lab-08_2_xor_nn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lab 9 XOR\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "# for reproducibility\n",
    "torch.manual_seed(777)\n",
    "if device == 'cuda':\n",
    "    torch.cuda.manual_seed_all(777)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = torch.FloatTensor([[0, 0], [0, 1], [1, 0], [1, 1]]).to(device)\n",
    "Y = torch.FloatTensor([[0], [1], [1], [0]]).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nn layers\n",
    "linear1 = torch.nn.Linear(2, 2, bias=True)\n",
    "linear2 = torch.nn.Linear(2, 1, bias=True)\n",
    "sigmoid = torch.nn.Sigmoid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model\n",
    "model = torch.nn.Sequential(linear1, sigmoid, linear2, sigmoid).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define cost/loss & optimizer\n",
    "criterion = torch.nn.BCELoss().to(device)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=1)  # modified learning rate from 0.1 to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "200 0.6931577920913696\n",
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      "400 0.6931463479995728\n",
      "500 0.6931411027908325\n",
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      "700 0.6931295394897461\n",
      "800 0.693122148513794\n",
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      "1000 0.6930999755859375\n",
      "1100 0.693082332611084\n",
      "1200 0.6930568814277649\n",
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      "1600 0.6927032470703125\n",
      "1700 0.6923960447311401\n",
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      "7300 0.0019184107659384608\n",
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      "7600 0.0018075400730594993\n",
      "7700 0.0017733527347445488\n",
      "7800 0.0017404207028448582\n",
      "7900 0.0017087138257920742\n",
      "8000 0.001678097527474165\n",
      "8100 0.0016485570231452584\n",
      "8200 0.001620002556592226\n",
      "8300 0.0015924491453915834\n",
      "8400 0.0015657917829230428\n",
      "8500 0.0015400308184325695\n",
      "8600 0.0015150615945458412\n",
      "8700 0.001490913680754602\n",
      "8800 0.0014674977865070105\n",
      "8900 0.001444813678972423\n",
      "9000 0.0014228166546672583\n",
      "9100 0.0014014765620231628\n",
      "9200 0.0013806892093271017\n",
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      "10000 0.0012345188297331333\n"
     ]
    }
   ],
   "source": [
    "for step in range(10001):\n",
    "    optimizer.zero_grad()\n",
    "    hypothesis = model(X)\n",
    "\n",
    "    # cost/loss function\n",
    "    cost = criterion(hypothesis, Y)\n",
    "    cost.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if step % 100 == 0:\n",
    "        print(step, cost.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Hypothesis:  [[0.00106364]\n",
      " [0.99889404]\n",
      " [0.99889404]\n",
      " [0.00165861]] \n",
      "Correct:  [[0.]\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]] \n",
      "Accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "# Accuracy computation\n",
    "# True if hypothesis>0.5 else False\n",
    "with torch.no_grad():\n",
    "    hypothesis = model(X)\n",
    "    predicted = (hypothesis > 0.5).float()\n",
    "    accuracy = (predicted == Y).float().mean()\n",
    "    print('\\nHypothesis: ', hypothesis.detach().cpu().numpy(), '\\nCorrect: ', predicted.detach().cpu().numpy(), '\\nAccuracy: ', accuracy.item())"
   ]
  }
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