teste_13.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FhGuhbZ6M5tl"
   },
   "source": [
    "##### Copyright 2018 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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    "cellView": "form",
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   "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": {
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    "id": "KyPEtTqk6VdG"
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   "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": "2021-02-13T03:21:08.785429Z",
     "iopub.status.busy": "2021-02-13T03:21:08.784706Z",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5.0\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": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:15.838508Z",
     "iopub.status.busy": "2021-02-13T03:21:15.837805Z",
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     "shell.execute_reply": "2021-02-13T03:21:16.643897Z"
    },
    "id": "p9kxxgzvzlyz"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Marcus_Eduardo\\\\.keras\\\\datasets\\\\auto-mpg.data'"
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:16.652792Z",
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     "shell.execute_reply": "2021-02-13T03:21:16.672347Z"
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   "outputs": [
    {
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       "    }\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>MPG</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Acceleration</th>\n",
       "      <th>Model Year</th>\n",
       "      <th>Origin</th>\n",
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       "  </thead>\n",
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       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
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       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
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       "      <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</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": 5,
     "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": 6,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2021-02-13T03:21:16.681427Z"
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    "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": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "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": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:16.686937Z",
     "iopub.status.busy": "2021-02-13T03:21:16.686278Z",
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     "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": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:16.695010Z",
     "iopub.status.busy": "2021-02-13T03:21:16.694348Z",
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     "shell.execute_reply": "2021-02-13T03:21:16.696178Z"
    },
    "id": "gWNTD2QjBWFJ"
   },
   "outputs": [],
   "source": [
    "origin = dataset.pop('Origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-13T03:21:16.713315Z",
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     "shell.execute_reply": "2021-02-13T03:21:16.715303Z"
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    "id": "ulXz4J7PAUzk"
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   "outputs": [
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       "      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": 9,
     "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": 10,
   "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": 11,
   "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 0x164c9107b50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "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": 12,
   "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": 12,
     "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": 13,
   "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": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "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": 18,
   "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([[0.16026905],\n",
       "       [0.35608578],\n",
       "       [1.2268451 ],\n",
       "       [0.41041765],\n",
       "       [0.35947436],\n",
       "       [0.25327632],\n",
       "       [0.3655386 ],\n",
       "       [0.855897  ],\n",
       "       [0.27286237],\n",
       "       [0.11623415]], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "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",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "....................................................................................................\n",
      "...................................................................................................."
     ]
    }
   ],
   "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": 20,
   "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": [
    {
     "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>loss</th>\n",
       "      <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>2.687582</td>\n",
       "      <td>1.079067</td>\n",
       "      <td>2.687582</td>\n",
       "      <td>8.839304</td>\n",
       "      <td>2.244625</td>\n",
       "      <td>8.839304</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.513630</td>\n",
       "      <td>1.017659</td>\n",
       "      <td>2.513630</td>\n",
       "      <td>8.821239</td>\n",
       "      <td>2.237973</td>\n",
       "      <td>8.821239</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.446637</td>\n",
       "      <td>0.994924</td>\n",
       "      <td>2.446637</td>\n",
       "      <td>9.786129</td>\n",
       "      <td>2.373727</td>\n",
       "      <td>9.786129</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.774288</td>\n",
       "      <td>1.101184</td>\n",
       "      <td>2.774288</td>\n",
       "      <td>8.923283</td>\n",
       "      <td>2.340163</td>\n",
       "      <td>8.923283</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.449755</td>\n",
       "      <td>1.008914</td>\n",
       "      <td>2.449755</td>\n",
       "      <td>8.902087</td>\n",
       "      <td>2.258846</td>\n",
       "      <td>8.902087</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  2.687582  1.079067  2.687582  8.839304  2.244625  8.839304    995\n",
       "996  2.513630  1.017659  2.513630  8.821239  2.237973  8.821239    996\n",
       "997  2.446637  0.994924  2.446637  9.786129  2.373727  9.786129    997\n",
       "998  2.774288  1.101184  2.774288  8.923283  2.340163  8.923283    998\n",
       "999  2.449755  1.008914  2.449755  8.902087  2.258846  8.902087    999"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "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": 22,
   "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",
      "........................................."
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "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": 23,
   "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: 6.8679 - mae: 2.0194 - mse: 6.8679\n",
      "Testing set Mean Abs Error:  2.02 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": 24,
   "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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ReBNN4nHGR9bAm+uBl4Gzw5+XgP+Tl1FOa/DKnkFuWfccADOmTh4JvOnuig/CLCXIRCPxPMCmvmSdwx9mZmdGjq+StCEHe5ycqZVjLFpwMpoIU3pGnMf96oXHsHTVxjFTgHbdi70ZySr43ZL+0sx+DSOBOB4R0WKMt/Bk+b2lL4q0XVwrJcJcGhaqLMcDbOpDVsH/DcG2UdMIRmTbCXaicVqI8RSeLLFifR9Xrnx01KaOWXZxTfK4e4BNY8k0hzezDWZ2HHAscIyZzTGzB/M1zak14w1fLY0I4nZwhWAX1/GSVxKPk420veU+aWY3SfrvZe0AmNk3c7TNqTFpvWv5/H7nnsExI4Io1QzDPe+9saQN6V8Tvr4u5lzzp9k5ozjlrTP54e82jfqPK/WucfP7NKodhnuATeNIq0v/z+HbfzWz+6LnQsed0yKsWN/Hrev6RoldwJknBOKbt2RNxd68HB+GtyZZ1+GvzdjmNClxDjsDfvnENmB8w/PpUzu5euEx3ku3IGlz+HcB7wZmls3jX0+QTOO0CGkOu0ohsjOmTmbHrr2J821Pemkd0ubwk4HXhtdF5/EvAWflZZRTe9IcdnHzewiW3ipt1zyRtX2n/qTN4e8F7pV0g5k9UyebnBrxlRUPs+z+ZxkyY5KC+Vt0F7eow658fg/wniP2T92bvZq1fadxZJ3Dfy+68YSk6ZJW5WOSUwu+suJhbvrdppHU1GELxN7VOWkk5r00D48TLcB/btuZ+hwvTdVaZI2029/M+ksHZrZD0gGVbpD0ZuAHwEEEf2vXmdk/SpoBLAdmAU8DZ5vZjvGb7lRi2f3PxrbvHTSeWvKhUW0TEa1HzrUWWXv4YUkHlw4kHUL6OvwgcJmZvQ04EfispKOAxcBqMzsCWB0eOzUmqejEkNmYveUOmjYl9tosovXIudYiaw//d8CvJd0bHp8EXFzpBjPbDGwO378s6XGgFzgDODm87EbgHuBL47LaSaUjoawUMKru+6sDQ0yJ2cwxq2g9cq61yFyXXtL+BD21gN+a2QuZHyLNAn4FvB3YZGbdkXM7zGx6zD0XE36pHHzwwSc884z7DMfDJ777W+774/bU6yZ3TGLIjE+deAh3P7bFRdsGVKppV1Hwkt5qZk9IOj7ufJYilpJeC9xLsGvNbZL6swg+ihexTCcuDj4p6aWctHX2pGf4l0JzMpEilpcBFwHfiDmXWsRSUifBzrM/NLPbwuYtknrMbLOkHmBrig1OCtXEwUfZvmvvyH1Ja+i+3t4eVHTamdlF4espMT9pYhfBBhaPl2XVrSTYlJLw9Y7qzXdWrO/jspsfHFccfCWStpDOa7tpp76khdYurHQ+0mvHMQ/4FPBwpBzWl4ElwM2SLgQ2AR/LbK0zilKvm+Scq5a45Thfb28P0ob0HwlfDyCIqV8THp9C4F1PFHxYDiupnump2U10kkgKmJkocctxvt7eHqQN6T9tZp8mmK8fZWZnhsUsK8dbOnWhUu/a2SE6q6gfnbQc5+vt7UHWdfhZ4bp6iS3AW3Kwx0kh6ilP2sKpQ2LpWccBcMUdj/Diq4OJnzd9aidTJ++X6nn39fb2IKvg7wlj55cR9PbnAr/MzSonlnJPeZzYS+WgF8zp5ZU9g3zxlocSP6+rs4MrPnJ0ZtF6pZrWJ+tWU5+T9FGCCDsI4uJvz88sJ46kOXuHxLDZqF63VDd+79BwzCcFeBGL4pG1hwd4AHjZzP5V0lRJrzOzl/MyzBlL0px92EYnxJTE/sCmHWNSYkv0dnexYE6vB9MUjKx7y10E3AKUatz1AitysslJIMkjHm2Pin2/SZNixV5euLKvf/eo+Hrf9ql9yZot91mCdfWXAMzsSYKlOqeOpHnKo9s/dXdNjh3Od0gV8+A9mKa9ySr4PWa2t3QgaT+8THXdWTCnl6sXHkNvd9eYIhZRsX/73Dns2LU39jOGzUaG7B5MUzyyCv5eSV8GuiS9D/gJ8NP8zHKSWDCnl/sWz+eac2YDcOnyDbzr6tV85Npfj4j9Q8f2ZBr+Z7nGaS+yCv5LwDbgYeAzwL8AX8nLqKKxYn0f85asGVOYotL10bn35hdf5akXdvKpEw8Z2estS6CMB9MUj1QvvaRJwENm9nbgu/mbVCyqyUJLWp67+7EtI0UnswTKeDBN8UgVvJkNS3pQ0sFmtqkeRhWJLFVfy5fOktJfS3Pv8uuvOWd2oog9mKZYZF2H7wEelfR7YKSUqZmdnotVBSLNcTaeXPc3dnd53rpTkayCvypXKwpMWhZa1oy40tzb68Q7lajotJM0RdIXCHLW3wrcZ2b3ln7qYWC7k+Y4q7REFrc850ttTiXSevgbgQHg34C/Ao4CPp+3UUUizXE2rasztjZdd1cn9y0eW3TI89adSqQtyx1lZp8Mt40+C3hPHWxyIighpT2p3ZfanEqk9fAjXYuZDSrpr8ypmjgn26KfPMhVP32U/l0DieGM/bviK9L6UptTiTTBHyfppfC9CCLtXgrfm5m9PlfrCkCck21g2NiRIOgSlYbovtTmJJG2e6zvAZ8z1TjTfIjuVMt48uGdMmqRS14pkKYchdf7EN2pFhd8ldQqwGXRaUey6CcPMjBcOflw+tRO1n/1/aOe7/N0Z7xkTZ5xyqhlLnlyEap9RMvXeeEKp1pc8FVSqwCXq376KEMpvTswai3eC1c41eJD+iqpNsClfCie5o0v0RFZEvVoOqdavIevkmoCXOKG4lmJlqT2whVOtbjgq6RSuakkJrI1VG9EzB5N51SLD+knwHgDXKodcpeL2aPpnGpxwdeQtKWy8ay5x20uEcWj6ZxqcMHXiCzr8otOO5LFtz7Eq4P7FuI6OwTGqHX46HZRjlNLfA5fI7Islb33qAPpiczFe7u7WHrWcSz92HHj8gU4TrV4D18j0pbKSnXjN23fxXc+fvxIddkSLnCnHngPXyMqLZWVbxJRLnbHqRcu+BqRtFR2yfzDXexO0+BD+hoRt1R2yfzDuWXdcy52p2nITfCSrgc+DGwNN7FA0gxgOTALeBo428x25GVDvYkulfkw3mlG8hzS3wB8oKxtMbDazI4AVofHbYeL3WlWchO8mf0K2F7WfAZBJVzC1wV5Pb9RuNidZqbeTrsDzWwzQPiauMe8pIslrZW0dtu2bXUzcCK42J1mp2m99GZ2nZnNNbO5M2fObLQ5qbjYnVag3oLfIqkHIHzdWufn54KL3WkV6i34lcD54fvzgTvq/Pya42J3WoncBC9pGfBb4EhJz0m6EFgCvE/Sk8D7wuOWxcXutBq5rcOb2XkJp07N65n1xMXutCJN67RrZlzsTqvigh8nLnanlXHBjwMXu9PquOAz4mJ32gEXfAZc7E674IJPwcXutBMu+Aq42J12wwWfgIvdaUdc8DG42J12xQVfhovdaWdc8BFc7E6744IPcbE7RcAFj4vdKQ6FF7yL3SkShRa8i90pGoUVvIvdKSKFFLyL3SkqhRO8i90pMoUSvIvdKTqFEbyL3XEKIngXu+MEtL3gXeyOs4+2FryL3XFG07aCd7E7zljaUvAudseJp+0E72J3nGTaSvAudsepTNsI3sXuOOm0heBd7I6TjZYXvIvdcbLT0oJ3sTvO+GhZwbvYHWf8tKTgXeyOUx0tJ3gXu+NUT0sJ3sXuOBOjZQTvYnecidMQwUv6gKSNkv5D0uK064fNXOyOUwP2q/cDJXUA3wHeBzwH/EHSSjN7LOmep17YyYsudseZMI3o4f8C+A8z+08z2wv8GDij0g279g652B2nBtS9hwd6gWcjx88B7yy/SNLFwMXh4Z4PH/fGR+pgW63YH3ih0UaMk1az2e1N5pCkE40QvGLabEyD2XXAdQCS1prZ3LwNqxWtZi+0ns1ub3U0Ykj/HPDmyPGbgOcbYIfjFI5GCP4PwBGSDpU0GTgXWNkAOxyncNR9SG9mg5I+B6wCOoDrzezRlNuuy9+ymtJq9kLr2ez2VoHMxkyfHcdpU1om0s5xnInjgnecAtHUgh9vCG4jkHS9pK2SHom0zZB0t6Qnw9fpjbQxiqQ3S/qlpMclPSrp82F7U9osaYqk30t6MLT3qrC9Ke0tIalD0npJd4bHTWFv0wo+EoL7V8BRwHmSjmqsVbHcAHygrG0xsNrMjgBWh8fNwiBwmZm9DTgR+Gz479qsNu8B5pvZccBs4AOSTqR57S3xeeDxyHFz2GtmTfkDvAtYFTm+HLi80XYl2DoLeCRyvBHoCd/3ABsbbWMF2+8gyGtoepuBqcADBJGZTWsvQWzJamA+cGcz/U00bQ9PfAhub4NsGS8HmtlmgPD1gAbbE4ukWcAc4H6a2OZweLwB2ArcbWZNbS/wLeCLwHCkrSnsbWbBZwrBdapD0muBW4EvmNlLjbanEmY2ZGazCXrOv5D09gablIikDwNbzWxdo22Jo5kF38ohuFsk9QCEr1sbbM8oJHUSiP2HZnZb2NzUNgOYWT9wD4HPpFntnQecLulpgkzQ+ZJuoknsbWbBt3II7krg/PD9+QTz5KZAkoDvA4+b2Tcjp5rSZkkzJXWH77uA9wJP0KT2mtnlZvYmM5tF8De7xsw+SbPY22gHR4rz44PAvwN/BP6u0fYk2LgM2AwMEIxKLgTeQOC0eTJ8ndFoOyP2/iXB1OghYEP488FmtRk4Flgf2vsI8NWwvSntLbP9ZPY57ZrCXg+tdZwC0cxDesdxaowL3nEKhAvecQqEC95xCoQL3nEKhAvecQqEC75JkPQGSRvCn/8nqS9yPLkGn3+lpKvL2mZLejzlnr+d6LMrfP7Tkh6WNDc8vkfSpjA4qHTNCkmvhO9nSdod/ps8JumfJE0Kzx0h6U5Jf5S0LkwBPik8d06YYn1nXr9Lq+CCbxLM7E9mNtuCmPF/Aq4pHZvZXkkTrT+4DDinrO1c4EcT/NyJcoqZrY0c9xOEpxJG2JXvPvLH8N/oWIK06QWSpgB3AdeZ2WFmdgJwCfDnAGa2HPivOf4OLYMLvomRdIOkb0r6JfC18h5X0iNhxhuSPhkWitgg6Z/DegIjmNlGoF9SdNOPs4EfS7pI0h/CIhO3SpoaY8s9kZ54/zBWvJTJtjS8/yFJnwnbeyT9KrTnEUnvyfhr/5jgiwhgIXBb3EVmNgj8Bjgc+ATwWzNbGTn/iJndkPGZhcEF3/y8BXivmV2WdIGktxH03vPC3m+IQATlLCMUU1hE4k9m9iRwm5m9w4IiE48ThAdn5ULgRTN7B/AO4CJJhwIfJ6hnMBs4jiCENwurgZPCL6xzgeVxF4VfSqcCDwNHE+TJOyk0YucZZ3z8xMyGUq45FTiBYGNOgC7is7F+DPxG0mUEYloWtr9d0j8A3cBrCUqIZ+X9wLGSzgqPpwFHECQ/XR9m5q0wsw0ZP28I+DXBF1iXmT0dmdIDHBbmxhtwh5n9TNL7ohdIuj204d/NbOE4fpe2xwXf/OyMvB9k9KhsSvgq4EYzu7zSB5nZs+FQ/L8AZxJUFYKgTNcCM3tQ0gUESR/lRJ89JdIu4BIzG/MlETrNPgT8X0lLzewHleyL8GPgduDKmHOlOXyUR4GTSgdm9tFw+vH1jM8rDD6kby2eBo4HkHQ8cGjYvho4S9IB4bkZkpI2FFwGXEMgnOfCttcBm8PeOG4qUHr2CeH7syLtq4C/Ce9F0lskvSZ8/lYz+y5BOu7x4/g9/w24mn0jkDR+BMyTdHqkbYwfwvEevtW4FfjrcEj7B4LUYczsMUlfAX4RLlMNAJ8Fnon5jJ8A/0jgxS7xPwjKXD1DMCd+Xcx9XwdulvQpYE2k/XsENf0eCJfTtgELCEYJiyQNAK8Af531l7QghTNz72xmuxVUmvmmpG8BW4CXgX/I+hlFwdNjnYYRTi/mmlnu2yhLOhn4WzP7cN7PamZ8SO80km3A6tJyX15IOgf438COPJ/TCngP7zgFwnt4xykQLnjHKRAueMcpEC54xykQ/x+G+Zk5DK7+wQAAAABJRU5ErkJggg==",
      "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": 25,
   "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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",
      "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": "3.9.11"
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 },
 "nbformat": 4,
 "nbformat_minor": 0
}