Lista2_Exercício3.ipynb

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  "metadata": {
    "colab": {
      "name": "Lista2-Exercício3.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPw7JaQqD9dW6Iz9+Wuc9KE",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nascimento-luciano/Artificial-Intelligence/blob/master/Lista2_Exerc%C3%ADcio3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oRmxYEZIMbUZ"
      },
      "source": [
        "*Aula prática: rede neural para regressão não linear com keras*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YGie8a0V04sp",
        "outputId": "853d4eaf-337f-4c99-eb56-cf975d096cfb"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def fobj(X): \n",
        "  OF=(1-np.exp(-X)*(np.cos(2*np.pi*X)+1)) #Cria uma subrotina para definir para definir a função que faremos a regressão não-linear\n",
        "  return OF\n",
        "xx=np.linspace(0,7,1000)\n",
        "y=fobj(xx)\n",
        "plt.plot(xx,y)\n",
        "\n",
        "#criando o data set de treino e teste\n",
        "s_d=200                                   #quantos Dtrain\n",
        "s_t=50                                    #quantos Dtest\n",
        "\n",
        "x_train = np.linspace(1,10.0,s_d)       #Define a entrada de treinamento da rede\n",
        "y_train = fobj(x_train)                 #Define as saídas de treinamento da rede, usando a subrotina\n",
        "x_test=np.linspace(1,8,s_t)             #Define a entrada de teste da rede\n",
        "y_test=fobj(x_test)                     #Define a saída de teste da rede.\n",
        "#criando a rede neural\n",
        "model=tf.keras.Sequential(name='rede_regressao_nao_linear')                      #Compilação da rede neural, definindo o otimizador e a função de Loss\n",
        "#configurando as camadas da rede para 4 camadas\n",
        "model.add(tf.keras.layers.Dense(18, input_dim=1, use_bias=1,activation='relu'))  #Adicionando camadas na rede neural\n",
        "model.add(tf.keras.layers.Dense(24, activation='relu'))                          #É a função de ativação. A funçã o “linear” é a padrão;\n",
        "model.add(tf.keras.layers.Dense(16, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(1, activation='linear'))                         #Adicionando camadas na rede neural\n",
        "\n",
        "#compilando a rede\n",
        "opt=tf.keras.optimizers.Adam(learning_rate=0.0005)\n",
        "model.compile(optimizer=opt,loss='mean_squared_error')\n",
        "print(model.summary())                                                           #Mostra na tela um resumo da rede neural\n",
        "#treinando a rede\n",
        "history=model.fit(x_train, y_train, epochs=20000, verbose=2)\n",
        "#mostra a performace do treinamento da rede\n",
        "plt.rcParams.update({'font.size':12})\n",
        "plt.figure()\n",
        "plt.semilogy(history.history['loss'],'k')\n",
        "plt.legend(['loss'])\n",
        "#testando a rede\n",
        "y_pred=model.predict(x_test)\n",
        "#mostra o resultado do ajuste em relação ao esperado\n",
        "plt.figure()\n",
        "plt.plot(x_train, model.predict(x_train),'r.')\n",
        "plt.plot(x_train, y_train, 'b-')\n",
        "plt.figure()\n",
        "plt.plot(x_test, y_pred,'r.')\n",
        "plt.plot(x_test, y_test, 'b-')\n",
        "plt.legend(['previsões', 'curva esperada'])\n",
        "plt.xlabel('x');plt.ylabel('y')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mA saída de streaming foi truncada nas últimas 5000 linhas.\u001b[0m\n",
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          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'y')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}