C_Fast_Feature_Extraction_VGG16_3.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Entreno de un ConvNet VGG16 preentrenado con Imagenet y\n",
    "entrenando conjunto de datos pequeño\n",
    "Conjunto de datos de 4 clases\n",
    "Utilizaremos un conjunto de datos reducido:\n",
    "- Conjunto de entrenamiento: 1.000 muestras x clase \n",
    "- Conjunto de validación: 300 muestras x clase\n",
    "- Conjunto de test: 300 muestras x clase\n",
    "El conjunto de datos: balanced_dataset\n",
    "contiene tres carpetas, train, validation y test, cada una con 4 clases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, shutil\n",
    "from os import walk, getcwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Retorna todos los archivos de un directorio dado\n",
    "def ls(ruta):  \n",
    "    return next(walk(ruta))[2]\n",
    "\n",
    "height_image = 224\n",
    "width_image = 224\n",
    "channels_image = 3\n",
    "nb_clases = 4\n",
    "batch_size = 64\n",
    "class_mode = 'categorical'\n",
    "nb_train = 4000        # 1000 x 4 clases\n",
    "nb_validation = 1200   #  300 x 4 clases\n",
    "nb_test = 1200\n",
    "\n",
    "base_dir = 'balanced_dataset'\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "test_dir = os.path.join(base_dir, 'test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.applications import VGG16\n",
    "conv_base = VGG16(weights = 'imagenet', \n",
    "                  include_top = False, \n",
    "                  input_shape = (width_image, height_image, channels_image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "FEATURE EXTRACTION (Extracción de características)\n",
    "\n",
    "Ahora hay dos formas de proceder:\n",
    "1- Ejecutar la base convolucional sobre el conjunto de datos, registrar la salida en una matriz Numpy en disco, y luego usar esa información como entrada para un clasificador independiente y densamente conectado. Esta solución es rápida y económica computacionalmente, pero no permite utilizar Data Augmentation.\n",
    "La primera parte es la base convolucional de un modelo preentrenado, segundo se entrena la parte convolucional con los nuevos datos de entrenamiento, depués añadimos un nuevo clasificador densamente conectado en la parte superior (final) y volvemos a entrenar. Si el modelo con el que se entrenó el modelo preentrenado es muy difernte del que queremos aplicar, es conveniente no utilizar toda la base convolucional (eliminar las últimas) ya que éstas són cada vez más especificas al conjunto de datos con el que se entrenó.\n",
    "2- Amplicar el modelo que tenemos (conv_base) agregando capas densas en la parte superior y ejecutando todo de punta a punta en los datos de entrada. Esto permite Data Augmentation, porque cada imagen de entrada pasa por la base convolucional. Esta técnica es mucho mas costosa y requiere GPU"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "1. Extracción de características sin Data Augmentation --> CPU\n",
    "\n",
    "Ejecutamos instancias de ImageDataGenerator para extraer imagenes como matrices Numpy y sus etiquetas. Extraeremos características de estas imágenes llamando al método del modelo conv_base\n",
    "\n",
    "- La ultima salida de la capa convolucional es de (7, 7, 512 )\n",
    "out_x, out_y, conv_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4000 images belonging to 4 classes.\n",
      "Found 1200 images belonging to 4 classes.\n",
      "Found 1200 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "#Extracción de características del modelo Preentrenado y nuestro dataset\n",
    "import numpy as np\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "#Tamaño de salida de la última capa convoluciónal del modelo preentrenado\n",
    "#lo vemos en el conv_base.summary()\n",
    "out_x = 7\n",
    "out_y = 7\n",
    "conv_len = 512\n",
    "\n",
    "datagen = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "def extract_features(directory, sample_count):\n",
    "    features = np.zeros(shape=(sample_count, out_x, out_x, conv_len))\n",
    "    labels = to_categorical(np.zeros(shape=(sample_count)),nb_clases) #INDICAR NUMERO DE CLASES\n",
    "    generator = datagen.flow_from_directory(\n",
    "        directory,\n",
    "        target_size = (height_image, width_image),\n",
    "        batch_size = batch_size,\n",
    "        #classes = 4,\n",
    "        class_mode = 'categorical')\n",
    "    i = 0\n",
    "    for inputs_batch, labels_batch in generator:\n",
    "        features_batch = conv_base.predict(inputs_batch) ##Asociamos al modelo preentrenado\n",
    "        features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
    "        labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n",
    "        i += 1\n",
    "        if i * batch_size >= sample_count:\n",
    "            break\n",
    "    return features, labels\n",
    "\n",
    "#Train: 1000 muestras x clase, con 4 clases, 1000 x 4 = 4000\n",
    "train_features, train_labels = extract_features(train_dir, nb_train)\n",
    "#validation 500 muestras x clase con 4 clases, 300 x 4 = 1200\n",
    "validation_features, validation_labels = extract_features(validation_dir, nb_validation) \n",
    "#test 500 muestras x clase con 4 clases, 300 x 4 = 1200\n",
    "test_features, test_labels = extract_features(test_dir, nb_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4000, 25088)\n",
      "(1200, 25088)\n",
      "(1200, 25088)\n"
     ]
    }
   ],
   "source": [
    "#Las características extraídas están como muetras en forma (muestra, 4, 4, 512).-->(muestras, out_x, out_y, conv_len)\n",
    "#Debemos aplanarlas (muestras, 8192) para alimentar un clasificador densamente conectado 4x4x512 = 8192 si entrada 150x150\n",
    "#Debemos aplanarlas (muestras, 25088) para alimentar un clasificador densamente conectado 7x7x512 = 25088 si entrada 224x224\n",
    "\n",
    "\n",
    "train_features = np.reshape(train_features, (nb_train, out_x * out_y * conv_len))\n",
    "validation_features = np.reshape(validation_features, (nb_validation, out_x * out_y * conv_len))\n",
    "test_features = np.reshape(test_features, (nb_test, out_x * out_y * conv_len))\n",
    "print(train_features.shape)\n",
    "print(validation_features.shape)\n",
    "print(test_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 256)               6422784   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 4)                 1028      \n",
      "=================================================================\n",
      "Total params: 6,423,812\n",
      "Trainable params: 6,423,812\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Definimos un clasificador densamente conectado capacitado con los datos y etiquetas obtenidas antes\n",
    "#Aplicamos capa de abandono Dropout y función de activación \"sigmoid\" al final\n",
    "from keras import layers\n",
    "from keras import models\n",
    "from keras import optimizers\n",
    "\n",
    "top_model = models.Sequential()\n",
    "top_model.add(layers.Dense(256, activation = 'relu', input_dim = out_x * out_y * conv_len))\n",
    "top_model.add(layers.Dropout(0.5))\n",
    "top_model.add(layers.Dense(nb_clases, activation = 'sigmoid'))  #INDICAR NUMERO DE CLASES\n",
    "\n",
    "top_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 4000 samples, validate on 1200 samples\n",
      "Epoch 1/80\n",
      "4000/4000 [==============================] - 1s 261us/step - loss: 1.2845 - acc: 0.3912 - val_loss: 1.1238 - val_acc: 0.5000\n",
      "Epoch 2/80\n",
      "4000/4000 [==============================] - 1s 201us/step - loss: 1.0870 - acc: 0.5228 - val_loss: 0.9999 - val_acc: 0.5608\n",
      "Epoch 3/80\n",
      "4000/4000 [==============================] - 1s 194us/step - loss: 0.9746 - acc: 0.5902 - val_loss: 0.9003 - val_acc: 0.6383\n",
      "Epoch 4/80\n",
      "4000/4000 [==============================] - 1s 193us/step - loss: 0.9106 - acc: 0.6160 - val_loss: 0.9216 - val_acc: 0.6150\n",
      "Epoch 5/80\n",
      "4000/4000 [==============================] - 1s 209us/step - loss: 0.8630 - acc: 0.6467 - val_loss: 0.8090 - val_acc: 0.6692\n",
      "Epoch 6/80\n",
      "4000/4000 [==============================] - 1s 189us/step - loss: 0.8089 - acc: 0.6743 - val_loss: 0.7885 - val_acc: 0.6858\n",
      "Epoch 7/80\n",
      "4000/4000 [==============================] - 1s 210us/step - loss: 0.7684 - acc: 0.6870 - val_loss: 0.7489 - val_acc: 0.7042\n",
      "Epoch 8/80\n",
      "4000/4000 [==============================] - 1s 207us/step - loss: 0.7368 - acc: 0.7092 - val_loss: 0.7621 - val_acc: 0.6817\n",
      "Epoch 9/80\n",
      "4000/4000 [==============================] - 1s 174us/step - loss: 0.7183 - acc: 0.7208 - val_loss: 0.7148 - val_acc: 0.7183\n",
      "Epoch 10/80\n",
      "4000/4000 [==============================] - 1s 190us/step - loss: 0.6853 - acc: 0.7332 - val_loss: 0.7088 - val_acc: 0.7133\n",
      "Epoch 11/80\n",
      "4000/4000 [==============================] - 1s 205us/step - loss: 0.6665 - acc: 0.7450 - val_loss: 0.6803 - val_acc: 0.7358\n",
      "Epoch 12/80\n",
      "4000/4000 [==============================] - 1s 206us/step - loss: 0.6338 - acc: 0.7595 - val_loss: 0.6626 - val_acc: 0.7458\n",
      "Epoch 13/80\n",
      "4000/4000 [==============================] - 1s 219us/step - loss: 0.6143 - acc: 0.7650 - val_loss: 0.6551 - val_acc: 0.7317\n",
      "Epoch 14/80\n",
      "4000/4000 [==============================] - 1s 187us/step - loss: 0.6021 - acc: 0.7772 - val_loss: 0.6448 - val_acc: 0.7575\n",
      "Epoch 15/80\n",
      "4000/4000 [==============================] - 1s 176us/step - loss: 0.5809 - acc: 0.7740 - val_loss: 0.6157 - val_acc: 0.7625\n",
      "Epoch 16/80\n",
      "4000/4000 [==============================] - 1s 196us/step - loss: 0.5660 - acc: 0.7923 - val_loss: 0.6079 - val_acc: 0.7658\n",
      "Epoch 17/80\n",
      "4000/4000 [==============================] - 1s 181us/step - loss: 0.5475 - acc: 0.7983 - val_loss: 0.6074 - val_acc: 0.7650\n",
      "Epoch 18/80\n",
      "4000/4000 [==============================] - 1s 190us/step - loss: 0.5245 - acc: 0.8067 - val_loss: 0.5846 - val_acc: 0.7742\n",
      "Epoch 19/80\n",
      "4000/4000 [==============================] - 1s 175us/step - loss: 0.5239 - acc: 0.8093 - val_loss: 0.5987 - val_acc: 0.7717\n",
      "Epoch 20/80\n",
      "4000/4000 [==============================] - 1s 155us/step - loss: 0.4972 - acc: 0.8197 - val_loss: 0.5915 - val_acc: 0.7717\n",
      "Epoch 21/80\n",
      "4000/4000 [==============================] - 1s 165us/step - loss: 0.4827 - acc: 0.8303 - val_loss: 0.5652 - val_acc: 0.7817\n",
      "Epoch 22/80\n",
      "4000/4000 [==============================] - 1s 205us/step - loss: 0.4775 - acc: 0.8325 - val_loss: 0.5585 - val_acc: 0.7908\n",
      "Epoch 23/80\n",
      "4000/4000 [==============================] - 1s 181us/step - loss: 0.4639 - acc: 0.8400 - val_loss: 0.5614 - val_acc: 0.7817\n",
      "Epoch 24/80\n",
      "4000/4000 [==============================] - 1s 198us/step - loss: 0.4460 - acc: 0.8473 - val_loss: 0.5553 - val_acc: 0.7900\n",
      "Epoch 25/80\n",
      "4000/4000 [==============================] - 1s 212us/step - loss: 0.4437 - acc: 0.8485 - val_loss: 0.5487 - val_acc: 0.7858\n",
      "Epoch 26/80\n",
      "4000/4000 [==============================] - 1s 205us/step - loss: 0.4237 - acc: 0.8572 - val_loss: 0.5687 - val_acc: 0.7692\n",
      "Epoch 27/80\n",
      "4000/4000 [==============================] - 1s 204us/step - loss: 0.4217 - acc: 0.8540 - val_loss: 0.5473 - val_acc: 0.7875\n",
      "Epoch 28/80\n",
      "4000/4000 [==============================] - 1s 201us/step - loss: 0.4094 - acc: 0.8605 - val_loss: 0.5344 - val_acc: 0.8008\n",
      "Epoch 29/80\n",
      "4000/4000 [==============================] - 1s 177us/step - loss: 0.3950 - acc: 0.8655 - val_loss: 0.5530 - val_acc: 0.7775\n",
      "Epoch 30/80\n",
      "4000/4000 [==============================] - 1s 189us/step - loss: 0.3831 - acc: 0.8750 - val_loss: 0.5402 - val_acc: 0.7892\n",
      "Epoch 31/80\n",
      "4000/4000 [==============================] - 1s 197us/step - loss: 0.3886 - acc: 0.8700 - val_loss: 0.5249 - val_acc: 0.8000\n",
      "Epoch 32/80\n",
      "4000/4000 [==============================] - 1s 212us/step - loss: 0.3695 - acc: 0.8785 - val_loss: 0.5399 - val_acc: 0.7850\n",
      "Epoch 33/80\n",
      "4000/4000 [==============================] - 1s 218us/step - loss: 0.3631 - acc: 0.8870 - val_loss: 0.5194 - val_acc: 0.8092\n",
      "Epoch 34/80\n",
      "4000/4000 [==============================] - 1s 187us/step - loss: 0.3563 - acc: 0.8845 - val_loss: 0.5180 - val_acc: 0.7958\n",
      "Epoch 35/80\n",
      "4000/4000 [==============================] - 1s 198us/step - loss: 0.3500 - acc: 0.8862 - val_loss: 0.5336 - val_acc: 0.7933\n",
      "Epoch 36/80\n",
      "4000/4000 [==============================] - 1s 210us/step - loss: 0.3402 - acc: 0.8932 - val_loss: 0.5235 - val_acc: 0.7992\n",
      "Epoch 37/80\n",
      "4000/4000 [==============================] - 1s 202us/step - loss: 0.3279 - acc: 0.9002 - val_loss: 0.5201 - val_acc: 0.7975\n",
      "Epoch 38/80\n",
      "4000/4000 [==============================] - 1s 211us/step - loss: 0.3154 - acc: 0.9038 - val_loss: 0.5271 - val_acc: 0.8000\n",
      "Epoch 39/80\n",
      "4000/4000 [==============================] - 1s 202us/step - loss: 0.3169 - acc: 0.9073 - val_loss: 0.5155 - val_acc: 0.8017\n",
      "Epoch 40/80\n",
      "4000/4000 [==============================] - 1s 178us/step - loss: 0.3018 - acc: 0.9020 - val_loss: 0.5348 - val_acc: 0.7850\n",
      "Epoch 41/80\n",
      "4000/4000 [==============================] - 1s 199us/step - loss: 0.3045 - acc: 0.9133 - val_loss: 0.5060 - val_acc: 0.7967\n",
      "Epoch 42/80\n",
      "4000/4000 [==============================] - 1s 206us/step - loss: 0.2885 - acc: 0.9137 - val_loss: 0.4957 - val_acc: 0.8075\n",
      "Epoch 43/80\n",
      "4000/4000 [==============================] - 1s 208us/step - loss: 0.2897 - acc: 0.9137 - val_loss: 0.5397 - val_acc: 0.7908\n",
      "Epoch 44/80\n",
      "4000/4000 [==============================] - 1s 221us/step - loss: 0.2789 - acc: 0.9210 - val_loss: 0.5045 - val_acc: 0.8008\n",
      "Epoch 45/80\n",
      "4000/4000 [==============================] - 1s 198us/step - loss: 0.2748 - acc: 0.9175 - val_loss: 0.5464 - val_acc: 0.7792\n",
      "Epoch 46/80\n",
      "4000/4000 [==============================] - 1s 192us/step - loss: 0.2687 - acc: 0.9230 - val_loss: 0.5047 - val_acc: 0.8100\n",
      "Epoch 47/80\n",
      "4000/4000 [==============================] - 1s 205us/step - loss: 0.2586 - acc: 0.9210 - val_loss: 0.4995 - val_acc: 0.8025\n",
      "Epoch 48/80\n",
      "4000/4000 [==============================] - 1s 186us/step - loss: 0.2520 - acc: 0.9303 - val_loss: 0.5074 - val_acc: 0.8008\n",
      "Epoch 49/80\n",
      "4000/4000 [==============================] - 1s 200us/step - loss: 0.2500 - acc: 0.9290 - val_loss: 0.5048 - val_acc: 0.7975\n",
      "Epoch 50/80\n",
      "4000/4000 [==============================] - 1s 197us/step - loss: 0.2464 - acc: 0.9305 - val_loss: 0.4946 - val_acc: 0.8067\n",
      "Epoch 51/80\n",
      "4000/4000 [==============================] - 1s 179us/step - loss: 0.2390 - acc: 0.9357 - val_loss: 0.4897 - val_acc: 0.8133\n",
      "Epoch 52/80\n",
      "4000/4000 [==============================] - 1s 193us/step - loss: 0.2364 - acc: 0.9370 - val_loss: 0.4914 - val_acc: 0.8067\n",
      "Epoch 53/80\n",
      "4000/4000 [==============================] - 1s 197us/step - loss: 0.2235 - acc: 0.9427 - val_loss: 0.4924 - val_acc: 0.8133\n",
      "Epoch 54/80\n",
      "4000/4000 [==============================] - 1s 194us/step - loss: 0.2255 - acc: 0.9382 - val_loss: 0.5071 - val_acc: 0.8050\n",
      "Epoch 55/80\n",
      "4000/4000 [==============================] - 1s 200us/step - loss: 0.2215 - acc: 0.9403 - val_loss: 0.4851 - val_acc: 0.8108\n",
      "Epoch 56/80\n",
      "4000/4000 [==============================] - 1s 203us/step - loss: 0.2103 - acc: 0.9485 - val_loss: 0.4856 - val_acc: 0.8167\n",
      "Epoch 57/80\n",
      "4000/4000 [==============================] - 1s 203us/step - loss: 0.2048 - acc: 0.9472 - val_loss: 0.5182 - val_acc: 0.7983\n",
      "Epoch 58/80\n",
      "4000/4000 [==============================] - 1s 212us/step - loss: 0.2116 - acc: 0.9457 - val_loss: 0.5053 - val_acc: 0.8000\n",
      "Epoch 59/80\n",
      "4000/4000 [==============================] - 1s 193us/step - loss: 0.1990 - acc: 0.9483 - val_loss: 0.4995 - val_acc: 0.8175\n",
      "Epoch 60/80\n",
      "4000/4000 [==============================] - 1s 190us/step - loss: 0.1991 - acc: 0.9502 - val_loss: 0.4919 - val_acc: 0.8150\n",
      "Epoch 61/80\n",
      "4000/4000 [==============================] - 1s 189us/step - loss: 0.1923 - acc: 0.9520 - val_loss: 0.4984 - val_acc: 0.8150\n",
      "Epoch 62/80\n",
      "4000/4000 [==============================] - 1s 192us/step - loss: 0.1859 - acc: 0.9553 - val_loss: 0.5256 - val_acc: 0.7958\n",
      "Epoch 63/80\n",
      "4000/4000 [==============================] - 1s 212us/step - loss: 0.1827 - acc: 0.9543 - val_loss: 0.4924 - val_acc: 0.8167\n",
      "Epoch 64/80\n",
      "4000/4000 [==============================] - 1s 211us/step - loss: 0.1786 - acc: 0.9597 - val_loss: 0.4895 - val_acc: 0.8150\n",
      "Epoch 65/80\n",
      "4000/4000 [==============================] - 1s 199us/step - loss: 0.1734 - acc: 0.9590 - val_loss: 0.4990 - val_acc: 0.8133\n",
      "Epoch 66/80\n",
      "4000/4000 [==============================] - 1s 204us/step - loss: 0.1749 - acc: 0.9597 - val_loss: 0.5111 - val_acc: 0.8083\n",
      "Epoch 67/80\n",
      "4000/4000 [==============================] - 1s 197us/step - loss: 0.1651 - acc: 0.9637 - val_loss: 0.4916 - val_acc: 0.8150\n",
      "Epoch 68/80\n",
      "4000/4000 [==============================] - 1s 201us/step - loss: 0.1603 - acc: 0.9627 - val_loss: 0.5217 - val_acc: 0.8025\n",
      "Epoch 69/80\n",
      "4000/4000 [==============================] - 1s 200us/step - loss: 0.1581 - acc: 0.9655 - val_loss: 0.5014 - val_acc: 0.8083\n",
      "Epoch 70/80\n",
      "4000/4000 [==============================] - 1s 181us/step - loss: 0.1536 - acc: 0.9675 - val_loss: 0.4843 - val_acc: 0.8150\n",
      "Epoch 71/80\n",
      "4000/4000 [==============================] - 1s 190us/step - loss: 0.1519 - acc: 0.9688 - val_loss: 0.4864 - val_acc: 0.8175\n",
      "Epoch 72/80\n",
      "4000/4000 [==============================] - 1s 196us/step - loss: 0.1485 - acc: 0.9675 - val_loss: 0.5121 - val_acc: 0.8000\n",
      "Epoch 73/80\n",
      "4000/4000 [==============================] - 1s 180us/step - loss: 0.1428 - acc: 0.9712 - val_loss: 0.5090 - val_acc: 0.8117\n",
      "Epoch 74/80\n",
      "4000/4000 [==============================] - 1s 192us/step - loss: 0.1458 - acc: 0.9710 - val_loss: 0.4851 - val_acc: 0.8200\n",
      "Epoch 75/80\n",
      "4000/4000 [==============================] - 1s 193us/step - loss: 0.1416 - acc: 0.9705 - val_loss: 0.5185 - val_acc: 0.8008\n",
      "Epoch 76/80\n",
      "4000/4000 [==============================] - 1s 202us/step - loss: 0.1402 - acc: 0.9675 - val_loss: 0.5137 - val_acc: 0.8033\n",
      "Epoch 77/80\n",
      "4000/4000 [==============================] - 1s 205us/step - loss: 0.1324 - acc: 0.9748 - val_loss: 0.5070 - val_acc: 0.8092\n",
      "Epoch 78/80\n",
      "4000/4000 [==============================] - 1s 187us/step - loss: 0.1293 - acc: 0.9750 - val_loss: 0.4967 - val_acc: 0.8200\n",
      "Epoch 79/80\n",
      "4000/4000 [==============================] - 1s 197us/step - loss: 0.1220 - acc: 0.9795 - val_loss: 0.5027 - val_acc: 0.8183\n",
      "Epoch 80/80\n",
      "4000/4000 [==============================] - 1s 208us/step - loss: 0.1174 - acc: 0.9785 - val_loss: 0.4977 - val_acc: 0.8125\n"
     ]
    }
   ],
   "source": [
    "#Para la compilación usaremos el optimizador RMSprop ya que termina la red con una sola\n",
    "#unidad sigmoidea, cómo función de pérdida se utiliza la entropia cruzada binaria\n",
    "\n",
    "top_model.compile(loss = 'categorical_crossentropy', \n",
    "              optimizer = optimizers.RMSprop(lr=2e-5), \n",
    "              metrics = ['acc'])\n",
    "\n",
    "epochs = 80\n",
    "\n",
    "history = top_model.fit(\n",
    "    train_features,\n",
    "    train_labels,  \n",
    "    batch_size = batch_size,\n",
    "    epochs = epochs, \n",
    "    verbose = 1, \n",
    "    validation_data = (validation_features, validation_labels))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top_model.save('Fast_Extraction_VGG_3.h5')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Guardamos el modelo"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Graficamos la Exactitud y la pérdida del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99c85995c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99c8503cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Mostramos otros graficos \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(acc)+ 1)\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "#plt.tittle('Trainning and validation accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "#plt.tittle('Trainning and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 0s 171us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5551292991638184, 0.7941666666666667]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   },
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   "source": []
  }
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