cap03/C03_E1_Ejemplo-NN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; clear: both;\">\n",
    "<div style=\"float: left; width: 50%;\">\n",
    "<img src=\"http://www.uoc.edu/portal/_resources/common/imatges/marca_UOC/UOC_Masterbrand.jpg\", align=\"left\">\n",
    "</div>\n",
    "<div style=\"float: right; width: 50%;\">\n",
    "<p style=\"margin: 0; padding-top: 22px; text-align:right;\"><a href=\"https://www.editorialuoc.cat/deep-learning\" target=\"_blank\\\">Deep learning: Principios y fundamentos</a></p>\n",
    "<p style=\"margin: 0; text-align:right;\">Estudios de Informática, Multimedia y Telecomunicación</p>\n",
    "<p style=\"margin: 0; text-align:right; padding-button: 100px;\">Universitat Oberta de Catalunya (UOC)</p>\n",
    "</div>\n",
    "</div>\n",
    "<div style=\"width:100%;\">&nbsp;</div>\n",
    "\n",
    "\n",
    "# Capítulo 3: Ejemplo de redes neuronales completamente conectadas\n",
    "\n",
    "En este ejemplo implementaremos una red neuronal completamente conectada utilizando la librería Keras y TensorFlow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Carga de datos\n",
    "\n",
    "El siguiente código carga los paquetes necesarios para el ejemplo y además lee los datos que utilizaremos para entrenar la red neuronal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape is (2000, 100) and sample data: [-3.80997895 -2.90418191 -2.73756329  1.93988891  2.27761932  2.29250939\n",
      "  0.80657717 -0.18411067 -0.42838168 -0.74681727 -1.73050585  0.09787124\n",
      " -0.2622376  -0.47714865  0.09652312  0.02163088 -0.46442759 -0.44279175\n",
      "  0.30522186  0.14028104 -0.81884    -0.1755735  -0.32787818 -0.01383203\n",
      " -0.92451808  0.05138323  0.92543899 -0.28682495 -0.75251595  0.21320492\n",
      " -0.23709864  0.94373726  0.69307069  0.5114043   0.40932795  0.15704461\n",
      "  0.35092131 -0.14505174  0.04883884  0.19069857 -0.36449453  0.05761488\n",
      "  0.03058404  0.37526033  0.16429508  0.17789281  0.12090412  0.12617789\n",
      "  0.00622596  0.1119326  -0.20559843 -0.14971844 -0.25820982  0.29480069\n",
      " -0.09804937  0.02398129 -0.03289546 -0.23438531  0.33442494 -0.36561303\n",
      " -0.17030636  0.1216377  -0.02450097  0.19675075  0.04785413  0.0186243\n",
      "  0.08853324  0.34336898 -0.09194883  0.03168607  0.16912165 -0.19320273\n",
      "  0.16965356  0.01574452 -0.02055554 -0.06386753 -0.01774633 -0.05936183\n",
      " -0.3109211  -0.09110711  0.10386121 -0.0816454  -0.05277287  0.04745927\n",
      "  0.18950338  0.03551209  0.05914535 -0.18293479  0.0400644   0.03657231\n",
      "  0.02882243 -0.11835669  0.00511612 -0.22489106  0.11483641  0.01020498\n",
      " -0.02709487  0.11601074 -0.1055054  -0.33433267]\n",
      "Y shape is (2000,) and sample data: [1 1 1 1 0 1 0 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "with open(\"../data/data.pickle\", \"rb\") as f:\n",
    "    data = pickle.load(f)\n",
    "\n",
    "X = data[\"features\"]\n",
    "Y = data[\"labels\"]\n",
    "\n",
    "print(\"X shape is {} and sample data: {}\".format(X.shape, X[0,:]))\n",
    "print(\"Y shape is {} and sample data: {}\".format(Y.shape, Y[0:10]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Redes neuronales utilizando Keras\n",
    "\n",
    "A continuación definiremos una red neuronal completamente conectada utilizando la librería [Keras](https://keras.io/).\n",
    "\n",
    "El siguiente código permite definir una red neuronal completamente conectada a partir de una lista que contiene el número de neuronas que debe tener cada capa de la red. Las primeras capas deben tener función de activación `relu` y la última capa debe tener función de activación `sigmoid`. Todas ellas tienen que tener `kernel_initializer=\"random_normal\"` y `bias_initializer=\"zeros\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import SGD\n",
    "\n",
    "def keras_model(layers_dims, learning_rate):\n",
    "    \"\"\"\n",
    "    Crea, utilizando Keras, una red neuronal de L capas completamente conectadas donde las L-1 primeras capas\n",
    "    tienen función de activación relu y la última capa tiene función de activación sigmoid.\n",
    "    \n",
    "    Argumentos:\n",
    "    layers_dims -- lista de longitud (número de capas + 1) que contiene el número de variables y el número \n",
    "                    de neuronas en cada capa, \n",
    "    learning_rate -- velocidad de aprendizaje para aplicar el método del descenso del gradiente\n",
    "    \n",
    "    Devuelve:\n",
    "    modelo -- objeto de Keras que representa la red neuronal\n",
    "    \"\"\"\n",
    "    \n",
    "    L = len(layers_dims)\n",
    "    \n",
    "    model = Sequential()\n",
    "    model.add(Dense(layers_dims[1], input_shape=(layers_dims[0],), activation=\"relu\"))\n",
    "    \n",
    "    for l in range(2, L-1):\n",
    "        model.add(Dense(layers_dims[l], activation=\"relu\", kernel_initializer=\"random_normal\",\n",
    "                bias_initializer=\"zeros\"))\n",
    "    \n",
    "    model.add(Dense(layers_dims[L-1], activation=\"sigmoid\", kernel_initializer=\"random_normal\",\n",
    "                bias_initializer=\"zeros\"))\n",
    "    \n",
    "    model.compile(optimizer=SGD(lr=learning_rate), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Entrenamiento de la red neuronal\n",
    "\n",
    "Con todas las funciones implementadas anteriormente es posible entrenar una red neuronal completamente conectada con cualquier número de capas y cualquier número de neuronas en cada capa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación definimos la estructura de capas que tendrá la red neuronal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "layers_dims = [100, 20, 5, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para entrenar la red neuronal que hemos construido utilizando Keras debemos ejecutar el siguiente código:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1600 samples, validate on 400 samples\n",
      "Epoch 1/250\n",
      " - 0s - loss: 0.6945 - acc: 0.3381 - val_loss: 0.6943 - val_acc: 0.3250\n",
      "Epoch 2/250\n",
      " - 0s - loss: 0.6942 - acc: 0.3575 - val_loss: 0.6940 - val_acc: 0.3475\n",
      "Epoch 3/250\n",
      " - 0s - loss: 0.6939 - acc: 0.3713 - val_loss: 0.6938 - val_acc: 0.3875\n",
      "Epoch 4/250\n",
      " - 0s - loss: 0.6937 - acc: 0.4019 - val_loss: 0.6937 - val_acc: 0.4150\n",
      "Epoch 5/250\n",
      " - 0s - loss: 0.6935 - acc: 0.4356 - val_loss: 0.6936 - val_acc: 0.4300\n",
      "Epoch 6/250\n",
      " - 0s - loss: 0.6934 - acc: 0.4631 - val_loss: 0.6935 - val_acc: 0.4475\n",
      "Epoch 7/250\n",
      " - 0s - loss: 0.6933 - acc: 0.4819 - val_loss: 0.6934 - val_acc: 0.4525\n",
      "Epoch 8/250\n",
      " - 0s - loss: 0.6933 - acc: 0.4856 - val_loss: 0.6934 - val_acc: 0.4650\n",
      "Epoch 9/250\n",
      " - 0s - loss: 0.6932 - acc: 0.4925 - val_loss: 0.6933 - val_acc: 0.4650\n",
      "Epoch 10/250\n",
      " - 0s - loss: 0.6931 - acc: 0.4975 - val_loss: 0.6933 - val_acc: 0.4700\n",
      "Epoch 11/250\n",
      " - 0s - loss: 0.6931 - acc: 0.5013 - val_loss: 0.6932 - val_acc: 0.4700\n",
      "Epoch 12/250\n",
      " - 0s - loss: 0.6930 - acc: 0.5038 - val_loss: 0.6932 - val_acc: 0.4700\n",
      "Epoch 13/250\n",
      " - 0s - loss: 0.6930 - acc: 0.5044 - val_loss: 0.6932 - val_acc: 0.4700\n",
      "Epoch 14/250\n",
      " - 0s - loss: 0.6929 - acc: 0.5050 - val_loss: 0.6931 - val_acc: 0.4700\n",
      "Epoch 15/250\n",
      " - 0s - loss: 0.6929 - acc: 0.5063 - val_loss: 0.6931 - val_acc: 0.4725\n",
      "Epoch 16/250\n",
      " - 0s - loss: 0.6928 - acc: 0.5056 - val_loss: 0.6931 - val_acc: 0.4725\n",
      "Epoch 17/250\n",
      " - 0s - loss: 0.6928 - acc: 0.5056 - val_loss: 0.6930 - val_acc: 0.4725\n",
      "Epoch 18/250\n",
      " - 0s - loss: 0.6927 - acc: 0.5056 - val_loss: 0.6930 - val_acc: 0.4725\n",
      "Epoch 19/250\n",
      " - 0s - loss: 0.6926 - acc: 0.5056 - val_loss: 0.6929 - val_acc: 0.4725\n",
      "Epoch 20/250\n",
      " - 0s - loss: 0.6926 - acc: 0.5056 - val_loss: 0.6928 - val_acc: 0.4725\n",
      "Epoch 21/250\n",
      " - 0s - loss: 0.6925 - acc: 0.5056 - val_loss: 0.6928 - val_acc: 0.4725\n",
      "Epoch 22/250\n",
      " - 0s - loss: 0.6924 - acc: 0.5069 - val_loss: 0.6927 - val_acc: 0.4725\n",
      "Epoch 23/250\n",
      " - 0s - loss: 0.6923 - acc: 0.5063 - val_loss: 0.6926 - val_acc: 0.4750\n",
      "Epoch 24/250\n",
      " - 0s - loss: 0.6923 - acc: 0.5063 - val_loss: 0.6925 - val_acc: 0.4750\n",
      "Epoch 25/250\n",
      " - 0s - loss: 0.6922 - acc: 0.5069 - val_loss: 0.6924 - val_acc: 0.4750\n",
      "Epoch 26/250\n",
      " - 0s - loss: 0.6920 - acc: 0.5075 - val_loss: 0.6923 - val_acc: 0.4800\n",
      "Epoch 27/250\n",
      " - 0s - loss: 0.6919 - acc: 0.5125 - val_loss: 0.6922 - val_acc: 0.4950\n",
      "Epoch 28/250\n",
      " - 0s - loss: 0.6918 - acc: 0.5181 - val_loss: 0.6921 - val_acc: 0.4975\n",
      "Epoch 29/250\n",
      " - 0s - loss: 0.6916 - acc: 0.5256 - val_loss: 0.6919 - val_acc: 0.5050\n",
      "Epoch 30/250\n",
      " - 0s - loss: 0.6915 - acc: 0.5387 - val_loss: 0.6917 - val_acc: 0.5175\n",
      "Epoch 31/250\n",
      " - 0s - loss: 0.6913 - acc: 0.5550 - val_loss: 0.6915 - val_acc: 0.5375\n",
      "Epoch 32/250\n",
      " - 0s - loss: 0.6911 - acc: 0.5719 - val_loss: 0.6913 - val_acc: 0.5700\n",
      "Epoch 33/250\n",
      " - 0s - loss: 0.6908 - acc: 0.5987 - val_loss: 0.6911 - val_acc: 0.5875\n",
      "Epoch 34/250\n",
      " - 0s - loss: 0.6906 - acc: 0.6331 - val_loss: 0.6908 - val_acc: 0.6300\n",
      "Epoch 35/250\n",
      " - 0s - loss: 0.6903 - acc: 0.6737 - val_loss: 0.6905 - val_acc: 0.6750\n",
      "Epoch 36/250\n",
      " - 0s - loss: 0.6900 - acc: 0.7044 - val_loss: 0.6901 - val_acc: 0.7250\n",
      "Epoch 37/250\n",
      " - 0s - loss: 0.6896 - acc: 0.7356 - val_loss: 0.6897 - val_acc: 0.7650\n",
      "Epoch 38/250\n",
      " - 0s - loss: 0.6892 - acc: 0.7625 - val_loss: 0.6893 - val_acc: 0.7975\n",
      "Epoch 39/250\n",
      " - 0s - loss: 0.6888 - acc: 0.7881 - val_loss: 0.6888 - val_acc: 0.8025\n",
      "Epoch 40/250\n",
      " - 0s - loss: 0.6883 - acc: 0.8231 - val_loss: 0.6882 - val_acc: 0.8175\n",
      "Epoch 41/250\n",
      " - 0s - loss: 0.6877 - acc: 0.8425 - val_loss: 0.6876 - val_acc: 0.8275\n",
      "Epoch 42/250\n",
      " - 0s - loss: 0.6871 - acc: 0.8506 - val_loss: 0.6869 - val_acc: 0.8450\n",
      "Epoch 43/250\n",
      " - 0s - loss: 0.6864 - acc: 0.8625 - val_loss: 0.6862 - val_acc: 0.8450\n",
      "Epoch 44/250\n",
      " - 0s - loss: 0.6857 - acc: 0.8625 - val_loss: 0.6853 - val_acc: 0.8450\n",
      "Epoch 45/250\n",
      " - 0s - loss: 0.6848 - acc: 0.8612 - val_loss: 0.6843 - val_acc: 0.8400\n",
      "Epoch 46/250\n",
      " - 0s - loss: 0.6839 - acc: 0.8575 - val_loss: 0.6833 - val_acc: 0.8275\n",
      "Epoch 47/250\n",
      " - 0s - loss: 0.6828 - acc: 0.8519 - val_loss: 0.6821 - val_acc: 0.8325\n",
      "Epoch 48/250\n",
      " - 0s - loss: 0.6817 - acc: 0.8481 - val_loss: 0.6808 - val_acc: 0.8300\n",
      "Epoch 49/250\n",
      " - 0s - loss: 0.6804 - acc: 0.8481 - val_loss: 0.6793 - val_acc: 0.8325\n",
      "Epoch 50/250\n",
      " - 0s - loss: 0.6789 - acc: 0.8431 - val_loss: 0.6776 - val_acc: 0.8300\n",
      "Epoch 51/250\n",
      " - 0s - loss: 0.6773 - acc: 0.8394 - val_loss: 0.6757 - val_acc: 0.8275\n",
      "Epoch 52/250\n",
      " - 0s - loss: 0.6754 - acc: 0.8375 - val_loss: 0.6737 - val_acc: 0.8200\n",
      "Epoch 53/250\n",
      " - 0s - loss: 0.6734 - acc: 0.8356 - val_loss: 0.6714 - val_acc: 0.8200\n",
      "Epoch 54/250\n",
      " - 0s - loss: 0.6712 - acc: 0.8338 - val_loss: 0.6688 - val_acc: 0.8150\n",
      "Epoch 55/250\n",
      " - 0s - loss: 0.6687 - acc: 0.8356 - val_loss: 0.6660 - val_acc: 0.8175\n",
      "Epoch 56/250\n",
      " - 0s - loss: 0.6659 - acc: 0.8325 - val_loss: 0.6627 - val_acc: 0.8175\n",
      "Epoch 57/250\n",
      " - 0s - loss: 0.6626 - acc: 0.8306 - val_loss: 0.6591 - val_acc: 0.8200\n",
      "Epoch 58/250\n",
      " - 0s - loss: 0.6591 - acc: 0.8269 - val_loss: 0.6551 - val_acc: 0.8200\n",
      "Epoch 59/250\n",
      " - 0s - loss: 0.6552 - acc: 0.8263 - val_loss: 0.6504 - val_acc: 0.8175\n",
      "Epoch 60/250\n",
      " - 0s - loss: 0.6506 - acc: 0.8238 - val_loss: 0.6454 - val_acc: 0.8250\n",
      "Epoch 61/250\n",
      " - 0s - loss: 0.6457 - acc: 0.8213 - val_loss: 0.6398 - val_acc: 0.8225\n",
      "Epoch 62/250\n",
      " - 0s - loss: 0.6402 - acc: 0.8225 - val_loss: 0.6336 - val_acc: 0.8225\n",
      "Epoch 63/250\n",
      " - 0s - loss: 0.6342 - acc: 0.8213 - val_loss: 0.6268 - val_acc: 0.8250\n",
      "Epoch 64/250\n",
      " - 0s - loss: 0.6274 - acc: 0.8206 - val_loss: 0.6189 - val_acc: 0.8250\n",
      "Epoch 65/250\n",
      " - 0s - loss: 0.6196 - acc: 0.8213 - val_loss: 0.6102 - val_acc: 0.8250\n",
      "Epoch 66/250\n",
      " - 0s - loss: 0.6111 - acc: 0.8225 - val_loss: 0.6010 - val_acc: 0.8275\n",
      "Epoch 67/250\n",
      " - 0s - loss: 0.6021 - acc: 0.8225 - val_loss: 0.5911 - val_acc: 0.8300\n",
      "Epoch 68/250\n",
      " - 0s - loss: 0.5923 - acc: 0.8225 - val_loss: 0.5804 - val_acc: 0.8300\n",
      "Epoch 69/250\n",
      " - 0s - loss: 0.5817 - acc: 0.8244 - val_loss: 0.5690 - val_acc: 0.8300\n",
      "Epoch 70/250\n",
      " - 0s - loss: 0.5705 - acc: 0.8269 - val_loss: 0.5570 - val_acc: 0.8325\n",
      "Epoch 71/250\n",
      " - 0s - loss: 0.5587 - acc: 0.8288 - val_loss: 0.5445 - val_acc: 0.8325\n",
      "Epoch 72/250\n",
      " - 0s - loss: 0.5463 - acc: 0.8306 - val_loss: 0.5316 - val_acc: 0.8400\n",
      "Epoch 73/250\n",
      " - 0s - loss: 0.5335 - acc: 0.8325 - val_loss: 0.5185 - val_acc: 0.8425\n",
      "Epoch 74/250\n",
      " - 0s - loss: 0.5205 - acc: 0.8375 - val_loss: 0.5054 - val_acc: 0.8550\n",
      "Epoch 75/250\n",
      " - 0s - loss: 0.5073 - acc: 0.8431 - val_loss: 0.4920 - val_acc: 0.8575\n",
      "Epoch 76/250\n",
      " - 0s - loss: 0.4939 - acc: 0.8519 - val_loss: 0.4792 - val_acc: 0.8575\n",
      "Epoch 77/250\n",
      " - 0s - loss: 0.4809 - acc: 0.8531 - val_loss: 0.4667 - val_acc: 0.8625\n",
      "Epoch 78/250\n",
      " - 0s - loss: 0.4681 - acc: 0.8606 - val_loss: 0.4545 - val_acc: 0.8625\n",
      "Epoch 79/250\n",
      " - 0s - loss: 0.4556 - acc: 0.8656 - val_loss: 0.4428 - val_acc: 0.8650\n",
      "Epoch 80/250\n",
      " - 0s - loss: 0.4434 - acc: 0.8700 - val_loss: 0.4314 - val_acc: 0.8775\n",
      "Epoch 81/250\n",
      " - 0s - loss: 0.4316 - acc: 0.8769 - val_loss: 0.4205 - val_acc: 0.8875\n",
      "Epoch 82/250\n",
      " - 0s - loss: 0.4200 - acc: 0.8825 - val_loss: 0.4099 - val_acc: 0.8900\n",
      "Epoch 83/250\n",
      " - 0s - loss: 0.4087 - acc: 0.8913 - val_loss: 0.3998 - val_acc: 0.8975\n",
      "Epoch 84/250\n",
      " - 0s - loss: 0.3977 - acc: 0.8925 - val_loss: 0.3899 - val_acc: 0.9025\n",
      "Epoch 85/250\n",
      " - 0s - loss: 0.3869 - acc: 0.8975 - val_loss: 0.3804 - val_acc: 0.9025\n",
      "Epoch 86/250\n",
      " - 0s - loss: 0.3765 - acc: 0.9094 - val_loss: 0.3711 - val_acc: 0.9025\n",
      "Epoch 87/250\n",
      " - 0s - loss: 0.3661 - acc: 0.9125 - val_loss: 0.3620 - val_acc: 0.9025\n",
      "Epoch 88/250\n",
      " - 0s - loss: 0.3561 - acc: 0.9162 - val_loss: 0.3533 - val_acc: 0.9100\n",
      "Epoch 89/250\n",
      " - 0s - loss: 0.3463 - acc: 0.9206 - val_loss: 0.3445 - val_acc: 0.9100\n",
      "Epoch 90/250\n",
      " - 0s - loss: 0.3365 - acc: 0.9237 - val_loss: 0.3360 - val_acc: 0.9175\n",
      "Epoch 91/250\n",
      " - 0s - loss: 0.3269 - acc: 0.9281 - val_loss: 0.3276 - val_acc: 0.9175\n",
      "Epoch 92/250\n",
      " - 0s - loss: 0.3175 - acc: 0.9306 - val_loss: 0.3195 - val_acc: 0.9200\n",
      "Epoch 93/250\n",
      " - 0s - loss: 0.3084 - acc: 0.9362 - val_loss: 0.3115 - val_acc: 0.9200\n",
      "Epoch 94/250\n",
      " - 0s - loss: 0.2994 - acc: 0.9375 - val_loss: 0.3037 - val_acc: 0.9200\n",
      "Epoch 95/250\n",
      " - 0s - loss: 0.2905 - acc: 0.9381 - val_loss: 0.2961 - val_acc: 0.9250\n",
      "Epoch 96/250\n",
      " - 0s - loss: 0.2820 - acc: 0.9425 - val_loss: 0.2887 - val_acc: 0.9250\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 97/250\n",
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      "Epoch 98/250\n",
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      "Epoch 99/250\n",
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      "Epoch 100/250\n",
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      "Epoch 101/250\n",
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      "Epoch 103/250\n",
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      "Epoch 104/250\n",
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      "Epoch 105/250\n",
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      "Epoch 106/250\n",
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      "Epoch 114/250\n",
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      "Epoch 115/250\n",
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      "Epoch 116/250\n",
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      "Epoch 117/250\n",
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      "Epoch 118/250\n",
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      "Epoch 119/250\n",
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      "Epoch 120/250\n",
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      "Epoch 121/250\n",
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      "Epoch 122/250\n",
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      "Epoch 123/250\n",
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      "Epoch 124/250\n",
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      "Epoch 125/250\n",
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      "Epoch 126/250\n",
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      "Epoch 127/250\n",
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      "Epoch 128/250\n",
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      "Epoch 129/250\n",
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      "Epoch 130/250\n",
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      "Epoch 131/250\n",
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      "Epoch 132/250\n",
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      "Epoch 133/250\n",
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      "Epoch 134/250\n",
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      "Epoch 135/250\n",
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      "Epoch 136/250\n",
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      "Epoch 137/250\n",
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      "Epoch 138/250\n",
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      "Epoch 139/250\n",
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      "Epoch 140/250\n",
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      "Epoch 141/250\n",
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      "Epoch 142/250\n",
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      "Epoch 143/250\n",
      " - 0s - loss: 0.1041 - acc: 0.9613 - val_loss: 0.1368 - val_acc: 0.9300\n",
      "Epoch 144/250\n",
      " - 0s - loss: 0.1030 - acc: 0.9613 - val_loss: 0.1357 - val_acc: 0.9300\n",
      "Epoch 145/250\n",
      " - 0s - loss: 0.1019 - acc: 0.9625 - val_loss: 0.1347 - val_acc: 0.9300\n",
      "Epoch 146/250\n",
      " - 0s - loss: 0.1008 - acc: 0.9625 - val_loss: 0.1336 - val_acc: 0.9325\n",
      "Epoch 147/250\n",
      " - 0s - loss: 0.0998 - acc: 0.9638 - val_loss: 0.1326 - val_acc: 0.9350\n",
      "Epoch 148/250\n",
      " - 0s - loss: 0.0988 - acc: 0.9656 - val_loss: 0.1316 - val_acc: 0.9350\n",
      "Epoch 149/250\n",
      " - 0s - loss: 0.0979 - acc: 0.9656 - val_loss: 0.1307 - val_acc: 0.9350\n",
      "Epoch 150/250\n",
      " - 0s - loss: 0.0969 - acc: 0.9663 - val_loss: 0.1297 - val_acc: 0.9350\n",
      "Epoch 151/250\n",
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      "Epoch 152/250\n",
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      "Epoch 153/250\n",
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      "Epoch 154/250\n",
      " - 0s - loss: 0.0934 - acc: 0.9669 - val_loss: 0.1262 - val_acc: 0.9375\n",
      "Epoch 155/250\n",
      " - 0s - loss: 0.0926 - acc: 0.9669 - val_loss: 0.1254 - val_acc: 0.9375\n",
      "Epoch 156/250\n",
      " - 0s - loss: 0.0918 - acc: 0.9669 - val_loss: 0.1246 - val_acc: 0.9375\n",
      "Epoch 157/250\n",
      " - 0s - loss: 0.0910 - acc: 0.9669 - val_loss: 0.1238 - val_acc: 0.9375\n",
      "Epoch 158/250\n",
      " - 0s - loss: 0.0903 - acc: 0.9669 - val_loss: 0.1230 - val_acc: 0.9375\n",
      "Epoch 159/250\n",
      " - 0s - loss: 0.0895 - acc: 0.9669 - val_loss: 0.1223 - val_acc: 0.9375\n",
      "Epoch 160/250\n",
      " - 0s - loss: 0.0888 - acc: 0.9669 - val_loss: 0.1215 - val_acc: 0.9375\n",
      "Epoch 161/250\n",
      " - 0s - loss: 0.0881 - acc: 0.9675 - val_loss: 0.1208 - val_acc: 0.9375\n",
      "Epoch 162/250\n",
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      "Epoch 163/250\n",
      " - 0s - loss: 0.0868 - acc: 0.9688 - val_loss: 0.1194 - val_acc: 0.9375\n",
      "Epoch 164/250\n",
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      "Epoch 165/250\n",
      " - 0s - loss: 0.0855 - acc: 0.9694 - val_loss: 0.1181 - val_acc: 0.9400\n",
      "Epoch 166/250\n",
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      "Epoch 167/250\n",
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      "Epoch 168/250\n",
      " - 0s - loss: 0.0836 - acc: 0.9706 - val_loss: 0.1162 - val_acc: 0.9400\n",
      "Epoch 169/250\n",
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      "Epoch 170/250\n",
      " - 0s - loss: 0.0825 - acc: 0.9706 - val_loss: 0.1149 - val_acc: 0.9400\n",
      "Epoch 171/250\n",
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      "Epoch 172/250\n",
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      "Epoch 173/250\n",
      " - 0s - loss: 0.0808 - acc: 0.9706 - val_loss: 0.1132 - val_acc: 0.9400\n",
      "Epoch 174/250\n",
      " - 0s - loss: 0.0803 - acc: 0.9706 - val_loss: 0.1127 - val_acc: 0.9425\n",
      "Epoch 175/250\n",
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      "Epoch 176/250\n",
      " - 0s - loss: 0.0792 - acc: 0.9706 - val_loss: 0.1116 - val_acc: 0.9425\n",
      "Epoch 177/250\n",
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      "Epoch 178/250\n",
      " - 0s - loss: 0.0782 - acc: 0.9706 - val_loss: 0.1106 - val_acc: 0.9425\n",
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      " - 0s - loss: 0.0763 - acc: 0.9712 - val_loss: 0.1086 - val_acc: 0.9450\n",
      "Epoch 183/250\n",
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      "Epoch 184/250\n",
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      "Epoch 185/250\n",
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      "Epoch 188/250\n",
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      " - 0s - loss: 0.0733 - acc: 0.9719 - val_loss: 0.1055 - val_acc: 0.9500\n",
      "Epoch 190/250\n",
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      "Epoch 191/250\n",
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      "Epoch 192/250\n",
      " - 0s - loss: 0.0721 - acc: 0.9719 - val_loss: 0.1043 - val_acc: 0.9500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 193/250\n",
      " - 0s - loss: 0.0717 - acc: 0.9719 - val_loss: 0.1039 - val_acc: 0.9500\n",
      "Epoch 194/250\n",
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      "Epoch 195/250\n",
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      "Epoch 196/250\n",
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      "Epoch 197/250\n",
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      "Epoch 198/250\n",
      " - 0s - loss: 0.0698 - acc: 0.9731 - val_loss: 0.1021 - val_acc: 0.9500\n",
      "Epoch 199/250\n",
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      "Epoch 200/250\n",
      " - 0s - loss: 0.0691 - acc: 0.9731 - val_loss: 0.1014 - val_acc: 0.9500\n",
      "Epoch 201/250\n",
      " - 0s - loss: 0.0688 - acc: 0.9731 - val_loss: 0.1011 - val_acc: 0.9500\n",
      "Epoch 202/250\n",
      " - 0s - loss: 0.0684 - acc: 0.9731 - val_loss: 0.1008 - val_acc: 0.9500\n",
      "Epoch 203/250\n",
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      "Epoch 204/250\n",
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      "Epoch 205/250\n",
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      "Epoch 206/250\n",
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      "Epoch 207/250\n",
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      "Epoch 208/250\n",
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      "Epoch 209/250\n",
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      "Epoch 210/250\n",
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      "Epoch 211/250\n",
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      "Epoch 212/250\n",
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      "Epoch 213/250\n",
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      "Epoch 214/250\n",
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      "Epoch 215/250\n",
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      "Epoch 216/250\n",
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      "Epoch 217/250\n",
      " - 0s - loss: 0.0638 - acc: 0.9769 - val_loss: 0.0966 - val_acc: 0.9600\n",
      "Epoch 218/250\n",
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      "Epoch 219/250\n",
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      "Epoch 220/250\n",
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      "Epoch 221/250\n",
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      "Epoch 222/250\n",
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      "Epoch 227/250\n",
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      " - 0s - loss: 0.0569 - acc: 0.9787 - val_loss: 0.0910 - val_acc: 0.9600\n",
      "Epoch 247/250\n",
      " - 0s - loss: 0.0567 - acc: 0.9787 - val_loss: 0.0909 - val_acc: 0.9600\n",
      "Epoch 248/250\n",
      " - 0s - loss: 0.0565 - acc: 0.9787 - val_loss: 0.0907 - val_acc: 0.9600\n",
      "Epoch 249/250\n",
      " - 0s - loss: 0.0562 - acc: 0.9787 - val_loss: 0.0906 - val_acc: 0.9600\n",
      "Epoch 250/250\n",
      " - 0s - loss: 0.0560 - acc: 0.9787 - val_loss: 0.0904 - val_acc: 0.9600\n"
     ]
    }
   ],
   "source": [
    "model = keras_model(layers_dims=layers_dims, learning_rate=0.1)\n",
    "history = model.fit(X, Y, validation_split=0.2, epochs=250, batch_size=X.shape[0], verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El siguiente código permite calcular el tiempo que tarda la red neuronal en entrenarse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.13 s ± 42.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "model = keras_model(layers_dims=layers_dims, learning_rate=0.1)\n",
    "model.fit(X, Y, epochs=250, batch_size=X.shape[0], verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este último fragmento de código veremos como emplear el objecto `history` para ver la evolución del proceso de entrenamniento. \n",
    "\n",
    "En primer lugar, veremos la información que contiene dicho objeto."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    }
   ],
   "source": [
    "# Ver los datos contenidos en 'history'\n",
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y finalmente, visualizaremos dos gráficos para ver como evoluciona la precisión y el error durante el proceso de entrenamiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Visualizamos la evolución de la accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "# Visualizamos la evolución del error cometido por la red\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}