Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'x')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = torch.randn(100, 1)*10\n",
    "y = X + 3*torch.randn(100, 1)\n",
    "plt.plot(X.numpy(), y.numpy(), 'o')\n",
    "plt.ylabel('y')\n",
    "plt.xlabel('x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LR(nn.Module):\n",
    "  def __init__(self, input_size, output_size):\n",
    "    super().__init__()\n",
    "    self.linear = nn.Linear(input_size, output_size)\n",
    "  def forward(self, x):\n",
    "    pred = self.linear(x)\n",
    "    return pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR(\n",
      "  (linear): Linear(in_features=1, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(1)\n",
    "model = LR(1, 1)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "[w,b] = model.parameters()\n",
    "\n",
    "def get_params():\n",
    "    return (w[0][0].item(), b[0].item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fit(title):\n",
    "  plt.title = title\n",
    "  w1, b1 = get_params()\n",
    "  x1 = np.array([-30, 30])\n",
    "  y1 = w1*x1 + b1\n",
    "  plt.plot(x1, y1, 'r')\n",
    "  plt.scatter(X, y)\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fit('Initial Model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 loss: 28.098552703857422\n",
      "epoch: 1 loss: 23.643329620361328\n",
      "epoch: 2 loss: 20.250816345214844\n",
      "epoch: 3 loss: 17.666976928710938\n",
      "epoch: 4 loss: 15.698525428771973\n",
      "epoch: 5 loss: 14.198383331298828\n",
      "epoch: 6 loss: 13.054638862609863\n",
      "epoch: 7 loss: 12.182149887084961\n",
      "epoch: 8 loss: 11.516135215759277\n",
      "epoch: 9 loss: 11.007296562194824\n",
      "epoch: 10 loss: 10.618125915527344\n",
      "epoch: 11 loss: 10.320084571838379\n",
      "epoch: 12 loss: 10.091449737548828\n",
      "epoch: 13 loss: 9.91569995880127\n",
      "epoch: 14 loss: 9.780254364013672\n",
      "epoch: 15 loss: 9.675541877746582\n",
      "epoch: 16 loss: 9.594278335571289\n",
      "epoch: 17 loss: 9.530913352966309\n",
      "epoch: 18 loss: 9.481230735778809\n",
      "epoch: 19 loss: 9.44201374053955\n",
      "epoch: 20 loss: 9.410815238952637\n",
      "epoch: 21 loss: 9.38576889038086\n",
      "epoch: 22 loss: 9.365455627441406\n",
      "epoch: 23 loss: 9.34879207611084\n",
      "epoch: 24 loss: 9.334953308105469\n",
      "epoch: 25 loss: 9.323307037353516\n",
      "epoch: 26 loss: 9.313375473022461\n",
      "epoch: 27 loss: 9.304786682128906\n",
      "epoch: 28 loss: 9.297262191772461\n",
      "epoch: 29 loss: 9.290584564208984\n",
      "epoch: 30 loss: 9.284588813781738\n",
      "epoch: 31 loss: 9.279149055480957\n",
      "epoch: 32 loss: 9.274165153503418\n",
      "epoch: 33 loss: 9.269560813903809\n",
      "epoch: 34 loss: 9.265275001525879\n",
      "epoch: 35 loss: 9.261265754699707\n",
      "epoch: 36 loss: 9.257494926452637\n",
      "epoch: 37 loss: 9.253931045532227\n",
      "epoch: 38 loss: 9.250555038452148\n",
      "epoch: 39 loss: 9.247346878051758\n",
      "epoch: 40 loss: 9.244290351867676\n",
      "epoch: 41 loss: 9.241373062133789\n",
      "epoch: 42 loss: 9.238587379455566\n",
      "epoch: 43 loss: 9.235920906066895\n",
      "epoch: 44 loss: 9.233366966247559\n",
      "epoch: 45 loss: 9.230920791625977\n",
      "epoch: 46 loss: 9.2285737991333\n",
      "epoch: 47 loss: 9.226323127746582\n",
      "epoch: 48 loss: 9.224163055419922\n",
      "epoch: 49 loss: 9.222088813781738\n",
      "epoch: 50 loss: 9.220098495483398\n",
      "epoch: 51 loss: 9.218185424804688\n",
      "epoch: 52 loss: 9.216348648071289\n",
      "epoch: 53 loss: 9.214585304260254\n",
      "epoch: 54 loss: 9.212888717651367\n",
      "epoch: 55 loss: 9.211261749267578\n",
      "epoch: 56 loss: 9.209696769714355\n",
      "epoch: 57 loss: 9.208192825317383\n",
      "epoch: 58 loss: 9.206748008728027\n",
      "epoch: 59 loss: 9.20535945892334\n",
      "epoch: 60 loss: 9.204024314880371\n",
      "epoch: 61 loss: 9.202742576599121\n",
      "epoch: 62 loss: 9.201510429382324\n",
      "epoch: 63 loss: 9.200325965881348\n",
      "epoch: 64 loss: 9.199187278747559\n",
      "epoch: 65 loss: 9.198095321655273\n",
      "epoch: 66 loss: 9.197042465209961\n",
      "epoch: 67 loss: 9.196033477783203\n",
      "epoch: 68 loss: 9.195061683654785\n",
      "epoch: 69 loss: 9.194129943847656\n",
      "epoch: 70 loss: 9.193232536315918\n",
      "epoch: 71 loss: 9.192370414733887\n",
      "epoch: 72 loss: 9.191542625427246\n",
      "epoch: 73 loss: 9.190747261047363\n",
      "epoch: 74 loss: 9.189983367919922\n",
      "epoch: 75 loss: 9.189247131347656\n",
      "epoch: 76 loss: 9.188539505004883\n",
      "epoch: 77 loss: 9.187862396240234\n",
      "epoch: 78 loss: 9.187209129333496\n",
      "epoch: 79 loss: 9.1865816116333\n",
      "epoch: 80 loss: 9.185979843139648\n",
      "epoch: 81 loss: 9.185399055480957\n",
      "epoch: 82 loss: 9.184842109680176\n",
      "epoch: 83 loss: 9.184309005737305\n",
      "epoch: 84 loss: 9.183794975280762\n",
      "epoch: 85 loss: 9.183300018310547\n",
      "epoch: 86 loss: 9.182826042175293\n",
      "epoch: 87 loss: 9.182368278503418\n",
      "epoch: 88 loss: 9.181929588317871\n",
      "epoch: 89 loss: 9.181509017944336\n",
      "epoch: 90 loss: 9.181102752685547\n",
      "epoch: 91 loss: 9.180713653564453\n",
      "epoch: 92 loss: 9.180339813232422\n",
      "epoch: 93 loss: 9.179980278015137\n",
      "epoch: 94 loss: 9.179636001586914\n",
      "epoch: 95 loss: 9.179303169250488\n",
      "epoch: 96 loss: 9.178984642028809\n",
      "epoch: 97 loss: 9.17867660522461\n",
      "epoch: 98 loss: 9.178380966186523\n",
      "epoch: 99 loss: 9.178099632263184\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "losses = []\n",
    "for i in range(epochs):\n",
    "  y_pred = model.forward(X)\n",
    "  loss = criterion(y_pred, y)\n",
    "  print(\"epoch:\", i, \"loss:\", loss.item())\n",
    "  \n",
    "  losses.append(loss)\n",
    "  optimizer.zero_grad()\n",
    "  loss.backward()\n",
    "  optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Epochs')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(epochs), losses)\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fit(\"Trained Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}