NNDL_Activationfunction-Iris (1).ipynb

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        "id": "iC70md_OucUt"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('IRIS.csv')"
      ],
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        "id": "BptAXcf6u9Zu"
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      "execution_count": 3,
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        "df.head(10)"
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          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width      species\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa\n",
              "5           5.4          3.9           1.7          0.4  Iris-setosa\n",
              "6           4.6          3.4           1.4          0.3  Iris-setosa\n",
              "7           5.0          3.4           1.5          0.2  Iris-setosa\n",
              "8           4.4          2.9           1.4          0.2  Iris-setosa\n",
              "9           4.9          3.1           1.5          0.1  Iris-setosa"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
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              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>Iris-setosa</td>\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "LOGISTIC ACTIVATION\n"
      ],
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      "cell_type": "code",
      "source": [
        "x=df['sepal_length']\n",
        "z=1/(1+np.exp(-x))\n",
        "\n",
        "print(\" x values \\n\")\n",
        "print(x)\n",
        "\n",
        "print(\" y values \\n\")\n",
        "print(z)\n",
        "print(\"\\n\")\n",
        "plt.plot(x,z)\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"LOgistict activation (x)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "dliPjyx0yIqE",
        "outputId": "0300d80f-e902-4373-b3ee-db4383d15311"
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      "execution_count": 18,
      "outputs": [
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          "output_type": "stream",
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          "text": [
            " x values \n",
            "\n",
            "0      5.1\n",
            "1      4.9\n",
            "2      4.7\n",
            "3      4.6\n",
            "4      5.0\n",
            "      ... \n",
            "145    6.7\n",
            "146    6.3\n",
            "147    6.5\n",
            "148    6.2\n",
            "149    5.9\n",
            "Name: sepal_length, Length: 150, dtype: float64\n",
            " y values \n",
            "\n",
            "0      0.993940\n",
            "1      0.992608\n",
            "2      0.990987\n",
            "3      0.990048\n",
            "4      0.993307\n",
            "         ...   \n",
            "145    0.998771\n",
            "146    0.998167\n",
            "147    0.998499\n",
            "148    0.997975\n",
            "149    0.997268\n",
            "Name: sepal_length, Length: 150, dtype: float64\n",
            "\n",
            "\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": [
        "SIGNUM ACTIVATION"
      ],
      "metadata": {
        "id": "jbTz-hEOyeb4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x1=df['sepal_length']\n",
        "z1 = []\n",
        "\n",
        "for i in x1:\n",
        "  if i < 0:\n",
        "    z1.append(-1)\n",
        "  elif i == 0:\n",
        "    z1.append(0)\n",
        "  else:\n",
        "    z1.append(1)\n",
        "\n",
        "print(\" x values \\n\")\n",
        "print(x1)\n",
        "print(\" y values \\n\")\n",
        "print(z1)\n",
        "print(\"\\n\")\n",
        "plt.plot(z1)\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"Signum Activation(x)\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "SlXVHIsMyfIh",
        "outputId": "1c63120b-014a-416a-d668-dbb7c25a47ae"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " x values \n",
            "\n",
            "0      5.1\n",
            "1      4.9\n",
            "2      4.7\n",
            "3      4.6\n",
            "4      5.0\n",
            "      ... \n",
            "145    6.7\n",
            "146    6.3\n",
            "147    6.5\n",
            "148    6.2\n",
            "149    5.9\n",
            "Name: sepal_length, Length: 150, dtype: float64\n",
            " y values \n",
            "\n",
            "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "\n",
            "\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": [
        "LINEAR ACTIVATION"
      ],
      "metadata": {
        "id": "du4n_MPbymUH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x2=df['sepal_length']\n",
        "z2=x2\n",
        "print(\" x values \\n\")\n",
        "print(x2)\n",
        "\n",
        "print(\" y values \\n\")\n",
        "print(z2)\n",
        "print(\"\\n\")\n",
        "plt.plot(x2,z2)\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"Linear activation (x)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "WwZIeb-rynDt",
        "outputId": "ca4b12b0-589b-453c-e60a-cbc9f1dc12e2"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " x values \n",
            "\n",
            "0      5.1\n",
            "1      4.9\n",
            "2      4.7\n",
            "3      4.6\n",
            "4      5.0\n",
            "      ... \n",
            "145    6.7\n",
            "146    6.3\n",
            "147    6.5\n",
            "148    6.2\n",
            "149    5.9\n",
            "Name: sepal_length, Length: 150, dtype: float64\n",
            " y values \n",
            "\n",
            "0      5.1\n",
            "1      4.9\n",
            "2      4.7\n",
            "3      4.6\n",
            "4      5.0\n",
            "      ... \n",
            "145    6.7\n",
            "146    6.3\n",
            "147    6.5\n",
            "148    6.2\n",
            "149    5.9\n",
            "Name: sepal_length, Length: 150, dtype: float64\n",
            "\n",
            "\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": [
        "TANGENT ACTIVATION"
      ],
      "metadata": {
        "id": "4Qxo76akytM-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x3=df['sepal_length']\n",
        "z3=np.tanh(x3)\n",
        "print(\" x values \\n\")\n",
        "print(x3)\n",
        "\n",
        "print(\" y values \\n\")\n",
        "print(z3)\n",
        "print(\"\\n\")\n",
        "plt.plot(x3,z3)\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"Tangent activation (x)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "s2-r6Mncyt6z",
        "outputId": "2ce79e6b-4681-4eac-f222-086283c0e278"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " x values \n",
            "\n",
            "0      5.1\n",
            "1      4.9\n",
            "2      4.7\n",
            "3      4.6\n",
            "4      5.0\n",
            "      ... \n",
            "145    6.7\n",
            "146    6.3\n",
            "147    6.5\n",
            "148    6.2\n",
            "149    5.9\n",
            "Name: sepal_length, Length: 150, dtype: float64\n",
            " y values \n",
            "\n",
            "0      0.999926\n",
            "1      0.999889\n",
            "2      0.999835\n",
            "3      0.999798\n",
            "4      0.999909\n",
            "         ...   \n",
            "145    0.999997\n",
            "146    0.999993\n",
            "147    0.999995\n",
            "148    0.999992\n",
            "149    0.999985\n",
            "Name: sepal_length, Length: 150, dtype: float64\n",
            "\n",
            "\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": [
        "HEAVISIDE ACTIVATION"
      ],
      "metadata": {
        "id": "HWakXX-VyzwK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x4=df['sepal_length']\n",
        "z4=np.heaviside(x4,2)\n",
        "print(\" x values \\n\")\n",
        "print(x4)\n",
        "\n",
        "print(\" y values \\n\")\n",
        "print(z4)\n",
        "print(\"\\n\")\n",
        "plt.plot(x4,z4)\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"Heaviside activation (x)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "8NL_a7NEy0lj",
        "outputId": "5dcce9bc-61c2-4cbd-f5d0-e23b0c368a70"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " x values \n",
            "\n",
            "0      5.1\n",
            "1      4.9\n",
            "2      4.7\n",
            "3      4.6\n",
            "4      5.0\n",
            "      ... \n",
            "145    6.7\n",
            "146    6.3\n",
            "147    6.5\n",
            "148    6.2\n",
            "149    5.9\n",
            "Name: sepal_length, Length: 150, dtype: float64\n",
            " y values \n",
            "\n",
            "0      1.0\n",
            "1      1.0\n",
            "2      1.0\n",
            "3      1.0\n",
            "4      1.0\n",
            "      ... \n",
            "145    1.0\n",
            "146    1.0\n",
            "147    1.0\n",
            "148    1.0\n",
            "149    1.0\n",
            "Name: sepal_length, Length: 150, dtype: float64\n",
            "\n",
            "\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 ACTIVATION FUCTION"
      ],
      "metadata": {
        "id": "UEiBprNcy8fL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x5=df['sepal_length']\n",
        "z5 = []\n",
        "\n",
        "for i in x5:\n",
        "  if i >= 1/2:\n",
        "    z5.append(1)\n",
        "  elif i < 1/2 or i > -1/2:\n",
        "    z5.append(i+(1/2))\n",
        "  else: # i <= - 1/2\n",
        "    z5.append(0)\n",
        "print(\" x values \\n\")\n",
        "print(x5)\n",
        "print(\" y values \\n\")\n",
        "print(z5)\n",
        "\n",
        "plt.xlim((-11,11))\n",
        "plt.ylim((-11,11))\n",
        "print(\"\\n\")\n",
        "plt.plot(z5)\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"PieceWiseLinear(x)\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "id": "zs2vET0Ly9Nt",
        "outputId": "b1d5a8c3-07ff-442d-e5c1-3c336475eb28"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " x values \n",
            "\n",
            "0      5.1\n",
            "1      4.9\n",
            "2      4.7\n",
            "3      4.6\n",
            "4      5.0\n",
            "      ... \n",
            "145    6.7\n",
            "146    6.3\n",
            "147    6.5\n",
            "148    6.2\n",
            "149    5.9\n",
            "Name: sepal_length, Length: 150, dtype: float64\n",
            " y values \n",
            "\n",
            "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "\n",
            "\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"
          }
        }
      ]
    }
  ]
}