Copy_of_Analise_COVID_19_Brasil.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Analise-COVID-19-Brasil.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO2r33W9UDN5V3OHiSG7818",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "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/Copy_of_Analise_COVID_19_Brasil.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tNWjJ10rocRp"
      },
      "source": [
        "1ª Etapa - Plotagem do Casos Confirmados de COVID-19 no Brasil"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_shQlW-OyqP"
      },
      "source": [
        "#---------------------------------Parte 1---------------------------------------\n",
        "#Importanto bibliotecas\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "#import geopandas as gpd -----> Verificar porque não carrega a biblioteca\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go"
      ],
      "execution_count": 734,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50HAQscEO-HT",
        "outputId": "70854eef-64a6-4197-fc38-1c1209473bcf"
      },
      "source": [
        "#Importação da biblioteca geopandas via instalação no Colab\n",
        "!pip install geopandas"
      ],
      "execution_count": 735,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: geopandas in /usr/local/lib/python3.7/dist-packages (0.10.2)\n",
            "Requirement already satisfied: pyproj>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (3.2.1)\n",
            "Requirement already satisfied: fiona>=1.8 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.20)\n",
            "Requirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.0)\n",
            "Requirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.1.5)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (0.7.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.15.0)\n",
            "Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2.5.0)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (21.2.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2021.10.8)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n",
            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.1.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (1.19.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2.8.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nc0h3H3rPDEq"
      },
      "source": [
        "#---------------------------------Parte 2---------------------------------------\n",
        "#Importando Arquivo CSV\n",
        "#Dados não \"Oficiais de 2020\"\n",
        "covid_cases = pd.read_csv('worldometer_coronavirus_daily_data.csv')\n",
        "covid_vaxxs = pd.read_csv('country_vaccinations.csv')\n",
        "df = pd.read_csv('covid_19_data.csv')\n",
        "train=pd.read_csv('train.csv')\n",
        "test=pd.read_csv('test.csv')\n",
        "sub=pd.read_csv('submission.csv')\n",
        "submission=pd.read_csv('submission.csv')\n",
        "confirmed_df = pd.read_csv('time_series_covid_19_confirmed.csv')"
      ],
      "execution_count": 736,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewu71qOlQQXk"
      },
      "source": [
        "#---------------------------------Parte 3---------------------------------------\n",
        "#Vamos marcar o datase e usar como amostra o Brazil\n",
        "\n",
        "def fix_country_name(country):\n",
        "    name_dict = {\n",
        "        'BRA': 'Brazil'\n",
        "     }\n",
        "    try:\n",
        "      return name_dict[country]\n",
        "    except KeyError:\n",
        "      return country\n",
        "\n",
        "covid_cases.country = covid_cases.country.apply(fix_country_name)"
      ],
      "execution_count": 737,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJLmTUSDQSS7"
      },
      "source": [
        "#---------------------------------Parte 4---------------------------------------\n",
        "#Vamos observar o caso do Brasil\n",
        "countries_of_interest = [\"Brazil\"]\n",
        "\n",
        "#Vamos começar a limpar os dados filtrando-o para o subconjunto de países que desejamos.\n",
        "covid_cases_subset = covid_cases[covid_cases.country.isin (countries_of_interest)]\n",
        "covid_vaxxs_subset = covid_vaxxs[covid_vaxxs.country.isin (countries_of_interest)]"
      ],
      "execution_count": 738,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y33OiuZBQT3y"
      },
      "source": [
        "#---------------------------------Parte 5---------------------------------------\n",
        "#Unindo os databases usando as datas e países\n",
        "merged_data = pd.merge_ordered(covid_cases_subset, covid_vaxxs_subset, \\\n",
        "                               left_on=['date', 'country'], \\\n",
        "                               right_on=['date', 'country'])\n",
        "\n",
        "#Remove as colunas desnecessárias\n",
        "merged_data.drop(columns=['iso_code', 'source_name', 'source_website', 'vaccines',\\\n",
        "                           'daily_vaccinations_per_million', 'daily_vaccinations_raw'], inplace=True)"
      ],
      "execution_count": 739,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyw-C9mpQVWa"
      },
      "source": [
        "#---------------------------------Parte 6---------------------------------------\n",
        "#Vamos lidar com os NaNs\n",
        "merged_data.fillna({'total_vaccinations':0, 'people_vaccinated':0, 'people_fully_vaccinated':0,\\\n",
        "                 'daily_vaccinations':0,'total_vaccinations_per_hundred':0,\\\n",
        "                 'people_vaccinated_per_hundred':0 ,'people_fully_vaccinated_per_hundred':0}\\\n",
        "                 ,inplace=True)\n",
        "\n",
        "covid_data = merged_data.dropna(how='any', subset=['cumulative_total_cases'])"
      ],
      "execution_count": 740,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1NLUjqmQWsC"
      },
      "source": [
        "#---------------------------------Parte 7---------------------------------------\n",
        "#Vamos também definir o índice para a data\n",
        "covid_data.set_index('date', inplace=True)"
      ],
      "execution_count": 741,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWBOmmGuQX_7"
      },
      "source": [
        "#Vamos mudar o índice para um objeto datetime agora\n",
        "covid_data.index = pd.to_datetime(covid_data.index, errors='coerce', infer_datetime_format=True)"
      ],
      "execution_count": 742,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "8GtjdXp-Qe_Z",
        "outputId": "06c554fa-694c-4c8c-9797-b048eb747b0c"
      },
      "source": [
        "#---------------------------------Parte 8---------------------------------------\n",
        "#Plotagem do casos de COVID-19 no Brasil em forma de barras\n",
        "BRA_infections = covid_data[covid_data.country == 'Brazil']\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "BRA_infections['daily_new_cases']\\\n",
        "  .groupby(BRA_infections.index.to_period('M'))\\\n",
        "  .sum().plot(kind='bar', legend=False, color='g')\n",
        "\n",
        "plt.legend(['Brasil'])\n",
        "plt.ylabel('Número de casos')\n",
        "plt.xlabel('Meses')\n",
        "plt.title('Casos de COVID em Meses');"
      ],
      "execution_count": 743,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "dqVmQ4fAQg_h",
        "outputId": "a7b46c7a-95c6-46bb-b6e2-2a770daae002"
      },
      "source": [
        "#---------------------------------Parte 9---------------------------------------\n",
        "#Vemos que o padrão muda de país para país\n",
        "plt.figure(figsize=(12, 6))\n",
        "covid_data[covid_data.country == 'Brazil'].daily_new_cases.plot(legend=False,rot=45)\n",
        "covid_data[covid_data.country == 'Brazil'].daily_vaccinations.plot(legend=False,rot=45)\n",
        "\n",
        "#Marca o início do lançamento da vacina  no Brazil -----> Mônica Calazans (Primeiro Vacinado)\n",
        "plt.axvline('17/01/2021', color='k', linestyle='dashed', linewidth=1)\n",
        "\n",
        "plt.legend(['Casos diários', 'Vacinações diárias'])\n",
        "plt.ylabel('Número de pessoas')\n",
        "plt.xlabel('Data')\n",
        "plt.title('Taxas de COVID e vacinas no Brasil');\n",
        "\n",
        "#É possível conferir se os gráficos abaixo estão certo,\n",
        "#checando nos sites da Wikipedia e Our World Data\n",
        "#https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Brazil\n",
        "#https://ourworldindata.org/coronavirus/country/brazil"
      ],
      "execution_count": 744,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lA9cU4t5tUS7"
      },
      "source": [
        "--------------------------Fim do Código---------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UyFrkAgALmT"
      },
      "source": [
        "2ª Etapa - Plotagem do Modelo de Predição de Casos de Confirmados no Brasil"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4DDlRZG2qslQ"
      },
      "source": [
        "#---------------------------------Parte 1---------------------------------------\n",
        "#Importanto bibliotecas\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "from plotly.offline import iplot, init_notebook_mode\n",
        "import plotly.express as px\n",
        "import bokeh \n",
        "from bokeh.io import output_notebook\n",
        "from bokeh.resources import INLINE\n",
        "import tensorflow as tf\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "output_notebook(resources=INLINE)\n",
        "%matplotlib inline\n",
        "\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from sklearn.metrics import classification_report,confusion_matrix,accuracy_score\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.utils import to_categorical"
      ],
      "execution_count": 745,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49rsEskQqunZ"
      },
      "source": [
        "#---------------------------------Parte 2---------------------------------------\n",
        "#Construindo o DataFrame a partir de um arquivo CSV.\n",
        "train = pd.DataFrame(pd.read_csv('train.csv'))"
      ],
      "execution_count": 746,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZr0HI-Xq1Oy",
        "outputId": "4bd46438-b65f-45de-de4e-bfce8b410245"
      },
      "source": [
        "#Platagem da forma de treinamento\n",
        "print(\"A forma dos dados de treinamento é = {}\".format(train.shape))"
      ],
      "execution_count": 747,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A forma dos dados de treinamento é = (35995, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEpXqWQuq30x"
      },
      "source": [
        "#---------------------------------Parte 3---------------------------------------\n",
        "#Considerando para o treinamento os dados das Provincias, Casos Confirmados e Fatalidades\n",
        "train.Province_State.fillna(\"\", inplace = True)\n",
        "train.ConfirmedCases.fillna(\"\", inplace = True)\n",
        "train.Fatalities.fillna(\"\", inplace = True)"
      ],
      "execution_count": 748,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxL8Uz9Kq5yo",
        "outputId": "0c299a2e-a427-4d9a-f378-4ee1bafa03c7"
      },
      "source": [
        "#---------------------------------Parte 4---------------------------------------\n",
        "#Treinamento exibe a forma matricial dos países com casos confirmados de COVID-19\n",
        "train[\"Country_Region\"].unique()"
      ],
      "execution_count": 749,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n",
              "       'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n",
              "       'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh',\n",
              "       'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',\n",
              "       'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
              "       'Brunei', 'Bulgaria', 'Burkina Faso', 'Burma', 'Burundi',\n",
              "       'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada',\n",
              "       'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',\n",
              "       'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Costa Rica',\n",
              "       \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark',\n",
              "       'Diamond Princess', 'Djibouti', 'Dominica', 'Dominican Republic',\n",
              "       'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea',\n",
              "       'Estonia', 'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France',\n",
              "       'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece',\n",
              "       'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana',\n",
              "       'Haiti', 'Holy See', 'Honduras', 'Hungary', 'Iceland', 'India',\n",
              "       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
              "       'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya',\n",
              "       'Korea, South', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia',\n",
              "       'Lebanon', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n",
              "       'Luxembourg', 'MS Zaandam', 'Madagascar', 'Malawi', 'Malaysia',\n",
              "       'Maldives', 'Mali', 'Malta', 'Mauritania', 'Mauritius', 'Mexico',\n",
              "       'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco',\n",
              "       'Mozambique', 'Namibia', 'Nepal', 'Netherlands', 'New Zealand',\n",
              "       'Nicaragua', 'Niger', 'Nigeria', 'North Macedonia', 'Norway',\n",
              "       'Oman', 'Pakistan', 'Panama', 'Papua New Guinea', 'Paraguay',\n",
              "       'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania',\n",
              "       'Russia', 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia',\n",
              "       'Saint Vincent and the Grenadines', 'San Marino',\n",
              "       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n",
              "       'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',\n",
              "       'Somalia', 'South Africa', 'South Sudan', 'Spain', 'Sri Lanka',\n",
              "       'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan*',\n",
              "       'Tanzania', 'Thailand', 'Timor-Leste', 'Togo',\n",
              "       'Trinidad and Tobago', 'Tunisia', 'Turkey', 'US', 'Uganda',\n",
              "       'Ukraine', 'United Arab Emirates', 'United Kingdom', 'Uruguay',\n",
              "       'Uzbekistan', 'Venezuela', 'Vietnam', 'West Bank and Gaza',\n",
              "       'Western Sahara', 'Zambia', 'Zimbabwe'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 749
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bn-NBB9cq82L"
      },
      "source": [
        "#---------------------------------Parte 5---------------------------------------\n",
        "#Aqui separamos os casos confimados e os casos fatais de COVID-19 no Brazil\n",
        "confirmed_cases_brazil = train[train[\"Country_Region\"] == \"Brazil\"].groupby([\"Date\"]).ConfirmedCases.sum()\n",
        "fatal_cases_brazil = train[train[\"Country_Region\"] == \"Brazil\"].groupby([\"Date\"]).Fatalities.sum()"
      ],
      "execution_count": 750,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "P3RqPhJ6r0LJ",
        "outputId": "8f038272-e01b-476f-853f-30292cc99d93"
      },
      "source": [
        "#---------------------------------Parte 6---------------------------------------\n",
        "#Fazendo o treinamento para Paíse/Região\n",
        "train_brazil = train[train[\"Country_Region\"] == \"Brazil\"]\n",
        "train_brazil"
      ],
      "execution_count": 751,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Province_State</th>\n",
              "      <th>Country_Region</th>\n",
              "      <th>Date</th>\n",
              "      <th>ConfirmedCases</th>\n",
              "      <th>Fatalities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3450</th>\n",
              "      <td>3451</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3451</th>\n",
              "      <td>3452</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3452</th>\n",
              "      <td>3453</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3453</th>\n",
              "      <td>3454</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3454</th>\n",
              "      <td>3455</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3560</th>\n",
              "      <td>3561</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-11</td>\n",
              "      <td>169594.0</td>\n",
              "      <td>11653.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3561</th>\n",
              "      <td>3562</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-12</td>\n",
              "      <td>178214.0</td>\n",
              "      <td>12461.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3562</th>\n",
              "      <td>3563</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-13</td>\n",
              "      <td>190137.0</td>\n",
              "      <td>13240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3563</th>\n",
              "      <td>3564</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-14</td>\n",
              "      <td>203165.0</td>\n",
              "      <td>13999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3564</th>\n",
              "      <td>3565</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-15</td>\n",
              "      <td>220291.0</td>\n",
              "      <td>14962.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>115 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Id Province_State  ... ConfirmedCases Fatalities\n",
              "3450  3451                 ...            0.0        0.0\n",
              "3451  3452                 ...            0.0        0.0\n",
              "3452  3453                 ...            0.0        0.0\n",
              "3453  3454                 ...            0.0        0.0\n",
              "3454  3455                 ...            0.0        0.0\n",
              "...    ...            ...  ...            ...        ...\n",
              "3560  3561                 ...       169594.0    11653.0\n",
              "3561  3562                 ...       178214.0    12461.0\n",
              "3562  3563                 ...       190137.0    13240.0\n",
              "3563  3564                 ...       203165.0    13999.0\n",
              "3564  3565                 ...       220291.0    14962.0\n",
              "\n",
              "[115 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 751
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ewbM62_ruMA"
      },
      "source": [
        "#---------------------------------Parte 7--------------------------------------\n",
        "#Analisando os casos confimados e casos fatais\n",
        "train_brazil[\"ConfirmedCases\"] = train_brazil[\"ConfirmedCases\"].astype(int)\n",
        "train_brazil[\"Fatalities\"] = train_brazil[\"Fatalities\"].astype(int)"
      ],
      "execution_count": 752,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Of9mgEJSrvjm",
        "outputId": "4d373dbe-ca50-4321-e419-e48479f5e9cc"
      },
      "source": [
        "#---------------------------------Parte 8---------------------------------------\n",
        "#Aqui vemos o tamanho do treinamento e da validação \n",
        "train_brazil_size = int(len(train_brazil) * 0.75)\n",
        "val_brazil_size = len(train_brazil) - train_brazil_size\n",
        "print(\"Training size = {}\".format(train_brazil_size))\n",
        "print(\"Validation size = {}\".format(val_brazil_size))"
      ],
      "execution_count": 753,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training size = 86\n",
            "Validation size = 29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K37HPRSDrh4B"
      },
      "source": [
        "#---------------------------------Parte 9---------------------------------------\n",
        "#Treinamento os casos confimados e casos fatais\n",
        "train_brazil_confirmed_cases = train_brazil[[\"ConfirmedCases\"]]\n",
        "train_brazil_fatal_cases = train_brazil[[\"Fatalities\"]]"
      ],
      "execution_count": 754,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AR4tHvVB0D0s",
        "outputId": "537799e2-062a-47d0-894c-80e20ff444a5"
      },
      "source": [
        "#PLotagem do casos confirmados e fatais em forma de matriz\n",
        "print(train_brazil_confirmed_cases, train_brazil_fatal_cases)"
      ],
      "execution_count": 755,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      ConfirmedCases\n",
            "3450               0\n",
            "3451               0\n",
            "3452               0\n",
            "3453               0\n",
            "3454               0\n",
            "...              ...\n",
            "3560          169594\n",
            "3561          178214\n",
            "3562          190137\n",
            "3563          203165\n",
            "3564          220291\n",
            "\n",
            "[115 rows x 1 columns]       Fatalities\n",
            "3450           0\n",
            "3451           0\n",
            "3452           0\n",
            "3453           0\n",
            "3454           0\n",
            "...          ...\n",
            "3560       11653\n",
            "3561       12461\n",
            "3562       13240\n",
            "3563       13999\n",
            "3564       14962\n",
            "\n",
            "[115 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "wWmVaIlczU7y",
        "outputId": "596c61b3-d275-4f4b-8040-03cdd20a3cb2"
      },
      "source": [
        "#---------------------------------Parte 11--------------------------------------\n",
        "#Plotagem dos casos confirmados e casos fatais de Janeiro a Maio (2020)\n",
        "plt.figure(figsize = (8, 8))\n",
        "x = np.arange(1, 116, 1)\n",
        "y1 = train_brazil_confirmed_cases \n",
        "y2 = train_brazil_fatal_cases \n",
        "plt.plot(x, y1, color = \"m\", label = \"Casos Confirmados no Brasil de Janeiro a Maio (2020)\")\n",
        "plt.plot(x, y2, color = \"r\", label = \"Casos Fatais no Brasil de Janeiro a Maio (2020)\")\n",
        "plt.grid(True)\n",
        "plt.legend()"
      ],
      "execution_count": 756,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f4153c90990>"
            ]
          },
          "metadata": {},
          "execution_count": 756
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXhBl4d2ztda",
        "outputId": "1088477c-61be-48f0-a753-23d774e8b4c9"
      },
      "source": [
        "#---------------------------------Parte 12--------------------------------------\n",
        "#O comando len () é uma função embutida que retorna o comprimento (número de itens) de um objeto.\n",
        "len(train_brazil_confirmed_cases)"
      ],
      "execution_count": 757,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "115"
            ]
          },
          "metadata": {},
          "execution_count": 757
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "YwQMFuKnzvpb",
        "outputId": "6fedb948-1ff9-40ad-8e74-dae0c1d091f5"
      },
      "source": [
        "#---------------------------------Parte 13--------------------------------------\n",
        "#Treinamento dos casos confirmados de COVID-19 no Brasil\n",
        "train_brazil_confirmed_cases"
      ],
      "execution_count": 758,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ConfirmedCases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3450</th>\n",
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              "</table>\n",
              "<p>115 rows × 1 columns</p>\n",
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            ],
            "text/plain": [
              "      ConfirmedCases\n",
              "3450               0\n",
              "3451               0\n",
              "3452               0\n",
              "3453               0\n",
              "3454               0\n",
              "...              ...\n",
              "3560          169594\n",
              "3561          178214\n",
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              "3564          220291\n",
              "\n",
              "[115 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 758
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdVndkPAsjjC"
      },
      "source": [
        "#---------------------------------Parte 14--------------------------------------\n",
        "#Aqui usa-se o iloc para indexação / seleção por posição baseada em localização de inteiros.\n",
        "train_brazil_confirmed_cases_data = train_brazil_confirmed_cases.iloc[0:train_brazil_size]\n",
        "val_brazil_confirmed_cases_data = train_brazil_confirmed_cases.iloc[train_brazil_size : len(train_brazil_confirmed_cases)]"
      ],
      "execution_count": 759,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j7lQ98xIsup5",
        "outputId": "73cb03ae-00a6-49ea-e2a6-009fd93df230"
      },
      "source": [
        "#---------------------------------Parte 15--------------------------------------\n",
        "#Plotagem da fixação de inteiros; treinamento e validação de casos\n",
        "print(len(train_brazil_confirmed_cases_data))\n",
        "print(len(val_brazil_confirmed_cases_data))"
      ],
      "execution_count": 760,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "86\n",
            "29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Drt7kV-mtEzL"
      },
      "source": [
        "#---------------------------------Parte 16--------------------------------------\n",
        "#O MinMaxScaler faz a estimação de escala e traduz cada recurso individualmente, \n",
        "#de modo que esteja no intervalo especificado no conjunto de treinamento, por exemplo. entre zero e um.\n",
        "scaler = MinMaxScaler(feature_range = (0,1))\n",
        "def createDataset(train) : \n",
        "    train_scaled = scaler.fit_transform(train)\n",
        "    x_train = [] \n",
        "    y_train = []\n",
        "    time_step = 2\n",
        "    for i in range(time_step, train_scaled.shape[0]):\n",
        "        x_train.append(train_scaled[i-time_step : i , 0])\n",
        "        y_train.append(train_scaled[i, 0])\n",
        "    \n",
        "    x_train, y_train = np.array(x_train), np.array(y_train)\n",
        "    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1 ))\n",
        "    y_train = np.reshape(y_train, (y_train.shape[0], 1 ))\n",
        "    return x_train, y_train     "
      ],
      "execution_count": 761,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwR6PESes1tS"
      },
      "source": [
        "#---------------------------------Parte 17--------------------------------------\n",
        "#Aqui será definido a criação do dataset (train)\n",
        "def createDataset(train) : \n",
        "    train_scaled = scaler.fit_transform(train)\n",
        "    x_train = [] \n",
        "    y_train = []\n",
        "    time_step = 2\n",
        "    for i in range(time_step, train_scaled.shape[0]):\n",
        "        x_train.append(train_scaled[i-time_step : i , 0])\n",
        "        y_train.append(train_scaled[i, 0])\n",
        "    \n",
        "    x_train, y_train = np.array(x_train), np.array(y_train)\n",
        "    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1 ))\n",
        "    y_train = np.reshape(y_train, (y_train.shape[0], 1 ))\n",
        "    return x_train, y_train   "
      ],
      "execution_count": 762,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvrDrCJRs4sZ"
      },
      "source": [
        "#---------------------------------Parte 18--------------------------------------\n",
        "#Conjunto de dados para treinar o respectivo modelo de aprendizado\n",
        "x_train, y_train = createDataset(train_brazil_confirmed_cases_data)\n",
        "x_val, y_val = createDataset(val_brazil_confirmed_cases_data)"
      ],
      "execution_count": 763,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dtw5ySo1thPZ",
        "outputId": "104eea3d-2bb3-4879-e8eb-de4aa93c2cd2"
      },
      "source": [
        "#---------------------------------Parte 19--------------------------------------\n",
        "#Plotagem do modelo de aprendizado\n",
        "print(x_train.shape, y_train.shape)\n",
        "print(x_val.shape, y_val.shape)"
      ],
      "execution_count": 764,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(84, 2, 1) (84, 1)\n",
            "(27, 2, 1) (27, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqWGqOKnrTZI"
      },
      "source": [
        "#---------------------------------Parte 20--------------------------------------\n",
        "#Aqui é criado e configurado a rede neural\n",
        "#LSTM cria camada longa de memória de curto prazo\n",
        "#A camada Dropout define aleatoriamente as unidades de entrada para 0 \n",
        "#com uma frequência de taxa em cada etapa durante o tempo de treinamento\n",
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.LSTM(units = 50, return_sequences = True, input_shape = (x_train.shape[1], 1)))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.LSTM(units = 50, return_sequences = True))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.LSTM(units = 50, return_sequences = True))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.LSTM(units = 50, return_sequences = False))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.Dense(units = 1))"
      ],
      "execution_count": 765,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HnqtBKzFtkVo"
      },
      "source": [
        "#Compilação do modelo de treinamento\n",
        "model.compile(tf.keras.optimizers.Adam(lr = 0.001), loss = \"mean_squared_error\", metrics=['accuracy'])"
      ],
      "execution_count": 766,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20cJDKv5tmOO",
        "outputId": "c9907251-1634-4eab-f292-f6cf70b8b218"
      },
      "source": [
        "#Sumário com o modelo de treinamento\n",
        "model.summary()"
      ],
      "execution_count": 767,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_52 (LSTM)              (None, 2, 50)             10400     \n",
            "                                                                 \n",
            " dropout_52 (Dropout)        (None, 2, 50)             0         \n",
            "                                                                 \n",
            " lstm_53 (LSTM)              (None, 2, 50)             20200     \n",
            "                                                                 \n",
            " dropout_53 (Dropout)        (None, 2, 50)             0         \n",
            "                                                                 \n",
            " lstm_54 (LSTM)              (None, 2, 50)             20200     \n",
            "                                                                 \n",
            " dropout_54 (Dropout)        (None, 2, 50)             0         \n",
            "                                                                 \n",
            " lstm_55 (LSTM)              (None, 50)                20200     \n",
            "                                                                 \n",
            " dropout_55 (Dropout)        (None, 50)                0         \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 1)                 51        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 71,051\n",
            "Trainable params: 71,051\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxHvQshP0PYn"
      },
      "source": [
        "#x_train.shape, y_train.shape"
      ],
      "execution_count": 768,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNshwY0ItvD5"
      },
      "source": [
        "#---------------------------------Parte 21--------------------------------------\n",
        "#Um epoch é uma iteração sobre todos os dados x e y fornecidos.\n",
        "#Um batch_size é o tamanho do lote é um termo usado no aprendizado de máquina, \n",
        "#e se refere ao número de exemplos de treinamento utilizados em uma iteração\n",
        "#sobre todos os dados x e y fornecidos.\n",
        "EPOCHS = 100\n",
        "#BATCH_SIZE = 1"
      ],
      "execution_count": 769,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_ZDolDutwb6",
        "outputId": "8b2405bb-220e-4c34-edf1-9bc9561b4d16"
      },
      "source": [
        "#---------------------------------Parte 22--------------------------------------\n",
        "#É possível usar as informações da variável “ history ” para\n",
        "#verificar a evolução da função de loss e da métrica\n",
        "with tf.device(\"/device:GPU:0\"):\n",
        "  history = model.fit(x_train, y_train,epochs = EPOCHS, verbose = 1,\n",
        "                     batch_size = BATCH_SIZE, validation_data = (x_val, y_val))"
      ],
      "execution_count": 770,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "84/84 [==============================] - 9s 36ms/step - loss: 0.0633 - accuracy: 0.3929 - val_loss: 0.1657 - val_accuracy: 0.0000e+00\n",
            "Epoch 2/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0332 - accuracy: 0.3929 - val_loss: 0.0149 - val_accuracy: 0.0370\n",
            "Epoch 3/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0203 - accuracy: 0.3929 - val_loss: 0.0036 - val_accuracy: 0.0370\n",
            "Epoch 4/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0071 - accuracy: 0.4048 - val_loss: 0.0027 - val_accuracy: 0.0370\n",
            "Epoch 5/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0027 - accuracy: 0.4048 - val_loss: 0.0052 - val_accuracy: 0.0370\n",
            "Epoch 6/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0030 - accuracy: 0.4048 - val_loss: 0.0091 - val_accuracy: 0.0370\n",
            "Epoch 7/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0045 - accuracy: 0.4048 - val_loss: 0.0083 - val_accuracy: 0.0370\n",
            "Epoch 8/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0031 - accuracy: 0.4048 - val_loss: 0.0166 - val_accuracy: 0.0370\n",
            "Epoch 9/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0046 - accuracy: 0.4048 - val_loss: 0.0018 - val_accuracy: 0.0370\n",
            "Epoch 10/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0015 - accuracy: 0.4048 - val_loss: 0.0086 - val_accuracy: 0.0370\n",
            "Epoch 11/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0035 - accuracy: 0.4048 - val_loss: 8.1670e-04 - val_accuracy: 0.0370\n",
            "Epoch 12/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0024 - accuracy: 0.4048 - val_loss: 0.0133 - val_accuracy: 0.0370\n",
            "Epoch 13/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0033 - accuracy: 0.4048 - val_loss: 0.0060 - val_accuracy: 0.0370\n",
            "Epoch 14/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0034 - accuracy: 0.4048 - val_loss: 4.4239e-04 - val_accuracy: 0.0370\n",
            "Epoch 15/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0017 - accuracy: 0.4048 - val_loss: 0.0016 - val_accuracy: 0.0370\n",
            "Epoch 16/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0028 - accuracy: 0.4048 - val_loss: 0.0046 - val_accuracy: 0.0370\n",
            "Epoch 17/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - accuracy: 0.4048 - val_loss: 0.0013 - val_accuracy: 0.0370\n",
            "Epoch 18/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - accuracy: 0.4048 - val_loss: 8.7958e-04 - val_accuracy: 0.0370\n",
            "Epoch 19/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0033 - accuracy: 0.4048 - val_loss: 0.0061 - val_accuracy: 0.0370\n",
            "Epoch 20/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0019 - accuracy: 0.4048 - val_loss: 6.0029e-04 - val_accuracy: 0.0370\n",
            "Epoch 21/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0012 - accuracy: 0.4048 - val_loss: 0.0077 - val_accuracy: 0.0370\n",
            "Epoch 22/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0021 - accuracy: 0.4048 - val_loss: 0.0059 - val_accuracy: 0.0370\n",
            "Epoch 23/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0035 - accuracy: 0.4048 - val_loss: 5.4672e-04 - val_accuracy: 0.0370\n",
            "Epoch 24/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0020 - accuracy: 0.4048 - val_loss: 0.0025 - val_accuracy: 0.0370\n",
            "Epoch 25/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0015 - accuracy: 0.4048 - val_loss: 7.4116e-04 - val_accuracy: 0.0370\n",
            "Epoch 26/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0018 - accuracy: 0.4048 - val_loss: 0.0010 - val_accuracy: 0.0370\n",
            "Epoch 27/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0023 - accuracy: 0.4048 - val_loss: 7.8574e-04 - val_accuracy: 0.0370\n",
            "Epoch 28/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0042 - accuracy: 0.4048 - val_loss: 9.8539e-04 - val_accuracy: 0.0370\n",
            "Epoch 29/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0049 - accuracy: 0.4048 - val_loss: 0.0050 - val_accuracy: 0.0370\n",
            "Epoch 30/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0023 - accuracy: 0.4048 - val_loss: 0.0018 - val_accuracy: 0.0370\n",
            "Epoch 31/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0016 - accuracy: 0.4048 - val_loss: 0.0012 - val_accuracy: 0.0370\n",
            "Epoch 32/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0031 - accuracy: 0.4048 - val_loss: 0.0042 - val_accuracy: 0.0370\n",
            "Epoch 33/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0011 - accuracy: 0.4048 - val_loss: 8.4853e-04 - val_accuracy: 0.0370\n",
            "Epoch 34/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0011 - accuracy: 0.4048 - val_loss: 8.9887e-04 - val_accuracy: 0.0370\n",
            "Epoch 35/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0011 - accuracy: 0.4048 - val_loss: 8.4725e-04 - val_accuracy: 0.0370\n",
            "Epoch 36/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0023 - accuracy: 0.4048 - val_loss: 0.0025 - val_accuracy: 0.0370\n",
            "Epoch 37/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0049 - accuracy: 0.4048 - val_loss: 0.0012 - val_accuracy: 0.0370\n",
            "Epoch 38/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0017 - accuracy: 0.4048 - val_loss: 8.8037e-04 - val_accuracy: 0.0370\n",
            "Epoch 39/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0015 - accuracy: 0.4048 - val_loss: 8.7551e-04 - val_accuracy: 0.0370\n",
            "Epoch 40/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0020 - accuracy: 0.4048 - val_loss: 0.0011 - val_accuracy: 0.0370\n",
            "Epoch 41/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0026 - accuracy: 0.4048 - val_loss: 0.0041 - val_accuracy: 0.0370\n",
            "Epoch 42/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0017 - accuracy: 0.4048 - val_loss: 0.0010 - val_accuracy: 0.0370\n",
            "Epoch 43/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0027 - accuracy: 0.4048 - val_loss: 0.0084 - val_accuracy: 0.0370\n",
            "Epoch 44/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0027 - accuracy: 0.4048 - val_loss: 0.0017 - val_accuracy: 0.0370\n",
            "Epoch 45/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0010 - accuracy: 0.4048 - val_loss: 0.0031 - val_accuracy: 0.0370\n",
            "Epoch 46/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0026 - accuracy: 0.4048 - val_loss: 0.0017 - val_accuracy: 0.0370\n",
            "Epoch 47/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0010 - accuracy: 0.4048 - val_loss: 0.0018 - val_accuracy: 0.0370\n",
            "Epoch 48/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0011 - accuracy: 0.4048 - val_loss: 6.3945e-04 - val_accuracy: 0.0370\n",
            "Epoch 49/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0012 - accuracy: 0.4048 - val_loss: 8.7118e-04 - val_accuracy: 0.0370\n",
            "Epoch 50/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0014 - accuracy: 0.4048 - val_loss: 0.0034 - val_accuracy: 0.0370\n",
            "Epoch 51/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0011 - accuracy: 0.4048 - val_loss: 6.9911e-04 - val_accuracy: 0.0370\n",
            "Epoch 52/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 9.4585e-04 - accuracy: 0.4048 - val_loss: 0.0037 - val_accuracy: 0.0370\n",
            "Epoch 53/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0045 - accuracy: 0.4048 - val_loss: 8.8452e-04 - val_accuracy: 0.0370\n",
            "Epoch 54/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0017 - accuracy: 0.4048 - val_loss: 0.0102 - val_accuracy: 0.0370\n",
            "Epoch 55/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0028 - accuracy: 0.4048 - val_loss: 0.0040 - val_accuracy: 0.0370\n",
            "Epoch 56/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0015 - accuracy: 0.4048 - val_loss: 0.0012 - val_accuracy: 0.0370\n",
            "Epoch 57/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0016 - accuracy: 0.4048 - val_loss: 0.0015 - val_accuracy: 0.0370\n",
            "Epoch 58/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 8.5029e-04 - accuracy: 0.4048 - val_loss: 5.8632e-04 - val_accuracy: 0.0370\n",
            "Epoch 59/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0020 - accuracy: 0.4048 - val_loss: 0.0011 - val_accuracy: 0.0370\n",
            "Epoch 60/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0012 - accuracy: 0.4048 - val_loss: 0.0019 - val_accuracy: 0.0370\n",
            "Epoch 61/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0042 - accuracy: 0.4048 - val_loss: 0.0051 - val_accuracy: 0.0370\n",
            "Epoch 62/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0015 - accuracy: 0.4048 - val_loss: 9.4027e-04 - val_accuracy: 0.0370\n",
            "Epoch 63/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0047 - accuracy: 0.4048 - val_loss: 9.1264e-04 - val_accuracy: 0.0370\n",
            "Epoch 64/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0023 - accuracy: 0.4048 - val_loss: 0.0048 - val_accuracy: 0.0370\n",
            "Epoch 65/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0030 - accuracy: 0.4048 - val_loss: 0.0072 - val_accuracy: 0.0370\n",
            "Epoch 66/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0023 - accuracy: 0.4048 - val_loss: 0.0021 - val_accuracy: 0.0370\n",
            "Epoch 67/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - accuracy: 0.4048 - val_loss: 0.0019 - val_accuracy: 0.0370\n",
            "Epoch 68/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0016 - accuracy: 0.4048 - val_loss: 5.2331e-04 - val_accuracy: 0.0370\n",
            "Epoch 69/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0019 - accuracy: 0.4048 - val_loss: 7.9462e-04 - val_accuracy: 0.0370\n",
            "Epoch 70/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0014 - accuracy: 0.4048 - val_loss: 5.2121e-04 - val_accuracy: 0.0370\n",
            "Epoch 71/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0036 - accuracy: 0.4048 - val_loss: 0.0036 - val_accuracy: 0.0370\n",
            "Epoch 72/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0019 - accuracy: 0.4048 - val_loss: 0.0031 - val_accuracy: 0.0370\n",
            "Epoch 73/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0016 - accuracy: 0.4048 - val_loss: 0.0010 - val_accuracy: 0.0370\n",
            "Epoch 74/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0019 - accuracy: 0.4048 - val_loss: 0.0017 - val_accuracy: 0.0370\n",
            "Epoch 75/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0022 - accuracy: 0.4048 - val_loss: 0.0011 - val_accuracy: 0.0370\n",
            "Epoch 76/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0019 - accuracy: 0.4048 - val_loss: 0.0043 - val_accuracy: 0.0370\n",
            "Epoch 77/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0037 - accuracy: 0.4048 - val_loss: 0.0098 - val_accuracy: 0.0370\n",
            "Epoch 78/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0016 - accuracy: 0.4048 - val_loss: 0.0039 - val_accuracy: 0.0370\n",
            "Epoch 79/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0017 - accuracy: 0.4048 - val_loss: 0.0055 - val_accuracy: 0.0370\n",
            "Epoch 80/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0013 - accuracy: 0.4048 - val_loss: 0.0018 - val_accuracy: 0.0370\n",
            "Epoch 81/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0016 - accuracy: 0.4048 - val_loss: 0.0076 - val_accuracy: 0.0370\n",
            "Epoch 82/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0023 - accuracy: 0.4048 - val_loss: 8.6673e-04 - val_accuracy: 0.0370\n",
            "Epoch 83/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0012 - accuracy: 0.4048 - val_loss: 0.0034 - val_accuracy: 0.0370\n",
            "Epoch 84/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 8.9076e-04 - accuracy: 0.4048 - val_loss: 9.0781e-04 - val_accuracy: 0.0370\n",
            "Epoch 85/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0018 - accuracy: 0.4048 - val_loss: 0.0062 - val_accuracy: 0.0370\n",
            "Epoch 86/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0015 - accuracy: 0.4048 - val_loss: 0.0061 - val_accuracy: 0.0370\n",
            "Epoch 87/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0036 - accuracy: 0.4048 - val_loss: 0.0082 - val_accuracy: 0.0370\n",
            "Epoch 88/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0027 - accuracy: 0.4048 - val_loss: 0.0040 - val_accuracy: 0.0370\n",
            "Epoch 89/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0031 - accuracy: 0.4048 - val_loss: 0.0018 - val_accuracy: 0.0370\n",
            "Epoch 90/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - accuracy: 0.4048 - val_loss: 0.0015 - val_accuracy: 0.0370\n",
            "Epoch 91/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0021 - accuracy: 0.4048 - val_loss: 0.0018 - val_accuracy: 0.0370\n",
            "Epoch 92/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0017 - accuracy: 0.4048 - val_loss: 0.0013 - val_accuracy: 0.0370\n",
            "Epoch 93/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0023 - accuracy: 0.4048 - val_loss: 0.0032 - val_accuracy: 0.0370\n",
            "Epoch 94/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0011 - accuracy: 0.4048 - val_loss: 0.0024 - val_accuracy: 0.0370\n",
            "Epoch 95/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0031 - accuracy: 0.4048 - val_loss: 0.0029 - val_accuracy: 0.0370\n",
            "Epoch 96/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0014 - accuracy: 0.4048 - val_loss: 0.0048 - val_accuracy: 0.0370\n",
            "Epoch 97/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - accuracy: 0.4048 - val_loss: 0.0012 - val_accuracy: 0.0370\n",
            "Epoch 98/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0015 - accuracy: 0.4048 - val_loss: 0.0031 - val_accuracy: 0.0370\n",
            "Epoch 99/100\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0021 - accuracy: 0.4048 - val_loss: 7.3416e-04 - val_accuracy: 0.0370\n",
            "Epoch 100/100\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0018 - accuracy: 0.4048 - val_loss: 5.6557e-04 - val_accuracy: 0.0370\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCh0udZB7Iik"
      },
      "source": [
        "#y_train = to_categorical(y_train, 3)\n",
        "#y_val = to_categorical(y_val, 3)"
      ],
      "execution_count": 771,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71j1jrT87ROH"
      },
      "source": [
        "#num_epochs = 100\n",
        "#history = model.fit(x_train, y_train, epochs=num_epochs, \n",
        "                    #validation_data=(x_val, y_val))"
      ],
      "execution_count": 772,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCGW1_kU068I"
      },
      "source": [
        "#acc = history.history['accuracy']\n",
        "#val_acc = history.history['val_accuracy']\n",
        "\n",
        "#loss = history.history['loss']\n",
        "#val_loss = history.history['val_loss']"
      ],
      "execution_count": 773,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wa2_2G8p1BxE"
      },
      "source": [
        "#print(f\"Accuracy on training data is: {acc[-1]*100} %\")\n",
        "#print(f\"Loss {loss[-1]*100}\")\n",
        "\n",
        "#print(f\"Accuracy on validation data is: {val_acc[-1]*100} %\")\n",
        "#print(f\"Loss {val_loss[-1]*100}\")"
      ],
      "execution_count": 774,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypHwcrei_2pV"
      },
      "source": [
        "#epochs = range(len(acc))\n",
        "\n",
        "#plt.plot(epochs, acc,'b',label='training acc')\n",
        "#plt.plot(epochs, val_acc, 'r', label='validation acc')\n",
        "#plt.legend()\n",
        "#plt.show()"
      ],
      "execution_count": 775,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tH6VW_V-Cpw8"
      },
      "source": [
        "#plt.plot(epochs, loss,'b',label='training loss')\n",
        "#plt.plot(epochs, val_loss, 'r', label='validation loss')\n",
        "#plt.legend()\n",
        "#plt.show()"
      ],
      "execution_count": 776,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSCqcgTa4-hP"
      },
      "source": [
        "y_pred=model.predict(x_val)"
      ],
      "execution_count": 777,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzLK-m5B4BeC",
        "outputId": "33a5dacb-d591-4df7-f7dd-dca6fcad50de"
      },
      "source": [
        "_,accuracy=model.evaluate(x_val, y_val)"
      ],
      "execution_count": 778,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step - loss: 5.6557e-04 - accuracy: 0.0370\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5CpDx8EK4Vyb",
        "outputId": "a0253471-547f-4093-f82a-dda5cef32a85"
      },
      "source": [
        "print('Accuracy: %.2f' %(accuracy*100))"
      ],
      "execution_count": 779,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 3.70\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "id": "hdBBOaZnuNSp",
        "outputId": "ccd0e535-3e27-4cff-8036-59f35a4fc6aa"
      },
      "source": [
        "#---------------------------------Parte 23--------------------------------------\n",
        "#Plotagem do treinamento e validação do Loss\n",
        "x = np.arange(0, EPOCHS, 1)                                                                  \n",
        "plt.figure(1, figsize = (22, 12))                                  \n",
        "plt.subplot(121)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.plot(x, history.history[\"loss\"], label = \"Treinamento do Loss\")\n",
        "plt.plot(x, history.history[\"val_loss\"], label = \"Validação Loss\")\n",
        "plt.grid(True)\n",
        "plt.legend()  "
      ],
      "execution_count": 780,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f414fa6f0d0>"
            ]
          },
          "metadata": {},
          "execution_count": 780
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1584x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "DznK2mxPunrC",
        "outputId": "02d5d391-c3b5-4cac-99da-7c53ce5ad77c"
      },
      "source": [
        "#---------------------------------Parte 24--------------------------------------\n",
        "#Aqui é possível usar os dados de teste do dataset para,\n",
        "# essa rede neural treinada, e assim prever as saídas que a rede desconhece\n",
        "#A seguir são apresentados os casos reais e uma predição rede não conhece;\n",
        "predicted_cases = model.predict(x_val)\n",
        "predicted_cases = scaler.inverse_transform(predicted_cases)\n",
        "real_cases = scaler.inverse_transform(y_val)\n",
        "plt.figure(figsize= (10, 6))\n",
        "plt.subplot(1,1,1)\n",
        "plt.plot(real_cases, color = \"green\", label = \"Número Real de Casos\")\n",
        "plt.plot(predicted_cases, color = \"blue\", label = \"Número de Predição de Casos\")\n",
        "plt.title(\"Casos de Covid-19 no Brasil\")\n",
        "plt.xlabel(\"Tempo\")\n",
        "plt.ylabel(\"Contagem de Casos\")\n",
        "plt.legend()\n",
        "plt.grid(\"both\")\n",
        "plt.show()"
      ],
      "execution_count": 781,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "StY6zua_D_xk"
      },
      "source": [
        "--------------------------Outro Modelo de Treinamemento-----------------------"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLBSsvaHD_B1"
      },
      "source": [
        "y_train = to_categorical(y_train, 3)\n",
        "y_val = to_categorical(y_val, 3)"
      ],
      "execution_count": 782,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXIR6RKjEFaq",
        "outputId": "95acde16-b563-4e39-e157-445aebc0e4f3"
      },
      "source": [
        "num_epochs = 100\n",
        "history = model.fit(x_train, y_train, epochs=num_epochs, \n",
        "                    validation_data=(x_val, y_val))"
      ],
      "execution_count": 783,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "3/3 [==============================] - 6s 919ms/step - loss: 0.3114 - accuracy: 0.6270 - val_loss: 0.3077 - val_accuracy: 0.5309\n",
            "Epoch 2/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2754 - accuracy: 0.6230 - val_loss: 0.3018 - val_accuracy: 0.5185\n",
            "Epoch 3/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2553 - accuracy: 0.6230 - val_loss: 0.2961 - val_accuracy: 0.4938\n",
            "Epoch 4/100\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.2434 - accuracy: 0.6230 - val_loss: 0.2835 - val_accuracy: 0.4938\n",
            "Epoch 5/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2415 - accuracy: 0.6230 - val_loss: 0.2679 - val_accuracy: 0.5185\n",
            "Epoch 6/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2346 - accuracy: 0.6310 - val_loss: 0.2524 - val_accuracy: 0.5432\n",
            "Epoch 7/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2294 - accuracy: 0.6349 - val_loss: 0.2409 - val_accuracy: 0.5802\n",
            "Epoch 8/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2287 - accuracy: 0.6349 - val_loss: 0.2337 - val_accuracy: 0.6049\n",
            "Epoch 9/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2261 - accuracy: 0.6548 - val_loss: 0.2294 - val_accuracy: 0.6420\n",
            "Epoch 10/100\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.2253 - accuracy: 0.6548 - val_loss: 0.2271 - val_accuracy: 0.6667\n",
            "Epoch 11/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2250 - accuracy: 0.6627 - val_loss: 0.2259 - val_accuracy: 0.6667\n",
            "Epoch 12/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2248 - accuracy: 0.6667 - val_loss: 0.2253 - val_accuracy: 0.6667\n",
            "Epoch 13/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2249 - accuracy: 0.6667 - val_loss: 0.2250 - val_accuracy: 0.6667\n",
            "Epoch 14/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2244 - accuracy: 0.6627 - val_loss: 0.2248 - val_accuracy: 0.6667\n",
            "Epoch 15/100\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.2243 - accuracy: 0.6627 - val_loss: 0.2248 - val_accuracy: 0.6667\n",
            "Epoch 16/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2238 - accuracy: 0.6667 - val_loss: 0.2247 - val_accuracy: 0.6667\n",
            "Epoch 17/100\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.2239 - accuracy: 0.6667 - val_loss: 0.2248 - val_accuracy: 0.6667\n",
            "Epoch 18/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2235 - accuracy: 0.6667 - val_loss: 0.2248 - val_accuracy: 0.6667\n",
            "Epoch 19/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2242 - accuracy: 0.6627 - val_loss: 0.2248 - val_accuracy: 0.6667\n",
            "Epoch 20/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2236 - accuracy: 0.6667 - val_loss: 0.2247 - val_accuracy: 0.6667\n",
            "Epoch 21/100\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.2234 - accuracy: 0.6667 - val_loss: 0.2244 - val_accuracy: 0.6667\n",
            "Epoch 22/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2232 - accuracy: 0.6667 - val_loss: 0.2242 - val_accuracy: 0.6667\n",
            "Epoch 23/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2231 - accuracy: 0.6667 - val_loss: 0.2240 - val_accuracy: 0.6667\n",
            "Epoch 24/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2228 - accuracy: 0.6667 - val_loss: 0.2239 - val_accuracy: 0.6667\n",
            "Epoch 25/100\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 0.2232 - accuracy: 0.6667 - val_loss: 0.2237 - val_accuracy: 0.6667\n",
            "Epoch 26/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2231 - accuracy: 0.6667 - val_loss: 0.2235 - val_accuracy: 0.6667\n",
            "Epoch 27/100\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.2232 - accuracy: 0.6667 - val_loss: 0.2234 - val_accuracy: 0.6667\n",
            "Epoch 28/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2229 - accuracy: 0.6667 - val_loss: 0.2233 - val_accuracy: 0.6667\n",
            "Epoch 29/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2230 - accuracy: 0.6667 - val_loss: 0.2231 - val_accuracy: 0.6667\n",
            "Epoch 30/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2228 - accuracy: 0.6667 - val_loss: 0.2230 - val_accuracy: 0.6667\n",
            "Epoch 31/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2230 - accuracy: 0.6667 - val_loss: 0.2229 - val_accuracy: 0.6667\n",
            "Epoch 32/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2228 - accuracy: 0.6667 - val_loss: 0.2227 - val_accuracy: 0.6667\n",
            "Epoch 33/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2230 - accuracy: 0.6667 - val_loss: 0.2226 - val_accuracy: 0.6667\n",
            "Epoch 34/100\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2225 - val_accuracy: 0.6667\n",
            "Epoch 35/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2228 - accuracy: 0.6667 - val_loss: 0.2224 - val_accuracy: 0.6667\n",
            "Epoch 36/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2224 - val_accuracy: 0.6667\n",
            "Epoch 37/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2228 - accuracy: 0.6667 - val_loss: 0.2224 - val_accuracy: 0.6667\n",
            "Epoch 38/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2228 - accuracy: 0.6667 - val_loss: 0.2223 - val_accuracy: 0.6667\n",
            "Epoch 39/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2223 - val_accuracy: 0.6667\n",
            "Epoch 40/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 41/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 42/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2228 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 43/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 44/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 45/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 46/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 47/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 48/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 49/100\n",
            "3/3 [==============================] - 0s 29ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 50/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 51/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2229 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 52/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 53/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2228 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 54/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 55/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 56/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 57/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 58/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 59/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 60/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 61/100\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 62/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 63/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2228 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 64/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 65/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2225 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 66/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 67/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 68/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 69/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 70/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 71/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2225 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 72/100\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 0.2225 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 73/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 74/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2225 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 75/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 76/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 77/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 78/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 79/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 80/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 81/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2223 - val_accuracy: 0.6667\n",
            "Epoch 82/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2223 - val_accuracy: 0.6667\n",
            "Epoch 83/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2223 - val_accuracy: 0.6667\n",
            "Epoch 84/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2223 - val_accuracy: 0.6667\n",
            "Epoch 85/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 86/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 87/100\n",
            "3/3 [==============================] - 0s 31ms/step - loss: 0.2225 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 88/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 89/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 90/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2225 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 91/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 92/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 93/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2227 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 94/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 95/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 96/100\n",
            "3/3 [==============================] - 0s 23ms/step - loss: 0.2225 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 97/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 98/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 99/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n",
            "Epoch 100/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 0.2226 - accuracy: 0.6667 - val_loss: 0.2222 - val_accuracy: 0.6667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-mXazlhAENfr"
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']"
      ],
      "execution_count": 784,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pMM0JnxNEQcJ",
        "outputId": "9ccc855e-6db6-449e-a352-f395c626a575"
      },
      "source": [
        "print(f\"Accuracy on training data is: {acc[-1]*100} %\")\n",
        "print(f\"Loss {loss[-1]*100}\")\n",
        "\n",
        "print(f\"Accuracy on validation data is: {val_acc[-1]*100} %\")\n",
        "print(f\"Loss {val_loss[-1]*100}\")"
      ],
      "execution_count": 785,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data is: 66.66666865348816 %\n",
            "Loss 22.264789044857025\n",
            "Accuracy on validation data is: 66.66666865348816 %\n",
            "Loss 22.222553193569183\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "7pzLbxXaEUex",
        "outputId": "9d962797-c7f7-406a-ae53-1277326d4224"
      },
      "source": [
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc,'b',label='training acc')\n",
        "plt.plot(epochs, val_acc, 'r', label='validation acc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 786,
      "outputs": [
        {
          "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": 265
        },
        "id": "CQAYhbwQEXyy",
        "outputId": "cc21cc47-7b19-41ca-d97d-6dd0438252c3"
      },
      "source": [
        "plt.plot(epochs, loss,'b',label='training loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 787,
      "outputs": [
        {
          "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": {
        "id": "2jV205eyEbmS"
      },
      "source": [
        "y_pred=model.predict(x_val)"
      ],
      "execution_count": 788,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kSXu79ncEeB0",
        "outputId": "f4b64bc3-6c9a-4018-a688-fa9c00bf7775"
      },
      "source": [
        "_,accuracy=model.evaluate(x_val, y_val)"
      ],
      "execution_count": 789,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 24ms/step - loss: 0.2222 - accuracy: 0.6667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d00lRzyaEgMy",
        "outputId": "abd0bf19-b3c4-4518-89df-0e0e2e5c8665"
      },
      "source": [
        "print('Accuracy: %.2f' %(accuracy*100))"
      ],
      "execution_count": 790,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 66.67\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "r2VMH-1AEmOq",
        "outputId": "be0788dd-92ab-415e-fdbc-01f1dc3551ce"
      },
      "source": [
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc,'b',label='training acc')\n",
        "plt.plot(epochs, val_acc, 'r', label='validation acc')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "#plt.plot(epochs, loss,'b',label='training loss')\n",
        "#plt.plot(epochs, val_loss, 'r', label='validation loss')\n",
        "#plt.legend()\n",
        "#plt.show()"
      ],
      "execution_count": 791,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}