PAVIC_ML_03_Activation_Functions.ipynb

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        },
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            "_model_module": "@jupyter-widgets/controls",
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            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
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            "box_style": "",
            "children": [
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              "IPY_MODEL_f512b135b2804b93a143cd2adeebed97",
              "IPY_MODEL_568b72ce916b4ebca3521a6e56dae6be",
              "IPY_MODEL_9479d9e2da5146e5a78c6f5b47f3b338"
            ],
            "layout": "IPY_MODEL_0adaa80be00342c0bb31cfa0c1662241"
          }
        },
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            "orientation": "horizontal",
            "readout": true,
            "readout_format": ".2f",
            "step": 0.1,
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        },
        "568b72ce916b4ebca3521a6e56dae6be": {
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          "model_name": "CheckboxModel",
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            "_model_module": "@jupyter-widgets/controls",
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            "layout": "IPY_MODEL_7090bf74c5444992b4fe4e7b759b348d",
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            "value": true
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        },
        "9479d9e2da5146e5a78c6f5b47f3b338": {
          "model_module": "@jupyter-widgets/output",
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          "state": {
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            "_model_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
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            "_view_module": "@jupyter-widgets/output",
            "_view_module_version": "1.0.0",
            "_view_name": "OutputView",
            "layout": "IPY_MODEL_70cc30ce4f674cf288082b9ec8a85373",
            "msg_id": "",
            "outputs": [
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 640x480 with 1 Axes>",
                  "image/png": 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              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 640x480 with 1 Axes>",
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\n"
                },
                "metadata": {}
              }
            ]
          }
        },
        "22ebae08de9d4c78a61b02653dfc2039": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
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  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wallisonferreira/machine-learning-pavic/blob/main/PAVIC_ML_03_Activation_Functions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import ipywidgets as wg\n",
        "from ipywidgets import interactive, fixed\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "xSaoX9nyOAUq"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def plot_interactive(w, b, func, ylim=fixed((0, 1)), show_der=False):\n",
        "    plt.figure(0)\n",
        "\n",
        "    x = np.linspace(-10, 10, num=1000)\n",
        "    z = w*x + b\n",
        "    y = func(z)\n",
        "\n",
        "    plt.plot(x, y, color='blue')\n",
        "    if show_der:\n",
        "        der = func(z, derivative=True)\n",
        "        y_der_z = der\n",
        "        y_der_x = w*der\n",
        "        plt.plot(x, y_der_z, color='red')\n",
        "        plt.plot(x, y_der_x, color='green')\n",
        "    plt.xlim(-10, 10)\n",
        "    plt.ylim(ylim[0], ylim[1])\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "MTkINh27OA7l"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Linear"
      ],
      "metadata": {
        "id": "-9kTBFDZOHun"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.ones_like(1)"
      ],
      "metadata": {
        "id": "-SWUB1Gum52t",
        "outputId": "36163917-82dd-43e5-9e9e-1502b46685fa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(1)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def linear(x, derivative=False):\n",
        "    return np.ones_like(x) if derivative else x"
      ],
      "metadata": {
        "id": "EjdTtfhtmarI"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "interactive_plot = interactive(plot_interactive, w=(-2.0, 2.0), b=(-3, 3, 0.5), func=fixed(linear), ylim=fixed((-10, 10)))\n",
        "interactive_plot\n",
        "#vermelho eh a derivada 1 fixa, em verde a derivada considerando a soma ponderada do neuronio"
      ],
      "metadata": {
        "id": "77m3L-eUOEwV",
        "outputId": "a3b2f20b-ab22-4d10-d514-2cd01dc99c02",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531,
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            "ccb15cb0316d4ba59eef88f7ef594374",
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            "5f075fb2e4254fbb81161f900b49ef6e",
            "7891f1faf42b4d34a4ecd99b1d7a103c"
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        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='w', max=2.0, min=-2.0), FloatSlider(value=0.0, descr…"
            ],
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              "version_minor": 0,
              "model_id": "87b4aa416338401890d44c986abb2b47"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sigmoid"
      ],
      "metadata": {
        "id": "AQmdRNz9PjIS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sigmoid(x, derivative=False):\n",
        "    if derivative:\n",
        "        y = sigmoid(x)\n",
        "        return y * (1 - y)\n",
        "    return 1.0 / (1.0 + np.exp(-x))"
      ],
      "metadata": {
        "id": "WA2RGBHLn8r2"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "interactive_plot = interactive(plot_interactive, w=(-2.0, 2.0), b=(-3, 3, 0.5), func=fixed(sigmoid))\n",
        "interactive_plot"
      ],
      "metadata": {
        "id": "QaYzuzRVPmv_",
        "outputId": "27203f65-ef06-4389-fc48-95ba75c75031",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531,
          "referenced_widgets": [
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            "0adaa80be00342c0bb31cfa0c1662241",
            "7cc25c3604b6428c937ca8bb2e97583b",
            "f27b8ee5bba940988634385da2e4f53b",
            "00303555681849f089b58a8ca1a44e03",
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            "7090bf74c5444992b4fe4e7b759b348d",
            "04bb4707de6f47b1849549d47a119cd7",
            "70cc30ce4f674cf288082b9ec8a85373"
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        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='w', max=2.0, min=-2.0), FloatSlider(value=0.0, descr…"
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          },
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tanh"
      ],
      "metadata": {
        "id": "3tFwotQLQHsH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tanh(x, derivative=False):\n",
        "    if derivative:\n",
        "        y = tanh(x)\n",
        "        return 1 - (y**2)\n",
        "    return ( np.exp(x) - np.exp(-x) ) / ( np.exp(x) + np.exp(-x) )"
      ],
      "metadata": {
        "id": "6yBvxE8Lply7"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "interactive_plot = interactive(plot_interactive, w=(-2.0, 2.0), b=(-3, 3, 0.5), func=fixed(tanh), ylim=fixed((-2, 2)))\n",
        "interactive_plot"
      ],
      "metadata": {
        "id": "I85rj4dgQGyV",
        "outputId": "9c6f32fc-8103-4b19-95f1-6ad658df5e84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531,
          "referenced_widgets": [
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            "7c5640353a4449f8b63d4bd36bdd538f",
            "25aa481ff5874c45b19e2db32ea00963",
            "8c26a5de985643e2b5422d484dbc94af",
            "f850ae64654841359ee9c9df0969b165",
            "44cc6745f6ba45dab29734b358a88e64",
            "02fd108bbfd5403198ae40d04d346a88",
            "d1058da76c5e4566ad6c991d74650a75",
            "fa1297a607ad4229a643df0f5dd6d324",
            "4b070d4addd1496b99b36628ae628bcc",
            "b525359487b049328b59b8296339d2e3",
            "a54fbdee0c8e458dacced96c2985b0d3",
            "b900dd84dca84b85b8632ff3a0841ffb"
          ]
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='w', max=2.0, min=-2.0), FloatSlider(value=0.0, descr…"
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Rectified Linear Unit (ReLU)\n"
      ],
      "metadata": {
        "id": "G22vcqk5Qmq3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def relu(x, derivative=False):\n",
        "    if derivative:\n",
        "        return np.where(x <= 0, 0, 1)\n",
        "    return np.maximum(0, x)"
      ],
      "metadata": {
        "id": "2k2QV2g0refE"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "interactive_plot = interactive(plot_interactive, w=(-2.0, 2.0), b=(-3, 3, 0.5), func=fixed(relu), ylim=fixed((-1, 10)))\n",
        "interactive_plot"
      ],
      "metadata": {
        "id": "CxRf3ASHQorg",
        "outputId": "7c930f5e-2237-4a03-f89c-4d52d36789f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531,
          "referenced_widgets": [
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            "d5df06ecc06143dd952bf85fbcd4d219",
            "b5b2862506934ca79d0a76fb56757983",
            "68cc5d88fd864b57a9559cb2be00390c",
            "2f54fc0e4afa4ca59c02cbdc92cc8743",
            "51e53104fde7476395af2522f298c6af",
            "22b514521c7942eb947fa81e54afaaea",
            "66e1a1d4f3004c0c855b8e96be006fae",
            "20b6402ab96340778b4d943f29140157",
            "7a23a5db17ab423788aa626d0b464855"
          ]
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='w', max=2.0, min=-2.0), FloatSlider(value=0.0, descr…"
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A ativação vai ficar entre 0 e 1 (vermelho)"
      ],
      "metadata": {
        "id": "7jCgryFzscYM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Leaky ReLU\n"
      ],
      "metadata": {
        "id": "TIGYVaQQRXCg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def leaky_relu(x, derivative=False):\n",
        "    alpha = 0.1\n",
        "    if derivative:\n",
        "        return np.where( x <= 0, alpha, 1)\n",
        "    return np.where( x <= 0, alpha * x, x)"
      ],
      "metadata": {
        "id": "c6EAH_3csYe-"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "interactive_plot = interactive(plot_interactive, w=(-2.0, 2.0), b=(-3, 3, 0.5), func=fixed(leaky_relu), ylim=fixed((-1, 10)))\n",
        "interactive_plot"
      ],
      "metadata": {
        "id": "u8Ks-H_NRX_Z",
        "outputId": "81b58b91-cdce-4f0c-f9c1-c4c106986711",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531,
          "referenced_widgets": [
            "e2ae6be1e92548b88c594d0d90b73f08",
            "3f8c3f025c374f6e8ba48c16daad47ef",
            "9162b73b94174a07af4fbe73cfe26fa5",
            "a695ef324e244bb997407cd0f64680f1",
            "b8a786b599624f218ef75a2848bbbe0e",
            "4cd89faf2a134baeb6edae8c6fa12764",
            "a760eebe0f7b4970acc7a5b3a72ee4f6",
            "4ef0b09054834a1ca57a4cd60bf29a47",
            "ff0aafb0dca148678275a697f5ae8794",
            "7cdc0126f593477ab6e1bd0a6fb373ba",
            "d9ffe7bb63444ad1856439af98bbb1a9",
            "82baf3c0f37c4f318768ff3cbd266d2b",
            "cc384356196c436ca322ed073dc07f30"
          ]
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='w', max=2.0, min=-2.0), FloatSlider(value=0.0, descr…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e2ae6be1e92548b88c594d0d90b73f08"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exponential Linear Unit (eLU)\n"
      ],
      "metadata": {
        "id": "tfmkjDsoR5gy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def elu(x, derivative=False):\n",
        "    alpha = 1.0\n",
        "    if derivative:\n",
        "        y = elu(x)\n",
        "        return np.where(x <= 0, y + alpha, 1)\n",
        "    return np.where(x <= 0, alpha * (np.exp(x) - 1), x)"
      ],
      "metadata": {
        "id": "dxWLr4BBtg9h"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "interactive_plot = interactive(plot_interactive, w=(-2.0, 2.0), b=(-3, 3, 0.5), func=fixed(elu), ylim=fixed((-2, 10)))\n",
        "interactive_plot"
      ],
      "metadata": {
        "id": "zR2bJVtDR5M6",
        "outputId": "bb970e34-49a8-433d-f396-28723f01b370",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531,
          "referenced_widgets": [
            "8cf58cfb57754280b96e9af8af0e4a29",
            "fe124ad256af4189879e4a4134d60aca",
            "47d50bace6c54875a509ef483f1fb461",
            "542d8adb6a6e44f4a575eae7dd2831c1",
            "af6b6e674afc4baaa62a47b9bfd39e86",
            "22ebae08de9d4c78a61b02653dfc2039",
            "6cdc5f51027e451da473fb058694c1cc",
            "277df996dfec47748b4d5d215ea4fdb9",
            "1c264b2371dc4bfc9658b9633e4ace49",
            "2b68f5f880804185aeb763bf6821b73a",
            "d655a5e9f757473fbb308ad1a9721f8e",
            "6131160ca43e4ab4b849d14ed45fbdfa",
            "6e6992b07c714c58b8ccc7eab65815d0"
          ]
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='w', max=2.0, min=-2.0), FloatSlider(value=0.0, descr…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8cf58cfb57754280b96e9af8af0e4a29"
            }
          },
          "metadata": {}
        }
      ]
    }
  ]
}