one_P.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ramyrahmeni/DeepLearning/blob/main/one_P.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "25bbdf19",
      "metadata": {
        "id": "25bbdf19"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "x = np.random.rand(30, 10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "d0d1f08d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0d1f08d",
        "outputId": "0fc04fb5-7dae-4fe3-8fa4-d0f25f330259"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(30, 1)\n"
          ]
        }
      ],
      "source": [
        "y=np.random.rand(30,1)\n",
        "y = y.reshape(30, -1)\n",
        "print(y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "fce026e9",
      "metadata": {
        "id": "fce026e9"
      },
      "outputs": [],
      "source": [
        "def sigmoid(x):\n",
        "    return  1/(1 + np.exp(-x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "71bcab1c",
      "metadata": {
        "id": "71bcab1c"
      },
      "outputs": [],
      "source": [
        "def sigmoid_derivative(x):\n",
        "    s = sigmoid(x)\n",
        "    ds = s*(1-s)\n",
        "    return ds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "8e50d63e",
      "metadata": {
        "id": "8e50d63e"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "W= np.random.rand(10,1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "40285caa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40285caa",
        "outputId": "7a12e8f5-196c-4a31-b6cc-29f96482daca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10, 1)\n"
          ]
        }
      ],
      "source": [
        "print(W.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Momentum Based Gredient Descent"
      ],
      "metadata": {
        "id": "HAcQySjTE-zv"
      },
      "id": "HAcQySjTE-zv"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "e05d4b9a",
      "metadata": {
        "id": "e05d4b9a",
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "b = 0\n",
        "alpha = 0.01\n",
        "beta = 0.9\n",
        "loss = 0.9\n",
        "W = np.random.rand(10, 1)\n",
        "L = []\n",
        "vdb=0\n",
        "vdw = np.zeros_like(W)\n",
        "x = np.random.rand(31, 10)\n",
        "y = np.random.randint(0, 2, size=(31, 1))\n",
        "\n",
        "def sigmoid(z):\n",
        "    return 1 / (1 + np.exp(-z))\n",
        "\n",
        "while loss > 0.001:\n",
        "    c = np.random.choice(range(31))\n",
        "\n",
        "    Z1 = np.matmul(W.T, x[c]) + b\n",
        "\n",
        "    A1 = sigmoid(Z1)\n",
        "    loss = -y[c] * np.log(A1) - (1 - y[c]) * np.log(1 - A1)\n",
        "    loss = loss.item()\n",
        "    dw = (A1 - y[c]) * x[c].reshape(-1, 1)\n",
        "    db = (A1 - y[c])\n",
        "\n",
        "    vdw = beta * vdw + (1 - beta) * dw\n",
        "    vdb = beta * vdb + (1 - beta) * db\n",
        "\n",
        "    L.append(loss)\n",
        "    W = W - alpha * vdw\n",
        "    b = b - alpha * vdb\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "eaa9b3e5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "eaa9b3e5",
        "outputId": "4109fb22-137f-4043-93d7-802ae2fa4cfb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "#datasets\n",
        "xx= [i for i in range (len(L))]\n",
        "#L=np.reshape(L,(len(L),-1))\n",
        "#scatter plot for the dataset\n",
        "plt.plot(xx,L)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adam Optimizer"
      ],
      "metadata": {
        "id": "p5TmGRefFGtJ"
      },
      "id": "p5TmGRefFGtJ"
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_gredient(x,y,W,b,L):\n",
        "    c = np.random.choice(range(31))\n",
        "\n",
        "    Z1 = np.matmul(W.T, x[c]) + b\n",
        "\n",
        "    A1 = sigmoid(Z1)\n",
        "    loss = -y[c] * np.log(A1) - (1 - y[c]) * np.log(1 - A1)\n",
        "    loss = loss.item()\n",
        "    L.append(loss)\n",
        "    dw = (A1 - y[c]) * x[c].reshape(-1, 1)\n",
        "    db = (A1 - y[c])\n",
        "    return (dw,db)"
      ],
      "metadata": {
        "id": "Wo_yuwotFWrX"
      },
      "id": "Wo_yuwotFWrX",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "f3cc29d2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3cc29d2",
        "outputId": "5e8930b7-eb35-428e-c687-4b57fbc79388"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-f03760762605>:7: RuntimeWarning: divide by zero encountered in log\n",
            "  loss = -y[c] * np.log(A1) - (1 - y[c]) * np.log(1 - A1)\n",
            "<ipython-input-14-f03760762605>:7: RuntimeWarning: invalid value encountered in multiply\n",
            "  loss = -y[c] * np.log(A1) - (1 - y[c]) * np.log(1 - A1)\n"
          ]
        }
      ],
      "source": [
        "num_iterations=10\n",
        "b=0\n",
        "m=0\n",
        "v=0\n",
        "beta1=0.9\n",
        "beta2=0.999\n",
        "W = np.random.rand(10, 1)\n",
        "alpha = 0.01\n",
        "epsilon=0.0001\n",
        "L=[]\n",
        "for t in range(1,250):\n",
        "  g=compute_gredient(x,y,W,b,L)\n",
        "  mw=beta1*m+(1-beta1)*g[0]\n",
        "  vw=beta2*v+(1-beta2)*g[0]**2\n",
        "  mb=beta1*m+(1-beta1)*g[1]\n",
        "  vb=beta2*v+(1-beta2)*g[1]**2\n",
        "  mw_hat=mw/(1-beta1**t)\n",
        "  vw_hat=vw/(1-beta2**t)\n",
        "  W=W-alpha*mw_hat/(vw_hat**2+epsilon)\n",
        "  mb_hat=mb/(1-beta1**t)\n",
        "  vb_hat=vb/(1-beta2**t)\n",
        "  b=b-alpha*mb_hat/(vb_hat**2+epsilon)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "aa502a23",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aa502a23",
        "outputId": "1509cb24-4c08-4096-d534-4a81c205e974"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.4831858432530827,\n",
              " 3.521073908131244,\n",
              " 0.24423615038270663,\n",
              " 20.19868557256878,\n",
              " 2.1789308159305596e-09,\n",
              " 19.743980541665618,\n",
              " 10.481111762117129,\n",
              " 0.7741868898125175,\n",
              " 11.221701418565388,\n",
              " 0.14518834256414834,\n",
              " 4.672339094160116e-06,\n",
              " 4.671232050416334e-06,\n",
              " 1.7417480436698135e-05,\n",
              " 6.461990297086189e-09,\n",
              " 17.201993465031062,\n",
              " 0.0005339078137987537,\n",
              " 12.410538534801285,\n",
              " 1.134144166762103,\n",
              " 4.821907342198756e-06,\n",
              " 4.506319032972873e-07,\n",
              " 18.522717112116855,\n",
              " 0.014649531560846571,\n",
              " 8.362775016489739,\n",
              " 3.060995970936699,\n",
              " 13.583640869229807,\n",
              " 0.014870556139519056,\n",
              " 0.7348386839664468,\n",
              " 13.219128979440251,\n",
              " 2.1651740040200553,\n",
              " 17.08165042892292,\n",
              " 0.48689201702618595,\n",
              " 19.396568649954975,\n",
              " 1.2319847059189795,\n",
              " 26.04490090574273,\n",
              " 8.8779228301322e-05,\n",
              " 0.40871375330221843,\n",
              " 2.0309717261942257e-09,\n",
              " 23.887038152526276,\n",
              " 15.239570521587606,\n",
              " 0.020927871544483313,\n",
              " 0.03113982441134049,\n",
              " 0.001252951907492657,\n",
              " 0.04152244266599284,\n",
              " 0.052977746410048134,\n",
              " 0.001151885577219986,\n",
              " 3.8658775459313644e-07,\n",
              " 0.00014236933204773018,\n",
              " 11.44055285843169,\n",
              " 13.001501255513713,\n",
              " 1.0921855380323145e-06,\n",
              " 0.0001376222270477051,\n",
              " 2.5880676387039113e-07,\n",
              " 20.454652565516312,\n",
              " 1.8951998026652164,\n",
              " 4.5266642050580335e-05,\n",
              " 1.1054043917802935e-05,\n",
              " 3.1456116038049717e-06,\n",
              " 4.515750336271591e-05,\n",
              " 7.4980198618954265e-06,\n",
              " 1.8450901005037975e-05,\n",
              " 0.05884384748044082,\n",
              " 18.441695577123234,\n",
              " 9.719835446378756,\n",
              " 21.13622724878616,\n",
              " 10.05246738734205,\n",
              " 18.751269369365975,\n",
              " 10.808774560250065,\n",
              " 3.041752067448135e-05,\n",
              " 4.976380733296859e-05,\n",
              " 9.4463882719772,\n",
              " 19.013320724988535,\n",
              " 16.66587397582918,\n",
              " 1.702553537647277e-07,\n",
              " 1.7025388460634908e-07,\n",
              " 9.716418938836405,\n",
              " 17.018826821200243,\n",
              " 1.144103085487608e-08,\n",
              " 12.876764522168045,\n",
              " 13.522459695289708,\n",
              " 10.950396863356852,\n",
              " 5.320436215875573,\n",
              " 1.6861542917940483e-07,\n",
              " 4.3442782713953336e-10,\n",
              " 12.983138304780807,\n",
              " 5.2368137916371375e-05,\n",
              " 1.742193686953257,\n",
              " 12.192021317005887,\n",
              " 0.0026610693389498225,\n",
              " 0.7788113525898334,\n",
              " 11.081718848116076,\n",
              " 3.931910786195935,\n",
              " 4.960055141835416,\n",
              " 0.009643243564377723,\n",
              " 1.1657204602552245,\n",
              " 0.10957810704342807,\n",
              " 4.213764854816037e-05,\n",
              " 9.94048825975375e-09,\n",
              " 3.7285797691657048,\n",
              " 3.6615155352144367e-13,\n",
              " 3.1933276126132316e-07,\n",
              " 6.443309767789551,\n",
              " 31.56863008314402,\n",
              " 1.7130106882290004,\n",
              " nan,\n",
              " 23.988403633321568,\n",
              " 1.1573560791746065,\n",
              " 22.36829950525802,\n",
              " 3.1831766082260763e-07,\n",
              " 0.0002951637150662998,\n",
              " 1.8395655093315177,\n",
              " 17.521985026198337,\n",
              " 6.796647691409876e-10,\n",
              " 0.049054748813195125,\n",
              " 3.491877732667904e-07,\n",
              " 0.0013838967953199045,\n",
              " 2.835251397085054,\n",
              " nan,\n",
              " 22.859711492060452,\n",
              " 0.00015685605476144712,\n",
              " 0.0034021926242357226,\n",
              " 0.010938251901676452,\n",
              " 0.30806746580250793,\n",
              " 4.720139012976149,\n",
              " 9.439560244617484e-12,\n",
              " 0.2377862557344692,\n",
              " 13.734345603158923,\n",
              " 26.9044868365955,\n",
              " 8.593886694066953e-08,\n",
              " 1.6934722392796195e-07,\n",
              " 0.0018127551906507223,\n",
              " 8.629805662285067e-07,\n",
              " 5.568686253628144e-06,\n",
              " 2.3307914514510092e-09,\n",
              " 0.14112709036630625,\n",
              " 0.012489878323847263,\n",
              " 7.76143534891443,\n",
              " 20.32335680558752,\n",
              " 6.9576398355530324,\n",
              " 2.2204460492503136e-16,\n",
              " 7.993605777301159e-15,\n",
              " 4.018582875773591,\n",
              " 0.00841059797562987,\n",
              " 9.230283204473689e-12,\n",
              " 6.972007272486359,\n",
              " nan,\n",
              " 1.1776130420300986e-07,\n",
              " 2.087219286295316e-14,\n",
              " 1.1776078484061777e-07,\n",
              " 1.8274270985331745e-13,\n",
              " 1.1776026570027033e-07,\n",
              " 23.697957941326145,\n",
              " 0.010322321337642413,\n",
              " 0.000916589307845713,\n",
              " 4.646481343094923,\n",
              " 3.8704893903291566,\n",
              " 9.606756230941349,\n",
              " inf,\n",
              " 2.89784186779521e-05,\n",
              " 2.8931868357535008e-05,\n",
              " 0.0025583587800408793,\n",
              " 8.304468224196515e-14,\n",
              " 0.038754336786438796,\n",
              " 3.0920761464267286,\n",
              " 32.48830532762774,\n",
              " 0.0006643071684452789,\n",
              " 8.694868307621055e-09,\n",
              " 6.641864836119586e-11,\n",
              " 8.69486519899656e-09,\n",
              " 1.7633635263494398e-07,\n",
              " 5.044416682387182,\n",
              " 13.99326988504277,\n",
              " 0.7785393995366642,\n",
              " 2.8865798640254113e-15,\n",
              " 1.03694830499995e-13,\n",
              " 6.176790436723987,\n",
              " 15.414278904187418,\n",
              " 44.614987322697345,\n",
              " 23.615733094254153,\n",
              " 1.904048491462779e-05,\n",
              " 5.48268751136421e-06,\n",
              " 3.330669073875475e-15,\n",
              " 0.00018012261010895563,\n",
              " 8.67521483420617e-08,\n",
              " 0.3006123854666701,\n",
              " 2.647491437931286,\n",
              " 4.2127992608656303e-07,\n",
              " 8.869805637739697,\n",
              " 5.830758098738355e-11,\n",
              " 20.903115548180306,\n",
              " 6.185496559415727e-12,\n",
              " 6.740472955305705,\n",
              " 0.008058578690031844,\n",
              " 1.6853185513811297e-13,\n",
              " 2.2204460492503136e-16,\n",
              " 3.7214885035661536,\n",
              " 18.045839554221878,\n",
              " 4.019751199377486e-10,\n",
              " 9.837717005147358,\n",
              " 21.63341188263661,\n",
              " 3.27944817452263e-07,\n",
              " 0.0634560048199702,\n",
              " 0.08986658416458775,\n",
              " 0.005601595096907029,\n",
              " 4.674860421544509e-06,\n",
              " 5.462297281157262e-13,\n",
              " 18.98508981939485,\n",
              " 17.53241222307613,\n",
              " 0.005493965335132585,\n",
              " 12.272565445471725,\n",
              " 27.156415034824015,\n",
              " 18.33164620889907,\n",
              " 23.551192007133757,\n",
              " 1.3686828783173736e-06,\n",
              " 1.0855538690238778e-11,\n",
              " 2.2054749162399077e-09,\n",
              " 2.764455331316678e-14,\n",
              " 1.0480193323686481e-08,\n",
              " 0.0010107118046560769,\n",
              " 0.0002386808928532067,\n",
              " 3.9039327327168726e-11,\n",
              " 29.863636735464585,\n",
              " 15.607408391522391,\n",
              " 18.291813111973987,\n",
              " 22.12257095835096,\n",
              " 16.72239265569707,\n",
              " 1.5543122344752203e-15,\n",
              " 8.881784197001292e-15,\n",
              " 2.5823787552784473e-13,\n",
              " 1.5543122344752203e-15,\n",
              " 4.567457523308937e-13,\n",
              " nan,\n",
              " inf,\n",
              " 32.35082915805541,\n",
              " 34.43421547668306,\n",
              " 23.39500368600427,\n",
              " 2.845857789988085,\n",
              " 0.000792616214700831,\n",
              " 19.549441296666625,\n",
              " 0.022507089423166666,\n",
              " 21.981741452157628,\n",
              " 0.0002332878931586586,\n",
              " -0.0,\n",
              " 2.728169912574961e-10,\n",
              " -0.0,\n",
              " 13.153964054124412,\n",
              " 0.032305692674099315,\n",
              " 0.000583106412245482,\n",
              " 6.8400212394853405e-09,\n",
              " 3.0348631206634732e-05]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "L"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "a59c2748",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "a59c2748",
        "outputId": "b6b5254f-0395-480c-b38b-354759387375"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "#datasets\n",
        "xx= [i for i in range (len(L))]\n",
        "#L=np.reshape(L,(len(L),-1))\n",
        "#scatter plot for the dataset\n",
        "plt.plot(xx,L)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "akVkaiTvz7NN"
      },
      "id": "akVkaiTvz7NN",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.11.5"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}