MLP_Clasificador_M3.ipynb

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  "metadata": {
    "colab": {
      "name": "MLP_Clasificador_M3.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "YCkpuVJoLdxR"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A8Rc1iIGlBXK"
      },
      "source": [
        "# Ejemplo de Clasificador basado en una Red Completamente Conectada.\n",
        "Este es un ejemplo de clasificador basado en una Red Completamente Conectada (MLP) con objetivo pronósticar si la suma de dos enteros es múltiplo de 3 (etiqueta 0) o no (etiqueta 1)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCkpuVJoLdxR"
      },
      "source": [
        "## Importar librerías"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpBMcQxaHKUF"
      },
      "source": [
        "# seed the pseudorandom number generator\n",
        "from random import seed\n",
        "import random\n",
        "from random import randint"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEUzc8d0Jqs-"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4ad7UB9iXeR"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6k3Fue2Mk3EI"
      },
      "source": [
        "import scipy.stats\n",
        "from pylab import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjr4bgyVRoRE"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.python.keras.layers import Dense, Dropout,Convolution1D, MaxPooling1D, Flatten\n",
        "from tensorflow.python.keras.layers import BatchNormalization, Activation, ZeroPadding1D, ReLU, add\n",
        "from tensorflow.python.keras import Sequential, Input, Model\n",
        "from tensorflow import keras\n",
        "\n",
        "from keras.models import Sequential  # Model type to be used\n",
        "\n",
        "from keras.layers.core import Dense, Dropout, Activation # Types of layers to be used in our model\n",
        "from keras.utils import np_utils                         # NumPy related tools"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgEjIeujpDSl"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SvFwqnphLlO_"
      },
      "source": [
        "## Fijar semilla del generador de números aleatorios"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UlyAtC9RLsSS"
      },
      "source": [
        "seed(1234)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFLRtTn1Lu-L"
      },
      "source": [
        "## Generados dos listas de números aleatorios y sumarlos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHhL72HunfWA"
      },
      "source": [
        "list_lenght=1000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhkHcJzuHh3Y",
        "outputId": "0c33a657-c32c-4996-fb0d-97caed4d2b61"
      },
      "source": [
        "randomList1 = []\n",
        "# Set a length of the list to 10\n",
        "for i in range(0, list_lenght):\n",
        "    # any random numbers from 0 to 10\n",
        "    randomList1.append(randint(0, 10))\n",
        "\n",
        "print(\"Primera lista\", randomList1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Primera lista [3, 6, 5, 4, 8, 9, 1, 7, 9, 6, 8, 0, 5, 0, 9, 6, 2, 0, 5, 2, 6, 3, 7, 0, 9, 0, 3, 2, 3, 1, 3, 1, 3, 7, 1, 7, 4, 0, 5, 1, 5, 9, 9, 4, 0, 9, 8, 8, 6, 8, 6, 3, 1, 2, 5, 2, 5, 6, 7, 4, 3, 5, 6, 4, 6, 2, 4, 2, 7, 9, 7, 7, 2, 9, 7, 4, 9, 0, 9, 2, 9, 1, 2, 9, 1, 5, 7, 4, 7, 7, 1, 4, 0, 5, 4, 9, 2, 9, 1, 3, 5, 9, 3, 0, 4, 4, 0, 6, 8, 4, 8, 1, 8, 9, 8, 2, 0, 2, 2, 3, 2, 9, 7, 4, 8, 1, 9, 2, 7, 4, 3, 2, 5, 5, 1, 0, 8, 4, 0, 0, 1, 0, 3, 9, 1, 9, 3, 7, 9, 3, 4, 4, 1, 1, 6, 2, 9, 8, 7, 8, 0, 8, 7, 9, 5, 6, 3, 9, 4, 7, 7, 1, 7, 2, 5, 2, 7, 2, 6, 8, 6, 1, 9, 3, 3, 4, 9, 7, 2, 8, 1, 9, 4, 3, 1, 0, 7, 8, 0, 9, 3, 9, 3, 9, 0, 5, 9, 2, 3, 1, 7, 1, 4, 7, 3, 8, 4, 5, 3, 8, 8, 8, 1, 3, 6, 8, 9, 1, 5, 8, 4, 1, 1, 1, 2, 3, 4, 2, 1, 9, 4, 0, 0, 2, 1, 2, 5, 9, 8, 8, 7, 5, 7, 0, 0, 6, 6, 1, 9, 4, 6, 7, 2, 0, 7, 0, 2, 8, 4, 8, 3, 2, 4, 7, 7, 0, 1, 1, 0, 0, 0, 1, 1, 9, 4, 9, 9, 5, 2, 4, 1, 0, 3, 5, 2, 3, 3, 5, 4, 5, 4, 0, 5, 6, 7, 3, 1, 5, 4, 5, 9, 1, 7, 6, 3, 3, 0, 0, 2, 3, 1, 6, 1, 3, 5, 6, 9, 2, 6, 6, 9, 7, 8, 9, 1, 3, 6, 6, 2, 2, 4, 9, 3, 8, 7, 5, 1, 3, 1, 3, 0, 2, 0, 3, 5, 2, 6, 6, 6, 4, 1, 0, 7, 4, 1, 8, 5, 9, 6, 2, 8, 7, 9, 7, 7, 3, 6, 5, 6, 8, 6, 5, 9, 1, 9, 2, 1, 9, 1, 5, 0, 2, 3, 8, 8, 1, 3, 7, 3, 5, 1, 5, 6, 9, 5, 0, 1, 3, 5, 8, 9, 9, 7, 8, 8, 9, 4, 4, 1, 5, 4, 6, 8, 7, 9, 4, 1, 7, 7, 0, 4, 6, 1, 9, 1, 1, 9, 0, 8, 1, 3, 7, 2, 8, 3, 2, 2, 3, 4, 1, 6, 3, 5, 2, 4, 2, 1, 3, 9, 7, 9, 7, 2, 5, 7, 5, 8, 5, 0, 4, 0, 9, 3, 1, 0, 0, 6, 4, 3, 4, 1, 8, 6, 8, 7, 1, 5, 7, 9, 1, 5, 6, 5, 1, 6, 0, 9, 2, 2, 1, 4, 9, 9, 6, 6, 2, 4, 1, 5, 5, 5, 5, 8, 6, 7, 2, 3, 9, 7, 2, 1, 1, 9, 6, 5, 9, 2, 1, 3, 8, 3, 8, 7, 3, 8, 0, 5, 0, 9, 9, 7, 4, 5, 6, 3, 8, 1, 7, 4, 1, 9, 2, 4, 3, 3, 9, 4, 2, 6, 0, 5, 4, 6, 6, 6, 3, 8, 8, 8, 5, 7, 9, 9, 0, 7, 8, 4, 6, 5, 7, 5, 9, 2, 6, 3, 0, 8, 1, 6, 0, 3, 7, 5, 0, 8, 9, 8, 5, 1, 9, 4, 6, 2, 6, 3, 3, 7, 3, 2, 8, 5, 2, 4, 8, 0, 5, 2, 3, 7, 2, 6, 1, 2, 4, 7, 2, 3, 5, 8, 4, 3, 5, 8, 0, 5, 3, 5, 3, 5, 6, 1, 1, 3, 1, 7, 3, 4, 1, 7, 0, 9, 8, 5, 9, 3, 8, 2, 3, 0, 9, 0, 4, 3, 4, 5, 0, 2, 7, 2, 8, 5, 3, 5, 0, 5, 9, 0, 6, 7, 2, 6, 6, 2, 2, 0, 1, 1, 3, 8, 3, 3, 2, 5, 9, 5, 4, 6, 9, 5, 9, 2, 5, 5, 6, 5, 7, 3, 0, 2, 3, 1, 9, 9, 8, 8, 0, 8, 6, 6, 1, 4, 3, 5, 5, 4, 9, 3, 8, 2, 3, 0, 6, 2, 8, 3, 3, 8, 2, 6, 2, 0, 3, 0, 3, 9, 0, 4, 2, 7, 1, 2, 2, 2, 1, 5, 9, 5, 3, 7, 1, 0, 1, 1, 8, 3, 5, 4, 4, 2, 4, 5, 6, 4, 2, 6, 1, 7, 6, 2, 3, 9, 4, 1, 4, 1, 0, 7, 3, 4, 5, 0, 8, 2, 2, 5, 4, 9, 5, 3, 3, 4, 6, 8, 4, 2, 2, 0, 6, 2, 8, 9, 2, 4, 3, 4, 4, 0, 8, 3, 4, 2, 9, 2, 8, 4, 0, 0, 6, 6, 7, 6, 7, 9, 3, 9, 7, 8, 2, 9, 1, 0, 6, 3, 8, 4, 9, 9, 6, 7, 9, 2, 3, 6, 4, 5, 8, 5, 9, 9, 7, 1, 0, 6, 3, 3, 4, 1, 8, 2, 8, 0, 3, 9, 0, 6, 6, 7, 7, 9, 2, 2, 1, 0, 9, 5, 0, 9, 7, 5, 3, 8, 2, 6, 4, 5, 6, 1, 9, 6, 3, 7, 0, 4, 9, 4, 4, 5, 1, 0, 9, 7, 0, 5, 3, 8, 9, 1, 7, 0, 9, 6, 8, 2, 1, 7, 5, 4, 0, 1, 1, 7, 8, 2, 7, 6, 9, 9, 2, 1, 1, 8, 9, 0, 7, 2, 4, 2, 9, 8, 1, 0, 1, 4, 4, 9, 9, 0, 1, 9, 9, 3, 1, 7, 2, 8, 4, 7, 4, 3, 2, 8, 4, 7, 7, 0, 1, 4, 6, 5, 5, 0, 5, 2, 0, 8, 3, 5, 7, 8, 4, 7, 6, 7, 7, 2, 7, 6, 0, 2, 6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aV7FhT58I0in",
        "outputId": "13a39d70-5010-4304-cb0e-60d56f9f9b87"
      },
      "source": [
        "randomList2 = []\n",
        "# Set a length of the list to 10\n",
        "for i in range(0, list_lenght):\n",
        "    # any random numbers from 0 to 10\n",
        "    randomList2.append(randint(0, 10))\n",
        "\n",
        "print(\"Printing list of 10 random numbers\")\n",
        "print(\"Segunda lista\",randomList2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Printing list of 10 random numbers\n",
            "Segunda lista [5, 7, 0, 2, 3, 4, 8, 4, 3, 1, 8, 9, 1, 7, 2, 8, 5, 9, 3, 0, 0, 2, 3, 1, 6, 8, 7, 0, 1, 9, 7, 0, 1, 4, 8, 1, 9, 0, 2, 6, 2, 5, 6, 3, 5, 0, 0, 2, 1, 1, 1, 3, 8, 2, 2, 5, 4, 9, 4, 1, 3, 8, 4, 4, 0, 4, 5, 6, 9, 4, 5, 6, 6, 6, 7, 1, 1, 0, 5, 9, 2, 1, 3, 1, 5, 0, 3, 2, 0, 5, 4, 8, 3, 0, 7, 9, 6, 3, 4, 3, 7, 0, 9, 7, 3, 7, 6, 0, 4, 4, 3, 2, 7, 0, 0, 0, 4, 2, 3, 7, 9, 6, 9, 1, 3, 2, 6, 2, 1, 6, 7, 0, 3, 3, 6, 8, 0, 8, 9, 2, 9, 7, 6, 5, 8, 2, 6, 0, 6, 9, 5, 4, 0, 1, 8, 8, 6, 9, 5, 5, 1, 8, 5, 9, 4, 8, 4, 4, 7, 1, 9, 0, 1, 6, 8, 3, 1, 3, 6, 3, 5, 0, 3, 5, 1, 3, 8, 0, 7, 3, 3, 2, 4, 8, 8, 0, 7, 8, 0, 8, 6, 4, 2, 3, 0, 3, 1, 1, 6, 7, 8, 4, 9, 0, 4, 0, 9, 9, 8, 7, 1, 9, 6, 7, 7, 5, 7, 8, 1, 1, 9, 3, 9, 3, 3, 5, 8, 7, 3, 9, 0, 3, 9, 7, 0, 1, 3, 8, 0, 3, 5, 7, 1, 4, 5, 0, 1, 7, 9, 7, 3, 3, 9, 1, 7, 1, 5, 6, 8, 4, 9, 2, 0, 6, 1, 7, 0, 5, 4, 7, 7, 4, 8, 0, 7, 7, 6, 2, 2, 3, 2, 3, 5, 7, 2, 3, 7, 5, 7, 8, 9, 9, 7, 4, 9, 7, 8, 0, 2, 1, 7, 6, 5, 1, 8, 8, 1, 8, 6, 3, 5, 6, 0, 1, 2, 6, 5, 5, 5, 6, 0, 0, 6, 2, 0, 5, 2, 3, 7, 6, 6, 3, 3, 4, 4, 0, 7, 3, 3, 4, 6, 1, 9, 4, 6, 8, 7, 9, 3, 0, 4, 0, 9, 8, 8, 4, 8, 6, 9, 5, 1, 6, 2, 9, 5, 5, 9, 8, 2, 4, 2, 3, 1, 5, 1, 7, 5, 4, 7, 6, 7, 6, 5, 8, 3, 3, 8, 0, 8, 1, 0, 8, 6, 9, 6, 1, 9, 5, 3, 8, 6, 0, 4, 1, 8, 9, 9, 3, 0, 5, 7, 4, 5, 3, 6, 0, 2, 0, 4, 1, 3, 0, 7, 4, 0, 8, 8, 3, 6, 0, 1, 7, 5, 3, 5, 1, 3, 5, 7, 4, 9, 6, 6, 2, 1, 5, 2, 4, 3, 3, 7, 3, 1, 4, 4, 5, 9, 7, 9, 2, 2, 7, 9, 5, 0, 2, 7, 8, 8, 5, 4, 8, 1, 4, 5, 9, 9, 9, 4, 7, 4, 9, 3, 9, 4, 1, 7, 6, 4, 5, 9, 1, 9, 6, 4, 6, 0, 9, 1, 3, 7, 7, 9, 4, 3, 8, 8, 8, 9, 5, 5, 3, 7, 6, 5, 1, 0, 9, 4, 3, 9, 3, 3, 4, 0, 6, 8, 2, 0, 7, 6, 8, 7, 4, 1, 2, 7, 0, 2, 7, 7, 3, 8, 0, 6, 2, 5, 1, 9, 1, 9, 8, 2, 7, 2, 0, 4, 8, 2, 2, 3, 3, 4, 8, 1, 3, 3, 6, 9, 8, 8, 7, 0, 1, 3, 3, 7, 1, 6, 7, 2, 4, 6, 6, 5, 9, 4, 5, 3, 5, 4, 6, 7, 4, 6, 8, 0, 1, 1, 1, 2, 4, 4, 0, 7, 3, 1, 4, 0, 3, 4, 2, 3, 5, 0, 1, 6, 0, 5, 7, 0, 1, 1, 4, 1, 6, 7, 2, 4, 8, 4, 8, 7, 1, 6, 9, 3, 5, 7, 0, 0, 2, 8, 1, 3, 6, 9, 9, 0, 5, 4, 3, 3, 0, 4, 1, 0, 3, 1, 8, 5, 9, 9, 2, 6, 2, 1, 0, 3, 6, 4, 4, 5, 6, 3, 2, 0, 6, 0, 7, 3, 1, 8, 5, 6, 1, 2, 4, 1, 0, 5, 4, 0, 9, 5, 3, 5, 1, 6, 3, 8, 1, 0, 3, 7, 6, 5, 6, 2, 4, 5, 5, 3, 8, 1, 7, 4, 9, 8, 4, 9, 7, 0, 2, 5, 9, 5, 6, 4, 7, 0, 8, 7, 4, 2, 8, 3, 2, 5, 1, 9, 6, 5, 4, 7, 4, 3, 4, 5, 6, 5, 6, 9, 3, 5, 4, 6, 6, 5, 3, 2, 0, 5, 7, 4, 7, 8, 7, 8, 9, 9, 9, 2, 3, 1, 8, 5, 3, 4, 4, 0, 8, 4, 7, 8, 8, 0, 3, 7, 3, 3, 5, 8, 0, 9, 9, 6, 2, 5, 0, 8, 8, 5, 7, 6, 1, 4, 1, 4, 4, 9, 6, 5, 1, 3, 0, 1, 1, 8, 5, 6, 6, 1, 9, 3, 0, 6, 4, 1, 4, 5, 4, 7, 8, 3, 6, 8, 4, 8, 9, 3, 7, 8, 9, 7, 9, 1, 5, 1, 6, 6, 3, 7, 6, 9, 5, 1, 4, 5, 8, 5, 4, 4, 7, 3, 8, 4, 7, 5, 1, 5, 6, 0, 9, 2, 9, 9, 5, 4, 1, 1, 4, 7, 8, 4, 2, 8, 8, 1, 4, 0, 4, 5, 3, 2, 9, 5, 0, 4, 6, 9, 6, 9, 0, 2, 0, 0, 3, 1, 1, 4, 5, 9, 2, 1, 4, 4, 7, 2, 7, 1, 5, 6, 3, 3, 3, 4, 6, 6, 1, 9, 8, 4, 1, 7, 9, 2, 1, 8, 9, 7, 7, 4, 5, 0, 3, 2, 5, 4, 9, 0, 9, 2, 6, 0, 4, 8, 1, 9, 0, 6, 1, 8, 7, 3, 4, 2, 5, 8, 9, 0, 0, 6, 1, 6, 3, 4, 4, 7, 1, 2, 1, 1, 8, 0, 1, 3, 7, 4, 7]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50312RYeJDRm",
        "outputId": "01b0c71b-28d3-49be-d12f-ab2ed8ee1367"
      },
      "source": [
        "randomSum=np.add(randomList1,randomList2)\n",
        "print(randomSum)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 8 13  5  6 11 13  9 11 12  7 16  9  6  7 11 14  7  9  8  2  6  5 10  1\n",
            " 15  8 10  2  4 10 10  1  4 11  9  8 13  0  7  7  7 14 15  7  5  9  8 10\n",
            "  7  9  7  6  9  4  7  7  9 15 11  5  6 13 10  8  6  6  9  8 16 13 12 13\n",
            "  8 15 14  5 10  0 14 11 11  2  5 10  6  5 10  6  7 12  5 12  3  5 11 18\n",
            "  8 12  5  6 12  9 12  7  7 11  6  6 12  8 11  3 15  9  8  2  4  4  5 10\n",
            " 11 15 16  5 11  3 15  4  8 10 10  2  8  8  7  8  8 12  9  2 10  7  9 14\n",
            "  9 11  9  7 15 12  9  8  1  2 14 10 15 17 12 13  1 16 12 18  9 14  7 13\n",
            " 11  8 16  1  8  8 13  5  8  5 12 11 11  1 12  8  4  7 17  7  9 11  4 11\n",
            "  8 11  9  0 14 16  0 17  9 13  5 12  0  8 10  3  9  8 15  5 13  7  7  8\n",
            " 13 14 11 15  9 17  7 10 13 13 16  9  6  9 13  4 10  4  5  8 12  9  4 18\n",
            "  4  3  9  9  1  3  8 17  8 11 12 12  8  4  5  6  7  8 18 11  9 10 11  1\n",
            " 14  1  7 14 12 12 12  4  4 13  8  7  1  6  4  7  7  5  9  9 11 16 15  7\n",
            "  4  7  3  3  8 12  4  6 10 10 11 13 13  9 12 10 16 10  9  5  6  6 16  7\n",
            " 12  7 11 11  1  8  8  6  6 12  1  4  7 12 14  7 11 12  9  7 14 11  1  8\n",
            "  8  9  9  8 10 12  6 12 11  5  8  6  4  7  6  3  9  7 11 10 13 15  9  4\n",
            "  5  0 16 12  9 12 13 15 15  7  9 13 11 16 12  8 15 13  8 12  8  8 10  6\n",
            " 10  9  6 13  8 11  7  8  8 16 11  4 11  7 11  6  1 13 12 18 11  1 10  8\n",
            "  8 16 15  9 11  9 16 18 13  7  1 10 11 10 13 10 15  4  3  7 11  1  7  6\n",
            "  8 13  1  9 17  3 14  1  4 14  7 11  8  3  5  8 11  5 15  9 11  4  5  7\n",
            "  3  7 12 10 16 10  3  9 11 10 17 12  9  6  2 16 12  6  0  2 13 12 11  9\n",
            "  5 16  7 12 12 10 14 16 13  8  9 15  8 10 10  1 16  8  6  6 13 10 18 12\n",
            " 10  8  4 10  6  8 12 12 17 10 10 10 11 17 16  7  6  4 16 12 10 10  2 10\n",
            "  7 11 12 11 10  7  8  6 13  2  9 16 13 12 12 10  4 10  8  7  6  8 16  5\n",
            " 12  3  9 11  9  3 15  1 14 12  8 13  8  3 12 16 10  7 10 12 13  8  8 11\n",
            "  7 12 14 15 13 16  2  7  6  3 15  2 12  7  5 11 11  6 13 18 12 10  4 14\n",
            "  8 12  9 10  9 11  7  4  3  9  7  6  8  8  7  8  3  7  7  5 10  3  5  9\n",
            "  7  3  9  5 13 11  3  6  9  4  6  9 12  5  9 14  5  9 10  2 13 12  7  6\n",
            " 14  0  9 10 13 10  6 14 11 12  0 14  4  7  6  4  9  1  2 10  3 16 10 12\n",
            " 14  2 11 11  1  6 10  8 10 10  7  8  3  3  1  9  8 10  6  3 13 14 11  5\n",
            "  8 13  6  9  7  9  5 15 10 10  8  1  8  6  9 10  9 11 15  6 13 12  8  5\n",
            "  9  8  8 13  5 16  7 17 10  7  9 13  2 10  8 12 13  8 10  9  0 11  7  7\n",
            " 11  8  7  4 12  2 11  8  7  5 12 13  8  7 12  7  5  7 10 11  8  9 10 10\n",
            "  7  7  7  6  9  9 10  8 15 13 10 12 18 13  3  7  2  8 12  6  8  9  0 16\n",
            "  6  9 13 12  9  8 10  6  7 11 16  4 11 11  6  8  7  8 17 10  9 10 10  5\n",
            "  4  9  7  8 11 15  7  9  7  0  1  7 14 12 12 13 10 12 12  7 14  6 10  5\n",
            "  5 10 10 16  7 15 17 10 15 18  5 10 14 13 12 17  6 14 10 13  7  3 13  9\n",
            " 12  9  2 12  7 16  5  7 13  7  9 14 11 14 14  3  7  7  0 18  7  9 18 12\n",
            "  9  4  9  6 13 12  9  8  9 17  7  7  7  4  9 12  6 13 10  1  4 15 16  6\n",
            " 14  3 10  9  1 10  1 10 10 13 11  3  8  9  8  7  3  8  8 13  8 10  9 12\n",
            " 13  8  7  2 17 17  4  8  9 13  4 10 16 10  7  8  8  9  9 12  2  6 13 18\n",
            "  3 10  9  8  8  8 15  5 12  2 14  5 15 14  3  5  6 11 13 14  0  5  8  1\n",
            " 14  6  9 11 15  5  9  7  8 15  2  8  9  7  6 13]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxj5T5Hp859s",
        "outputId": "b2999278-6660-401f-ac13-63d28cf782a7"
      },
      "source": [
        "Y_label=mod(randomSum,3)\n",
        "print(Y_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2 1 2 0 2 1 0 2 0 1 1 0 0 1 2 2 1 0 2 2 0 2 1 1 0 2 1 2 1 1 1 1 1 2 0 2 1\n",
            " 0 1 1 1 2 0 1 2 0 2 1 1 0 1 0 0 1 1 1 0 0 2 2 0 1 1 2 0 0 0 2 1 1 0 1 2 0\n",
            " 2 2 1 0 2 2 2 2 2 1 0 2 1 0 1 0 2 0 0 2 2 0 2 0 2 0 0 0 0 1 1 2 0 0 0 2 2\n",
            " 0 0 0 2 2 1 1 2 1 2 0 1 2 2 0 0 1 2 1 1 2 2 2 1 2 2 0 0 2 1 1 0 2 0 2 0 1\n",
            " 0 0 0 2 1 2 2 1 0 2 0 1 1 1 0 0 0 2 1 1 2 2 1 1 2 2 1 2 2 2 0 2 2 1 0 2 1\n",
            " 1 2 1 0 2 1 2 2 2 0 0 2 1 0 2 0 1 2 0 0 2 1 0 0 2 0 2 1 1 1 2 1 2 2 0 0 2\n",
            " 1 1 1 1 1 0 0 0 1 1 1 1 2 2 0 0 1 0 1 0 0 0 1 0 2 2 2 2 0 0 2 1 2 0 1 2 0\n",
            " 2 0 1 2 1 2 1 1 2 0 0 0 1 1 1 2 1 1 0 1 1 1 2 0 0 2 1 0 1 1 1 0 0 2 0 1 0\n",
            " 1 1 2 1 1 0 0 1 1 1 0 2 0 0 1 1 0 1 2 2 1 2 2 0 0 0 1 1 1 0 2 1 2 0 0 1 2\n",
            " 2 1 2 2 0 0 2 1 0 0 0 2 2 2 0 1 1 0 0 0 1 2 1 1 0 0 1 2 0 1 0 0 0 1 0 0 1\n",
            " 0 1 2 1 0 2 0 1 2 0 2 2 1 0 1 0 0 1 2 2 1 2 2 1 2 1 2 1 2 0 1 1 0 0 2 1 1\n",
            " 2 2 1 0 0 2 0 1 0 1 1 1 1 2 1 1 1 0 1 0 1 2 1 1 0 2 1 1 0 2 0 2 1 1 2 1 2\n",
            " 2 0 2 2 2 2 0 0 2 1 2 1 0 1 0 1 1 1 0 0 2 1 2 0 0 0 2 1 0 0 0 2 1 0 2 0 2\n",
            " 1 1 0 0 1 2 1 1 2 0 0 2 1 1 1 1 2 0 0 1 1 0 0 1 2 1 1 0 2 0 0 2 1 1 1 2 2\n",
            " 1 1 0 1 1 0 1 1 2 1 1 2 0 2 1 1 2 0 1 2 0 1 1 0 0 1 1 1 2 1 0 2 1 2 0 0 0\n",
            " 2 0 0 0 1 2 0 2 1 2 0 0 1 1 1 1 0 1 2 2 2 1 0 2 0 1 1 2 1 0 0 0 2 0 1 2 2\n",
            " 2 0 1 0 0 1 1 2 2 0 0 1 0 2 1 1 0 0 1 0 2 2 1 2 0 1 1 2 1 0 2 0 1 0 0 2 1\n",
            " 2 0 0 0 1 0 0 0 2 0 2 2 0 1 2 1 0 1 0 2 0 0 1 1 1 0 2 2 0 0 2 1 1 0 1 0 1\n",
            " 2 1 0 1 1 0 2 2 2 2 1 0 1 2 1 1 1 2 0 0 1 0 2 1 0 0 1 2 2 2 2 1 0 0 1 0 2\n",
            " 0 1 1 2 1 2 0 0 1 0 2 0 0 1 0 2 2 0 2 2 1 2 1 1 2 1 1 0 1 2 1 2 0 1 2 1 0\n",
            " 0 2 1 1 2 2 1 1 0 2 2 2 1 2 0 1 2 1 0 1 2 1 1 2 2 0 1 1 1 1 1 0 0 0 1 2 0\n",
            " 1 1 0 0 1 0 1 2 2 0 0 2 0 0 1 0 0 1 0 0 2 1 0 1 2 1 1 2 2 0 2 1 2 2 1 0 1\n",
            " 1 2 1 0 1 2 2 0 1 0 1 0 1 1 2 0 0 1 1 0 0 1 2 0 1 2 2 1 1 1 1 0 2 1 0 0 2\n",
            " 1 2 1 0 2 0 2 1 1 1 0 1 0 0 0 2 0 1 1 2 1 1 1 0 2 2 2 2 0 1 1 0 0 1 0 0 0\n",
            " 0 1 0 0 1 0 0 2 0 2 1 1 1 1 0 0 0 1 1 1 1 0 1 0 2 0 1 0 1 1 1 1 1 1 2 0 2\n",
            " 0 2 1 0 2 2 1 2 1 0 0 1 2 1 2 2 2 1 2 0 1 1 1 1 1 1 2 2 0 0 0 2 0 1 0 0 1\n",
            " 0 2 2 2 0 2 0 2 2 2 0 2 0 2 0 2 1 2 0 2 2 1 2 0 0 2 0 2 0 1 2 0 2 2 0 1 0\n",
            " 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hlg1D7jw85z_",
        "outputId": "97407f50-87fb-4c5c-e656-4db4644ad38a"
      },
      "source": [
        "Y_label[Y_label >=1] = 1\n",
        "print(Y_label)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 1 0 1 1 0 1 0 1 1 0 0 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1\n",
            " 0 1 1 1 1 0 1 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1 1 0 0 0 1 1 1 0 1 1 0\n",
            " 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 0 0 1 1 0 1 0 1 0 0 0 0 1 1 1 0 0 0 1 1\n",
            " 0 0 0 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 1 0 1\n",
            " 0 0 0 1 1 1 1 1 0 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1\n",
            " 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 0 1 1 0 0 1 1 0 0 1 0 1 1 1 1 1 1 1 1 0 0 1\n",
            " 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 1 0 1 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 1 1 0\n",
            " 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 0 1 1 1 0 0 1 0 1 0\n",
            " 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1 1 0 1 1 1 1 1 1 0 0 0 1 1 1 0 1 1 1 0 0 1 1\n",
            " 1 1 1 1 0 0 1 1 0 0 0 1 1 1 0 1 1 0 0 0 1 1 1 1 0 0 1 1 0 1 0 0 0 1 0 0 1\n",
            " 0 1 1 1 0 1 0 1 1 0 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1\n",
            " 1 1 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1\n",
            " 1 0 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 1 1 0 1 0 1\n",
            " 1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 1 1\n",
            " 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 0\n",
            " 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 0 0 0 1 0 1 1 1\n",
            " 1 0 1 0 0 1 1 1 1 0 0 1 0 1 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 0 1 0 1 0 0 1 1\n",
            " 1 0 0 0 1 0 0 0 1 0 1 1 0 1 1 1 0 1 0 1 0 0 1 1 1 0 1 1 0 0 1 1 1 0 1 0 1\n",
            " 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 0 1\n",
            " 0 1 1 1 1 1 0 0 1 0 1 0 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0\n",
            " 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 1 1 0\n",
            " 1 1 0 0 1 0 1 1 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1\n",
            " 1 1 1 0 1 1 1 0 1 0 1 0 1 1 1 0 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1\n",
            " 1 1 1 0 1 0 1 1 1 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 0 1 0 0 0\n",
            " 0 1 0 0 1 0 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 0 1 1 1 1 1 1 1 0 1\n",
            " 0 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 0 1 0 0 1\n",
            " 0 1 1 1 0 1 0 1 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 0 0 1 0 1 0 1 1 0 1 1 0 1 0\n",
            " 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lz89pTNvl0le"
      },
      "source": [
        "Se construye un dataframe con las anteriores listas distribuidas en forma de columnas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZHEA5_YvJZIj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99022f36-5823-41f1-8d47-3f30a533cfae"
      },
      "source": [
        "# Some arrangements for next plots\n",
        "df2=np.column_stack((randomList1, randomList2, randomSum,Y_label))\n",
        "print(df2.shape)\n",
        "dfnew=pd.DataFrame(data=df2)\n",
        "print(dfnew.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1000, 4)\n",
            "(1000, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNz3CIyuRzMO"
      },
      "source": [
        "Se pone nombre a las columnas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dq7S0KWRRzls",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "383fdca1-0212-4577-bc2b-88faba6552f1"
      },
      "source": [
        "print(dfnew.columns)\n",
        "dfnew.columns = [\"V1\",\"V2\",\"Suma\",\"Y_label\"]\n",
        "print(dfnew.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RangeIndex(start=0, stop=4, step=1)\n",
            "Index(['V1', 'V2', 'Suma', 'Y_label'], dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NGidlW3Rnsl"
      },
      "source": [
        "Se borran estructuras de datos innecesarias."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksxWmJD-RwKG"
      },
      "source": [
        "del(df2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3J75JEXmKGry",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45be23bb-4632-4460-b11e-1ad2d043e7a3"
      },
      "source": [
        "print(dfnew)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     V1  V2  Suma  Y_label\n",
            "0     3   5     8        1\n",
            "1     6   7    13        1\n",
            "2     5   0     5        1\n",
            "3     4   2     6        0\n",
            "4     8   3    11        1\n",
            "..   ..  ..   ...      ...\n",
            "995   7   1     8        1\n",
            "996   6   3     9        0\n",
            "997   0   7     7        1\n",
            "998   2   4     6        0\n",
            "999   6   7    13        1\n",
            "\n",
            "[1000 rows x 4 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3YFmUE4lvsH"
      },
      "source": [
        "Se eliminan las variables no necesarias."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuLt5jPPmLcS"
      },
      "source": [
        "del(randomList1,randomList2,randomSum)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5JDha7BmRD_"
      },
      "source": [
        "A partir del dataframe, se separan las variables independientes (X) y las dependientes (Y)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HpxU3-4hKiN2",
        "outputId": "344c6089-a773-4a7f-c8f6-8d6442a50680"
      },
      "source": [
        "# Para las variables independientes se seleccionan las dos primeras columnas del dataframe dfnew.\n",
        "X = dfnew.iloc[:,0:2]\n",
        "print(X.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1000, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0-jGhTMbKswW",
        "outputId": "2daf834e-3546-4b91-9e80-0069c6b99081"
      },
      "source": [
        "# y para la variable dependiente se selecciona la cuarta columna (índice 3).\n",
        "Y = dfnew.iloc[:,3]\n",
        "print(Y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6gM5MQLK68I"
      },
      "source": [
        "# Quality check\n",
        "assert X.shape[0] == Y.shape[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3QGkp0AMLOI_"
      },
      "source": [
        "## Separación de los conjuntos de entrenamiento y test.\n",
        "Se verifica la dimensionalidad."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-7z4Y6KaLFaI",
        "outputId": "2d7e6118-ec46-4f5c-baaf-5f36bcb1901c"
      },
      "source": [
        "print(X.shape)\n",
        "test_size = int(np.floor(0.30*X.shape[0]) )\n",
        "print(test_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1000, 2)\n",
            "300\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxwcduzFQjo1"
      },
      "source": [
        " X = np.asarray(X)# convertirnos los datos de DataFrame a Numpy Arrays\n",
        " Y = np.asarray(Y)# para que sean leidos por los modulos de Keras"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8vxfjfFuLNDD",
        "outputId": "e64694ef-8d3f-4a81-d2fa-235bd88d4092"
      },
      "source": [
        "trainX, testX = X[:-test_size], X[-test_size:]\n",
        "trainY, testY = Y[:-test_size], Y[-test_size:]\n",
        "print(trainX.shape,testX.shape)\n",
        "print(len(trainY),len(testY))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(700, 2) (300, 2)\n",
            "700 300\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlhPCrA4MBMu",
        "outputId": "ed081a97-3755-414e-a111-aa852cc69a5e"
      },
      "source": [
        " trainX_num=trainX.shape[1]\n",
        " print(trainX_num)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MVEhMG8WpEA5"
      },
      "source": [
        "Definición de la Red Neuronal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qhmjlgItQ66v",
        "outputId": "7daccf2c-7754-4e85-bd1c-a1c2e95a4b13"
      },
      "source": [
        "model = keras.Sequential() # creamos el modelo secuencial\n",
        "\n",
        "# Ponemos una primera capa oculta\n",
        "model.add(Dense(100, activation='relu', input_shape=(2,))) \n",
        "print(model.layers[-1].output_shape)\n",
        "# Segunda capa\n",
        "model.add(Dense(50, activation='relu')) \n",
        "print(model.layers[-1].output_shape)\n",
        "# Tercera capa\n",
        "model.add(Dense(50, activation='relu')) \n",
        "print(model.layers[-1].output_shape)\n",
        "# Incorporamos la capa de salida\n",
        "model.add(Dense( 1, activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(None, 100)\n",
            "(None, 50)\n",
            "(None, 50)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAJ76CKqpD7-"
      },
      "source": [
        "Se muestra un resumen de la red creada."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ds2gz3S7dbuK",
        "outputId": "1d0b156a-b880-4320-950d-93c3a2712009"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 100)               300       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 50)                5050      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 50)                2550      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 7,951\n",
            "Trainable params: 7,951\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6TCICToeo7mC"
      },
      "source": [
        "Se compila el modelo, incluyendo la elección del optimizador y del error que se minimizará durante el entrenamiento. A continuación se entrena el modelo, almacenando la información del error en la variable \"history\". "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6_0STjWcGSA"
      },
      "source": [
        "model.compile(loss='binary_crossentropy',  optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2_es52ISayE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d19795f7-4851-4fff-8545-d34ebaec1a50"
      },
      "source": [
        " history=model.fit(trainX, trainY, epochs=100, batch_size=16, validation_data=(testX, testY), verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "44/44 - 1s - loss: 0.6531 - accuracy: 0.6543 - val_loss: 0.6498 - val_accuracy: 0.6667\n",
            "Epoch 2/100\n",
            "44/44 - 0s - loss: 0.6553 - accuracy: 0.6629 - val_loss: 0.6441 - val_accuracy: 0.6667\n",
            "Epoch 3/100\n",
            "44/44 - 0s - loss: 0.6474 - accuracy: 0.6629 - val_loss: 0.6599 - val_accuracy: 0.6667\n",
            "Epoch 4/100\n",
            "44/44 - 0s - loss: 0.6450 - accuracy: 0.6629 - val_loss: 0.6434 - val_accuracy: 0.6667\n",
            "Epoch 5/100\n",
            "44/44 - 0s - loss: 0.6395 - accuracy: 0.6757 - val_loss: 0.6407 - val_accuracy: 0.6833\n",
            "Epoch 6/100\n",
            "44/44 - 0s - loss: 0.6453 - accuracy: 0.6757 - val_loss: 0.6406 - val_accuracy: 0.6833\n",
            "Epoch 7/100\n",
            "44/44 - 0s - loss: 0.6374 - accuracy: 0.6757 - val_loss: 0.6357 - val_accuracy: 0.6833\n",
            "Epoch 8/100\n",
            "44/44 - 0s - loss: 0.6395 - accuracy: 0.6757 - val_loss: 0.6313 - val_accuracy: 0.6833\n",
            "Epoch 9/100\n",
            "44/44 - 0s - loss: 0.6334 - accuracy: 0.6757 - val_loss: 0.6337 - val_accuracy: 0.6833\n",
            "Epoch 10/100\n",
            "44/44 - 0s - loss: 0.6278 - accuracy: 0.6757 - val_loss: 0.6339 - val_accuracy: 0.6833\n",
            "Epoch 11/100\n",
            "44/44 - 0s - loss: 0.6295 - accuracy: 0.6757 - val_loss: 0.6239 - val_accuracy: 0.6833\n",
            "Epoch 12/100\n",
            "44/44 - 0s - loss: 0.6261 - accuracy: 0.6757 - val_loss: 0.6351 - val_accuracy: 0.6833\n",
            "Epoch 13/100\n",
            "44/44 - 0s - loss: 0.6222 - accuracy: 0.6757 - val_loss: 0.6188 - val_accuracy: 0.6833\n",
            "Epoch 14/100\n",
            "44/44 - 0s - loss: 0.6212 - accuracy: 0.6757 - val_loss: 0.6203 - val_accuracy: 0.6833\n",
            "Epoch 15/100\n",
            "44/44 - 0s - loss: 0.6200 - accuracy: 0.6757 - val_loss: 0.6182 - val_accuracy: 0.6833\n",
            "Epoch 16/100\n",
            "44/44 - 0s - loss: 0.6157 - accuracy: 0.6757 - val_loss: 0.6165 - val_accuracy: 0.6833\n",
            "Epoch 17/100\n",
            "44/44 - 0s - loss: 0.6167 - accuracy: 0.6757 - val_loss: 0.6098 - val_accuracy: 0.6833\n",
            "Epoch 18/100\n",
            "44/44 - 0s - loss: 0.6093 - accuracy: 0.6757 - val_loss: 0.6122 - val_accuracy: 0.6833\n",
            "Epoch 19/100\n",
            "44/44 - 0s - loss: 0.6093 - accuracy: 0.6757 - val_loss: 0.6072 - val_accuracy: 0.6833\n",
            "Epoch 20/100\n",
            "44/44 - 0s - loss: 0.6046 - accuracy: 0.6771 - val_loss: 0.6122 - val_accuracy: 0.6967\n",
            "Epoch 21/100\n",
            "44/44 - 0s - loss: 0.6050 - accuracy: 0.6771 - val_loss: 0.6040 - val_accuracy: 0.6833\n",
            "Epoch 22/100\n",
            "44/44 - 0s - loss: 0.6068 - accuracy: 0.6757 - val_loss: 0.6169 - val_accuracy: 0.6833\n",
            "Epoch 23/100\n",
            "44/44 - 0s - loss: 0.6063 - accuracy: 0.6757 - val_loss: 0.6021 - val_accuracy: 0.6833\n",
            "Epoch 24/100\n",
            "44/44 - 0s - loss: 0.6072 - accuracy: 0.6757 - val_loss: 0.6062 - val_accuracy: 0.6833\n",
            "Epoch 25/100\n",
            "44/44 - 0s - loss: 0.6048 - accuracy: 0.6757 - val_loss: 0.6020 - val_accuracy: 0.6833\n",
            "Epoch 26/100\n",
            "44/44 - 0s - loss: 0.6024 - accuracy: 0.6757 - val_loss: 0.6023 - val_accuracy: 0.6833\n",
            "Epoch 27/100\n",
            "44/44 - 0s - loss: 0.6031 - accuracy: 0.6757 - val_loss: 0.6100 - val_accuracy: 0.6833\n",
            "Epoch 28/100\n",
            "44/44 - 0s - loss: 0.6080 - accuracy: 0.6714 - val_loss: 0.6022 - val_accuracy: 0.6833\n",
            "Epoch 29/100\n",
            "44/44 - 0s - loss: 0.6019 - accuracy: 0.6757 - val_loss: 0.6038 - val_accuracy: 0.6833\n",
            "Epoch 30/100\n",
            "44/44 - 0s - loss: 0.6006 - accuracy: 0.6757 - val_loss: 0.5993 - val_accuracy: 0.6833\n",
            "Epoch 31/100\n",
            "44/44 - 0s - loss: 0.6002 - accuracy: 0.6757 - val_loss: 0.6000 - val_accuracy: 0.6833\n",
            "Epoch 32/100\n",
            "44/44 - 0s - loss: 0.5994 - accuracy: 0.6757 - val_loss: 0.5986 - val_accuracy: 0.6833\n",
            "Epoch 33/100\n",
            "44/44 - 0s - loss: 0.5997 - accuracy: 0.6757 - val_loss: 0.6002 - val_accuracy: 0.6833\n",
            "Epoch 34/100\n",
            "44/44 - 0s - loss: 0.5987 - accuracy: 0.6757 - val_loss: 0.6026 - val_accuracy: 0.6833\n",
            "Epoch 35/100\n",
            "44/44 - 0s - loss: 0.5982 - accuracy: 0.6757 - val_loss: 0.5991 - val_accuracy: 0.6833\n",
            "Epoch 36/100\n",
            "44/44 - 0s - loss: 0.5987 - accuracy: 0.6757 - val_loss: 0.6013 - val_accuracy: 0.6833\n",
            "Epoch 37/100\n",
            "44/44 - 0s - loss: 0.5989 - accuracy: 0.6757 - val_loss: 0.6035 - val_accuracy: 0.6833\n",
            "Epoch 38/100\n",
            "44/44 - 0s - loss: 0.6007 - accuracy: 0.6757 - val_loss: 0.5984 - val_accuracy: 0.6833\n",
            "Epoch 39/100\n",
            "44/44 - 0s - loss: 0.5955 - accuracy: 0.6757 - val_loss: 0.5990 - val_accuracy: 0.6833\n",
            "Epoch 40/100\n",
            "44/44 - 0s - loss: 0.5949 - accuracy: 0.6757 - val_loss: 0.5968 - val_accuracy: 0.6833\n",
            "Epoch 41/100\n",
            "44/44 - 0s - loss: 0.5953 - accuracy: 0.6757 - val_loss: 0.6042 - val_accuracy: 0.6833\n",
            "Epoch 42/100\n",
            "44/44 - 0s - loss: 0.5969 - accuracy: 0.6757 - val_loss: 0.5974 - val_accuracy: 0.6833\n",
            "Epoch 43/100\n",
            "44/44 - 0s - loss: 0.5960 - accuracy: 0.6714 - val_loss: 0.5992 - val_accuracy: 0.6833\n",
            "Epoch 44/100\n",
            "44/44 - 0s - loss: 0.5908 - accuracy: 0.6757 - val_loss: 0.5977 - val_accuracy: 0.6833\n",
            "Epoch 45/100\n",
            "44/44 - 0s - loss: 0.5905 - accuracy: 0.6757 - val_loss: 0.5962 - val_accuracy: 0.6833\n",
            "Epoch 46/100\n",
            "44/44 - 0s - loss: 0.5890 - accuracy: 0.6786 - val_loss: 0.5925 - val_accuracy: 0.6867\n",
            "Epoch 47/100\n",
            "44/44 - 0s - loss: 0.5879 - accuracy: 0.6943 - val_loss: 0.5976 - val_accuracy: 0.6867\n",
            "Epoch 48/100\n",
            "44/44 - 0s - loss: 0.5927 - accuracy: 0.6914 - val_loss: 0.5929 - val_accuracy: 0.6867\n",
            "Epoch 49/100\n",
            "44/44 - 0s - loss: 0.5833 - accuracy: 0.6957 - val_loss: 0.5915 - val_accuracy: 0.6867\n",
            "Epoch 50/100\n",
            "44/44 - 0s - loss: 0.5880 - accuracy: 0.7014 - val_loss: 0.5886 - val_accuracy: 0.6967\n",
            "Epoch 51/100\n",
            "44/44 - 0s - loss: 0.5813 - accuracy: 0.7057 - val_loss: 0.5855 - val_accuracy: 0.7100\n",
            "Epoch 52/100\n",
            "44/44 - 0s - loss: 0.5820 - accuracy: 0.7129 - val_loss: 0.5866 - val_accuracy: 0.7100\n",
            "Epoch 53/100\n",
            "44/44 - 0s - loss: 0.5743 - accuracy: 0.7143 - val_loss: 0.5804 - val_accuracy: 0.7100\n",
            "Epoch 54/100\n",
            "44/44 - 0s - loss: 0.5748 - accuracy: 0.7114 - val_loss: 0.5778 - val_accuracy: 0.7100\n",
            "Epoch 55/100\n",
            "44/44 - 0s - loss: 0.5713 - accuracy: 0.7143 - val_loss: 0.5847 - val_accuracy: 0.7100\n",
            "Epoch 56/100\n",
            "44/44 - 0s - loss: 0.5736 - accuracy: 0.7143 - val_loss: 0.5842 - val_accuracy: 0.7100\n",
            "Epoch 57/100\n",
            "44/44 - 0s - loss: 0.5702 - accuracy: 0.7171 - val_loss: 0.5874 - val_accuracy: 0.7100\n",
            "Epoch 58/100\n",
            "44/44 - 0s - loss: 0.5654 - accuracy: 0.7143 - val_loss: 0.5807 - val_accuracy: 0.7100\n",
            "Epoch 59/100\n",
            "44/44 - 0s - loss: 0.5598 - accuracy: 0.7171 - val_loss: 0.5721 - val_accuracy: 0.7233\n",
            "Epoch 60/100\n",
            "44/44 - 0s - loss: 0.5606 - accuracy: 0.7157 - val_loss: 0.5771 - val_accuracy: 0.7167\n",
            "Epoch 61/100\n",
            "44/44 - 0s - loss: 0.5584 - accuracy: 0.7129 - val_loss: 0.5698 - val_accuracy: 0.7100\n",
            "Epoch 62/100\n",
            "44/44 - 0s - loss: 0.5544 - accuracy: 0.7157 - val_loss: 0.5708 - val_accuracy: 0.7100\n",
            "Epoch 63/100\n",
            "44/44 - 0s - loss: 0.5520 - accuracy: 0.7143 - val_loss: 0.5653 - val_accuracy: 0.7100\n",
            "Epoch 64/100\n",
            "44/44 - 0s - loss: 0.5487 - accuracy: 0.7186 - val_loss: 0.5668 - val_accuracy: 0.7100\n",
            "Epoch 65/100\n",
            "44/44 - 0s - loss: 0.5473 - accuracy: 0.7143 - val_loss: 0.5649 - val_accuracy: 0.7233\n",
            "Epoch 66/100\n",
            "44/44 - 0s - loss: 0.5511 - accuracy: 0.7214 - val_loss: 0.5598 - val_accuracy: 0.7233\n",
            "Epoch 67/100\n",
            "44/44 - 0s - loss: 0.5466 - accuracy: 0.7214 - val_loss: 0.5621 - val_accuracy: 0.7100\n",
            "Epoch 68/100\n",
            "44/44 - 0s - loss: 0.5458 - accuracy: 0.7157 - val_loss: 0.5592 - val_accuracy: 0.7100\n",
            "Epoch 69/100\n",
            "44/44 - 0s - loss: 0.5498 - accuracy: 0.7100 - val_loss: 0.5626 - val_accuracy: 0.7233\n",
            "Epoch 70/100\n",
            "44/44 - 0s - loss: 0.5494 - accuracy: 0.7214 - val_loss: 0.5635 - val_accuracy: 0.7167\n",
            "Epoch 71/100\n",
            "44/44 - 0s - loss: 0.5437 - accuracy: 0.7186 - val_loss: 0.5595 - val_accuracy: 0.7100\n",
            "Epoch 72/100\n",
            "44/44 - 0s - loss: 0.5403 - accuracy: 0.7214 - val_loss: 0.5629 - val_accuracy: 0.7233\n",
            "Epoch 73/100\n",
            "44/44 - 0s - loss: 0.5428 - accuracy: 0.7257 - val_loss: 0.5601 - val_accuracy: 0.7100\n",
            "Epoch 74/100\n",
            "44/44 - 0s - loss: 0.5386 - accuracy: 0.7186 - val_loss: 0.5626 - val_accuracy: 0.7100\n",
            "Epoch 75/100\n",
            "44/44 - 0s - loss: 0.5399 - accuracy: 0.7186 - val_loss: 0.5570 - val_accuracy: 0.7167\n",
            "Epoch 76/100\n",
            "44/44 - 0s - loss: 0.5448 - accuracy: 0.7171 - val_loss: 0.5581 - val_accuracy: 0.7100\n",
            "Epoch 77/100\n",
            "44/44 - 0s - loss: 0.5369 - accuracy: 0.7214 - val_loss: 0.5543 - val_accuracy: 0.7233\n",
            "Epoch 78/100\n",
            "44/44 - 0s - loss: 0.5327 - accuracy: 0.7214 - val_loss: 0.5506 - val_accuracy: 0.7233\n",
            "Epoch 79/100\n",
            "44/44 - 0s - loss: 0.5331 - accuracy: 0.7214 - val_loss: 0.5509 - val_accuracy: 0.7233\n",
            "Epoch 80/100\n",
            "44/44 - 0s - loss: 0.5315 - accuracy: 0.7271 - val_loss: 0.5646 - val_accuracy: 0.7333\n",
            "Epoch 81/100\n",
            "44/44 - 0s - loss: 0.5347 - accuracy: 0.7243 - val_loss: 0.5509 - val_accuracy: 0.7233\n",
            "Epoch 82/100\n",
            "44/44 - 0s - loss: 0.5275 - accuracy: 0.7271 - val_loss: 0.5550 - val_accuracy: 0.7333\n",
            "Epoch 83/100\n",
            "44/44 - 0s - loss: 0.5270 - accuracy: 0.7286 - val_loss: 0.5475 - val_accuracy: 0.7233\n",
            "Epoch 84/100\n",
            "44/44 - 0s - loss: 0.5280 - accuracy: 0.7314 - val_loss: 0.5452 - val_accuracy: 0.7333\n",
            "Epoch 85/100\n",
            "44/44 - 0s - loss: 0.5210 - accuracy: 0.7214 - val_loss: 0.5475 - val_accuracy: 0.7333\n",
            "Epoch 86/100\n",
            "44/44 - 0s - loss: 0.5287 - accuracy: 0.7214 - val_loss: 0.5474 - val_accuracy: 0.7200\n",
            "Epoch 87/100\n",
            "44/44 - 0s - loss: 0.5181 - accuracy: 0.7314 - val_loss: 0.5412 - val_accuracy: 0.7200\n",
            "Epoch 88/100\n",
            "44/44 - 0s - loss: 0.5182 - accuracy: 0.7357 - val_loss: 0.5459 - val_accuracy: 0.7233\n",
            "Epoch 89/100\n",
            "44/44 - 0s - loss: 0.5183 - accuracy: 0.7257 - val_loss: 0.5381 - val_accuracy: 0.7200\n",
            "Epoch 90/100\n",
            "44/44 - 0s - loss: 0.5097 - accuracy: 0.7529 - val_loss: 0.5387 - val_accuracy: 0.7267\n",
            "Epoch 91/100\n",
            "44/44 - 0s - loss: 0.5045 - accuracy: 0.7400 - val_loss: 0.5379 - val_accuracy: 0.7367\n",
            "Epoch 92/100\n",
            "44/44 - 0s - loss: 0.5087 - accuracy: 0.7300 - val_loss: 0.5451 - val_accuracy: 0.7300\n",
            "Epoch 93/100\n",
            "44/44 - 0s - loss: 0.5061 - accuracy: 0.7457 - val_loss: 0.5335 - val_accuracy: 0.7333\n",
            "Epoch 94/100\n",
            "44/44 - 0s - loss: 0.5048 - accuracy: 0.7371 - val_loss: 0.5298 - val_accuracy: 0.7333\n",
            "Epoch 95/100\n",
            "44/44 - 0s - loss: 0.4977 - accuracy: 0.7500 - val_loss: 0.5344 - val_accuracy: 0.7533\n",
            "Epoch 96/100\n",
            "44/44 - 0s - loss: 0.4928 - accuracy: 0.7571 - val_loss: 0.5235 - val_accuracy: 0.7533\n",
            "Epoch 97/100\n",
            "44/44 - 0s - loss: 0.4896 - accuracy: 0.7586 - val_loss: 0.5338 - val_accuracy: 0.7400\n",
            "Epoch 98/100\n",
            "44/44 - 0s - loss: 0.4987 - accuracy: 0.7500 - val_loss: 0.5207 - val_accuracy: 0.7533\n",
            "Epoch 99/100\n",
            "44/44 - 0s - loss: 0.4870 - accuracy: 0.7571 - val_loss: 0.5210 - val_accuracy: 0.7367\n",
            "Epoch 100/100\n",
            "44/44 - 0s - loss: 0.4900 - accuracy: 0.7571 - val_loss: 0.5193 - val_accuracy: 0.7733\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "se6go8Fdgxls",
        "outputId": "c2e4b72c-1c0c-4070-c3c1-be3acbbf10b2"
      },
      "source": [
        "from keras.utils.layer_utils import count_params\n",
        "\n",
        "trainable_count = count_params(model.trainable_weights)\n",
        "non_trainable_count = count_params(model.non_trainable_weights)\n",
        "print(trainable_count,non_trainable_count)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7951 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQyPvNYUg2-n"
      },
      "source": [
        "# save model and architecture to single file\n",
        "#model.save('model_CNN1d_neckeventsClass_Filters_'+str(filters)+'_Kernels_'+str(kernel_sz)+'_Epochs_'+str(nepochs)+'_Batch_'+str(sizeofbatch)+'.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EoZKsYcKg_2C"
      },
      "source": [
        "pred = model.predict(testX)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T50T59BBhBin",
        "outputId": "67c34aca-6a22-4f49-b15c-a2ad39162844"
      },
      "source": [
        "# Errors calculation ECM y EAM\n",
        "testScoreECM = mean_squared_error(testY, pred)\n",
        "print('ECM: %.4f' % (testScoreECM))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ECM: 0.1779\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eXaUNSfCkvxm"
      },
      "source": [
        "## Visualización de resultados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugDhsd5PSsXR"
      },
      "source": [
        "En la siguiente gráfica se visualiza la evolución del error para el conjunto de entrenamiento y test en función de las épocas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "3lX4jW4Rgllt",
        "outputId": "75ef3bcb-0e08-403d-be42-4d9a67a2b283"
      },
      "source": [
        "plt.figure(1, figsize=(8,8))\n",
        "plt.plot(history.history['loss'])\n",
        "\n",
        "plt.plot(history.history['val_loss'])\n",
        "\n",
        "#plt.yscale(\"log\")\n",
        "#plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "#plt.xlim(-0.5,20.1)\n",
        "plt.legend(['train', 'test'], loc='upper center')\n",
        "\n",
        "from google.colab import files\n",
        "plt.savefig('loss_MLP.eps')\n",
        "#files.download('loss_MLP.eps') # Descomentar para descargar localmente"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
            "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uZQZmAlUSQTq"
      },
      "source": [
        "En la siguiente gráfica se visualiza la evolución de la calidad de la clasificación para el conjunto de entrenamiento y test en función de las épocas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "QRJLuHl3F3qg",
        "outputId": "4dbc9f2d-919f-402b-8298-f855a9cd9db2"
      },
      "source": [
        "plt.figure(1, figsize=(8,8))\n",
        "plt.plot(history.history['accuracy'])\n",
        "\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.ylabel('accuray')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper center')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f28aea9b250>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A3AO4VG5i144",
        "outputId": "6090462d-3076-4b19-95bb-d94297ac8360"
      },
      "source": [
        "print('V1','V2','\\t\\t','Real','\\t','Pronóstico')\n",
        "for i in range(len(testY)):\n",
        "  print(i, testX[i],'\\t',testY[i],'\\t',pred[i])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "V1 V2 \t\t Real \t Pronóstico\n",
            "0 [2 5] \t 1 \t [0.70090896]\n",
            "1 [5 4] \t 0 \t [0.4980222]\n",
            "2 [5 0] \t 1 \t [0.96421397]\n",
            "3 [6 9] \t 0 \t [0.65390503]\n",
            "4 [5 5] \t 1 \t [0.58757085]\n",
            "5 [7 3] \t 1 \t [0.77810323]\n",
            "6 [3 5] \t 1 \t [0.6852763]\n",
            "7 [0 1] \t 1 \t [0.9999213]\n",
            "8 [2 6] \t 1 \t [0.60593486]\n",
            "9 [3 3] \t 0 \t [0.56304705]\n",
            "10 [1 8] \t 0 \t [0.5503015]\n",
            "11 [9 1] \t 1 \t [0.67350316]\n",
            "12 [9 0] \t 0 \t [0.4373721]\n",
            "13 [8 3] \t 1 \t [0.7769079]\n",
            "14 [8 7] \t 0 \t [0.62459815]\n",
            "15 [0 6] \t 0 \t [0.56246525]\n",
            "16 [8 5] \t 1 \t [0.6371336]\n",
            "17 [6 6] \t 0 \t [0.61014605]\n",
            "18 [6 2] \t 1 \t [0.8072833]\n",
            "19 [1 4] \t 1 \t [0.9583831]\n",
            "20 [4 5] \t 0 \t [0.60010314]\n",
            "21 [3 5] \t 1 \t [0.6852763]\n",
            "22 [5 3] \t 1 \t [0.7632425]\n",
            "23 [5 8] \t 1 \t [0.6452526]\n",
            "24 [4 1] \t 1 \t [0.9045775]\n",
            "25 [9 7] \t 1 \t [0.5557067]\n",
            "26 [3 4] \t 1 \t [0.6350658]\n",
            "27 [8 9] \t 1 \t [0.6549438]\n",
            "28 [2 8] \t 1 \t [0.5828988]\n",
            "29 [3 4] \t 1 \t [0.6350658]\n",
            "30 [0 9] \t 0 \t [0.49433798]\n",
            "31 [6 7] \t 1 \t [0.63269436]\n",
            "32 [2 0] \t 1 \t [0.9996033]\n",
            "33 [8 2] \t 1 \t [0.81548184]\n",
            "34 [3 5] \t 1 \t [0.6852763]\n",
            "35 [3 9] \t 0 \t [0.60108787]\n",
            "36 [8 5] \t 1 \t [0.6371336]\n",
            "37 [2 6] \t 1 \t [0.60593486]\n",
            "38 [6 4] \t 1 \t [0.7074858]\n",
            "39 [2 7] \t 0 \t [0.59686077]\n",
            "40 [0 0] \t 0 \t [0.00020245]\n",
            "41 [3 8] \t 1 \t [0.6132326]\n",
            "42 [0 7] \t 1 \t [0.53935385]\n",
            "43 [3 4] \t 1 \t [0.6350658]\n",
            "44 [9 2] \t 1 \t [0.81593966]\n",
            "45 [0 8] \t 1 \t [0.51606894]\n",
            "46 [4 3] \t 1 \t [0.7478789]\n",
            "47 [2 2] \t 1 \t [0.99634075]\n",
            "48 [7 5] \t 0 \t [0.41552022]\n",
            "49 [1 1] \t 1 \t [0.99959725]\n",
            "50 [2 9] \t 1 \t [0.56869376]\n",
            "51 [2 6] \t 1 \t [0.60593486]\n",
            "52 [2 5] \t 1 \t [0.70090896]\n",
            "53 [1 4] \t 1 \t [0.9583831]\n",
            "54 [5 7] \t 0 \t [0.6371195]\n",
            "55 [9 4] \t 1 \t [0.7439392]\n",
            "56 [5 3] \t 1 \t [0.7632425]\n",
            "57 [3 4] \t 1 \t [0.6350658]\n",
            "58 [7 5] \t 0 \t [0.41552022]\n",
            "59 [1 6] \t 1 \t [0.5808178]\n",
            "60 [0 5] \t 1 \t [0.8832369]\n",
            "61 [1 6] \t 1 \t [0.5808178]\n",
            "62 [1 9] \t 1 \t [0.5345327]\n",
            "63 [8 3] \t 1 \t [0.77690804]\n",
            "64 [3 5] \t 1 \t [0.6852763]\n",
            "65 [5 4] \t 0 \t [0.4980222]\n",
            "66 [4 6] \t 1 \t [0.63352823]\n",
            "67 [4 6] \t 1 \t [0.63352823]\n",
            "68 [2 5] \t 1 \t [0.70090896]\n",
            "69 [4 3] \t 1 \t [0.7478789]\n",
            "70 [5 2] \t 1 \t [0.72913325]\n",
            "71 [6 0] \t 0 \t [0.7093947]\n",
            "72 [4 5] \t 0 \t [0.60010314]\n",
            "73 [2 7] \t 0 \t [0.59686077]\n",
            "74 [6 4] \t 1 \t [0.7074858]\n",
            "75 [1 7] \t 1 \t [0.5659699]\n",
            "76 [7 8] \t 0 \t [0.64634144]\n",
            "77 [6 7] \t 1 \t [0.6326943]\n",
            "78 [2 8] \t 1 \t [0.5828988]\n",
            "79 [3 9] \t 0 \t [0.60108787]\n",
            "80 [9 9] \t 0 \t [0.648924]\n",
            "81 [4 9] \t 1 \t [0.62623394]\n",
            "82 [1 2] \t 0 \t [0.00116125]\n",
            "83 [4 3] \t 1 \t [0.7478789]\n",
            "84 [1 1] \t 1 \t [0.99959725]\n",
            "85 [0 8] \t 1 \t [0.51606894]\n",
            "86 [7 5] \t 0 \t [0.41552022]\n",
            "87 [3 3] \t 0 \t [0.56304705]\n",
            "88 [4 4] \t 1 \t [0.564619]\n",
            "89 [5 4] \t 0 \t [0.4980222]\n",
            "90 [0 0] \t 0 \t [0.00020245]\n",
            "91 [8 8] \t 1 \t [0.640656]\n",
            "92 [2 4] \t 0 \t [0.7706617]\n",
            "93 [2 7] \t 0 \t [0.59686077]\n",
            "94 [5 8] \t 1 \t [0.6452526]\n",
            "95 [4 8] \t 0 \t [0.6300707]\n",
            "96 [9 0] \t 0 \t [0.4373721]\n",
            "97 [5 3] \t 1 \t [0.7632425]\n",
            "98 [3 7] \t 1 \t [0.6215582]\n",
            "99 [3 3] \t 0 \t [0.56304705]\n",
            "100 [4 3] \t 1 \t [0.7478789]\n",
            "101 [6 5] \t 1 \t [0.55349445]\n",
            "102 [8 8] \t 1 \t [0.640656]\n",
            "103 [4 0] \t 1 \t [0.99847937]\n",
            "104 [2 9] \t 1 \t [0.56869376]\n",
            "105 [2 9] \t 1 \t [0.56869376]\n",
            "106 [0 6] \t 0 \t [0.56246525]\n",
            "107 [6 2] \t 1 \t [0.8072833]\n",
            "108 [2 5] \t 1 \t [0.70090896]\n",
            "109 [8 0] \t 1 \t [0.5644644]\n",
            "110 [9 8] \t 1 \t [0.6352247]\n",
            "111 [2 8] \t 1 \t [0.5828988]\n",
            "112 [4 5] \t 0 \t [0.60010314]\n",
            "113 [3 7] \t 1 \t [0.6215582]\n",
            "114 [4 6] \t 1 \t [0.63352823]\n",
            "115 [4 1] \t 1 \t [0.9045775]\n",
            "116 [0 4] \t 1 \t [0.9848498]\n",
            "117 [8 1] \t 0 \t [0.7258264]\n",
            "118 [3 4] \t 1 \t [0.6350658]\n",
            "119 [4 4] \t 1 \t [0.564619]\n",
            "120 [2 9] \t 1 \t [0.56869376]\n",
            "121 [9 6] \t 0 \t [0.3517726]\n",
            "122 [2 5] \t 1 \t [0.70090896]\n",
            "123 [8 1] \t 0 \t [0.7258264]\n",
            "124 [4 3] \t 1 \t [0.7478789]\n",
            "125 [0 0] \t 0 \t [0.00020245]\n",
            "126 [0 1] \t 1 \t [0.9999213]\n",
            "127 [6 1] \t 1 \t [0.80734086]\n",
            "128 [6 8] \t 1 \t [0.6495079]\n",
            "129 [7 5] \t 0 \t [0.41552022]\n",
            "130 [6 6] \t 0 \t [0.61014605]\n",
            "131 [7 6] \t 1 \t [0.60596883]\n",
            "132 [9 1] \t 1 \t [0.67350316]\n",
            "133 [3 9] \t 0 \t [0.60108787]\n",
            "134 [9 3] \t 0 \t [0.7806461]\n",
            "135 [7 0] \t 1 \t [0.6482376]\n",
            "136 [8 6] \t 1 \t [0.45719695]\n",
            "137 [2 4] \t 0 \t [0.7706617]\n",
            "138 [9 1] \t 1 \t [0.67350316]\n",
            "139 [1 4] \t 1 \t [0.9583831]\n",
            "140 [0 5] \t 1 \t [0.8832369]\n",
            "141 [6 4] \t 1 \t [0.7074858]\n",
            "142 [3 7] \t 1 \t [0.6215582]\n",
            "143 [8 8] \t 1 \t [0.640656]\n",
            "144 [4 3] \t 1 \t [0.7478789]\n",
            "145 [9 6] \t 0 \t [0.3517726]\n",
            "146 [9 8] \t 1 \t [0.6352247]\n",
            "147 [6 4] \t 1 \t [0.7074858]\n",
            "148 [7 8] \t 0 \t [0.64634144]\n",
            "149 [9 9] \t 0 \t [0.648924]\n",
            "150 [2 3] \t 1 \t [0.975837]\n",
            "151 [3 7] \t 1 \t [0.6215582]\n",
            "152 [6 8] \t 1 \t [0.6495079]\n",
            "153 [4 9] \t 1 \t [0.62623394]\n",
            "154 [5 7] \t 0 \t [0.6371195]\n",
            "155 [8 9] \t 1 \t [0.6549438]\n",
            "156 [5 1] \t 0 \t [0.41898564]\n",
            "157 [9 5] \t 1 \t [0.6908587]\n",
            "158 [9 1] \t 1 \t [0.67350316]\n",
            "159 [7 6] \t 1 \t [0.60596883]\n",
            "160 [1 6] \t 1 \t [0.5808178]\n",
            "161 [0 3] \t 0 \t [0.00892329]\n",
            "162 [6 7] \t 1 \t [0.6326943]\n",
            "163 [3 6] \t 0 \t [0.6288988]\n",
            "164 [3 9] \t 0 \t [0.60108787]\n",
            "165 [4 5] \t 0 \t [0.60010314]\n",
            "166 [1 1] \t 1 \t [0.99959725]\n",
            "167 [8 4] \t 0 \t [0.73499614]\n",
            "168 [2 5] \t 1 \t [0.70090896]\n",
            "169 [8 8] \t 1 \t [0.640656]\n",
            "170 [0 5] \t 1 \t [0.8832369]\n",
            "171 [3 4] \t 1 \t [0.6350658]\n",
            "172 [9 4] \t 1 \t [0.7439392]\n",
            "173 [0 7] \t 1 \t [0.53935385]\n",
            "174 [6 3] \t 0 \t [0.7713723]\n",
            "175 [6 8] \t 1 \t [0.6495079]\n",
            "176 [7 4] \t 1 \t [0.7283981]\n",
            "177 [7 7] \t 1 \t [0.6281894]\n",
            "178 [9 5] \t 1 \t [0.6908587]\n",
            "179 [2 1] \t 0 \t [0.00140682]\n",
            "180 [2 5] \t 1 \t [0.70090896]\n",
            "181 [1 6] \t 1 \t [0.5808178]\n",
            "182 [0 0] \t 0 \t [0.00020245]\n",
            "183 [9 9] \t 0 \t [0.648924]\n",
            "184 [5 2] \t 1 \t [0.72913325]\n",
            "185 [0 9] \t 0 \t [0.49433795]\n",
            "186 [9 9] \t 0 \t [0.648924]\n",
            "187 [7 5] \t 0 \t [0.41552022]\n",
            "188 [5 4] \t 0 \t [0.4980222]\n",
            "189 [3 1] \t 1 \t [0.9954965]\n",
            "190 [8 1] \t 0 \t [0.72582644]\n",
            "191 [2 4] \t 0 \t [0.7706618]\n",
            "192 [6 7] \t 1 \t [0.6326943]\n",
            "193 [4 8] \t 0 \t [0.63007075]\n",
            "194 [5 4] \t 0 \t [0.4980222]\n",
            "195 [6 2] \t 1 \t [0.8072833]\n",
            "196 [1 8] \t 0 \t [0.5503015]\n",
            "197 [9 8] \t 1 \t [0.6352247]\n",
            "198 [6 1] \t 1 \t [0.80734086]\n",
            "199 [3 4] \t 1 \t [0.6350658]\n",
            "200 [7 0] \t 1 \t [0.6482376]\n",
            "201 [0 4] \t 1 \t [0.9848498]\n",
            "202 [4 5] \t 0 \t [0.60010314]\n",
            "203 [9 3] \t 0 \t [0.7806461]\n",
            "204 [4 2] \t 0 \t [0.62047416]\n",
            "205 [4 9] \t 1 \t [0.62623394]\n",
            "206 [5 5] \t 1 \t [0.58757085]\n",
            "207 [1 0] \t 1 \t [0.9999648]\n",
            "208 [0 4] \t 1 \t [0.9848498]\n",
            "209 [9 6] \t 0 \t [0.3517726]\n",
            "210 [7 9] \t 1 \t [0.66079193]\n",
            "211 [0 6] \t 0 \t [0.56246525]\n",
            "212 [5 9] \t 1 \t [0.642642]\n",
            "213 [3 0] \t 0 \t [0.00321138]\n",
            "214 [8 2] \t 1 \t [0.81548184]\n",
            "215 [9 0] \t 0 \t [0.4373721]\n",
            "216 [1 0] \t 1 \t [0.9999648]\n",
            "217 [7 3] \t 1 \t [0.77810323]\n",
            "218 [0 1] \t 1 \t [0.9999213]\n",
            "219 [9 1] \t 1 \t [0.67350316]\n",
            "220 [6 4] \t 1 \t [0.7074858]\n",
            "221 [8 5] \t 1 \t [0.6371336]\n",
            "222 [2 9] \t 1 \t [0.5686938]\n",
            "223 [1 2] \t 0 \t [0.00116125]\n",
            "224 [7 1] \t 1 \t [0.7840348]\n",
            "225 [5 4] \t 0 \t [0.4980222]\n",
            "226 [4 4] \t 1 \t [0.564619]\n",
            "227 [0 7] \t 1 \t [0.53935385]\n",
            "228 [1 2] \t 0 \t [0.00116125]\n",
            "229 [1 7] \t 1 \t [0.5659699]\n",
            "230 [7 1] \t 1 \t [0.7840348]\n",
            "231 [8 5] \t 1 \t [0.6371336]\n",
            "232 [2 6] \t 1 \t [0.60593486]\n",
            "233 [7 3] \t 1 \t [0.77810323]\n",
            "234 [6 3] \t 0 \t [0.7713723]\n",
            "235 [9 3] \t 0 \t [0.7806461]\n",
            "236 [9 4] \t 1 \t [0.7439392]\n",
            "237 [2 6] \t 1 \t [0.60593486]\n",
            "238 [1 6] \t 1 \t [0.5808178]\n",
            "239 [1 1] \t 1 \t [0.99959725]\n",
            "240 [8 9] \t 1 \t [0.6549438]\n",
            "241 [9 8] \t 1 \t [0.6352247]\n",
            "242 [0 4] \t 1 \t [0.9848498]\n",
            "243 [7 1] \t 1 \t [0.7840348]\n",
            "244 [2 7] \t 0 \t [0.59686077]\n",
            "245 [4 9] \t 1 \t [0.62623394]\n",
            "246 [2 2] \t 1 \t [0.99634075]\n",
            "247 [9 1] \t 1 \t [0.67350316]\n",
            "248 [8 8] \t 1 \t [0.640656]\n",
            "249 [1 9] \t 1 \t [0.53453267]\n",
            "250 [0 7] \t 1 \t [0.53935385]\n",
            "251 [1 7] \t 1 \t [0.5659699]\n",
            "252 [4 4] \t 1 \t [0.564619]\n",
            "253 [4 5] \t 0 \t [0.60010314]\n",
            "254 [9 0] \t 0 \t [0.43737197]\n",
            "255 [9 3] \t 0 \t [0.7806461]\n",
            "256 [0 2] \t 1 \t [0.9999168]\n",
            "257 [1 5] \t 0 \t [0.62550664]\n",
            "258 [9 4] \t 1 \t [0.7439392]\n",
            "259 [9 9] \t 0 \t [0.648924]\n",
            "260 [3 0] \t 0 \t [0.00321138]\n",
            "261 [1 9] \t 1 \t [0.53453267]\n",
            "262 [7 2] \t 0 \t [0.8107091]\n",
            "263 [2 6] \t 1 \t [0.60593486]\n",
            "264 [8 0] \t 1 \t [0.5644644]\n",
            "265 [4 4] \t 1 \t [0.564619]\n",
            "266 [7 8] \t 0 \t [0.64634144]\n",
            "267 [4 1] \t 1 \t [0.9045775]\n",
            "268 [3 9] \t 0 \t [0.60108787]\n",
            "269 [2 0] \t 1 \t [0.9996033]\n",
            "270 [8 6] \t 1 \t [0.45719695]\n",
            "271 [4 1] \t 1 \t [0.9045775]\n",
            "272 [7 8] \t 0 \t [0.64634144]\n",
            "273 [7 7] \t 1 \t [0.6281894]\n",
            "274 [0 3] \t 0 \t [0.00892329]\n",
            "275 [1 4] \t 1 \t [0.9583831]\n",
            "276 [4 2] \t 0 \t [0.62047416]\n",
            "277 [6 5] \t 1 \t [0.55349445]\n",
            "278 [5 8] \t 1 \t [0.6452526]\n",
            "279 [5 9] \t 1 \t [0.642642]\n",
            "280 [0 0] \t 0 \t [0.00020245]\n",
            "281 [5 0] \t 1 \t [0.96421397]\n",
            "282 [2 6] \t 1 \t [0.60593486]\n",
            "283 [0 1] \t 1 \t [0.9999213]\n",
            "284 [8 6] \t 1 \t [0.45719695]\n",
            "285 [3 3] \t 0 \t [0.56304705]\n",
            "286 [5 4] \t 0 \t [0.4980222]\n",
            "287 [7 4] \t 1 \t [0.72839814]\n",
            "288 [8 7] \t 0 \t [0.62459815]\n",
            "289 [4 1] \t 1 \t [0.9045775]\n",
            "290 [7 2] \t 0 \t [0.8107091]\n",
            "291 [6 1] \t 1 \t [0.80734086]\n",
            "292 [7 1] \t 1 \t [0.7840348]\n",
            "293 [7 8] \t 0 \t [0.64634144]\n",
            "294 [2 0] \t 1 \t [0.9996033]\n",
            "295 [7 1] \t 1 \t [0.7840348]\n",
            "296 [6 3] \t 0 \t [0.7713723]\n",
            "297 [0 7] \t 1 \t [0.53935385]\n",
            "298 [2 4] \t 0 \t [0.7706617]\n",
            "299 [6 7] \t 1 \t [0.6326943]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "PJ201jFzGP5p",
        "outputId": "302ee6cd-472f-4842-9812-749777b1592e"
      },
      "source": [
        "plt.figure(1,figsize=(8,8)) # figura numero 4\n",
        "plt.style.use('seaborn-deep') \n",
        "#plt.hist(pred,100,histtype='step')\n",
        "# seleccionamos los datos que cumplen que testY==0 es argo\n",
        "c=np.where(testY==0)\n",
        "print(\"test 0s number:  \",len(c[0]))\n",
        "plt.hist(pred[c[0]],50,histtype='step',color='darkorange', label= \"Múltiplo de 3 ( %d ) \" % (len(c[0]) ))\n",
        "# selecionamos los de cuello testY==1\n",
        "c=np.where(testY==1)\n",
        "print(\"test 1s number:  \", len(c[0]))\n",
        "plt.hist(pred[c[0]],50,histtype='step',color='blue', label= \"No múltiplo de 3 ( %d ) \" % (len(c[0]) ))\n",
        "plt.ylabel('Cuentas')\n",
        "plt.xlabel('Predicción')\n",
        "plt.yscale(\"log\")\n",
        "plt.xlim(-0.1,1.1)\n",
        "plt.legend( loc='upper center')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test 0s number:   100\n",
            "test 1s number:   200\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f28ac0d0350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmK_NAEW1B0q"
      },
      "source": [
        "## Propuestas adicionales.\n",
        "\n",
        "*   Modificar el número de neuronas y capas para mejorar el rendimiento de la red. \n",
        "*   Hacer un clasificador multivariante en la salida que pronostique independiente si la suma será múltiplo de 3 y de 5. \n",
        "*   Modificar la red añadiendo dropout."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WOYETe0an752"
      },
      "source": [
        "## Propuestas adicionales: no te quedes con hambre.\n",
        "\n",
        "*   Construir un clasificador de múltiplos de 11. ¿Es el rendimiento del clasificador mejor que el múltiplos de 3? \n",
        "*   En el conjunto de entrenamiento ¿cuál es la proporción entre instancias/ejemplos de ambas clases? ¿Está más o menos desbalanceadas las clases que en clasificador de múltiplos de 3?   \n",
        "*   Si el batch_size es 16 ¿cuántos instancias/ejemplos de la clase minoritaria entran de media en cada batch? ¿Si los pesos de la red se actualizan con cada batch, entonces el aprendizaje de las características de la clase minoritaria está siendo adecuado?\n",
        "*   ¿Qué técnicas existen para corregir los problemas de clasificación con clases fuertemente desbalanceadas?\n"
      ]
    }
  ]
}