{
 "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": "2020-09-23T00:13:19.251944Z",
     "iopub.status.busy": "2020-09-23T00:13:19.251312Z",
     "iopub.status.idle": "2020-09-23T00:13:19.253787Z",
     "shell.execute_reply": "2020-09-23T00:13:19.253203Z"
    },
    "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": "2020-09-23T00:13:19.257382Z",
     "iopub.status.busy": "2020-09-23T00:13:19.256735Z",
     "iopub.status.idle": "2020-09-23T00:13:19.258597Z",
     "shell.execute_reply": "2020-09-23T00:13:19.258994Z"
    },
    "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": [
    "# Regresion Basica: Predecir eficiencia de gasolina"
   ]
  },
  {
   "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\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/es-419/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/es-419/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/es-419/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Au0QDqNXgRb_"
   },
   "source": [
    "Note: Nuestra comunidad de Tensorflow ha traducido estos documentos. Como las traducciones de la comunidad\n",
    "son basados en el \"mejor esfuerzo\", no hay ninguna garantia que esta sea un reflejo preciso y actual \n",
    "de la [Documentacion Oficial en Ingles](https://www.tensorflow.org/?hl=en).\n",
    "Si tienen sugerencias sobre como mejorar esta traduccion, por favor envian un \"Pull request\"\n",
    "al siguiente repositorio [tensorflow/docs](https://github.com/tensorflow/docs).\n",
    "Para ofrecerse como voluntario o hacer revision de las traducciones de la Comunidad\n",
    "por favor contacten al siguiente grupo [docs@tensorflow.org list](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AHp3M9ZmrIxj"
   },
   "source": [
    "En un problema de *regresion*, buscamos predecir la salida de un valor continuo como la probabilidad de un precio. En contraste en un problema de *Clasificacion*, buscamos seleccionar una clase de una lista de clases (por ejemplo, en donde una imagen contenga una manzana o una naranja queremos reconocer cual es la fruta en la imagen).\n",
    "\n",
    "Este libro usa el set de datos clasico [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) y construye un modelo para predecir la eficiencia de vehiculos de 1970 y 1980. Para hacer esto proveeremos el modelo con una descripcion de muchos automoviles de ese periodo. Esta descripcion incluye atributos como: Cilindros, desplazamiento, potencia y peso.\n",
    "\n",
    "Este ejemplo usa el API `tf.keras` , revise [Esta Guia](https://www.tensorflow.org/guide/keras) para obtener mas detalles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:19.265623Z",
     "iopub.status.busy": "2020-09-23T00:13:19.262712Z",
     "iopub.status.idle": "2020-09-23T00:13:20.610944Z",
     "shell.execute_reply": "2020-09-23T00:13:20.611527Z"
    },
    "id": "moB4tpEHxKB3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 20.2.2; however, version 20.2.3 is available.\r\n",
      "You should consider upgrading via the '/tmpfs/src/tf_docs_env/bin/python -m pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "# Use seaborn for pairplot\n",
    "!pip install -q seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:20.617330Z",
     "iopub.status.busy": "2020-09-23T00:13:20.616344Z",
     "iopub.status.idle": "2020-09-23T00:13:27.321654Z",
     "shell.execute_reply": "2020-09-23T00:13:27.321020Z"
    },
    "id": "1rRo8oNqZ-Rj"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.0\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": [
    "## El set de Datos de MPG\n",
    "\n",
    "el set de datos esta disponible de el siguiente repositorio [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gFh9ne3FZ-On"
   },
   "source": [
    "### Obtenga la data\n",
    "Primero descargue el set de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:27.328243Z",
     "iopub.status.busy": "2020-09-23T00:13:27.327585Z",
     "iopub.status.idle": "2020-09-23T00:13:27.661960Z",
     "shell.execute_reply": "2020-09-23T00:13:27.662382Z"
    },
    "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",
      "32768/30286 [================================] - 0s 1us/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": [
    "Importelo usando pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:27.670510Z",
     "iopub.status.busy": "2020-09-23T00:13:27.667688Z",
     "iopub.status.idle": "2020-09-23T00:13:27.689919Z",
     "shell.execute_reply": "2020-09-23T00:13:27.689290Z"
    },
    "id": "CiX2FI4gZtTt"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
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       "    <tr>\n",
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       "      <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",
       "      <td>84.0</td>\n",
       "      <td>2295.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>28.0</td>\n",
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       "      <td>79.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>18.6</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": 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": [
    "### Limpie la data\n",
    "\n",
    "El set de datos contiene algunos valores desconocidos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:27.696269Z",
     "iopub.status.busy": "2020-09-23T00:13:27.695586Z",
     "iopub.status.idle": "2020-09-23T00:13:27.699098Z",
     "shell.execute_reply": "2020-09-23T00:13:27.698583Z"
    },
    "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** Mantener este tutorial inicial sencillo eliminemos las siguientes filas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:27.704846Z",
     "iopub.status.busy": "2020-09-23T00:13:27.704211Z",
     "iopub.status.idle": "2020-09-23T00:13:27.708906Z",
     "shell.execute_reply": "2020-09-23T00:13:27.708399Z"
    },
    "id": "4ZUDosChC1UN"
   },
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8XKitwaH4v8h"
   },
   "source": [
    "La columna de `\"Origin\"` realmente es categorica, no numerica. Entonces conviertala a un \"one-hot\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:27.713743Z",
     "iopub.status.busy": "2020-09-23T00:13:27.713079Z",
     "iopub.status.idle": "2020-09-23T00:13:27.715710Z",
     "shell.execute_reply": "2020-09-23T00:13:27.715193Z"
    },
    "id": "gWNTD2QjBWFJ"
   },
   "outputs": [],
   "source": [
    "origin = dataset.pop('Origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:27.731879Z",
     "iopub.status.busy": "2020-09-23T00:13:27.731117Z",
     "iopub.status.idle": "2020-09-23T00:13:27.734434Z",
     "shell.execute_reply": "2020-09-23T00:13:27.733917Z"
    },
    "id": "ulXz4J7PAUzk"
   },
   "outputs": [
    {
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       "      <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": [
    "### Dividamos la data en entrenamiento y prueba\n",
    "\n",
    "Ahora divida el set de datos en un set de entrenamiento y otro de pruebas.\n",
    "\n",
    "Usaremos el set de pruebas en la evaluacion final de nuestro modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:27.740592Z",
     "iopub.status.busy": "2020-09-23T00:13:27.739928Z",
     "iopub.status.idle": "2020-09-23T00:13:27.742001Z",
     "shell.execute_reply": "2020-09-23T00:13:27.742428Z"
    },
    "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": [
    "### Inspeccione la data\n",
    "\n",
    "Revise rapidamente la distribucion conjunta de un par de columnas de el set de entrenamiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:27.749652Z",
     "iopub.status.busy": "2020-09-23T00:13:27.748596Z",
     "iopub.status.idle": "2020-09-23T00:13:33.056550Z",
     "shell.execute_reply": "2020-09-23T00:13:33.057143Z"
    },
    "id": "oRKO_x8gWKv-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fbbccd3bd30>"
      ]
     },
     "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": [
    "Tambien revise las estadisticas generales:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:33.068038Z",
     "iopub.status.busy": "2020-09-23T00:13:33.067343Z",
     "iopub.status.idle": "2020-09-23T00:13:33.100862Z",
     "shell.execute_reply": "2020-09-23T00:13:33.100322Z"
    },
    "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 las caracteristicas de las etiquetas.\n",
    "\n",
    "Separe el valor objetivo, o la \"etiqueta\" \n",
    "de las caracteristicas. Esta etiqueta es el valor que entrenara el modelo para predecir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:33.107188Z",
     "iopub.status.busy": "2020-09-23T00:13:33.106198Z",
     "iopub.status.idle": "2020-09-23T00:13:33.108329Z",
     "shell.execute_reply": "2020-09-23T00:13:33.108774Z"
    },
    "id": "t2sluJdCW7jN"
   },
   "outputs": [],
   "source": [
    "train_labels = train_dataset.pop('MPG')\n",
    "test_labels = test_dataset.pop('MPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mRklxK5s388r"
   },
   "source": [
    "### Normalice la data\n",
    "\n",
    "Revise otra vez el bloque de `train_stats` que se presento antes y note la diferencia de rangos de cada caracteristica."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-ywmerQ6dSox"
   },
   "source": [
    "Es una buena práctica normalizar funciones que utilizan diferentes escalas y rangos. Aunque el modelo * podría * converger sin normalización de características, dificulta el entrenamiento y hace que el modelo resultante dependa de la elección de las unidades utilizadas en la entrada.\n",
    "\n",
    "Nota: Aunque generamos intencionalmente estas estadísticas solo del conjunto de datos de entrenamiento, estas estadísticas también se utilizarán para normalizar el conjunto de datos de prueba. Necesitamos hacer eso para proyectar el conjunto de datos de prueba en la misma distribución en la que el modelo ha sido entrenado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:33.117479Z",
     "iopub.status.busy": "2020-09-23T00:13:33.116765Z",
     "iopub.status.idle": "2020-09-23T00:13:33.118784Z",
     "shell.execute_reply": "2020-09-23T00:13:33.119274Z"
    },
    "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": [
    "Estos datos normalizados es lo que usaremos para entrenar el modelo.\n",
    "\n",
    "Precaución: las estadísticas utilizadas para normalizar las entradas aquí (media y desviación estándar) deben aplicarse a cualquier otro dato que se alimente al modelo, junto con la codificación de un punto que hicimos anteriormente. Eso incluye el conjunto de pruebas, así como los datos en vivo cuando el modelo se usa en producción."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SmjdzxKzEu1-"
   },
   "source": [
    "## El modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6SWtkIjhrZwa"
   },
   "source": [
    "### Construye el modelo\n",
    "\n",
    "Construyamos nuestro modelo. Aquí, utilizaremos un modelo `secuencial` con dos capas ocultas densamente conectadas y una capa de salida que devuelve un único valor continuo. Los pasos de construcción del modelo se envuelven en una función, `build_model`, ya que crearemos un segundo modelo, más adelante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:33.125030Z",
     "iopub.status.busy": "2020-09-23T00:13:33.124402Z",
     "iopub.status.idle": "2020-09-23T00:13:33.126998Z",
     "shell.execute_reply": "2020-09-23T00:13:33.126470Z"
    },
    "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": "2020-09-23T00:13:33.130964Z",
     "iopub.status.busy": "2020-09-23T00:13:33.130309Z",
     "iopub.status.idle": "2020-09-23T00:13:34.872552Z",
     "shell.execute_reply": "2020-09-23T00:13:34.871944Z"
    },
    "id": "cGbPb-PHGbhs"
   },
   "outputs": [],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Sj49Og4YGULr"
   },
   "source": [
    "### Inspeccione el modelo\n",
    "\n",
    "Use el método `.summary` para imprimir una descripción simple del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:34.877263Z",
     "iopub.status.busy": "2020-09-23T00:13:34.876610Z",
     "iopub.status.idle": "2020-09-23T00:13:34.879990Z",
     "shell.execute_reply": "2020-09-23T00:13:34.880521Z"
    },
    "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": [
    "Ahora pruebe el modelo. Tome un lote de ejemplos `10` de los datos de entrenamiento y llame a` model.predict` en él."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:34.885465Z",
     "iopub.status.busy": "2020-09-23T00:13:34.884854Z",
     "iopub.status.idle": "2020-09-23T00:13:35.279864Z",
     "shell.execute_reply": "2020-09-23T00:13:35.280423Z"
    },
    "id": "-d-gBaVtGTSC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.01398571],\n",
       "       [ 0.09287428],\n",
       "       [-0.06822745],\n",
       "       [ 0.01392132],\n",
       "       [ 0.2776244 ],\n",
       "       [-0.02446011],\n",
       "       [ 0.24629724],\n",
       "       [ 0.585566  ],\n",
       "       [-0.01556234],\n",
       "       [ 0.31573048]], 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 estar funcionando, y produce un resultado de la forma y tipo esperados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0-qWCsh6DlyH"
   },
   "source": [
    "### Entrenar a la modelo\n",
    "\n",
    "Entrene el modelo durante 1000 épocas y registre la precisión de entrenamiento y validación en el objeto `history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:13:35.289253Z",
     "iopub.status.busy": "2020-09-23T00:13:35.288254Z",
     "iopub.status.idle": "2020-09-23T00:14:08.890777Z",
     "shell.execute_reply": "2020-09-23T00:14:08.891232Z"
    },
    "id": "sD7qHCmNIOY0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
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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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    {
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      "......"
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      "......."
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      "......."
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    },
    {
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     "output_type": "stream",
     "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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      "......."
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      "......."
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...\n",
      "...."
     ]
    },
    {
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     "output_type": "stream",
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      "......."
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    },
    {
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      "......."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".....\n",
      ".."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "......."
     ]
    },
    {
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      "......"
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      "......"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "....\n",
      "..."
     ]
    },
    {
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      "......."
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    {
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     "output_type": "stream",
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    {
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    {
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     "text": [
      "....."
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    }
   ],
   "source": [
    "# Display training progress by printing a single dot for each completed epoch\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": [
    "Visualice el progreso de entrenamiento del modelo usando las estadísticas almacenadas en el objeto `history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:14:08.904442Z",
     "iopub.status.busy": "2020-09-23T00:14:08.896689Z",
     "iopub.status.idle": "2020-09-23T00:14:08.907100Z",
     "shell.execute_reply": "2020-09-23T00:14:08.907542Z"
    },
    "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>2.595970</td>\n",
       "      <td>0.978913</td>\n",
       "      <td>2.595970</td>\n",
       "      <td>11.760169</td>\n",
       "      <td>2.524347</td>\n",
       "      <td>11.760169</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.479838</td>\n",
       "      <td>0.976182</td>\n",
       "      <td>2.479838</td>\n",
       "      <td>11.067865</td>\n",
       "      <td>2.483008</td>\n",
       "      <td>11.067865</td>\n",
       "      <td>996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.674930</td>\n",
       "      <td>1.019684</td>\n",
       "      <td>2.674930</td>\n",
       "      <td>11.104455</td>\n",
       "      <td>2.580837</td>\n",
       "      <td>11.104455</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2.610440</td>\n",
       "      <td>0.966319</td>\n",
       "      <td>2.610440</td>\n",
       "      <td>10.906663</td>\n",
       "      <td>2.532035</td>\n",
       "      <td>10.906663</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.501746</td>\n",
       "      <td>1.019541</td>\n",
       "      <td>2.501746</td>\n",
       "      <td>10.737952</td>\n",
       "      <td>2.528607</td>\n",
       "      <td>10.737952</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  2.595970  0.978913  2.595970  11.760169  2.524347  11.760169    995\n",
       "996  2.479838  0.976182  2.479838  11.067865  2.483008  11.067865    996\n",
       "997  2.674930  1.019684  2.674930  11.104455  2.580837  11.104455    997\n",
       "998  2.610440  0.966319  2.610440  10.906663  2.532035  10.906663    998\n",
       "999  2.501746  1.019541  2.501746  10.737952  2.528607  10.737952    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": "2020-09-23T00:14:08.916363Z",
     "iopub.status.busy": "2020-09-23T00:14:08.915232Z",
     "iopub.status.idle": "2020-09-23T00:14:09.466039Z",
     "shell.execute_reply": "2020-09-23T00:14:09.466618Z"
    },
    "id": "B6XriGbVPh2t"
   },
   "outputs": [
    {
     "data": {
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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 gráfico muestra poca mejora, o incluso degradación en el error de validación después de aproximadamente 100 épocas. Actualicemos la llamada `model.fit` para detener automáticamente el entrenamiento cuando el puntaje de validación no mejore. Utilizaremos una * devolución de llamada de EarlyStopping * que pruebe una condición de entrenamiento para cada época. Si transcurre una cantidad determinada de épocas sin mostrar mejoría, entonces detiene automáticamente el entrenamiento.\n",
    "\n",
    "Puedes obtener más información sobre esta devolución de llamada [Aca](https://www.tensorflow.org/versions/master/api_docs/python/tf/keras/callbacks/EarlyStopping)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:14:09.475435Z",
     "iopub.status.busy": "2020-09-23T00:14:09.474702Z",
     "iopub.status.idle": "2020-09-23T00:14:13.240258Z",
     "shell.execute_reply": "2020-09-23T00:14:13.239617Z"
    },
    "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",
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      "......."
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      "......."
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      "......."
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      "......."
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     "output_type": "stream",
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      "...."
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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": [
    "model = build_model()\n",
    "\n",
    "# The patience parameter is the amount of epochs to check for improvement\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": [
    "El gráfico muestra que en el conjunto de validación, el error promedio generalmente es de alrededor de +/- 2 MPG. ¿Es esto bueno? Le dejaremos esa decisión a usted.\n",
    "\n",
    "Veamos qué tan bien generaliza el modelo al usar el conjunto ** test **, que no usamos al entrenar el modelo. Esto nos dice qué tan bien podemos esperar que el modelo prediga cuándo lo usamos en el mundo real."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:14:13.245748Z",
     "iopub.status.busy": "2020-09-23T00:14:13.244728Z",
     "iopub.status.idle": "2020-09-23T00:14:13.297849Z",
     "shell.execute_reply": "2020-09-23T00:14:13.298268Z"
    },
    "id": "jl_yNr5n1kms"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 - 0s - loss: 5.9382 - mae: 1.9334 - mse: 5.9382\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing set Mean Abs Error:  1.93 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": [
    "### Haga Predicciones\n",
    "\n",
    "Finalmente, prediga los valores de MPG utilizando datos en el conjunto de pruebas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:14:13.304696Z",
     "iopub.status.busy": "2020-09-23T00:14:13.303733Z",
     "iopub.status.idle": "2020-09-23T00:14:13.491809Z",
     "shell.execute_reply": "2020-09-23T00:14:13.491188Z"
    },
    "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 nuestro modelo predice razonablemente bien. Echemos un vistazo a la distribución de errores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-23T00:14:13.505689Z",
     "iopub.status.busy": "2020-09-23T00:14:13.504621Z",
     "iopub.status.idle": "2020-09-23T00:14:13.660425Z",
     "shell.execute_reply": "2020-09-23T00:14:13.659893Z"
    },
    "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": [
    "No es del todo gaussiano, pero podríamos esperar eso porque el número de muestras es muy pequeño."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vgGQuV-yqYZH"
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "Este cuaderno introdujo algunas técnicas para manejar un problema de regresión.\n",
    "\n",
    "* El error cuadrático medio (MSE) es una función de pérdida común utilizada para problemas de regresión (se utilizan diferentes funciones de pérdida para problemas de clasificación).\n",
    "* Del mismo modo, las métricas de evaluación utilizadas para la regresión difieren de la clasificación. Una métrica de regresión común es el error absoluto medio (MAE).\n",
    "* Cuando las características de datos de entrada numéricos tienen valores con diferentes rangos, cada característica debe escalarse independientemente al mismo rango.\n",
    "* Si no hay muchos datos de entrenamiento, una técnica es preferir una red pequeña con pocas capas ocultas para evitar el sobreajuste.\n",
    "* La detención temprana es una técnica útil para evitar el sobreajuste."
   ]
  }
 ],
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