Funciones_de_activacion.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Funciones_de_activacion.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMc66kRCjo3pv98Xan0xbRr"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "mcCd67rDe5Ia"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1Aw2JUsfUOe"
      },
      "source": [
        "#Creamos la funcion sigmoide\n",
        "def sigmoid(a):\n",
        "  return 1 / (1+np.exp(-a))"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tux20ckufj9C"
      },
      "source": [
        "#Generamos datos aleatorios\n",
        "x = np.linspace(10, -10, 100)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "Dv6M7266fu9A",
        "outputId": "4ee727d5-5a63-43d3-d9b0-15017b52d45d"
      },
      "source": [
        "#Graficamos la funcion sigmoide\n",
        "plt.plot(x,sigmoid(x))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fac2dbbb150>]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8rxb5oAgUtR"
      },
      "source": [
        "#Creamos la funcion step\n",
        "def step(x):\n",
        "  return np.piecewise(x,[x<0.0, x>0.0],[0,1])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "kUkC7EUmgs9H",
        "outputId": "34bea164-ab25-407b-856d-3a5a6e6683e9"
      },
      "source": [
        "#Graficamos\n",
        "plt.plot(x,step(x))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fac29a58b10>]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ktZBT4nVhpfv"
      },
      "source": [
        "#Funcion tangente hiperbolica\n",
        "def tanh(x):\n",
        "  return np.sinh(x)/ np.cosh(x)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "TEdsoQYLiBhV",
        "outputId": "c9659f6c-37cc-4855-e27e-ea2d00d4bb26"
      },
      "source": [
        "#La tangente hiperbolica va de -1 a 1\n",
        "plt.plot(x,tanh(x))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fac298dee50>]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3J7K3KT5iZdO"
      },
      "source": [
        "#Definimos la funcion relu\n",
        "def relu(x):\n",
        "  return np.piecewise(x,[x<0.0, x>0.0],[0, lambda x: x])"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "_AvfJP1TjHAB",
        "outputId": "51ab1024-940a-4069-910c-b144b7307bc8"
      },
      "source": [
        "#La funcion relu lo que hace es que si el valor es menor a cero, el resultado es 0, pero si el resultado es mayor a cero, el resultado se queda como el valor de x \n",
        "plt.plot(x,relu(x))\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fac296e9710>]"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjYYBhXHhtJc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}