dl_az_iris_simples.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPd5sS83yKWO+joatOmbUgr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fjmsouza/ML2DL/blob/main/dl_az_iris_simples.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "MNeVyDrOICYS"
      },
      "outputs": [],
      "source": [
        "from math import ceil\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras.utils import np_utils\n",
        "\n",
        "base = pd.read_csv('iris.csv')\n",
        "n_atributes = 4\n",
        "n_classes = 3\n",
        "n_inputs_neurons = ceil((n_atributes+n_classes)/2)\n",
        "\n",
        "#split:\n",
        "previsores = base.iloc[:,0:n_atributes].values\n",
        "classe = base.iloc[:,n_atributes].values\n",
        "\n",
        "#de string para numérico\n",
        "labelencoder = LabelEncoder()\n",
        "classe = labelencoder.fit_transform(classe)\n",
        "#iris setosa      1\n",
        "#iris versicolor  2\n",
        "#iris virginica   3\n",
        "\n",
        "#de numerico para ohe(array)\n",
        "classe_dummy = np_utils.to_categorical(classe)\n",
        "#iris setosa      100\n",
        "#iris versicolor  010\n",
        "#iris virginica   001 \n",
        "\n",
        "previsores_treinamento, previsores_teste, classe_treinamento, classe_teste = train_test_split(previsores, classe_dummy, test_size=0.25)\n",
        "\n",
        "classificador = Sequential()\n",
        "#neurons = (4 atributos + 3 classes )/2\n",
        "classificador.add(Dense(units=n_inputs_neurons, activation='relu', input_dim=n_atributes))\n",
        "classificador.add(Dense(units=n_inputs_neurons, activation='relu'))\n",
        "classificador.add(Dense(units=n_classes, activation='softmax'))\n",
        "classificador.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classificador.fit(previsores_treinamento, classe_treinamento, batch_size=10, epochs=1000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_xf-I-NE32e",
        "outputId": "54aa7da6-9b7c-4a97-a34e-acf88d84ed21"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/1000\n",
            "12/12 [==============================] - 2s 3ms/step - loss: 2.5993 - categorical_accuracy: 0.3482\n",
            "Epoch 2/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 2.3359 - categorical_accuracy: 0.3482\n",
            "Epoch 3/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 2.1231 - categorical_accuracy: 0.3482\n",
            "Epoch 4/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 1.9545 - categorical_accuracy: 0.3482\n",
            "Epoch 5/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 1.8060 - categorical_accuracy: 0.3482\n",
            "Epoch 6/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 1.6731 - categorical_accuracy: 0.3482\n",
            "Epoch 7/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 1.5582 - categorical_accuracy: 0.3482\n",
            "Epoch 8/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 1.4552 - categorical_accuracy: 0.3482\n",
            "Epoch 9/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 1.3584 - categorical_accuracy: 0.3482\n",
            "Epoch 10/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 1.2686 - categorical_accuracy: 0.3482\n",
            "Epoch 11/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 1.1949 - categorical_accuracy: 0.3482\n",
            "Epoch 12/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 1.1197 - categorical_accuracy: 0.3482\n",
            "Epoch 13/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 1.0588 - categorical_accuracy: 0.3482\n",
            "Epoch 14/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 1.0001 - categorical_accuracy: 0.3482\n",
            "Epoch 15/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.9504 - categorical_accuracy: 0.3571\n",
            "Epoch 16/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.9034 - categorical_accuracy: 0.3750\n",
            "Epoch 17/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.8592 - categorical_accuracy: 0.5357\n",
            "Epoch 18/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.8210 - categorical_accuracy: 0.6161\n",
            "Epoch 19/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7846 - categorical_accuracy: 0.6696\n",
            "Epoch 20/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.7520 - categorical_accuracy: 0.7232\n",
            "Epoch 21/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.7222 - categorical_accuracy: 0.7679\n",
            "Epoch 22/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6953 - categorical_accuracy: 0.8214\n",
            "Epoch 23/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6718 - categorical_accuracy: 0.8661\n",
            "Epoch 24/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6498 - categorical_accuracy: 0.9018\n",
            "Epoch 25/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.6311 - categorical_accuracy: 0.9554\n",
            "Epoch 26/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.6115 - categorical_accuracy: 0.9554\n",
            "Epoch 27/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.5933 - categorical_accuracy: 0.9464\n",
            "Epoch 28/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.5791 - categorical_accuracy: 0.9375\n",
            "Epoch 29/1000\n",
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            "Epoch 30/1000\n",
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            "Epoch 31/1000\n",
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            "Epoch 32/1000\n",
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            "Epoch 33/1000\n",
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            "Epoch 34/1000\n",
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            "Epoch 35/1000\n",
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            "Epoch 36/1000\n",
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            "Epoch 37/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.4842 - categorical_accuracy: 0.9286\n",
            "Epoch 38/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.4768 - categorical_accuracy: 0.9375\n",
            "Epoch 39/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.4693 - categorical_accuracy: 0.9375\n",
            "Epoch 40/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.4616 - categorical_accuracy: 0.9286\n",
            "Epoch 41/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.4554 - categorical_accuracy: 0.9286\n",
            "Epoch 42/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.4492 - categorical_accuracy: 0.9375\n",
            "Epoch 43/1000\n",
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            "Epoch 44/1000\n",
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            "Epoch 45/1000\n",
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            "Epoch 46/1000\n",
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            "Epoch 48/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.4124 - categorical_accuracy: 0.9375\n",
            "Epoch 49/1000\n",
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            "Epoch 50/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.4012 - categorical_accuracy: 0.9554\n",
            "Epoch 51/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.3956 - categorical_accuracy: 0.9643\n",
            "Epoch 52/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.3896 - categorical_accuracy: 0.9643\n",
            "Epoch 53/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.3846 - categorical_accuracy: 0.9643\n",
            "Epoch 54/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.3793 - categorical_accuracy: 0.9643\n",
            "Epoch 55/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.3743 - categorical_accuracy: 0.9643\n",
            "Epoch 56/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.3692 - categorical_accuracy: 0.9554\n",
            "Epoch 57/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.3635 - categorical_accuracy: 0.9643\n",
            "Epoch 58/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.3582 - categorical_accuracy: 0.9643\n",
            "Epoch 59/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.3535 - categorical_accuracy: 0.9643\n",
            "Epoch 60/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.3490 - categorical_accuracy: 0.9643\n",
            "Epoch 61/1000\n",
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            "12/12 [==============================] - 0s 3ms/step - loss: 0.3350 - categorical_accuracy: 0.9643\n",
            "Epoch 64/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.3313 - categorical_accuracy: 0.9643\n",
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            "12/12 [==============================] - 0s 3ms/step - loss: 0.3171 - categorical_accuracy: 0.9643\n",
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            "12/12 [==============================] - 0s 3ms/step - loss: 0.3130 - categorical_accuracy: 0.9732\n",
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            "12/12 [==============================] - 0s 3ms/step - loss: 0.3094 - categorical_accuracy: 0.9643\n",
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            "Epoch 71/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.3020 - categorical_accuracy: 0.9643\n",
            "Epoch 72/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2975 - categorical_accuracy: 0.9732\n",
            "Epoch 73/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2931 - categorical_accuracy: 0.9643\n",
            "Epoch 74/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2883 - categorical_accuracy: 0.9643\n",
            "Epoch 75/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.2847 - categorical_accuracy: 0.9732\n",
            "Epoch 76/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2796 - categorical_accuracy: 0.9643\n",
            "Epoch 77/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.2786 - categorical_accuracy: 0.9464\n",
            "Epoch 78/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2740 - categorical_accuracy: 0.9464\n",
            "Epoch 79/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2703 - categorical_accuracy: 0.9732\n",
            "Epoch 80/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2662 - categorical_accuracy: 0.9643\n",
            "Epoch 81/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2641 - categorical_accuracy: 0.9643\n",
            "Epoch 82/1000\n",
            "12/12 [==============================] - 0s 2ms/step - loss: 0.2594 - categorical_accuracy: 0.9554\n",
            "Epoch 83/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2576 - categorical_accuracy: 0.9732\n",
            "Epoch 84/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2536 - categorical_accuracy: 0.9732\n",
            "Epoch 85/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2486 - categorical_accuracy: 0.9643\n",
            "Epoch 86/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2461 - categorical_accuracy: 0.9554\n",
            "Epoch 87/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2420 - categorical_accuracy: 0.9643\n",
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            "12/12 [==============================] - 0s 3ms/step - loss: 0.2394 - categorical_accuracy: 0.9643\n",
            "Epoch 89/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2361 - categorical_accuracy: 0.9643\n",
            "Epoch 90/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2321 - categorical_accuracy: 0.9554\n",
            "Epoch 91/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2315 - categorical_accuracy: 0.9554\n",
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            "12/12 [==============================] - 0s 3ms/step - loss: 0.2293 - categorical_accuracy: 0.9554\n",
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            "12/12 [==============================] - 0s 3ms/step - loss: 0.2245 - categorical_accuracy: 0.9464\n",
            "Epoch 94/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2221 - categorical_accuracy: 0.9643\n",
            "Epoch 95/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2201 - categorical_accuracy: 0.9732\n",
            "Epoch 96/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2173 - categorical_accuracy: 0.9732\n",
            "Epoch 97/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2143 - categorical_accuracy: 0.9732\n",
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            "12/12 [==============================] - 0s 3ms/step - loss: 0.2133 - categorical_accuracy: 0.9732\n",
            "Epoch 99/1000\n",
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            "Epoch 100/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2056 - categorical_accuracy: 0.9643\n",
            "Epoch 101/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2039 - categorical_accuracy: 0.9554\n",
            "Epoch 102/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.2022 - categorical_accuracy: 0.9554\n",
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            "12/12 [==============================] - 0s 3ms/step - loss: 0.1959 - categorical_accuracy: 0.9643\n",
            "Epoch 105/1000\n",
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            "Epoch 106/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.1938 - categorical_accuracy: 0.9554\n",
            "Epoch 107/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.1909 - categorical_accuracy: 0.9643\n",
            "Epoch 108/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.1880 - categorical_accuracy: 0.9732\n",
            "Epoch 109/1000\n",
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            "Epoch 110/1000\n",
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            "Epoch 111/1000\n",
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            "Epoch 112/1000\n",
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            "Epoch 764/1000\n",
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            "Epoch 768/1000\n",
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            "Epoch 782/1000\n",
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            "Epoch 784/1000\n",
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            "Epoch 788/1000\n",
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            "Epoch 789/1000\n",
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            "Epoch 790/1000\n",
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            "Epoch 791/1000\n",
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            "Epoch 792/1000\n",
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            "Epoch 795/1000\n",
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            "Epoch 796/1000\n",
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            "Epoch 797/1000\n",
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            "Epoch 798/1000\n",
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            "Epoch 799/1000\n",
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            "Epoch 800/1000\n",
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            "Epoch 841/1000\n",
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            "Epoch 888/1000\n",
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            "Epoch 891/1000\n",
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            "Epoch 894/1000\n",
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            "Epoch 896/1000\n",
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            "Epoch 897/1000\n",
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            "Epoch 898/1000\n",
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            "Epoch 899/1000\n",
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            "Epoch 900/1000\n",
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            "Epoch 901/1000\n",
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            "Epoch 902/1000\n",
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            "Epoch 903/1000\n",
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            "Epoch 904/1000\n",
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            "Epoch 905/1000\n",
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            "Epoch 906/1000\n",
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            "Epoch 907/1000\n",
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            "Epoch 908/1000\n",
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            "Epoch 909/1000\n",
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            "Epoch 910/1000\n",
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            "Epoch 911/1000\n",
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            "Epoch 912/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0662 - categorical_accuracy: 0.9821\n",
            "Epoch 913/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0699 - categorical_accuracy: 0.9643\n",
            "Epoch 914/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0658 - categorical_accuracy: 0.9643\n",
            "Epoch 915/1000\n",
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            "Epoch 916/1000\n",
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            "Epoch 917/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0612 - categorical_accuracy: 0.9643\n",
            "Epoch 918/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0695 - categorical_accuracy: 0.9732\n",
            "Epoch 919/1000\n",
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            "Epoch 920/1000\n",
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            "Epoch 921/1000\n",
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            "Epoch 922/1000\n",
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            "Epoch 923/1000\n",
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            "Epoch 924/1000\n",
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            "Epoch 925/1000\n",
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            "Epoch 926/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0645 - categorical_accuracy: 0.9821\n",
            "Epoch 927/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0648 - categorical_accuracy: 0.9821\n",
            "Epoch 928/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0667 - categorical_accuracy: 0.9732\n",
            "Epoch 929/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0721 - categorical_accuracy: 0.9732\n",
            "Epoch 930/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0673 - categorical_accuracy: 0.9732\n",
            "Epoch 931/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0662 - categorical_accuracy: 0.9732\n",
            "Epoch 932/1000\n",
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            "Epoch 933/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0646 - categorical_accuracy: 0.9643\n",
            "Epoch 934/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0664 - categorical_accuracy: 0.9732\n",
            "Epoch 935/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0633 - categorical_accuracy: 0.9732\n",
            "Epoch 936/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0655 - categorical_accuracy: 0.9643\n",
            "Epoch 937/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0643 - categorical_accuracy: 0.9643\n",
            "Epoch 938/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0659 - categorical_accuracy: 0.9732\n",
            "Epoch 939/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0662 - categorical_accuracy: 0.9732\n",
            "Epoch 940/1000\n",
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            "Epoch 941/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0655 - categorical_accuracy: 0.9821\n",
            "Epoch 942/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0664 - categorical_accuracy: 0.9821\n",
            "Epoch 943/1000\n",
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            "Epoch 944/1000\n",
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            "Epoch 945/1000\n",
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            "Epoch 946/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0653 - categorical_accuracy: 0.9732\n",
            "Epoch 947/1000\n",
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            "Epoch 948/1000\n",
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            "Epoch 949/1000\n",
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            "Epoch 950/1000\n",
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            "Epoch 951/1000\n",
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            "Epoch 952/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0670 - categorical_accuracy: 0.9643\n",
            "Epoch 953/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0717 - categorical_accuracy: 0.9821\n",
            "Epoch 954/1000\n",
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            "Epoch 955/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0632 - categorical_accuracy: 0.9732\n",
            "Epoch 956/1000\n",
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            "Epoch 957/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0701 - categorical_accuracy: 0.9643\n",
            "Epoch 958/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0674 - categorical_accuracy: 0.9821\n",
            "Epoch 959/1000\n",
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            "Epoch 960/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0639 - categorical_accuracy: 0.9732\n",
            "Epoch 961/1000\n",
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            "Epoch 962/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0697 - categorical_accuracy: 0.9643\n",
            "Epoch 963/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0639 - categorical_accuracy: 0.9643\n",
            "Epoch 964/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0635 - categorical_accuracy: 0.9732\n",
            "Epoch 965/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0639 - categorical_accuracy: 0.9821\n",
            "Epoch 966/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0656 - categorical_accuracy: 0.9643\n",
            "Epoch 967/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0686 - categorical_accuracy: 0.9643\n",
            "Epoch 968/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0626 - categorical_accuracy: 0.9732\n",
            "Epoch 969/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0650 - categorical_accuracy: 0.9643\n",
            "Epoch 970/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0644 - categorical_accuracy: 0.9643\n",
            "Epoch 971/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0642 - categorical_accuracy: 0.9732\n",
            "Epoch 972/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0637 - categorical_accuracy: 0.9732\n",
            "Epoch 973/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0649 - categorical_accuracy: 0.9643\n",
            "Epoch 974/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0648 - categorical_accuracy: 0.9643\n",
            "Epoch 975/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0628 - categorical_accuracy: 0.9732\n",
            "Epoch 976/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0664 - categorical_accuracy: 0.9821\n",
            "Epoch 977/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0653 - categorical_accuracy: 0.9821\n",
            "Epoch 978/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0633 - categorical_accuracy: 0.9732\n",
            "Epoch 979/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0637 - categorical_accuracy: 0.9732\n",
            "Epoch 980/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0653 - categorical_accuracy: 0.9821\n",
            "Epoch 981/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0642 - categorical_accuracy: 0.9732\n",
            "Epoch 982/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0634 - categorical_accuracy: 0.9732\n",
            "Epoch 983/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0739 - categorical_accuracy: 0.9732\n",
            "Epoch 984/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0662 - categorical_accuracy: 0.9821\n",
            "Epoch 985/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0671 - categorical_accuracy: 0.9732\n",
            "Epoch 986/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0657 - categorical_accuracy: 0.9732\n",
            "Epoch 987/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0655 - categorical_accuracy: 0.9643\n",
            "Epoch 988/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0629 - categorical_accuracy: 0.9732\n",
            "Epoch 989/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0656 - categorical_accuracy: 0.9821\n",
            "Epoch 990/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0629 - categorical_accuracy: 0.9732\n",
            "Epoch 991/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0640 - categorical_accuracy: 0.9732\n",
            "Epoch 992/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0627 - categorical_accuracy: 0.9821\n",
            "Epoch 993/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0714 - categorical_accuracy: 0.9643\n",
            "Epoch 994/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0713 - categorical_accuracy: 0.9643\n",
            "Epoch 995/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0632 - categorical_accuracy: 0.9732\n",
            "Epoch 996/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0707 - categorical_accuracy: 0.9732\n",
            "Epoch 997/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0697 - categorical_accuracy: 0.9732\n",
            "Epoch 998/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0714 - categorical_accuracy: 0.9821\n",
            "Epoch 999/1000\n",
            "12/12 [==============================] - 0s 3ms/step - loss: 0.0653 - categorical_accuracy: 0.9821\n",
            "Epoch 1000/1000\n",
            "12/12 [==============================] - 0s 4ms/step - loss: 0.0643 - categorical_accuracy: 0.9643\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb9600ade50>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "resultado = classificador.evaluate(previsores_teste,classe_teste)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Q1K6XXoL5e9",
        "outputId": "959e7616-1ff8-4732-beb1-e71c6aea1005"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 6ms/step - loss: 0.0223 - categorical_accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsoes = classificador.predict(previsores_teste)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y7a9Cg1EO587",
        "outputId": "ad79e989-f638-4c0e-e06b-84e4cef57bf6"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 5ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris1 = np.array([[5.1,3.5,1.4,0.2]])#setosa\n",
        "iris2 = np.array([[4.9,3.0,1.4,0.2]])#setosa\n",
        "iris3 = np.array([[5.6,3.0,4.1,1.3]])#versicolor\n",
        "iris4 = np.array([[5.7,3.0,4.2,1.2]])#versicolor\n",
        "iris5 = np.array([[6.7,3.3,5.7,2.5]])#virginica\n",
        "iris6 = np.array([[4.9,2.5,4.5,1.7]])#virginica\n",
        "\n",
        "\n",
        "previsao1 = classificador.predict(iris1)\n",
        "previsao2 = classificador.predict(iris2)\n",
        "previsao3 = classificador.predict(iris3)\n",
        "previsao4 = classificador.predict(iris4)\n",
        "previsao5 = classificador.predict(iris5)\n",
        "previsao6 = classificador.predict(iris6)\n",
        "\n",
        "print(previsao1)\n",
        "print(previsao2)\n",
        "print(previsao3)\n",
        "print(previsao4)\n",
        "print(previsao5)\n",
        "print(previsao6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QuNRJGlwVLu8",
        "outputId": "a59daafe-669d-4e09-a225-9457cd8f97a6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 14ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "1/1 [==============================] - 0s 15ms/step\n",
            "[[9.9999344e-01 6.4974720e-06 4.7890965e-24]]\n",
            "[[9.9997783e-01 2.2163113e-05 9.2016280e-22]]\n",
            "[[9.6129006e-06 9.9974626e-01 2.4409474e-04]]\n",
            "[[1.8503197e-05 9.9987388e-01 1.0767668e-04]]\n",
            "[[5.3096769e-16 3.5219677e-04 9.9964786e-01]]\n",
            "[[1.8904298e-11 5.4141255e-03 9.9458587e-01]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsoes = (previsoes > 0.5)\n",
        "import numpy as np\n",
        "classe_teste2 = [np.argmax(t) for t in classe_teste]\n",
        "previsoes2 = [np.argmax(t) for t in previsoes]\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "matriz = confusion_matrix(previsoes2, classe_teste2)\n",
        "\n",
        "print(matriz)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uxp7c763soGh",
        "outputId": "28d8a27d-b8ef-41b0-c4d7-53c26b3f8b3e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[13  0  0]\n",
            " [ 0 11  0]\n",
            " [ 0  0 14]]\n"
          ]
        }
      ]
    }
  ]
}