Linear regression example using Pytorch.

regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.autograd import Variable\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 202.09515380859375\n",
      "10 171.15576171875\n",
      "20 144.06959533691406\n",
      "30 120.9667739868164\n",
      "40 101.63298034667969\n",
      "50 85.61366271972656\n",
      "60 72.35327911376953\n",
      "70 61.30906295776367\n",
      "80 52.017799377441406\n",
      "90 44.118812561035156\n",
      "100 37.34712219238281\n",
      "110 31.512853622436523\n",
      "120 26.4785099029541\n",
      "130 22.140188217163086\n",
      "140 18.414417266845703\n",
      "150 15.23002815246582\n",
      "160 12.523662567138672\n",
      "170 10.237588882446289\n",
      "180 8.318814277648926\n",
      "190 6.718794822692871\n",
      "200 5.393377304077148\n",
      "210 4.302750110626221\n",
      "220 3.411351442337036\n",
      "230 2.6877212524414062\n",
      "240 2.1042821407318115\n",
      "250 1.6370915174484253\n",
      "260 1.2655516862869263\n",
      "270 0.9721100926399231\n",
      "280 0.7419456839561462\n",
      "290 0.5626537799835205\n",
      "300 0.42395275831222534\n",
      "310 0.31739136576652527\n",
      "320 0.23608431220054626\n",
      "330 0.17447291314601898\n",
      "340 0.12810657918453217\n",
      "350 0.0934523195028305\n",
      "360 0.06772942841053009\n",
      "370 0.04876668006181717\n",
      "380 0.03488374873995781\n",
      "390 0.024789126589894295\n",
      "400 0.017499692738056183\n",
      "410 0.01227207574993372\n",
      "420 0.008548851124942303\n",
      "430 0.0059155626222491264\n",
      "440 0.0040659294463694096\n",
      "450 0.0027757221832871437\n",
      "460 0.001882058335468173\n",
      "470 0.0012674160534515977\n",
      "480 0.00084764912026003\n",
      "490 0.0005629826919175684\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "N = 64\n",
    "\n",
    "alpha = 1.3\n",
    "beta = np.array([[0.5], [1.9]])\n",
    "\n",
    "x_data = np.random.randn(N, 2)\n",
    "y_data = x_data.dot(beta) + alpha\n",
    "\n",
    "x = Variable(torch.Tensor(x_data), requires_grad=False)\n",
    "y = Variable(torch.Tensor(y_data), requires_grad=False)\n",
    "\n",
    "w_beta = Variable(torch.randn(2, 1), requires_grad=True)\n",
    "w_alpha = Variable(torch.randn(1), requires_grad=True)\n",
    "\n",
    "learning_rate = 1e-2\n",
    "optimizer = torch.optim.Adam([w_beta, w_alpha], lr=learning_rate)\n",
    "\n",
    "for t in range(500):\n",
    "    y_pred = x.mm(w_beta).add(w_alpha.expand(N))\n",
    "    \n",
    "    loss = (y_pred - y).pow(2).sum()\n",
    "    \n",
    "    if t % 10 == 0:\n",
    "        print(t, loss.data[0])\n",
    "    \n",
    "    optimizer.zero_grad()   \n",
    "    \n",
    "    loss.backward()\n",
    "    \n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimized Beta: \n",
      " 0.4973\n",
      " 1.9002\n",
      "[torch.FloatTensor of size 2x1]\n",
      "\n",
      "Optimized Alpha: \n",
      " 1.2997\n",
      "[torch.FloatTensor of size 1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Optimized Beta: {0}\".format(w_beta.data))\n",
    "print(\"Optimized Alpha: {0}\".format(w_alpha.data))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}