{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FhGuhbZ6M5tl"
   },
   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:07.268586Z",
     "iopub.status.busy": "2021-02-13T03:21:07.267890Z",
     "iopub.status.idle": "2021-02-13T03:21:07.270466Z",
     "shell.execute_reply": "2021-02-13T03:21:07.269906Z"
    },
    "id": "AwOEIRJC6Une"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:07.274506Z",
     "iopub.status.busy": "2021-02-13T03:21:07.273809Z",
     "iopub.status.idle": "2021-02-13T03:21:07.276435Z",
     "shell.execute_reply": "2021-02-13T03:21:07.275834Z"
    },
    "id": "KyPEtTqk6VdG"
   },
   "outputs": [],
   "source": [
    "#@title MIT License\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EIdT9iu_Z4Rb"
   },
   "source": [
    "# Regressão: Preveja consumo de combustível"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bBIlTPscrIT9"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/regression\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />Veja em TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/pt-br/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Execute em Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/pt-br/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />Veja a fonte em GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/pt-br/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Baixe o notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JzsKDVTh7UR4"
   },
   "source": [
    "Note: A nossa comunidade TensorFlow traduziu estes documentos. Como as traduções da comunidade são *o melhor esforço*, não há garantias de que sejam uma reflexão exata e atualizada da [documentação oficial em Inglês](https://www.tensorflow.org/?hl=en). Se tem alguma sugestão para melhorar esta tradução, por favor envie um pull request para o repositório do GitHub [tensorflow/docs](https://github.com/tensorflow/docs). Para se voluntariar para escrever ou rever as traduções da comunidade, contacte a [lista docs@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AHp3M9ZmrIxj"
   },
   "source": [
    "Em um problema de regressão, o objetivo é prever as saídas (*outputs*) de um valor contínuo, como um preço ou probabilidade. Em contraste de problemas de classificação, onde temos o propósito de escolher uma classe em uma lista de classificações (por exemplo, se uma imagem contém uma maçã ou laranja, assim reconhecendo qual fruta é representada na imagem).\n",
    "\n",
    "Este *notebook* usa a clássica base de dados [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) e constrói um modelo para prever a economia de combustíveis de automóveis do final dos anos 1970, início dos anos 1980. Para isso, forneceremos um modelo com descrição de vários automóveis desse período. Essa descrição inclui atributos como: cilindros, deslocamento, potência do motor, e peso.\n",
    "\n",
    "Este exemplo usa a API `tf.keras`. Veja [este guia](https://www.tensorflow.org/guide/keras) para mais detalhes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:07.287694Z",
     "iopub.status.busy": "2021-02-13T03:21:07.284928Z",
     "iopub.status.idle": "2021-02-13T03:21:08.779486Z",
     "shell.execute_reply": "2021-02-13T03:21:08.778876Z"
    },
    "id": "moB4tpEHxKB3"
   },
   "outputs": [],
   "source": [
    "# Use seaborn para pairplot\n",
    "!pip install -q seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:08.785429Z",
     "iopub.status.busy": "2021-02-13T03:21:08.784706Z",
     "iopub.status.idle": "2021-02-13T03:21:15.831412Z",
     "shell.execute_reply": "2021-02-13T03:21:15.831984Z"
    },
    "id": "1rRo8oNqZ-Rj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.1\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F_72b0LCNbjx"
   },
   "source": [
    "## Base de dados Auto MPG\n",
    "\n",
    "A base de dados está disponível  em [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gFh9ne3FZ-On"
   },
   "source": [
    "### Pegando os dados\n",
    "Primeiro baixe a base de dados dos automóveis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:15.838508Z",
     "iopub.status.busy": "2021-02-13T03:21:15.837805Z",
     "iopub.status.idle": "2021-02-13T03:21:16.643399Z",
     "shell.execute_reply": "2021-02-13T03:21:16.643897Z"
    },
    "id": "p9kxxgzvzlyz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 8192/30286 [=======>......................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "16384/30286 [===============>..............] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "32768/30286 [================================] - 0s 4us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/home/kbuilder/.keras/datasets/auto-mpg.data'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_path = keras.utils.get_file(\"auto-mpg.data\", \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\")\n",
    "dataset_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nslsRLh7Zss4"
   },
   "source": [
    "Utilizando o pandas, importe os dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:16.652792Z",
     "iopub.status.busy": "2021-02-13T03:21:16.652100Z",
     "iopub.status.idle": "2021-02-13T03:21:16.672873Z",
     "shell.execute_reply": "2021-02-13T03:21:16.672347Z"
    },
    "id": "CiX2FI4gZtTt"
   },
   "outputs": [
    {
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       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
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       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  Origin  \n",
       "393          82       1  \n",
       "394          82       2  \n",
       "395          82       1  \n",
       "396          82       1  \n",
       "397          82       1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',\n",
    "                'Acceleration', 'Model Year', 'Origin']\n",
    "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n",
    "                      na_values = \"?\", comment='\\t',\n",
    "                      sep=\" \", skipinitialspace=True)\n",
    "\n",
    "dataset = raw_dataset.copy()\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3MWuJTKEDM-f"
   },
   "source": [
    "### Limpe os dados\n",
    "\n",
    "Esta base contém alguns valores não conhecidos (*unknown*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:16.679147Z",
     "iopub.status.busy": "2021-02-13T03:21:16.678472Z",
     "iopub.status.idle": "2021-02-13T03:21:16.680992Z",
     "shell.execute_reply": "2021-02-13T03:21:16.681427Z"
    },
    "id": "JEJHhN65a2VV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MPG             0\n",
       "Cylinders       0\n",
       "Displacement    0\n",
       "Horsepower      6\n",
       "Weight          0\n",
       "Acceleration    0\n",
       "Model Year      0\n",
       "Origin          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9UPN0KBHa_WI"
   },
   "source": [
    "Para manter esse tutorial básico, remova as linhas com esses valores não conhecidos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:16.686937Z",
     "iopub.status.busy": "2021-02-13T03:21:16.686278Z",
     "iopub.status.idle": "2021-02-13T03:21:16.689908Z",
     "shell.execute_reply": "2021-02-13T03:21:16.690315Z"
    },
    "id": "4ZUDosChC1UN"
   },
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8XKitwaH4v8h"
   },
   "source": [
    "A coluna \"Origin\" é uma coluna categórica e não numérica. Logo converta para *one-hot* :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:16.695010Z",
     "iopub.status.busy": "2021-02-13T03:21:16.694348Z",
     "iopub.status.idle": "2021-02-13T03:21:16.696792Z",
     "shell.execute_reply": "2021-02-13T03:21:16.696178Z"
    },
    "id": "gWNTD2QjBWFJ"
   },
   "outputs": [],
   "source": [
    "origin = dataset.pop('Origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:16.713315Z",
     "iopub.status.busy": "2021-02-13T03:21:16.712593Z",
     "iopub.status.idle": "2021-02-13T03:21:16.715836Z",
     "shell.execute_reply": "2021-02-13T03:21:16.715303Z"
    },
    "id": "ulXz4J7PAUzk"
   },
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
       "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
       "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
       "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
       "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
       "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
       "\n",
       "     Model Year  USA  Europe  Japan  \n",
       "393          82  1.0     0.0    0.0  \n",
       "394          82  0.0     1.0    0.0  \n",
       "395          82  1.0     0.0    0.0  \n",
       "396          82  1.0     0.0    0.0  \n",
       "397          82  1.0     0.0    0.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['USA'] = (origin == 1)*1.0\n",
    "dataset['Europe'] = (origin == 2)*1.0\n",
    "dataset['Japan'] = (origin == 3)*1.0\n",
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cuym4yvk76vU"
   },
   "source": [
    "### Separando dados de treinamento e teste\n",
    "\n",
    "Agora separe os dados em um conjunto de treinamento e outro teste.\n",
    "\n",
    "Iremos utilizar o conjunto de teste no final da análise do modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:16.721936Z",
     "iopub.status.busy": "2021-02-13T03:21:16.721255Z",
     "iopub.status.idle": "2021-02-13T03:21:16.723164Z",
     "shell.execute_reply": "2021-02-13T03:21:16.723589Z"
    },
    "id": "qn-IGhUE7_1H"
   },
   "outputs": [],
   "source": [
    "train_dataset = dataset.sample(frac=0.8,random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J4ubs136WLNp"
   },
   "source": [
    "### Inspecione o dado\n",
    "\n",
    "Dê uma rápida olhada em como está a distribuição de algumas colunas do conjunto de treinamento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:16.746477Z",
     "iopub.status.busy": "2021-02-13T03:21:16.742014Z",
     "iopub.status.idle": "2021-02-13T03:21:19.333372Z",
     "shell.execute_reply": "2021-02-13T03:21:19.333863Z"
    },
    "id": "oRKO_x8gWKv-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f088d352978>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(train_dataset[[\"MPG\", \"Cylinders\", \"Displacement\", \"Weight\"]], diag_kind=\"kde\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gavKO_6DWRMP"
   },
   "source": [
    "Repare na visão geral das estatísticas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:19.346771Z",
     "iopub.status.busy": "2021-02-13T03:21:19.342019Z",
     "iopub.status.idle": "2021-02-13T03:21:19.381187Z",
     "shell.execute_reply": "2021-02-13T03:21:19.380541Z"
    },
    "id": "yi2FzC3T21jR"
   },
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>314.0</td>\n",
       "      <td>5.477707</td>\n",
       "      <td>1.699788</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Displacement</th>\n",
       "      <td>314.0</td>\n",
       "      <td>195.318471</td>\n",
       "      <td>104.331589</td>\n",
       "      <td>68.0</td>\n",
       "      <td>105.50</td>\n",
       "      <td>151.0</td>\n",
       "      <td>265.75</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsepower</th>\n",
       "      <td>314.0</td>\n",
       "      <td>104.869427</td>\n",
       "      <td>38.096214</td>\n",
       "      <td>46.0</td>\n",
       "      <td>76.25</td>\n",
       "      <td>94.5</td>\n",
       "      <td>128.00</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>314.0</td>\n",
       "      <td>2990.251592</td>\n",
       "      <td>843.898596</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>2256.50</td>\n",
       "      <td>2822.5</td>\n",
       "      <td>3608.00</td>\n",
       "      <td>5140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acceleration</th>\n",
       "      <td>314.0</td>\n",
       "      <td>15.559236</td>\n",
       "      <td>2.789230</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.20</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model Year</th>\n",
       "      <td>314.0</td>\n",
       "      <td>75.898089</td>\n",
       "      <td>3.675642</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.624204</td>\n",
       "      <td>0.485101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.178344</td>\n",
       "      <td>0.383413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.197452</td>\n",
       "      <td>0.398712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean         std     min      25%     50%  \\\n",
       "Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \n",
       "Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \n",
       "Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \n",
       "Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \n",
       "Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \n",
       "Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \n",
       "USA           314.0     0.624204    0.485101     0.0     0.00     1.0   \n",
       "Europe        314.0     0.178344    0.383413     0.0     0.00     0.0   \n",
       "Japan         314.0     0.197452    0.398712     0.0     0.00     0.0   \n",
       "\n",
       "                  75%     max  \n",
       "Cylinders        8.00     8.0  \n",
       "Displacement   265.75   455.0  \n",
       "Horsepower     128.00   225.0  \n",
       "Weight        3608.00  5140.0  \n",
       "Acceleration    17.20    24.8  \n",
       "Model Year      79.00    82.0  \n",
       "USA              1.00     1.0  \n",
       "Europe           0.00     1.0  \n",
       "Japan            0.00     1.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"MPG\")\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Db7Auq1yXUvh"
   },
   "source": [
    "### Separe features de labels\n",
    "\n",
    "Separe o valor alvo (*labels*), das *features*. Esta label é o valor no qual o model é treinado para prever."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:19.386694Z",
     "iopub.status.busy": "2021-02-13T03:21:19.385923Z",
     "iopub.status.idle": "2021-02-13T03:21:19.388564Z",
     "shell.execute_reply": "2021-02-13T03:21:19.388039Z"
    },
    "id": "t2sluJdCW7jN"
   },
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop('MPG')\n",
    "test_labels = test_dataset.pop('MPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mRklxK5s388r"
   },
   "source": [
    "### Normalize os dados\n",
    "\n",
    "Olhe novamente para o bloco `train_stats` acima e note quão diferente é a variação de feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-ywmerQ6dSox"
   },
   "source": [
    "Uma boa prática é normalizar as *features* que usam diferentes escalas e intervalos. Apesar do modelo poder convergir sem a normalização, isso torna o treinamento mais difícil, e torna o resultado do modelo dependente da escolha das unidades da entrada.\n",
    "\n",
    "Note: embora geramos intencionalmente essas estatísticas para os dados de treinamento, essas estatísticas serão usadas também para normalizar o conjunto de teste. Precisamos delinear o conjunto de teste na mesma distribuição que o modelo foi treinado. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:19.397053Z",
     "iopub.status.busy": "2021-02-13T03:21:19.396293Z",
     "iopub.status.idle": "2021-02-13T03:21:19.398876Z",
     "shell.execute_reply": "2021-02-13T03:21:19.398381Z"
    },
    "id": "JlC5ooJrgjQF"
   },
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "  return (x - train_stats['mean']) / train_stats['std']\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BuiClDk45eS4"
   },
   "source": [
    "O dado normalizado é o que nós usaremos para treinar o modelo.\n",
    "\n",
    "Caution: A estatística usada para normalizar as entradas aqui (média e desvio padrão) precisa ser aplicada em qualquer outro dado que alimenta o modelo, junto com o codificação one-hot que fizemos mais cedo.  Isso inclui o conjunto de teste, assim como dados que o modelo usará em produção."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SmjdzxKzEu1-"
   },
   "source": [
    "## O Modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6SWtkIjhrZwa"
   },
   "source": [
    "### Construindo o modelo\n",
    "\n",
    "Vamos construir o modelo. Aqui usaremos o modelo `Sequential` com duas camadas *densely connected*, e a camada de saída que retorna um único valor contínuo. Os passos de construção do modelo são agrupados em uma função, `build_model`, já que criaremos um segundo modelo mais tarde."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:19.404942Z",
     "iopub.status.busy": "2021-02-13T03:21:19.404191Z",
     "iopub.status.idle": "2021-02-13T03:21:19.406271Z",
     "shell.execute_reply": "2021-02-13T03:21:19.406698Z"
    },
    "id": "c26juK7ZG8j-"
   },
   "outputs": [],
   "source": [
    "def build_model():\n",
    "  model = keras.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(1)\n",
    "  ])\n",
    "\n",
    "  optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
    "\n",
    "  model.compile(loss='mse',\n",
    "                optimizer=optimizer,\n",
    "                metrics=['mae', 'mse'])\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:19.410642Z",
     "iopub.status.busy": "2021-02-13T03:21:19.409970Z",
     "iopub.status.idle": "2021-02-13T03:21:21.238083Z",
     "shell.execute_reply": "2021-02-13T03:21:21.237449Z"
    },
    "id": "cGbPb-PHGbhs"
   },
   "outputs": [],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Sj49Og4YGULr"
   },
   "source": [
    "###  Examine o modelo\n",
    "\n",
    "Use o método `.summary` para exibir uma descrição simples do modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:21.244202Z",
     "iopub.status.busy": "2021-02-13T03:21:21.243208Z",
     "iopub.status.idle": "2021-02-13T03:21:21.246235Z",
     "shell.execute_reply": "2021-02-13T03:21:21.246698Z"
    },
    "id": "ReAD0n6MsFK-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                640       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 4,865\n",
      "Trainable params: 4,865\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vt6W50qGsJAL"
   },
   "source": [
    "Agora teste o modelo. Pegue um batch de 10 exemplos do conjunto de treinamento e chame `model.predict` nestes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:21.252023Z",
     "iopub.status.busy": "2021-02-13T03:21:21.250955Z",
     "iopub.status.idle": "2021-02-13T03:21:21.821851Z",
     "shell.execute_reply": "2021-02-13T03:21:21.821230Z"
    },
    "id": "-d-gBaVtGTSC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.32038543],\n",
       "       [-0.03224104],\n",
       "       [ 0.13639623],\n",
       "       [-0.18148893],\n",
       "       [-0.06226134],\n",
       "       [ 0.35602754],\n",
       "       [-0.0232144 ],\n",
       "       [-0.38673395],\n",
       "       [ 0.13995253],\n",
       "       [-0.20493355]], dtype=float32)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_batch = normed_train_data[:10]\n",
    "example_result = model.predict(example_batch)\n",
    "example_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QlM8KrSOsaYo"
   },
   "source": [
    "Parece que está funcionando e ele produz o resultado de forma e tipo esperados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0-qWCsh6DlyH"
   },
   "source": [
    "### Treinando o modelo\n",
    "\n",
    "Treine o modelo com 1000 *epochs*,  e grave a acurácia do treinamento e da validação em um objeto `history`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:21.832484Z",
     "iopub.status.busy": "2021-02-13T03:21:21.831183Z",
     "iopub.status.idle": "2021-02-13T03:22:15.220886Z",
     "shell.execute_reply": "2021-02-13T03:22:15.220088Z"
    },
    "id": "sD7qHCmNIOY0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "...."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "...."
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    {
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      "...."
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      "...."
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      "....."
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    {
     "name": "stdout",
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     "text": [
      "....\n",
      "."
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    },
    {
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      "....."
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    {
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      "....."
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      "...."
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    {
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     "text": [
      "\n",
      "...."
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    },
    {
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      "...."
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    {
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     "text": [
      ".."
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      "....."
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    {
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    {
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   ],
   "source": [
    "# Mostra o progresso do treinamento imprimindo um único ponto para cada epoch completada\n",
    "class PrintDot(keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs):\n",
    "    if epoch % 100 == 0: print('')\n",
    "    print('.', end='')\n",
    "\n",
    "EPOCHS = 1000\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=EPOCHS, validation_split = 0.2, verbose=0,\n",
    "  callbacks=[PrintDot()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tQm3pc0FYPQB"
   },
   "source": [
    "Visualize o progresso do modelo de treinamento usando o estados armazenados no objeto `history`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:22:15.238684Z",
     "iopub.status.busy": "2021-02-13T03:22:15.237823Z",
     "iopub.status.idle": "2021-02-13T03:22:15.241303Z",
     "shell.execute_reply": "2021-02-13T03:22:15.241773Z"
    },
    "id": "4Xj91b-dymEy"
   },
   "outputs": [
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       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_mse</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.522043</td>\n",
       "      <td>1.006257</td>\n",
       "      <td>2.522043</td>\n",
       "      <td>8.909552</td>\n",
       "      <td>2.256334</td>\n",
       "      <td>8.909552</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.579235</td>\n",
       "      <td>1.034346</td>\n",
       "      <td>2.579235</td>\n",
       "      <td>9.359710</td>\n",
       "      <td>2.365973</td>\n",
       "      <td>9.359710</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.812614</td>\n",
       "      <td>1.036293</td>\n",
       "      <td>2.812614</td>\n",
       "      <td>10.182361</td>\n",
       "      <td>2.454575</td>\n",
       "      <td>10.182361</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.828955</td>\n",
       "      <td>1.043446</td>\n",
       "      <td>2.828955</td>\n",
       "      <td>8.992881</td>\n",
       "      <td>2.277874</td>\n",
       "      <td>8.992881</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.869476</td>\n",
       "      <td>1.049073</td>\n",
       "      <td>2.869476</td>\n",
       "      <td>9.126456</td>\n",
       "      <td>2.287193</td>\n",
       "      <td>9.126456</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss       mae       mse   val_loss   val_mae    val_mse  epoch\n",
       "995  2.522043  1.006257  2.522043   8.909552  2.256334   8.909552    995\n",
       "996  2.579235  1.034346  2.579235   9.359710  2.365973   9.359710    996\n",
       "997  2.812614  1.036293  2.812614  10.182361  2.454575  10.182361    997\n",
       "998  2.828955  1.043446  2.828955   8.992881  2.277874   8.992881    998\n",
       "999  2.869476  1.049073  2.869476   9.126456  2.287193   9.126456    999"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:22:15.252513Z",
     "iopub.status.busy": "2021-02-13T03:22:15.251711Z",
     "iopub.status.idle": "2021-02-13T03:22:15.708300Z",
     "shell.execute_reply": "2021-02-13T03:22:15.707627Z"
    },
    "id": "B6XriGbVPh2t"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(history):\n",
    "  hist = pd.DataFrame(history.history)\n",
    "  hist['epoch'] = history.epoch\n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Mean Abs Error [MPG]')\n",
    "  plt.plot(hist['epoch'], hist['mae'],\n",
    "           label='Train Error')\n",
    "  plt.plot(hist['epoch'], hist['val_mae'],\n",
    "           label = 'Val Error')\n",
    "  plt.ylim([0,5])\n",
    "  plt.legend()\n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Mean Square Error [$MPG^2$]')\n",
    "  plt.plot(hist['epoch'], hist['mse'],\n",
    "           label='Train Error')\n",
    "  plt.plot(hist['epoch'], hist['val_mse'],\n",
    "           label = 'Val Error')\n",
    "  plt.ylim([0,20])\n",
    "  plt.legend()\n",
    "  plt.show()\n",
    "\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AqsuANc11FYv"
   },
   "source": [
    "Este grafo mostra as pequenas melhoras, ou mesmo a diminuição do `validation error` após 100 *epochs*. Vamos atualizar o `model.fit` para que pare automaticamente o treinamento quando o `validation score` não aumentar mais. Usaremos o `EarlyStopping callback` que testa a condição do treinamento a cada `epoch`.  Se um grupo de `epochs` decorre sem mostrar melhoras, o treinamento irá parar automaticamente.\n",
    "\n",
    "Você pode aprender mais sobre este callback [aqui](https://www.tensorflow.org/versions/master/api_docs/python/tf/keras/callbacks/EarlyStopping)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:22:15.717435Z",
     "iopub.status.busy": "2021-02-13T03:22:15.716767Z",
     "iopub.status.idle": "2021-02-13T03:22:19.584303Z",
     "shell.execute_reply": "2021-02-13T03:22:19.584757Z"
    },
    "id": "fdMZuhUgzMZ4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "....."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "....."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "..."
     ]
    },
    {
     "data": {
      "image/png": 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vcMaYgLVUgr8CmKKqDwCTgRtDHZAJvwHp8VwxJJ3nVuyl1lfvrOx7KQyZDu8/DBWFYY3PGBOYlhJ8jar6AFS1AmdwMdMFzJ7Yl6Ml1U6XyZM+8yOoKYPl/y98gRljAtZSgh8mIhv8r40NljeKyIb2CNCEx+ShGfRJ6Xa6sRUgYziM/hKsfBJO7A1bbMaYwLSU4IcD1/lf1zZYvtb/blzKE+F0mVy99wSfHiw+/cXkB0EiYOnPwxecMSYgLXWT3Nvcq72CNOFxy7jexEZ5+MtHeadXJmXB+Lmw4e9wZGPYYjPGtKylXjSlIlLS4FXa8L29gjThkdQtihvHZPHv9Yc4UV5z+ovL73XGil/843CFZowJQEtVNEuAzcBPgQtVNUFVE0++hz48E26zL+1LdV09C1ftP70yNhkmfQt2Lobdzc4HY4wJo5aqaG4EpgEFwB9F5F0R+T8iktIewZnwG9YzkQkDUvjrx3upO9llEuCSOyGpN7z5faj3hS9AY0yTWnySVVWLVXUBzhOt/wP8BJgT4rhMB/KVy/pzsKiSlzccOr0yyus8/HR0I6x5OmyxGWOa1mKCF5FLReQxYC1wKXCTqv4m5JGZDmPq8B4M7ZHA4+/sxFffYGz4ETdB38vhnZ/aw0/GdEAtNbLmAb8DDgJ3Ak8B5SIyVkTGhj480xFERAh3XTWIXQXlzoxPJ4nA9P+GqiJnnBpjTIcS2cL3eYDi1MN/ljOfZFXA5mXtIqZfmMmgjB089s4OZozMJCLC/6fQ80LIud2ZFGTcHOgxIqxxGmNOa6mRdbKq5vpfUxp8zlVVS+5diCdCuGvKILYfLeOtzUfO/DL3+063yde/a9P7GdOBtFRF02I1jFXVdB3XjupF/7Q4HlmyE22YyLulwJSHIG85bP7fsMVnjDlTS42sC0QkWURSmnoBf25sRxHxishKEflERDaJyPzgh2/akydC+HruILYcLmHxlvwzvxz3FegxEt58yMaMN6aDaCnBJwFrWnjVNrFvNc5QwxcBo4GrRWRCEGI2YXTD6F70SenGo0t2nFmKj/A4Da4lB+CDR8IXoDHmlJbq4Pup6gBV7d/M65Im9lVVLfMvRvlfVkHbyUV5Ivh67kA2Hixm2faCM7/sd5kz+9MHD9s4NcZ0AIFM2XfeRMQjIuuBfOBtVV3RyDZ3ishqEVldUFBwzjFMx3PTmGyyusfyyOKzSvEA034OsSnw3Beg+GB4AjTGACFO8KrqU9XRQDZwiYhc2Mg2T6pqjqrmpKenhzIcEyTRkRHMmzyQ9fuLeH/nsTO/TMyE2/4J1aXw3C1QVdz4QYwxIRfIk6wiIr3bchJVLQKWAle35Tim47glJ5teSV4eXLSRwoYjTYLTN/7Wv8CxbfD3/4C6msYPYowJqUDGolHgtdYeWETSRaS7/3MsMBXY2trjmI4pJtLD47eN5WhJNXOfXUN13VkDjg2cAtc9CnvehZfvtv7xxoRBoFU0a0Xk4lYeOxNY6p/abxVOHfwrrTyG6cDG9knmVzePYmVeId9f9Om59fFjbnNmgPrkeZsBypgwaGmogpPGA7eJyF6gHGfIAlXVUU3toKobgDFtD9F0ZDeMzmJXQTmPLtnB4Ix4/vPKgWducOX9ULQP3vslJPSEi+8IT6DGdEGBJvhpIY3CdGr3XjWYXQVl/OKNrQxIj2fqBT1OfykC1z0M5fnw6jedRtfL73PWG2NCKqAqGv/8q905PQF3d5uT1ZwUESH8+uaLGJmVxD0L17Hp0Fk9ZzxRcOtzMPIWWDIf3vge1Nc3fjBjTNAElOBF5B7gOSDD//qriNwVysBM5xIb7eFPs3JI9Ebx1WdWs+No6ZkbREbDTU/ChK/Dij/Ai3dAXXV4gjWmiwi0kfUOYLyq/lBVfwhMAL4WurBMZ5SR6OXPc3Ko9dVzwxMf8ErDGaAAIiJg2s+cmaA2/cvfT97mbjcmVAJN8AI07Afn48yx4Y0BYESvJF65axLDeibwjb+t4ycvb6a24VyuInDZPXDjHyDvfVgwHZb/Bra+Csd2gq8ufMEb4zKBNrIuAFaIyCL/8o00MYqkMT2TvCy8cyI/f20LT32wh40Hi3jiS2PJSPSe3mj0TIhLg1e+6dTLn+SJhpSBMPY/YOLX2z94Y1xEzum7fPYGIhE4VTJVwOX+1ctVdV2wg8nJydHVq1cH+7AmjP69/iDfe3EjcTGR/OHLY8npl3LuRlXFcGwHFGxznn7d9zHsXwFffB6GXdP+QRvTiYjIGlXNafS7lhK8/wDrVDXkfdotwbvTtiOl/OezqzlaUs2Cr1zMhAGpze9QVw1/ngon9sLc5dC9T/sEakwn1FyCD7QOfomIfF7EOi+b1hvaM4F/zr2UrORYvrJgFSt2H29+h8gYuHkB1PvghdvB19SUA8aY5gSa4P8T+CdQLSIlIlIqItb9wQQsPSGGv31tPL26e/nK06tYuaew+R1SB8L1j8KBVbDkJ+0TpDEuE8hokhHA1aoaoarRqpqoqgmqmtgO8RkXyUjw8vydE8hM8jJnwUpW5bWQ5C/8HOTcDh8+CtvfbJ8gjXGRQEaTrAceb4dYTBeQkeDl+a9NoGeSlzlPrWR1S0l+2n9Bjwth0VybQMSYVrI6eNPuMhK9LPzaBHokepn91Ere2Xq06Y2jvHDL007Dq9XHG9MqVgdvwiIj0amu6ZcWxx3PrObxdxqZ/u+ktMHOgGX7P4anpsHxXe0aqzGdVaCDjSVYHbwJth6JXl6Yeyk3XNSLX7+1nXl/XUtZdRNPso76glOSP74L/jAJ1v7FJhExpgXNJngR+XKDz5ed9d03QhWU6Tpioz389tbRPDRjOG9tPsJNT3zAnmPljW884iaY9yFkjYWX7oK/fxkqWqjDN6YLa6kE/80Gnx8767vbgxyL6aJEhK9OGsCzd4znWFk11z/+Pi+sOUBFTSOl+aQsmPWSM2DZ9jfhdxNh80s2ho0xjWj2SdaGT7Ce/TRrKJ5utSdZzf7CCub+dQ2bDpUQG+XhquEZXHdRL64cko43ynPmxoc/gRe/5gxvEJfhjDd/0Reh50ibUMR0Gec9VIGIrFXVsWd/bmw5GCzBGwBfvbJyTyGvbDjE658eobC8hoSYSD47oif3TR1MdnK30xvX1cCOt5x5X7e/CfW1kDECRt0CqYMhtjt4u4M3yfkcHW/J37hKWxJ8BbATZ2jggf7P+JcHqGpcMAO1BG/OVuer58Ndx3llwyFe2XAYT4Twi8+NYsaozHM3rih0xpn/ZKHzBGxjErPg0rth3GyIig1t8Ma0g7Yk+L7NHTjY0/ZZgjfN2Xe8grsXrmP9/iJuzenNj66/gG7RTYx4XXIYyo46I1VWFUFlkfO+7Q3Y96FTpXPZ3c6TstFnlVPK8uHQOkBg8FQr8ZsOrc2jSbYXS/CmJbW+eh5evJ3fLdtF/9Q4Hp05hguzklp3kLz34d1fwp53oVsqjJ/n/CY9tN5J7CUNnpjtNwmufRjSBgXxKowJHkvwxnU+3HWMb/79E46XV/ON3MHMmtiX5Ljo1h1k3wp475ewc7GznDoIMkdDrzHOq2ArLJ4PdZUw6dtw+b3OSJfGdCCW4I0rnSiv4cFFG3n90yNER0Zw/UW9mDWxL6Oyu7fyQHv9jbGN/BIoPQpvPgCfvug02l73MPS7/NztTOv56sAT6KRypilBTfAikgz0VtUNwQiuIUvw5nxsO1LKsx/n8a+1B6mo8XFR7+7MmtCXqSN6kOiNCs5JdrwNr34TivZBbIrTGyc6DmL8794kSOoN3fs6E5R07+28R8ZCfR2oz3mv90FEJHi78IPglUXw4WOw4g8wZBrc8MT5N3jXVEB0t5a3a0m9D07kOb/aCrZC+TG4+KvOsNUdXDBmdFoGXI8zh+saIB/4QFW/2dx+rWUJ3rRFSVUt/1pzgL98vJfdBeV4IoSxfbpzxeB0rhiSzsisJCIi2tBgWlMBq/4ERXuhphyqS533mnKoLITiA1BXFdix4tIhbSikDzn9njkaujUypaFbVJfBit87yb2q2GnfyHsfssbBzOchPiPwY5Xlwyv3wfY3YPxcmPw9iEloXTz5W2H1n2HvR3BsO/iqT38XEQURHpjyA5gwz/kcSqrn3ZgftCn7ROSrOKX3H4nIBlUddV4RNcESvAkGVWVV3gmWbcvnvR0FfHrQGRcvuVsUkwanM2VYBlcMSSeltXX2LZ8YygucUv7Jl68WIiKcUntEJIjHuQkc3wEF252HtKqK/QcQ6DUaBkx2Xr0nOKNpNjx+bQVUHIeYRKdaqTMoK4ANf4f3f+PEPvQayH3QeSBty8vOw2px6fClv0OPC1o+3uZ/O8m9ugwG5jpJPr4nfPanMPLm5hNlvQ+2vQ4rn3Qa2T0x0H8SZAyH9GHOK22Ic9N+5T7Y/jpkXww3/M65CQdbXY0z38HBtfDF584ryQcjwW8EPgs8A3xfVVdZgjedxbGyaj7YeYx3txfw3vYCjpXVECEwund3pgzLIHdYBhdkJhKW0bBVndJowVbY9xHsXub04a+vg0gv9BwFtZVOYqwsPPMXQnI/yLzI2SZztDPqZuUJKDnk9AQqOeh8rqtyqpSiujnVSdHxTrWGePwJRZx3EefG0b2PU9UU38O5OTXkq4XSI1B62LkxSYRTupUI53goFO6Go5shf5PzXnHM2XdALkx5CLLPykUH18LzM52k+oWnYdBnGv+3qjwBr90PG//hXPdN/+Mk5gNr4LVvOT2g+l4G1/wKeoxw/m0rT5yO9/AnsHoBFO+DxGy4+HYYOxvi0pr+b7PxBXj9O86vt9wHYOJdwWs32PsRvHKv899++HXO9ZzdZTcAwUjwtwA/wKmWmSciA4BfqernWx1NMyzBm1Crr1c2Hizmna35LNuWzycHnNLzyKwk5k0eyLQRPfG0pRonGKpLYe+HsGspHNngJN1uqU71zcn38mNOwjr8CZzY0/hxIqIgMdNpB6itgJoyJ4n6agKLwxPjJPvEXk4yLz3s3IwIoN0uqptTGu5xgfNkce/xkD2u6e2LD8Dfvgj5m+HK+yFlAGfceKpLYdl/Q3k+XPEdmPQt8DRoX6n3OSOMLpkPVSXOA21lR8+sdgGnWuiSO51fEYEm6tKjzg1ky8sQmwzJ/Z2ba3Jf50aYlO3chKuKnBtK5QmnnSE6DvpMgD4Tz7yJVBTC2z+Edc867TbX/BqGXh1YLI2wXjTGNKGgtJo3Nx3hT8t3k3e8ggHpccy9YiA3jskiOjLQ6RLCrLIIjmyE4zudRJLYyymhxqWfWwIHpxReUw5a7x9yWU9/rip22hiK9jq9i4r2Or8CvEmQkOkkzsRMSOjlVBGp+huQfc67qnNTSO7f+LmbU10KL37VqXJpTPowuOkPThfWplQUOlVBpUchoWeDV6a/Ebx362I6SRW2vgo733b+XU7kQfF+55fW2SKinH+b6tLTv7jShkLfic4N4aMnnJvAxP8Dkx84r1J7Q8EowQ8AHgEm4Ny+PwLuU9XdbYrsLJbgTbj46pXXPz3M75buYvPhEjKTvHx5Ql/G90/hwqykcwc6a6Cmrp6SqlqSu0WHv/Tf2ak6bRf1dWfefMAp1XuC1CsqGOp9/uqwQ06Vl7e7U8KPjnN+ddRVOw/P7f3AqX7b9zFUl0BWjtPdtufIoIQRjAT/MfAE8Lx/1ReBu1R1fFAi9LMEb8JNVXlvxzF+t3QnK/Y4Y81HeYQLMhMZ0yeZUdlJVNb62F1Qzu6CMvYcK2f/iUp89YonQuiREEOPJC89E730TPIybURPJgxIDfNVmQ6h3ueU+pP6tP7XTTOCkeDPaVAVkU9U9aIgxQhYgjcdS0FpNev2nWDd/iLW7TvBJ/uLqaz1AeCNiqB/WjwD0uMYkBZHalw0BWXVHCmu5khJJUeKqzhUVEVlrY/PDM/ge9OHMygjPsxXZNyoLYONneyU+13gBLAQp4rmViBZVR8IZqCW4E1HVuerZ1dBOfHeSDITvS32qa+q9fHUB3v43dJdVNb6uG18H+65ajCp8U0Pd1Drq6ey1kdVrY+qmnoiPUKv7jbqpWlaWxL8HpyE3thfsqrqgOCE6LAEb9zoWFk1jyzewd9W7qNblIcvXtKbenV+IRSUVnOsrJqCsmrKquqoqz/3/8dpI3pw/9XDGJhuvwDMuULSi0ZEolS1tpnvewN/AXrg3CSeVNVHmjumJXjjZjvzy/jF61tYvCWfuGgPaQkxpMfHkJ4QQ1p8DImxkXgjPcRGe/BGOa99hRX8efluqurqufXi3tx71WAyEr0tnktVqa6rp6y6jviYyGYbiU3nFrQEL86TIFOALwHXqmqPZrbNBDJVda2IJOAMcXCjqm5uah9L8KYrqKmrb1UXzGNl1Ty2ZAfPrdhHlCeCr03qz+WD0zlUVMnBk68TlRwtqaK0qo6y6jrKq0//GvBGRXD5oHSmXuA81JWRcOYNoqrWx9YjpXx60Hkm4Moh6fROCcL4LqZdBKORdQJOUr8RSAG+DrykqidaEcS/gcdV9e2mtrEEb0zT8o6V86u3tvHqhsNnrE+JiyareyyZSV4SvFHEx3iIi4kk3htJXHQkuwvKWLwln4NFlYDzBO9lg1I5XFzFpoMl7Cwow3dW1dDgjHhyh2UweWg6OX1TGr0hqSolVXVOFZO/qqmwvIa46EgyEmPokeglIyGGpNio8Dwl3EW0pQ7+58AtwD6cLpKLgNWq2r+VAfQD3gMuVNWSs767E7gToE+fPuP27g3qJFHGuM7WIyUcLakmq3ssWd1jiY1uufpFVdl6pJTFm4+yeGs+n+wvIiMhhhG9ErkwK4kRvZIY0SuRGl89y7YVsGxbPit2F1Ljqyc2yrlhgFKvzrHqFSprfdTU1bd47ujICNLjY8hIPF0llZ4QQ0aCl5S4aP8riu7doukeG0Wkp5M8YNZBtCXB5wPbgYeBl1W1WkR2t6ZxVUTigXeBn6nqv5rb1krwxrSPqlpfi/Xy5dV1fLjrOB/uOkZVbT0R/lEDIkQQwBvlIa1Bwk6LjyElLpqy6jryS6rIL612Xv7Px8qqyS9xGpQLy5seLiElLpreKd3ol9qNvind6JsaR9/UbijOHAAnKmooLK/lREUNdT5l4sBULh2Y6r8JnetwcSXvbM1ny+ESRvdOZtLgNHoE2I5RXFnL/sJK9p+oYF9hBYneKD4zPCOgdpD20pYE7wGmAjOBq4ClwGdwRpRs5Bndc/aPAl4B3lTV37S0vSV4Y7qGWl/9qSqdE+W1FFbUcKK8hsLyGvJLq9lXWM7e4xUcKqqkkY5FAMT4q42q6+qJ9kQwfkAKVw5JZ/LQDIora3ln61He2VrAlsNOpYE3KoKqWucXx9AeCUwanMakIemkdIvmYFEFB05UcuDE6TaN/YUVlFY3nubG9OnOtBE9mTaiJ/3T2jbUQFsFpZFVRGKAa3GS/SRgiap+qZntBWf0yUJVvTeQc1iCN8Y0VFNXzwF/6dkTISR3iyY5LpqUbtHERnuoqatndV4hS7fls2xbATvyy07t64kQcvomM2VYBlcNz2BAWjxbj5SyfEcBy3ccY2Ve4TlVTHHRHrKSnaqvPind6J3Sjezkbv7PsRwuruLNT4/w5uYjp4ahHpQRT/+0uDN+zaTHx5DcLYrYaA+x/h5RsdEeoiMjyC+pYn9hJfsKK9hfWMH+ExXUK/xxVqM5ukVB7yYpIok4PWL+0sw2lwPLgY3AyX/FB1X1tab2sQRvjGmL/YUVLN9xjARvJFcMTiepW9Nj11TW+FiVV0hlrY+s7rFkJ8e2qkH4YFElb206wrvbCzhSXEVBaTWFFTW0JqV6oyLITu7GoPR4/vAfzYy22QwbTdIYY9pBna/+VDVTcWUtlTU+Kmt9p55Orq6tJz0hht7+XwTp8TFt7mHUXIK3GW+NMSZIIj0RZCR6O0wjrPVHMsYYlwq4BC8ilwL9Gu7TXB28McaY8AoowYvIs8BAYD3g869WnLFmjDHGdECBluBzgAu0I7XIGmOMaVagdfCfAj1DGYgxxpjgCrQEnwZsFpGVwKlpylX1+pBEZYwxps0CTfA/DmUQxhhjgi+gBK+q74Y6EGOMMcEVUB28iEwQkVUiUiYiNSLiE5GSlvc0xhgTLoE2sj6OM8jYDiAW+CrwRKiCMsYY03YBP8mqqjsBj6r6VHUBcHXowjLGGNNWgTayVohINLBeRH4JHMaGOTDGmA4t0CT9H/5tvwGUA72Bz4cqKGOMMW0XaC+avSISC2Sq6vwQx2SMMSYIAu1Fcx3OODRv+JdHi8hLIYzLGGNMGwVaRfNj4BKgCEBV1wP9QxKRMcaYoAg0wdeqavFZ62zgMWOM6cAC7UWzSUS+BHhEZDBwN/Bh6MIyxhjTVoGW4O8CRuAMNPY8UALcG6KYjDHGBEGgvWgqgO/7X8YYYzqBZhN8Sz1lbLhgY4zpuFoqwU8E9uNUy6wAJOQRGWOMCYqWEnxPYCrOQGNfAl4FnlfVTaEOzBhjTNs028jqH1jsDVWdDUwAdgLLROQb7RKdMcaY89ZiI6uIxAAzcErx/YBHgUWhDcsYY0xbtdTI+hfgQuA1YL6qftouURljjGmzlkrwX8YZPfIe4G6RU22sAqiqJoYwNmOMMW3QbIJXVRvz3RhjOilL4MYY41KW4I0xxqUswRtjjEtZgjfGGJeyBG+MMS4VsgQvIk+JSL6IWN95Y4wJg1CW4J8Grg7h8Y0xxjQjZAleVd8DCkN1fGOMMc0Lex28iNwpIqtFZHVBQUG4wzHGGNcIe4JX1SdVNUdVc9LT08MdjjHGuEbYE7wxxpjQsARvjDEuFcpuks8DHwFDReSAiNwRqnMZY4w5V4sTfpwvVZ0ZqmMbY4xpmVXRGGOMS1mCN8YYl7IEb4wxLmUJ3hhjXMoSvDHGuJQleGOMcSlL8MYY41KW4I0xxqUswRtjjEtZgjfGGJeyBG+MMS5lCd4YY1zKErwxxriUJXhjjHEpS/DGGONSluCNMcalLMEbY4xLWYI3xhiXsgRvjDEuZQneGGNcyhK8Mca4lCV4Y4xxKUvwxhjjUpbgjTHGpSzBG2OMS1mCN8YYl7IEb4wxLmUJ3hhjXMoSvDHGuJQleGOMcSlL8MYY41KW4I0xxqUswRtjjEtZgjfGGJeyBG+MMS4V0gQvIleLyDYR2Ski3wvluYwxxpwpZAleRDzAE8B04AJgpohcEKrzGWOMOVMoS/CXADtVdbeq1gALgRtCeD5jjDENRIbw2FnA/gbLB4DxZ28kIncCd/oXy0Rk23meLw04dp77dnR2bZ2Xm6/Prq1j6NvUF6FM8AFR1SeBJ9t6HBFZrao5QQipw7Fr67zcfH12bR1fKKtoDgK9Gyxn+9cZY4xpB6FM8KuAwSLSX0SigS8CL4XwfMYYYxoIWRWNqtaJyDeANwEP8JSqbgrV+QhCNU8HZtfWebn5+uzaOjhR1XDHYIwxJgTsSVZjjHEpS/DGGONSnT7Bu204BBF5SkTyReTTButSRORtEdnhf08OZ4znS0R6i8hSEdksIptE5B7/+k5/fSLiFZGVIvKJ/9rm+9f3F5EV/r/Pv/s7HHRKIuIRkXUi8op/2U3XliciG0VkvYis9q/r9H+XnTrBu3Q4hKeBq89a9z1giaoOBpb4lzujOuBbqnoBMAH4uv+/lxuurxqYoqoXAaOBq0VkAvDfwG9VdRBwArgjfCG22T3AlgbLbro2gFxVHd2g/3un/7vs1AkeFw6HoKrvAYVnrb4BeMb/+RngxvaMKVhU9bCqrvV/LsVJFlm44PrUUeZfjPK/FJgCvOBf3ymvDUBEsoEZwJ/8y4JLrq0Znf7vsrMn+MaGQ8gKUyyh1ENVD/s/HwF6hDOYYBCRfsAYYAUuuT5/FcZ6IB94G9gFFKlqnX+Tzvz3+TBwP1DvX07FPdcGzs34LRFZ4x8+BVzwdxn2oQpM66iqikin7tsqIvHAi8C9qlriFAYdnfn6VNUHjBaR7sAiYFh4IwoOEbkWyFfVNSIyOczhhMrlqnpQRDKAt0Vka8MvO+vfZWcvwXeV4RCOikgmgP89P8zxnDcRicJJ7s+p6r/8q11zfQCqWgQsBSYC3UXkZEGqs/59XgZcLyJ5ONWgU4BHcMe1AaCqB/3v+Tg350twwd9lZ0/wXWU4hJeA2f7Ps4F/hzGW8+avt/0zsEVVf9Pgq05/fSKS7i+5IyKxwFScNoalwM3+zTrltanqA6qarar9cP4fe0dVb8MF1wYgInEiknDyM/BZ4FPc8HfZ2Z9kFZFrcOoHTw6H8LPwRtQ2IvI8MBlnuNKjwI+A/wX+AfQB9gJfUNWzG2I7PBG5HFgObOR0Xe6DOPXwnfr6RGQUTkOcB6fg9A9V/YmIDMAp9aYA64Avq2p1+CJtG38VzbdV9Vq3XJv/Ohb5FyOBv6nqz0Qklc7+d9nZE7wxxpjGdfYqGmOMMU2wBG+MMS5lCd4YY1zKErwxxriUJXhjjHEpS/CmSxERn3/EwJOvoA0gJSL9Go4Caky42VAFpqupVNXR4Q7CmPZgJXhjODUe+C/9Y4KvFJFB/vX9ROQdEdkgIktEpI9/fQ8RWeQf//0TEbnUfyiPiPzRPyb8W/6nWo0JC0vwpquJPauK5tYG3xWr6kjgcZynowEeA55R1VHAc8Cj/vWPAu/6x38fC5ycUH4w8ISqjgCKgM+H9GqMaYY9yWq6FBEpU9X4Rtbn4UzYsds/INoRVU0VkWNApqrW+tcfVtU0ESkAshs+mu8fAvlt/wQRiMh3gShV/Wk7XJox57ASvDGnaROfW6PhWCw+rJ3LhJEleGNOu7XB+0f+zx/ijKAIcBvOYGngTOE2D05N9JHUXkEaEygrXZiuJtY/69JJb6jqya6SySKyAacUPtO/7i5ggYh8BygAvuJffw/wpIjcgVNSnwccxpgOxOrgjeFUHXyOqh4LdyzGBItV0RhjjEtZCd4YY1zKSvDGGONSluCNMcalLMEbY4xLWYI3xhiXsgRvjDEu9f8BaeY4dKLm2s0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "\n",
    "# O parâmetro patience é o quantidade de epochs para checar as melhoras\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
    "\n",
    "history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n",
    "                    validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n",
    "\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3St8-DmrX8P4"
   },
   "source": [
    "O gráfico mostra que no conjunto de validação, a média de erro é próximo de +/- 2MPG. Isso é bom? Deixaremos essa decisão a você.\n",
    "\n",
    "Vamos ver quão bem o modelo generaliza usando o conjunto de **teste**, que não usamos para treinar o modelo. Isso diz quão bem podemos esperar que o modelo se saia quando usarmos na vida real."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:22:19.590193Z",
     "iopub.status.busy": "2021-02-13T03:22:19.589191Z",
     "iopub.status.idle": "2021-02-13T03:22:19.646500Z",
     "shell.execute_reply": "2021-02-13T03:22:19.646911Z"
    },
    "id": "jl_yNr5n1kms"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 - 0s - loss: 5.9054 - mae: 1.8057 - mse: 5.9054\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing set Mean Abs Error:  1.81 MPG\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=2)\n",
    "\n",
    "print(\"Testing set Mean Abs Error: {:5.2f} MPG\".format(mae))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ft603OzXuEZC"
   },
   "source": [
    "### Faça predições\n",
    "\n",
    "Finalmente, prevejam os valores MPG usando o conjunto de teste."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:22:19.653675Z",
     "iopub.status.busy": "2021-02-13T03:22:19.652638Z",
     "iopub.status.idle": "2021-02-13T03:22:19.855201Z",
     "shell.execute_reply": "2021-02-13T03:22:19.854659Z"
    },
    "id": "Xe7RXH3N3CWU"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_predictions = model.predict(normed_test_data).flatten()\n",
    "\n",
    "plt.scatter(test_labels, test_predictions)\n",
    "plt.xlabel('True Values [MPG]')\n",
    "plt.ylabel('Predictions [MPG]')\n",
    "plt.axis('equal')\n",
    "plt.axis('square')\n",
    "plt.xlim([0,plt.xlim()[1]])\n",
    "plt.ylim([0,plt.ylim()[1]])\n",
    "_ = plt.plot([-100, 100], [-100, 100])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "19wyogbOSU5t"
   },
   "source": [
    "Parece que o nosso modelo prediz razoavelmente bem. Vamos dar uma olhada na distribuição dos erros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:22:19.976791Z",
     "iopub.status.busy": "2021-02-13T03:22:19.976015Z",
     "iopub.status.idle": "2021-02-13T03:22:20.157942Z",
     "shell.execute_reply": "2021-02-13T03:22:20.157322Z"
    },
    "id": "f-OHX4DiXd8x"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = test_predictions - test_labels\n",
    "plt.hist(error, bins = 25)\n",
    "plt.xlabel(\"Prediction Error [MPG]\")\n",
    "_ = plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m0CB5tBjSU5w"
   },
   "source": [
    "Não é tão gaussiana, porém podemos esperar que por conta do número de exemplo é bem pequeno."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vgGQuV-yqYZH"
   },
   "source": [
    "## Conclusão\n",
    "\n",
    "Este notebook introduz algumas técnicas para trabalhar com problema de regressão.\n",
    "\n",
    "* Mean Squared Error(MSE), é uma função comum de *loss* usada para problemas de regressão (diferentes funções de *loss* são usadas para problemas de classificação).\n",
    "* Similarmente, as métricas de evolução usadas na regressão são diferentes da classificação. Uma métrica comum de regressão é  Mean Absolute Error (MAE).\n",
    "* Quando o dado de entrada de *features*  tem diferentes intervalos, cada *feature* deve ser escalada para o mesmo intervalo.\n",
    "* Se não possuir muitos dados de treinamento, uma técnica é preferir uma pequena rede com poucas camadas para evitar *overfitting*.\n",
    "* *Early stopping* é uma boa técnica para evitar *overfitting*."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "regression.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
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 },
 "nbformat": 4,
 "nbformat_minor": 0
}