ACTIVATION_FUNCTIONS_DRAW.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ACTIVATION_FUNCTIONS_DRAW.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDxUiE2vDaBr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "329cdfc1-c982-4014-a765-8a89784a887d"
      },
      "source": [
        "from matplotlib import pyplot\n",
        " \n",
        "# rectified linear function\n",
        "def rectified(x):\n",
        "\treturn max(0.0, x)\n",
        " \n",
        "# define a series of inputs\n",
        "series_in = [x for x in range(-10, 11)]\n",
        "# calculate outputs for our inputs\n",
        "series_out = [rectified(x) for x in series_in]\n",
        "# line plot of raw inputs to rectified outputs\n",
        "pyplot.plot(series_in, series_out)\n",
        "pyplot.show()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O1KZiruDPwwm8y2FljHKtIDDXNzf49WrFXLHcXpU9fuBxAi8wAHWC+86K4L7U\nNQbb6BQCU1/zy22n4cDth/czYGxw28wjcDD4pBjkqvR5qZDLgJeC23MB5d49FuVs9QkU5Mblrov5\n9iLwArIJKAnWrlsIHDP5BFgOfAw0Cy6bCbxW7ndvDu5nK4Cbwlm/PkovIpKgEmUKRUREKlABFxFJ\nUCrgIiIJSgVcRCRBqYCLiCQoFXARkQSlAi4ikqD+D1x6TY4nnbr4AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwUy2RsqDcnV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "35a6683a-1ff9-4e75-e6d0-7c44aca1e667"
      },
      "source": [
        "\"\"\"\n",
        "relu\n",
        "~~~~\n",
        "Plots a graph of the squashing function used by a rectified linear\n",
        "unit.\"\"\"\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "z = np.arange(-2, 2, .1)\n",
        "zero = np.zeros(len(z))\n",
        "y = np.max([zero, z], axis=0)\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111)\n",
        "ax.plot(z, y)\n",
        "ax.set_ylim([-2.0, 2.0])\n",
        "ax.set_xlim([-2.0, 2.0])\n",
        "ax.grid(True)\n",
        "ax.set_xlabel('z')\n",
        "ax.set_title('Rectified linear unit')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IIiICKBBERCSgQBAREUCBICIiAQWCiIgACgQREQkoEEREBFAgiIhIQIEgIiKA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\nREQABYKIiAT+HyfyKNa1Xr+qAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wzm3m54RD-tQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "f4c74e8b-cea3-47c0-87a2-94b4bfe11924"
      },
      "source": [
        "\"\"\"\n",
        "sigmoid\n",
        "~~~~~~~\n",
        "Plots a graph of the sigmoid function.\"\"\"\n",
        "\n",
        "import numpy\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "z = numpy.arange(-5, 5, .1)\n",
        "sigma_fn = numpy.vectorize(lambda z: 1/(1+numpy.exp(-z)))\n",
        "sigma = sigma_fn(z)\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111)\n",
        "ax.plot(z, sigma)\n",
        "ax.set_ylim([-0.5, 1.5])\n",
        "ax.set_xlim([-5,5])\n",
        "ax.grid(True)\n",
        "ax.set_xlabel('z')\n",
        "ax.set_title('sigmoid function')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g8sh+fPX88Zx9/AAmjehHVqZu+yzSkykIejh3Z3tVIysrq3i7soqVlVWs2FrF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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rD7RpOFPELm1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "301eb993-8ebd-43f7-9287-faa88e668642"
      },
      "source": [
        "\"\"\"\n",
        "step\n",
        "~~~~~~~\n",
        "Plots a graph of a step function.\"\"\"\n",
        "\n",
        "import numpy\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "z = numpy.arange(-5, 5, .02)\n",
        "step_fn = numpy.vectorize(lambda z: 1.0 if z >= 0.0 else 0.0)\n",
        "step = step_fn(z)\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111)\n",
        "ax.plot(z, step)\n",
        "ax.set_ylim([-0.5, 1.5])\n",
        "ax.set_xlim([-5,5])\n",
        "ax.grid(True)\n",
        "ax.set_xlabel('z')\n",
        "ax.set_title('step function')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PwY/+mdNPO42uUzuKLqdw/rloaPe2SHFpqA+Yk5ufnS0bEhG7I+LlbPZmYMFY9zUri6Gx\nhnwvnpVMih/ZdcA8SSdJmgJcCvTkN5A0Mzd7MfCzbPou4AJJ0yVNBy7IlpmVjoehtrJq+dJQROyT\ntIz6L/BJwMqI2CTpemB9RPQAH5Z0MbAP2ANcnu27R9InqYcJwPURsafVmsyKMDjWkPuKrWyS9BFE\nxGpg9bBl1+SmrwaubrLvSmBlijrMiuQhJqysfDXTLBG/qtLKykFglsjA0FhDTgIrFweBWSKDncU+\nI7CycRCYJdJ4Z7GTwMrFQWCWSPjFNFZSDgKzRPyqSisrB4FZIn5VpZWVg8AsEd8+amXlIDBLZPCM\nwC+rtLJxEJgl4jMCKysHgVkijVdVOgmsXBwEZon4VZVWVg4Cs0R8+6iVlYPALJFGZ7FZuTgIzBJp\ndBb7jMDKxUFgloiHmLCychCYJTLgMwIrKQeBWSKN20cLLsRsnJIEgaTFkrZI6pV01Qjr/0rSZkkP\nSbpH0tzcuv2SNmZfPcP3NSuLoRfTOAisZFp+Z7GkScBNwPnATmCdpJ6I2Jzb7KdAZ0S8IOnPgc8A\n78vWvRgR81utw6xofmexlVWKM4KFQG9EbI+IvcAqYEl+g4hYGxEvZLP3A7MTHNesrQy9mKbYMszG\nreUzAmAWsCM3vxM4a5TtrwC+l5s/QtJ6YB+wPCK+NdJOkrqBboCOjg5qtVorNbesv7+/8Brahdui\nbvOT+wBY95Of8MTr3P3mn4uGdm+LFEEwZpL+GOgE3p1bPDci+iSdDNwr6eGIeHT4vhGxAlgB0NnZ\nGV1dXRNRclO1Wo2ia2gXbou63Rt2wkMP8o53nMXcN7yu6HIK55+LhnZvixR/tvQBc3Lzs7Nlv0XS\necAngIsj4uXB5RHRl33fDtSAMxPUZDbhGi+v98UhK5cUQbAOmCfpJElTgEuB37r7R9KZwBeoh8Cu\n3PLpkqZm0zOAs4F8J7NZaQy9jcA5YCXT8qWhiNgnaRlwFzAJWBkRmyRdD6yPiB7gH4BpwDeyOyqe\niIiLgbcCX5A0QD2Ulg+728isNHzXkJVVkj6CiFgNrB627Jrc9HlN9rsPOC1FDWZF84tprKx8a4NZ\nIkMPlPkGUisZB4FZIh5iwsrKQWCWyIDfXW8l5SAwSyR8+6iVlIPALBG/mMbKykFglsjgA2WOASsb\nB4FZIj4jsLJyEJglMuDhR62kHARmifiBMisrB4FZIoPPEXiICSsbB4FZIgM+I7CSchCYJeLOYisr\nB4FZIkOdxWYl4yAwS8xnBFY2DgKzRAYGBjuLCy7EbJwcBGaJDLiPwErKQWCWiIehtrJyEJglMvRi\nGp8RWMkkCQJJiyVtkdQr6aoR1k+VdHu2/seSTsytuzpbvkXSe1LUY1aICI8uYaXUchBImgTcBLwX\nOBW4TNKpwza7Ang2Ik4BbgA+ne17KnAp8DZgMfC57PPMSmfAd49aSaV4ef1CoDcitgNIWgUsATbn\ntlkCXJdN3wHcqPr58xJgVUS8DDwmqTf7vB+NdsBfv/gKd296OkHpr94jv9zH3oJraBdui7rtz/S7\nf8BKKUUQzAJ25OZ3Amc12yYi9kl6HnhDtvz+YfvOGukgkrqBboApx59C95c3JCi9RT9tgxrahdsC\ngGmTg1qtVnQZbaG/v99tkWn3tkgRBBMiIlYAKwDedsaZcfuH3lloPRs2rGfBgs5Ca2gXbouGRx95\ngK6urqLLaAu1Ws1tkWn3tkgRBH3AnNz87GzZSNvslHQ48Hpg9xj3PcCRkyfx9lmvb6Xmlj2zrfga\n2oXbouGZbb42ZOWT4q6hdcA8SSdJmkK987dn2DY9wNJs+hLg3qi/6bsHuDS7q+gkYB7wkwQ1mZnZ\nGLV8RpBd818G3AVMAlZGxCZJ1wPrI6IH+CLw5awzeA/1sCDb7uvUO5b3AR+MiP2t1mRmZmOXpI8g\nIlYDq4ctuyY3/RLwh032/RTwqRR1mJnZ+PnJYjOzinMQmJlVnIPAzKziHARmZhXnIDAzqzgHgZlZ\nxTkIzMwqzkFgZlZxDgIzs4pzEJiZVZyDwMys4hwEZmYV5yAwM6s4B4GZWcU5CMzMKs5BYGZWcQ4C\nM7OKcxCYmVVcS0Eg6VhJayRty75PH2Gb+ZJ+JGmTpIckvS+37kuSHpO0Mfua30o9ZmY2fq2eEVwF\n3BMR84B7svnhXgA+EBFvAxYDn5V0TG79f42I+dnXxhbrMTOzcWo1CJYAt2TTtwB/MHyDiNgaEduy\n6SeBXcAbWzyumZkl0moQdETEU9n000DHaBtLWghMAR7NLf5UdsnoBklTW6zHzMzGSREx+gbSD4Dj\nR1j1CeCWiDgmt+2zEXFAP0G2biZQA5ZGxP25ZU9TD4cVwKMRcX2T/buBboCOjo4Fq1atGv1fdoj1\n9/czbdq0QmtoF26LBrdFg9uioV3aYtGiRRsiovOAFRHxqr+ALcDMbHomsKXJdkcDDwCXjPJZXcB3\nx3LcBQsWRNHWrl1bdAltw23R4LZocFs0tEtbAOtjhN+prV4a6gGWZtNLgW8P30DSFOBO4NaIuGPY\nupnZd1HvX3ikxXrMzGycWg2C5cD5krYB52XzSOqUdHO2zR8B7wIuH+E20a9Kehh4GJgB/G2L9ZiZ\n2Tgd3srOEbEbOHeE5euBK7PprwBfabL/Oa0c38zMWucni83MKs5BYGZWcQ4CM7OKcxCYmVWcg8DM\nrOIcBGZmFecgMDOrOAeBmVnFOQjMzCrOQWBmVnEOAjOzinMQmJlVnIPAzKziHARmZhXnIDAzqzgH\ngZlZxTkIzMwqzkFgZlZxDgIzs4prKQgkHStpjaRt2ffpTbbbn3txfU9u+UmSfiypV9Ltkqa0Uo+Z\nmY1fq2cEVwH3RMQ84J5sfiQvRsT87Ovi3PJPAzdExCnAs8AVLdZjZmbj1GoQLAFuyaZvAf5grDtK\nEnAOcMer2d/MzNI4vMX9OyLiqWz6aaCjyXZHSFoP7AOWR8S3gDcAz0XEvmybncCsZgeS1A10Z7P9\nkra0WHurZgDPFFxDu3BbNLgtGtwWDe3SFnNHWnjQIJD0A+D4EVZ9Ij8TESEpmh08IvoknQzcK+lh\n4PmDHXvY568AVoxnn0NJ0vqI6Cy6jnbgtmhwWzS4LRravS0OGgQRcV6zdZJ+KWlmRDwlaSawq8ln\n9GXft0uqAWcC/wc4RtLh2VnBbKDvVfwbzMysBa32EfQAS7PppcC3h28gabqkqdn0DOBsYHNEBLAW\nuGS0/c3M7NBqNQiWA+dL2gacl80jqVPSzdk2bwXWS3qQ+i/+5RGxOVv3ceCvJPVS7zP4Yov1TKS2\nuUzVBtwWDW6LBrdFQ1u3hep/mJuZWVX5yWIzs4pzEJiZVZyDIAFJfy0pss7wSpL0D5J+LukhSXdK\nOqbomiaapMWStmRDpjR7yv41T9IcSWslbZa0SdJfFF1TkSRNkvRTSd8tupZmHAQtkjQHuAB4ouha\nCrYGeHtEnA5sBa4uuJ4JJWkScBPwXuBU4DJJpxZbVWH2AX8dEacC7wA+WOG2APgL4GdFFzEaB0Hr\nbgA+BlS61z0i7s49JX4/9edCqmQh0BsR2yNiL7CK+hAslRMRT0XEA9n0b6j/Emw6asBrmaTZwO8D\nNx9s2yI5CFogaQnQFxEPFl1Lm/kT4HtFFzHBZgE7cvOjDplSFZJOpP4A6Y+LraQwn6X+h+JA0YWM\nptWxhl7zDjLExt9QvyxUCaO1RUR8O9vmE9QvDXx1Imuz9iNpGvURBD4SEb8uup6JJukiYFdEbJDU\nVXQ9o3EQHESzITYknQacBDxYH0iV2cADkhZGxNMTWOKEGW24EQBJlwMXAedG9R5Q6QPm5OYrPWSK\npMnUQ+CrEfHNouspyNnAxZIuBI4Ajpb0lYj444LrOoAfKEtE0uNAZ0S0wwiDE07SYuB/Au+OiF8V\nXc9Ek3Q49U7yc6kHwDrgP0bEpkILK0A2xPwtwJ6I+EjR9bSD7IzgoxFxUdG1jMR9BJbKjcBRwJrs\nTXSfL7qgiZR1lC8D7qLeOfr1KoZA5mzg/cA5uTcTXlh0UdaczwjMzCrOZwRmZhXnIDAzqzgHgZlZ\nxTkIzMwqzkFgZlZxDgIzs4pzEJiZVZyDwCwBSX+We3jqMUlri67JbKz8QJlZQtkYO/cCn4mI7xRd\nj9lY+IzALK3/BdzrELAy8eijZolko6/OpT7mkFlp+NKQWQKSFlAfcfPfRsSzRddjNh6+NGSWxjLg\nWGBt1mHc1q8mNMvzGYGZWcX5jMDMrOIcBGZmFecgMDOrOAeBmVnFOQjMzCrOQWBmVnEOAjOzivv/\n99QYZ8BOyr8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ilL3fuqERXJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "096d73ae-4028-456b-bcea-ae4bebd8bbac"
      },
      "source": [
        "\"\"\"\n",
        "tanh\n",
        "~~~~\n",
        "Plots a graph of the tanh function.\"\"\"\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "z = np.arange(-5, 5, .1)\n",
        "t = np.tanh(z)\n",
        "\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111)\n",
        "ax.plot(z, t)\n",
        "ax.set_ylim([-1.0, 1.0])\n",
        "ax.set_xlim([-5,5])\n",
        "ax.grid(True)\n",
        "ax.set_xlabel('z')\n",
        "ax.set_title('tanh function')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AFWa2hugX/6PuXhys+ypwn5mVAL2BX8RZj8hpe+LV7fTJz2H6mJZGPkXSxymH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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzDF61OkEX7M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}