Gradient_Descent_from_Scratch.ipynb

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      "name": "Gradient Descent from Scratch.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMyKcyxdRXxfqxKnzjtqOL1",
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/smlra-kjsce/ML-DL101/blob/master/Gradient_Descent_from_Scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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        "id": "CxJJ6cXRamL9",
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      "source": [
        "import numpy as np\n",
        "\n",
        "class NeuralNetwork:\n",
        "  def __init__(self):\n",
        "    np.random.seed(42)\n",
        "    self.Wij = np.random.rand(4,5)\n",
        "    self.Wjk = np.random.rand(5,1)\n",
        "\n",
        "  def sigmoid(self, x, w):\n",
        "    z = np.dot(x,w)\n",
        "    return 1/(1 + np.exp(-z))\n",
        "  \n",
        "  def diff_sigmoid(self, x, w):\n",
        "    return self.sigmoid(x,w)*(1-self.sigmoid(x,w))\n",
        "  \n",
        "  def gradient_descent(self,x,y, epochs):\n",
        "    for i in range(epochs):\n",
        "      Xi = x #Input layer\n",
        "      Xj = self.sigmoid(Xi, self.Wij) #Hidden layer\n",
        "      Y_cap = self.sigmoid(Xj, self.Wjk) #Final layer\n",
        "      grad_Wjk = np.dot(Xj.T, (y-Y_cap)*self.diff_sigmoid(Xj, self.Wjk))\n",
        "      grad_Wij = np.dot(Xi.T, np.dot((y-Y_cap)* self.diff_sigmoid(Xj, self.Wjk), self.Wjk.T) * self.diff_sigmoid(Xi, self.Wij))\n",
        "\n",
        "      self.Wjk += grad_Wjk\n",
        "      self.Wij += grad_Wij\n",
        "\n",
        "    print('The final prediction from neural network are: ')\n",
        "    print(Y_cap)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gYCR_7B1xM6",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "ed9ace23-2948-4cb0-c425-acf75d6fdec3"
      },
      "source": [
        "if __name__ == '__main__':\n",
        "    NN = NeuralNetwork()\n",
        "    print('Random starting input to hidden weights: ')\n",
        "    print(NN.Wij)\n",
        "    print('Random starting hidden to output weights: ')\n",
        "    print(NN.Wjk)\n",
        "    X = np.array([[0, 0, 1, 1], [1, 1, 0, 1], [1, 0, 1, 1], [0, 1, 0, 1], [0, 0, 0, 1]])\n",
        "    y = np.array([[0, 1, 1, 0, 1]]).T\n",
        "    neural_network.gradient_descent(X, y, 10000)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random starting input to hidden weights: \n",
            "[[0.37454012 0.95071431 0.73199394 0.59865848 0.15601864]\n",
            " [0.15599452 0.05808361 0.86617615 0.60111501 0.70807258]\n",
            " [0.02058449 0.96990985 0.83244264 0.21233911 0.18182497]\n",
            " [0.18340451 0.30424224 0.52475643 0.43194502 0.29122914]]\n",
            "Random starting hidden to output weights: \n",
            "[[0.61185289]\n",
            " [0.13949386]\n",
            " [0.29214465]\n",
            " [0.36636184]\n",
            " [0.45606998]]\n",
            "The final prediction from neural network are: \n",
            "[[0.00589178]\n",
            " [0.99658221]\n",
            " [0.99669157]\n",
            " [0.00609915]\n",
            " [0.99361295]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "metadata": {
        "id": "MFIGkAqq10lT",
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
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