AA_PRACTICA3_GRUPO_4.ipynb

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        "<a href=\"https://colab.research.google.com/github/javierarteagapuell/deeplearning/blob/main/AA_PRACTICA3_GRUPO_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# AA_PRACTICA3_GRUPO_4\n",
        "\n",
        "Daniel Alonso\n",
        "\n",
        "Javier Arteaga\n",
        "\n",
        "Marina Garrido\n",
        "\n",
        "Elena González"
      ],
      "metadata": {
        "id": "NAt_CleDyO_d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importar librerías"
      ],
      "metadata": {
        "id": "ghXqavarzEn_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from keras import models\n",
        "from keras import layers\n",
        "%matplotlib inline\n"
      ],
      "metadata": {
        "id": "lcAKpTlrZKu2"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "Vamos a **predecir** el **porcentaje de grasa corporal** de un **individuo** con ayuda de la siguiente base de datos.\n",
        "https://www.kaggle.com/datasets/fedesoriano/body-fat-prediction-dataset\n",
        "\n",
        "Las variables de la base de datos son:\n",
        "- Density determined from underwater weighing\n",
        "- Percent body fat from Siri's (1956) equation\n",
        "- Age (years)\n",
        "- Weight (lbs)\n",
        "- Height (inches)\n",
        "- Neck circumference (cm)\n",
        "- Chest circumference (cm)\n",
        "- Abdomen 2 circumference (cm)\n",
        "- Hip circumference (cm)\n",
        "- Thigh circumference (cm)\n",
        "- Knee circumference (cm)\n",
        "- Ankle circumference (cm)\n",
        "- Biceps (extended) circumference (cm)\n",
        "- Forearm circumference (cm)\n",
        "- Wrist circumference (cm)"
      ],
      "metadata": {
        "id": "8BDFkWIYqOfy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cargar los datos"
      ],
      "metadata": {
        "id": "6jVVtg9N0ZgL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('bodyfat.csv', encoding='latin-1')\n",
        "data.head()\n"
      ],
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        "outputId": "773e1c64-03fa-4691-e6dd-6b6990ed73a4"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Density  BodyFat  Age  Weight  Height  Neck  Chest  Abdomen    Hip  Thigh  \\\n",
              "0   1.0708     12.3   23  154.25   67.75  36.2   93.1     85.2   94.5   59.0   \n",
              "1   1.0853      6.1   22  173.25   72.25  38.5   93.6     83.0   98.7   58.7   \n",
              "2   1.0414     25.3   22  154.00   66.25  34.0   95.8     87.9   99.2   59.6   \n",
              "3   1.0751     10.4   26  184.75   72.25  37.4  101.8     86.4  101.2   60.1   \n",
              "4   1.0340     28.7   24  184.25   71.25  34.4   97.3    100.0  101.9   63.2   \n",
              "\n",
              "   Knee  Ankle  Biceps  Forearm  Wrist  \n",
              "0  37.3   21.9    32.0     27.4   17.1  \n",
              "1  37.3   23.4    30.5     28.9   18.2  \n",
              "2  38.9   24.0    28.8     25.2   16.6  \n",
              "3  37.3   22.8    32.4     29.4   18.2  \n",
              "4  42.2   24.0    32.2     27.7   17.7  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Density</th>\n",
              "      <th>BodyFat</th>\n",
              "      <th>Age</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Height</th>\n",
              "      <th>Neck</th>\n",
              "      <th>Chest</th>\n",
              "      <th>Abdomen</th>\n",
              "      <th>Hip</th>\n",
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              "      <th>Ankle</th>\n",
              "      <th>Biceps</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0708</td>\n",
              "      <td>12.3</td>\n",
              "      <td>23</td>\n",
              "      <td>154.25</td>\n",
              "      <td>67.75</td>\n",
              "      <td>36.2</td>\n",
              "      <td>93.1</td>\n",
              "      <td>85.2</td>\n",
              "      <td>94.5</td>\n",
              "      <td>59.0</td>\n",
              "      <td>37.3</td>\n",
              "      <td>21.9</td>\n",
              "      <td>32.0</td>\n",
              "      <td>27.4</td>\n",
              "      <td>17.1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0853</td>\n",
              "      <td>6.1</td>\n",
              "      <td>22</td>\n",
              "      <td>173.25</td>\n",
              "      <td>72.25</td>\n",
              "      <td>38.5</td>\n",
              "      <td>93.6</td>\n",
              "      <td>83.0</td>\n",
              "      <td>98.7</td>\n",
              "      <td>58.7</td>\n",
              "      <td>37.3</td>\n",
              "      <td>23.4</td>\n",
              "      <td>30.5</td>\n",
              "      <td>28.9</td>\n",
              "      <td>18.2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0414</td>\n",
              "      <td>25.3</td>\n",
              "      <td>22</td>\n",
              "      <td>154.00</td>\n",
              "      <td>66.25</td>\n",
              "      <td>34.0</td>\n",
              "      <td>95.8</td>\n",
              "      <td>87.9</td>\n",
              "      <td>99.2</td>\n",
              "      <td>59.6</td>\n",
              "      <td>38.9</td>\n",
              "      <td>24.0</td>\n",
              "      <td>28.8</td>\n",
              "      <td>25.2</td>\n",
              "      <td>16.6</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0751</td>\n",
              "      <td>10.4</td>\n",
              "      <td>26</td>\n",
              "      <td>184.75</td>\n",
              "      <td>72.25</td>\n",
              "      <td>37.4</td>\n",
              "      <td>101.8</td>\n",
              "      <td>86.4</td>\n",
              "      <td>101.2</td>\n",
              "      <td>60.1</td>\n",
              "      <td>37.3</td>\n",
              "      <td>22.8</td>\n",
              "      <td>32.4</td>\n",
              "      <td>29.4</td>\n",
              "      <td>18.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0340</td>\n",
              "      <td>28.7</td>\n",
              "      <td>24</td>\n",
              "      <td>184.25</td>\n",
              "      <td>71.25</td>\n",
              "      <td>34.4</td>\n",
              "      <td>97.3</td>\n",
              "      <td>100.0</td>\n",
              "      <td>101.9</td>\n",
              "      <td>63.2</td>\n",
              "      <td>42.2</td>\n",
              "      <td>24.0</td>\n",
              "      <td>32.2</td>\n",
              "      <td>27.7</td>\n",
              "      <td>17.7</td>\n",
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          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Imprimimos la forma de nuestra base de datos para realizar la separación de estos.\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g1KVTqwtrITa",
        "outputId": "3676c6d6-08c5-460a-b0ff-495a1df4d8c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(252, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Realizamos la partición de nuestros datos\n",
        "En total tenemos 252 observaciones, por tanto 3/4 partes irán para entrenar nuestro modelo y 1/4 parte para el test."
      ],
      "metadata": {
        "id": "bd-fbocr09Zq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Nuestro objetivo será calcular el porcentaje masa muscular por tanto añadiremos esa columna a nuestras targets\n",
        "\n",
        "# 3/4 de 252\n",
        "train_targets=data.BodyFat[:189]\n",
        "# 1/4 de 252\n",
        "test_targets=data.BodyFat[190:]\n",
        "data=data.drop(['BodyFat'], axis=1)\n",
        "\n",
        "# El resto de variables serán los datos del modelo\n",
        "# 3/4 de 252\n",
        "train_data=data[:189]\n",
        "# 1/4 de 252\n",
        "test_data=data[190:]"
      ],
      "metadata": {
        "id": "uQSf9SCDdlkO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imprimimos la forma de los datos"
      ],
      "metadata": {
        "id": "O17TH2eg4Erz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ihq6r5uOOW6n",
        "outputId": "ff17adf6-a7d7-4481-adf6-bf93091d9ab9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(189, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-bVV-rSOaD0",
        "outputId": "f161141a-a0ea-4bb5-db3b-72b63ceeeed4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(62, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_targets.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgibHgHhOagB",
        "outputId": "752da9ef-1d50-4350-87d6-50261be8e080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(189,)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_targets.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwcoEhr0OaxP",
        "outputId": "6ff4044a-768c-4f79-f85a-9c8258f81555"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(62,)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparamos los datos normalizándolos"
      ],
      "metadata": {
        "id": "rjq3Myrt4ouH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizamos basándonos en las características\n",
        "##############################################\n",
        "# Calculamos la media del primer eje de los datos de entrenamiento\n",
        "mean = train_data.mean(axis=0)\n",
        "print(\"##### Media de las 13 características de los datos de entrenamiento ##### \")\n",
        "print(mean)\n",
        "# A los datos de entrenamiento le restamos la media\n",
        "train_data -= mean\n",
        "\n",
        "# Calculamos la desviación estandar del primer eje de los datos de entrenamiento\n",
        "std = train_data.std(axis=0)\n",
        "print(\"##### Desviación estandar de las 13 características de los datos de entrenamiento ##### \")\n",
        "print(std)\n",
        "# A los datos de entrenamiento los dividimos por la desviación estándar\n",
        "train_data /= std\n",
        "\n",
        "\n",
        "# USAMOS LA MEDIA Y LA DESVIACIÓN ESTANDAR DE LOS DATOS DE \n",
        "# ENTRENAMIENTO PARA NORMALIZAR EL SET DE DATOS DE LOS DATOS DE TEST \n",
        "test_data -= mean\n",
        "test_data /= std\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vm0wC3sVOnKA",
        "outputId": "46a4a1db-195d-4c46-c44b-c3dcee344309"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "##### Media de las 13 características de los datos de entrenamiento ##### \n",
            "Density      1.056924\n",
            "Age         41.888889\n",
            "Weight     178.469841\n",
            "Height      70.240741\n",
            "Neck        37.895767\n",
            "Chest      100.125926\n",
            "Abdomen     91.725397\n",
            "Hip         99.893122\n",
            "Thigh       59.613757\n",
            "Knee        38.515344\n",
            "Ankle       23.143915\n",
            "Biceps      32.289947\n",
            "Forearm     28.694709\n",
            "Wrist       18.173016\n",
            "dtype: float64\n",
            "##### Desviación estandar de las 13 características de los datos de entrenamiento ##### \n",
            "Density     0.018409\n",
            "Age        12.009453\n",
            "Weight     29.818886\n",
            "Height      3.966228\n",
            "Neck        2.492880\n",
            "Chest       8.167412\n",
            "Abdomen    10.598002\n",
            "Hip         7.353296\n",
            "Thigh       5.527727\n",
            "Knee        2.333641\n",
            "Ankle       1.793139\n",
            "Biceps      3.056377\n",
            "Forearm     2.015254\n",
            "Wrist       0.932773\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Construimos el modelo "
      ],
      "metadata": {
        "id": "juRKc9CK5B8n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model():\n",
        "    model = models.Sequential()\n",
        "    model.add(layers.Dense(90, activation='relu',\n",
        "                           input_shape=(train_data.shape[1],)))\n",
        "    model.add(layers.Dense(90, activation='relu'))\n",
        "    model.add(layers.Dense(1))\n",
        "    # MSE: Mean Squared Error\n",
        "    # MAE: Mean Absolute Error\n",
        "    model.compile(optimizer='rmsprop', loss='mse', \n",
        "                  metrics=[tf.keras.metrics.MeanAbsoluteError()]) # metrics = ['mae']\n",
        "    return model"
      ],
      "metadata": {
        "id": "1Ko1B4Q3ZC_d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Realizamos las particiones de los datos de entrenamiento"
      ],
      "metadata": {
        "id": "jy25uZZn5PSn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 3/4 de 189 = 141\n",
        "# 1/4 de 189 = 48\n",
        "partial_train_data = train_data[:141]\n",
        "partial_eval_train_data = train_data[142:]\n",
        "partial_train_targets = train_targets[:141]\n",
        "partial_eval_train_targets = train_targets[142:]"
      ],
      "metadata": {
        "id": "019PQQ2MZmNd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Llamamos a nuestra función de construir modelo\n",
        "mymodel = build_model()"
      ],
      "metadata": {
        "id": "znQpv5fCZOOf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Entrenamos nuestro modelo\n",
        "\n",
        "Lo haremos con 100 épocas y un batch_size = 1"
      ],
      "metadata": {
        "id": "N4zr-wS96D8H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = mymodel.fit(partial_train_data, partial_train_targets,\n",
        "            validation_data=(partial_eval_train_data, partial_eval_train_targets),\n",
        "                        epochs=100, batch_size=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-UAUOTQJZQte",
        "outputId": "8dba324f-7d80-4528-c618-7c7835ec12cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "141/141 [==============================] - 1s 3ms/step - loss: 243.0866 - mean_absolute_error: 13.1632 - val_loss: 84.4576 - val_mean_absolute_error: 7.4264\n",
            "Epoch 2/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 70.2180 - mean_absolute_error: 6.3927 - val_loss: 37.7253 - val_mean_absolute_error: 5.0606\n",
            "Epoch 3/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 33.8475 - mean_absolute_error: 4.1254 - val_loss: 20.7644 - val_mean_absolute_error: 3.6673\n",
            "Epoch 4/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 15.8590 - mean_absolute_error: 2.6642 - val_loss: 14.3638 - val_mean_absolute_error: 3.1115\n",
            "Epoch 5/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 8.3915 - mean_absolute_error: 2.0653 - val_loss: 12.0062 - val_mean_absolute_error: 2.7874\n",
            "Epoch 6/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 6.6549 - mean_absolute_error: 1.8771 - val_loss: 13.0084 - val_mean_absolute_error: 2.6689\n",
            "Epoch 7/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 5.0918 - mean_absolute_error: 1.6694 - val_loss: 11.1154 - val_mean_absolute_error: 2.7658\n",
            "Epoch 8/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 4.2406 - mean_absolute_error: 1.4818 - val_loss: 7.8412 - val_mean_absolute_error: 2.1345\n",
            "Epoch 9/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 3.1241 - mean_absolute_error: 1.2955 - val_loss: 7.5603 - val_mean_absolute_error: 2.1194\n",
            "Epoch 10/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 2.8631 - mean_absolute_error: 1.2343 - val_loss: 7.1453 - val_mean_absolute_error: 1.9321\n",
            "Epoch 11/100\n",
            "141/141 [==============================] - 0s 3ms/step - loss: 3.2132 - mean_absolute_error: 1.2310 - val_loss: 5.8186 - val_mean_absolute_error: 1.8204\n",
            "Epoch 12/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 2.1612 - mean_absolute_error: 1.0569 - val_loss: 5.0278 - val_mean_absolute_error: 1.5786\n",
            "Epoch 13/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 2.0108 - mean_absolute_error: 1.0111 - val_loss: 5.8400 - val_mean_absolute_error: 1.6324\n",
            "Epoch 14/100\n",
            "141/141 [==============================] - 0s 3ms/step - loss: 1.8944 - mean_absolute_error: 0.8962 - val_loss: 4.5048 - val_mean_absolute_error: 1.5208\n",
            "Epoch 15/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.5614 - mean_absolute_error: 0.8566 - val_loss: 5.0878 - val_mean_absolute_error: 1.5440\n",
            "Epoch 16/100\n",
            "141/141 [==============================] - 0s 3ms/step - loss: 1.3542 - mean_absolute_error: 0.8907 - val_loss: 4.1981 - val_mean_absolute_error: 1.4683\n",
            "Epoch 17/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.4668 - mean_absolute_error: 0.8768 - val_loss: 3.5193 - val_mean_absolute_error: 1.2849\n",
            "Epoch 18/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.1886 - mean_absolute_error: 0.7893 - val_loss: 3.6326 - val_mean_absolute_error: 1.5222\n",
            "Epoch 19/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.2358 - mean_absolute_error: 0.8047 - val_loss: 3.5533 - val_mean_absolute_error: 1.3244\n",
            "Epoch 20/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.1567 - mean_absolute_error: 0.7349 - val_loss: 3.4215 - val_mean_absolute_error: 1.3172\n",
            "Epoch 21/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.2675 - mean_absolute_error: 0.7379 - val_loss: 3.2807 - val_mean_absolute_error: 1.4164\n",
            "Epoch 22/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.1294 - mean_absolute_error: 0.6808 - val_loss: 2.6476 - val_mean_absolute_error: 1.2551\n",
            "Epoch 23/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.0316 - mean_absolute_error: 0.6592 - val_loss: 2.6333 - val_mean_absolute_error: 1.1644\n",
            "Epoch 24/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.1263 - mean_absolute_error: 0.6857 - val_loss: 3.1023 - val_mean_absolute_error: 1.2931\n",
            "Epoch 25/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.9417 - mean_absolute_error: 0.6623 - val_loss: 3.3456 - val_mean_absolute_error: 1.1869\n",
            "Epoch 26/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.0060 - mean_absolute_error: 0.6817 - val_loss: 2.5011 - val_mean_absolute_error: 1.2510\n",
            "Epoch 27/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.9209 - mean_absolute_error: 0.6437 - val_loss: 2.6712 - val_mean_absolute_error: 1.1837\n",
            "Epoch 28/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.0250 - mean_absolute_error: 0.6250 - val_loss: 3.3968 - val_mean_absolute_error: 1.3086\n",
            "Epoch 29/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.7875 - mean_absolute_error: 0.6271 - val_loss: 3.1913 - val_mean_absolute_error: 1.3859\n",
            "Epoch 30/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.8772 - mean_absolute_error: 0.5899 - val_loss: 2.3211 - val_mean_absolute_error: 1.0788\n",
            "Epoch 31/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.8765 - mean_absolute_error: 0.6159 - val_loss: 2.5538 - val_mean_absolute_error: 1.0662\n",
            "Epoch 32/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.8719 - mean_absolute_error: 0.6339 - val_loss: 2.0036 - val_mean_absolute_error: 0.9865\n",
            "Epoch 33/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.7528 - mean_absolute_error: 0.5705 - val_loss: 3.8298 - val_mean_absolute_error: 1.4868\n",
            "Epoch 34/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.8843 - mean_absolute_error: 0.6052 - val_loss: 1.9006 - val_mean_absolute_error: 0.9190\n",
            "Epoch 35/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.7350 - mean_absolute_error: 0.6188 - val_loss: 2.0441 - val_mean_absolute_error: 1.0465\n",
            "Epoch 36/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.9271 - mean_absolute_error: 0.6160 - val_loss: 2.1908 - val_mean_absolute_error: 1.0193\n",
            "Epoch 37/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.8183 - mean_absolute_error: 0.6042 - val_loss: 1.8626 - val_mean_absolute_error: 0.9912\n",
            "Epoch 38/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6461 - mean_absolute_error: 0.5251 - val_loss: 2.0505 - val_mean_absolute_error: 1.0735\n",
            "Epoch 39/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.7516 - mean_absolute_error: 0.6090 - val_loss: 2.0707 - val_mean_absolute_error: 0.9681\n",
            "Epoch 40/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.7656 - mean_absolute_error: 0.5631 - val_loss: 2.1843 - val_mean_absolute_error: 1.0964\n",
            "Epoch 41/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.8728 - mean_absolute_error: 0.5622 - val_loss: 2.2958 - val_mean_absolute_error: 1.1336\n",
            "Epoch 42/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6435 - mean_absolute_error: 0.5435 - val_loss: 1.9861 - val_mean_absolute_error: 1.0464\n",
            "Epoch 43/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5978 - mean_absolute_error: 0.5566 - val_loss: 2.0446 - val_mean_absolute_error: 1.1455\n",
            "Epoch 44/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.7358 - mean_absolute_error: 0.5226 - val_loss: 2.7808 - val_mean_absolute_error: 1.2231\n",
            "Epoch 45/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.7650 - mean_absolute_error: 0.5541 - val_loss: 1.8093 - val_mean_absolute_error: 0.9813\n",
            "Epoch 46/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.7475 - mean_absolute_error: 0.5774 - val_loss: 2.4027 - val_mean_absolute_error: 1.0539\n",
            "Epoch 47/100\n",
            "141/141 [==============================] - 0s 3ms/step - loss: 0.7427 - mean_absolute_error: 0.5723 - val_loss: 2.2627 - val_mean_absolute_error: 1.2393\n",
            "Epoch 48/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.7254 - mean_absolute_error: 0.5756 - val_loss: 2.5151 - val_mean_absolute_error: 1.2087\n",
            "Epoch 49/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.8515 - mean_absolute_error: 0.5691 - val_loss: 2.3764 - val_mean_absolute_error: 1.2233\n",
            "Epoch 50/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6509 - mean_absolute_error: 0.4862 - val_loss: 3.4243 - val_mean_absolute_error: 1.4669\n",
            "Epoch 51/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6682 - mean_absolute_error: 0.5286 - val_loss: 2.7015 - val_mean_absolute_error: 1.2806\n",
            "Epoch 52/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.8167 - mean_absolute_error: 0.5364 - val_loss: 1.9186 - val_mean_absolute_error: 0.9323\n",
            "Epoch 53/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5976 - mean_absolute_error: 0.5603 - val_loss: 2.2544 - val_mean_absolute_error: 1.2080\n",
            "Epoch 54/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6438 - mean_absolute_error: 0.5366 - val_loss: 1.6531 - val_mean_absolute_error: 0.8990\n",
            "Epoch 55/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5487 - mean_absolute_error: 0.4814 - val_loss: 2.2856 - val_mean_absolute_error: 0.9854\n",
            "Epoch 56/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5611 - mean_absolute_error: 0.5207 - val_loss: 2.7914 - val_mean_absolute_error: 1.3079\n",
            "Epoch 57/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5974 - mean_absolute_error: 0.5384 - val_loss: 2.1715 - val_mean_absolute_error: 1.0410\n",
            "Epoch 58/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5277 - mean_absolute_error: 0.5419 - val_loss: 2.4423 - val_mean_absolute_error: 1.2180\n",
            "Epoch 59/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.7452 - mean_absolute_error: 0.5598 - val_loss: 2.7979 - val_mean_absolute_error: 1.2151\n",
            "Epoch 60/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6236 - mean_absolute_error: 0.5039 - val_loss: 1.9638 - val_mean_absolute_error: 0.9970\n",
            "Epoch 61/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6125 - mean_absolute_error: 0.5659 - val_loss: 2.0458 - val_mean_absolute_error: 0.9950\n",
            "Epoch 62/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5185 - mean_absolute_error: 0.4709 - val_loss: 2.9746 - val_mean_absolute_error: 1.2820\n",
            "Epoch 63/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6261 - mean_absolute_error: 0.5118 - val_loss: 2.6949 - val_mean_absolute_error: 1.2871\n",
            "Epoch 64/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6831 - mean_absolute_error: 0.5285 - val_loss: 2.0782 - val_mean_absolute_error: 1.0097\n",
            "Epoch 65/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5720 - mean_absolute_error: 0.5106 - val_loss: 2.5226 - val_mean_absolute_error: 1.0665\n",
            "Epoch 66/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6139 - mean_absolute_error: 0.5335 - val_loss: 1.8246 - val_mean_absolute_error: 1.0070\n",
            "Epoch 67/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5752 - mean_absolute_error: 0.4992 - val_loss: 2.1932 - val_mean_absolute_error: 1.0708\n",
            "Epoch 68/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5403 - mean_absolute_error: 0.5244 - val_loss: 2.1085 - val_mean_absolute_error: 1.0782\n",
            "Epoch 69/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5855 - mean_absolute_error: 0.5619 - val_loss: 2.1749 - val_mean_absolute_error: 1.1502\n",
            "Epoch 70/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5087 - mean_absolute_error: 0.5058 - val_loss: 2.1703 - val_mean_absolute_error: 1.1700\n",
            "Epoch 71/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5828 - mean_absolute_error: 0.5459 - val_loss: 2.6447 - val_mean_absolute_error: 1.2542\n",
            "Epoch 72/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5746 - mean_absolute_error: 0.5058 - val_loss: 2.2835 - val_mean_absolute_error: 1.1042\n",
            "Epoch 73/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.4767 - mean_absolute_error: 0.4682 - val_loss: 2.6364 - val_mean_absolute_error: 1.1884\n",
            "Epoch 74/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5806 - mean_absolute_error: 0.5168 - val_loss: 1.9212 - val_mean_absolute_error: 0.9779\n",
            "Epoch 75/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.7492 - mean_absolute_error: 0.5280 - val_loss: 1.9694 - val_mean_absolute_error: 1.0473\n",
            "Epoch 76/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.9003 - mean_absolute_error: 0.4916 - val_loss: 1.6754 - val_mean_absolute_error: 0.9196\n",
            "Epoch 77/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.4307 - mean_absolute_error: 0.4473 - val_loss: 2.1270 - val_mean_absolute_error: 1.0869\n",
            "Epoch 78/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6620 - mean_absolute_error: 0.5095 - val_loss: 2.1272 - val_mean_absolute_error: 1.0176\n",
            "Epoch 79/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5131 - mean_absolute_error: 0.4852 - val_loss: 2.1376 - val_mean_absolute_error: 1.1806\n",
            "Epoch 80/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6098 - mean_absolute_error: 0.4838 - val_loss: 1.7432 - val_mean_absolute_error: 0.9786\n",
            "Epoch 81/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.4174 - mean_absolute_error: 0.4687 - val_loss: 3.4390 - val_mean_absolute_error: 1.4368\n",
            "Epoch 82/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5037 - mean_absolute_error: 0.5168 - val_loss: 1.6842 - val_mean_absolute_error: 0.9502\n",
            "Epoch 83/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5359 - mean_absolute_error: 0.4900 - val_loss: 2.2280 - val_mean_absolute_error: 1.0163\n",
            "Epoch 84/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.4988 - mean_absolute_error: 0.4673 - val_loss: 2.0936 - val_mean_absolute_error: 1.1230\n",
            "Epoch 85/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5012 - mean_absolute_error: 0.4815 - val_loss: 2.3851 - val_mean_absolute_error: 1.1985\n",
            "Epoch 86/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6380 - mean_absolute_error: 0.4992 - val_loss: 1.9853 - val_mean_absolute_error: 1.1657\n",
            "Epoch 87/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5100 - mean_absolute_error: 0.4730 - val_loss: 1.9061 - val_mean_absolute_error: 1.0023\n",
            "Epoch 88/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.4474 - mean_absolute_error: 0.5038 - val_loss: 1.9924 - val_mean_absolute_error: 1.0037\n",
            "Epoch 89/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.4837 - mean_absolute_error: 0.4925 - val_loss: 2.0992 - val_mean_absolute_error: 1.0749\n",
            "Epoch 90/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.4428 - mean_absolute_error: 0.4693 - val_loss: 1.9697 - val_mean_absolute_error: 1.0412\n",
            "Epoch 91/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6657 - mean_absolute_error: 0.5194 - val_loss: 1.9246 - val_mean_absolute_error: 0.9896\n",
            "Epoch 92/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.4340 - mean_absolute_error: 0.4827 - val_loss: 2.0226 - val_mean_absolute_error: 1.0278\n",
            "Epoch 93/100\n",
            "141/141 [==============================] - 0s 3ms/step - loss: 0.4134 - mean_absolute_error: 0.4468 - val_loss: 1.7504 - val_mean_absolute_error: 1.0389\n",
            "Epoch 94/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.4418 - mean_absolute_error: 0.4871 - val_loss: 2.0449 - val_mean_absolute_error: 1.0000\n",
            "Epoch 95/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.3909 - mean_absolute_error: 0.4578 - val_loss: 2.7483 - val_mean_absolute_error: 1.3099\n",
            "Epoch 96/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.5914 - mean_absolute_error: 0.4818 - val_loss: 1.6698 - val_mean_absolute_error: 0.9271\n",
            "Epoch 97/100\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 0.6208 - mean_absolute_error: 0.5278 - val_loss: 1.7448 - val_mean_absolute_error: 0.9103\n",
            "Epoch 98/100\n",
            "141/141 [==============================] - 0s 3ms/step - loss: 0.4216 - mean_absolute_error: 0.4341 - val_loss: 1.6617 - val_mean_absolute_error: 0.9982\n",
            "Epoch 99/100\n",
            "141/141 [==============================] - 1s 7ms/step - loss: 0.4008 - mean_absolute_error: 0.4376 - val_loss: 1.6649 - val_mean_absolute_error: 0.9664\n",
            "Epoch 100/100\n",
            "141/141 [==============================] - 0s 3ms/step - loss: 0.4038 - mean_absolute_error: 0.4389 - val_loss: 1.7629 - val_mean_absolute_error: 0.9608\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mae_history = history.history['val_mean_absolute_error']\n",
        "accuracy= np.mean(mae_history)\n",
        "accuracy"
      ],
      "metadata": {
        "id": "i_mRL-sMd8_C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3dddcbb-da6d-4d4b-e0e1-bfd1f589bea1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.3768435555696488"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aún estamos desviados 1.37% de la masa corporal. "
      ],
      "metadata": {
        "id": "23UbDwJY66N5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mse_history = history.history['val_loss']"
      ],
      "metadata": {
        "id": "TghrxcY-ek0B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confrontamos en una gráfica de épocas en el eje de abcisas contra pérdida (MSE) en el eje de ordenadas\n",
        "\n",
        "Podemos ver que a partir de la época 20 se genera sobreentrenamiento debido a que nuestro modelo deja de mejorar ya que como podemos ver la gráfica se estabiliza y dejamos de mejorar a partir de ese momento."
      ],
      "metadata": {
        "id": "DllLRVSn7Ere"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1, len(mse_history) + 1), mse_history)\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Validación MSE')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "gcfWuM20hopF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "4baf3aaa-b67d-4472-db45-76801202e0d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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kgr+6TD1+EYm2yAV/RUkSM6hXj19EIipywR+PGRUlSeq1xy8iERW54IdgSKdOxiIiURXR4E9qOKeIRFZEg18zdIpIdOUs+M1sipk9YWarzGylmV0fLq82s8fMbF34uypXNRxIVVlKwzlFJLJyucffRTCz5/HAmcCnzex44EZgobvPBhaG14fVuFFF7GjuoKNL8/WISPTkLPjdfZu7vxBebiY4a9ck4DLgtvButxGc0WtYnXBUBV1ZZ23tnuF+aRGRvBuWHr+ZTQdOBRYB4919W3hTLTD+AI+51swWm9niurq6Ia1n7qQKAJZt0bz8IhI9OQ9+MysH7gE+6+5NvW9zdwe8v8e5+83uPt/d59fU1AxpTVOqS6goSbJiS+OQPq+IyEiQ0+A3syRB6N/u7veGi7eb2cTw9onAjlzWcIC6mDupgmWbFfwiEj25HNVjwK3Aanf/Tq+bHgCuDi9fDdyfqxoGMndyBWu3N9Oe1gFeEYmWXO7xnwP8NXCRmb0Y/lwKfAN4q5mtA94SXh92cydVkM44L9c25+PlRUTyZqCTrb8h7v40cKCT2i7I1esOVvcB3uVbGjl5SmWeqxERGT6R/OYuwOSqEipLkyxXn19EIiaywd99gHe5RvaISMRENvghaPfoAK+IRE2kg/+kycE3eNfoAK+IREikg//E7gO8m/UNXhGJjkgH/6TKEqrLUurzi0ikRDr4zYwT9Q1eEYmYSAc/wCmTK1i3Yw9bGtryXYqIyLCIfPBfecZU4jHj24++nO9SRESGReSDf1JlCR87ezq/W7qFVVubDv4AEZERLvLBD/CpC2cxujjJNx5ZA4C789OnN/C27z7JK3U6WYuIFBYFP1BRmuS6i2bx1No6/rCyluvuWMq/PbiK9Tv28Pm7XqIrk813iSIiQ0bBH/rrs6YxuaqET/xyCQ8t38YXL57Dd688haWbGvjJU6/muzwRkSGj4A8VJeJ85V0nMGtcOb+85k186sJZvPvko3jH3Il8749rWb1N/X8RKQwWnP3wyDZ//nxfvHhxXl57d0snb/vuk9SMKub+T59DKqH3ShEZGcxsibvP33e5UuwgqstSfO3yE1m9rYkHl23NdzkiIm+Ygn8Q3n7CBKaPKeXO51/PdykiIm+Ygn8QzIwrT5/Kcxt2a3iniIx4Cv5Beu9pk0jEjN9or19ERjgF/yCNG1XMguPGcc+SzXR2aVy/iIxcCv5DcNUZU9nV0skfV2/PdykiIodNwX8Izp9dw6TKEu54blO+SxEROWwK/kMQjxnvmz+Zp9fv5NanN7Bsc4PaPiIy4iTyXcBI84EzpvLAi1v59wdXAVCUiHHurLFcfOIE3nLceKrKUnmuUERkYAr+QzR+dDELb7iAbY3tLN3UwPMbd/PYqu0sXLODeMy48eJj+dvzZ+a7TBGRA1LwHwYz46jKEo6qLOEdJ03kK+86nhVbmvjB4+v4+kOriceMj587I99lioj0Sz3+IWBmzJ1cwY8+NI+LT5jAvz24il89+1q+yxIR6ZeCfwgl4zFu+sCpLDh2HP/vvhX8YOG6Pgd/tza0cd0dS/nWH15mJEyOJyKFSa2eIZZKxPjRh+bx+bte4tuPreX+l7byb+8+gTW1zXz70Zdp78qSyTrpbJYbLz4WM8t3ySISMQr+HChOxvnhB+fxnnnb+Zf7V/LBWxYBcMExNXzt8hP5yVOv8JMnX6U4EeeTFx7NfUu3cOvTGwD4z/fMZf706nyWLyIFTvPx51hbZ4af/d8GplaX8o65EzEzslnnS/cs464lmxldnKCpvYsTjhpNU3uaLfVtfOKCo/ncW47R3P8i8oYcaD5+7fHnWEkqzqcunNVnWSxmfOO9J1GUjFHb2M7Hz53BWTPH0NKZ4WsPruLHf3qFhau388W3H8uC48YVRDvI3QtiPUQKgfb4j0ALV2/na79fzYadLcybWsmHz5xGa2eGXXs6aensorwowajiBJWlScaNKmb86CLGjy5mVHEy36X366Hl2/jq/67k1qtP58RJFfkuZ0h0ZbIk4vpElg+ZbJBZ8Zh2JA7mQHv8Cv4jVDqT5a7Fm7lp4Tpqm9r3Li9OxmhP9z9NRFkqzoSKYiZXlfL++VO45MQJxPr5nyObdZZsqufFTQ2s2tbEy7XNnDGjms+/fQ7lRft/CHxuw27+46HVvFq3h6PHlTOrppyzjh7D5adM6vf5e9vS0MbF33uK5vYujp0wigf+/twR38J6ePk2vnD3MuZNq+KfLj2OORNG5bukyGhPZ7jq5mdJxIxf/+2ZI/5vKdcU/CNUezrDxl0tVJWmqC5LkYzHSGey7Gnvor61kx3NHWxvaqe2sZ3a8PfKrU1s2t3K7HHlfOrNR3N0TTkxM9KZLI+t2s79L25lS0MbABNGFzN9bCmLNuzmqIoS/vM9czlv9ljqW9Ns2LmHW/68gYdX1DKxopgL54xj484W1u3Yw849HZw7ayzfvOIkJlWW9Ft7Jut86JZnWb65kc+/fQ5f/d9VXHfRLG5425zh/Cfc64VN9aza2sRVp085rL31bNb53sJ13LRwHcdOGMXWhjb2dHRx5elTueFtxzC2vOiQnq89neGRFbU0t6f5q3mT+33TrWvu4On1dWxrbOeDZ0ylsnT/KUHcnd88/zrfenQt40YVccrUSk6dUsnbTphARUluPgW2pzPUNXcwuaqkTwuvsytLfWsn40cX5+R1//F3y/n1omCSxGvPn8k/XnpcTl5nqDz76i5iZpwxIz8DNhT8EZLJOr9fvo0fPr6Otdv7njEsHjPOmz2Wvzp1EufOGsuYMKyWvFbPF+9+iVfqWigvSrCnowuA0lScT15wNH9z3kxKUnEgCJpfP7eJr/9+NXEz3n/6FLbUt7G6tonWzgxXnDaZD585jQdf2sp/PryG/7riJN4/fwo3/PYl7ntxC/d96hzmTq7Yr+bdLZ2MKUsd9FNEdw1NbV3sbOlg155Odu3poKwowbxpVfsF6NaGNr75yBrufzE4Z/IZM6r5wQdOZfzoYtydP71cx8I126kuTTG+ImiZrd/ezPItjbxS10JFSZIJFcU0taVZtGE37zttMl/7qxNp7cjw/YXr+NWzr1GaivOFi4/lg2dMJR4LDuCv3dFMdVmKcaP6huC67c388tnX+N3SLTS3B//OFSVJPnr2dN5+wgTW1DaxbHMjz23YzaptTXsfN7a8iH+/7AQumTtx77LG1jRf/t0yHlpey2nTqihJxnnp9QaaO7oYVZTgI2dP4+PnzKC+tZMHXtrGH1dt59gJo/jcW49hSnXp3udp7eyipSPD6JIERYlgO2ezTms6s/e7KAas3d7MvS9s4aHl22ju6OKoimIumFPDzLHlLNqwm2de2UlLZ4aLT5jAly45lhljyw66LQfrvqVb+OxvXuTvLjialo4ufvnsa/zsY6fz5jnjANjT0cWW+jbGjSqisjRJJuus3tbMC5vqqWvu4LzZYzltWtWwtOhWb2viGw+v4cm1dZjBP15yHH9z3oxhP86l4I+gbNZ5fuNumtq79vZF502r3C+IurWnM9z69AbqmjuYUl3K1OpSTp1aecA92dd3t/LFu5fx7IZdzBhTxnETR9OZybIwPF+BmfHW48bz4w/Pw8xobEvztu8+SXlRggXHjWdrQxu1je1sCz+tZLJOVWmSM2eO4YwZ1XR2Zdmws4WNu1po6cjQlXW6Mlma2tPsbukkndn/bzdmcMJRFUwbU0p7OkNLR4alr9fjHuwhTqkq5SsPrKQ0Feea82Zw/9KtvLy9mbJUnLZ0hvCfiZjB7HGjmDW+nD3tXdQ2ttPUnuba82fy0bOn9/kfeP2OZv75vpU88+ouTppcwaTKEp55dRcNrWlS8RjvmTeJa8+fSUdXlh88vo6HV9SSjMe45MQJXHn6FEpTCX70xHoeW9VznofSVJyTJldw3uwazp9dA8CN9y5j5dYmzps9lqrSFE3taVZtbWJ3Syc3vG0Onzh/JrHwTWfZlkZufuoVHl5RS9yMrqwTMzh1ahUrtjTiDh85axrjRhfxp5freH7j7r3/nsXJGHEzWjoz/W730lScS+dOZO6kCp55ZRd/Wb+T5o4uplSXcP7sGipKkvz8/zbS2ZXlitMmM7a8iLZ0hkzWmTNhFPOnVTGzppzFG3dz/0tbeXTldprb0wB4+J+sO1l3Zo8bxVuOH8fcSRV87jcvMXdSBb/+2zfRlXUu/9FfqGvu4IcfnMfDK7Zxz5LNe2suSsQwY29b1Azcoao0ybmza6gpL2J0SYKSZJw9HV00tqVpaA3+rna1dNLUlmZ0SZJxo4oYN6qIsqIExck4RYkYrZ1d1LemaQg/2Zw8pZJTplTS3J5m8cZ6ntuwm8df3sHo4iSffvPRvPh6Aw8tr+X98yfztcvnkkoEn9ozWQ/r7P/NoD2dob61k5ryosN+s1LwS86kM1mSvf4wN9e38qtnN7FyayM3XXVqnxlLn3h5B9f8/HmS8RgTK4qZUFHMURUlTKwsprqsiFVbm3jmlZ1sbQyOa4wpSzFtTCkVJUnisRiJmDG6JMGY8iLGlhcxpizF2PIiqstS7Grp4PkNu1m0YTc7mjsoScYpK4ozc2w51y2YxeSqYA93/Y5mPnX7C6zdvodjJ4ziExfM5J0nHYUBdXs6aGhNM31M2d5POIPh7jzw0la+8fAaDDh71ljeNKOaZZsb+e3i1+nMZHGH8qIEHz17Oh8/dwbV+8zkuqa2iVVbmzjhqApmjSvf7+BlOpPllj9v4BfPbKQoEWN0SZKx5UVcv2A2J0+p7Leu9Tv2cOdzm5hcVcKlJ01k3KhitjW28Z1H13LPC5vJOhw7YRQXzKlhcmUJjW1pGtvSZB3KihKUF8VJ9dq2Y8qLWHDcOEpTPZ+q0pksu1v6tnd2NLfzvT+u47fPv07WnZJk8G/ZHczJuJHOBMsXHDeOSVU97cKYGTGDrMPSTfU8v7GeTNYZU5bioevP2/s663c0864f/IW2dIZUIsa7TjqK848Zy849nWxvaqcr45wytZLTplVRUZLkqbV1PLqyluc31tPUlqY5/FQbMxhdkqSiJEl1WYoxZSlGlyRpakuzvamDHc3ttHZkaO/KkM44xckY1aXBfbbUt+19nm7Tx5Ry8YkT+eQFR1NRmgxahH9cy02Pr2dUcYJ0Jrv3DSkVD7Zjaa+/ta5MlvrWNG3p4N/qic9feNifnBT8csRoT2cG3NNxd7Y1tlNWlMhpj/q1Xa0cM758SD9+d///1Ps5d+7p4PZnN5GIGx9+0zQqSo+M0Veb61uJx4yJFf0foxkKmfCThpnh7mzY2cKS1+pZta2JU6ZU8tbjx/d5E+lPQ2snT63byTHjyzl2wug+tz25to61tc28Z96kvW3LQ6mtLZ2hNBkfVHux+zG935CzWefVnS0s29xAWVGC06ZVHfAT8iMranly7Y5wVF6SeMxobg8+bbR2dmEE/07xmFFZkqSqLEVVaYpL507o99jOYBxRwW9mFwPfB+LALe7+jYHur+AXETl0Bwr+YR8LZWZx4EfAJcDxwAfM7PjhrkNEJKryMQj2DGC9u7/q7p3AncBleahDRCSS8hH8k4DXe13fHC7rw8yuNbPFZra4rq5u2IoTESl0R+zX3tz9Znef7+7za2pq8l2OiEjByEfwbwGm9Lo+OVwmIiLDIB/B/zww28xmmFkKuAp4IA91iIhE0rBPy+zuXWb298AfCIZz/tTdVw53HSIiUZWX+fjd/SHgoXy8tohI1I2Ib+6aWR3w2iE8ZCywM0flHKmiuM4QzfWO4jpDNNf7ja7zNHffb3TMiAj+Q2Vmi/v7tlohi+I6QzTXO4rrDNFc71yt8xE7nFNERHJDwS8iEjGFGvw357uAPIjiOkM01zuK6wzRXO+crHNB9vhFROTACnWPX0REDkDBLyISMQUV/GZ2sZm9bGbrzezGfNeTK2Y2xcyeMLNVZrbSzK4Pl1eb2WNmti78XZXvWoeamcXNbKmZPRhen2Fmi8Jt/ptwGpCCYmaVZna3ma0xs9Vmdlahb2sz+1z4t73CzO4ws+JC3NZm9lMz22FmK3ot63fbWuCmcP2Xmdm8w33dggn+iJ3gpQu4wd2PB84EPh2u643AQnefDSwMrzv59PkAAARtSURBVBea64HVva5/E/iuu88C6oFr8lJVbn0feMTdjwVOJlj/gt3WZjYJ+Aww391PJJja5SoKc1v/HLh4n2UH2raXALPDn2uBHx/uixZM8BOhE7y4+zZ3fyG83EwQBJMI1ve28G63AZfnp8LcMLPJwDuAW8LrBlwE3B3epRDXuQI4H7gVwN073b2BAt/WBNPJlJhZAigFtlGA29rdnwJ277P4QNv2MuAXHngWqDSziYfzuoUU/IM6wUuhMbPpwKnAImC8u28Lb6oFxueprFz5HvBFIBteHwM0uHtXeL0Qt/kMoA74WdjiusXMyijgbe3uW4BvAZsIAr8RWELhb+tuB9q2Q5ZxhRT8kWNm5cA9wGfdvan3bR6M0y2Ysbpm9k5gh7svyXctwywBzAN+7O6nAi3s09YpwG1dRbB3OwM4Cihj/3ZIJORq2xZS8EfqBC9mliQI/dvd/d5w8fbuj37h7x35qi8HzgHebWYbCdp4FxH0vivDdgAU5jbfDGx290Xh9bsJ3ggKeVu/Bdjg7nXungbuJdj+hb6tux1o2w5ZxhVS8EfmBC9hb/tWYLW7f6fXTQ8AV4eXrwbuH+7acsXdv+zuk919OsG2fdzdPwQ8AVwR3q2g1hnA3WuB181sTrhoAbCKAt7WBC2eM82sNPxb717ngt7WvRxo2z4AfCQc3XMm0NirJXRo3L1gfoBLgbXAK8A/5bueHK7nuQQf/5YBL4Y/lxL0vBcC64A/AtX5rjVH638h8GB4eSbwHLAeuAsoynd9OVjfU4DF4fa+D6gq9G0NfBVYA6wAfgkUFeK2Bu4gOI6RJvh0d82Bti1gBCMXXwGWE4x6OqzX1ZQNIiIRU0itHhERGQQFv4hIxCj4RUQiRsEvIhIxCn4RkYhR8EtkmVnGzF7s9TNkE52Z2fTeMy6KHEkSB7+LSMFqc/dT8l2EyHDTHr/IPsxso5n9l5ktN7PnzGxWuHy6mT0ezoW+0MymhsvHm9nvzOyl8Ofs8KniZvY/4bzyj5pZSXj/z4TnUlhmZnfmaTUlwhT8EmUl+7R6rux1W6O7zwV+SDArKMAPgNvc/STgduCmcPlNwJPufjLBPDorw+WzgR+5+wlAA/DecPmNwKnh8/xdrlZO5ED0zV2JLDPb4+7l/SzfCFzk7q+Gk+HVuvsYM9sJTHT3dLh8m7uPNbM6YLK7d/R6junAYx6cTAMz+xKQdPevmdkjwB6C6Rfuc/c9OV5VkT60xy/SPz/A5UPR0etyhp5jau8gmHNlHvB8rxknRYaFgl+kf1f2+v1MePn/CGYGBfgQ8Ofw8kLgk7D3nMAVB3pSM4sBU9z9CeBLQAWw36cOkVzSnoZEWYmZvdjr+iPu3j2ks8rMlhHstX8gXHYdwZmwvkBwVqyPhcuvB242s2sI9uw/STDjYn/iwK/CNwcDbvLgVIoiw0Y9fpF9hD3++e6+M9+1iOSCWj0iIhGjPX4RkYjRHr+ISMQo+EVEIkbBLyISMQp+EZGIUfCLiETM/weXdsTPdbBFlwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Esta gráfica nos sirve para visualizar como se ajustan los datos de validación con los de entreno, \n",
        "como están bastante juntos podemos asegurar que está funcionando correctamente"
      ],
      "metadata": {
        "id": "fg865tXpElRh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Pérdida del entreno')\n",
        "plt.plot(epochs, val_loss, 'b', label='Pérdida de la validación')\n",
        "plt.title('Perdidas del entreno y la validación')\n",
        "plt.xlabel('Épocas')\n",
        "plt.ylabel('Pérdida')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "DNuqDK1Zhq1X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "18da0404-b330-430d-d9af-250681c386f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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eemq8feOll15i/vz5HH744fH92H///ZPu59y5c1m0aBFHHnkkANu2baN///7x+UOHDgWgT58+8e0lMmzYMLKysigqKuLNN99k2LBh8Xnff/99fPjMM8+kVq1adOnShVWrgtuyXn/9dc455xyysrJo2bIlRx11FO+8884un0lVkXFJQaS6adcuqDJKNH1fxNoUotydWbNmceihh+4y/a233qJBgwb7tsGI0aNH89RTT5Gfn8+UKVOYPXt2uZd1d0aNGsUf/vCHcpc//vjjefTRRxPOr1u3LhA0vJfWXhDb/x9++IGcnJzdPruS64ttu7rJqDYFkeqoMtvCTjzxRP785z/Hv8zee++9hOV+/OMf88gjjwDBr/4FCxYkLPPUU0+xZcsWNm7cyN/+9rf4vI0bN9KqVSu2b9/O9CSNI9FtPP/886xbtw6AY489lscff5zVq1cD8O2337I0UdYMHXHEEbzxxhssXrwYgE2bNvHZZ5+V+jk0atSIjRs3JpzXuHFjOnTowP/93/8BwRf/Bx98UOr6Bg4cyMyZM9m5cyeFhYXMmTOHvn2rZn+fSgoiVdzIkTBpEuTlgVnwPmlSaqpBr7/+erZv306PHj3o2rUr119/fcJyY8eOpaioiM6dO3PDDTfQp0+f3cr07t2bESNGkJ+fz8knnxyv7gG46aab6NevH0ceeSSHHXZYwm2MHz+eOXPm0LVrV5544ol4X2ddunTh97//PSeccAI9evTg+OOPZ+XKlUn3qUWLFkyZMoVzzjmHHj160L9//3jDcDKnn346Tz75ZLyhuaTp06czefJk8vPz6dq1a7xBPpkhQ4bQo0cP8vPzGTRoELfccgsHHHBAqcukS7V+RnNBQYHrITtSHX388cd07tw53WFIBkj0t2Zm8929IFF5nSmIiEickoKIiMQpKYiISJySgoiIxCkpiIhInJKCiCT017/+NX5vgFQNL7zwQtKb5iqKkoJIhqqJXWcniykVRo8eHe/w7qKLLmLRokW7lZkyZQqXXnrpXq2/5Of48ssv88ILL5Cfn793AZeTurkQyVDRbi5GjhzJvffey5VXXgkEXWcns2PHDmrXTv7V8dxzSZ+jVWPdf//9Fb7Okp/joEGDGDRoUIVvpySdKYhIje86e+zYsRQUFNC1a1fGjx+/2/xPPvlkl24nlixZQvfu3YHgrOnwww+nW7dujBkzJmF/RtGzkgcffJBDDjmEvn378sYbb8TL/O1vf6Nfv3706tWL4447Lt5ZXlFREeeffz7du3enR48ezJo1a7fP8fbbb6dbt25069aNO++8Mx5j586dufjii+natSsnnHDCLsdhb+lMQSTNrrgCKrqauGdPCL87yrRjxw6ef/55TjrpJCZOnMigQYN44IEHWL9+PX379uW4444D4N1332XBggU0bdqU22+/nezsbD7++GMWLFhA7969d1vv/PnzmTFjBu+//z47duygd+/e8e4whg4dysUXXwzAuHHjmDx5Mpdddtkuy994440MGDCAG264gb///e9MnjwZCO7QnTlzJm+88QZ16tThF7/4BdOnT+e8885Luo8TJ06kadOm7Ny5k2OPPZYFCxbs0rPrYYcdxrZt2/jyyy/p0KEDM2fOZMSIEQBceuml8TOnc889l2effZbTTz894XZWrlzJ+PHjmT9/Pk2aNOGYY46hV69eAAwYMIC5c+diZtx///3ccsst3Hbbbdx00000adKEDz/8EGC3dpz58+fz4IMP8tZbb+Hu9OvXj6OOOorc3Fw+//xzHn30Ue677z6GDx/OrFmz+NnPfpb0cygPJQWRDJUpXWcDPPbYY0yaNIkdO3awcuVKFi1atFvMw4cPZ+bMmVx99dXMnDkz/iCcV155hVtuuYXNmzfz7bff0rVr16RJ4a233uLoo4+mRYsWAIwYMSLe+d6KFSsYMWIEK1euZNu2bXTo0AGAf/3rX8yYMSO+jpLtOK+//jpDhgyJ99I6dOhQXnvtNQYPHkyHDh3ix7BPnz4sWbKk1M+hPJQURNKsvL/oK1qmdJ395Zdfcuutt/LOO++Qm5vL6NGj2bp1627lRowYwbBhwxg6dChmRqdOndi6dSu/+MUvmDdvHm3btmXChAkJly2Pyy67jCuvvJLBgwcze/ZsJkyYsFfriYp2052VlVUh1UdqUxCRuJrYdfZ3331HgwYNaNKkCatWreL5559PWK5jx45kZWVx0003xauOYgmgefPmFBUVxa82SqZfv368+uqrrF27lu3bt8e71wbYsGEDrVu3BmDq1Knx6ccffzx33313fLxk9dHAgQN56qmn2Lx5M5s2beLJJ59k4MCBpcaxL5QURCSuJnadnZ+fT69evTjssMP46U9/Gn8CWyIjRoxg2rRpDB8+HICcnBwuvvhiunXrxoknnrjLPiTSqlUrJkyYQP/+/TnyyCN36Z10woQJDBs2jD59+tC8efP49HHjxrFu3Tq6detGfn4+r7zyyi7r7N27N6NHj6Zv377069ePiy66KN5OkQrqOlskDdR1tlQWdZ0tIiJ7TUlBRETilBRE0qQ6V91K9bA3f2NKCiJpUK9ePdauXavEICnj7qxdu5Z69ert0XK6T0EkDdq0acOKFSsoLCxMdyhSg9WrV482bdrs0TJKCiJpUKdOnfgdrSJViaqPREQkTklBRETiUpYUzKytmb1iZovM7CMz+1U4vamZvWhmn4fvueF0M7O7zGyxmS0ws927XRQRkZRK5ZnCDuDX7t4FOAL4pZl1Aa4GXnL3TsBL4TjAyUCn8DUGuCeFsYmISAIpSwruvtLd3w2HNwIfA62BM4BYb1BTgTPD4TOAhzwwF8gxs1apik9ERHZXKW0KZtYe6AW8BbR091jvVd8ALcPh1sDyyGIrwmkl1zXGzOaZ2TxdziciUrFSnhTMrCEwC7jC3b+LzvPgzp09unvH3Se5e4G7F8QeZCEiIhUjpUnBzOoQJITp7v5EOHlVrFoofF8dTv8KaBtZvE04TUREKkkqrz4yYDLwsbvfHpn1DDAqHB4FPB2Zfl54FdIRwIZINZOIiFSCVN7RfCRwLvChmcWe+XctcDPwmJldCCwFhofzngNOARYDm4HzUxibiIgkkLKk4O6vA5Zk9rEJyjvwy1TFIyIiZdMdzSIiEqekICIicUoKIiISp6QgIiJxSgoiIhKnpCAiInFKCiIiEqekICIicUoKIiISp6QgIiJxSgoiIhKnpCAiInFKCiIiEqekICIicUoKIiISp6QgIiJxSgoiIhKnpCAiInFKCiIiEqekICIicUoKIiISp6QgIiJxSgoiIhKnpCAiInFKCiIiEqekICIicUoKIiISp6QgIiJxSgoiIhKnpCAiInFKCiIiEqekICIicSlLCmb2gJmtNrOFkWkTzOwrM3s/fJ0SmXeNmS02s0/N7MRUxSUiIsml8kxhCnBSgul3uHvP8PUcgJl1Ac4GuobL/MXMslIYm4iIJJCypODuc4Bvy1n8DGCGu3/v7l8Ci4G+qYpNREQSS0ebwqVmtiCsXsoNp7UGlkfKrAin7cbMxpjZPDObV1hYmOpYRUQySmUnhXuAjkBPYCVw256uwN0nuXuBuxe0aNGiouMTEclolZoU3H2Vu+909x+A+yiuIvoKaBsp2iacJiIilahSk4KZtYqMDgFiVyY9A5xtZnXNrAPQCXi7MmMTERGonaoVm9mjwNFAczNbAYwHjjaznoADS4BLANz9IzN7DFgE7AB+6e47UxWbiIgkZu6e7hj2WkFBgc+bNy/dYYiIVCtmNt/dCxLN0x3NIiISp6QgIiJx5WpTMLN6wIUEdxzXi0139wtSFJeIiKRBec8UHgYOAE4EXiW4ZHRjqoISEZH0KG9SONjdrwc2uftU4FSgX+rCEhGRdChvUtgevq83s25AE2D/1IQkIiLpUt77FCaF/RRdT3CjWUPghpRFJSIiaVGupODu94eDrwIHpS4cERFJp1KTgpldWdp8d7+9YsMREZF0KutMoVH4fihwOEHVEcDpqG8iEZEap9Sk4O43ApjZHKC3u28MxycAf095dCIiUqnKe/VRS2BbZHxbOE1ERGqQ8l599BDwtpk9GY6fSfAMZhERqUHKe/XRRDN7HhgYTjrf3d9LXVgiIpIOZV191NjdvzOzpgTPP1gSmdfU3b9NbXgiIlKZyjpTeAQ4DZhP8GCcGAvHdc+CiEgNUtbVR6eF7x0qJxwREUmnsqqPepc2393frdhwREQkncqqProtfK8HFAAfEFQd9QDmAf1TF5qIiFS2Uu9TcPdj3P0YYCXBzWsF7t4H6AV8VRkBiohI5SnvzWuHuvuHsRF3Xwh0Tk1IIiKSLuW9ee1DM7sfmBaOjwQWpCYkERFJl/ImhdHAWOBX4fgc4J5UBCQiIulTZlIwsyzg+bBt4Y7UhyQiIulSZpuCu+8EfjCzJpUQj4iIpFF5q4+KCNoVXgQ2xSa6++UpiUpERNKivEnhifAlIiI1WHl7SZ1qZvWBdu7+aYpjEhGRNEnaphBtQzCz04H3gX+E4z3N7Jlky4qISPVUWkPzCDM7KxyeAPQF1gO4+/uoh1QRkRonaVJw90kU37W83d03lCjyQ8qiEhGRtCir6+ybwsGPzOynQJaZdQIuB95MdXAiIlK5ytv30WVAV+B7ggfvbACuKG0BM3vAzFab2cLItKZm9qKZfR6+54bTzczuMrPFZragrC67RUQkNUpNCmZWz8yuAG4BlgH93f1wdx/n7lvLWPcU4KQS064GXnL3TsBL4TjAyUCn8DUGdaEhIpIWZZ0pTCV4jsKHBF/ct5Z3xe4+Byj5DOczwnXG1n1mZPpDHpgL5JhZq/JuS0REKkZZ9yl0cffuAGY2GXh7H7fX0t1XhsPfAC3D4dbA8ki5FeG0lZRgZmMIziZo167dPoYjIiJRZZ0pbI8NuPuOitywuzvge7HcpPBhPwUtWrSoyJBERDJeWWcK+Wb2XThsQP1w3Ai+1xvv4fZWmVkrd18ZVg+tDqd/BbSNlGuDnuwmIlLpynocZ5a7Nw5fjdy9dmR4TxMCwDPAqHB4FPB0ZPp54VVIRwAbItVMIiJSScrbId4eM7NHgaOB5ma2AhgP3Aw8ZmYXAkuB4WHx54BTgMXAZuD8VMUlIiLJpSwpuPs5SWYdm6CsA79MVSwiIlI+5b15TUREMoCSgoiIxCkpiIhInJKCiIjEKSmIiEhcRiaFr7+GJ5+ETZvSHYmISNWSkUnhjTdg6FD44ot0RyIiUrVkZFJo3jx4X7MmvXGIiFQ1GZkUYv3oFRamNw4RkapGSUFEROIyMik0bRq8q/pIRGRXGZkU6tSB3FydKYiIlJSRSQGCKiQlBRGRXWVsUmjeXNVHIiIlZWxS0JmCiMjuMjopLFsG7dtDrVrB+/Tp6Y5KRCS9UvaQnapu1SrYsCF4ASxdCmPGBMMjR6YvLhGRdMrYM4XXX9992ubNcN11lR+LiEhVkbFJYd26xNOXLavcOEREqpKMTQr77594ert2lRuHiEhVkrFJ4dJLd5+WnQ0TJ1Z+LCIiVUXGJoXzzgvemzYFM8jLg0mT1MgsIpktY68+inWKd9VV8NvfpjcWEZGqImPPFLKzoX593cAmIhKVsUkBdFeziEhJGZ8U1P+RiEixjE4KzZvrTEFEJCqjk4Kqj0REdpXxSUHVRyIixTI6KTRvDkVFsHVruiMREakaMjopxO5VUBWSiEggLUnBzJaY2Ydm9r6ZzQunNTWzF83s8/A9N9VxxJKCqpBERALpPFM4xt17untBOH418JK7dwJeCsdTqnnz4F1nCiIigapUfXQGMDUcngqcmeoNqvpIRGRX6UoKDvzTzOabWfi8M1q6+8pw+BugZaqDUPWRiMiu0tUh3gB3/8rM9gdeNLNPojPd3c3MEy0YJpExAO328eEHOTmQlaUzBRGRmLScKbj7V+H7auBJoC+wysxaAYTvq5MsO8ndC9y9oEXsp/5eqlULmjVTUhARian0pGBmDcysUWwYOAFYCDwDjAqLjQKerox4dAObiEixdFQftQSeNLPY9h9x93+Y2TvAY2Z2IbAUGF4ZwairCxGRYpWeFNz9CyA/wfS1wLGVHU/z5rBwYWVvVUSkaqpKl/WpauYAAAxYSURBVKSmhaqPRESKKSm0gLVrYefOdEciIpJ+GZ8UmjcHd/j223RHIiKSfhmfFHQDm4hIsYxPCgccELyvWJHeOEREqoKMTwr54XVQb78N06dD+/bBTW3t2wfjIiKZJF3dXFQZubnQuTM8/jh89hls3hxMX7oUxoS9Mo0cmb74REQqU8afKQD07w8LFhQnhJjNm+G669ITk4hIOigpECSFH35IPG/ZssqNRUQknZQUCJJCMvvYEauISLWipEDQppCdDbVLtLBkZ8PEiemJSUQkHZQUCK42GjAADjwQ8vLALHifNEmNzCKSWTL+6qOY/v3hX/+C9euhUaN0RyMikh46UwjFGpvffjvdkYiIpI+SQqhfv+D93/9ObxwiIumkpBDKyYEuXZQURCSzKSlE9O8Pc+cGvaaKiGQiJYWI/v2DLrQ/+yzdkYiIpIeSQsRRRwXv99+f3jhERNJFSSHi4IPhggvgzjvhj39Uj6kiknnMq3EFekFBgc+bN69C17l6NXToAFu37tofUna2bmYTkZrBzOa7e0GieTpTKGH//aFu3d07yFOPqSKSCZQUEli3LvF09ZgqIjWdkkICeXmJp8d6TN20KXlX2yIi1ZmSQgITJwZtCFG1a0P37lBQEPSN1K0bzJgBO3emJ0YRkVRQUkhg5MigUTl2xlC3LuzYAc8+CwsXwmmnBVclnXMO9OgRdKQnIlITKCkkMXIkLFkC06ZBVlbx9O+/h5degt/+FmbODJLFSScFSUREpLpT19lluO66xM9uPu+84Exi3Dh44gm45BL48sug6qmWUq2IVFO6T6EMtWqV3heSWTC/YUMoKoK2baFlS2jWLHhv0yaY1r170I2GEoaIpFtp9ynoTKEM7drB0qXJ58cSRlFR8L58ORQWQqtWsGgRfP11cWN0u3ZBO8TQodCzJ+y3X/E6li+HVaugd+9dq6tERCqTzhTKMH06jBmzexVSWWJnEE2bBpevrl8PderA9u3FZWrXDtoksrKKE0fr1nD22UE7xddfwyefBO89ewaPDO3Zc/dnSYuI7InSzhSUFMph+vSgbaG0M4aKUjJxlBRLNnXrBslk8+biy2djw2bBvRQNGwbTi4qgceNg+oYNQaIyC3qEbdo0KBMbdg+Gc3KC4dLK5+YWJ7yWLeGKK+Dcc4NnXZuVva/LlgWdD3bqFJxBJUp2sc9+2bLgTGvixKrb1Yh7cHXad98FV6Xpsa6Va8MG+Oij4IFZOtsuXWlJAXevUi/gJOBTYDFwdWll+/Tp45Vp2jT37Gz34N9fr9Je++1X/FnVq+det24w3LChe+PGu5evVSuYB+45Oe5NmpS+/pyc4AVB2Vj5xo3dGzUq3lZsnY0aFW/3wAPdR4xwP+CAYLxuXffatYuXyc0tXia2Dw0aBC9wr18/2KfYcKyM2e77BMH82LabNCmOr0kT96ZNg+Vyc4vLNG7s3rFj8WeWnV28H9nZwTbBvU6d4rizs4s/g+jnV7ducRxZWcXl69cvXmeDBsXrbNiw+HPNySmOKfr5RT/j/fd3P+UU9xYtio/1fvsVbyP2mUWnN2y4a6yx7TVt6t6sWTCcm7vrMY1uOzackxMsE1t/7POvVat4u23bup91VnF80f1o0KD42DVqVLy93Nziv4GSf2fR4xj9nBKVz80tPr5NmxZPb9Cg+NjWr1+83bw897Fjg3ez4LNo1qz04by84HtpTwHz3BN/r1apMwUzywI+A44HVgDvAOe4+6JE5SvrTCEqetYQ+9UuIpIue9NZZ3XqEK8vsNjdv3D3bcAM4Iw0x7SL2P0L7vDww8U3uJWnukREpKJVdGedVS0ptAaWR8ZXhNPizGyMmc0zs3mFhYWVGlxJShAiUhVUZGedVS0plMndJ7l7gbsXtGjRIt3hxCVKEGbB/QrNmiUfBiUREdk3sc46K0JVSwpfAW0j423CadVKLEH88AOsWRO8kg2XN4nk5cHYseVPNukYjt13UdFiSVPJU2R32dnBVXkVJlkLdDpeBDfTfQF0APYDPgC6Jitf2VcfSemmTduzKyf29OqKilr/3lzlUZnDVT2+6hRrVY9vX2Ot8VcfAZjZKcCdQBbwgLsnzYHpuPpIRKS6q1bdXLj7c8Bz6Y5DRCQTVbU2BRERSSMlBRERiVNSEBGROCUFERGJq3JXH+0JMysE9qTv0ubAmhSFU5Vl4n5n4j5DZu53Ju4z7Nt+57l7wrt/q3VS2FNmNi/ZZVg1WSbudybuM2TmfmfiPkPq9lvVRyIiEqekICIicZmWFCalO4A0ycT9zsR9hszc70zcZ0jRfmdUm4KIiJQu084URESkFEoKIiISlzFJwcxOMrNPzWyxmV2d7nhSwczamtkrZrbIzD4ys1+F05ua2Ytm9nn4npvuWFPBzLLM7D0zezYc72Bmb4XHfKaZpeiJD+lhZjlm9riZfWJmH5tZ/0w41mb2X+Hf90Ize9TM6tW0Y21mD5jZajNbGJmW8Nha4K5w3xeYWe992XZGJAUzywLuBk4GugDnmFmX9EaVEjuAX7t7F+AI4Jfhfl4NvOTunYCXwvGa6FfAx5Hx/wHucPeDgXXAhWmJKnX+BPzD3Q8D8gn2vUYfazNrDVwOFLh7N4Iu9s+m5h3rKcBJJaYlO7YnA53C1xjgnn3ZcEYkBaAvsNjdv3D3bcAM4Iw0x1Th3H2lu78bDm8k+JJoTbCvU8NiU4Ez0xNh6phZG+BU4P5w3IBBwONhkRq132bWBPgxMBnA3be5+3oy4FgTdPlf38xqA9nASmrYsXb3OcC3JSYnO7ZnAA+Fz8+ZC+SYWau93XamJIXWwPLI+IpwWo1lZu2BXsBbQEt3XxnO+gZomaawUulO4Crgh3C8GbDe3XeE4zXtmHcACoEHwyqz+82sATX8WLv7V8CtwDKCZLABmE/NPtYxyY5thX6/ZUpSyChm1hCYBVzh7t9F54WP4qtR1yGb2WnAanefn+5YKlFtoDdwj7v3AjZRoqqohh7rXIJfxh2AA4EG7F7NUuOl8thmSlL4CmgbGW8TTqtxzKwOQUKY7u5PhJNXxU4nw/fV6YovRY4EBpvZEoKqwUEE9e05YRUD1LxjvgJY4e5vheOPEySJmn6sjwO+dPdCd98OPEFw/GvysY5Jdmwr9PstU5LCO0Cn8AqF/Qgapp5Jc0wVLqxHnwx87O63R2Y9A4wKh0cBT1d2bKnk7te4ext3b09wbF9295HAK8BZYbEatd/u/g2w3MwODScdCyyihh9rgmqjI8wsO/x7j+13jT3WEcmO7TPAeeFVSEcAGyLVTHssY+5oNrNTCOqds4AH3H1imkOqcGY2AHgN+JDiuvVrCdoVHgPaEXQ1PtzdSzZi1QhmdjTw/9z9NDM7iODMoSnwHvAzd/8+nfFVJDPrSdCwvh/wBXA+wQ+9Gn2szexGYATB1XbvARcR1KHXmGNtZo8CRxN0j70KGA88RYJjGybH/yWoRtsMnO/u8/Z625mSFEREpGyZUn0kIiLloKQgIiJxSgoiIhKnpCAiInFKCiIiEqekIJKEmdUys3+YWbt0xyJSWXRJqkgSZtYRaOPur6Y7FpHKoqQgkoCZ7SS4CTBmhrvfnK54RCqLkoJIAmZW5O4N0x2HSGVTm4LIHjCzJWZ2i5l9aGZvm9nB4fT2ZvZy+OSrl2LtEGbW0syeNLMPwtePwulPmdn88AliY8JpWWY2JXyi2Idm9l/p21PJVLXLLiKSkeqb2fuR8T+4+8xweIO7dzez8wj60zoN+DMw1d2nmtkFwF0ED0G5C3jV3YeETwCMnX1cEPZbUx94x8xmAe2B1uETxTCznFTvpEhJqj4SSSBZ9VHYPfcgd/8i7Kb8G3dvZmZrgFbuvj2cvtLdm5tZIUFj9fcl1jMBGBKOtgdOBD4F5gHPAX8H/unuPyBSiVR9JLLnPMlwuYQ9uR4H9Hf3fIJePeu5+zqCZy3PBn5O+GhRkcqkpCCy50ZE3v8dDr9J8CwHgJEEXZhD8ID1sRBvM2gCNAHWuftmMzsMOCKc3xyo5e6zgHEED80RqVSqPhJJIMElqf9w96vD6qOZwMnA98A57r7YzPKABwn6vy8k6NN+mZm1BCYBBwE7CRLEuwR947cnqDLKASYA68J1xH6sXePuz6dwN0V2o6QgsgfCpFDg7mvSHYtIKqj6SERE4nSmICIicTpTEBGROCUFERGJU1IQEZE4JQUREYlTUhARkbj/DxHMmNKpdwRCAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confrontamos en una gráfica de épocas en el eje de abcisas contra la métrica de \n",
        "error absoluto medio (MAE)\n",
        "\n",
        "Esta gráfica nos ayuda a verificar que se genera sobreentramiento a partir de la época 20."
      ],
      "metadata": {
        "id": "PBwTC5gm71Pb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1, len(mae_history) + 1), mae_history)\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Validación MAE')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "mClz2sAGhsra",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "9c98012c-1e0b-4ed9-e413-ffea7524c013"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Esta gráfica funciona algo peor que la anterior pero sigue funcionando bastante bien"
      ],
      "metadata": {
        "id": "uoJysI14D3qL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history.history['mean_absolute_error']\n",
        "val_loss = history.history['val_mean_absolute_error']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Pérdida del entreno')\n",
        "plt.plot(epochs, val_loss, 'b', label='Pérdida de la validación')\n",
        "plt.title('Perdidas del entreno y la validación')\n",
        "plt.xlabel('Épocas')\n",
        "plt.ylabel('Pérdida')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "njh5NgIDhutt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "b9259ac1-40d6-41ad-911c-b1a5faf92464"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Entrenamos de nuevo el modelo el número de épocas adecuado para evitar overfitting"
      ],
      "metadata": {
        "id": "vR0_hj9N8LWf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mymodel2 = build_model()\n",
        "history2 = mymodel2.fit(partial_train_data, partial_train_targets,\n",
        "            validation_data=(partial_eval_train_data, partial_eval_train_targets),\n",
        "                        epochs=20, batch_size=1)"
      ],
      "metadata": {
        "id": "jfLmuPaUhwhV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d7bccb4-7831-454f-9a3c-1930f6c83208"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "141/141 [==============================] - 1s 3ms/step - loss: 253.0373 - mean_absolute_error: 13.8530 - val_loss: 72.1150 - val_mean_absolute_error: 6.5754\n",
            "Epoch 2/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 69.6591 - mean_absolute_error: 6.1296 - val_loss: 37.2228 - val_mean_absolute_error: 4.8390\n",
            "Epoch 3/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 32.3354 - mean_absolute_error: 3.9806 - val_loss: 21.0864 - val_mean_absolute_error: 3.6850\n",
            "Epoch 4/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 16.1589 - mean_absolute_error: 2.7267 - val_loss: 12.0527 - val_mean_absolute_error: 2.9223\n",
            "Epoch 5/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 11.0177 - mean_absolute_error: 2.2366 - val_loss: 8.5719 - val_mean_absolute_error: 2.4017\n",
            "Epoch 6/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 7.6707 - mean_absolute_error: 1.7600 - val_loss: 8.0124 - val_mean_absolute_error: 2.3996\n",
            "Epoch 7/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 5.7405 - mean_absolute_error: 1.6218 - val_loss: 6.3558 - val_mean_absolute_error: 2.0519\n",
            "Epoch 8/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 4.1488 - mean_absolute_error: 1.4652 - val_loss: 5.4668 - val_mean_absolute_error: 1.7794\n",
            "Epoch 9/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 3.5662 - mean_absolute_error: 1.2992 - val_loss: 5.2138 - val_mean_absolute_error: 1.9304\n",
            "Epoch 10/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 3.3364 - mean_absolute_error: 1.2870 - val_loss: 5.3105 - val_mean_absolute_error: 1.6969\n",
            "Epoch 11/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 2.4250 - mean_absolute_error: 1.1554 - val_loss: 4.0337 - val_mean_absolute_error: 1.6749\n",
            "Epoch 12/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.9761 - mean_absolute_error: 1.0414 - val_loss: 4.2180 - val_mean_absolute_error: 1.5747\n",
            "Epoch 13/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.7929 - mean_absolute_error: 0.9535 - val_loss: 2.8718 - val_mean_absolute_error: 1.3501\n",
            "Epoch 14/20\n",
            "141/141 [==============================] - 0s 3ms/step - loss: 1.7106 - mean_absolute_error: 0.9113 - val_loss: 2.8574 - val_mean_absolute_error: 1.3598\n",
            "Epoch 15/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.5806 - mean_absolute_error: 0.8480 - val_loss: 2.7579 - val_mean_absolute_error: 1.3942\n",
            "Epoch 16/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.4208 - mean_absolute_error: 0.7964 - val_loss: 2.4930 - val_mean_absolute_error: 1.2737\n",
            "Epoch 17/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.2820 - mean_absolute_error: 0.7914 - val_loss: 2.5440 - val_mean_absolute_error: 1.1920\n",
            "Epoch 18/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.2144 - mean_absolute_error: 0.7207 - val_loss: 2.9535 - val_mean_absolute_error: 1.4509\n",
            "Epoch 19/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.0045 - mean_absolute_error: 0.7070 - val_loss: 1.7914 - val_mean_absolute_error: 1.0122\n",
            "Epoch 20/20\n",
            "141/141 [==============================] - 0s 2ms/step - loss: 1.1333 - mean_absolute_error: 0.6630 - val_loss: 2.5113 - val_mean_absolute_error: 1.2797\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mae_history2 = history2.history['val_mean_absolute_error']\n",
        "mse_history2 = history2.history['val_loss']\n",
        "\n",
        "\n",
        "plt.plot(range(1, len(mse_history2) + 1), mse_history2)\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Validación MSE')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "gU22Ja5mh0T9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "a8efde87-74fa-4bd2-f063-9aef47b736a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history2.history['loss']\n",
        "val_loss = history2.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Pérdida del entreno')\n",
        "plt.plot(epochs, val_loss, 'b', label='Pérdida de la validación')\n",
        "plt.title('Perdidas del entreno y la validación')\n",
        "plt.xlabel('Épocas')\n",
        "plt.ylabel('Pérdida')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "fndDkin2zg-2",
        "outputId": "9cead144-1913-4350-b48e-8e3c2854605d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1, len(mae_history2) + 1), mae_history2)\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Validación MAE')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "T7EKvl0ozS_z",
        "outputId": "f96e223b-d000-4b1a-88a1-53e9b7f23980"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos ver como funciona bastante bien durante todas las épocas. Esto significa que hemos ajustado bien las épocas a nuestro modelo."
      ],
      "metadata": {
        "id": "hK50c2NJEuvD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history2.history['mean_absolute_error']\n",
        "val_loss = history2.history['val_mean_absolute_error']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Pérdida del entreno')\n",
        "plt.plot(epochs, val_loss, 'b', label='Pérdida de la validación')\n",
        "plt.title('Perdidas del entreno y la validación')\n",
        "plt.xlabel('Épocas')\n",
        "plt.ylabel('Pérdida')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "r3YsiMvFzrEM",
        "outputId": "c0f38ad7-df64-448f-ef36-35a7835d2818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Validamos nuestro enfoque usando la **validación K-fold o K-iteraciones**"
      ],
      "metadata": {
        "id": "T2QcEVdm83Uh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# k= 4 particiones\n",
        "k = 4\n",
        "num_val_samples = len(train_data) // k # 404//4=101\n",
        "print(\"num_val_samples\", num_val_samples)\n",
        "num_epochs = 100\n",
        "all_scores = []\n",
        "for i in range(k):\n",
        "    print('processing fold #', i)\n",
        "    # Preparamos la validación de los datos: datos de la partición #k\n",
        "    print(\"Uso para validar el siguiente rango de datos de entrenamiento -> [\",(i * num_val_samples),\":\",((i + 1) * num_val_samples),\"]\")\n",
        "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "\n",
        "    # Preparamos los datos de entrenamiento: Datos de las demás particiones\n",
        "    print(\"Uso para entrenar el siguiente rango de datos de entrenamiento -> [0:\",(i * num_val_samples),\"]U[\",(i + 1) * num_val_samples,\":\",len(train_data),\"]\")\n",
        "    partial_train_data = np.concatenate(\n",
        "        [train_data[:i * num_val_samples],\n",
        "         train_data[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "    partial_train_targets = np.concatenate(\n",
        "        [train_targets[:i * num_val_samples],\n",
        "         train_targets[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "\n",
        "    # Construcción del modelo Keras (ya compilado)\n",
        "    model = build_model()\n",
        "    # Entrenando el modelo (en modo silencioso, verbose=0)\n",
        "    model.fit(partial_train_data, partial_train_targets,\n",
        "              epochs=num_epochs, batch_size=1, verbose=0)\n",
        "    # Evalua el modelo sobre los datos de validación\n",
        "    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\n",
        "    all_scores.append(val_mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DOW7NSVl1enn",
        "outputId": "4852de9e-5da7-4de5-f564-5e9399e56594"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "num_val_samples 47\n",
            "processing fold # 0\n",
            "Uso para validar el siguiente rango de datos de entrenamiento -> [ 0 : 47 ]\n",
            "Uso para entrenar el siguiente rango de datos de entrenamiento -> [0: 0 ]U[ 47 : 189 ]\n",
            "processing fold # 1\n",
            "Uso para validar el siguiente rango de datos de entrenamiento -> [ 47 : 94 ]\n",
            "Uso para entrenar el siguiente rango de datos de entrenamiento -> [0: 47 ]U[ 94 : 189 ]\n",
            "processing fold # 2\n",
            "Uso para validar el siguiente rango de datos de entrenamiento -> [ 94 : 141 ]\n",
            "Uso para entrenar el siguiente rango de datos de entrenamiento -> [0: 94 ]U[ 141 : 189 ]\n",
            "processing fold # 3\n",
            "Uso para validar el siguiente rango de datos de entrenamiento -> [ 141 : 188 ]\n",
            "Uso para entrenar el siguiente rango de datos de entrenamiento -> [0: 141 ]U[ 188 : 189 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrWdQ8qE1jn7",
        "outputId": "f8234bc2-57e4-4253-a26b-d8f0f10b34de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.5663825273513794,\n",
              " 1.0886306762695312,\n",
              " 0.7022271752357483,\n",
              " 0.9458131790161133]"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(all_scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAnoweCw1mx0",
        "outputId": "ec694fe1-cb84-4155-ca87-7e6f62ec3770"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0230272114276886"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import backend as K\n",
        "\n",
        "# Some memory clean-up\n",
        "K.clear_session()"
      ],
      "metadata": {
        "id": "QtbTBYQv_M6O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 100\n",
        "all_mae_histories = []\n",
        "for i in range(k):\n",
        "    print('processing fold #', i)\n",
        "    # Preparamos la validación de los datos: datos de la partición # k\n",
        "    print(\"Uso para validar el siguiente rango de datos de entrenamiento -> [\",(i * num_val_samples),\":\",((i + 1) * num_val_samples),\"]\")\n",
        "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "\n",
        "    # Preparamos los datos de entrenamiento: Datos de todas la demás particiones\n",
        "    print(\"Uso para entrenar el siguiente rango de datos de entrenamiento -> [0:\",(i * num_val_samples),\"]U[\",(i + 1) * num_val_samples,\":\",len(train_data),\"]\")\n",
        "    partial_train_data = np.concatenate(\n",
        "        [train_data[:i * num_val_samples],\n",
        "         train_data[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "    partial_train_targets = np.concatenate(\n",
        "        [train_targets[:i * num_val_samples],\n",
        "         train_targets[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "\n",
        "    # Construcción del modelo Keras (ya compilado)\n",
        "    model = build_model()\n",
        "    # Entrenando el modelo (en modo silencioso, verbose=0)\n",
        "    history = model.fit(partial_train_data, partial_train_targets,\n",
        "                        validation_data=(val_data, val_targets),\n",
        "                        epochs=num_epochs, batch_size=1, verbose=0)\n",
        "    mae_history = history.history['val_mean_absolute_error']\n",
        "    all_mae_histories.append(mae_history)"
      ],
      "metadata": {
        "id": "jnC02RQq_PM9",
        "outputId": "17f1aaf0-c445-4178-e67e-d1c42289bb18",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "processing fold # 0\n",
            "Uso para validar el siguiente rango de datos de entrenamiento -> [ 0 : 47 ]\n",
            "Uso para entrenar el siguiente rango de datos de entrenamiento -> [0: 0 ]U[ 47 : 189 ]\n",
            "processing fold # 1\n",
            "Uso para validar el siguiente rango de datos de entrenamiento -> [ 47 : 94 ]\n",
            "Uso para entrenar el siguiente rango de datos de entrenamiento -> [0: 47 ]U[ 94 : 189 ]\n",
            "processing fold # 2\n",
            "Uso para validar el siguiente rango de datos de entrenamiento -> [ 94 : 141 ]\n",
            "Uso para entrenar el siguiente rango de datos de entrenamiento -> [0: 94 ]U[ 141 : 189 ]\n",
            "processing fold # 3\n",
            "Uso para validar el siguiente rango de datos de entrenamiento -> [ 141 : 188 ]\n",
            "Uso para entrenar el siguiente rango de datos de entrenamiento -> [0: 141 ]U[ 188 : 189 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "average_mae_history = [\n",
        "    np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]"
      ],
      "metadata": {
        "id": "enAXuhkG1yQW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Validación MAE')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "e-dHg8A913Cx",
        "outputId": "e90373cd-c761-4a48-9946-7cabef53b560"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Al igual que con el método que hemos hecho antes de hacer los K-Folds vemos que se empieza a sobreentrenar alrededor de la época 20."
      ],
      "metadata": {
        "id": "x63s7aZOFwN7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def smooth_curve(points, factor=0.9):\n",
        "  smoothed_points = []\n",
        "  for point in points:\n",
        "    if smoothed_points:\n",
        "      previous = smoothed_points[-1]\n",
        "      smoothed_points.append(previous * factor + point * (1 - factor))\n",
        "    else:\n",
        "      smoothed_points.append(point)\n",
        "  return smoothed_points\n",
        "\n",
        "smooth_mae_history = smooth_curve(average_mae_history[10:])\n",
        "\n",
        "plt.plot(range(1, len(smooth_mae_history) + 1), smooth_mae_history)\n",
        "plt.xlabel('Repeticiones')\n",
        "plt.ylabel('MAE de validación')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "B5whK5Pl15tk",
        "outputId": "a804fb3d-cd6c-46a5-dbf8-e8b17fdd87c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Mo64oTUSLlEQe+1YOFZXV3PC3XPZUVAUdSUQamboUgsfMrDVwN/AKsAz4XURTSb3qmdmcBy8fyrKNO7nzxUU0tQ4CIhJZdSkEk9x9e3ha6mx3b+fuj0Y8mdSrU/u15/ZxfXhl4QbeW6l1C0Tkc3UpBPlm9piZnWZm6oPYhN14ck+6tG7Gg++sVqtARA6oSyHoB7wD3AQUmNmfzWxMZGNJJCTGx3HTKb1YuL6E91dtCTqOiDQSdZmGere7P+fuFwJDgXRC3UilCbrouC50bqVWgYh8ri4tAszsJDN7BJgHpAATI5pKIiYpIdQqWLBuB9PVKhAR6jayuIDQimQzgUHuPtHdX4x0MImci4eFWwXT1CoQkbq1CAa7+wXuPsXdyyKeSCIuKSGO753ck/lrdzBzdXHQcUQkYHW5RrCzIYJIw7okJ9Qq+MNbK9UqEIlxdbpGINEnOSGe207vw6L1JUxdvDHoOCISIBWCGHbBsZ3p16EFv39zJRWV1UHHEZGA1OVicXsze9LMXg8/HmBm10U+mkRafJxx5/h+fLZ1N8/MXRt0HBEJSF1aBJOBN4FO4cerCPUikihwct9MRmVn8OA7q9m1tzLoOCISgLoUgrbu/hxQDeDulYCmsIwSZsZPz+rP1rIKHpuxJug4IhKAuhSCMjNrAziAmY0CSiKaShrU0K6tOGdQRx6fsYY1W3YFHUdEGlhdCsHthKaf7mlms4C/ATdHNJU0uLsnDCA5MY5bn13AvipdOBaJJXUZR/AJcBJwPPBdYKC7L4p0MGlYHVqm8NsLB7NofQl/fHtV0HFEpAEl1LTBzC6sYVMfM8PdX4pQJgnI+GM6cNnwrvxl+qec2CeTUdltgo4kIg2gxkIAnBv+2Y5Qa+Dd8ONTgA8BFYIodPeEAczO38btzy7g9VtOpGVqYtCRRCTCajw15O7XuPs1QCIwwN0vcveLgIHh52plZk+ZWZGZLTnMfsPNrNLMLj7S8FL/0pITeODSoRSV7uX25xZQXa3pJ0SiXV0uFnd194PnINgMdKvD6yYD42vbwcziCa1//FYd3k8ayJCurbh7wgCmrSjiT+/mBR1HRCKstlND+00zszeBKeHHlxJasaxW7j7DzLIOs9vNwIvA8DrkkAb0rdHdWbh+Bw9MW8WgLumc2q990JFEJELq0mvoB8BfgSHh22PuftTdR82sM3AB8Jc67HuDmeWaWe6WLVpMpSGYGf99wSAGdEznlmcWUFCsGchFolWdJp1z93+7+23h27/r6bMfAO5098N2Wnf3x9w9x91zMjMz6+nj5XBSEuP561XDiI8zbp4yX9cLRKJUkLOP5gDPhFdAuxh4xMzODzCPHELXjFR+ce4AFheW8JqmqxaJSoEVAnfv4e5Z7p4FvAB8393/L6g8UrPzhoSmq77/rZUadSwShWosBGaWXsu2w/YaMrMpwEdAXzNbb2bXmdmNZnbj14sqQYmLM+4Y35eCrbt5Lndd0HFEpJ7V1mvofeA4ADOb5u6nHbTt//Zvq4m7X17XEO7+7bruK8E4pW87crq35sF3VnPhsV1olhQfdCQRqSe1nRqyg+5n1LJNYoCZcedZ/Sgq3cvkDwuCjiMi9ai2QuA13D/UY4kBw7MyOLVfO/7yfh7Fu/YGHUdE6klthaCdmd1uZj866P7+x+rDGaPuHN+PvZXVXPXEbLaqGIhEhdoKweNAC6D5Qff3P34i8tGkMerboQVPfXs4+cVlXKliIBIVzP3Iz/KY2XB3nxuBPIeVk5Pjubm5QXy0HGRWXjHXTp5Lj7Zp/Os7o8hISwo6kojUwszmuXvOobbVeRyBmQ0ws3vNLI86TAsh0e2EXm158upQy+DmKZ/wdf6gEJHGodZCYGZZZnaXmS0C/g58DxhXU1WR2DKmd1v+65z+zMrbyutLNgUdR0S+ptoGlH0ETCU01uAidx8GlLp7QQNlkybgihHd6N8xnV+/tow9FVVBxxGRr6G2FsFmQheH2/N5LyG1/+ULEuLj+NU3BrKhpJxH3tfaBSJNUW0rlJ0PDALmAb80s3ygtZmNaKhw0jSM6JHB+UM78ej0NXy2VdNVizQ1tV4jcPcSd5/k7mcAo4B7gD+amSackS+46+z+JMYb/+/VZbpwLNLE1LnXkLtvdvc/ufsJwJgIZpImqH16CreO68O0FUX8SsVApEmpcdI5M3vlMK/9Rj1nkSbu+rE92FhSzlOz8tlTUcV/XziI+DhNSyXS2NU2++hoYB2htYpno4nm5DDMjLsn9CctOZ4/vZvHnn1V3DdxCInxQa5/JCKHU1sh6ACcDlwOXEGoK+kUd1/aEMGkaTIzfnRGX5olxfO/b6xk/fbd/OGSIWRnNg86mojUoLZeQ1Xu/oa7X03oQnEe8L6Z/aDB0kmT9f2Te/HgZUPJK9rF2Q/NZNKsfK15LNJIHW5kcbKZXQj8A7gJeAior8XrJcqdN7Qzb912EqOy2/CrV5dxzeS5VGqpS5FGp7aRxX8jtNTkccCv3H24u9/r7oUNlk6avA4tU5j07eHcPWEA01dt4ZH3Pw06koh8SW0tgquA3sAtwIdmtjN8KzWznQ0TT6KBmXHdmB58Y0gnHpy2moXrdgQdSUQOUts1gjh3bxG+pR90a+HuNS5sL1KTe887hnYtkrnt2QXsrqgMOo6IhKlfnzSYlqmJ3HfJENYUl/GbqctZvbmUKXPW8uPnFzJpVn7Q8URiVm3dR0Xq3fG92nL9mB488UE+/5y9FoCUxDhemLeevh1acHzPtgEnFIk9KgTS4H58Zl9aNkukQ8sUhnVvTYeWKUx46AN+/NxC3rjtRNJTEoOOKBJTdGpIGlxKYjw3n9abS3K6kp3ZnNSkBO6/dCibS/fyy1c0XlGkoakQSKMwtGsrbjqlFy99UsgbSzYGHUckpqgQSKNx86m9GNylJXe9tJjVm0uDjiMSM1QIpNFIjI/jj5cOJT4ujgsf+ZDpq7YEHUkkJqgQSKPSM7M5L//gBLpkpHLNpDk8/WFBjfvmFmzTeASReqBCII1O51bNeOHG0Zzarz2/eGUpd720iPJ9VQe2V1c79762jIv/+hE3/fMTTWYncpRUCKRRSktO4NFvDuP7J/dkypx1nP/wLNZs2UX5vipufmY+T36Qz/Cs1ry3cguPzlgTdFyRJk3jCKTRio8z7hjfj+FZGdz+3ALO/dMHZGc2Z3FhCf91dn+uH9uDH0yZzx/eWslx3VoxMrtN0JFFmiS1CKTRO6VfO6b+cCz9OqazclMpD11+LN85MRsz47cXDqJbRio3T5lP8a69QUcVaZKsqS0ynpOT47m5uUHHkABUVTsle/aRkZb0heeXbdjJBY/MYmCndB65chgdWqYElFCk8TKzee6ec6htahFIkxEfZ18pAgADOqVz/8ShLN9YypkPzOC1RRsCSCfSdKkQSFQ4Z3BHpv5wDFlt0/jBv+Zz6zPz2V5WEXQskSYhYoXAzJ4ysyIzW1LD9ivNbJGZLTazD81sSKSySGzIzmzOizeO5rZxfXh10UZOu386z+Wuo6md/hRpaJFsEUwGxteyPR84yd0HAfcCj0Uwi8SIhPg4bhnXm9duHkOPtmnc8cIiJj76EUs3lAQdTaTRilghcPcZwLZatn/o7tvDDz8GukQqi8Se/h3Tef67o/ndRYNYXbSLcx76gGsnzyW3oMavZMSV76tSzyZplBrLNYLrgNdr2mhmN5hZrpnlbtmi+WekbuLijEuHd2P6j0/hR6f3Yf7a7Vz814+44vGP2Vm+LyKfWVFZzYpNX13Se+uuvZz/8CxO/v37LClU60Qal8ALgZmdQqgQ3FnTPu7+mLvnuHtOZmZmw4WTqNAyNZGbT+vNrJ+eys/P6c/s/G38/N9LjujagbsfdioLd+dHzy9k/AMz+cnzC9m1NzQPUlFpOZc99jEFW8tonpzA1U/NYc2WXUf1O4nUp0ALgZkNBp4AznP3rUFmkeiXmpTA9WOzufW03ryycAMvfVJ4YFt1tfPgO6u59NGPmPfZF08fLVq/g7Mf+oAzHphR63/gz+eu59WFGxiVncGLn6zn7Adn8ubSTVz22McU7tjDpG+PYMoNowD45pNz2FiyJzK/qMgRCqwQmFk34CXgm+6+KqgcEnu+f0ovRvTI4J6Xl1BQXMaeiipunjKfP76zimUbdnLRXz7iJ88vZMOOPfz29RWc//Astu7ay7ayCs5/eBYzV3/19GReUSm/eGUpx/dswz+vH8Wz3x1NtTvf/fs8NpeU8/S1Ixjdsw092qbx9LUj2LlnH998cg5bdc1AGoGIjSw2synAyUBbYDPwCyARwN3/amZPABcBn4VfUlnTqLeDaWSx1IcNO/Zw1oMz6ZaRihksLizhrrP6ceXI7vzp3TyemLmGyvCpoEtzuvKzc/qzc88+rn86l7wtu7jrrH5MHN6V9JREyvdVcf7Dsygq3cvrt4ylfXpoZHNp+T4en5nPuP7tGNyl1Rc+/+M1W7n6qTl0atWMv107gq4ZqQ1+DCS21DayWFNMSMx6Y8lGbvzHJ6QmxfPgZcdy+oD2B7blFZXyxMx8zhnckbG9P78utWtvJbc+s4B3lm8GQlNmt0hJYMWmUiZ9ezin9GtX58/PLdjGtZPnkpIYz9PXjqB/x/T6++VEvkSFQKQG/1m8kd7tmtO7fYs6v6a62pmZV8zSDSWs3FTKyk2lnHVMR24Z1/uIP3/V5lK+9eQcyioq+a+z+9O7fQu6ZaTStnkSZnbE7ydSExUCkUascMcern5qDnlFn1+IbpGSwITBHZmY05WhXUOnlfKLy5ixagsleyq5YmQ3MlskBxVZmiAVApFGbl9VNQXFZazbvpu1W3ezaH0Jry/ZxJ59VfTMTGNvZTXrt3/eyyg1KZ7rxvTgOydmk56SGGByaSpUCESaoNLyfUxdtJGXF2wgLTmBk/q05cQ+mVRWO/e/vYqpizbSKjWRe887hnOHdAo6rjRyKgQiUWhJYQn3vLyET9bu4MaTevKTM/sSH6frCnJoWo9AJAod07klz9wwmitGduOv0z/l2slzKdkdmakzJLqpEIg0YUkJcfz3BYP4zQXH8OGnxZz+x+k8+UE+eyqqgo4mTYgKgUgUuHJkd5777miyM9O497VljPnduzz8Xt6B+Y5EaqNrBCJRJrdgG39+L4/3V26hdWoi3zkxm6tHZ5GWnBB0NAmQLhaLxKCF63bwwDureC9cEK4fm82VI7vRKvWr6z7vN++zbeQX7+bCYzsTpwvPUUWFQCSGzV+7nQenreb9lVtolhjPpcO7cvXxWWS1ST0wenn99t38z+srmLpoIwBje7flvolDaNciJcjoUo9UCESEFZt28viMfF5ZWMi+Kic9JYHe7VvQqVUz3lq6CTP47ok9adsimd9MXUZaUgJ/mDiEU/rWff4kabxUCETkgE0l5by5dBOrNpeyumgXBcVljO7ZhjvH96NTq2YArN5cys1T5rNiUylXjerGXWf1r/M1hg/zivn3/EIGdkpnRI829OvQQqeZGgEVAhE5YuX7qvjfN1Yy6cN8Ordqxv9ePJjje7at9TUvLyjkR88tJCHeKN9XDUB6SgK92jWna0YqXVo3I7ttcwZ3aUl2ZnMNgGtAKgQi8rXNLdjGHS8sIr+4jCtHduOO8f1o2eyr8xs99UE+/++1ZYzskcHjV+ewc88+5uRvY27B9gPzKG0sKacqvM5DWlI8g7q05KpR3Tn7mI5qNUSYCoGIHJU9FVXc99ZKnpqVT5vmydwzYQATBnekqtqZv24HL31SyJQ5axk/sAMPXDaUlMT4Q75PZVU1+cVlLFpfwqL1O5i5upg1xWX0bd+CW8f15syBHVQQIkSFQETqxZLCEu56aTGLC0sY2Cmdddt2s7O8kvg446qR3bjn3IFHdLqnqtp5bdEGHpy2mjVbymiVmkivzOb0zGzOMZ3TuWxENxLj62/cq7uzdMNOXlu0kZWbdpKTlcFJfTIZ0DE96guQCoGI1JuqaudvHxXwXO56jumUzsl92zGmd9tDni46kvecungjH326lU+37GLNll0U76pgVHYGj1w5jIy0msc+1MW6bbt5ft56XllQSMHW3STEGd0yUllTXAZA2+ZJ9MxsTvv0FNq1SGZQlxmez48AAAmSSURBVJacO7hTVBUHFQIRaXL+PX89d764mPbpyTzxreFkZ6bx4adbeXXhBgq37+HkvpmcObADWW3T2FdVzZLCEmbnb2NbWQUZaUm0SUvCHV5eWMisvK2YwfE923Du4E6cObADrdOSKCotZ+aqYj7IK2b99t0Ule6laOde9uyr4viebfjdRYOjZj1pFQIRaZIWrNvBDX/LZdfeSlIS49lWVkGL5AQ6t27Gik2lAGS3TWPTznJ2hyfaS06IY29l9YH36NK6GRNzunLxsC4HusfWxt15Zu46fjN1OdXu3HFmX4Z2a31ge7eM1KNuoQRBhUBEmqzNO8v55StLSYyP49whnTixT1uSE+JZt203by3bzKy8Yrq2bsbI7DaM6JFBm7QkdldUsa2sgt0VVfRu1/xrneIp3LGHn764iJmri7/wfEpiHFeN7M4NJ2U3qZHXKgQiIl+DuzO3YDtl4Vlcq935z+JN/N+CQhLjjYk5XRnQMZ32LVPo2DKFHm3TSE44dI+poNVWCDQdoYhIDcyMET0yvvDcaf3bc/Opvfjze3n8a/ZaKqs//2O6WWI8o7IzOLFPJif1ySQ7s/lX3jOvaBcJcUZW27SI568rtQhERL6mispqikrL2byznMId5eQWbGPGqi0UbN0NQFabVE7t154TerVhSeFOpi7ewKrNu4iPM24b15vvndzrC91ti0rLiTejTfPkes+qU0MiIg1o7dbdvL+qiHdXFPHhp1upqKzGDIZ3z+DsQR2Yt3YHry7cwKjsDO6bOJQVG3fyr9lreW9lEWbG2N5tueDYzpwxoAPNkurnVJMKgYhIQHZXVLJg7Q56tguNU4DQtYcX5q3nF68sPdDbKbNFMhNzulDt8PL8QjaUlJOaFM+4/u2ZMLgjJ/bJrHHEdl2oEIiINEJrtuzi7x9/xoisDMYNaH9gFHV1tTM7fxsvLyjkjaWb2LF7H82TE7h1XG+uH5v9tT5LhUBEpInaV1XNh59uZeqiDZzYJ5MJgzt9rfdRryERkSYqMT6Ok8K9kCKl/mZzEhGRJkmFQEQkxqkQiIjEOBUCEZEYp0IgIhLjVAhERGKcCoGISIxTIRARiXFNbmSxmW0BPjuCl7QFig+7V+zQ8fgqHZMv0vH4qmg4Jt3d/ZCj0ppcIThSZpZb07DqWKTj8VU6Jl+k4/FV0X5MdGpIRCTGqRCIiMS4WCgEjwUdoJHR8fgqHZMv0vH4qqg+JlF/jUBERGoXCy0CERGphQqBiEiMi9pCYGbjzWylmeWZ2U+DzhMEM+tqZu+Z2TIzW2pmt4SfzzCzt81sdfhn66CzNiQzizez+Wb2WvhxDzObHf6uPGtmSUFnbEhm1srMXjCzFWa23MxGx/J3xMxuC/97WWJmU8wsJdq/I1FZCMwsHngYOAsYAFxuZgOCTRWISuBH7j4AGAXcFD4OPwWmuXtvYFr4cSy5BVh+0OPfAX90917AduC6QFIF50HgDXfvBwwhdGxi8jtiZp2BHwI57n4MEA9cRpR/R6KyEAAjgDx3X+PuFcAzwHkBZ2pw7r7R3T8J3y8l9A+8M6Fj8XR4t6eB84NJ2PDMrAtwDvBE+LEBpwIvhHeJtePREjgReBLA3SvcfQcx/B0htIRvMzNLAFKBjUT5dyRaC0FnYN1Bj9eHn4tZZpYFHAvMBtq7+8bwpk1A+4BiBeEB4A6gOvy4DbDD3SvDj2Ptu9ID2AJMCp8ue8LM0ojR74i7FwJ/ANYSKgAlwDyi/DsSrYVADmJmzYEXgVvdfefB2zzUfzgm+hCb2QSgyN3nBZ2lEUkAjgP+4u7HAmV86TRQjH1HWhNqDfUAOgFpwPhAQzWAaC0EhUDXgx53CT8Xc8wskVAR+Ke7vxR+erOZdQxv7wgUBZWvgZ0AfMPMCgidLjyV0PnxVuHTABB735X1wHp3nx1+/AKhwhCr35FxQL67b3H3fcBLhL43Uf0didZCMBfoHb7Sn0ToYs8rAWdqcOHz308Cy939/oM2vQJcHb5/NfByQ2cLgrvf5e5d3D2L0HfiXXe/EngPuDi8W8wcDwB33wSsM7O+4adOA5YRo98RQqeERplZavjfz/7jEdXfkagdWWxmZxM6HxwPPOXuvwk4UoMzszHATGAxn58T/xmh6wTPAd0ITek90d23BRIyIGZ2MvBjd59gZtmEWggZwHzgKnffG2S+hmRmQwldPE8C1gDXEPojMSa/I2b2K+BSQr3u5gPXE7omELXfkagtBCIiUjfRempIRETqSIVARCTGqRCIiMQ4FQIRkRinQiAiEuNUCCTqmFmVmS0Izx75qpm1quf3zzKzKw56nGNmDx3mNf+p7xwi9UXdRyXqmNkud28evv80sKo+x5EcPAahvt5TJEhqEUi0+4jwBGFm1tPM3jCzeWY208z6hZ+fbGZ/NbNcM1sVnpNo/7oFvzezuWa2yMy+G37P3wJjw62O28zs5IPWNmhuZpPMbHH4NReFny8ws7bh+7eHWytLzOzW8HNZ4bUAHg/Phf+WmTU7TO5Lwu+x0MxmNNgRlaiTcPhdRJqm8LoUpxGeYpnQAuQ3uvtqMxsJPEJoviGALELTl/cE3jOzXsC3gBJ3H25mycAsM3uL0KRsB1oE4RbCfneHXzMovO0LC7qY2TBCI3dHAgbMNrPphOa47w1c7u7fMbPngIuAf9SS+x7gTHcv1GknORoqBBKNmpnZAkItgeXA2+EZWI8Hng9NIQNA8kGvec7dq4HVZrYG6AecAQw2s/1zzLQk9J91RS2fPY7QPEYAuPv2L20fA/zb3csAzOwlYCyhuX3y3X1BeL95QNZhcs8CJoeLxv4JBUWOmAqBRKM97j7UzFKBN4GbgMmE5pQfWsNrvnyxzAn9xX6zu7958IYvtQDq08Fz11QBzQidvj1kbne/MdxCOAeYZ2bD3H1rhLJJFNM1Aola7r6b0LKDPwJ2A/lmdgmEZmY1syEH7X6JmcWZWU8gG1hJqIh8LzyVN2bWJ7xoSynQooaPfZtQ4SH8mi+v9TsTOD88u2UacEH4uZp+h5015Taznu4+293vIbS4TNea3kekNioEEtXcfT6wCLgcuBK4zswWAkv54vKla4E5wOuEzseXE5qRcxnwiZktAR4l1IpeBFSFL9Le9qWP/DXQev9FXOCUL+X5hFDrZA6hWWCfCGesTU25fx++KL0E+BBYWJdjIvJl6j4qMc/MJgOvufsLh9tXJBqpRSAiEuPUIhARiXFqEYiIxDgVAhGRGKdCICIS41QIRERinAqBiEiM+/9piNgUhB5BqQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtenemos un modelo compilado nuevo\n",
        "model = build_model()\n",
        "# Lo entrenamos en todos los datos.\n",
        "model.fit(train_data, train_targets,\n",
        "          epochs=80, batch_size=16, verbose=0)\n",
        "test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QFV8KTez19H0",
        "outputId": "bdc9494c-578c-4b83-c309-2d2a1cb5c761"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 7ms/step - loss: 2.0041 - mean_absolute_error: 1.0902\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_mae_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KjOJs-zd194k",
        "outputId": "b323e734-990b-416d-9a48-91040fc4f882"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0902025699615479"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aún estamos desviados 1.09% de la masa corporal.\n",
        "\n",
        "Esto nos indica que si lo hacemos con K-Folds hemos conseguido disminuir nuestro índice de masa temporal de 1,37% a un 1,09% lo cúal es un cambio bastante significativo."
      ],
      "metadata": {
        "id": "GaanaGqzAaHi"
      }
    }
  ]
}