IA_teoria_Redes_neurais.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "IA_teoria_Redes_neurais.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNUaKjB+BTGBPbwwot3TJ3y"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "0r4tNZ_2mR4U",
        "outputId": "784dfe06-e9c5-44e8-df01-b82a1f70b23e"
      },
      "source": [
        "#exemplo 1\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "x = np.array([-1,2,4])\n",
        "phi=[x, x**2]\n",
        "W=np.array([2, -1])\n",
        "score=np.dot(W,phi)\n",
        "print('score= ',score)\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "plt.plot(x,score,'.',markersize=12)\n",
        "xt=np.linspace(-3,6,1000)\n",
        "plt.plot(xt,W[0]*xt+W[1]*xt**2,'r--')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "score=  [-3  0 -8]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1ed1ab3990>]"
            ]
          },
          "metadata": {},
          "execution_count": 1
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "CCxB29gFo2Oo",
        "outputId": "80db2f26-7d1d-4a89-f058-e71c5c7a8cbd"
      },
      "source": [
        "#exemplo 2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def sigmoid(z):\n",
        "  out=1/(1+np.exp(-z))\n",
        "  return out\n",
        "\n",
        "x = np.array([2, 1, 3])\n",
        "phi=x\n",
        "W=np.array([0.5, -1, 2])\n",
        "B = -4\n",
        "score=np.dot(W,phi)+B\n",
        "print('score= ',score)\n",
        "saida=sigmoid(score)\n",
        "print('saida= ',saida)\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "plt.plot(score,saida,'.',markersize=12,label='previsão')\n",
        "xt=np.linspace(-6,6,1000)\n",
        "plt.xlabel('score');plt.ylabel('saida')\n",
        "plt.plot(xt,sigmoid(xt),'r--',label='sigmoid')\n",
        "plt.grid();plt.legend()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "score=  2.0\n",
            "saida=  0.8807970779778823\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f1ed7595810>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        },
        {
          "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wjpJkE0itA4A",
        "outputId": "bee00adf-c918-4747-8885-8c58e689a2a0"
      },
      "source": [
        "#exemplo 3\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def relu(z):\n",
        "  return np.maximum(z,np.zeros(1))\n",
        "\n",
        "x = np.array([1, 2, 3])\n",
        "phi=x\n",
        "V1=np.array([0.1, -0.2, -0.2])\n",
        "V2=np.array([2, -1, 2])\n",
        "W=np.array([4, 1])\n",
        "Bh1 = 0\n",
        "Bh2=0\n",
        "Bsaida=1\n",
        "h1=relu(np.dot(V1,phi)+Bh1)\n",
        "h2=relu(np.dot(V2,phi)+Bh2)\n",
        "h=np.concatenate((h1,h2),axis=0)\n",
        "print('h= ',h)\n",
        "saida=np.dot(W,h)+Bsaida\n",
        "print('saida= ',saida)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "h=  [0. 6.]\n",
            "saida=  7.0\n"
          ]
        }
      ]
    }
  ]
}