PytorchLR_py.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMGGmy5ugF1m1j2gkDGbk2F",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/123-code/Deep-Learning-From-Scratch/blob/main/PytorchLR_py.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "OQGCTRACUqJP"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import torch.nn as nn"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = torch.linspace(1,50,50).reshape(-1,1)\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1z_ptXyXU2yu",
        "outputId": "15dd58f8-abe5-409a-bc09-8c4b14917248"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 1.],\n",
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              "        [50.]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(71)\n",
        "e = torch.randint(-8,9,(50,1),dtype=torch.float)\n",
        "e"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z9HU7jpqVOSK",
        "outputId": "f3ab126e-8ca6-4f3c-e55c-9028d79039dd"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 2.],\n",
              "        [ 7.],\n",
              "        [ 2.],\n",
              "        [ 6.],\n",
              "        [ 2.],\n",
              "        [-4.],\n",
              "        [ 2.],\n",
              "        [-5.],\n",
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              "        [-7.],\n",
              "        [-2.]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = 2*X + 1 +e\n",
        "y.shape\n",
        "plt.scatter(X.numpy(),y.numpy())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "YcBybDU3VuBw",
        "outputId": "38a69d99-fa2a-4d1a-fa9f-38862ffe1809"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8f2cb350d0>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(59)\n",
        "model = nn.Linear(in_features=1,out_features=1)\n",
        "print(model.weight)\n",
        "print(model.bias)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "14SuUcvuWHUu",
        "outputId": "7da7d63c-d6e3-4ec8-9bbf-e4643a7219b5"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parameter containing:\n",
            "tensor([[0.1060]], requires_grad=True)\n",
            "Parameter containing:\n",
            "tensor([0.9638], requires_grad=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class Model(nn.Module):\n",
        "  def __init__(self,in_features,out_features):\n",
        "    super().__init__()\n",
        "    self.linear = nn.Linear(in_features,out_features)\n",
        "\n",
        "  def forward(self,x):\n",
        "    y_pred = self.linear(x)\n",
        "    return y_pred"
      ],
      "metadata": {
        "id": "A2aZxbzwW3Bg"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(59)\n",
        "model = Model(1,1)\n",
        "print(model.linear.weight)\n",
        "print(model.linear.bias)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HjeoY63sXJVJ",
        "outputId": "bf2e8109-6df2-4a0c-d3a8-1c3d1941920a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Parameter containing:\n",
            "tensor([[0.1060]], requires_grad=True)\n",
            "Parameter containing:\n",
            "tensor([0.9638], requires_grad=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for name,param in model.named_parameters():\n",
        "  print(name,\"\\t\",param.item())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eSuBSSfyXmVL",
        "outputId": "3f44c1b9-1374-44df-d241-556409a9914c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "linear.weight \t 0.10597813129425049\n",
            "linear.bias \t 0.9637961387634277\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3xrxHgYYrvf",
        "outputId": "fc2612da-740d-4ba2-802c-665150d7325c"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: torch: command not found\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = torch.tensor([2.0])\n",
        "print(model.forward(x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxMyQV6oYLu-",
        "outputId": "3cbeb89d-5032-4ebe-cd6b-27d315d20f54"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1.1758], grad_fn=<AddBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = np.linspace(0.0,50.0,50)"
      ],
      "metadata": {
        "id": "ukO3un7QY1e1"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x1\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DN69edJGY8Ou",
        "outputId": "ef29bb42-2bc1-41e2-b782-038c4565e7d2"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.        ,  1.02040816,  2.04081633,  3.06122449,  4.08163265,\n",
              "        5.10204082,  6.12244898,  7.14285714,  8.16326531,  9.18367347,\n",
              "       10.20408163, 11.2244898 , 12.24489796, 13.26530612, 14.28571429,\n",
              "       15.30612245, 16.32653061, 17.34693878, 18.36734694, 19.3877551 ,\n",
              "       20.40816327, 21.42857143, 22.44897959, 23.46938776, 24.48979592,\n",
              "       25.51020408, 26.53061224, 27.55102041, 28.57142857, 29.59183673,\n",
              "       30.6122449 , 31.63265306, 32.65306122, 33.67346939, 34.69387755,\n",
              "       35.71428571, 36.73469388, 37.75510204, 38.7755102 , 39.79591837,\n",
              "       40.81632653, 41.83673469, 42.85714286, 43.87755102, 44.89795918,\n",
              "       45.91836735, 46.93877551, 47.95918367, 48.97959184, 50.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "w1 = 0.1059\n",
        "b1 = 0.9637\n",
        "y1 = w1*x1 + b1\n",
        "y1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-o1zqJSSY-L-",
        "outputId": "8c9e9968-daad-4921-922c-a1c03e2acf75"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.9637    , 1.07176122, 1.17982245, 1.28788367, 1.3959449 ,\n",
              "       1.50400612, 1.61206735, 1.72012857, 1.8281898 , 1.93625102,\n",
              "       2.04431224, 2.15237347, 2.26043469, 2.36849592, 2.47655714,\n",
              "       2.58461837, 2.69267959, 2.80074082, 2.90880204, 3.01686327,\n",
              "       3.12492449, 3.23298571, 3.34104694, 3.44910816, 3.55716939,\n",
              "       3.66523061, 3.77329184, 3.88135306, 3.98941429, 4.09747551,\n",
              "       4.20553673, 4.31359796, 4.42165918, 4.52972041, 4.63778163,\n",
              "       4.74584286, 4.85390408, 4.96196531, 5.07002653, 5.17808776,\n",
              "       5.28614898, 5.3942102 , 5.50227143, 5.61033265, 5.71839388,\n",
              "       5.8264551 , 5.93451633, 6.04257755, 6.15063878, 6.2587    ])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X.numpy(),y.numpy())\n",
        "\n",
        "plt.plot(x1,y1,'r')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "vHEcZIKZZkv3",
        "outputId": "a8db1c5d-8ca8-47e9-ae04-f40845e82aca"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8f2cb9b940>]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "criterion = nn.MSELoss()"
      ],
      "metadata": {
        "id": "f8-xnziAaPxn"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Learning rate tells the optimizer how much to optimize the learning rate on each round. "
      ],
      "metadata": {
        "id": "S5ndzYftaivk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = torch.optim.SGD(model.parameters(),lr=.001)\n"
      ],
      "metadata": {
        "id": "kIuRk9kRage1"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "choose a number of times we iterate over the dataset, and record the losses."
      ],
      "metadata": {
        "id": "NMzH0eJycFzl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 50\n",
        "losses = []\n",
        "\n",
        "for i in range(epochs):\n",
        "  #increments the counter\n",
        "  i+=1\n",
        "  # predicts the output \n",
        "  y_pred = model.forward(X)\n",
        "  #calculates the loss \n",
        "  loss = criterion(y_pred,y)\n",
        "  #appends loss to the losses array \n",
        "  losses.append(loss)\n",
        "  print(f\"epoch{i} Loss:{loss.item()} weight:{model.linear.weight.item()} Bias:{model.linear.bias.item()}\")\n",
        "  optimizer.zero_grad()\n",
        "  loss.backward()\n",
        "  optimizer.step()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xuMInc-Gbnnt",
        "outputId": "58f8104e-3795-4970-e3d1-6c47414daa46"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch1 Loss:3057.216796875 weight:0.10597813129425049 Bias:0.9637961387634277\n",
            "epoch2 Loss:1588.5306396484375 weight:3.334900140762329 Bias:1.0604636669158936\n",
            "epoch3 Loss:830.2999267578125 weight:1.014832854270935 Bias:0.9922628402709961\n",
            "epoch4 Loss:438.8521728515625 weight:2.6817994117736816 Bias:1.0425218343734741\n",
            "epoch5 Loss:236.76144409179688 weight:1.4840213060379028 Bias:1.0076650381088257\n",
            "epoch6 Loss:132.4291229248047 weight:2.3446059226989746 Bias:1.0339646339416504\n",
            "epoch7 Loss:78.56572723388672 weight:1.7262253761291504 Bias:1.0163217782974243\n",
            "epoch8 Loss:50.75775909423828 weight:2.170504093170166 Bias:1.0302516222000122\n",
            "epoch9 Loss:36.4012336730957 weight:1.8512457609176636 Bias:1.0214954614639282\n",
            "epoch10 Loss:28.989229202270508 weight:2.0806007385253906 Bias:1.029038906097412\n",
            "epoch11 Loss:25.162382125854492 weight:1.9157683849334717 Bias:1.0248701572418213\n",
            "epoch12 Loss:23.186473846435547 weight:2.034165620803833 Bias:1.0291162729263306\n",
            "epoch13 Loss:22.166126251220703 weight:1.9490584135055542 Bias:1.027315616607666\n",
            "epoch14 Loss:21.639110565185547 weight:2.010172128677368 Bias:1.0298590660095215\n",
            "epoch15 Loss:21.366771697998047 weight:1.9662237167358398 Bias:1.0292805433273315\n",
            "epoch16 Loss:21.22591781616211 weight:1.997764229774475 Bias:1.030944585800171\n",
            "epoch17 Loss:21.15294647216797 weight:1.9750648736953735 Bias:1.0309966802597046\n",
            "epoch18 Loss:21.115009307861328 weight:1.991337537765503 Bias:1.032206416130066\n",
            "epoch19 Loss:21.095176696777344 weight:1.9796085357666016 Bias:1.0325838327407837\n",
            "epoch20 Loss:21.084684371948242 weight:1.9879988431930542 Bias:1.0335586071014404\n",
            "epoch21 Loss:21.07901382446289 weight:1.981933355331421 Bias:1.034103512763977\n",
            "epoch22 Loss:21.075830459594727 weight:1.9862544536590576 Bias:1.034956693649292\n",
            "epoch23 Loss:21.073938369750977 weight:1.9831126928329468 Bias:1.0355877876281738\n",
            "epoch24 Loss:21.07270050048828 weight:1.9853330850601196 Bias:1.0363779067993164\n",
            "epoch25 Loss:21.071819305419922 weight:1.9837009906768799 Bias:1.037053108215332\n",
            "epoch26 Loss:21.07110595703125 weight:1.9848365783691406 Bias:1.037810206413269\n",
            "epoch27 Loss:21.070486068725586 weight:1.9839837551116943 Bias:1.0385079383850098\n",
            "epoch28 Loss:21.069913864135742 weight:1.9845597743988037 Bias:1.039247751235962\n",
            "epoch29 Loss:21.069368362426758 weight:1.9841090440750122 Bias:1.0399566888809204\n",
            "epoch30 Loss:21.068838119506836 weight:1.9843961000442505 Bias:1.0406872034072876\n",
            "epoch31 Loss:21.068307876586914 weight:1.984152913093567 Bias:1.0414016246795654\n",
            "epoch32 Loss:21.067781448364258 weight:1.9842908382415771 Bias:1.042127013206482\n",
            "epoch33 Loss:21.0672607421875 weight:1.9841549396514893 Bias:1.0428439378738403\n",
            "epoch34 Loss:21.066740036010742 weight:1.9842157363891602 Bias:1.043566346168518\n",
            "epoch35 Loss:21.066225051879883 weight:1.9841355085372925 Bias:1.0442842245101929\n",
            "epoch36 Loss:21.06570816040039 weight:1.9841564893722534 Bias:1.0450047254562378\n",
            "epoch37 Loss:21.065187454223633 weight:1.9841045141220093 Bias:1.0457227230072021\n",
            "epoch38 Loss:21.064668655395508 weight:1.9841052293777466 Bias:1.046441912651062\n",
            "epoch39 Loss:21.064157485961914 weight:1.9840680360794067 Bias:1.0471596717834473\n",
            "epoch40 Loss:21.063636779785156 weight:1.984058141708374 Bias:1.0478779077529907\n",
            "epoch41 Loss:21.063125610351562 weight:1.984028697013855 Bias:1.0485951900482178\n",
            "epoch42 Loss:21.062606811523438 weight:1.9840131998062134 Bias:1.0493125915527344\n",
            "epoch43 Loss:21.062095642089844 weight:1.98398756980896 Bias:1.0500292778015137\n",
            "epoch44 Loss:21.06157684326172 weight:1.9839695692062378 Bias:1.0507458448410034\n",
            "epoch45 Loss:21.06106948852539 weight:1.9839458465576172 Bias:1.051461935043335\n",
            "epoch46 Loss:21.06055450439453 weight:1.9839262962341309 Bias:1.0521777868270874\n",
            "epoch47 Loss:21.060043334960938 weight:1.9839037656784058 Bias:1.0528931617736816\n",
            "epoch48 Loss:21.059532165527344 weight:1.9838833808898926 Bias:1.0536082983016968\n",
            "epoch49 Loss:21.05901336669922 weight:1.9838614463806152 Bias:1.0543230772018433\n",
            "epoch50 Loss:21.058509826660156 weight:1.9838409423828125 Bias:1.055037498474121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.linspace(0.0,50.0,50)\n",
        "current_weight = model.linear.weight.item()\n",
        "current_bias = model.linear.bias.item()\n",
        "predicted_y = current_weight * x + current_bias\n"
      ],
      "metadata": {
        "id": "aJiRnUN4gHGn"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"x{x}\")\n",
        "print(f\"pred:{predicted_y}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0vhTw_MngtpE",
        "outputId": "d95b8a9c-d79c-46d1-cde7-6a03575c59a4"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x[ 0.          1.02040816  2.04081633  3.06122449  4.08163265  5.10204082\n",
            "  6.12244898  7.14285714  8.16326531  9.18367347 10.20408163 11.2244898\n",
            " 12.24489796 13.26530612 14.28571429 15.30612245 16.32653061 17.34693878\n",
            " 18.36734694 19.3877551  20.40816327 21.42857143 22.44897959 23.46938776\n",
            " 24.48979592 25.51020408 26.53061224 27.55102041 28.57142857 29.59183673\n",
            " 30.6122449  31.63265306 32.65306122 33.67346939 34.69387755 35.71428571\n",
            " 36.73469388 37.75510204 38.7755102  39.79591837 40.81632653 41.83673469\n",
            " 42.85714286 43.87755102 44.89795918 45.91836735 46.93877551 47.95918367\n",
            " 48.97959184 50.        ]\n",
            "pred:[  1.05575156   3.08005679   5.10436203   7.12866726   9.15297249\n",
            "  11.17727772  13.20158295  15.22588818  17.25019342  19.27449865\n",
            "  21.29880388  23.32310911  25.34741434  27.37171957  29.39602481\n",
            "  31.42033004  33.44463527  35.4689405   37.49324573  39.51755096\n",
            "  41.5418562   43.56616143  45.59046666  47.61477189  49.63907712\n",
            "  51.66338236  53.68768759  55.71199282  57.73629805  59.76060328\n",
            "  61.78490851  63.80921375  65.83351898  67.85782421  69.88212944\n",
            "  71.90643467  73.9307399   75.95504514  77.97935037  80.0036556\n",
            "  82.02796083  84.05226606  86.07657129  88.10087653  90.12518176\n",
            "  92.14948699  94.17379222  96.19809745  98.22240268 100.24670792]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qRdmVuPsg7_s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "uOhqCJ7RgsNK"
      }
    }
  ]
}