├── LICENSE ├── README.md ├── cap03 ├── .ipynb_checkpoints │ └── C03_E1_Ejemplo NN-checkpoint.ipynb └── C03_E1_Ejemplo-NN.ipynb ├── cap05 └── C05_E1_Shallow-AutoEncoder.ipynb ├── cap07 ├── .ipynb_checkpoints │ └── C07_E1_Ejemplo-CNN-MNIST-Keras-checkpoint.ipynb └── C07_E1_Ejemplo-CNN-MNIST-Keras.ipynb ├── cap09 ├── .ipynb_checkpoints │ └── C09_E1_Ejemplo-Neural-Style-Transfer-checkpoint.ipynb └── C09_E1_Ejemplo-Neural-Style-Transfer.ipynb ├── cap10 └── C10_E1_Ejemplo-series-temporales-RNN.ipynb ├── data └── data.pickle └── figs ├── lion.jpg └── violin.jpg /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep learning: Principios y fundamentos 2 | ## Material adicional del libro "**Deep learning: Principios y fundamentos**" 3 | ### Por Anna Bosch Rué, Jordi Casas Roma y Toni Lozano Bagén 4 | 5 | Deep learning: Principios y fundamentos 6 | 7 | **Este github contiene el código del libro [Deep learning: Principios y fundamentos](https://www.editorialuoc.cat/deep-learning), publicado por Editorial UOC** 8 | 9 | En esta página encontraréis ejercicios prácticos en [Jupyter](http://jupyter.org/) que os permitirán trabajar más a fondo los contenidos aprendidos en el libro [Deep learning: Principios y fundamentos](https://www.editorialuoc.cat/deep-learning). 10 | 11 | La mejor forma de trabajar estos ejercicios es seguir los capítulos del libro, descargarse los archivos de esta página y abrirlos desde un navegador web accediendo a tu instalación Jupyter. 12 | 13 | ## Introducción 14 | Este libro es el resultado de 3 años de docencia en *machine learning* y *deep learning* dentro del [Máster Universitario en Ciencia de datos (Data Science)](https://estudios.uoc.edu/es/masters-universitarios/data-science/presentacion) de la UOC, a través de los cuales hemos reflexionado junto con nuestros alumnos, la mejor manera de aproximarse al mundo del aprendizaje profundo. 15 | 16 | ## Ejecución de los ejemplos 17 | Recomendamos los siguientes enlaces para poder instalar los componentes de software necesarios para seguir los ejercicios propuestos: 18 | 19 | - [Anaconda](https://www.anaconda.com/products/individual) 20 | - [Entorno de trabajo Jupyter](http://jupyter.org/install.html) 21 | 22 | Aunque es posible instalar el software necesario para poder seguir los ejemplos, recomendamos el uso de plataformas gratuitas en la nube (*cloud*) que permiten la ejecución de estos ejemplos de forma mucho más rápida, ya que permiten el uso de GPUs para el entrenamiento de los modelos. Entre múltiples plataformas, recomendamos algunas opciones interesantes: 23 | 24 | - [Kaggle notebooks](https://www.kaggle.com/) 25 | - [Google Colab](https://colab.research.google.com/) 26 | 27 | # Parte I: Redes neuronales artificiales 28 | 29 | ## Capítulo 3. Principios y fundamentos 30 | En este capítulo veremos ejemplos básicos de funcionamiento de redes neuronales complemtamente conectadas (feed-forward neural networks). 31 | 32 | Ejemplos prácticos de este capítulo: 33 | 34 | - [Ejemplo de NN básico](cap03/C03_E1_Ejemplo-NN.ipynb): Ejemplo de redes neuronales completamente conectadas empleando Keras. 35 | 36 | ## Capítulo 5. *Autoencoders* 37 | En este capítulo veremos ejemplos básicos de funcionamiento de los *autoencoders*. 38 | 39 | Ejemplos prácticos de este capítulo: 40 | 41 | - [Ejemplo de autoencoder](cap05/C05_E1_Shallow-AutoEncoder.ipynb): Ejemplo de redes neuronales completamente conectadas para crear un *autoencoder*. 42 | 43 | # Parte II: Redes neuronales convolucionales 44 | 45 | ## Capítulo 7. Principios y fundamentos 46 | En este capítulo veremos ejemplos básicos de funcionamiento de redes neuronales convolucionales (CNN). 47 | 48 | Ejemplos prácticos de este capítulo: 49 | 50 | - [Ejemplo de CNN básico con MNIST](cap07/C07_E1_Ejemplo-CNN-MNIST-Keras.ipynb): Ejemplo básico de red neuronal convolucional empleando Keras y trabajando con el dataset de dígitos MNIST. 51 | 52 | ## Capítulo 9. Consejos prácticos y ejemplos 53 | En este capítulo veremos algunas aplicaciones y *best practices* de funcionamiento de redes neuronales convolucionales. 54 | 55 | Ejemplos prácticos de este capítulo: 56 | 57 | - [Ejemplo de transferencia de estilo (NST)](cap09/C09_E1_Ejemplo-Neural-Style-Transfer.ipynb): Ejemplo de uso de una red neuronal para el problema de la transferencia de estilo (Neural Style Transfer, NST). 58 | 59 | # Parte III: Redes neuronales recurrentes 60 | 61 | ## Capítulo 10. Fundamentos de las redes recurrentes 62 | En este capítulo veremos ejemplos básicos de funcionamiento de redes neuronales recurrentes (RNN). 63 | 64 | Ejemplos prácticos de este capítulo: 65 | 66 | - [Ejemplo de RNN básico aplicado a TSA](cap10/C10_E1_Ejemplo-series-temporales-RNN.ipynb): Ejemplo básico de red neuronal recurrente empleando Keras y aplicado a un problema de análisis de series temporales (TSA). 67 | -------------------------------------------------------------------------------- /cap03/.ipynb_checkpoints/C03_E1_Ejemplo NN-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
\n", 8 | "
\n", 9 | "\n", 10 | "
\n", 11 | "
\n", 12 | "

Deep learning: Principios y fundamentos

\n", 13 | "

Estudios de Informática, Multimedia y Telecomunicación

\n", 14 | "

Universitat Oberta de Catalunya (UOC)

\n", 15 | "
\n", 16 | "
\n", 17 | "
 
\n", 18 | "\n", 19 | "\n", 20 | "# Capítulo 3: Ejemplo de redes neuronales completamente conectadas\n", 21 | "\n", 22 | "En este ejemplo implementaremos una red neuronal completamente conectada utilizando la librería Keras y TensorFlow." 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": {}, 28 | "source": [ 29 | "## 1. Carga de datos\n", 30 | "\n", 31 | "El siguiente código carga los paquetes necesarios para el ejemplo y además lee los datos que utilizaremos para entrenar la red neuronal." 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 1, 37 | "metadata": {}, 38 | "outputs": [ 39 | { 40 | "name": "stdout", 41 | "output_type": "stream", 42 | "text": [ 43 | "X shape is (2000, 100) and sample data: [-3.80997895 -2.90418191 -2.73756329 1.93988891 2.27761932 2.29250939\n", 44 | " 0.80657717 -0.18411067 -0.42838168 -0.74681727 -1.73050585 0.09787124\n", 45 | " -0.2622376 -0.47714865 0.09652312 0.02163088 -0.46442759 -0.44279175\n", 46 | " 0.30522186 0.14028104 -0.81884 -0.1755735 -0.32787818 -0.01383203\n", 47 | " -0.92451808 0.05138323 0.92543899 -0.28682495 -0.75251595 0.21320492\n", 48 | " -0.23709864 0.94373726 0.69307069 0.5114043 0.40932795 0.15704461\n", 49 | " 0.35092131 -0.14505174 0.04883884 0.19069857 -0.36449453 0.05761488\n", 50 | " 0.03058404 0.37526033 0.16429508 0.17789281 0.12090412 0.12617789\n", 51 | " 0.00622596 0.1119326 -0.20559843 -0.14971844 -0.25820982 0.29480069\n", 52 | " -0.09804937 0.02398129 -0.03289546 -0.23438531 0.33442494 -0.36561303\n", 53 | " -0.17030636 0.1216377 -0.02450097 0.19675075 0.04785413 0.0186243\n", 54 | " 0.08853324 0.34336898 -0.09194883 0.03168607 0.16912165 -0.19320273\n", 55 | " 0.16965356 0.01574452 -0.02055554 -0.06386753 -0.01774633 -0.05936183\n", 56 | " -0.3109211 -0.09110711 0.10386121 -0.0816454 -0.05277287 0.04745927\n", 57 | " 0.18950338 0.03551209 0.05914535 -0.18293479 0.0400644 0.03657231\n", 58 | " 0.02882243 -0.11835669 0.00511612 -0.22489106 0.11483641 0.01020498\n", 59 | " -0.02709487 0.11601074 -0.1055054 -0.33433267]\n", 60 | "Y shape is (2000,) and sample data: [1 1 1 1 0 1 0 0 1 1]\n" 61 | ] 62 | } 63 | ], 64 | "source": [ 65 | "import numpy as np\n", 66 | "import pickle\n", 67 | "\n", 68 | "with open(\"../data/data.pickle\", \"rb\") as f:\n", 69 | " data = pickle.load(f)\n", 70 | "\n", 71 | "X = data[\"features\"]\n", 72 | "Y = data[\"labels\"]\n", 73 | "\n", 74 | "print(\"X shape is {} and sample data: {}\".format(X.shape, X[0,:]))\n", 75 | "print(\"Y shape is {} and sample data: {}\".format(Y.shape, Y[0:10]))" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "## 2. Redes neuronales utilizando Keras\n", 83 | "\n", 84 | "A continuación definiremos una red neuronal completamente conectada utilizando la librería [Keras](https://keras.io/).\n", 85 | "\n", 86 | "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\"`." 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 2, 92 | "metadata": {}, 93 | "outputs": [ 94 | { 95 | "name": "stderr", 96 | "output_type": "stream", 97 | "text": [ 98 | "Using TensorFlow backend.\n" 99 | ] 100 | } 101 | ], 102 | "source": [ 103 | "import keras\n", 104 | "from keras.models import Sequential\n", 105 | "from keras.layers import Dense\n", 106 | "from keras.optimizers import SGD\n", 107 | "\n", 108 | "def keras_model(layers_dims, learning_rate):\n", 109 | " \"\"\"\n", 110 | " Crea, utilizando Keras, una red neuronal de L capas completamente conectadas donde las L-1 primeras capas\n", 111 | " tienen función de activación relu y la última capa tiene función de activación sigmoid.\n", 112 | " \n", 113 | " Argumentos:\n", 114 | " layers_dims -- lista de longitud (número de capas + 1) que contiene el número de variables y el número \n", 115 | " de neuronas en cada capa, \n", 116 | " learning_rate -- velocidad de aprendizaje para aplicar el método del descenso del gradiente\n", 117 | " \n", 118 | " Devuelve:\n", 119 | " modelo -- objeto de Keras que representa la red neuronal\n", 120 | " \"\"\"\n", 121 | " \n", 122 | " L = len(layers_dims)\n", 123 | " \n", 124 | " model = Sequential()\n", 125 | " model.add(Dense(layers_dims[1], input_shape=(layers_dims[0],), activation=\"relu\"))\n", 126 | " \n", 127 | " for l in range(2, L-1):\n", 128 | " model.add(Dense(layers_dims[l], activation=\"relu\", kernel_initializer=\"random_normal\",\n", 129 | " bias_initializer=\"zeros\"))\n", 130 | " \n", 131 | " model.add(Dense(layers_dims[L-1], activation=\"sigmoid\", kernel_initializer=\"random_normal\",\n", 132 | " bias_initializer=\"zeros\"))\n", 133 | " \n", 134 | " model.compile(optimizer=SGD(lr=learning_rate), loss='binary_crossentropy', metrics=['accuracy'])\n", 135 | " \n", 136 | " return model" 137 | ] 138 | }, 139 | { 140 | "cell_type": "markdown", 141 | "metadata": {}, 142 | "source": [ 143 | "## 3. Entrenamiento de la red neuronal\n", 144 | "\n", 145 | "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." 146 | ] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "metadata": {}, 151 | "source": [ 152 | "A continuación definimos la estructura de capas que tendrá la red neuronal." 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 3, 158 | "metadata": {}, 159 | "outputs": [], 160 | "source": [ 161 | "layers_dims = [100, 20, 5, 1]" 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "metadata": {}, 167 | "source": [ 168 | "Para entrenar la red neuronal que hemos construido utilizando Keras debemos ejecutar el siguiente código:" 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": 4, 174 | "metadata": {}, 175 | "outputs": [ 176 | { 177 | "name": "stdout", 178 | "output_type": "stream", 179 | "text": [ 180 | "Train on 1600 samples, validate on 400 samples\n", 181 | "Epoch 1/250\n", 182 | " - 0s - loss: 0.6945 - acc: 0.3381 - val_loss: 0.6943 - val_acc: 0.3250\n", 183 | "Epoch 2/250\n", 184 | " - 0s - loss: 0.6942 - acc: 0.3575 - val_loss: 0.6940 - val_acc: 0.3475\n", 185 | "Epoch 3/250\n", 186 | " - 0s - loss: 0.6939 - acc: 0.3713 - val_loss: 0.6938 - val_acc: 0.3875\n", 187 | "Epoch 4/250\n", 188 | " - 0s - loss: 0.6937 - acc: 0.4019 - val_loss: 0.6937 - val_acc: 0.4150\n", 189 | "Epoch 5/250\n", 190 | " - 0s - loss: 0.6935 - acc: 0.4356 - val_loss: 0.6936 - val_acc: 0.4300\n", 191 | "Epoch 6/250\n", 192 | " - 0s - loss: 0.6934 - acc: 0.4631 - val_loss: 0.6935 - val_acc: 0.4475\n", 193 | "Epoch 7/250\n", 194 | " - 0s - loss: 0.6933 - acc: 0.4819 - val_loss: 0.6934 - val_acc: 0.4525\n", 195 | "Epoch 8/250\n", 196 | " - 0s - loss: 0.6933 - acc: 0.4856 - val_loss: 0.6934 - val_acc: 0.4650\n", 197 | "Epoch 9/250\n", 198 | " - 0s - loss: 0.6932 - acc: 0.4925 - val_loss: 0.6933 - val_acc: 0.4650\n", 199 | "Epoch 10/250\n", 200 | " - 0s - loss: 0.6931 - acc: 0.4975 - val_loss: 0.6933 - val_acc: 0.4700\n", 201 | "Epoch 11/250\n", 202 | " - 0s - loss: 0.6931 - acc: 0.5013 - val_loss: 0.6932 - val_acc: 0.4700\n", 203 | "Epoch 12/250\n", 204 | " - 0s - loss: 0.6930 - acc: 0.5038 - val_loss: 0.6932 - val_acc: 0.4700\n", 205 | "Epoch 13/250\n", 206 | " - 0s - loss: 0.6930 - acc: 0.5044 - val_loss: 0.6932 - val_acc: 0.4700\n", 207 | "Epoch 14/250\n", 208 | " - 0s - loss: 0.6929 - acc: 0.5050 - val_loss: 0.6931 - val_acc: 0.4700\n", 209 | "Epoch 15/250\n", 210 | " - 0s - loss: 0.6929 - acc: 0.5063 - val_loss: 0.6931 - val_acc: 0.4725\n", 211 | "Epoch 16/250\n", 212 | " - 0s - loss: 0.6928 - acc: 0.5056 - val_loss: 0.6931 - val_acc: 0.4725\n", 213 | "Epoch 17/250\n", 214 | " - 0s - loss: 0.6928 - acc: 0.5056 - val_loss: 0.6930 - val_acc: 0.4725\n", 215 | "Epoch 18/250\n", 216 | " - 0s - loss: 0.6927 - acc: 0.5056 - val_loss: 0.6930 - val_acc: 0.4725\n", 217 | "Epoch 19/250\n", 218 | " - 0s - loss: 0.6926 - acc: 0.5056 - val_loss: 0.6929 - val_acc: 0.4725\n", 219 | "Epoch 20/250\n", 220 | " - 0s - loss: 0.6926 - acc: 0.5056 - val_loss: 0.6928 - val_acc: 0.4725\n", 221 | "Epoch 21/250\n", 222 | " - 0s - loss: 0.6925 - acc: 0.5056 - val_loss: 0.6928 - val_acc: 0.4725\n", 223 | "Epoch 22/250\n", 224 | " - 0s - loss: 0.6924 - acc: 0.5069 - val_loss: 0.6927 - val_acc: 0.4725\n", 225 | "Epoch 23/250\n", 226 | " - 0s - loss: 0.6923 - acc: 0.5063 - val_loss: 0.6926 - val_acc: 0.4750\n", 227 | "Epoch 24/250\n", 228 | " - 0s - loss: 0.6923 - acc: 0.5063 - val_loss: 0.6925 - val_acc: 0.4750\n", 229 | "Epoch 25/250\n", 230 | " - 0s - loss: 0.6922 - acc: 0.5069 - val_loss: 0.6924 - val_acc: 0.4750\n", 231 | "Epoch 26/250\n", 232 | " - 0s - loss: 0.6920 - acc: 0.5075 - val_loss: 0.6923 - val_acc: 0.4800\n", 233 | "Epoch 27/250\n", 234 | " - 0s - loss: 0.6919 - acc: 0.5125 - val_loss: 0.6922 - val_acc: 0.4950\n", 235 | "Epoch 28/250\n", 236 | " - 0s - loss: 0.6918 - acc: 0.5181 - val_loss: 0.6921 - val_acc: 0.4975\n", 237 | "Epoch 29/250\n", 238 | " - 0s - loss: 0.6916 - acc: 0.5256 - val_loss: 0.6919 - val_acc: 0.5050\n", 239 | "Epoch 30/250\n", 240 | " - 0s - loss: 0.6915 - acc: 0.5387 - val_loss: 0.6917 - val_acc: 0.5175\n", 241 | "Epoch 31/250\n", 242 | " - 0s - loss: 0.6913 - acc: 0.5550 - val_loss: 0.6915 - val_acc: 0.5375\n", 243 | "Epoch 32/250\n", 244 | " - 0s - loss: 0.6911 - acc: 0.5719 - val_loss: 0.6913 - val_acc: 0.5700\n", 245 | "Epoch 33/250\n", 246 | " - 0s - loss: 0.6908 - acc: 0.5987 - val_loss: 0.6911 - val_acc: 0.5875\n", 247 | "Epoch 34/250\n", 248 | " - 0s - loss: 0.6906 - acc: 0.6331 - val_loss: 0.6908 - val_acc: 0.6300\n", 249 | "Epoch 35/250\n", 250 | " - 0s - loss: 0.6903 - acc: 0.6737 - val_loss: 0.6905 - val_acc: 0.6750\n", 251 | "Epoch 36/250\n", 252 | " - 0s - loss: 0.6900 - acc: 0.7044 - val_loss: 0.6901 - val_acc: 0.7250\n", 253 | "Epoch 37/250\n", 254 | " - 0s - loss: 0.6896 - acc: 0.7356 - val_loss: 0.6897 - val_acc: 0.7650\n", 255 | "Epoch 38/250\n", 256 | " - 0s - loss: 0.6892 - acc: 0.7625 - val_loss: 0.6893 - val_acc: 0.7975\n", 257 | "Epoch 39/250\n", 258 | " - 0s - loss: 0.6888 - acc: 0.7881 - val_loss: 0.6888 - val_acc: 0.8025\n", 259 | "Epoch 40/250\n", 260 | " - 0s - loss: 0.6883 - acc: 0.8231 - val_loss: 0.6882 - val_acc: 0.8175\n", 261 | "Epoch 41/250\n", 262 | " - 0s - loss: 0.6877 - acc: 0.8425 - val_loss: 0.6876 - val_acc: 0.8275\n", 263 | "Epoch 42/250\n", 264 | " - 0s - loss: 0.6871 - acc: 0.8506 - val_loss: 0.6869 - val_acc: 0.8450\n", 265 | "Epoch 43/250\n", 266 | " - 0s - loss: 0.6864 - acc: 0.8625 - val_loss: 0.6862 - val_acc: 0.8450\n", 267 | "Epoch 44/250\n", 268 | " - 0s - loss: 0.6857 - acc: 0.8625 - val_loss: 0.6853 - val_acc: 0.8450\n", 269 | "Epoch 45/250\n", 270 | " - 0s - loss: 0.6848 - acc: 0.8612 - val_loss: 0.6843 - val_acc: 0.8400\n", 271 | "Epoch 46/250\n", 272 | " - 0s - loss: 0.6839 - acc: 0.8575 - val_loss: 0.6833 - val_acc: 0.8275\n", 273 | "Epoch 47/250\n", 274 | " - 0s - loss: 0.6828 - acc: 0.8519 - val_loss: 0.6821 - val_acc: 0.8325\n", 275 | "Epoch 48/250\n", 276 | " - 0s - loss: 0.6817 - acc: 0.8481 - val_loss: 0.6808 - val_acc: 0.8300\n", 277 | "Epoch 49/250\n", 278 | " - 0s - loss: 0.6804 - acc: 0.8481 - val_loss: 0.6793 - val_acc: 0.8325\n", 279 | "Epoch 50/250\n", 280 | " - 0s - loss: 0.6789 - acc: 0.8431 - val_loss: 0.6776 - val_acc: 0.8300\n", 281 | "Epoch 51/250\n", 282 | " - 0s - loss: 0.6773 - acc: 0.8394 - val_loss: 0.6757 - val_acc: 0.8275\n", 283 | "Epoch 52/250\n", 284 | " - 0s - loss: 0.6754 - acc: 0.8375 - val_loss: 0.6737 - val_acc: 0.8200\n", 285 | "Epoch 53/250\n", 286 | " - 0s - loss: 0.6734 - acc: 0.8356 - val_loss: 0.6714 - val_acc: 0.8200\n", 287 | "Epoch 54/250\n", 288 | " - 0s - loss: 0.6712 - acc: 0.8338 - val_loss: 0.6688 - val_acc: 0.8150\n", 289 | "Epoch 55/250\n", 290 | " - 0s - loss: 0.6687 - acc: 0.8356 - val_loss: 0.6660 - val_acc: 0.8175\n", 291 | "Epoch 56/250\n", 292 | " - 0s - loss: 0.6659 - acc: 0.8325 - val_loss: 0.6627 - val_acc: 0.8175\n", 293 | "Epoch 57/250\n", 294 | " - 0s - loss: 0.6626 - acc: 0.8306 - val_loss: 0.6591 - val_acc: 0.8200\n", 295 | "Epoch 58/250\n", 296 | " - 0s - loss: 0.6591 - acc: 0.8269 - val_loss: 0.6551 - val_acc: 0.8200\n", 297 | "Epoch 59/250\n", 298 | " - 0s - loss: 0.6552 - acc: 0.8263 - val_loss: 0.6504 - val_acc: 0.8175\n", 299 | "Epoch 60/250\n", 300 | " - 0s - loss: 0.6506 - acc: 0.8238 - val_loss: 0.6454 - val_acc: 0.8250\n", 301 | "Epoch 61/250\n", 302 | " - 0s - loss: 0.6457 - acc: 0.8213 - val_loss: 0.6398 - val_acc: 0.8225\n", 303 | "Epoch 62/250\n", 304 | " - 0s - loss: 0.6402 - acc: 0.8225 - val_loss: 0.6336 - val_acc: 0.8225\n", 305 | "Epoch 63/250\n", 306 | " - 0s - loss: 0.6342 - acc: 0.8213 - val_loss: 0.6268 - val_acc: 0.8250\n", 307 | "Epoch 64/250\n", 308 | " - 0s - loss: 0.6274 - acc: 0.8206 - val_loss: 0.6189 - val_acc: 0.8250\n", 309 | "Epoch 65/250\n", 310 | " - 0s - loss: 0.6196 - acc: 0.8213 - val_loss: 0.6102 - val_acc: 0.8250\n", 311 | "Epoch 66/250\n", 312 | " - 0s - loss: 0.6111 - acc: 0.8225 - val_loss: 0.6010 - val_acc: 0.8275\n", 313 | "Epoch 67/250\n", 314 | " - 0s - loss: 0.6021 - acc: 0.8225 - val_loss: 0.5911 - val_acc: 0.8300\n", 315 | "Epoch 68/250\n", 316 | " - 0s - loss: 0.5923 - acc: 0.8225 - val_loss: 0.5804 - val_acc: 0.8300\n", 317 | "Epoch 69/250\n", 318 | " - 0s - loss: 0.5817 - acc: 0.8244 - val_loss: 0.5690 - val_acc: 0.8300\n", 319 | "Epoch 70/250\n", 320 | " - 0s - loss: 0.5705 - acc: 0.8269 - val_loss: 0.5570 - val_acc: 0.8325\n", 321 | "Epoch 71/250\n", 322 | " - 0s - loss: 0.5587 - acc: 0.8288 - val_loss: 0.5445 - val_acc: 0.8325\n", 323 | "Epoch 72/250\n", 324 | " - 0s - loss: 0.5463 - acc: 0.8306 - val_loss: 0.5316 - val_acc: 0.8400\n", 325 | "Epoch 73/250\n", 326 | " - 0s - loss: 0.5335 - acc: 0.8325 - val_loss: 0.5185 - val_acc: 0.8425\n", 327 | "Epoch 74/250\n", 328 | " - 0s - loss: 0.5205 - acc: 0.8375 - val_loss: 0.5054 - val_acc: 0.8550\n", 329 | "Epoch 75/250\n", 330 | " - 0s - loss: 0.5073 - acc: 0.8431 - val_loss: 0.4920 - val_acc: 0.8575\n", 331 | "Epoch 76/250\n", 332 | " - 0s - loss: 0.4939 - acc: 0.8519 - val_loss: 0.4792 - val_acc: 0.8575\n", 333 | "Epoch 77/250\n", 334 | " - 0s - loss: 0.4809 - acc: 0.8531 - val_loss: 0.4667 - val_acc: 0.8625\n", 335 | "Epoch 78/250\n", 336 | " - 0s - loss: 0.4681 - acc: 0.8606 - val_loss: 0.4545 - val_acc: 0.8625\n", 337 | "Epoch 79/250\n", 338 | " - 0s - loss: 0.4556 - acc: 0.8656 - val_loss: 0.4428 - val_acc: 0.8650\n", 339 | "Epoch 80/250\n", 340 | " - 0s - loss: 0.4434 - acc: 0.8700 - val_loss: 0.4314 - val_acc: 0.8775\n", 341 | "Epoch 81/250\n", 342 | " - 0s - loss: 0.4316 - acc: 0.8769 - val_loss: 0.4205 - val_acc: 0.8875\n", 343 | "Epoch 82/250\n", 344 | " - 0s - loss: 0.4200 - acc: 0.8825 - val_loss: 0.4099 - val_acc: 0.8900\n", 345 | "Epoch 83/250\n", 346 | " - 0s - loss: 0.4087 - acc: 0.8913 - val_loss: 0.3998 - val_acc: 0.8975\n", 347 | "Epoch 84/250\n", 348 | " - 0s - loss: 0.3977 - acc: 0.8925 - val_loss: 0.3899 - val_acc: 0.9025\n", 349 | "Epoch 85/250\n", 350 | " - 0s - loss: 0.3869 - acc: 0.8975 - val_loss: 0.3804 - val_acc: 0.9025\n", 351 | "Epoch 86/250\n", 352 | " - 0s - loss: 0.3765 - acc: 0.9094 - val_loss: 0.3711 - val_acc: 0.9025\n", 353 | "Epoch 87/250\n", 354 | " - 0s - loss: 0.3661 - acc: 0.9125 - val_loss: 0.3620 - val_acc: 0.9025\n", 355 | "Epoch 88/250\n", 356 | " - 0s - loss: 0.3561 - acc: 0.9162 - val_loss: 0.3533 - val_acc: 0.9100\n", 357 | "Epoch 89/250\n", 358 | " - 0s - loss: 0.3463 - acc: 0.9206 - val_loss: 0.3445 - val_acc: 0.9100\n", 359 | "Epoch 90/250\n", 360 | " - 0s - loss: 0.3365 - acc: 0.9237 - val_loss: 0.3360 - val_acc: 0.9175\n", 361 | "Epoch 91/250\n", 362 | " - 0s - loss: 0.3269 - acc: 0.9281 - val_loss: 0.3276 - val_acc: 0.9175\n", 363 | "Epoch 92/250\n", 364 | " - 0s - loss: 0.3175 - acc: 0.9306 - val_loss: 0.3195 - val_acc: 0.9200\n", 365 | "Epoch 93/250\n", 366 | " - 0s - loss: 0.3084 - acc: 0.9362 - val_loss: 0.3115 - val_acc: 0.9200\n", 367 | "Epoch 94/250\n", 368 | " - 0s - loss: 0.2994 - acc: 0.9375 - val_loss: 0.3037 - val_acc: 0.9200\n", 369 | "Epoch 95/250\n", 370 | " - 0s - loss: 0.2905 - acc: 0.9381 - val_loss: 0.2961 - val_acc: 0.9250\n", 371 | "Epoch 96/250\n", 372 | " - 0s - loss: 0.2820 - acc: 0.9425 - val_loss: 0.2887 - val_acc: 0.9250\n" 373 | ] 374 | }, 375 | { 376 | "name": "stdout", 377 | "output_type": "stream", 378 | "text": [ 379 | "Epoch 97/250\n", 380 | " - 0s - loss: 0.2736 - acc: 0.9425 - val_loss: 0.2815 - val_acc: 0.9250\n", 381 | "Epoch 98/250\n", 382 | " - 0s - loss: 0.2655 - acc: 0.9438 - val_loss: 0.2745 - val_acc: 0.9250\n", 383 | "Epoch 99/250\n", 384 | " - 0s - loss: 0.2575 - acc: 0.9469 - val_loss: 0.2677 - val_acc: 0.9225\n", 385 | "Epoch 100/250\n", 386 | " - 0s - loss: 0.2498 - acc: 0.9469 - val_loss: 0.2611 - val_acc: 0.9225\n", 387 | "Epoch 101/250\n", 388 | " - 0s - loss: 0.2422 - acc: 0.9488 - val_loss: 0.2548 - val_acc: 0.9225\n", 389 | "Epoch 102/250\n", 390 | " - 0s - loss: 0.2349 - acc: 0.9481 - val_loss: 0.2486 - val_acc: 0.9225\n", 391 | "Epoch 103/250\n", 392 | " - 0s - loss: 0.2279 - acc: 0.9488 - val_loss: 0.2427 - val_acc: 0.9250\n", 393 | "Epoch 104/250\n", 394 | " - 0s - loss: 0.2211 - acc: 0.9494 - val_loss: 0.2370 - val_acc: 0.9225\n", 395 | "Epoch 105/250\n", 396 | " - 0s - loss: 0.2146 - acc: 0.9500 - val_loss: 0.2316 - val_acc: 0.9225\n", 397 | "Epoch 106/250\n", 398 | " - 0s - loss: 0.2083 - acc: 0.9506 - val_loss: 0.2264 - val_acc: 0.9200\n", 399 | "Epoch 107/250\n", 400 | " - 0s - loss: 0.2023 - acc: 0.9513 - val_loss: 0.2214 - val_acc: 0.9200\n", 401 | "Epoch 108/250\n", 402 | " - 0s - loss: 0.1966 - acc: 0.9538 - val_loss: 0.2166 - val_acc: 0.9200\n", 403 | "Epoch 109/250\n", 404 | " - 0s - loss: 0.1911 - acc: 0.9531 - val_loss: 0.2121 - val_acc: 0.9200\n", 405 | "Epoch 110/250\n", 406 | " - 0s - loss: 0.1859 - acc: 0.9531 - val_loss: 0.2078 - val_acc: 0.9200\n", 407 | "Epoch 111/250\n", 408 | " - 0s - loss: 0.1810 - acc: 0.9531 - val_loss: 0.2037 - val_acc: 0.9200\n", 409 | "Epoch 112/250\n", 410 | " - 0s - loss: 0.1763 - acc: 0.9531 - val_loss: 0.1999 - val_acc: 0.9200\n", 411 | "Epoch 113/250\n", 412 | " - 0s - loss: 0.1718 - acc: 0.9531 - val_loss: 0.1962 - val_acc: 0.9200\n", 413 | "Epoch 114/250\n", 414 | " - 0s - loss: 0.1676 - acc: 0.9531 - val_loss: 0.1927 - val_acc: 0.9200\n", 415 | "Epoch 115/250\n", 416 | " - 0s - loss: 0.1636 - acc: 0.9538 - val_loss: 0.1894 - val_acc: 0.9225\n", 417 | "Epoch 116/250\n", 418 | " - 0s - loss: 0.1598 - acc: 0.9544 - val_loss: 0.1862 - val_acc: 0.9225\n", 419 | "Epoch 117/250\n", 420 | " - 0s - loss: 0.1562 - acc: 0.9550 - val_loss: 0.1832 - val_acc: 0.9250\n", 421 | "Epoch 118/250\n", 422 | " - 0s - loss: 0.1528 - acc: 0.9556 - val_loss: 0.1804 - val_acc: 0.9250\n", 423 | "Epoch 119/250\n", 424 | " - 0s - loss: 0.1496 - acc: 0.9556 - val_loss: 0.1777 - val_acc: 0.9250\n", 425 | "Epoch 120/250\n", 426 | " - 0s - loss: 0.1465 - acc: 0.9556 - val_loss: 0.1751 - val_acc: 0.9250\n", 427 | "Epoch 121/250\n", 428 | " - 0s - loss: 0.1436 - acc: 0.9563 - val_loss: 0.1726 - val_acc: 0.9250\n", 429 | "Epoch 122/250\n", 430 | " - 0s - loss: 0.1408 - acc: 0.9556 - val_loss: 0.1703 - val_acc: 0.9250\n", 431 | "Epoch 123/250\n", 432 | " - 0s - loss: 0.1382 - acc: 0.9556 - val_loss: 0.1680 - val_acc: 0.9250\n", 433 | "Epoch 124/250\n", 434 | " - 0s - loss: 0.1357 - acc: 0.9556 - val_loss: 0.1659 - val_acc: 0.9250\n", 435 | "Epoch 125/250\n", 436 | " - 0s - loss: 0.1333 - acc: 0.9563 - val_loss: 0.1638 - val_acc: 0.9250\n", 437 | "Epoch 126/250\n", 438 | " - 0s - loss: 0.1311 - acc: 0.9563 - val_loss: 0.1618 - val_acc: 0.9250\n", 439 | "Epoch 127/250\n", 440 | " - 0s - loss: 0.1289 - acc: 0.9569 - val_loss: 0.1599 - val_acc: 0.9275\n", 441 | "Epoch 128/250\n", 442 | " - 0s - loss: 0.1269 - acc: 0.9569 - val_loss: 0.1581 - val_acc: 0.9275\n", 443 | "Epoch 129/250\n", 444 | " - 0s - loss: 0.1249 - acc: 0.9569 - val_loss: 0.1563 - val_acc: 0.9275\n", 445 | "Epoch 130/250\n", 446 | " - 0s - loss: 0.1230 - acc: 0.9569 - val_loss: 0.1546 - val_acc: 0.9275\n", 447 | "Epoch 131/250\n", 448 | " - 0s - loss: 0.1212 - acc: 0.9581 - val_loss: 0.1529 - val_acc: 0.9275\n", 449 | "Epoch 132/250\n", 450 | " - 0s - loss: 0.1194 - acc: 0.9581 - val_loss: 0.1513 - val_acc: 0.9275\n", 451 | "Epoch 133/250\n", 452 | " - 0s - loss: 0.1178 - acc: 0.9588 - val_loss: 0.1498 - val_acc: 0.9275\n", 453 | "Epoch 134/250\n", 454 | " - 0s - loss: 0.1162 - acc: 0.9594 - val_loss: 0.1483 - val_acc: 0.9300\n", 455 | "Epoch 135/250\n", 456 | " - 0s - loss: 0.1146 - acc: 0.9600 - val_loss: 0.1469 - val_acc: 0.9300\n", 457 | "Epoch 136/250\n", 458 | " - 0s - loss: 0.1131 - acc: 0.9600 - val_loss: 0.1455 - val_acc: 0.9300\n", 459 | "Epoch 137/250\n", 460 | " - 0s - loss: 0.1117 - acc: 0.9600 - val_loss: 0.1441 - val_acc: 0.9300\n", 461 | "Epoch 138/250\n", 462 | " - 0s - loss: 0.1103 - acc: 0.9600 - val_loss: 0.1428 - val_acc: 0.9300\n", 463 | "Epoch 139/250\n", 464 | " - 0s - loss: 0.1090 - acc: 0.9600 - val_loss: 0.1415 - val_acc: 0.9275\n", 465 | "Epoch 140/250\n", 466 | " - 0s - loss: 0.1077 - acc: 0.9600 - val_loss: 0.1403 - val_acc: 0.9275\n", 467 | "Epoch 141/250\n", 468 | " - 0s - loss: 0.1065 - acc: 0.9600 - val_loss: 0.1391 - val_acc: 0.9275\n", 469 | "Epoch 142/250\n", 470 | " - 0s - loss: 0.1053 - acc: 0.9600 - val_loss: 0.1380 - val_acc: 0.9275\n", 471 | "Epoch 143/250\n", 472 | " - 0s - loss: 0.1041 - acc: 0.9613 - val_loss: 0.1368 - val_acc: 0.9300\n", 473 | "Epoch 144/250\n", 474 | " - 0s - loss: 0.1030 - acc: 0.9613 - val_loss: 0.1357 - val_acc: 0.9300\n", 475 | "Epoch 145/250\n", 476 | " - 0s - loss: 0.1019 - acc: 0.9625 - val_loss: 0.1347 - val_acc: 0.9300\n", 477 | "Epoch 146/250\n", 478 | " - 0s - loss: 0.1008 - acc: 0.9625 - val_loss: 0.1336 - val_acc: 0.9325\n", 479 | "Epoch 147/250\n", 480 | " - 0s - loss: 0.0998 - acc: 0.9638 - val_loss: 0.1326 - val_acc: 0.9350\n", 481 | "Epoch 148/250\n", 482 | " - 0s - loss: 0.0988 - acc: 0.9656 - val_loss: 0.1316 - val_acc: 0.9350\n", 483 | "Epoch 149/250\n", 484 | " - 0s - loss: 0.0979 - acc: 0.9656 - val_loss: 0.1307 - val_acc: 0.9350\n", 485 | "Epoch 150/250\n", 486 | " - 0s - loss: 0.0969 - acc: 0.9663 - val_loss: 0.1297 - val_acc: 0.9350\n", 487 | "Epoch 151/250\n", 488 | " - 0s - loss: 0.0960 - acc: 0.9669 - val_loss: 0.1288 - val_acc: 0.9375\n", 489 | "Epoch 152/250\n", 490 | " - 0s - loss: 0.0951 - acc: 0.9669 - val_loss: 0.1279 - val_acc: 0.9375\n", 491 | "Epoch 153/250\n", 492 | " - 0s - loss: 0.0943 - acc: 0.9669 - val_loss: 0.1271 - val_acc: 0.9375\n", 493 | "Epoch 154/250\n", 494 | " - 0s - loss: 0.0934 - acc: 0.9669 - val_loss: 0.1262 - val_acc: 0.9375\n", 495 | "Epoch 155/250\n", 496 | " - 0s - loss: 0.0926 - acc: 0.9669 - val_loss: 0.1254 - val_acc: 0.9375\n", 497 | "Epoch 156/250\n", 498 | " - 0s - loss: 0.0918 - acc: 0.9669 - val_loss: 0.1246 - val_acc: 0.9375\n", 499 | "Epoch 157/250\n", 500 | " - 0s - loss: 0.0910 - acc: 0.9669 - val_loss: 0.1238 - val_acc: 0.9375\n", 501 | "Epoch 158/250\n", 502 | " - 0s - loss: 0.0903 - acc: 0.9669 - val_loss: 0.1230 - val_acc: 0.9375\n", 503 | "Epoch 159/250\n", 504 | " - 0s - loss: 0.0895 - acc: 0.9669 - val_loss: 0.1223 - val_acc: 0.9375\n", 505 | "Epoch 160/250\n", 506 | " - 0s - loss: 0.0888 - acc: 0.9669 - val_loss: 0.1215 - val_acc: 0.9375\n", 507 | "Epoch 161/250\n", 508 | " - 0s - loss: 0.0881 - acc: 0.9675 - val_loss: 0.1208 - val_acc: 0.9375\n", 509 | "Epoch 162/250\n", 510 | " - 0s - loss: 0.0874 - acc: 0.9675 - val_loss: 0.1201 - val_acc: 0.9375\n", 511 | "Epoch 163/250\n", 512 | " - 0s - loss: 0.0868 - acc: 0.9688 - val_loss: 0.1194 - val_acc: 0.9375\n", 513 | "Epoch 164/250\n", 514 | " - 0s - loss: 0.0861 - acc: 0.9688 - val_loss: 0.1187 - val_acc: 0.9375\n", 515 | "Epoch 165/250\n", 516 | " - 0s - loss: 0.0855 - acc: 0.9694 - val_loss: 0.1181 - val_acc: 0.9400\n", 517 | "Epoch 166/250\n", 518 | " - 0s - loss: 0.0848 - acc: 0.9700 - val_loss: 0.1174 - val_acc: 0.9400\n", 519 | "Epoch 167/250\n", 520 | " - 0s - loss: 0.0842 - acc: 0.9700 - val_loss: 0.1168 - val_acc: 0.9400\n", 521 | "Epoch 168/250\n", 522 | " - 0s - loss: 0.0836 - acc: 0.9706 - val_loss: 0.1162 - val_acc: 0.9400\n", 523 | "Epoch 169/250\n", 524 | " - 0s - loss: 0.0830 - acc: 0.9712 - val_loss: 0.1155 - val_acc: 0.9400\n", 525 | "Epoch 170/250\n", 526 | " - 0s - loss: 0.0825 - acc: 0.9706 - val_loss: 0.1149 - val_acc: 0.9400\n", 527 | "Epoch 171/250\n", 528 | " - 0s - loss: 0.0819 - acc: 0.9706 - val_loss: 0.1144 - val_acc: 0.9400\n", 529 | "Epoch 172/250\n", 530 | " - 0s - loss: 0.0813 - acc: 0.9706 - val_loss: 0.1138 - val_acc: 0.9400\n", 531 | "Epoch 173/250\n", 532 | " - 0s - loss: 0.0808 - acc: 0.9706 - val_loss: 0.1132 - val_acc: 0.9400\n", 533 | "Epoch 174/250\n", 534 | " - 0s - loss: 0.0803 - acc: 0.9706 - val_loss: 0.1127 - val_acc: 0.9425\n", 535 | "Epoch 175/250\n", 536 | " - 0s - loss: 0.0797 - acc: 0.9706 - val_loss: 0.1121 - val_acc: 0.9425\n", 537 | "Epoch 176/250\n", 538 | " - 0s - loss: 0.0792 - acc: 0.9706 - val_loss: 0.1116 - val_acc: 0.9425\n", 539 | "Epoch 177/250\n", 540 | " - 0s - loss: 0.0787 - acc: 0.9706 - val_loss: 0.1111 - val_acc: 0.9425\n", 541 | "Epoch 178/250\n", 542 | " - 0s - loss: 0.0782 - acc: 0.9706 - val_loss: 0.1106 - val_acc: 0.9425\n", 543 | "Epoch 179/250\n", 544 | " - 0s - loss: 0.0777 - acc: 0.9706 - val_loss: 0.1101 - val_acc: 0.9450\n", 545 | "Epoch 180/250\n", 546 | " - 0s - loss: 0.0773 - acc: 0.9706 - val_loss: 0.1096 - val_acc: 0.9450\n", 547 | "Epoch 181/250\n", 548 | " - 0s - loss: 0.0768 - acc: 0.9706 - val_loss: 0.1091 - val_acc: 0.9450\n", 549 | "Epoch 182/250\n", 550 | " - 0s - loss: 0.0763 - acc: 0.9712 - val_loss: 0.1086 - val_acc: 0.9450\n", 551 | "Epoch 183/250\n", 552 | " - 0s - loss: 0.0759 - acc: 0.9712 - val_loss: 0.1082 - val_acc: 0.9450\n", 553 | "Epoch 184/250\n", 554 | " - 0s - loss: 0.0754 - acc: 0.9712 - val_loss: 0.1077 - val_acc: 0.9475\n", 555 | "Epoch 185/250\n", 556 | " - 0s - loss: 0.0750 - acc: 0.9719 - val_loss: 0.1072 - val_acc: 0.9475\n", 557 | "Epoch 186/250\n", 558 | " - 0s - loss: 0.0746 - acc: 0.9719 - val_loss: 0.1068 - val_acc: 0.9500\n", 559 | "Epoch 187/250\n", 560 | " - 0s - loss: 0.0741 - acc: 0.9719 - val_loss: 0.1064 - val_acc: 0.9500\n", 561 | "Epoch 188/250\n", 562 | " - 0s - loss: 0.0737 - acc: 0.9719 - val_loss: 0.1059 - val_acc: 0.9500\n", 563 | "Epoch 189/250\n", 564 | " - 0s - loss: 0.0733 - acc: 0.9719 - val_loss: 0.1055 - val_acc: 0.9500\n", 565 | "Epoch 190/250\n", 566 | " - 0s - loss: 0.0729 - acc: 0.9719 - val_loss: 0.1051 - val_acc: 0.9500\n", 567 | "Epoch 191/250\n", 568 | " - 0s - loss: 0.0725 - acc: 0.9719 - val_loss: 0.1047 - val_acc: 0.9500\n", 569 | "Epoch 192/250\n", 570 | " - 0s - loss: 0.0721 - acc: 0.9719 - val_loss: 0.1043 - val_acc: 0.9500\n" 571 | ] 572 | }, 573 | { 574 | "name": "stdout", 575 | "output_type": "stream", 576 | "text": [ 577 | "Epoch 193/250\n", 578 | " - 0s - loss: 0.0717 - acc: 0.9719 - val_loss: 0.1039 - val_acc: 0.9500\n", 579 | "Epoch 194/250\n", 580 | " - 0s - loss: 0.0713 - acc: 0.9719 - val_loss: 0.1036 - val_acc: 0.9500\n", 581 | "Epoch 195/250\n", 582 | " - 0s - loss: 0.0709 - acc: 0.9731 - val_loss: 0.1032 - val_acc: 0.9500\n", 583 | "Epoch 196/250\n", 584 | " - 0s - loss: 0.0706 - acc: 0.9731 - val_loss: 0.1028 - val_acc: 0.9500\n", 585 | "Epoch 197/250\n", 586 | " - 0s - loss: 0.0702 - acc: 0.9731 - val_loss: 0.1025 - val_acc: 0.9500\n", 587 | "Epoch 198/250\n", 588 | " - 0s - loss: 0.0698 - acc: 0.9731 - val_loss: 0.1021 - val_acc: 0.9500\n", 589 | "Epoch 199/250\n", 590 | " - 0s - loss: 0.0695 - acc: 0.9731 - val_loss: 0.1018 - val_acc: 0.9500\n", 591 | "Epoch 200/250\n", 592 | " - 0s - loss: 0.0691 - acc: 0.9731 - val_loss: 0.1014 - val_acc: 0.9500\n", 593 | "Epoch 201/250\n", 594 | " - 0s - loss: 0.0688 - acc: 0.9731 - val_loss: 0.1011 - val_acc: 0.9500\n", 595 | "Epoch 202/250\n", 596 | " - 0s - loss: 0.0684 - acc: 0.9731 - val_loss: 0.1008 - val_acc: 0.9500\n", 597 | "Epoch 203/250\n", 598 | " - 0s - loss: 0.0681 - acc: 0.9744 - val_loss: 0.1005 - val_acc: 0.9500\n", 599 | "Epoch 204/250\n", 600 | " - 0s - loss: 0.0678 - acc: 0.9744 - val_loss: 0.1002 - val_acc: 0.9525\n", 601 | "Epoch 205/250\n", 602 | " - 0s - loss: 0.0674 - acc: 0.9744 - val_loss: 0.0999 - val_acc: 0.9525\n", 603 | "Epoch 206/250\n", 604 | " - 0s - loss: 0.0671 - acc: 0.9750 - val_loss: 0.0996 - val_acc: 0.9550\n", 605 | "Epoch 207/250\n", 606 | " - 0s - loss: 0.0668 - acc: 0.9750 - val_loss: 0.0993 - val_acc: 0.9550\n", 607 | "Epoch 208/250\n", 608 | " - 0s - loss: 0.0665 - acc: 0.9750 - val_loss: 0.0990 - val_acc: 0.9575\n", 609 | "Epoch 209/250\n", 610 | " - 0s - loss: 0.0662 - acc: 0.9750 - val_loss: 0.0987 - val_acc: 0.9600\n", 611 | "Epoch 210/250\n", 612 | " - 0s - loss: 0.0659 - acc: 0.9756 - val_loss: 0.0984 - val_acc: 0.9600\n", 613 | "Epoch 211/250\n", 614 | " - 0s - loss: 0.0656 - acc: 0.9762 - val_loss: 0.0982 - val_acc: 0.9600\n", 615 | "Epoch 212/250\n", 616 | " - 0s - loss: 0.0653 - acc: 0.9762 - val_loss: 0.0979 - val_acc: 0.9600\n", 617 | "Epoch 213/250\n", 618 | " - 0s - loss: 0.0650 - acc: 0.9762 - val_loss: 0.0976 - val_acc: 0.9600\n", 619 | "Epoch 214/250\n", 620 | " - 0s - loss: 0.0647 - acc: 0.9762 - val_loss: 0.0974 - val_acc: 0.9600\n", 621 | "Epoch 215/250\n", 622 | " - 0s - loss: 0.0644 - acc: 0.9762 - val_loss: 0.0971 - val_acc: 0.9600\n", 623 | "Epoch 216/250\n", 624 | " - 0s - loss: 0.0641 - acc: 0.9762 - val_loss: 0.0969 - val_acc: 0.9600\n", 625 | "Epoch 217/250\n", 626 | " - 0s - loss: 0.0638 - acc: 0.9769 - val_loss: 0.0966 - val_acc: 0.9600\n", 627 | "Epoch 218/250\n", 628 | " - 0s - loss: 0.0636 - acc: 0.9769 - val_loss: 0.0964 - val_acc: 0.9600\n", 629 | "Epoch 219/250\n", 630 | " - 0s - loss: 0.0633 - acc: 0.9769 - val_loss: 0.0962 - val_acc: 0.9600\n", 631 | "Epoch 220/250\n", 632 | " - 0s - loss: 0.0630 - acc: 0.9769 - val_loss: 0.0959 - val_acc: 0.9600\n", 633 | "Epoch 221/250\n", 634 | " - 0s - loss: 0.0628 - acc: 0.9769 - val_loss: 0.0957 - val_acc: 0.9600\n", 635 | "Epoch 222/250\n", 636 | " - 0s - loss: 0.0625 - acc: 0.9775 - val_loss: 0.0955 - val_acc: 0.9600\n", 637 | "Epoch 223/250\n", 638 | " - 0s - loss: 0.0622 - acc: 0.9775 - val_loss: 0.0953 - val_acc: 0.9600\n", 639 | "Epoch 224/250\n", 640 | " - 0s - loss: 0.0620 - acc: 0.9775 - val_loss: 0.0951 - val_acc: 0.9600\n", 641 | "Epoch 225/250\n", 642 | " - 0s - loss: 0.0617 - acc: 0.9775 - val_loss: 0.0948 - val_acc: 0.9600\n", 643 | "Epoch 226/250\n", 644 | " - 0s - loss: 0.0615 - acc: 0.9775 - val_loss: 0.0946 - val_acc: 0.9600\n", 645 | "Epoch 227/250\n", 646 | " - 0s - loss: 0.0612 - acc: 0.9775 - val_loss: 0.0944 - val_acc: 0.9600\n", 647 | "Epoch 228/250\n", 648 | " - 0s - loss: 0.0610 - acc: 0.9775 - val_loss: 0.0942 - val_acc: 0.9600\n", 649 | "Epoch 229/250\n", 650 | " - 0s - loss: 0.0608 - acc: 0.9775 - val_loss: 0.0940 - val_acc: 0.9600\n", 651 | "Epoch 230/250\n", 652 | " - 0s - loss: 0.0605 - acc: 0.9775 - val_loss: 0.0938 - val_acc: 0.9600\n", 653 | "Epoch 231/250\n", 654 | " - 0s - loss: 0.0603 - acc: 0.9775 - val_loss: 0.0936 - val_acc: 0.9600\n", 655 | "Epoch 232/250\n", 656 | " - 0s - loss: 0.0600 - acc: 0.9781 - val_loss: 0.0934 - val_acc: 0.9600\n", 657 | "Epoch 233/250\n", 658 | " - 0s - loss: 0.0598 - acc: 0.9781 - val_loss: 0.0932 - val_acc: 0.9600\n", 659 | "Epoch 234/250\n", 660 | " - 0s - loss: 0.0595 - acc: 0.9787 - val_loss: 0.0931 - val_acc: 0.9600\n", 661 | "Epoch 235/250\n", 662 | " - 0s - loss: 0.0593 - acc: 0.9787 - val_loss: 0.0929 - val_acc: 0.9600\n", 663 | "Epoch 236/250\n", 664 | " - 0s - loss: 0.0591 - acc: 0.9787 - val_loss: 0.0927 - val_acc: 0.9600\n", 665 | "Epoch 237/250\n", 666 | " - 0s - loss: 0.0588 - acc: 0.9787 - val_loss: 0.0925 - val_acc: 0.9600\n", 667 | "Epoch 238/250\n", 668 | " - 0s - loss: 0.0586 - acc: 0.9787 - val_loss: 0.0923 - val_acc: 0.9600\n", 669 | "Epoch 239/250\n", 670 | " - 0s - loss: 0.0584 - acc: 0.9787 - val_loss: 0.0922 - val_acc: 0.9600\n", 671 | "Epoch 240/250\n", 672 | " - 0s - loss: 0.0582 - acc: 0.9787 - val_loss: 0.0920 - val_acc: 0.9600\n", 673 | "Epoch 241/250\n", 674 | " - 0s - loss: 0.0579 - acc: 0.9787 - val_loss: 0.0918 - val_acc: 0.9600\n", 675 | "Epoch 242/250\n", 676 | " - 0s - loss: 0.0577 - acc: 0.9787 - val_loss: 0.0916 - val_acc: 0.9600\n", 677 | "Epoch 243/250\n", 678 | " - 0s - loss: 0.0575 - acc: 0.9794 - val_loss: 0.0915 - val_acc: 0.9600\n", 679 | "Epoch 244/250\n", 680 | " - 0s - loss: 0.0573 - acc: 0.9794 - val_loss: 0.0913 - val_acc: 0.9600\n", 681 | "Epoch 245/250\n", 682 | " - 0s - loss: 0.0571 - acc: 0.9787 - val_loss: 0.0912 - val_acc: 0.9600\n", 683 | "Epoch 246/250\n", 684 | " - 0s - loss: 0.0569 - acc: 0.9787 - val_loss: 0.0910 - val_acc: 0.9600\n", 685 | "Epoch 247/250\n", 686 | " - 0s - loss: 0.0567 - acc: 0.9787 - val_loss: 0.0909 - val_acc: 0.9600\n", 687 | "Epoch 248/250\n", 688 | " - 0s - loss: 0.0565 - acc: 0.9787 - val_loss: 0.0907 - val_acc: 0.9600\n", 689 | "Epoch 249/250\n", 690 | " - 0s - loss: 0.0562 - acc: 0.9787 - val_loss: 0.0906 - val_acc: 0.9600\n", 691 | "Epoch 250/250\n", 692 | " - 0s - loss: 0.0560 - acc: 0.9787 - val_loss: 0.0904 - val_acc: 0.9600\n" 693 | ] 694 | } 695 | ], 696 | "source": [ 697 | "model = keras_model(layers_dims=layers_dims, learning_rate=0.1)\n", 698 | "history = model.fit(X, Y, validation_split=0.2, epochs=250, batch_size=X.shape[0], verbose=2)" 699 | ] 700 | }, 701 | { 702 | "cell_type": "markdown", 703 | "metadata": {}, 704 | "source": [ 705 | "El siguiente código permite calcular el tiempo que tarda la red neuronal en entrenarse." 706 | ] 707 | }, 708 | { 709 | "cell_type": "code", 710 | "execution_count": 5, 711 | "metadata": {}, 712 | "outputs": [ 713 | { 714 | "name": "stdout", 715 | "output_type": "stream", 716 | "text": [ 717 | "1.13 s ± 42.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" 718 | ] 719 | } 720 | ], 721 | "source": [ 722 | "%%timeit\n", 723 | "model = keras_model(layers_dims=layers_dims, learning_rate=0.1)\n", 724 | "model.fit(X, Y, epochs=250, batch_size=X.shape[0], verbose=0)" 725 | ] 726 | }, 727 | { 728 | "cell_type": "markdown", 729 | "metadata": {}, 730 | "source": [ 731 | "En este último fragmento de código veremos como emplear el objecto `history` para ver la evolución del proceso de entrenamniento. \n", 732 | "\n", 733 | "En primer lugar, veremos la información que contiene dicho objeto." 734 | ] 735 | }, 736 | { 737 | "cell_type": "code", 738 | "execution_count": 6, 739 | "metadata": {}, 740 | "outputs": [ 741 | { 742 | "name": "stdout", 743 | "output_type": "stream", 744 | "text": [ 745 | "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n" 746 | ] 747 | } 748 | ], 749 | "source": [ 750 | "# Ver los datos contenidos en 'history'\n", 751 | "print(history.history.keys())" 752 | ] 753 | }, 754 | { 755 | "cell_type": "markdown", 756 | "metadata": {}, 757 | "source": [ 758 | "Y finalmente, visualizaremos dos gráficos para ver como evoluciona la precisión y el error durante el proceso de entrenamiento." 759 | ] 760 | }, 761 | { 762 | "cell_type": "code", 763 | "execution_count": 7, 764 | "metadata": {}, 765 | "outputs": [ 766 | { 767 | "data": { 768 | "image/png": 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Bju9WZiXwJeA+4GIgRUSyVHWfuXdFZB4wD2DMGOtOGBLqdsKL30CzD+NX0d9lw+4G/nzNcUSnDIOMcW5HZ0zEcnu+gJuBU0XkU+BUoBTY71ZQVX1UVQtUtSAnJyfUMZpgWPRLtK2RFyb+ike3ZlNw8heJHjPTEoIxLgtmS6EUGO23nOdb10VVd+K0FBCRZODLqnoIUz2aQaFkGbriaZ7znMdti5o4a8owrjt9ottRGWMIblJYCkwSkXycZHAZsM+F4iKSDVSpqhe4HZgfxHhMOFj+BPzzh9RHZXBvywXcd9k0Zh85PLKmvjYmjAWt+0hVO4DrgTeAdcDzqrpGRO4Skbm+YqcBG0RkI5AL/CJY8Zgw0NEGb/+MtuHHcnbLrzhzxlQunDbKmdPIGBMWgnrzmqouBBZ2W3eH3+sXgReDGYMJI+tfhaY9PJ59KxWdyXz7lPFuR2SM6cbtgWYTSQr/QlPiKH65cQTXnT6R/OwktyMyxnRjScGERn0Zuu0DnmiZxdF5GfzgzEluR2SM6YHNfWRCY92rCMpLLTO456KjiBnMT08zZgizpGBCovyTF6jzjmTmcSdxVF6a2+EYY3phSWGQ2lHZRG1zO2OzE0mNj3E7nJ51dkD5GopWvM+4iqV8lHYpd1ww1e2ojDEHYElhENq6p5Ezf/8uXoVojzD3mJH88ktHER8TRpd2dnbA374Km//NRGB19FTO/sYddvmpMWHOksIg9OrKnXgV/nDpNFYU1/DXj7axq7aFx785k9hol/vqa0vh/d/C7jVQ8gmvZX2TZ8vz+Pn115KUmexubMaYPtlo3yD0z1U7OW5cBhdNH8Wdc4/g9189hsVbKvn9mxvcDaxqKzxwPKx8Bu1oZuvR/811pWdxzKzzGZttCcGYwcCSwiCzoayejbsbuOCYkV3rvjwjjyuPH8Mj72/hpWUl7gW3+gVoa4Br3+e6lPs4/ZMChqXEMe9Uu0nNmMHCksIg8+cPthAb7eHco0bss/4n50/l5IlZ3PTCSr7wm0X8+l/rUdXQBrd2AYw+nlWtuSxcXcY1J43jjRu/EL4D4caY/VhSGER2VDax4dP/sDThB2Q/OBV+OxHe+w0A8TFRzL/mOK47fQJjMhN56N3NPP7RttAFV7UFdq+GqXN55L0tpMRHc9MXJ5ORFBu6GIwxh8wGmgeRJ5ds4xxPIakdlXDM150+/EW/cJ5BcPQlxEVHccs5h+P1KvOeXMbP/rmWKI9w1QljkWA+yax8Hbz8HUBYljCLhZ+V8L3TJpBiLQRjBh1rKQwiy7ZXc2p8ETLiaDj/XrjyBRh9PLzxI/B+/mwij0f40xXTOeOwYfzklTVc9MCHLFy9i06voqq0duz3HKP+6+yA566C2hLqL/g/vrewgvHZSfZ8BGMGKWspDBLtnV427KxicuxGGP1NZ2VUDMycBy99C4o/htwjID4N2pqIFw+PXD2D5wqLefT9LXzv6eWMy0okNSGGovIGfnHxkVw8Pe/QgmqogFXPQmURHZc8zX/9J4uaphrmX3McibH2p2XMYGT/cweJDWX1TOzcQoy3Fcb4Pep68jkQFQcLb3HuDZh4FuxcDlFxRM+5mytnzuWy48bwxpoyHn1/C42tHUzOTeGHz63k6SU7uHPuERw5KsBpJ0qXO+cA2PkpFM4HlIqM6Vy2MInNe6r4w6XTOGKkTWNhzGBlSWGQWFVSS4Fno7Mw+oTPN8SlwIQzYOPrkD0Ztv0Hhh8N7c3w/Ndg8myivvpXzj1qRNcVSx2dXp5asp0H393MZY8u4fZzD+fE8VmMz+nlXoKmKnjrDvj0ya5VirBu1Fd4rmwEL+86krwR0Tx05bHM6XZVlDFmcLGkMEisLK7hipiP0ezDkNRuH7wnfR+87XDxIxAdD7FJzhjD4j/B2z+Fjx+GWT/sKh4d5eGak/OZfeQIrvnLJ/zo5c+Y5inij+nPkXXWDZB7JPFL7sNTvNjZoakK7WihYcZ1NB79NVbvbOCBD3ayYnMUJ03I4k+nTuCUSdnBHcw2xoSEJYVBorn4U45hE8z41f4bx53s/PiLioZZN8L2j+A/90LycJh4JiQPc7arMnzPYhaeVkZJwmHIKz9ndNNaWDAPgDaiWZE0i6iYePZ4hHtbTmX9h2Pgw80ATMhJ5fFvHsGpk3OCWW1jTIhZUhgkZtW9RrvEEnPMZQe341l3wmNnwT++A5kT4JxfwMY3YM9G2P4hHmAMAiglJ/+ShVs6GBXfyuroI3lndxJV9W3kZycxuyCbb6YlADAsNY4vTMrB47GWgTFDjYT8rtdDVFBQoIWFhW6HEVItbR3U/2I81cNmMvm6fjzSurkadnzsXDrqbYe4VEjMhBOug/GnwccPQd0uuPQpp4VhjBlyRGSZqhb0Vc4+AQaBPZuXkye1bB91av8OkJABh82Gr/7FaSEc/12ITfx8+/n3DkygxphBL6g3r4nIbBHZICJFInJbD9vHiMgiEflURFaJyLnBjGewat/0DgAy4YxDO9CUC+CUm/ZNCMYY4ydoSUFEooAHgDnAVOByEen+2K0fA8+r6nTgMuDBYMUzmCUUv89G7yhyRua7HYoxZogLZkthJlCkqltUtQ14FriwWxkFUn2v04CdQYxncNpTRPaeQj7wHkVuWpzb0RhjhrhgJoVRQLHfcolvnb87gatEpARYCHy/pwOJyDwRKRSRwoqKimDEGp7amuDZy2nxJPD3+IvsUZbGmKBze0K8y4G/qmoecC7wpIjsF5OqPqqqBapakJMTQdfF7/gI9mzkz+k3EJUxxu1ojDERIJhJoRQY7bec51vn71vA8wCquhiIB7KDGNPgUrMDgI9axjIyLd7lYIwxkSCYSWEpMElE8kUkFmcgeUG3MjuAMwFEZApOUoig/qE+1OxAPTGsqU9kZHqC29EYYyJA0JKCqnYA1wNvAOtwrjJaIyJ3ichcX7GbgG+LyErgGeAaHWx30wVTTTHelJE0tCmjMywpGGOCL6g3r6nqQpwBZP91d/i9Xguc3H0/41Ozg4aEkQBMGNbLDKbGGDOA3B5oNgdSs4OK6OEAvU9rbYwxA8iSQrhqb4GGMkq82STERDEi1QaajTHBZ0khXNWWALCxNYP87CSbkdQYExKWFMJVzXYAVjWk2XiCMSZkLCmEK989CsvrUhmfneRyMMaYSGFJIVzVlaLiYbemW0vBGBMylhTCVX0ZrXFZdBJlLQVjTMhYUghX9WXURTszfozPsaRgjAkNSwrhqr6McjIYmRZPYqw9IM8YExqWFMJV/S5KOtLspjVjTEjZV9Bw1NEGTXso0hQmWNeRMSaErKUQjhp2A1BsLQVjTIhZUghH9WUAzuWolhSMMSFkSSEc1e8CoFwz7MojY0xIWVIIR77uo+qoLIbbRHjGmBCypBCO6nfRSRRJ6cNsIjxjTEhZUghH9WVUejIZnWXjCcaY0LKkEI4adlPuTWV0ZqLbkRhjIowlhTDUUV9BRWcyYywpGGNCLKCkICJ/F5HzRMSSSAh4GyupIpW8DEsKxpjQCvRD/kHgCmCTiNwtIocFMaaI52mupEpTGJ2Z4HYoxpgIE1BSUNW3VfVK4FhgG/C2iHwkIt8QkZje9hOR2SKyQUSKROS2HrbfKyIrfD8bRaSmvxUZMtqaiO5spkptTMEYE3oBz30kIlnAVcDVwKfA08As4OvAaT2UjwIeAM4GSoClIrJAVdfuLaOqP/Qr/31ger9qMZQ0VQLQHJtOanyv+dYYY4Ii0DGFl4H/AInABao6V1WfU9XvA71dNzkTKFLVLaraBjwLXHiA01wOPBN46ENU0x4AYpKzXQ7EGBOJAm0p3K+qi3raoKoFvewzCij2Wy4Bju+poIiMBfKBfwcYz9DV6LQU4tJyXQ7EGBOJAh1onioi6XsXRCRDRL43gHFcBryoqp09bRSReSJSKCKFFRUVA3ja8ONtdOqXkjXc5UiMMZEo0KTwbVXtGgRW1Wrg233sUwqM9lvO863ryWUcoOtIVR9V1QJVLcjJyQkw5MGpvsqZ9ygzZ4TLkRhjIlGgSSFKRLom4fENIsf2sc9SYJKI5ItILM4H/4LuhUTkcCADWBxgLENaQ1UZ7RrF8GHWfWSMCb1Ak8K/gOdE5EwRORPnW/2/DrSDqnYA1wNvAOuA51V1jYjcJSJz/YpeBjyrqnrw4Q89rbXlVJPC6EybMtsYE3qBDjTfClwLfNe3/BbwWF87qepCYGG3dXd0W74zwBgigrexkmpNIT/dblwzxoReQElBVb3AQ74fE0Se5kpqo9OIjbYZRYwxoRdQUhCRScCvgKlA11NfVHV8kOKKWHFtVbTFTnA7DGNMhAr06+hfcFoJHcDpwBPAU8EKKpKldNbgTcxyOwxjTIQKNCkkqOo7gKjqdt84wHnBCysydba1kEojmjTM7VCMMREq0IHmVt+02ZtE5Hqc+w3ssWADrLqihGwgKtVuXDPGuCPQlsINOPMe/QCYgTMx3teDFVSkqi137u2LS7ekYIxxR58tBd+Napeq6s1AA/CNoEcVoRqqdgKQlDnS5UiMMZGqz5aCbz6iWSGIJeK1Vu8CIG1YnsuRGGMiVaBjCp+KyALgBaBx70pV/XtQoopQHXV75z0a5XIkxphIFWhSiAcqgTP81ilgSWEASWM5tSSTFm93Mxtj3BHoHc02jhACMc0V1HoySHM7EGNMxAr0jua/4LQM9qGq3xzwiCJYQlsVjTGZbodhjIlggXYf/dPvdTxwMbBz4MOJbKkdVVQmTXU7DGNMBAu0++gl/2UReQb4ICgRRahOr5Kh1exOHNoPETLGhLf+TsU5CbC5GAZQVU01ydKCJ9l+rcYY9wQ6plDPvmMKZTjPWDADpLKshBwgNt1uXDPGuCfQ7qOUYAcS6WrKiwFIybakYIxxT0DdRyJysYik+S2ni8hFwQsr8jRUlQGQOcxuXDPGuCfQMYWfqmrt3gVVrQF+GpyQIlNrjTPFRXKWJQVjjHsCTQo9lQv0clYTAK3fjRdBkuzqI2OMewJNCoUico+ITPD93AMsC2ZgkSaquYIGTxpEWa41xrgn0KTwfaANeA54FmgBrgtWUJEooXUPjbH2GE5jjLsCvfqoEbjtYA8uIrOB+4Ao4DFVvbuHMpcAd+Jc8rpSVa842PMMdi3tnaR1VtMen+12KMaYCBfo1UdviUi633KGiLzRxz5RwAPAHGAqcLmITO1WZhJwO3Cyqh4B3HiQ8Q8JO2uayaYW7MY1Y4zLAu0+yvZdcQSAqlbT9x3NM4EiVd2iqm043U4XdivzbeAB3/FQ1fIA4xlSdlY3kyM1RNuzmY0xLgs0KXhFZMzeBREZRw+zpnYzCij2Wy7xrfM3GZgsIh+KyBJfd9N+RGSeiBSKSGFFRUWAIQ8eZRXlxEs7ifYYTmOMywK91OVHwAci8h4gwCnAvAE6/yTgNCAPeF9EjvJvlQCo6qPAowAFBQV9JaNBp6LMyZ2pdjezMcZlAbUUVPVfQAGwAXgGuAlo7mO3UmC033Keb52/EmCBqrar6lZgI06SiCh1Fc6vxZOS63IkxphIF+iEeP8F3IDzwb4COAFYzL6P5+xuKTBJRPJxksFlQPcri/4BXA78RUSycbqTthxMBYaC9uoS50WyJQVjjLsCHVO4ATgO2K6qpwPTgZoD7aCqHcD1wBvAOuB5VV0jIneJyFxfsTeAShFZCywCblHVyn7UY9Bq7/SS17SWNk88ZEdcI8kYE2YCHVNoUdUWEUFE4lR1vYgc1tdOqroQWNht3R1+rxX4b99PRCquauJY2UBNxlEMi4pxOxxjTIQLNCmU+O5T+AfwlohUA9uDF1bk2LGrnFmynYq8890OxRhjAr6j+WLfyztFZBGQBvwraFFFkMYtHxMtXlImnex2KMYYc/Aznarqe8EIJFJ5Sj/Bi5A88SS3QzHGmH4/o9kMkKiqIqqicyE+re/CxhgTZJYUXFRa00xmexntKaP7LmyMMSFgScFFn2ytJE8qiM/JdzsUY4wBLCm4qrCojOFSTdqICW6HYowxgCUFV+3YuhEAT8ZYlyMxxhiHJQWXlNe1oDU7nIX0MQcubIwxIWJJwSWfbKsiT3zTgFtSMMaECUsKLvl4SxX50ZWoJxpSRrgdjjFd0DgOAAAShklEQVTGAJYUXPPJ1iqOTKpDUkdB1EHfQ2iMMUFhScEF5XUtbNhdz/joCus6MsaEFUsKLnhr3W48eMltKoJhU90OxxhjulhScMFba3dzcno1no4mGDnd7XCMMaaLJYUQa2jt4KOiSr48wnflkSUFY0wYsaQQYs8tLaat08uJ8TsgJsmetmaMCSuWFEKorqWdP/17E7MmZpPbsA5GHA2eKLfDMsaYLpYUQuh3b2yguqmd27+YD7tWwYhpbodkjDH7sKQQIm+v3c0Ti7fzzZPzOaJlBXQ0w4Qz3A7LGGP2YUkhBLbuaeSHz69g6ohUbp1zGKxbAHGpMP5Ut0Mzxph9BPVWWhGZDdwHRAGPqerd3bZfA/wWKPWt+pOqPhbMmPpDVdlV20Lh9mpWl9QwPC2BhJj9xwLaO718srWK5vZOhqfF09bhZdueRtaX1RMTJTxy9QziRGH9azB5NkTHuVAbY4zpXdCSgohEAQ8AZwMlwFIRWaCqa7sVfU5Vrw9WHIdiRXENd7++jlUltTS1dQIQ7RE6vNrrPsNS4shMimVlcQ3RUcLYrCQuOGYEVx4/ltGZibB2ATRXwdS5oaqGMcYELJgthZlAkapuARCRZ4ELge5JISw9uWQ7d7zyGcNS4rj0uNFMyElmyohUpo1Op7a5nfZO7377CJCdHIfHIz0ftLMD/v2/kD0ZJs8JbgWMMaYfgpkURgHFfsslwPE9lPuyiHwB2Aj8UFWLuxcQkXnAPIAxY4I/V9CTi7fxk1fWcNaUYdx76TRS4mP22Z6ZFHvwB22phX/9D+zZCJc+bZPgGWPCktsDza8C41T1aOAt4PGeCqnqo6paoKoFOTk5QQ1o8eZK7nx1LWdNGcZDV83YLyH026s3wsq/wUnfh8PPG5hjGmPMAAtmUigFRvst5/H5gDIAqlqpqq2+xceAGUGMp0/NbZ3c/MJKxmYlcu+l04iJGqBfT2cHFL0N06+GL/4cpJfuJWOMcVkwk8JSYJKI5ItILHAZsMC/gIj4P11mLrAuiPH06eH3NlNa08wvLjpq4FoIAKXLoLXO7kswxoS9oHVsq2qHiFwPvIFzSep8VV0jIncBhaq6APiBiMwFOoAq4JpgxdOX55bu4I//3sT5R4/gxAlZA3vwzf8GBPK/MLDHNcaYARbU0U5VXQgs7LbuDr/XtwO3BzOGQJRUN3Hb31cza2I2v/nK0QN7cFUoesuZDTUxc2CPbYwxA8wugQGWba9GFW6fM4XE2AH4lXR2QM125/XafzjdR7N/fejHNcaYILOkAHy6o4aEmCgm5yYfuGBzDWx4HTrbei/jbYePH4U9Gz5fN+UCmDlvYII1xpggsqQAfFpcw1F5aUTvvdpo9xpY/KDzAb+XKmx5FxrL+z5g+hg47x6IS3Gmsph0DnjcvvrXGGP6FvFJobWjk3U76/jGrHHQ1gjV2+Cpr0BrPSR1G3DOOQwufRLSRvd0qM8lD4OoAbx6yRhjQiTik8LanXW0dXqZnpcKD53kJIWYRPjWmzD8KLfDM8aYkIr4pLBuVz0Ax3qKnIRwwvecm8xyp7obmDHGuCDik8LWPQ3ERXvIKX4TPDFw2m0Qn+Z2WMYY44qIH/3cuqeRcZmJyPoFMOF0SwjGmIgW8Ulhy55GvhH7FtTsgCO+5HY4xhjjqojuPuro9JJStYZLYh50nm9w9KVuh2SMMa6K6JZCaU0zZ0ghoHDRg3YvgTEm4kX0p+CWPY3MkA00Zx5u8xIZYwwRnhS27a5luqcIz5gT3A7FGGPCQkSPKbSWriRJWtEJJ7sdijHGhIWIbimkViwHQKylYIwxQIQnhbH1y9kTnQtpeW6HYowxYSFyk0JnB0e1r2R72nFuR2KMMWEjYpNC47alpNJEda6NJxhjzF4RmxSa1r2FV4XO/FPdDsUYY8JGxF59FL3jA9boWHJyR7odijEmBNrb2ykpKaGlpcXtUIIqPj6evLw8YmL690yXiE0K8TUbWe2dxlnpCW6HYowJgZKSElJSUhg3bhwi4nY4QaGqVFZWUlJSQn5+fr+OEdTuIxGZLSIbRKRIRG47QLkvi4iKSEEw4+nSXENCWzXFMpLs5LiQnNIY466WlhaysrKGbEIAEBGysrIOqTUUtKQgIlHAA8AcYCpwuYjs9+QaEUkBbgA+DlYs+6naDEBd4hg8nqH7B2KM2ddQTgh7HWodg9lSmAkUqeoWVW0DngUu7KHc/wK/BkLX0VfpJIXWtPEhO6UxxgwGwUwKo4Biv+US37ouInIsMFpVXzvQgURknogUikhhRUXFoUdWWUQnHmKzLSkYY0KjpqaGBx988KD3O/fcc6mpqQlCRD1z7ZJUEfEA9wA39VVWVR9V1QJVLcjJyTnkc3dUFFHqzWJUTvohH8sYYwLRW1Lo6Og44H4LFy4kPT10n1XBvPqoFBjtt5znW7dXCnAk8K6vD2w4sEBE5qpqYRDjoqNiE1t1BKMzE4N5GmNMmPrZq2tYu7NuQI85dWQqP73giF6333bbbWzevJlp06YRExNDfHw8GRkZrF+/no0bN3LRRRdRXFxMS0sLN9xwA/PmzQNg3LhxFBYW0tDQwJw5c5g1axYfffQRo0aN4pVXXiEhYWCvoAxmS2EpMElE8kUkFrgMWLB3o6rWqmq2qo5T1XHAEiDoCQFVoqs3s0VHMMaSgjEmRO6++24mTJjAihUr+O1vf8vy5cu577772LhxIwDz589n2bJlFBYWcv/991NZWbnfMTZt2sR1113HmjVrSE9P56WXXhrwOIPWUlDVDhG5HngDiALmq+oaEbkLKFTVBQc+QpC01BDd0UiJZnOhJQVjItKBvtGHysyZM/e5l+D+++/n5ZdfBqC4uJhNmzaRlZW1zz75+flMmzYNgBkzZrBt27YBjyuoN6+p6kJgYbd1d/RS9rRgxtKlvgyA2ugcMhL7d8efMcYcqqSkpK7X7777Lm+//TaLFy8mMTGR0047rcd7DeLiPr+vKioqiubm5gGPK/LmPqrfBYCmDI+Ia5aNMeEhJSWF+vr6HrfV1taSkZFBYmIi69evZ8mSJSGO7nORN81F/W4A4jNG9VHQGGMGTlZWFieffDJHHnkkCQkJ5Obmdm2bPXs2Dz/8MFOmTOGwww7jhBPce/BXxCUFb90uPEBKtj1YxxgTWn/72996XB8XF8frr7/e47a94wbZ2dl89tlnXetvvvnmAY8PIjApVJZtJ04TOXxMbt+FjTEmwkRcUqgu24FoBqdOPvSb4IwxZqiJuKTQWbeT9vgcMpJi3Q7FGGPCTkRdfVRe10JK+x5ibZDZGGN6FFFJ4ZOtleRQQ2buGLdDMcaYsBRRSWHjth3ESQdZI8a6HYoxxoSliEoKpcVbAYhKHeFyJMaYSNPfqbMB/vCHP9DU1DTAEfUsYpJCp1dpKy9yFtJHH7iwMcYMsMGSFCLm6qOi8gbGd25DPYLkTHE7HGOMm16/DcpWD+wxhx8Fc+7udbP/1Nlnn302w4YN4/nnn6e1tZWLL76Yn/3sZzQ2NnLJJZdQUlJCZ2cnP/nJT9i9ezc7d+7k9NNPJzs7m0WLFg1s3N1ETFJYWVzD4Z4dtKfnExtrs6MaY0Lr7rvv5rPPPmPFihW8+eabvPjii3zyySeoKnPnzuX999+noqKCkSNH8tprzsMoa2trSUtL45577mHRokVkZ2cHPc6ISQop8dFMjyslZsRxbodijHHbAb7Rh8Kbb77Jm2++yfTp0wFoaGhg06ZNnHLKKdx0003ceuutnH/++Zxyyikhjy1iksKcySnQsQuGH+l2KMaYCKeq3H777Vx77bX7bVu+fDkLFy7kxz/+MWeeeSZ33NHj0waCJmIGmqlYDyjkuv9wDWNM5PGfOvucc85h/vz5NDQ0AFBaWkp5eTk7d+4kMTGRq666iltuuYXly5fvt2+wRUxLgd2+2QUtKRhjXOA/dfacOXO44oorOPHEEwFITk7mqaeeoqioiFtuuQWPx0NMTAwPPfQQAPPmzWP27NmMHDky6APNoqpBPcFAKygo0MLCfjzGef1r8OnTcOlT4ImcBpIxxrFu3TqmTImMKw97qquILFPVgr72jZyWwuHnOT/GGGN6ZV+ZjTHGdLGkYIyJGIOtu7w/DrWOQU0KIjJbRDaISJGI3NbD9u+IyGoRWSEiH4jI1GDGY4yJXPHx8VRWVg7pxKCqVFZWEh8f3+9jBG1MQUSigAeAs4ESYKmILFDVtX7F/qaqD/vKzwXuAWYHKyZjTOTKy8ujpKSEiooKt0MJqvj4ePLy+v8M+mAONM8EilR1C4CIPAtcCHQlBVWt8yufBAzdFG6McVVMTAz5+fluhxH2gpkURgHFfsslwPHdC4nIdcB/A7HAGT0dSETmAfMAxoyxB+QYY0ywuD7QrKoPqOoE4Fbgx72UeVRVC1S1ICcnJ7QBGmNMBAlmUigF/B9ckOdb15tngYuCGI8xxpg+BLP7aCkwSUTycZLBZcAV/gVEZJKqbvItngdsog/Lli3bIyLb+xlTNrCnn/sOVpFYZ4jMeludI0N/6xzQc4iDlhRUtUNErgfeAKKA+aq6RkTuAgpVdQFwvYicBbQD1cDXAzhuv/uPRKQwkNu8h5JIrDNEZr2tzpEh2HUO6jQXqroQWNht3R1+r28I5vmNMcYcHNcHmo0xxoSPSEsKj7odgAsisc4QmfW2OkeGoNZ50E2dbYwxJngiraVgjDHmACwpGGOM6RIxSaGvGVuHChHZ5jfzbKFvXaaIvCUim3z/Zrgd56EQkfkiUi4in/mt67GO4rjf976vEpFj3Yu8/3qp850iUup7r1eIyLl+22731XmDiJzjTtSHRkRGi8giEVkrImtE5Abf+iH7Xh+gzqF7r1V1yP/g3CexGRiPM8fSSmCq23EFqa7bgOxu634D3OZ7fRvwa7fjPMQ6fgE4FvisrzoC5wKvAwKcAHzsdvwDWOc7gZt7KDvV9zceB+T7/vaj3K5DP+o8AjjW9zoF2Oir25B9rw9Q55C915HSUuiasVVV23Cm1LjQ5ZhC6ULgcd/rxxnk04mo6vtAVbfVvdXxQuAJdSwB0kVkRGgiHTi91Lk3FwLPqmqrqm4FinD+DwwqqrpLVZf7XtcD63Am2hyy7/UB6tybAX+vIyUp9DRj64F+0YOZAm+KyDLf7LIAuaq6y/e6DMh1J7Sg6q2OQ/29v97XVTLfr1twyNVZRMYB04GPiZD3uludIUTvdaQkhUgyS1WPBeYA14nIF/w3qtPmHNLXIUdCHX0eAiYA04BdwO/dDSc4RCQZeAm4Ufd9BsuQfa97qHPI3utISQoHO2ProKWqpb5/y4GXcZqSu/c2o33/lrsXYdD0Vsch+96r6m5V7VRVL/B/fN5tMGTqLCIxOB+OT6vq332rh/R73VOdQ/leR0pS6JqxVURicWZsXeByTANORJJEJGXva+CLwGc4dd072eDXgVfciTCoeqvjAuBrvitTTgBq/boeBrVu/eUX47zX4NT5MhGJ881SPAn4JNTxHSoREeDPwDpVvcdv05B9r3urc0jfa7dH20M4qn8uzkj+ZuBHbscTpDqOx7kSYSWwZm89gSzgHZypyd8GMt2O9RDr+QxOE7odpw/1W73VEedKlAd87/tqoMDt+Aewzk/66rTK9+Ewwq/8j3x13gDMcTv+ftZ5Fk7X0Cpghe/n3KH8Xh+gziF7r22aC2OMMV0ipfvIGGNMACwpGGOM6WJJwRhjTBdLCsYYY7pYUjDGGNPFkoIxISQip4nIP92Ow5jeWFIwxhjTxZKCMT0QkatE5BPf3PWPiEiUiDSIyL2+ee7fEZEcX9lpIrLEN1nZy37z+08UkbdFZKWILBeRCb7DJ4vIiyKyXkSe9t3FakxYsKRgTDciMgW4FDhZVacBncCVQBJQqKpHAO8BP/Xt8gRwq6oejXPX6d71TwMPqOoxwEk4dySDM/PljThz4Y8HTg56pYwJULTbARgThs4EZgBLfV/iE3AmXfMCz/nKPAX8XUTSgHRVfc+3/nHgBd8cVKNU9WUAVW0B8B3vE1Ut8S2vAMYBHwS/Wsb0zZKCMfsT4HFVvX2flSI/6Vauv3PEtPq97sT+H5owYt1HxuzvHeArIjIMup4JPBbn/8tXfGWuAD5Q1VqgWkRO8a2/GnhPnadmlYjIRb5jxIlIYkhrYUw/2DcUY7pR1bUi8mOcJ9h5cGYmvQ5oBGb6tpXjjDuAM33zw74P/S3AN3zrrwYeEZG7fMf4agirYUy/2CypxgRIRBpUNdntOIwJJus+MsYY08VaCsYYY7pYS8EYY0wXSwrGGGO6WFIwxhjTxZKCMcaYLpYUjDHGdPn/ubshaI01r3kAAAAASUVORK5CYII=\n", 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\n", 781 | "text/plain": [ 782 | "
" 783 | ] 784 | }, 785 | "metadata": { 786 | "needs_background": "light" 787 | }, 788 | "output_type": "display_data" 789 | } 790 | ], 791 | "source": [ 792 | "%matplotlib inline\n", 793 | "import matplotlib.pyplot as plt\n", 794 | "\n", 795 | "# Visualizamos la evolución de la accuracy\n", 796 | "plt.plot(history.history['acc'])\n", 797 | "plt.plot(history.history['val_acc'])\n", 798 | "plt.title('model accuracy')\n", 799 | "plt.ylabel('accuracy')\n", 800 | "plt.xlabel('epoch')\n", 801 | "plt.legend(['train', 'test'], loc='lower right')\n", 802 | "plt.show()\n", 803 | "\n", 804 | "# Visualizamos la evolución del error cometido por la red\n", 805 | "plt.plot(history.history['loss'])\n", 806 | "plt.plot(history.history['val_loss'])\n", 807 | "plt.title('model loss')\n", 808 | "plt.ylabel('loss')\n", 809 | "plt.xlabel('epoch')\n", 810 | "plt.legend(['train', 'test'], loc='upper right')\n", 811 | "plt.show()" 812 | ] 813 | } 814 | ], 815 | "metadata": { 816 | "celltoolbar": "Raw Cell Format", 817 | "kernelspec": { 818 | "display_name": "Python 3", 819 | "language": "python", 820 | "name": "python3" 821 | }, 822 | "language_info": { 823 | "codemirror_mode": { 824 | "name": "ipython", 825 | "version": 3 826 | }, 827 | "file_extension": ".py", 828 | "mimetype": "text/x-python", 829 | "name": "python", 830 | "nbconvert_exporter": "python", 831 | "pygments_lexer": "ipython3", 832 | "version": "3.6.10" 833 | } 834 | }, 835 | "nbformat": 4, 836 | "nbformat_minor": 2 837 | } 838 | -------------------------------------------------------------------------------- /cap03/C03_E1_Ejemplo-NN.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
\n", 8 | "
\n", 9 | "\n", 10 | "
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Deep learning: Principios y fundamentos

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Estudios de Informática, Multimedia y Telecomunicación

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Universitat Oberta de Catalunya (UOC)

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\n", 18 | "\n", 19 | "\n", 20 | "# Capítulo 3: Ejemplo de redes neuronales completamente conectadas\n", 21 | "\n", 22 | "En este ejemplo implementaremos una red neuronal completamente conectada utilizando la librería Keras y TensorFlow." 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": {}, 28 | "source": [ 29 | "## 1. Carga de datos\n", 30 | "\n", 31 | "El siguiente código carga los paquetes necesarios para el ejemplo y además lee los datos que utilizaremos para entrenar la red neuronal." 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 1, 37 | "metadata": {}, 38 | "outputs": [ 39 | { 40 | "name": "stdout", 41 | "output_type": "stream", 42 | "text": [ 43 | "X shape is (2000, 100) and sample data: [-3.80997895 -2.90418191 -2.73756329 1.93988891 2.27761932 2.29250939\n", 44 | " 0.80657717 -0.18411067 -0.42838168 -0.74681727 -1.73050585 0.09787124\n", 45 | " -0.2622376 -0.47714865 0.09652312 0.02163088 -0.46442759 -0.44279175\n", 46 | " 0.30522186 0.14028104 -0.81884 -0.1755735 -0.32787818 -0.01383203\n", 47 | " -0.92451808 0.05138323 0.92543899 -0.28682495 -0.75251595 0.21320492\n", 48 | " -0.23709864 0.94373726 0.69307069 0.5114043 0.40932795 0.15704461\n", 49 | " 0.35092131 -0.14505174 0.04883884 0.19069857 -0.36449453 0.05761488\n", 50 | " 0.03058404 0.37526033 0.16429508 0.17789281 0.12090412 0.12617789\n", 51 | " 0.00622596 0.1119326 -0.20559843 -0.14971844 -0.25820982 0.29480069\n", 52 | " -0.09804937 0.02398129 -0.03289546 -0.23438531 0.33442494 -0.36561303\n", 53 | " -0.17030636 0.1216377 -0.02450097 0.19675075 0.04785413 0.0186243\n", 54 | " 0.08853324 0.34336898 -0.09194883 0.03168607 0.16912165 -0.19320273\n", 55 | " 0.16965356 0.01574452 -0.02055554 -0.06386753 -0.01774633 -0.05936183\n", 56 | " -0.3109211 -0.09110711 0.10386121 -0.0816454 -0.05277287 0.04745927\n", 57 | " 0.18950338 0.03551209 0.05914535 -0.18293479 0.0400644 0.03657231\n", 58 | " 0.02882243 -0.11835669 0.00511612 -0.22489106 0.11483641 0.01020498\n", 59 | " -0.02709487 0.11601074 -0.1055054 -0.33433267]\n", 60 | "Y shape is (2000,) and sample data: [1 1 1 1 0 1 0 0 1 1]\n" 61 | ] 62 | } 63 | ], 64 | "source": [ 65 | "import numpy as np\n", 66 | "import pickle\n", 67 | "\n", 68 | "with open(\"../data/data.pickle\", \"rb\") as f:\n", 69 | " data = pickle.load(f)\n", 70 | "\n", 71 | "X = data[\"features\"]\n", 72 | "Y = data[\"labels\"]\n", 73 | "\n", 74 | "print(\"X shape is {} and sample data: {}\".format(X.shape, X[0,:]))\n", 75 | "print(\"Y shape is {} and sample data: {}\".format(Y.shape, Y[0:10]))" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "## 2. Redes neuronales utilizando Keras\n", 83 | "\n", 84 | "A continuación definiremos una red neuronal completamente conectada utilizando la librería [Keras](https://keras.io/).\n", 85 | "\n", 86 | "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\"`." 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 2, 92 | "metadata": {}, 93 | "outputs": [ 94 | { 95 | "name": "stderr", 96 | "output_type": "stream", 97 | "text": [ 98 | "Using TensorFlow backend.\n" 99 | ] 100 | } 101 | ], 102 | "source": [ 103 | "import keras\n", 104 | "from keras.models import Sequential\n", 105 | "from keras.layers import Dense\n", 106 | "from keras.optimizers import SGD\n", 107 | "\n", 108 | "def keras_model(layers_dims, learning_rate):\n", 109 | " \"\"\"\n", 110 | " Crea, utilizando Keras, una red neuronal de L capas completamente conectadas donde las L-1 primeras capas\n", 111 | " tienen función de activación relu y la última capa tiene función de activación sigmoid.\n", 112 | " \n", 113 | " Argumentos:\n", 114 | " layers_dims -- lista de longitud (número de capas + 1) que contiene el número de variables y el número \n", 115 | " de neuronas en cada capa, \n", 116 | " learning_rate -- velocidad de aprendizaje para aplicar el método del descenso del gradiente\n", 117 | " \n", 118 | " Devuelve:\n", 119 | " modelo -- objeto de Keras que representa la red neuronal\n", 120 | " \"\"\"\n", 121 | " \n", 122 | " L = len(layers_dims)\n", 123 | " \n", 124 | " model = Sequential()\n", 125 | " model.add(Dense(layers_dims[1], input_shape=(layers_dims[0],), activation=\"relu\"))\n", 126 | " \n", 127 | " for l in range(2, L-1):\n", 128 | " model.add(Dense(layers_dims[l], activation=\"relu\", kernel_initializer=\"random_normal\",\n", 129 | " bias_initializer=\"zeros\"))\n", 130 | " \n", 131 | " model.add(Dense(layers_dims[L-1], activation=\"sigmoid\", kernel_initializer=\"random_normal\",\n", 132 | " bias_initializer=\"zeros\"))\n", 133 | " \n", 134 | " model.compile(optimizer=SGD(lr=learning_rate), loss='binary_crossentropy', metrics=['accuracy'])\n", 135 | " \n", 136 | " return model" 137 | ] 138 | }, 139 | { 140 | "cell_type": "markdown", 141 | "metadata": {}, 142 | "source": [ 143 | "## 3. Entrenamiento de la red neuronal\n", 144 | "\n", 145 | "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." 146 | ] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "metadata": {}, 151 | "source": [ 152 | "A continuación definimos la estructura de capas que tendrá la red neuronal." 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 3, 158 | "metadata": {}, 159 | "outputs": [], 160 | "source": [ 161 | "layers_dims = [100, 20, 5, 1]" 162 | ] 163 | }, 164 | { 165 | "cell_type": "markdown", 166 | "metadata": {}, 167 | "source": [ 168 | "Para entrenar la red neuronal que hemos construido utilizando Keras debemos ejecutar el siguiente código:" 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": 4, 174 | "metadata": {}, 175 | "outputs": [ 176 | { 177 | "name": "stdout", 178 | "output_type": "stream", 179 | "text": [ 180 | "Train on 1600 samples, validate on 400 samples\n", 181 | "Epoch 1/250\n", 182 | " - 0s - loss: 0.6945 - acc: 0.3381 - val_loss: 0.6943 - val_acc: 0.3250\n", 183 | "Epoch 2/250\n", 184 | " - 0s - loss: 0.6942 - acc: 0.3575 - val_loss: 0.6940 - val_acc: 0.3475\n", 185 | "Epoch 3/250\n", 186 | " - 0s - loss: 0.6939 - acc: 0.3713 - val_loss: 0.6938 - val_acc: 0.3875\n", 187 | "Epoch 4/250\n", 188 | " - 0s - loss: 0.6937 - acc: 0.4019 - val_loss: 0.6937 - val_acc: 0.4150\n", 189 | "Epoch 5/250\n", 190 | " - 0s - loss: 0.6935 - acc: 0.4356 - val_loss: 0.6936 - val_acc: 0.4300\n", 191 | "Epoch 6/250\n", 192 | " - 0s - loss: 0.6934 - acc: 0.4631 - val_loss: 0.6935 - val_acc: 0.4475\n", 193 | "Epoch 7/250\n", 194 | " - 0s - loss: 0.6933 - acc: 0.4819 - val_loss: 0.6934 - val_acc: 0.4525\n", 195 | "Epoch 8/250\n", 196 | " - 0s - loss: 0.6933 - acc: 0.4856 - val_loss: 0.6934 - val_acc: 0.4650\n", 197 | "Epoch 9/250\n", 198 | " - 0s - loss: 0.6932 - acc: 0.4925 - val_loss: 0.6933 - val_acc: 0.4650\n", 199 | "Epoch 10/250\n", 200 | " - 0s - loss: 0.6931 - acc: 0.4975 - val_loss: 0.6933 - val_acc: 0.4700\n", 201 | "Epoch 11/250\n", 202 | " - 0s - loss: 0.6931 - acc: 0.5013 - val_loss: 0.6932 - val_acc: 0.4700\n", 203 | "Epoch 12/250\n", 204 | " - 0s - loss: 0.6930 - acc: 0.5038 - val_loss: 0.6932 - val_acc: 0.4700\n", 205 | "Epoch 13/250\n", 206 | " - 0s - loss: 0.6930 - acc: 0.5044 - val_loss: 0.6932 - val_acc: 0.4700\n", 207 | "Epoch 14/250\n", 208 | " - 0s - loss: 0.6929 - acc: 0.5050 - val_loss: 0.6931 - val_acc: 0.4700\n", 209 | "Epoch 15/250\n", 210 | " - 0s - loss: 0.6929 - acc: 0.5063 - val_loss: 0.6931 - val_acc: 0.4725\n", 211 | "Epoch 16/250\n", 212 | " - 0s - loss: 0.6928 - acc: 0.5056 - val_loss: 0.6931 - val_acc: 0.4725\n", 213 | "Epoch 17/250\n", 214 | " - 0s - loss: 0.6928 - acc: 0.5056 - val_loss: 0.6930 - val_acc: 0.4725\n", 215 | "Epoch 18/250\n", 216 | " - 0s - loss: 0.6927 - acc: 0.5056 - val_loss: 0.6930 - val_acc: 0.4725\n", 217 | "Epoch 19/250\n", 218 | " - 0s - loss: 0.6926 - acc: 0.5056 - val_loss: 0.6929 - val_acc: 0.4725\n", 219 | "Epoch 20/250\n", 220 | " - 0s - loss: 0.6926 - acc: 0.5056 - val_loss: 0.6928 - val_acc: 0.4725\n", 221 | "Epoch 21/250\n", 222 | " - 0s - loss: 0.6925 - acc: 0.5056 - val_loss: 0.6928 - val_acc: 0.4725\n", 223 | "Epoch 22/250\n", 224 | " - 0s - loss: 0.6924 - acc: 0.5069 - val_loss: 0.6927 - val_acc: 0.4725\n", 225 | "Epoch 23/250\n", 226 | " - 0s - loss: 0.6923 - acc: 0.5063 - val_loss: 0.6926 - val_acc: 0.4750\n", 227 | "Epoch 24/250\n", 228 | " - 0s - loss: 0.6923 - acc: 0.5063 - val_loss: 0.6925 - val_acc: 0.4750\n", 229 | "Epoch 25/250\n", 230 | " - 0s - loss: 0.6922 - acc: 0.5069 - val_loss: 0.6924 - val_acc: 0.4750\n", 231 | "Epoch 26/250\n", 232 | " - 0s - loss: 0.6920 - acc: 0.5075 - val_loss: 0.6923 - val_acc: 0.4800\n", 233 | "Epoch 27/250\n", 234 | " - 0s - loss: 0.6919 - acc: 0.5125 - val_loss: 0.6922 - val_acc: 0.4950\n", 235 | "Epoch 28/250\n", 236 | " - 0s - loss: 0.6918 - acc: 0.5181 - val_loss: 0.6921 - val_acc: 0.4975\n", 237 | "Epoch 29/250\n", 238 | " - 0s - loss: 0.6916 - acc: 0.5256 - val_loss: 0.6919 - val_acc: 0.5050\n", 239 | "Epoch 30/250\n", 240 | " - 0s - loss: 0.6915 - acc: 0.5387 - val_loss: 0.6917 - val_acc: 0.5175\n", 241 | "Epoch 31/250\n", 242 | " - 0s - loss: 0.6913 - acc: 0.5550 - val_loss: 0.6915 - val_acc: 0.5375\n", 243 | "Epoch 32/250\n", 244 | " - 0s - loss: 0.6911 - acc: 0.5719 - val_loss: 0.6913 - val_acc: 0.5700\n", 245 | "Epoch 33/250\n", 246 | " - 0s - loss: 0.6908 - acc: 0.5987 - val_loss: 0.6911 - val_acc: 0.5875\n", 247 | "Epoch 34/250\n", 248 | " - 0s - loss: 0.6906 - acc: 0.6331 - val_loss: 0.6908 - val_acc: 0.6300\n", 249 | "Epoch 35/250\n", 250 | " - 0s - loss: 0.6903 - acc: 0.6737 - val_loss: 0.6905 - val_acc: 0.6750\n", 251 | "Epoch 36/250\n", 252 | " - 0s - loss: 0.6900 - acc: 0.7044 - val_loss: 0.6901 - val_acc: 0.7250\n", 253 | "Epoch 37/250\n", 254 | " - 0s - loss: 0.6896 - acc: 0.7356 - val_loss: 0.6897 - val_acc: 0.7650\n", 255 | "Epoch 38/250\n", 256 | " - 0s - loss: 0.6892 - acc: 0.7625 - val_loss: 0.6893 - val_acc: 0.7975\n", 257 | "Epoch 39/250\n", 258 | " - 0s - loss: 0.6888 - acc: 0.7881 - val_loss: 0.6888 - val_acc: 0.8025\n", 259 | "Epoch 40/250\n", 260 | " - 0s - loss: 0.6883 - acc: 0.8231 - val_loss: 0.6882 - val_acc: 0.8175\n", 261 | "Epoch 41/250\n", 262 | " - 0s - loss: 0.6877 - acc: 0.8425 - val_loss: 0.6876 - val_acc: 0.8275\n", 263 | "Epoch 42/250\n", 264 | " - 0s - loss: 0.6871 - acc: 0.8506 - val_loss: 0.6869 - val_acc: 0.8450\n", 265 | "Epoch 43/250\n", 266 | " - 0s - loss: 0.6864 - acc: 0.8625 - val_loss: 0.6862 - val_acc: 0.8450\n", 267 | "Epoch 44/250\n", 268 | " - 0s - loss: 0.6857 - acc: 0.8625 - val_loss: 0.6853 - val_acc: 0.8450\n", 269 | "Epoch 45/250\n", 270 | " - 0s - loss: 0.6848 - acc: 0.8612 - val_loss: 0.6843 - val_acc: 0.8400\n", 271 | "Epoch 46/250\n", 272 | " - 0s - loss: 0.6839 - acc: 0.8575 - val_loss: 0.6833 - val_acc: 0.8275\n", 273 | "Epoch 47/250\n", 274 | " - 0s - loss: 0.6828 - acc: 0.8519 - val_loss: 0.6821 - val_acc: 0.8325\n", 275 | "Epoch 48/250\n", 276 | " - 0s - loss: 0.6817 - acc: 0.8481 - val_loss: 0.6808 - val_acc: 0.8300\n", 277 | "Epoch 49/250\n", 278 | " - 0s - loss: 0.6804 - acc: 0.8481 - val_loss: 0.6793 - val_acc: 0.8325\n", 279 | "Epoch 50/250\n", 280 | " - 0s - loss: 0.6789 - acc: 0.8431 - val_loss: 0.6776 - val_acc: 0.8300\n", 281 | "Epoch 51/250\n", 282 | " - 0s - loss: 0.6773 - acc: 0.8394 - val_loss: 0.6757 - val_acc: 0.8275\n", 283 | "Epoch 52/250\n", 284 | " - 0s - loss: 0.6754 - acc: 0.8375 - val_loss: 0.6737 - val_acc: 0.8200\n", 285 | "Epoch 53/250\n", 286 | " - 0s - loss: 0.6734 - acc: 0.8356 - val_loss: 0.6714 - val_acc: 0.8200\n", 287 | "Epoch 54/250\n", 288 | " - 0s - loss: 0.6712 - acc: 0.8338 - val_loss: 0.6688 - val_acc: 0.8150\n", 289 | "Epoch 55/250\n", 290 | " - 0s - loss: 0.6687 - acc: 0.8356 - val_loss: 0.6660 - val_acc: 0.8175\n", 291 | "Epoch 56/250\n", 292 | " - 0s - loss: 0.6659 - acc: 0.8325 - val_loss: 0.6627 - val_acc: 0.8175\n", 293 | "Epoch 57/250\n", 294 | " - 0s - loss: 0.6626 - acc: 0.8306 - val_loss: 0.6591 - val_acc: 0.8200\n", 295 | "Epoch 58/250\n", 296 | " - 0s - loss: 0.6591 - acc: 0.8269 - val_loss: 0.6551 - val_acc: 0.8200\n", 297 | "Epoch 59/250\n", 298 | " - 0s - loss: 0.6552 - acc: 0.8263 - val_loss: 0.6504 - val_acc: 0.8175\n", 299 | "Epoch 60/250\n", 300 | " - 0s - loss: 0.6506 - acc: 0.8238 - val_loss: 0.6454 - val_acc: 0.8250\n", 301 | "Epoch 61/250\n", 302 | " - 0s - loss: 0.6457 - acc: 0.8213 - val_loss: 0.6398 - val_acc: 0.8225\n", 303 | "Epoch 62/250\n", 304 | " - 0s - loss: 0.6402 - acc: 0.8225 - val_loss: 0.6336 - val_acc: 0.8225\n", 305 | "Epoch 63/250\n", 306 | " - 0s - loss: 0.6342 - acc: 0.8213 - val_loss: 0.6268 - val_acc: 0.8250\n", 307 | "Epoch 64/250\n", 308 | " - 0s - loss: 0.6274 - acc: 0.8206 - val_loss: 0.6189 - val_acc: 0.8250\n", 309 | "Epoch 65/250\n", 310 | " - 0s - loss: 0.6196 - acc: 0.8213 - val_loss: 0.6102 - val_acc: 0.8250\n", 311 | "Epoch 66/250\n", 312 | " - 0s - loss: 0.6111 - acc: 0.8225 - val_loss: 0.6010 - val_acc: 0.8275\n", 313 | "Epoch 67/250\n", 314 | " - 0s - loss: 0.6021 - acc: 0.8225 - val_loss: 0.5911 - val_acc: 0.8300\n", 315 | "Epoch 68/250\n", 316 | " - 0s - loss: 0.5923 - acc: 0.8225 - val_loss: 0.5804 - val_acc: 0.8300\n", 317 | "Epoch 69/250\n", 318 | " - 0s - loss: 0.5817 - acc: 0.8244 - val_loss: 0.5690 - val_acc: 0.8300\n", 319 | "Epoch 70/250\n", 320 | " - 0s - loss: 0.5705 - acc: 0.8269 - val_loss: 0.5570 - val_acc: 0.8325\n", 321 | "Epoch 71/250\n", 322 | " - 0s - loss: 0.5587 - acc: 0.8288 - val_loss: 0.5445 - val_acc: 0.8325\n", 323 | "Epoch 72/250\n", 324 | " - 0s - loss: 0.5463 - acc: 0.8306 - val_loss: 0.5316 - val_acc: 0.8400\n", 325 | "Epoch 73/250\n", 326 | " - 0s - loss: 0.5335 - acc: 0.8325 - val_loss: 0.5185 - val_acc: 0.8425\n", 327 | "Epoch 74/250\n", 328 | " - 0s - loss: 0.5205 - acc: 0.8375 - val_loss: 0.5054 - val_acc: 0.8550\n", 329 | "Epoch 75/250\n", 330 | " - 0s - loss: 0.5073 - acc: 0.8431 - val_loss: 0.4920 - val_acc: 0.8575\n", 331 | "Epoch 76/250\n", 332 | " - 0s - loss: 0.4939 - acc: 0.8519 - val_loss: 0.4792 - val_acc: 0.8575\n", 333 | "Epoch 77/250\n", 334 | " - 0s - loss: 0.4809 - acc: 0.8531 - val_loss: 0.4667 - val_acc: 0.8625\n", 335 | "Epoch 78/250\n", 336 | " - 0s - loss: 0.4681 - acc: 0.8606 - val_loss: 0.4545 - val_acc: 0.8625\n", 337 | "Epoch 79/250\n", 338 | " - 0s - loss: 0.4556 - acc: 0.8656 - val_loss: 0.4428 - val_acc: 0.8650\n", 339 | "Epoch 80/250\n", 340 | " - 0s - loss: 0.4434 - acc: 0.8700 - val_loss: 0.4314 - val_acc: 0.8775\n", 341 | "Epoch 81/250\n", 342 | " - 0s - loss: 0.4316 - acc: 0.8769 - val_loss: 0.4205 - val_acc: 0.8875\n", 343 | "Epoch 82/250\n", 344 | " - 0s - loss: 0.4200 - acc: 0.8825 - val_loss: 0.4099 - val_acc: 0.8900\n", 345 | "Epoch 83/250\n", 346 | " - 0s - loss: 0.4087 - acc: 0.8913 - val_loss: 0.3998 - val_acc: 0.8975\n", 347 | "Epoch 84/250\n", 348 | " - 0s - loss: 0.3977 - acc: 0.8925 - val_loss: 0.3899 - val_acc: 0.9025\n", 349 | "Epoch 85/250\n", 350 | " - 0s - loss: 0.3869 - acc: 0.8975 - val_loss: 0.3804 - val_acc: 0.9025\n", 351 | "Epoch 86/250\n", 352 | " - 0s - loss: 0.3765 - acc: 0.9094 - val_loss: 0.3711 - val_acc: 0.9025\n", 353 | "Epoch 87/250\n", 354 | " - 0s - loss: 0.3661 - acc: 0.9125 - val_loss: 0.3620 - val_acc: 0.9025\n", 355 | "Epoch 88/250\n", 356 | " - 0s - loss: 0.3561 - acc: 0.9162 - val_loss: 0.3533 - val_acc: 0.9100\n", 357 | "Epoch 89/250\n", 358 | " - 0s - loss: 0.3463 - acc: 0.9206 - val_loss: 0.3445 - val_acc: 0.9100\n", 359 | "Epoch 90/250\n", 360 | " - 0s - loss: 0.3365 - acc: 0.9237 - val_loss: 0.3360 - val_acc: 0.9175\n", 361 | "Epoch 91/250\n", 362 | " - 0s - loss: 0.3269 - acc: 0.9281 - val_loss: 0.3276 - val_acc: 0.9175\n", 363 | "Epoch 92/250\n", 364 | " - 0s - loss: 0.3175 - acc: 0.9306 - val_loss: 0.3195 - val_acc: 0.9200\n", 365 | "Epoch 93/250\n", 366 | " - 0s - loss: 0.3084 - acc: 0.9362 - val_loss: 0.3115 - val_acc: 0.9200\n", 367 | "Epoch 94/250\n", 368 | " - 0s - loss: 0.2994 - acc: 0.9375 - val_loss: 0.3037 - val_acc: 0.9200\n", 369 | "Epoch 95/250\n", 370 | " - 0s - loss: 0.2905 - acc: 0.9381 - val_loss: 0.2961 - val_acc: 0.9250\n", 371 | "Epoch 96/250\n", 372 | " - 0s - loss: 0.2820 - acc: 0.9425 - val_loss: 0.2887 - val_acc: 0.9250\n" 373 | ] 374 | }, 375 | { 376 | "name": "stdout", 377 | "output_type": "stream", 378 | "text": [ 379 | "Epoch 97/250\n", 380 | " - 0s - loss: 0.2736 - acc: 0.9425 - val_loss: 0.2815 - val_acc: 0.9250\n", 381 | "Epoch 98/250\n", 382 | " - 0s - loss: 0.2655 - acc: 0.9438 - val_loss: 0.2745 - val_acc: 0.9250\n", 383 | "Epoch 99/250\n", 384 | " - 0s - loss: 0.2575 - acc: 0.9469 - val_loss: 0.2677 - val_acc: 0.9225\n", 385 | "Epoch 100/250\n", 386 | " - 0s - loss: 0.2498 - acc: 0.9469 - val_loss: 0.2611 - val_acc: 0.9225\n", 387 | "Epoch 101/250\n", 388 | " - 0s - loss: 0.2422 - acc: 0.9488 - val_loss: 0.2548 - val_acc: 0.9225\n", 389 | "Epoch 102/250\n", 390 | " - 0s - loss: 0.2349 - acc: 0.9481 - val_loss: 0.2486 - val_acc: 0.9225\n", 391 | "Epoch 103/250\n", 392 | " - 0s - loss: 0.2279 - acc: 0.9488 - val_loss: 0.2427 - val_acc: 0.9250\n", 393 | "Epoch 104/250\n", 394 | " - 0s - loss: 0.2211 - acc: 0.9494 - val_loss: 0.2370 - val_acc: 0.9225\n", 395 | "Epoch 105/250\n", 396 | " - 0s - loss: 0.2146 - acc: 0.9500 - val_loss: 0.2316 - val_acc: 0.9225\n", 397 | "Epoch 106/250\n", 398 | " - 0s - loss: 0.2083 - acc: 0.9506 - val_loss: 0.2264 - val_acc: 0.9200\n", 399 | "Epoch 107/250\n", 400 | " - 0s - loss: 0.2023 - acc: 0.9513 - val_loss: 0.2214 - val_acc: 0.9200\n", 401 | "Epoch 108/250\n", 402 | " - 0s - loss: 0.1966 - acc: 0.9538 - val_loss: 0.2166 - val_acc: 0.9200\n", 403 | "Epoch 109/250\n", 404 | " - 0s - loss: 0.1911 - acc: 0.9531 - val_loss: 0.2121 - val_acc: 0.9200\n", 405 | "Epoch 110/250\n", 406 | " - 0s - loss: 0.1859 - acc: 0.9531 - val_loss: 0.2078 - val_acc: 0.9200\n", 407 | "Epoch 111/250\n", 408 | " - 0s - loss: 0.1810 - acc: 0.9531 - val_loss: 0.2037 - val_acc: 0.9200\n", 409 | "Epoch 112/250\n", 410 | " - 0s - loss: 0.1763 - acc: 0.9531 - val_loss: 0.1999 - val_acc: 0.9200\n", 411 | "Epoch 113/250\n", 412 | " - 0s - loss: 0.1718 - acc: 0.9531 - val_loss: 0.1962 - val_acc: 0.9200\n", 413 | "Epoch 114/250\n", 414 | " - 0s - loss: 0.1676 - acc: 0.9531 - val_loss: 0.1927 - val_acc: 0.9200\n", 415 | "Epoch 115/250\n", 416 | " - 0s - loss: 0.1636 - acc: 0.9538 - val_loss: 0.1894 - val_acc: 0.9225\n", 417 | "Epoch 116/250\n", 418 | " - 0s - loss: 0.1598 - acc: 0.9544 - val_loss: 0.1862 - val_acc: 0.9225\n", 419 | "Epoch 117/250\n", 420 | " - 0s - loss: 0.1562 - acc: 0.9550 - val_loss: 0.1832 - val_acc: 0.9250\n", 421 | "Epoch 118/250\n", 422 | " - 0s - loss: 0.1528 - acc: 0.9556 - val_loss: 0.1804 - val_acc: 0.9250\n", 423 | "Epoch 119/250\n", 424 | " - 0s - loss: 0.1496 - acc: 0.9556 - val_loss: 0.1777 - val_acc: 0.9250\n", 425 | "Epoch 120/250\n", 426 | " - 0s - loss: 0.1465 - acc: 0.9556 - val_loss: 0.1751 - val_acc: 0.9250\n", 427 | "Epoch 121/250\n", 428 | " - 0s - loss: 0.1436 - acc: 0.9563 - val_loss: 0.1726 - val_acc: 0.9250\n", 429 | "Epoch 122/250\n", 430 | " - 0s - loss: 0.1408 - acc: 0.9556 - val_loss: 0.1703 - val_acc: 0.9250\n", 431 | "Epoch 123/250\n", 432 | " - 0s - loss: 0.1382 - acc: 0.9556 - val_loss: 0.1680 - val_acc: 0.9250\n", 433 | "Epoch 124/250\n", 434 | " - 0s - loss: 0.1357 - acc: 0.9556 - val_loss: 0.1659 - val_acc: 0.9250\n", 435 | "Epoch 125/250\n", 436 | " - 0s - loss: 0.1333 - acc: 0.9563 - val_loss: 0.1638 - val_acc: 0.9250\n", 437 | "Epoch 126/250\n", 438 | " - 0s - loss: 0.1311 - acc: 0.9563 - val_loss: 0.1618 - val_acc: 0.9250\n", 439 | "Epoch 127/250\n", 440 | " - 0s - loss: 0.1289 - acc: 0.9569 - val_loss: 0.1599 - val_acc: 0.9275\n", 441 | "Epoch 128/250\n", 442 | " - 0s - loss: 0.1269 - acc: 0.9569 - val_loss: 0.1581 - val_acc: 0.9275\n", 443 | "Epoch 129/250\n", 444 | " - 0s - loss: 0.1249 - acc: 0.9569 - val_loss: 0.1563 - val_acc: 0.9275\n", 445 | "Epoch 130/250\n", 446 | " - 0s - loss: 0.1230 - acc: 0.9569 - val_loss: 0.1546 - val_acc: 0.9275\n", 447 | "Epoch 131/250\n", 448 | " - 0s - loss: 0.1212 - acc: 0.9581 - val_loss: 0.1529 - val_acc: 0.9275\n", 449 | "Epoch 132/250\n", 450 | " - 0s - loss: 0.1194 - acc: 0.9581 - val_loss: 0.1513 - val_acc: 0.9275\n", 451 | "Epoch 133/250\n", 452 | " - 0s - loss: 0.1178 - acc: 0.9588 - val_loss: 0.1498 - val_acc: 0.9275\n", 453 | "Epoch 134/250\n", 454 | " - 0s - loss: 0.1162 - acc: 0.9594 - val_loss: 0.1483 - val_acc: 0.9300\n", 455 | "Epoch 135/250\n", 456 | " - 0s - loss: 0.1146 - acc: 0.9600 - val_loss: 0.1469 - val_acc: 0.9300\n", 457 | "Epoch 136/250\n", 458 | " - 0s - loss: 0.1131 - acc: 0.9600 - val_loss: 0.1455 - val_acc: 0.9300\n", 459 | "Epoch 137/250\n", 460 | " - 0s - loss: 0.1117 - acc: 0.9600 - val_loss: 0.1441 - val_acc: 0.9300\n", 461 | "Epoch 138/250\n", 462 | " - 0s - loss: 0.1103 - acc: 0.9600 - val_loss: 0.1428 - val_acc: 0.9300\n", 463 | "Epoch 139/250\n", 464 | " - 0s - loss: 0.1090 - acc: 0.9600 - val_loss: 0.1415 - val_acc: 0.9275\n", 465 | "Epoch 140/250\n", 466 | " - 0s - loss: 0.1077 - acc: 0.9600 - val_loss: 0.1403 - val_acc: 0.9275\n", 467 | "Epoch 141/250\n", 468 | " - 0s - loss: 0.1065 - acc: 0.9600 - val_loss: 0.1391 - val_acc: 0.9275\n", 469 | "Epoch 142/250\n", 470 | " - 0s - loss: 0.1053 - acc: 0.9600 - val_loss: 0.1380 - val_acc: 0.9275\n", 471 | "Epoch 143/250\n", 472 | " - 0s - loss: 0.1041 - acc: 0.9613 - val_loss: 0.1368 - val_acc: 0.9300\n", 473 | "Epoch 144/250\n", 474 | " - 0s - loss: 0.1030 - acc: 0.9613 - val_loss: 0.1357 - val_acc: 0.9300\n", 475 | "Epoch 145/250\n", 476 | " - 0s - loss: 0.1019 - acc: 0.9625 - val_loss: 0.1347 - val_acc: 0.9300\n", 477 | "Epoch 146/250\n", 478 | " - 0s - loss: 0.1008 - acc: 0.9625 - val_loss: 0.1336 - val_acc: 0.9325\n", 479 | "Epoch 147/250\n", 480 | " - 0s - loss: 0.0998 - acc: 0.9638 - val_loss: 0.1326 - val_acc: 0.9350\n", 481 | "Epoch 148/250\n", 482 | " - 0s - loss: 0.0988 - acc: 0.9656 - val_loss: 0.1316 - val_acc: 0.9350\n", 483 | "Epoch 149/250\n", 484 | " - 0s - loss: 0.0979 - acc: 0.9656 - val_loss: 0.1307 - val_acc: 0.9350\n", 485 | "Epoch 150/250\n", 486 | " - 0s - loss: 0.0969 - acc: 0.9663 - val_loss: 0.1297 - val_acc: 0.9350\n", 487 | "Epoch 151/250\n", 488 | " - 0s - loss: 0.0960 - acc: 0.9669 - val_loss: 0.1288 - val_acc: 0.9375\n", 489 | "Epoch 152/250\n", 490 | " - 0s - loss: 0.0951 - acc: 0.9669 - val_loss: 0.1279 - val_acc: 0.9375\n", 491 | "Epoch 153/250\n", 492 | " - 0s - loss: 0.0943 - acc: 0.9669 - val_loss: 0.1271 - val_acc: 0.9375\n", 493 | "Epoch 154/250\n", 494 | " - 0s - loss: 0.0934 - acc: 0.9669 - val_loss: 0.1262 - val_acc: 0.9375\n", 495 | "Epoch 155/250\n", 496 | " - 0s - loss: 0.0926 - acc: 0.9669 - val_loss: 0.1254 - val_acc: 0.9375\n", 497 | "Epoch 156/250\n", 498 | " - 0s - loss: 0.0918 - acc: 0.9669 - val_loss: 0.1246 - val_acc: 0.9375\n", 499 | "Epoch 157/250\n", 500 | " - 0s - loss: 0.0910 - acc: 0.9669 - val_loss: 0.1238 - val_acc: 0.9375\n", 501 | "Epoch 158/250\n", 502 | " - 0s - loss: 0.0903 - acc: 0.9669 - val_loss: 0.1230 - val_acc: 0.9375\n", 503 | "Epoch 159/250\n", 504 | " - 0s - loss: 0.0895 - acc: 0.9669 - val_loss: 0.1223 - val_acc: 0.9375\n", 505 | "Epoch 160/250\n", 506 | " - 0s - loss: 0.0888 - acc: 0.9669 - val_loss: 0.1215 - val_acc: 0.9375\n", 507 | "Epoch 161/250\n", 508 | " - 0s - loss: 0.0881 - acc: 0.9675 - val_loss: 0.1208 - val_acc: 0.9375\n", 509 | "Epoch 162/250\n", 510 | " - 0s - loss: 0.0874 - acc: 0.9675 - val_loss: 0.1201 - val_acc: 0.9375\n", 511 | "Epoch 163/250\n", 512 | " - 0s - loss: 0.0868 - acc: 0.9688 - val_loss: 0.1194 - val_acc: 0.9375\n", 513 | "Epoch 164/250\n", 514 | " - 0s - loss: 0.0861 - acc: 0.9688 - val_loss: 0.1187 - val_acc: 0.9375\n", 515 | "Epoch 165/250\n", 516 | " - 0s - loss: 0.0855 - acc: 0.9694 - val_loss: 0.1181 - val_acc: 0.9400\n", 517 | "Epoch 166/250\n", 518 | " - 0s - loss: 0.0848 - acc: 0.9700 - val_loss: 0.1174 - val_acc: 0.9400\n", 519 | "Epoch 167/250\n", 520 | " - 0s - loss: 0.0842 - acc: 0.9700 - val_loss: 0.1168 - val_acc: 0.9400\n", 521 | "Epoch 168/250\n", 522 | " - 0s - loss: 0.0836 - acc: 0.9706 - val_loss: 0.1162 - val_acc: 0.9400\n", 523 | "Epoch 169/250\n", 524 | " - 0s - loss: 0.0830 - acc: 0.9712 - val_loss: 0.1155 - val_acc: 0.9400\n", 525 | "Epoch 170/250\n", 526 | " - 0s - loss: 0.0825 - acc: 0.9706 - val_loss: 0.1149 - val_acc: 0.9400\n", 527 | "Epoch 171/250\n", 528 | " - 0s - loss: 0.0819 - acc: 0.9706 - val_loss: 0.1144 - val_acc: 0.9400\n", 529 | "Epoch 172/250\n", 530 | " - 0s - loss: 0.0813 - acc: 0.9706 - val_loss: 0.1138 - val_acc: 0.9400\n", 531 | "Epoch 173/250\n", 532 | " - 0s - loss: 0.0808 - acc: 0.9706 - val_loss: 0.1132 - val_acc: 0.9400\n", 533 | "Epoch 174/250\n", 534 | " - 0s - loss: 0.0803 - acc: 0.9706 - val_loss: 0.1127 - val_acc: 0.9425\n", 535 | "Epoch 175/250\n", 536 | " - 0s - loss: 0.0797 - acc: 0.9706 - val_loss: 0.1121 - val_acc: 0.9425\n", 537 | "Epoch 176/250\n", 538 | " - 0s - loss: 0.0792 - acc: 0.9706 - val_loss: 0.1116 - val_acc: 0.9425\n", 539 | "Epoch 177/250\n", 540 | " - 0s - loss: 0.0787 - acc: 0.9706 - val_loss: 0.1111 - val_acc: 0.9425\n", 541 | "Epoch 178/250\n", 542 | " - 0s - loss: 0.0782 - acc: 0.9706 - val_loss: 0.1106 - val_acc: 0.9425\n", 543 | "Epoch 179/250\n", 544 | " - 0s - loss: 0.0777 - acc: 0.9706 - val_loss: 0.1101 - val_acc: 0.9450\n", 545 | "Epoch 180/250\n", 546 | " - 0s - loss: 0.0773 - acc: 0.9706 - val_loss: 0.1096 - val_acc: 0.9450\n", 547 | "Epoch 181/250\n", 548 | " - 0s - loss: 0.0768 - acc: 0.9706 - val_loss: 0.1091 - val_acc: 0.9450\n", 549 | "Epoch 182/250\n", 550 | " - 0s - loss: 0.0763 - acc: 0.9712 - val_loss: 0.1086 - val_acc: 0.9450\n", 551 | "Epoch 183/250\n", 552 | " - 0s - loss: 0.0759 - acc: 0.9712 - val_loss: 0.1082 - val_acc: 0.9450\n", 553 | "Epoch 184/250\n", 554 | " - 0s - loss: 0.0754 - acc: 0.9712 - val_loss: 0.1077 - val_acc: 0.9475\n", 555 | "Epoch 185/250\n", 556 | " - 0s - loss: 0.0750 - acc: 0.9719 - val_loss: 0.1072 - val_acc: 0.9475\n", 557 | "Epoch 186/250\n", 558 | " - 0s - loss: 0.0746 - acc: 0.9719 - val_loss: 0.1068 - val_acc: 0.9500\n", 559 | "Epoch 187/250\n", 560 | " - 0s - loss: 0.0741 - acc: 0.9719 - val_loss: 0.1064 - val_acc: 0.9500\n", 561 | "Epoch 188/250\n", 562 | " - 0s - loss: 0.0737 - acc: 0.9719 - val_loss: 0.1059 - val_acc: 0.9500\n", 563 | "Epoch 189/250\n", 564 | " - 0s - loss: 0.0733 - acc: 0.9719 - val_loss: 0.1055 - val_acc: 0.9500\n", 565 | "Epoch 190/250\n", 566 | " - 0s - loss: 0.0729 - acc: 0.9719 - val_loss: 0.1051 - val_acc: 0.9500\n", 567 | "Epoch 191/250\n", 568 | " - 0s - loss: 0.0725 - acc: 0.9719 - val_loss: 0.1047 - val_acc: 0.9500\n", 569 | "Epoch 192/250\n", 570 | " - 0s - loss: 0.0721 - acc: 0.9719 - val_loss: 0.1043 - val_acc: 0.9500\n" 571 | ] 572 | }, 573 | { 574 | "name": "stdout", 575 | "output_type": "stream", 576 | "text": [ 577 | "Epoch 193/250\n", 578 | " - 0s - loss: 0.0717 - acc: 0.9719 - val_loss: 0.1039 - val_acc: 0.9500\n", 579 | "Epoch 194/250\n", 580 | " - 0s - loss: 0.0713 - acc: 0.9719 - val_loss: 0.1036 - val_acc: 0.9500\n", 581 | "Epoch 195/250\n", 582 | " - 0s - loss: 0.0709 - acc: 0.9731 - val_loss: 0.1032 - val_acc: 0.9500\n", 583 | "Epoch 196/250\n", 584 | " - 0s - loss: 0.0706 - acc: 0.9731 - val_loss: 0.1028 - val_acc: 0.9500\n", 585 | "Epoch 197/250\n", 586 | " - 0s - loss: 0.0702 - acc: 0.9731 - val_loss: 0.1025 - val_acc: 0.9500\n", 587 | "Epoch 198/250\n", 588 | " - 0s - loss: 0.0698 - acc: 0.9731 - val_loss: 0.1021 - val_acc: 0.9500\n", 589 | "Epoch 199/250\n", 590 | " - 0s - loss: 0.0695 - acc: 0.9731 - val_loss: 0.1018 - val_acc: 0.9500\n", 591 | "Epoch 200/250\n", 592 | " - 0s - loss: 0.0691 - acc: 0.9731 - val_loss: 0.1014 - val_acc: 0.9500\n", 593 | "Epoch 201/250\n", 594 | " - 0s - loss: 0.0688 - acc: 0.9731 - val_loss: 0.1011 - val_acc: 0.9500\n", 595 | "Epoch 202/250\n", 596 | " - 0s - loss: 0.0684 - acc: 0.9731 - val_loss: 0.1008 - val_acc: 0.9500\n", 597 | "Epoch 203/250\n", 598 | " - 0s - loss: 0.0681 - acc: 0.9744 - val_loss: 0.1005 - val_acc: 0.9500\n", 599 | "Epoch 204/250\n", 600 | " - 0s - loss: 0.0678 - acc: 0.9744 - val_loss: 0.1002 - val_acc: 0.9525\n", 601 | "Epoch 205/250\n", 602 | " - 0s - loss: 0.0674 - acc: 0.9744 - val_loss: 0.0999 - val_acc: 0.9525\n", 603 | "Epoch 206/250\n", 604 | " - 0s - loss: 0.0671 - acc: 0.9750 - val_loss: 0.0996 - val_acc: 0.9550\n", 605 | "Epoch 207/250\n", 606 | " - 0s - loss: 0.0668 - acc: 0.9750 - val_loss: 0.0993 - val_acc: 0.9550\n", 607 | "Epoch 208/250\n", 608 | " - 0s - loss: 0.0665 - acc: 0.9750 - val_loss: 0.0990 - val_acc: 0.9575\n", 609 | "Epoch 209/250\n", 610 | " - 0s - loss: 0.0662 - acc: 0.9750 - val_loss: 0.0987 - val_acc: 0.9600\n", 611 | "Epoch 210/250\n", 612 | " - 0s - loss: 0.0659 - acc: 0.9756 - val_loss: 0.0984 - val_acc: 0.9600\n", 613 | "Epoch 211/250\n", 614 | " - 0s - loss: 0.0656 - acc: 0.9762 - val_loss: 0.0982 - val_acc: 0.9600\n", 615 | "Epoch 212/250\n", 616 | " - 0s - loss: 0.0653 - acc: 0.9762 - val_loss: 0.0979 - val_acc: 0.9600\n", 617 | "Epoch 213/250\n", 618 | " - 0s - loss: 0.0650 - acc: 0.9762 - val_loss: 0.0976 - val_acc: 0.9600\n", 619 | "Epoch 214/250\n", 620 | " - 0s - loss: 0.0647 - acc: 0.9762 - val_loss: 0.0974 - val_acc: 0.9600\n", 621 | "Epoch 215/250\n", 622 | " - 0s - loss: 0.0644 - acc: 0.9762 - val_loss: 0.0971 - val_acc: 0.9600\n", 623 | "Epoch 216/250\n", 624 | " - 0s - loss: 0.0641 - acc: 0.9762 - val_loss: 0.0969 - val_acc: 0.9600\n", 625 | "Epoch 217/250\n", 626 | " - 0s - loss: 0.0638 - acc: 0.9769 - val_loss: 0.0966 - val_acc: 0.9600\n", 627 | "Epoch 218/250\n", 628 | " - 0s - loss: 0.0636 - acc: 0.9769 - val_loss: 0.0964 - val_acc: 0.9600\n", 629 | "Epoch 219/250\n", 630 | " - 0s - loss: 0.0633 - acc: 0.9769 - val_loss: 0.0962 - val_acc: 0.9600\n", 631 | "Epoch 220/250\n", 632 | " - 0s - loss: 0.0630 - acc: 0.9769 - val_loss: 0.0959 - val_acc: 0.9600\n", 633 | "Epoch 221/250\n", 634 | " - 0s - loss: 0.0628 - acc: 0.9769 - val_loss: 0.0957 - val_acc: 0.9600\n", 635 | "Epoch 222/250\n", 636 | " - 0s - loss: 0.0625 - acc: 0.9775 - val_loss: 0.0955 - val_acc: 0.9600\n", 637 | "Epoch 223/250\n", 638 | " - 0s - loss: 0.0622 - acc: 0.9775 - val_loss: 0.0953 - val_acc: 0.9600\n", 639 | "Epoch 224/250\n", 640 | " - 0s - loss: 0.0620 - acc: 0.9775 - val_loss: 0.0951 - val_acc: 0.9600\n", 641 | "Epoch 225/250\n", 642 | " - 0s - loss: 0.0617 - acc: 0.9775 - val_loss: 0.0948 - val_acc: 0.9600\n", 643 | "Epoch 226/250\n", 644 | " - 0s - loss: 0.0615 - acc: 0.9775 - val_loss: 0.0946 - val_acc: 0.9600\n", 645 | "Epoch 227/250\n", 646 | " - 0s - loss: 0.0612 - acc: 0.9775 - val_loss: 0.0944 - val_acc: 0.9600\n", 647 | "Epoch 228/250\n", 648 | " - 0s - loss: 0.0610 - acc: 0.9775 - val_loss: 0.0942 - val_acc: 0.9600\n", 649 | "Epoch 229/250\n", 650 | " - 0s - loss: 0.0608 - acc: 0.9775 - val_loss: 0.0940 - val_acc: 0.9600\n", 651 | "Epoch 230/250\n", 652 | " - 0s - loss: 0.0605 - acc: 0.9775 - val_loss: 0.0938 - val_acc: 0.9600\n", 653 | "Epoch 231/250\n", 654 | " - 0s - loss: 0.0603 - acc: 0.9775 - val_loss: 0.0936 - val_acc: 0.9600\n", 655 | "Epoch 232/250\n", 656 | " - 0s - loss: 0.0600 - acc: 0.9781 - val_loss: 0.0934 - val_acc: 0.9600\n", 657 | "Epoch 233/250\n", 658 | " - 0s - loss: 0.0598 - acc: 0.9781 - val_loss: 0.0932 - val_acc: 0.9600\n", 659 | "Epoch 234/250\n", 660 | " - 0s - loss: 0.0595 - acc: 0.9787 - val_loss: 0.0931 - val_acc: 0.9600\n", 661 | "Epoch 235/250\n", 662 | " - 0s - loss: 0.0593 - acc: 0.9787 - val_loss: 0.0929 - val_acc: 0.9600\n", 663 | "Epoch 236/250\n", 664 | " - 0s - loss: 0.0591 - acc: 0.9787 - val_loss: 0.0927 - val_acc: 0.9600\n", 665 | "Epoch 237/250\n", 666 | " - 0s - loss: 0.0588 - acc: 0.9787 - val_loss: 0.0925 - val_acc: 0.9600\n", 667 | "Epoch 238/250\n", 668 | " - 0s - loss: 0.0586 - acc: 0.9787 - val_loss: 0.0923 - val_acc: 0.9600\n", 669 | "Epoch 239/250\n", 670 | " - 0s - loss: 0.0584 - acc: 0.9787 - val_loss: 0.0922 - val_acc: 0.9600\n", 671 | "Epoch 240/250\n", 672 | " - 0s - loss: 0.0582 - acc: 0.9787 - val_loss: 0.0920 - val_acc: 0.9600\n", 673 | "Epoch 241/250\n", 674 | " - 0s - loss: 0.0579 - acc: 0.9787 - val_loss: 0.0918 - val_acc: 0.9600\n", 675 | "Epoch 242/250\n", 676 | " - 0s - loss: 0.0577 - acc: 0.9787 - val_loss: 0.0916 - val_acc: 0.9600\n", 677 | "Epoch 243/250\n", 678 | " - 0s - loss: 0.0575 - acc: 0.9794 - val_loss: 0.0915 - val_acc: 0.9600\n", 679 | "Epoch 244/250\n", 680 | " - 0s - loss: 0.0573 - acc: 0.9794 - val_loss: 0.0913 - val_acc: 0.9600\n", 681 | "Epoch 245/250\n", 682 | " - 0s - loss: 0.0571 - acc: 0.9787 - val_loss: 0.0912 - val_acc: 0.9600\n", 683 | "Epoch 246/250\n", 684 | " - 0s - loss: 0.0569 - acc: 0.9787 - val_loss: 0.0910 - val_acc: 0.9600\n", 685 | "Epoch 247/250\n", 686 | " - 0s - loss: 0.0567 - acc: 0.9787 - val_loss: 0.0909 - val_acc: 0.9600\n", 687 | "Epoch 248/250\n", 688 | " - 0s - loss: 0.0565 - acc: 0.9787 - val_loss: 0.0907 - val_acc: 0.9600\n", 689 | "Epoch 249/250\n", 690 | " - 0s - loss: 0.0562 - acc: 0.9787 - val_loss: 0.0906 - val_acc: 0.9600\n", 691 | "Epoch 250/250\n", 692 | " - 0s - loss: 0.0560 - acc: 0.9787 - val_loss: 0.0904 - val_acc: 0.9600\n" 693 | ] 694 | } 695 | ], 696 | "source": [ 697 | "model = keras_model(layers_dims=layers_dims, learning_rate=0.1)\n", 698 | "history = model.fit(X, Y, validation_split=0.2, epochs=250, batch_size=X.shape[0], verbose=2)" 699 | ] 700 | }, 701 | { 702 | "cell_type": "markdown", 703 | "metadata": {}, 704 | "source": [ 705 | "El siguiente código permite calcular el tiempo que tarda la red neuronal en entrenarse." 706 | ] 707 | }, 708 | { 709 | "cell_type": "code", 710 | "execution_count": 5, 711 | "metadata": {}, 712 | "outputs": [ 713 | { 714 | "name": "stdout", 715 | "output_type": "stream", 716 | "text": [ 717 | "1.13 s ± 42.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" 718 | ] 719 | } 720 | ], 721 | "source": [ 722 | "%%timeit\n", 723 | "model = keras_model(layers_dims=layers_dims, learning_rate=0.1)\n", 724 | "model.fit(X, Y, epochs=250, batch_size=X.shape[0], verbose=0)" 725 | ] 726 | }, 727 | { 728 | "cell_type": "markdown", 729 | "metadata": {}, 730 | "source": [ 731 | "En este último fragmento de código veremos como emplear el objecto `history` para ver la evolución del proceso de entrenamniento. \n", 732 | "\n", 733 | "En primer lugar, veremos la información que contiene dicho objeto." 734 | ] 735 | }, 736 | { 737 | "cell_type": "code", 738 | "execution_count": 6, 739 | "metadata": {}, 740 | "outputs": [ 741 | { 742 | "name": "stdout", 743 | "output_type": "stream", 744 | "text": [ 745 | "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n" 746 | ] 747 | } 748 | ], 749 | "source": [ 750 | "# Ver los datos contenidos en 'history'\n", 751 | "print(history.history.keys())" 752 | ] 753 | }, 754 | { 755 | "cell_type": "markdown", 756 | "metadata": {}, 757 | "source": [ 758 | "Y finalmente, visualizaremos dos gráficos para ver como evoluciona la precisión y el error durante el proceso de entrenamiento." 759 | ] 760 | }, 761 | { 762 | "cell_type": "code", 763 | "execution_count": 7, 764 | "metadata": {}, 765 | "outputs": [ 766 | { 767 | "data": { 768 | "image/png": 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\n", 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\n", 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" 783 | ] 784 | }, 785 | "metadata": { 786 | "needs_background": "light" 787 | }, 788 | "output_type": "display_data" 789 | } 790 | ], 791 | "source": [ 792 | "%matplotlib inline\n", 793 | "import matplotlib.pyplot as plt\n", 794 | "\n", 795 | "# Visualizamos la evolución de la accuracy\n", 796 | "plt.plot(history.history['acc'])\n", 797 | "plt.plot(history.history['val_acc'])\n", 798 | "plt.title('model accuracy')\n", 799 | "plt.ylabel('accuracy')\n", 800 | "plt.xlabel('epoch')\n", 801 | "plt.legend(['train', 'test'], loc='lower right')\n", 802 | "plt.show()\n", 803 | "\n", 804 | "# Visualizamos la evolución del error cometido por la red\n", 805 | "plt.plot(history.history['loss'])\n", 806 | "plt.plot(history.history['val_loss'])\n", 807 | "plt.title('model loss')\n", 808 | "plt.ylabel('loss')\n", 809 | "plt.xlabel('epoch')\n", 810 | "plt.legend(['train', 'test'], loc='upper right')\n", 811 | "plt.show()" 812 | ] 813 | } 814 | ], 815 | "metadata": { 816 | "celltoolbar": "Raw Cell Format", 817 | "kernelspec": { 818 | "display_name": "Python 3", 819 | "language": "python", 820 | "name": "python3" 821 | }, 822 | "language_info": { 823 | "codemirror_mode": { 824 | "name": "ipython", 825 | "version": 3 826 | }, 827 | "file_extension": ".py", 828 | "mimetype": "text/x-python", 829 | "name": "python", 830 | "nbconvert_exporter": "python", 831 | "pygments_lexer": "ipython3", 832 | "version": "3.6.10" 833 | } 834 | }, 835 | "nbformat": 4, 836 | "nbformat_minor": 2 837 | } 838 | -------------------------------------------------------------------------------- /cap07/.ipynb_checkpoints/C07_E1_Ejemplo-CNN-MNIST-Keras-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "
\n", 8 | "
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Deep learning: Principios y fundamentos

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Estudios de Informática, Multimedia y Telecomunicación

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Universitat Oberta de Catalunya (UOC)

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\n", 18 | "\n", 19 | "\n", 20 | "# Capítulo 7: Ejemplo de Redes Neuronales Convolucionales (CNN) con MNIST y Keras" 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": {}, 26 | "source": [ 27 | "En este ejemplo veremos la implementación de una red neuronal convolucional (CNN) para reconocer los dígitos del conjunto de datos de referencia MNIST. Se utilizará la librería Keras para la implementación, compilación y entrenamiento del modelo." 28 | ] 29 | }, 30 | { 31 | "cell_type": "markdown", 32 | "metadata": {}, 33 | "source": [ 34 | "# 1. Carga de datos" 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "metadata": {}, 40 | "source": [ 41 | "El siguiente código carga los paquetes necesarios para el ejercicio y, además, lee los datos que utilizaremos para entrenar la red neuronal. El Dataset [MNIST](https://en.wikipedia.org/wiki/MNIST_database) corresponde a imágenes de digitos del 0 al 9 de tamaño 28x28 píxeles." 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 1, 47 | "metadata": {}, 48 | "outputs": [ 49 | { 50 | "name": "stderr", 51 | "output_type": "stream", 52 | "text": [ 53 | "Using TensorFlow backend.\n" 54 | ] 55 | } 56 | ], 57 | "source": [ 58 | "import numpy as np\n", 59 | "import keras\n", 60 | "# En caso de emplear TF 2.x, usar:\n", 61 | "# from tensorflow import keras\n", 62 | "from keras.datasets import mnist\n", 63 | "from keras.utils import to_categorical\n", 64 | "from keras.models import Sequential\n", 65 | "from keras.layers import Dense, Conv2D, Flatten\n", 66 | "import matplotlib.pyplot as plt" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "metadata": {}, 72 | "source": [ 73 | "Descarga de el dataset MNIST y hace la partición train/test" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 2, 79 | "metadata": {}, 80 | "outputs": [], 81 | "source": [ 82 | "# Descarga de el dataset MNIST y hace la partición train/test\n", 83 | "(x_train_orig, y_train_orig), (x_test_orig, y_test_orig) = mnist.load_data()" 84 | ] 85 | }, 86 | { 87 | "cell_type": "markdown", 88 | "metadata": {}, 89 | "source": [ 90 | "Visualizamos la primera imagen del dataset" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 3, 96 | "metadata": {}, 97 | "outputs": [ 98 | { 99 | "data": { 100 | "image/png": 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101 | "text/plain": [ 102 | "
" 103 | ] 104 | }, 105 | "metadata": { 106 | "needs_background": "light" 107 | }, 108 | "output_type": "display_data" 109 | } 110 | ], 111 | "source": [ 112 | "# Visualizamos la primera imagen del dataset\n", 113 | "first_image = x_train_orig[0]\n", 114 | "first_image = np.array(first_image, dtype='float')\n", 115 | "pixels = first_image.reshape((28, 28))\n", 116 | "plt.imshow(pixels, cmap='gray')\n", 117 | "plt.show()" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": 4, 123 | "metadata": {}, 124 | "outputs": [ 125 | { 126 | "data": { 127 | "text/plain": [ 128 | "(28, 28)" 129 | ] 130 | }, 131 | "execution_count": 4, 132 | "metadata": {}, 133 | "output_type": "execute_result" 134 | } 135 | ], 136 | "source": [ 137 | "# Comprobación del tamaño de la imagen\n", 138 | "x_train_orig[0].shape" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "A continuación implementaremos una red neuronal convolucional de una capa y haremos el entrenamiento y test sobre el dataset MNIST. Tenemos 60.000 imágenes para entrenar y 10.000 para testear. " 146 | ] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "metadata": {}, 151 | "source": [ 152 | "# 2. Pre-procesado de datos\n", 153 | "\n", 154 | "El primer paso para entrenar una red neuronal consiste en pre-procesar los datos de entrenamiento y test." 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": 5, 160 | "metadata": {}, 161 | "outputs": [ 162 | { 163 | "name": "stdout", 164 | "output_type": "stream", 165 | "text": [ 166 | "Tamaño de los datos originales de train: (60000, 28, 28)\n", 167 | "Tamaño de los datos originales de test : (10000, 28, 28)\n" 168 | ] 169 | } 170 | ], 171 | "source": [ 172 | "# Tamaño de los datos originales\n", 173 | "print(\"Tamaño de los datos originales de train: {}\".format(x_train_orig.shape))\n", 174 | "print(\"Tamaño de los datos originales de test : {}\".format(x_test_orig.shape))" 175 | ] 176 | }, 177 | { 178 | "cell_type": "markdown", 179 | "metadata": {}, 180 | "source": [ 181 | "Seguidamente, aplicaremos una simple normalización de los datos para que queden comprendidos entre el rango 0 y 1. Aunque existen clases para aplicar la normalización, aquí emplearemos el método más simple, que es dividir los valores por el máximo para obtener valores en el rango entre 0 y 1." 182 | ] 183 | }, 184 | { 185 | "cell_type": "code", 186 | "execution_count": 6, 187 | "metadata": {}, 188 | "outputs": [ 189 | { 190 | "name": "stdout", 191 | "output_type": "stream", 192 | "text": [ 193 | "Los valores del primer registro son: [[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", 194 | " 0 0 0 0 0 0 0 0 0 0]\n", 195 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", 196 | " 0 0 0 0 0 0 0 0 0 0]\n", 197 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", 198 | " 0 0 0 0 0 0 0 0 0 0]\n", 199 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", 200 | " 0 0 0 0 0 0 0 0 0 0]\n", 201 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", 202 | " 0 0 0 0 0 0 0 0 0 0]\n", 203 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 3 18 18 18 126 136\n", 204 | " 175 26 166 255 247 127 0 0 0 0]\n", 205 | " [ 0 0 0 0 0 0 0 0 30 36 94 154 170 253 253 253 253 253\n", 206 | " 225 172 253 242 195 64 0 0 0 0]\n", 207 | " [ 0 0 0 0 0 0 0 49 238 253 253 253 253 253 253 253 253 251\n", 208 | " 93 82 82 56 39 0 0 0 0 0]\n", 209 | " [ 0 0 0 0 0 0 0 18 219 253 253 253 253 253 198 182 247 241\n", 210 | " 0 0 0 0 0 0 0 0 0 0]\n", 211 | " [ 0 0 0 0 0 0 0 0 80 156 107 253 253 205 11 0 43 154\n", 212 | " 0 0 0 0 0 0 0 0 0 0]\n", 213 | " [ 0 0 0 0 0 0 0 0 0 14 1 154 253 90 0 0 0 0\n", 214 | " 0 0 0 0 0 0 0 0 0 0]\n", 215 | " [ 0 0 0 0 0 0 0 0 0 0 0 139 253 190 2 0 0 0\n", 216 | " 0 0 0 0 0 0 0 0 0 0]\n", 217 | " [ 0 0 0 0 0 0 0 0 0 0 0 11 190 253 70 0 0 0\n", 218 | " 0 0 0 0 0 0 0 0 0 0]\n", 219 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 35 241 225 160 108 1\n", 220 | " 0 0 0 0 0 0 0 0 0 0]\n", 221 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 81 240 253 253 119\n", 222 | " 25 0 0 0 0 0 0 0 0 0]\n", 223 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 45 186 253 253\n", 224 | " 150 27 0 0 0 0 0 0 0 0]\n", 225 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 93 252\n", 226 | " 253 187 0 0 0 0 0 0 0 0]\n", 227 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 249\n", 228 | " 253 249 64 0 0 0 0 0 0 0]\n", 229 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 46 130 183 253\n", 230 | " 253 207 2 0 0 0 0 0 0 0]\n", 231 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 39 148 229 253 253 253\n", 232 | " 250 182 0 0 0 0 0 0 0 0]\n", 233 | " [ 0 0 0 0 0 0 0 0 0 0 24 114 221 253 253 253 253 201\n", 234 | " 78 0 0 0 0 0 0 0 0 0]\n", 235 | " [ 0 0 0 0 0 0 0 0 23 66 213 253 253 253 253 198 81 2\n", 236 | " 0 0 0 0 0 0 0 0 0 0]\n", 237 | " [ 0 0 0 0 0 0 18 171 219 253 253 253 253 195 80 9 0 0\n", 238 | " 0 0 0 0 0 0 0 0 0 0]\n", 239 | " [ 0 0 0 0 55 172 226 253 253 253 253 244 133 11 0 0 0 0\n", 240 | " 0 0 0 0 0 0 0 0 0 0]\n", 241 | " [ 0 0 0 0 136 253 253 253 212 135 132 16 0 0 0 0 0 0\n", 242 | " 0 0 0 0 0 0 0 0 0 0]\n", 243 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", 244 | " 0 0 0 0 0 0 0 0 0 0]\n", 245 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", 246 | " 0 0 0 0 0 0 0 0 0 0]\n", 247 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n", 248 | " 0 0 0 0 0 0 0 0 0 0]]\n", 249 | "Los valores mínimo y máximo son 0 y 255.\n", 250 | "Los valores del primer registro después de la normalización son: [[0. 0. 0. 0. 0. 0.\n", 251 | " 0. 0. 0. 0. 0. 0.\n", 252 | " 0. 0. 0. 0. 0. 0.\n", 253 | " 0. 0. 0. 0. 0. 0.\n", 254 | " 0. 0. 0. 0. ]\n", 255 | " [0. 0. 0. 0. 0. 0.\n", 256 | " 0. 0. 0. 0. 0. 0.\n", 257 | " 0. 0. 0. 0. 0. 0.\n", 258 | " 0. 0. 0. 0. 0. 0.\n", 259 | " 0. 0. 0. 0. ]\n", 260 | " [0. 0. 0. 0. 0. 0.\n", 261 | " 0. 0. 0. 0. 0. 0.\n", 262 | " 0. 0. 0. 0. 0. 0.\n", 263 | " 0. 0. 0. 0. 0. 0.\n", 264 | " 0. 0. 0. 0. ]\n", 265 | " [0. 0. 0. 0. 0. 0.\n", 266 | " 0. 0. 0. 0. 0. 0.\n", 267 | " 0. 0. 0. 0. 0. 0.\n", 268 | " 0. 0. 0. 0. 0. 0.\n", 269 | " 0. 0. 0. 0. ]\n", 270 | " [0. 0. 0. 0. 0. 0.\n", 271 | " 0. 0. 0. 0. 0. 0.\n", 272 | " 0. 0. 0. 0. 0. 0.\n", 273 | " 0. 0. 0. 0. 0. 0.\n", 274 | " 0. 0. 0. 0. ]\n", 275 | " [0. 0. 0. 0. 0. 0.\n", 276 | " 0. 0. 0. 0. 0. 0.\n", 277 | " 0.01176471 0.07058824 0.07058824 0.07058824 0.49411765 0.53333333\n", 278 | " 0.68627451 0.10196078 0.65098039 1. 0.96862745 0.49803922\n", 279 | " 0. 0. 0. 0. ]\n", 280 | " [0. 0. 0. 0. 0. 0.\n", 281 | " 0. 0. 0.11764706 0.14117647 0.36862745 0.60392157\n", 282 | " 0.66666667 0.99215686 0.99215686 0.99215686 0.99215686 0.99215686\n", 283 | " 0.88235294 0.6745098 0.99215686 0.94901961 0.76470588 0.25098039\n", 284 | " 0. 0. 0. 0. ]\n", 285 | " [0. 0. 0. 0. 0. 0.\n", 286 | " 0. 0.19215686 0.93333333 0.99215686 0.99215686 0.99215686\n", 287 | " 0.99215686 0.99215686 0.99215686 0.99215686 0.99215686 0.98431373\n", 288 | " 0.36470588 0.32156863 0.32156863 0.21960784 0.15294118 0.\n", 289 | " 0. 0. 0. 0. ]\n", 290 | " [0. 0. 0. 0. 0. 0.\n", 291 | " 0. 0.07058824 0.85882353 0.99215686 0.99215686 0.99215686\n", 292 | " 0.99215686 0.99215686 0.77647059 0.71372549 0.96862745 0.94509804\n", 293 | " 0. 0. 0. 0. 0. 0.\n", 294 | " 0. 0. 0. 0. ]\n", 295 | " [0. 0. 0. 0. 0. 0.\n", 296 | " 0. 0. 0.31372549 0.61176471 0.41960784 0.99215686\n", 297 | " 0.99215686 0.80392157 0.04313725 0. 0.16862745 0.60392157\n", 298 | " 0. 0. 0. 0. 0. 0.\n", 299 | " 0. 0. 0. 0. ]\n", 300 | " [0. 0. 0. 0. 0. 0.\n", 301 | " 0. 0. 0. 0.05490196 0.00392157 0.60392157\n", 302 | " 0.99215686 0.35294118 0. 0. 0. 0.\n", 303 | " 0. 0. 0. 0. 0. 0.\n", 304 | " 0. 0. 0. 0. ]\n", 305 | " [0. 0. 0. 0. 0. 0.\n", 306 | " 0. 0. 0. 0. 0. 0.54509804\n", 307 | " 0.99215686 0.74509804 0.00784314 0. 0. 0.\n", 308 | " 0. 0. 0. 0. 0. 0.\n", 309 | " 0. 0. 0. 0. ]\n", 310 | " [0. 0. 0. 0. 0. 0.\n", 311 | " 0. 0. 0. 0. 0. 0.04313725\n", 312 | " 0.74509804 0.99215686 0.2745098 0. 0. 0.\n", 313 | " 0. 0. 0. 0. 0. 0.\n", 314 | " 0. 0. 0. 0. ]\n", 315 | " [0. 0. 0. 0. 0. 0.\n", 316 | " 0. 0. 0. 0. 0. 0.\n", 317 | " 0.1372549 0.94509804 0.88235294 0.62745098 0.42352941 0.00392157\n", 318 | " 0. 0. 0. 0. 0. 0.\n", 319 | " 0. 0. 0. 0. ]\n", 320 | " [0. 0. 0. 0. 0. 0.\n", 321 | " 0. 0. 0. 0. 0. 0.\n", 322 | " 0. 0.31764706 0.94117647 0.99215686 0.99215686 0.46666667\n", 323 | " 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0.99215686 0.83137255 0.52941176 0.51764706 0.0627451\n", 372 | " 0. 0. 0. 0. 0. 0.\n", 373 | " 0. 0. 0. 0. 0. 0.\n", 374 | " 0. 0. 0. 0. ]\n", 375 | " [0. 0. 0. 0. 0. 0.\n", 376 | " 0. 0. 0. 0. 0. 0.\n", 377 | " 0. 0. 0. 0. 0. 0.\n", 378 | " 0. 0. 0. 0. 0. 0.\n", 379 | " 0. 0. 0. 0. ]\n", 380 | " [0. 0. 0. 0. 0. 0.\n", 381 | " 0. 0. 0. 0. 0. 0.\n", 382 | " 0. 0. 0. 0. 0. 0.\n", 383 | " 0. 0. 0. 0. 0. 0.\n", 384 | " 0. 0. 0. 0. ]\n", 385 | " [0. 0. 0. 0. 0. 0.\n", 386 | " 0. 0. 0. 0. 0. 0.\n", 387 | " 0. 0. 0. 0. 0. 0.\n", 388 | " 0. 0. 0. 0. 0. 0.\n", 389 | " 0. 0. 0. 0. ]]\n" 390 | ] 391 | } 392 | ], 393 | "source": [ 394 | "print(\"Los valores del primer registro son: {}\".format(x_train_orig[0,:,:]))\n", 395 | "\n", 396 | "# Determinar valores mínimo y máximo para la normalización\n", 397 | "v_min = np.min(x_train_orig)\n", 398 | "v_max = np.max(x_train_orig)\n", 399 | "print(\"Los valores mínimo y máximo son {} y {}.\".format(v_min, v_max))\n", 400 | "\n", 401 | "# Normalización entre 0 y 1\n", 402 | "x_train_orig = (x_train_orig - v_min) / (v_max - v_min)\n", 403 | "x_test_orig = (x_test_orig - v_min) / (v_max - v_min)\n", 404 | "\n", 405 | "print(\"Los valores del primer registro después de la normalización son: {}\".format(x_train_orig[0,:,:]))" 406 | ] 407 | }, 408 | { 409 | "cell_type": "markdown", 410 | "metadata": {}, 411 | "source": [ 412 | "A continuación, ajustaremos el tamaño de los datos de entrenamiento y test utilizando 4 dimensiones, ya que la última dimensión debe ser 1 para indicar que las imágenes están en escala de grises." 413 | ] 414 | }, 415 | { 416 | "cell_type": "code", 417 | "execution_count": 7, 418 | "metadata": {}, 419 | "outputs": [ 420 | { 421 | "name": "stdout", 422 | "output_type": "stream", 423 | "text": [ 424 | "Tamaño de los datos de train: (60000, 28, 28, 1)\n", 425 | "Tamaño de los datos de test : (10000, 28, 28, 1)\n" 426 | ] 427 | } 428 | ], 429 | "source": [ 430 | "x_train = x_train_orig.reshape(60000,28,28,1)\n", 431 | "x_test = x_test_orig.reshape(10000,28,28,1)\n", 432 | "\n", 433 | "print(\"Tamaño de los datos de train: {}\".format(x_train.shape))\n", 434 | "print(\"Tamaño de los datos de test : {}\".format(x_test.shape))" 435 | ] 436 | }, 437 | { 438 | "cell_type": "markdown", 439 | "metadata": {}, 440 | "source": [ 441 | "Finalemte, codificaremos los valores de las etiquetas de salida en un vector `one-hot`. El objetivo de esta acción es convertir los datos al mismo formato que tendrá la salida de la red neuronal. Por ejemplo, el vector de salida para una imagen que contiene un 5 sería: [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.]. \n", 442 | "\n", 443 | "Para realizar esta tarea de forma sencilla, se puede utilizar la función `to_categorical` de `Keras.utils`. " 444 | ] 445 | }, 446 | { 447 | "cell_type": "code", 448 | "execution_count": 8, 449 | "metadata": {}, 450 | "outputs": [ 451 | { 452 | "name": "stdout", 453 | "output_type": "stream", 454 | "text": [ 455 | "El tamaño de y_train_orig es (60000,) y el valor de y_train_orig[0] es 5\n", 456 | "El tamaño de y_train es (60000, 10) y el valor de y_train[0] es [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n" 457 | ] 458 | } 459 | ], 460 | "source": [ 461 | "y_train = to_categorical(y_train_orig)\n", 462 | "y_test = to_categorical(y_test_orig)\n", 463 | "\n", 464 | "print(\"El tamaño de y_train_orig es {} y el valor de y_train_orig[0] es {}\".format(y_train_orig.shape, y_train_orig[0]))\n", 465 | "print(\"El tamaño de y_train es {} y el valor de y_train[0] es {}\".format(y_train.shape, y_train[0]))" 466 | ] 467 | }, 468 | { 469 | "cell_type": "markdown", 470 | "metadata": {}, 471 | "source": [ 472 | "# 3. Creación del modelo\n", 473 | "\n", 474 | "Vamos a usar un modelo `Secuencial` de Keras, ya que es muy intuitivo y fácil de utilizar. Este tipo de modelos nos permiten construir el modelo capa a capa. En concreto:\n", 475 | "\n", 476 | "- La primera capa que añadiremos será una capa convolucional con las siguientes propiedades: \n", 477 | " - Número de kernels (neuronas) primera capa: 64 neuronas\n", 478 | " - Tamaño de los kernels: 3x3\n", 479 | " - Activacion de los kernels: ReLU\n", 480 | "- A continuación añadiremos una capa *Flatten* para conectar la salida de la capa convolucional con la entrada de una capa densa.\n", 481 | "- Por último, añadiremos una capa densa de salida, y por lo tanto tendrá tantas neuronas como clases queremos predecir. La activación de esta última capa sera *Softmax*. La predicción final del modelo será entonces la clase que tenga una probabilidad más alta. " 482 | ] 483 | }, 484 | { 485 | "cell_type": "markdown", 486 | "metadata": {}, 487 | "source": [ 488 | "El siguiente código implementa el modelo anterior, teniendo en cuenta que la salida de la red neuronal será un vector de probabilidades de las 10 clases anteriormente comentadas, i.e. 0...9." 489 | ] 490 | }, 491 | { 492 | "cell_type": "code", 493 | "execution_count": 9, 494 | "metadata": {}, 495 | "outputs": [ 496 | { 497 | "name": "stdout", 498 | "output_type": "stream", 499 | "text": [ 500 | "WARNING:tensorflow:From /Users/jcasasr/opt/anaconda3/envs/py_36_tf_15/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n", 501 | "Instructions for updating:\n", 502 | "If using Keras pass *_constraint arguments to layers.\n" 503 | ] 504 | } 505 | ], 506 | "source": [ 507 | "# Número de clases\n", 508 | "num_classes = 10\n", 509 | "\n", 510 | "model = Sequential()\n", 511 | "\n", 512 | "# Añadir las capas indicadas\n", 513 | "model.add(Conv2D(64, kernel_size=3, activation=\"relu\", input_shape=(28,28,1)))\n", 514 | "model.add(Flatten())\n", 515 | "model.add(Dense(num_classes, activation=\"softmax\"))" 516 | ] 517 | }, 518 | { 519 | "cell_type": "markdown", 520 | "metadata": {}, 521 | "source": [ 522 | "# 4. Compilación el modelo\n", 523 | "\n", 524 | "Una vez definido el modelo, se debe compilar de forma previa al proceso de entrenamiento. Para ello vamos a utilizar el algoritmo de optimización `Adam`, la función de coste `categorical_crossentropy` y la métrica `accuracy`." 525 | ] 526 | }, 527 | { 528 | "cell_type": "code", 529 | "execution_count": 10, 530 | "metadata": {}, 531 | "outputs": [], 532 | "source": [ 533 | "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])" 534 | ] 535 | }, 536 | { 537 | "cell_type": "markdown", 538 | "metadata": {}, 539 | "source": [ 540 | "# 5. Entrenamiento del modelo\n", 541 | "\n", 542 | "En este punto ya podemos entrenar el modelo. Haremos que el modelo vea cada imagen 10 veces diferentes (es decir, `epochs`=10), fijaremos un valor de `bacth` igual a 128 y utilizaremos el conjunto de test para la validación del proceso." 543 | ] 544 | }, 545 | { 546 | "cell_type": "code", 547 | "execution_count": null, 548 | "metadata": {}, 549 | "outputs": [ 550 | { 551 | "name": "stdout", 552 | "output_type": "stream", 553 | "text": [ 554 | "WARNING:tensorflow:From /Users/jcasasr/opt/anaconda3/envs/py_36_tf_15/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n", 555 | "\n", 556 | "Train on 60000 samples, validate on 10000 samples\n", 557 | "Epoch 1/10\n" 558 | ] 559 | } 560 | ], 561 | "source": [ 562 | "# Entrenamiento del modelo\n", 563 | "n_epochs = 10\n", 564 | "mfit = model.fit(x_train, y_train, validation_data=(x_test, y_test), batch_size=128, epochs=n_epochs)" 565 | ] 566 | }, 567 | { 568 | "cell_type": "markdown", 569 | "metadata": {}, 570 | "source": [ 571 | "# 6. Evolución de la precisión" 572 | ] 573 | }, 574 | { 575 | "cell_type": "markdown", 576 | "metadata": {}, 577 | "source": [ 578 | "A continuación, vamos a crear una función que nos permitirá ver la evolución de la *accuracy* y el *loss* en el conjunto de entrenamiento y test en función de las diferentes épocas (*epochs*) del entrenamiento." 579 | ] 580 | }, 581 | { 582 | "cell_type": "code", 583 | "execution_count": null, 584 | "metadata": {}, 585 | "outputs": [], 586 | "source": [ 587 | "# Plot del training loss y el accuracy\n", 588 | "def plot_prediction(n_epochs, mfit):\n", 589 | " N = n_epochs\n", 590 | " plt.style.use(\"ggplot\")\n", 591 | " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,6))\n", 592 | " fig.suptitle('Training Loss and Accuracy')\n", 593 | " \n", 594 | " ax1.plot(np.arange(0, N), mfit.history[\"acc\"], label=\"train\")\n", 595 | " ax1.plot(np.arange(0, N), mfit.history[\"val_acc\"], label=\"val\")\n", 596 | " ax1.set_title(\"Accuracy\")\n", 597 | " ax1.set_xlabel(\"Epoch #\")\n", 598 | " ax1.set_ylabel(\"Accuracy\")\n", 599 | " ax1.legend(loc=\"lower right\")\n", 600 | " \n", 601 | " ax2.plot(np.arange(0, N), mfit.history[\"loss\"], label=\"train\")\n", 602 | " ax2.plot(np.arange(0, N), mfit.history[\"val_loss\"], label=\"val\")\n", 603 | " ax2.set_title(\"Loss\")\n", 604 | " ax2.set_xlabel(\"Epoch #\")\n", 605 | " ax2.set_ylabel(\"Loss\")\n", 606 | " ax2.legend(loc=\"upper right\")\n", 607 | " \n", 608 | " plt.show()" 609 | ] 610 | }, 611 | { 612 | "cell_type": "code", 613 | "execution_count": null, 614 | "metadata": {}, 615 | "outputs": [], 616 | "source": [ 617 | "plot_prediction(n_epochs, mfit)" 618 | ] 619 | }, 620 | { 621 | "cell_type": "markdown", 622 | "metadata": {}, 623 | "source": [ 624 | "# 7. Predicción\n", 625 | "\n", 626 | "Finalmente, podemos hacer la predicción para cuatro de las imágenes del conjunto de test y mirar si los resultados son o no correctos." 627 | ] 628 | }, 629 | { 630 | "cell_type": "code", 631 | "execution_count": null, 632 | "metadata": {}, 633 | "outputs": [], 634 | "source": [ 635 | "# Hacemos la predicción para las 4 primeras imágenes del set de test\n", 636 | "print(model.predict(x_test[:4]))\n", 637 | "\n", 638 | "# Mostramos el ground truth para las primeras 4 imágenes\n", 639 | "y_test[:4]" 640 | ] 641 | } 642 | ], 643 | "metadata": { 644 | "kernelspec": { 645 | "display_name": "Python 3", 646 | "language": "python", 647 | "name": "python3" 648 | }, 649 | "language_info": { 650 | "codemirror_mode": { 651 | "name": "ipython", 652 | "version": 3 653 | }, 654 | "file_extension": ".py", 655 | "mimetype": "text/x-python", 656 | "name": "python", 657 | "nbconvert_exporter": "python", 658 | "pygments_lexer": "ipython3", 659 | "version": "3.6.10" 660 | } 661 | }, 662 | "nbformat": 4, 663 | "nbformat_minor": 2 664 | } 665 | -------------------------------------------------------------------------------- /data/data.pickle: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcasasr/Libro-Deep-Learning/209603211222fd5b0daecf3a7dc34cfe206fcd33/data/data.pickle -------------------------------------------------------------------------------- /figs/lion.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcasasr/Libro-Deep-Learning/209603211222fd5b0daecf3a7dc34cfe206fcd33/figs/lion.jpg -------------------------------------------------------------------------------- /figs/violin.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jcasasr/Libro-Deep-Learning/209603211222fd5b0daecf3a7dc34cfe206fcd33/figs/violin.jpg --------------------------------------------------------------------------------