \n",
      " Cost: 4.406352\n",
      "\n",
      "Epoch   5/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.6902, 156.3364]) \n",
      " Cost: 16.210831\n",
      "\n",
      "Epoch   5/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.7644, 186.5341]) \n",
      " Cost: 15.701297\n",
      "\n",
      "Epoch   5/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.7744, 175.4899]) \n",
      " Cost: 32.049843\n",
      "\n",
      "Epoch   5/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.4433, 160.2272]) \n",
      " Cost: 3.737830\n",
      "\n",
      "Epoch   5/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.0994, 174.4410]) \n",
      " Cost: 7.763524\n",
      "\n",
      "Epoch   5/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7225, 154.5248]) \n",
      " Cost: 58.297569\n",
      "\n",
      "Epoch   5/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.48709106445312 \n",
      " Cost: 6.185622\n",
      "\n",
      "Epoch   6/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7843, 186.8635]) \n",
      " Cost: 2.043847\n",
      "\n",
      "Epoch   6/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8119, 197.9600]) \n",
      " Cost: 3.562370\n",
      "\n",
      "Epoch   6/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1912, 105.2512]) \n",
      " Cost: 9.054586\n",
      "\n",
      "Epoch   6/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.0001, 106.8714]) \n",
      " Cost: 37.536758\n",
      "\n",
      "Epoch   6/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9902, 162.3477]) \n",
      " Cost: 1.365017\n",
      "\n",
      "Epoch   6/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3605, 142.7997]) \n",
      " Cost: 4.405499\n",
      "\n",
      "Epoch   6/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.6725, 156.3122]) \n",
      " Cost: 16.041180\n",
      "\n",
      "Epoch   6/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.7793, 186.5390]) \n",
      " Cost: 15.656208\n",
      "\n",
      "Epoch   6/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.7471, 175.5076]) \n",
      " Cost: 31.840933\n",
      "\n",
      "Epoch   6/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.4322, 160.2186]) \n",
      " Cost: 3.700264\n",
      "\n",
      "Epoch   6/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.1151, 174.4476]) \n",
      " Cost: 7.698765\n",
      "\n",
      "Epoch   6/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7311, 154.5064]) \n",
      " Cost: 58.116859\n",
      "\n",
      "Epoch   6/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.4808807373047 \n",
      " Cost: 6.154769\n",
      "\n",
      "Epoch   7/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7859, 186.8634]) \n",
      " Cost: 2.044991\n",
      "\n",
      "Epoch   7/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8120, 197.9667]) \n",
      " Cost: 3.575598\n",
      "\n",
      "Epoch   7/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1836, 105.2555]) \n",
      " Cost: 9.071370\n",
      "\n",
      "Epoch   7/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.0146, 106.8966]) \n",
      " Cost: 37.288498\n",
      "\n",
      "Epoch   7/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9902, 162.3620]) \n",
      " Cost: 1.341496\n",
      "\n",
      "Epoch   7/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3596, 142.8004]) \n",
      " Cost: 4.404566\n",
      "\n",
      "Epoch   7/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.6550, 156.2881]) \n",
      " Cost: 15.873274\n",
      "\n",
      "Epoch   7/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.7942, 186.5439]) \n",
      " Cost: 15.611542\n",
      "\n",
      "Epoch   7/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.7199, 175.5254]) \n",
      " Cost: 31.633732\n",
      "\n",
      "Epoch   7/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.4211, 160.2101]) \n",
      " Cost: 3.663061\n",
      "\n",
      "Epoch   7/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.1307, 174.4541]) \n",
      " Cost: 7.634683\n",
      "\n",
      "Epoch   7/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7396, 154.4881]) \n",
      " Cost: 57.937401\n",
      "\n",
      "Epoch   7/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.47470092773438 \n",
      " Cost: 6.124145\n",
      "\n",
      "Epoch   8/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7875, 186.8633]) \n",
      " Cost: 2.046024\n",
      "\n",
      "Epoch   8/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8121, 197.9733]) \n",
      " Cost: 3.588923\n",
      "\n",
      "Epoch   8/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1760, 105.2597]) \n",
      " Cost: 9.088066\n",
      "\n",
      "Epoch   8/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.0291, 106.9218]) \n",
      " Cost: 37.042194\n",
      "\n",
      "Epoch   8/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9902, 162.3763]) \n",
      " Cost: 1.318278\n",
      "\n",
      "Epoch   8/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3587, 142.8011]) \n",
      " Cost: 4.403769\n",
      "\n",
      "Epoch   8/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.6376, 156.2641]) \n",
      " Cost: 15.707184\n",
      "\n",
      "Epoch   8/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.8089, 186.5487]) \n",
      " Cost: 15.567628\n",
      "\n",
      "Epoch   8/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.6928, 175.5431]) \n",
      " Cost: 31.428555\n",
      "\n",
      "Epoch   8/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.4101, 160.2017]) \n",
      " Cost: 3.626200\n",
      "\n",
      "Epoch   8/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.1463, 174.4605]) \n",
      " Cost: 7.571088\n",
      "\n",
      "Epoch   8/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7481, 154.4699]) \n",
      " Cost: 57.759018\n",
      "\n",
      "Epoch   8/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.46853637695312 \n",
      " Cost: 6.093672\n",
      "\n",
      "Epoch   9/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7891, 186.8632]) \n",
      " Cost: 2.047100\n",
      "\n",
      "Epoch   9/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8122, 197.9799]) \n",
      " Cost: 3.602179\n",
      "\n",
      "Epoch   9/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1685, 105.2639]) \n",
      " Cost: 9.104649\n",
      "\n",
      "Epoch   9/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.0435, 106.9468]) \n",
      " Cost: 36.797558\n",
      "\n",
      "Epoch   9/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9902, 162.3904]) \n",
      " Cost: 1.295461\n",
      "\n",
      "Epoch   9/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3578, 142.8018]) \n",
      " Cost: 4.402847\n",
      "\n",
      "Epoch   9/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.6202, 156.2402]) \n",
      " Cost: 15.542749\n",
      "\n",
      "Epoch   9/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.8237, 186.5535]) \n",
      " Cost: 15.524007\n",
      "\n",
      "Epoch   9/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.6659, 175.5607]) \n",
      " Cost: 31.225018\n",
      "\n",
      "Epoch   9/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.3991, 160.1933]) \n",
      " Cost: 3.589824\n",
      "\n",
      "Epoch   9/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.1618, 174.4669]) \n",
      " Cost: 7.508162\n",
      "\n",
      "Epoch   9/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7566, 154.4519]) \n",
      " Cost: 57.581963\n",
      "\n",
      "Epoch   9/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.46237182617188 \n",
      " Cost: 6.063275\n",
      "\n",
      "Epoch  10/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7907, 186.8631]) \n",
      " Cost: 2.048191\n",
      "\n",
      "Epoch  10/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8124, 197.9865]) \n",
      " Cost: 3.615470\n",
      "\n",
      "Epoch  10/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1609, 105.2681]) \n",
      " Cost: 9.121178\n",
      "\n",
      "Epoch  10/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.0578, 106.9717]) \n",
      " Cost: 36.554714\n",
      "\n",
      "Epoch  10/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9901, 162.4044]) \n",
      " Cost: 1.272964\n",
      "\n",
      "Epoch  10/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3570, 142.8025]) \n",
      " Cost: 4.402125\n",
      "\n",
      "Epoch  10/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.6030, 156.2165]) \n",
      " Cost: 15.380278\n",
      "\n",
      "Epoch  10/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.8383, 186.5583]) \n",
      " Cost: 15.480864\n",
      "\n",
      "Epoch  10/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.6391, 175.5783]) \n",
      " Cost: 31.023438\n",
      "\n",
      "Epoch  10/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.3882, 160.1849]) \n",
      " Cost: 3.553784\n",
      "\n",
      "Epoch  10/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.1772, 174.4733]) \n",
      " Cost: 7.445689\n",
      "\n",
      "Epoch  10/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7650, 154.4339]) \n",
      " Cost: 57.406246\n",
      "\n",
      "Epoch  10/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.45620727539062 \n",
      " Cost: 6.032954\n",
      "\n",
      "Epoch  11/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7924, 186.8629]) \n",
      " Cost: 2.049211\n",
      "\n",
      "Epoch  11/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8125, 197.9930]) \n",
      " Cost: 3.628631\n",
      "\n",
      "Epoch  11/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1534, 105.2722]) \n",
      " Cost: 9.137590\n",
      "\n",
      "Epoch  11/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.0721, 106.9966]) \n",
      " Cost: 36.313522\n",
      "\n",
      "Epoch  11/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9901, 162.4184]) \n",
      " Cost: 1.250833\n",
      "\n",
      "Epoch  11/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3561, 142.8031]) \n",
      " Cost: 4.401259\n",
      "\n",
      "Epoch  11/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.5858, 156.1929]) \n",
      " Cost: 15.219376\n",
      "\n",
      "Epoch  11/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.8529, 186.5630]) \n",
      " Cost: 15.438278\n",
      "\n",
      "Epoch  11/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.6124, 175.5958]) \n",
      " Cost: 30.823509\n",
      "\n",
      "Epoch  11/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.3774, 160.1766]) \n",
      " Cost: 3.518187\n",
      "\n",
      "Epoch  11/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.1925, 174.4795]) \n",
      " Cost: 7.383900\n",
      "\n",
      "Epoch  11/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7734, 154.4160]) \n",
      " Cost: 57.231682\n",
      "\n",
      "Epoch  11/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.45008850097656 \n",
      " Cost: 6.002934\n",
      "\n",
      "Epoch  12/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7940, 186.8628]) \n",
      " Cost: 2.050262\n",
      "\n",
      "Epoch  12/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8126, 197.9996]) \n",
      " Cost: 3.641975\n",
      "\n",
      "Epoch  12/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1459, 105.2763]) \n",
      " Cost: 9.153911\n",
      "\n",
      "Epoch  12/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.0864, 107.0213]) \n",
      " Cost: 36.074226\n",
      "\n",
      "Epoch  12/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9900, 162.4322]) \n",
      " Cost: 1.229041\n",
      "\n",
      "Epoch  12/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3552, 142.8038]) \n",
      " Cost: 4.400456\n",
      "\n",
      "Epoch  12/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.5688, 156.1694]) \n",
      " Cost: 15.060162\n",
      "\n",
      "Epoch  12/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.8675, 186.5677]) \n",
      " Cost: 15.396079\n",
      "\n",
      "Epoch  12/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.5858, 175.6132]) \n",
      " Cost: 30.625439\n",
      "\n",
      "Epoch  12/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.3666, 160.1683]) \n",
      " Cost: 3.482883\n",
      "\n",
      "Epoch  12/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.2078, 174.4858]) \n",
      " Cost: 7.322617\n",
      "\n",
      "Epoch  12/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7818, 154.3982]) \n",
      " Cost: 57.058384\n",
      "\n",
      "Epoch  12/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.44393920898438 \n",
      " Cost: 5.972839\n",
      "\n",
      "Epoch  13/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7957, 186.8627]) \n",
      " Cost: 2.051288\n",
      "\n",
      "Epoch  13/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8127, 198.0061]) \n",
      " Cost: 3.655188\n",
      "\n",
      "Epoch  13/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1384, 105.2803]) \n",
      " Cost: 9.170148\n",
      "\n",
      "Epoch  13/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.1006, 107.0460]) \n",
      " Cost: 35.836498\n",
      "\n",
      "Epoch  13/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9899, 162.4460]) \n",
      " Cost: 1.207558\n",
      "\n",
      "Epoch  13/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3544, 142.8045]) \n",
      " Cost: 4.399664\n",
      "\n",
      "Epoch  13/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.5518, 156.1461]) \n",
      " Cost: 14.902630\n",
      "\n",
      "Epoch  13/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.8819, 186.5724]) \n",
      " Cost: 15.354447\n",
      "\n",
      "Epoch  13/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.5594, 175.6306]) \n",
      " Cost: 30.429182\n",
      "\n",
      "Epoch  13/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.3559, 160.1601]) \n",
      " Cost: 3.448087\n",
      "\n",
      "Epoch  13/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.2230, 174.4920]) \n",
      " Cost: 7.261932\n",
      "\n",
      "Epoch  13/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7901, 154.3806]) \n",
      " Cost: 56.886311\n",
      "\n",
      "Epoch  13/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.43783569335938 \n",
      " Cost: 5.943043\n",
      "\n",
      "Epoch  14/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7973, 186.8625]) \n",
      " Cost: 2.052312\n",
      "\n",
      "Epoch  14/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8129, 198.0126]) \n",
      " Cost: 3.668523\n",
      "\n",
      "Epoch  14/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1310, 105.2843]) \n",
      " Cost: 9.186328\n",
      "\n",
      "Epoch  14/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.1147, 107.0705]) \n",
      " Cost: 35.600529\n",
      "\n",
      "Epoch  14/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9897, 162.4596]) \n",
      " Cost: 1.186406\n",
      "\n",
      "Epoch  14/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3536, 142.8051]) \n",
      " Cost: 4.398873\n",
      "\n",
      "Epoch  14/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.5349, 156.1229]) \n",
      " Cost: 14.746804\n",
      "\n",
      "Epoch  14/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.8963, 186.5771]) \n",
      " Cost: 15.313098\n",
      "\n",
      "Epoch  14/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.5330, 175.6479]) \n",
      " Cost: 30.234571\n",
      "\n",
      "Epoch  14/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.3452, 160.1520]) \n",
      " Cost: 3.413542\n",
      "\n",
      "Epoch  14/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.2381, 174.4982]) \n",
      " Cost: 7.201756\n",
      "\n",
      "Epoch  14/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.7984, 154.3630]) \n",
      " Cost: 56.715328\n",
      "\n",
      "Epoch  14/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.4317169189453 \n",
      " Cost: 5.913247\n",
      "\n",
      "Epoch  15/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.7990, 186.8623]) \n",
      " Cost: 2.053380\n",
      "\n",
      "Epoch  15/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8130, 198.0190]) \n",
      " Cost: 3.681724\n",
      "\n",
      "Epoch  15/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1235, 105.2883]) \n",
      " Cost: 9.202351\n",
      "\n",
      "Epoch  15/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.1288, 107.0950]) \n",
      " Cost: 35.366398\n",
      "\n",
      "Epoch  15/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9896, 162.4732]) \n",
      " Cost: 1.165605\n",
      "\n",
      "Epoch  15/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3528, 142.8058]) \n",
      " Cost: 4.398127\n",
      "\n",
      "Epoch  15/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.5181, 156.0997]) \n",
      " Cost: 14.592510\n",
      "\n",
      "Epoch  15/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.9107, 186.5817]) \n",
      " Cost: 15.272375\n",
      "\n",
      "Epoch  15/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.5068, 175.6652]) \n",
      " Cost: 30.041670\n",
      "\n",
      "Epoch  15/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.3346, 160.1439]) \n",
      " Cost: 3.379516\n",
      "\n",
      "Epoch  15/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.2532, 174.5043]) \n",
      " Cost: 7.142177\n",
      "\n",
      "Epoch  15/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.8067, 154.3455]) \n",
      " Cost: 56.545715\n",
      "\n",
      "Epoch  15/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.42564392089844 \n",
      " Cost: 5.883749\n",
      "\n",
      "Epoch  16/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.8007, 186.8621]) \n",
      " Cost: 2.054325\n",
      "\n",
      "Epoch  16/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8132, 198.0254]) \n",
      " Cost: 3.694992\n",
      "\n",
      "Epoch  16/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1161, 105.2922]) \n",
      " Cost: 9.218316\n",
      "\n",
      "Epoch  16/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.1428, 107.1194]) \n",
      " Cost: 35.133736\n",
      "\n",
      "Epoch  16/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9894, 162.4867]) \n",
      " Cost: 1.145103\n",
      "\n",
      "Epoch  16/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3519, 142.8064]) \n",
      " Cost: 4.397354\n",
      "\n",
      "Epoch  16/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.5014, 156.0768]) \n",
      " Cost: 14.440036\n",
      "\n",
      "Epoch  16/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.9250, 186.5863]) \n",
      " Cost: 15.232012\n",
      "\n",
      "Epoch  16/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.4807, 175.6824]) \n",
      " Cost: 29.850502\n",
      "\n",
      "Epoch  16/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.3241, 160.1358]) \n",
      " Cost: 3.345684\n",
      "\n",
      "Epoch  16/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.2682, 174.5103]) \n",
      " Cost: 7.083097\n",
      "\n",
      "Epoch  16/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.8149, 154.3281]) \n",
      " Cost: 56.377090\n",
      "\n",
      "Epoch  16/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.41958618164062 \n",
      " Cost: 5.854397\n",
      "\n",
      "Epoch  17/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.8024, 186.8619]) \n",
      " Cost: 2.055325\n",
      "\n",
      "Epoch  17/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8133, 198.0318]) \n",
      " Cost: 3.708179\n",
      "\n",
      "Epoch  17/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1086, 105.2961]) \n",
      " Cost: 9.234223\n",
      "\n",
      "Epoch  17/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.1568, 107.1437]) \n",
      " Cost: 34.902775\n",
      "\n",
      "Epoch  17/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9892, 162.5001]) \n",
      " Cost: 1.124898\n",
      "\n",
      "Epoch  17/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3512, 142.8070]) \n",
      " Cost: 4.396655\n",
      "\n",
      "Epoch  17/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.4848, 156.0539]) \n",
      " Cost: 14.288996\n",
      "\n",
      "Epoch  17/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.9392, 186.5908]) \n",
      " Cost: 15.192137\n",
      "\n",
      "Epoch  17/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.4548, 175.6996]) \n",
      " Cost: 29.661060\n",
      "\n",
      "Epoch  17/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.3136, 160.1278]) \n",
      " Cost: 3.312309\n",
      "\n",
      "Epoch  17/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.2831, 174.5164]) \n",
      " Cost: 7.024521\n",
      "\n",
      "Epoch  17/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.8232, 154.3108]) \n",
      " Cost: 56.209637\n",
      "\n",
      "Epoch  17/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.41351318359375 \n",
      " Cost: 5.825046\n",
      "\n",
      "Epoch  18/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.8041, 186.8617]) \n",
      " Cost: 2.056288\n",
      "\n",
      "Epoch  18/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8135, 198.0382]) \n",
      " Cost: 3.721495\n",
      "\n",
      "Epoch  18/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.1012, 105.3000]) \n",
      " Cost: 9.249971\n",
      "\n",
      "Epoch  18/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.1707, 107.1679]) \n",
      " Cost: 34.673592\n",
      "\n",
      "Epoch  18/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9890, 162.5134]) \n",
      " Cost: 1.105055\n",
      "\n",
      "Epoch  18/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3504, 142.8076]) \n",
      " Cost: 4.395893\n",
      "\n",
      "Epoch  18/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.4683, 156.0311]) \n",
      " Cost: 14.139566\n",
      "\n",
      "Epoch  18/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.9534, 186.5954]) \n",
      " Cost: 15.152680\n",
      "\n",
      "Epoch  18/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.4289, 175.7167]) \n",
      " Cost: 29.473360\n",
      "\n",
      "Epoch  18/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.3032, 160.1199]) \n",
      " Cost: 3.279353\n",
      "\n",
      "Epoch  18/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.2980, 174.5224]) \n",
      " Cost: 6.966516\n",
      "\n",
      "Epoch  18/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.8313, 154.2936]) \n",
      " Cost: 56.043495\n",
      "\n",
      "Epoch  18/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.407470703125 \n",
      " Cost: 5.795915\n",
      "\n",
      "Epoch  19/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.8058, 186.8615]) \n",
      " Cost: 2.057250\n",
      "\n",
      "Epoch  19/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8136, 198.0445]) \n",
      " Cost: 3.734643\n",
      "\n",
      "Epoch  19/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.0938, 105.3038]) \n",
      " Cost: 9.265695\n",
      "\n",
      "Epoch  19/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.1846, 107.1920]) \n",
      " Cost: 34.445827\n",
      "\n",
      "Epoch  19/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9888, 162.5266]) \n",
      " Cost: 1.085500\n",
      "\n",
      "Epoch  19/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3496, 142.8082]) \n",
      " Cost: 4.395167\n",
      "\n",
      "Epoch  19/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.4519, 156.0085]) \n",
      " Cost: 13.991785\n",
      "\n",
      "Epoch  19/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.9675, 186.5999]) \n",
      " Cost: 15.113773\n",
      "\n",
      "Epoch  19/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.4032, 175.7337]) \n",
      " Cost: 29.287176\n",
      "\n",
      "Epoch  19/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.2928, 160.1119]) \n",
      " Cost: 3.246620\n",
      "\n",
      "Epoch  19/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.3128, 174.5283]) \n",
      " Cost: 6.909070\n",
      "\n",
      "Epoch  19/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.8395, 154.2766]) \n",
      " Cost: 55.878296\n",
      "\n",
      "Epoch  19/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.4014129638672 \n",
      " Cost: 5.766784\n",
      "\n",
      "Epoch  20/20 Batch   1/13 \n",
      " y: tensor([152., 185.]) Hypothesis: tensor([152.8076, 186.8613]) \n",
      " Cost: 2.058203\n",
      "\n",
      "Epoch  20/20 Batch   2/13 \n",
      " y: tensor([180., 196.]) Hypothesis: tensor([181.8138, 198.0509]) \n",
      " Cost: 3.747883\n",
      "\n",
      "Epoch  20/20 Batch   3/13 \n",
      " y: tensor([142., 101.]) Hypothesis: tensor([142.0865, 105.3076]) \n",
      " Cost: 9.281323\n",
      "\n",
      "Epoch  20/20 Batch   4/13 \n",
      " y: tensor([149., 115.]) Hypothesis: tensor([146.1985, 107.2160]) \n",
      " Cost: 34.219822\n",
      "\n",
      "Epoch  20/20 Batch   5/13 \n",
      " y: tensor([175., 164.]) Hypothesis: tensor([174.9885, 162.5397]) \n",
      " Cost: 1.066232\n",
      "\n",
      "Epoch  20/20 Batch   6/13 \n",
      " y: tensor([141., 141.]) Hypothesis: tensor([143.3489, 142.8088]) \n",
      " Cost: 4.394523\n",
      "\n",
      "Epoch  20/20 Batch   7/13 \n",
      " y: tensor([184., 152.]) Hypothesis: tensor([187.4356, 155.9859]) \n",
      " Cost: 13.845533\n",
      "\n",
      "Epoch  20/20 Batch   8/13 \n",
      " y: tensor([148., 192.]) Hypothesis: tensor([146.9816, 186.6043]) \n",
      " Cost: 15.075213\n",
      "\n",
      "Epoch  20/20 Batch   9/13 \n",
      " y: tensor([147., 183.]) Hypothesis: tensor([149.3776, 175.7507]) \n",
      " Cost: 29.102602\n",
      "\n",
      "Epoch  20/20 Batch  10/13 \n",
      " y: tensor([177., 159.]) Hypothesis: tensor([179.2825, 160.1041]) \n",
      " Cost: 3.214402\n",
      "\n",
      "Epoch  20/20 Batch  11/13 \n",
      " y: tensor([177., 175.]) Hypothesis: tensor([173.3275, 174.5342]) \n",
      " Cost: 6.852125\n",
      "\n",
      "Epoch  20/20 Batch  12/13 \n",
      " y: tensor([175., 149.]) Hypothesis: tensor([165.8476, 154.2595]) \n",
      " Cost: 55.714340\n",
      "\n",
      "Epoch  20/20 Batch  13/13 \n",
      " y: 192.0 Hypothesis: 194.39541625976562 \n",
      " Cost: 5.738019\n"
     ]
    }
   ],
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ]
}