NNDL_Activation-function-L1P1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kMTKYrOVePjM"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SIGMOIDAL / LogisticFUNCTION"
      ],
      "metadata": {
        "id": "0BQHLsfPgo5O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "def sig(x):\n",
        " return 1/(1 + np.exp(-x))\n",
        "\n",
        "\n",
        "x = 1.0\n",
        "print('Applying Sigmoid Activation on (%.1f) gives %.1f' % (x, sig(x)))\n",
        "\n",
        "x = -10.0\n",
        "print('Applying Sigmoid Activation on (%.1f) gives %.1f' % (x, sig(x)))\n",
        "\n",
        "x = 0.0\n",
        "print('Applying Sigmoid Activation on (%.1f) gives %.1f' % (x, sig(x)))\n",
        "\n",
        "x = 15.0\n",
        "print('Applying Sigmoid Activation on (%.1f) gives %.1f' % (x, sig(x)))\n",
        "\n",
        "x = -2.0\n",
        "print('Applying Sigmoid Activation on (%.1f) gives %.1f' % (x, sig(x)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HoPEGvXCejnf",
        "outputId": "a840f9d7-2ca0-4bb6-e33b-af38d5ddce10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying Sigmoid Activation on (1.0) gives 0.7\n",
            "Applying Sigmoid Activation on (-10.0) gives 0.0\n",
            "Applying Sigmoid Activation on (0.0) gives 0.5\n",
            "Applying Sigmoid Activation on (15.0) gives 1.0\n",
            "Applying Sigmoid Activation on (-2.0) gives 0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.linspace(-10, 10, 50)   \n",
        "p = sig(x)\n",
        "plt.xlabel(\"x\") \n",
        "plt.ylabel(\"Sigmoid(x)\")  \n",
        "plt.plot(x, p) \n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "I4POXVQGgUpM",
        "outputId": "ab3b0f33-5a91-4601-cabd-d10719062a3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "LINEAR ACTIVATION FUNCTION"
      ],
      "metadata": {
        "id": "bWCygbzmhBkI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_data = np.linspace(-6,6,100)\n",
        "z=x\n",
        "plt.plot(x, z)\n",
        "plt.title('Linear Activation Function ')\n",
        "plt.legend(['linea'])\n",
        "plt.grid()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "VMbewexWkAR4",
        "outputId": "8d45a22b-a63e-4096-cc22-db5f55b8c7e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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kAHuAP6vqOh/TTwQmAsTFxcUnJiZWKEd2djZRUVEVmtZNlss/lss/luv0dh4v5pXUAnYcL+HMWOXmvg1pHBFcJ4Mru74SEhLWqGr/n41QVdcfQD3gEBDnY1wjIMp5PgrYfLr5xcfHa0UlJSVVeFo3WS7/WC7/WK6y5RYU6eMfbtAu097X+AeX6dLkPUGRy5fK5gJWq49tanWVu8vw7A3s91GIjqtqtvN8KVBXRJpVUy5jTBhbs+MIl//jC55JSmfMWW1Y/qcLuaxvq0DHqnbVdY7gemCurxEi0hLYr6oqIgOBWsDhaspljAlDJ/I9ncS9+s12Wsc04NVbBnJR9+aBjhUwrhcCEYkELgbu8Bp2J4CqPg+MBX4jIkVALjDO2YUxxpgq9/mmg0xbkMKezFzGn9uBv9TATuKqmuvvXlVPALGlhj3v9fwZ4JnS0xljTFU6llPArPfXM29NBp2bR/KfOwbTv2PN7CSuqoV3GTTGhIUPUvZy/7vrOJpTwF0JXfjdsJrdSVxVs0JgjAlZB47nMf3ddXy4bh+9Wzfi1VsG0Lt1ze8krqpZITDGhBxVZd6aDB5ckkZeUQmTR/bg9gtCp5O4qmaFwBgTUnYdyeGehSl8sfkQAzo24ZFr+9GleXB8aS1YWSEwxoSEkhLltW+289hHGxHgwTG9+dWgDiHZSVxVs0JgjKnx0g9kMWV+Cmt2HOWi7s352zV9adO4QaBj1RhWCIwxNVZhcQkvfr6Vp5dvpmH92jzxizO55pw2Id9JXFWzQmCMqZFSd2cyeV4yaXuPc3m/Vsy4ojfNo+sHOlaNZIXAGFOj5BUW8/Qnm3nx8600jazHCzfGc2nvloGOVaNZITDG1BjfbTvC1PnJbD10guv6t+OeUWcQ07BuoGPVeFYIjDFBLzu/iEc/2MDrK3fQtkkD3rh1EOd3s06Kq4oVAmNMUEvaeIB7F6Sw93getwzpxJ8v7U7Derbpqkq2No0xQenoiQIeXJLGgh9207VFFPPuPI/4Dk0CHSskWSEwxgQVVeX9lL389d11ZOYW8vthXblrWFfq17FO4txihcAYEzT2H8/j/kWpfJy2n75tYnj91kH0at0o0LFCnhUCY0zAqSrvrN7FrPfXU1BUwrTLenLr+Z2oY53EVYvquEPZdiALKAaKVLV/qfECPI3nxvU5wARV/d7tXMaY4LDzcA7TFibzVfphBnZqyqPX9qNTs8hAxwor1bVHkKCqh8oYdxnQzXkMAv7l/DTGhLDiEuWj7YUs+uRzatcSHrq6D9cPaG+dxAVAMBwaGgO85tyneKWINBaRVqq6N9DBjDHu2Lw/i8nzk/lhZwHDerZg1lV9aG2dxAWMuH2feBHZBhwFFHhBVV8sNX4J8Iiqfum8/gSYoqqrS7WbCEwEiIuLi09MTKxQnuzsbKKigq9vcsvlH8vln2DJVVSivL+1kPe2FBJRB67tpAztFBl0ncQFy/oqrbK5EhIS1pQ+PA94TtK4+QDaOD9bAGuBC0uNXwKc7/X6E6D/qeYZHx+vFZWUlFThad1kufxjufwTDLnW7jqqlz71mXaYskQnvfW9HsrKC4pcvoRqLmC1+timun5oSFV3Oz8PiMhCYCDwuVeT3UA7r9dtnWHGmBCQV1jMU8s28dIXW2keXZ+Xxvfn4l5xgY5lvLhaCEQkEqilqlnO80uAmaWaLQYmiUginpPEmWrnB4wJCSu3Hmbq/GS2H85h3IB2TBt1BjENrJO4YOP2HkEcsNA5/lcHeEtVPxSROwFU9XlgKZ5LR9PxXD56s8uZjDEuy8or5JEPNvDmtztp37Qhb902iPO6WidxwcrVQqCqW4EzfQx/3uu5Ane5mcMYU30+3bCfexemsv94Hred34m7L+lBg3rWPUQwC4bLR40xIeDIiQJmvreORT/uoXtcFM/96jzObm+dxNUEVgiMMZWiqryXvJcZi9eRlVfI74d3466ELtZJXA1ihcAYU2H7MvO4b1EKy9cf4My2MTw6dhA9W1oncTWNFQJjjN9KSpTEVbt4eOl6CktKuO/yM7h5SCdqW/cQNZIVAmOMX7YfOsHUBcms3HqEwZ1jeeTavnSItU7iajIrBMaYcikuUWZ/uY0nlm2kbq1aPHxNX8YNaBd03UMY/1khMMac1sZ9WUyet5a1GZmMOKMFs67qS8uYiEDHMlXECoExpkwFRSU8m5TOcyvSaRRRl39efzaj+7WyvYAQY4XAGOPTj7uOMXneWjbtz+aqs1oz/YreNI2sF+hYxgVWCIwxP5FbUMwTH29k9lfbiGsUwewJ/RnW0zqJC2VWCIwx//V1+iGmLkhh55Ecfn1ue6aM7El0hHUSF+qsEBhjyMwt5OGl60lctYuOsQ1JnHgu53aODXQsU02sEBgT5pal7ee+RSkczMrnjos688cR3Ymoa91DhBMrBMaEqUPZ+cxYvI4lyXvp2TKal8b3p1/bxoGOZQLACoExYUZVeffHPTzw3jpO5Bdz98XdueOiLtSrUyvQ0UyAuFYIRKQd8Bqem9Mo8KKqPl2qzVDgXWCbM2iBqpa+g5kxporsOZbLfYtS+XTDAc5u35jHru1Ht7joQMcyAebmHkERcLeqfi8i0cAaEVmmqmml2n2hqqNdzGFM2CtR5Y2VO3jkgw0UlyjTR/fipvM6WidxBnCxEDj3Hd7rPM8SkfVAG6B0ITDGuGjboRM8+l0eG4+mMqRrLA9f3Y/2sQ0DHcsEEfHcKdLlhYh0BD4H+qjqca/hQ4H5QAawB/izqq4rYx4TgYkAcXFx8YmJiRXKkp2dTVRUVIWmdZPl8o/lOr3iEuWjHYUs3FxIHVGuP6M+F7SpE1TdQwTT+vIWqrkSEhLWqGr/n41QVVcfQBSwBrjGx7hGQJTzfBSwuTzzjI+P14pKSkqq8LRuslz+sVyntm53po7+xxfaYcoSvf3VVbrgg08CHcmnYFlfpYVqLmC1+timunqZgIjUxfMf/5uqusBHETquqtnO86VAXRFp5mYmY0JZfpGne4grn/mSvZm5PPerc3jhxniaRNgVQaZsbl41JMArwHpVfbKMNi2B/aqqIjIQqAUcdiuTMaFszY6jTJmfTPqBbK45uw33j+5FE+skzpSDm1cNDQFuBFJE5Edn2D1AewBVfR4YC/xGRIqAXGCcs/tijCmnnIIiHv9oI3O+3k7rmAbMuXkAQ3u0CHQsU4O4edXQl8Apz0qp6jPAM25lMCbUfbn5EFMXJJNxNJfxgzsweWRPourb90SNf+wTY0wNlJlTyENL03hndQadm0Xyzh2DGdipaaBjmRrKCoExNcyHqfu4/91Ujpwo4M6LuvCHEd2skzhTKVYIjKkhDmZ5Ool7P2UvvVo14t8TBtCnTUygY5kQYIXAmCCnqiz4fjczl6SRW1DMXy7twcQLO1O3tl0SaqqGFQJjgtjuY7ncsyCFzzYdJL5DEx69th9dWwTfN15NzWaFwJggVFKivPHtDh79YAMK/PWKXowfbJ3EGXdYITAmyGw5mM3U+cms2n6UC7o1429X96VdU+skzrjHCoExQaKwuISXvtjK/y7fTESdWjw+th9j49sGVSdxJjRZITAmCKzbk8nkecms23Ockb1bMvOq3rSIjgh0LBMmrBAYE0B5hcX889PNPP/ZVpo0rMdzvzqHUX1bBTqWCTNWCIwJkNXbjzB5fjJbD55gbHxb7rv8DBo3tE7iTPWzQmBMNcvOL+LxDzfw2sodtI5pwGu3DOTC7s0DHcuEMSsExlSjzzYd5J4FKezJzOWmwR35y6U9iLRO4kyA2SfQmGpwLKeAB5esZ/73GXRpHsm8OwcT38E6iTPBwQqBMS77IGUv97+7jmM5BUxK6MqkYV2tkzgTVKwQGOOSA8fzmP7uOj5ct48+bRrx6i0D6N3aOokzwcf1QiAiI4GngdrAy6r6SKnx9YHXgHg8t6m8TlW3u53LGLeoKvPWZPDgkjTyikqYMrInt1/QiTrWSZwJUq4WAhGpDTwLXAxkAKtEZLGqpnk1uxU4qqpdRWQc8ChwnZu5jHHLwZwSxs/+ji82H2JAR08ncZ2bWydxJri5vUcwEEhX1a0AIpIIjAG8C8EYYIbzfB7wjIiI3bvY1CTFJcpr32znka9yqVO7gAfH9OZXgzpQyzqJMzWAuLm9FZGxwEhVvc15fSMwSFUnebVJddpkOK+3OG0OlZrXRGAiQFxcXHxiYmKFMmVnZxMVFXz/oVku/wRTrj3ZJcxOzSf9WAm9mii39mtIbIPgOgwUTOvLm+XyT2VzJSQkrFHV/qWH15iTxar6IvAiQP/+/XXo0KEVms+KFSuo6LRuslz+CYZchcUlvPDZFv7xTToN69fmyV/2pUnmZhISEgKay5dgWF++WC7/uJXL7UKwG2jn9bqtM8xXmwwRqQPE4DlpbEzQSt2dyV/mJbN+73Eu79eKGVf0pnl0fVasSA90NGP85nYhWAV0E5FOeDb444AbSrVZDNwEfAOMBT618wMmWOUVFvO/yzfz0hdbaRpZjxdujOfS3i0DHcuYSnG1EKhqkYhMAj7Cc/nobFVdJyIzgdWquhh4BXhdRNKBI3iKhTFB57ttR5g6P5mth05wXf923DPqDGIa1g10LGMqzfVzBKq6FFhaath0r+d5wC/czmFMRWXlFfLYhxt5feUO2jZpwBu3DuL8bs0CHcuYKlNjThYbEwhJGw9w74IU9h7P4+Yhnk7iGtazPxsTWuwTbYwPR08U8OCSNBb8sJtuLaKYd+d5xHdoEuhYxrjCCoExXlSV91P28td315GZW8jvh3XlrmFdqV/HOokzocsKgTGO/cfzuG9RKsvS9tO3TQxv3DaIM1o1CnQsY1xnhcCEPVXlndW7mPX+egqKSph2WU9uPd86iTPhwwqBCWs7D+cwdUEyX285zKBOTXn02n50bBYZ6FjGVCsrBCYsFZcoc77ezt8/2kjtWsJDV/fh+gHtrZM4E5asEJiws3l/FpPnJ/PDzmMM69mCh67uQ6uYBoGOZUzAWCEwYaOgqITnP9vCPz/dTFT9Ojw97iyuPLM1IrYXYMKbFQITFtbuOsaU+cls2JfFlWe25q9X9CI2qn6gYxkTFKwQmJCWW1DMU8s38fIXW2kRHcHL4/szoldcoGMZE1SsEJiQ9c2Ww0xdkMyOwzlcP7A900b1pFGEdRJnTGlWCEzIOZ5XyMNLNzD3u510iG3IW7cP4rwu1kmcMWWxQmBCyifr93PvwlQOZOVx+wWd+NPFPWhQz7qHMOZUrBCYkHA4O58H3ktj8do99IiL5vkb4zmrXeNAxzKmRrBCYGo0VWXx2j088F4aWXmF/GFEN347tCv16lj3EMaUlyuFQEQeB64ACoAtwM2qesxHu+1AFlAMFKlqfzfymNC0LzOP+xalsHz9Ac5s15jHru1Hj5bRgY5lTI3j1h7BMmCac6vKR4FpwJQy2iao6iGXcpgQpKqs2FXI75I+o7CkhPsuP4Obh3SitnUPYUyFuFIIVPVjr5cr8dyU3phK237oBNMWpPDN1gIGd47lkWv70iHWOokzpjJEVd1dgMh7wNuq+oaPcduAo4ACL6jqi6eYz0RgIkBcXFx8YmJihfJkZ2cTFRVVoWndZLlOrUSVj7YXsXBzAbVrwZiOyqVdIoOue4hgWV+lWS7/hGquhISENT4PwatqhR7AciDVx2OMV5t7gYU4BcfHPNo4P1sAa4ELy7Ps+Ph4raikpKQKT+smy1W29Xsz9cp/fqEdpizRW+es0r3HcoMily+Wyz+Wyz+VzQWsVh/b1AofGlLVEacaLyITgNHAcCeAr3nsdn4eEJGFwEDg84pmMqElv6iYZ5O28FxSOjEN6vLP689mdL9WiAgbAh3OmBDi1lVDI4HJwEWqmlNGm0iglqpmOc8vAWa6kcfUPD/sPMqU+cls2p/N1We34f7RvWgaWS/QsYwJSW5dNfQMUB9Y5hzDXamqd4pIa+BlVR0FxAELnfF1gLdU9UOX8pgaIqegiCc+3sTsr7bRslEE/54wgISeLQIdy5iQ5tZVQ13LGL4HGOU83wqc6cbyTc30dfohpi5IYeeRHH59bnumjOxJtHUSZ4zr7JvFJuAycwt5eOl6ElftolOzSN6eeC6DOscGOkMGPiYAABIOSURBVJYxYcMKgQmoZWn7uW9RCgez8rnjos78cUR3IupaJ3HGVCcrBCYgDmXnM2PxOpYk76Vny2heGt+ffm2tkzhjAsEKgalWqsqiH3fzwHtp5OQXc/fF3bnjoi7WSZwxAWSFwFSbPcdyuXdhCkkbD3J2e08ncd3irJM4YwLNCoFxXUmJ8uZ3O3n0gw0UlyjTR/fipvM6WidxxgQJKwTGVdsOnWDK/GS+23aE87s24+Fr+tKuacNAxzLGeLFCYFxRVFzCy19u46llm6hXpxaPXduPX/RvG3SdxBljrBAYF6TtOc7k+WtJ3X2cS3rF8eBVfYhrFBHoWMaYMlghMFUmv6iYZz5N518rttC4YV2eveEcRvVtaXsBxgQ5KwSmSqzZ4ekkLv1ANtec04b7L+9FE+skzpgawQqBqZQT+UX8/eONzPl6O61jGjDn5gEM7WGdxBlTk1ghMBX2xeaDTFuQQsbRXMYP7sDkkT2Jqm8fKWNqGvurNX7LzClk1vtp/GdNBp2bRfLOHYMZ2KlpoGMZYyrICoHxy4ep+7j/3VSOnCjgN0O78D/Du1knccbUcK518CIiM0Rkt4j86DxGldFupIhsFJF0EZnqVh5TOQey8vjtm2u48401NI+qz7t3DWHKyJ5WBIwJAW7vETylqn8va6SI1AaeBS4GMoBVIrJYVdNczmXKSVWZvyaDmUvSyC0s5i+X9mDihZ2pW9s6iTMmVAT60NBAIN25WxkikgiMAawQBIGMozk8sSaf1ENrie/QhEev7UfXFlGBjmWMqWKiqu7MWGQGMAE4DqwG7lbVo6XajAVGquptzusbgUGqOsnH/CYCEwHi4uLiExMTK5QrOzubqKjg25gFU64SVT7dWcS8TQWUqPLLHvUZ1r4OtYLoi2HBtL68WS7/WC7/VDZXQkLCGlXt/7MRqlrhB7AcSPXxGIPn5vS18ZyHeAiY7WP6sXhuZn/y9Y3AM6dbbnx8vFZUUlJShad1U7DkSj+QpWP/9ZV2mLJEb3zlW/3P0k8CHcmnYFlfpVku/1gu/1Q2F7BafWxTK3VoSFVHlKediLwELPExajfQzut1W2eYqWaFxSW8+PlWnv5kMw3q1ubxsf0YG9+Wzz77LNDRjDEuc+0cgYi0UtW9zsur8ewplLYK6CYinfAUgHHADW5lMr6l7s5kyvxk1u05zqi+LZlxZW9aRFsnccaECzdPFj8mImcBCmwH7gAQkdZ4DgeNUtUiEZkEfITnMNJsVV3nYibjJa+wmH98spkXPt9K08h6PP/rcxjZp1WgYxljqplrhUBVbyxj+B5glNfrpcBSt3IY31ZvP8Lk+clsPXiCX8S35b7LexHTsG6gYxljAiDQl4+aapadX8TjH27gtZU7aNO4Aa/fOpALujUPdCxjTABZIQgjn206yD0LUtiTmctNgzvyl0t7EGmdxBkT9mwrEAaO5RTw4JL1zP8+gy7NI5l352DiO1gnccYYDysEIW5pyl6mv5vKsZxCJiV0ZdKwrtY/kDHmJ6wQhKgDx/OY/u46Ply3jz5tGvHqLQPp3Tom0LGMMUHICkGIUVX+syaDWUvSyC8qYeplPbnt/E7UsU7ijDFlsEIQQnYdyeGehSl8sfkQAzs25ZFr+9K5efD1l2KMCS5WCEJAcYny+jfbeeyjjQjw4FV9+NXA9tSqFTydxBljgpcVghou/UAWk+cl8/3OYwzt0ZyHru5Lm8YNAh3LGFODWCGooQqLS3jhsy3845N0GtavzVPXnclVZ7VBgqiraGNMzWCFoAZKycjkL/PWsmFfFpf3a8UDV/amWVT9QMcyxtRQVghqkLzCYp5avomXv9hGbGQ9Xrgxnkt7twx0LGNMDWeFoIb4duthpi5IYduhE4wb0I5po84gpoF1EmeMqTwrBEEuK6+QRz/cwBsrd9KuaQPevG0QQ7o2C3QsY0wIsUIQxJI2HOCehSnsO57Hred34u5LutOwnv3KjDFVy7YqQejIiQIeXJLGwh92061FFPN/cx7ntG8S6FjGmBDlSiEQkbeBHs7LxsAxVT3LR7vtQBZQDBSpan838tQUqsqS5L3MWLyOzNxCfj+8G3cldKF+HeskzhjjHlcKgaped/K5iDwBZJ6ieYKqHnIjR01yNK+E219bw/L1++nXNoY3bx9Ez5aNAh3LGBMGXD00JJ5vN/0SGObmcmoyVeXtVbt44MtcSsjn3lFncPOQjtZJnDGm2oiqujdzkQuBJ8s65CMi24CjeG5w/4KqvniKeU0EJgLExcXFJyYmVihTdnY2UVHB0RHbgZwS/p2az/ojJXSLUW7r15C4yOAqAMG0vrxZLv9YLv+Eaq6EhIQ1PrfHqlqhB7AcSPXxGOPV5l/A3aeYRxvnZwtgLXBheZYdHx+vFZWUlFThaatKUXGJvvT5Fu1x31LtM/1DfWPldv3k008DHcunYFhfvlgu/1gu/4RqLmC1+timVvjQkKqOONV4EakDXAPEn2Ieu52fB0RkITAQ+LyimWqCTfs9ncT9uOsYw3u2YNbVfWgV04AVK7YFOpoxJky5eY5gBLBBVTN8jRSRSKCWqmY5zy8BZrqYJ6AKikr414otPJO0meiIujw97iyuPLO1dRJnjAk4NwvBOGCu9wARaQ28rKqjgDhgobMhrAO8paofupgnYNbuOsaU+cls2JfFmLNaM310L2KtkzhjTJBwrRCo6gQfw/YAo5znW4Ez3Vp+MMgtONlJ3FZaREfw8vj+jOgVF+hYxhjzE/bNYpd8s+UwUxcks+NwDjcMas/Uy3rSKMI6iTPGBB8rBFXseF4hDy/dwNzvdtIhtiFv3T6I87pYJ3HGmOBlhaAKfbJ+P/cuTOVAVh4TL+zMH0d0p0E96x7CGBPcrBBUgcPZ+TzwXhqL1+6hZ8toXrgxnjPbNQ50LGOMKRcrBJWgqixeu4cH3ksjK6+QP47ozm+GdqFeneD6drAxxpyKFYIK2puZy30LU/lkwwHOateYx8b2o3tcdKBjGWOM36wQ+KmkRJm7aicPL91AUUkJ911+BjcP6UTtWvbFMGNMzWSFwA/bD51g6oJkVm49wnldYnnkmn60j20Y6FjGGFMpVgjKoai4hNlfbeOJjzdRr04tHr22L7/s3866hzDGhAQrBKexYd9xpsxLZm1GJhf3imPWVX2IaxQR6FjGGFNlrBCUIb+omGeTtvBcUjoxDeryzA1nc3nfVrYXYIwJOVYIfPh+51GmzEtm84Fsrj67DdNH96JJZL1AxzLGGFdYIfCSU1DEEx9vYvZX22jZKIJ/TxhAQs8WgY5ljDGuskLg+Cr9EFMXJLPrSC6/Prc9U0b2JNo6iTPGhIGwLwSZuYU8vHQ9iat20alZJG9PPJdBnWMDHcsYY6pNWBeCj9ft475FqRzKzueOizydxEXUtU7ijDHhpVKd4ojIL0RknYiUiEj/UuOmiUi6iGwUkUvLmL6TiHzrtHtbRKrljOzBrHzueut7Jr6+hqaR9Vh01xCmXXaGFQFjTFiq7B5BKp4b1L/gPVBEeuG5VWVvoDWwXES6q2pxqekfBZ5S1UQReR64FfhXJTOVSVX5ek8Rf/j8M3Lyi/nzJd2546Iu1K1tncQZY8JXpbaAqrpeVTf6GDUGSFTVfFXdBqQDA70biOeC/GHAPGfQq8BVlclzKoXFJdwyZxUvJufTuVkkS//nfCYN62ZFwBgT9kRVKz8TkRXAn1V1tfP6GWClqr7hvH4F+EBV53lN08xp09V53c5p06eMZUwEJgLExcXFJyYm+p1z7vp8omsXMqpbJLWC7Ith2dnZREVFBTrGz1gu/1gu/1gu/1Q2V0JCwhpV7f+zEap6ygewHM8hoNKPMV5tVgD9vV4/A/za6/UrwNhS820GpHu9bgekni6PqhIfH68VlZSUVOFp3WS5/GO5/GO5/BOquYDV6mObetpzBKo6ogKFZ7ezYT+prTPM22GgsYjUUdWiMtoYY4xxmVsHyBcD40Skvoh0AroB33k3cKpTEjDWGXQT8K5LeYwxxpShspePXi0iGcBg4H0R+QhAVdcB7wBpwIfAXepcMSQiS0WktTOLKcCfRCQdiMVzCMkYY0w1qtTlo6q6EFhYxriHgId8DB/l9Xwrpa4mMsYYU73s2kljjAlzVgiMMSbMWSEwxpgwZ4XAGGPCXJV8s7i6ichBYEcFJ28GHKrCOFXFcvnHcvnHcvknVHN1UNXmpQfWyEJQGSKyWn19xTrALJd/LJd/LJd/wi2XHRoyxpgwZ4XAGGPCXDgWghcDHaAMlss/lss/lss/YZUr7M4RGGOM+alw3CMwxhjjxQqBMcaEuZAsBCLyCxFZJyIlItK/1LhpIpIuIhtF5NIypu8kIt867d4WkXouZHxbRH50HttF5Mcy2m0XkRSn3eqqzuFjeTNEZLdXtlFltBvprMN0EZlaDbkeF5ENIpIsIgtFpHEZ7aplfZ3u/TtdsL/tjP9WRDq6lcVrme1EJElE0pzP///4aDNURDK9fr/T3c7lLPeUvxfx+IezvpJF5JxqyNTDaz38KCLHReQPpdpUy/oSkdkickBEUr2GNRWRZSKy2fnZpIxpb3LabBaRmyoUwNfdamr6AzgD6MHP75zWC1gL1Ac6AVuA2j6mfwcY5zx/HviNy3mfAKaXMW470Kwa190MPLcdPVWb2s666wzUc9ZpL5dzXQLUcZ4/CjwaqPVVnvcP/BZ43nk+Dni7Gn53rYBznOfRwCYfuYYCS6rr81Te3wswCvgAEOBc4Ntqzlcb2IfnC1fVvr6AC4Fz8LpLI/AYMNV5PtXXZx5oCmx1fjZxnjfxd/khuUegqutVdaOPUWOARFXNV9VtQDqlusEWEQGGASfvr/wqcJVbWZ3l/RKY69YyXDAQz21Gt6pqAZCIZ926RlU/Vs+d7ABW4rmjXaCU5/2PwfPZAc9nabjzu3aNqu5V1e+d51nAeqCNm8usQmOA19RjJZ67F7aqxuUPB7aoakV7LKgUVf0cOFJqsPdnqKzt0KXAMlU9oqpHgWXASH+XH5KF4BTaALu8Xmfw8z+UWOCY10bHV5uqdAGwX1U3lzFegY9FZI2ITHQxh7dJzu757DJ2R8uzHt10C57/Hn2pjvVVnvf/3zbOZykTz2erWjiHos4GvvUxerCIrBWRD0SkdzVFOt3vJdCfqXGU/c9YINYXQJyq7nWe7wPifLSpkvVWqRvTBJKILAda+hh1r6oGxS0vy5nxek69N3C+qu4WkRbAMhHZ4Pz34Eou4F/Ag3j+cB/Ec9jqlsosrypynVxfInIvUAS8WcZsqnx91TQiEgXMB/6gqsdLjf4ez+GPbOf8zyI8t5J1W9D+XpxzgFcC03yMDtT6+glVVRFx7Vr/GlsIVHVEBSbbDbTzet3WGebtMJ7d0jrOf3K+2lRJRhGpA1wDxJ9iHrudnwdEZCGewxKV+gMq77oTkZeAJT5GlWc9VnkuEZkAjAaGq3OA1Mc8qnx9+VCe93+yTYbze47B89lylYjUxVME3lTVBaXHexcGVV0qIs+JSDNVdbWDtXL8Xlz5TJXTZcD3qrq/9IhArS/HfhFppap7ncNkB3y02Y3nPMZJbfGcG/VLuB0aWgyMc67o6ISnsn/n3cDZwCQBY51BNwFu7WGMADaoaoavkSISKSLRJ5/jOWGa6qttVSl1XPbqMpa3Cugmnqur6uHZrV7scq6RwGTgSlXNKaNNda2v8rz/xXg+O+D5LH1aVvGqKs45iFeA9ar6ZBltWp48VyEiA/FsA1wtUOX8vSwGxjtXD50LZHodFnFbmXvlgVhfXrw/Q2Vthz4CLhGRJs5h3EucYf5x+2x4IB54NmAZQD6wH/jIa9y9eK742Ahc5jV8KdDaed4ZT4FIB/4D1Hcp5xzgzlLDWgNLvXKsdR7r8BwicXvdvQ6kAMnOB7FV6VzO61F4rkrZUk250vEcC/3ReTxfOld1ri9f7x+YiadQAUQ4n51057PUuRrW0fl4Dukle62nUcCdJz9nwCRn3azFc9L9vGrI5fP3UiqXAM866zMFr6v9XM4WiWfDHuM1rNrXF55CtBcodLZdt+I5p/QJsBlYDjR12vYHXvaa9hbnc5YO3FyR5VsXE8YYE+bC7dCQMcaYUqwQGGNMmLNCYIwxYc4KgTHGhDkrBMYYE+asEBhjTJizQmCMMWHu/wB6HsYG+W2UswAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "HYPERBLIC TANGENT FUNCTION"
      ],
      "metadata": {
        "id": "GXiH0KD5ireJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def htan(x):\n",
        "  return (np.exp(x) - np.exp(-x))/(np.exp(x) + np.exp(-x))"
      ],
      "metadata": {
        "id": "0vWPSdrViYOu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y_data = htan(x_data)"
      ],
      "metadata": {
        "id": "AGGOwhSuieaM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_data = np.linspace(-6,6,100)\n",
        "plt.plot(x, z)\n",
        "plt.title('htanr Activation Function ')\n",
        "plt.legend(['htan'])\n",
        "plt.grid()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "YtBfhiZ7iidZ",
        "outputId": "df3a23d7-6f8b-449a-a8ce-61fddc9f4849"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SIGN(SIGNUM) ACTIVATION FUNCTION"
      ],
      "metadata": {
        "id": "OC7E1lrdlGeu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=np.arange(-20,20,1)\n",
        "z = []\n",
        "\n",
        "for i in x:\n",
        "  if i < 0:\n",
        "    z.append(-1)\n",
        "  elif i == 0:\n",
        "    z.append(0)\n",
        "  else:\n",
        "    z.append(1)\n",
        "\n",
        "print(\"x value:\\n\")\n",
        "print(x)\n",
        "print(\"z value :\\n\")\n",
        "print(z)\n",
        "\n",
        "plt.plot(z)\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"Signum(x)\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "3kFb72D7lCAa",
        "outputId": "ef7b5b33-3985-4a5f-8ab7-6bd9492e4eb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x value:\n",
            "\n",
            "[-20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10  -9  -8  -7  -6  -5  -4  -3\n",
            "  -2  -1   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15\n",
            "  16  17  18  19]\n",
            "z value :\n",
            "\n",
            "[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "UNIT STEP (HEAVISIDE)"
      ],
      "metadata": {
        "id": "A5WNxOJ0qePE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=np.arange(-10,10,1)\n",
        "z = []\n",
        "\n",
        "for i in x:\n",
        "  if i < 0:\n",
        "    z.append(0)\n",
        "  elif i == 0:\n",
        "    z.append(0.5)\n",
        "  else:\n",
        "    z.append(1)\n",
        "\n",
        "print(\"x value :\\n\")\n",
        "print(x)\n",
        "print(\"z value :\\n\")\n",
        "print(z)\n",
        "\n",
        "plt.plot(z)\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"Heaviside(x)\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "nR4r5MO7l4cY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "767988f5-e89e-4cf4-cfe3-8105185f79e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x value :\n",
            "\n",
            "[-10  -9  -8  -7  -6  -5  -4  -3  -2  -1   0   1   2   3   4   5   6   7\n",
            "   8   9]\n",
            "z value :\n",
            "\n",
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PIECE-WISE LINEAR"
      ],
      "metadata": {
        "id": "h9NilzYnqprA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=np.arange(-10,10,5)\n",
        "z = []\n",
        "\n",
        "for i in x:\n",
        "  if i >= 1/2:\n",
        "    z.append(1)\n",
        "  elif i < 1/2 or i > -1/2:\n",
        "    z.append(i+(1/2))\n",
        "  else: # i <= - 1/2\n",
        "    z.append(0)\n",
        "\n",
        "print(x)\n",
        "print(z)\n",
        "\n",
        "plt.xlim((-12,12))\n",
        "plt.ylim((-12,12))\n",
        "plt.plot(z)\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"PieceWiseLinear(x)\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "iOBYpUcXqrb0",
        "outputId": "d1a01813-c2fc-4cfc-80ff-480bf53251c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-10  -5   0   5]\n",
            "[-9.5, -4.5, 0.5, 1]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}