E_Fine_Tuning_Xception_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": [
    "import os\n",
    "import sys\n",
    "import glob\n",
    "import argparse\n",
    "import os, shutil\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from os import walk, getcwd\n",
    "from keras import __version__\n",
    "#from keras.applications.inception_v3 import InceptionV3, preprocess_input\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, GlobalAveragePooling2D\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "IM_WIDTH, IM_HEIGHT = 299, 299 #fixed size for InceptionV3\n",
    "nb_epoch = 1\n",
    "batch_size = 32\n",
    "FC_SIZE = 1001\n",
    "nb_classes = 4        # 4 clases\n",
    "nb_train = 4000       # 1000 x clase\n",
    "nb_validation = 1200  # 500 x clase\n",
    "nb_tes = 1200         # 500 x clase\n",
    "\n",
    "#Asociamos a dataset y carpetas, traim, validation y test\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')\n",
    "loss_mode = 'binary_crossentropy'\n",
    "output_model_file = 'Xception_FT_model_1.h5'\n",
    "class_mode= 'categorical'\n",
    "\n",
    "#inceptionv3_weights = 'inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
    "\n",
    "# Retorna todos los archivos de un directorio dado\n",
    "def ls(ruta):  \n",
    "    return next(walk(ruta))[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.applications import Xception\n",
    "#Load Pretrained model\n",
    "conv_base = Xception(include_top=False, \n",
    "                     weights='imagenet', \n",
    "                     input_shape = (IM_WIDTH, IM_HEIGHT, 3)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def setup_to_transfer_learn(model, base_model):\n",
    "    \n",
    "  #Freeze all layers and compile the model\"\"\"\n",
    "    for layer in base_model.layers:\n",
    "        layer.trainable = False\n",
    "\n",
    "    model.compile(optimizer='rmsprop', loss=loss_mode, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def add_new_last_layer(base_model, nb_classes):\n",
    "#\"\"\"\n",
    "#Add last layer to the convnet\n",
    "#Args:\n",
    "#  base_model: keras model excluding top\n",
    "#  nb_classes: # of classes\n",
    "#Returns:\n",
    "#  new keras model with last layer\n",
    "#\"\"\"\n",
    "    FC_SIZE =1001\n",
    "    x = base_model.output\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Dense(FC_SIZE, activation='relu')(x) #new FC layer, random init\n",
    "    \n",
    "    if class_mode == \"binary\":\n",
    "        predictions = Dense(1, activation='sigmoid')(x) #new softmax layer\n",
    "    else:\n",
    "        predictions = Dense(nb_classes, activation='softmax')(x) #new softmax layer\n",
    "        \n",
    "    model = Model(inputs=base_model.input, outputs=predictions)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def setup_to_finetune(model):\n",
    "#\"\"\"Freeze the bottom NB_IV3_LAYERS and retrain the remaining top layers.\n",
    "#note: NB_IV3_LAYERS corresponds to the top 2 inception blocks in the inceptionv3 arch\n",
    "#Args:\n",
    "#model: keras model\n",
    "#\"\"\"\n",
    "    for layer in model.layers[:NB_IV3_LAYERS_TO_FREEZE]:\n",
    "        layer.trainable = False\n",
    "    for layer in model.layers[NB_IV3_LAYERS_TO_FREEZE:]:\n",
    "        layer.trainable = True\n",
    "        \n",
    "    #model.compile(optimizer=\"rmsprop\", loss=loss_mode, metrics=['accuracy'])\n",
    "    #default values rmsprop\n",
    "    #rmsprop  keras.optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)\n",
    "    model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Graficas loss y accuracy\n",
    "def plot_training(history):\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",
    "    epochs = range(len(acc))\n",
    "\n",
    "    plt.plot(epochs, acc, 'r.')\n",
    "    plt.plot(epochs, val_acc, 'r')\n",
    "    plt.title('Training and validation accuracy')\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(epochs, loss, 'r.')\n",
    "    plt.plot(epochs, val_loss, 'r-')\n",
    "    plt.title('Training and validation loss')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "#PREPROCESSING - \n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale = 1./255,              #Normalizamos el valor de pixel\n",
    "    rotation_range = 40,          #rotamos imagne 40 grados\n",
    "    width_shift_range =0.2,       #aplica una fracción del ancho de la imagen orig\n",
    "    height_shift_range =0.2,      #aplica una fracción del alto de la imagen orig\n",
    "    shear_range = 0.2,            #aplica al azar operaciones de corte\n",
    "    zoom_range = 0.2,             #aplica zoom aleatorio dentro de las imagenes\n",
    "    horizontal_flip = True,       #invierte aleatoriamente la mitad de las imágenes\n",
    "    fill_mode = 'nearest'\n",
    ") \n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "validation_datagen = ImageDataGenerator(\n",
    "    rescale = 1./255,              #Normalizamos el valor de pixel\n",
    "    rotation_range = 40,          #rotamos imagne 40 grados\n",
    "    width_shift_range =0.2,       #aplica una fracción del ancho de la imagen orig\n",
    "    height_shift_range =0.2,      #aplica una fracción del alto de la imagen orig\n",
    "    shear_range = 0.2,            #aplica al azar operaciones de corte\n",
    "    zoom_range = 0.2,             #aplica zoom aleatorio dentro de las imagenes\n",
    "    horizontal_flip = True,       #invierte aleatoriamente la mitad de las imágenes\n",
    "    fill_mode = 'nearest'\n",
    ")\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(IM_WIDTH, IM_HEIGHT),\n",
    "    batch_size=batch_size,\n",
    "    class_mode=class_mode #'categorical'\n",
    "  )\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory( #OJO HE CAMBIADO en la prueba 3\n",
    "    validation_dir,\n",
    "    target_size=(IM_WIDTH, IM_HEIGHT),\n",
    "    batch_size=batch_size,\n",
    "    class_mode=class_mode\n",
    "  )\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    validation_dir,\n",
    "    target_size=(IM_WIDTH, IM_HEIGHT),\n",
    "    batch_size = batch_size, \n",
    "    class_mode = class_mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#base_model = InceptionV3(weights='imagenet', include_top=False) #include_top=False excludes final FC layer\n",
    "#model = add_new_last_layer(base_model, nb_classes)\n",
    "\n",
    "model = add_new_last_layer(conv_base, nb_classes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 299, 299, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 149, 149, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1_bn (BatchNormaliza (None, 149, 149, 32) 128         block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1_act (Activation)   (None, 149, 149, 32) 0           block1_conv1_bn[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 147, 147, 64) 18432       block1_conv1_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2_bn (BatchNormaliza (None, 147, 147, 64) 256         block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2_act (Activation)   (None, 147, 147, 64) 0           block1_conv2_bn[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv1 (SeparableConv2 (None, 147, 147, 128 8768        block1_conv2_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv1_bn (BatchNormal (None, 147, 147, 128 512         block2_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2_act (Activation (None, 147, 147, 128 0           block2_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2 (SeparableConv2 (None, 147, 147, 128 17536       block2_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2_bn (BatchNormal (None, 147, 147, 128 512         block2_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 74, 74, 128)  8192        block1_conv2_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)      (None, 74, 74, 128)  0           block2_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 74, 74, 128)  512         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 74, 74, 128)  0           block2_pool[0][0]                \n",
      "                                                                 batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1_act (Activation (None, 74, 74, 128)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1 (SeparableConv2 (None, 74, 74, 256)  33920       block3_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1_bn (BatchNormal (None, 74, 74, 256)  1024        block3_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2_act (Activation (None, 74, 74, 256)  0           block3_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2 (SeparableConv2 (None, 74, 74, 256)  67840       block3_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2_bn (BatchNormal (None, 74, 74, 256)  1024        block3_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 37, 37, 256)  32768       add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)      (None, 37, 37, 256)  0           block3_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 37, 37, 256)  1024        conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 37, 37, 256)  0           block3_pool[0][0]                \n",
      "                                                                 batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1_act (Activation (None, 37, 37, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1 (SeparableConv2 (None, 37, 37, 728)  188672      block4_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1_bn (BatchNormal (None, 37, 37, 728)  2912        block4_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2_act (Activation (None, 37, 37, 728)  0           block4_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2 (SeparableConv2 (None, 37, 37, 728)  536536      block4_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2_bn (BatchNormal (None, 37, 37, 728)  2912        block4_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 19, 19, 728)  186368      add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block4_pool (MaxPooling2D)      (None, 19, 19, 728)  0           block4_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 19, 19, 728)  2912        conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 19, 19, 728)  0           block4_pool[0][0]                \n",
      "                                                                 batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1_act (Activation (None, 19, 19, 728)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2_act (Activation (None, 19, 19, 728)  0           block5_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3_act (Activation (None, 19, 19, 728)  0           block5_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 19, 19, 728)  0           block5_sepconv3_bn[0][0]         \n",
      "                                                                 add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1_act (Activation (None, 19, 19, 728)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2_act (Activation (None, 19, 19, 728)  0           block6_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3_act (Activation (None, 19, 19, 728)  0           block6_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 19, 19, 728)  0           block6_sepconv3_bn[0][0]         \n",
      "                                                                 add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1_act (Activation (None, 19, 19, 728)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2_act (Activation (None, 19, 19, 728)  0           block7_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3_act (Activation (None, 19, 19, 728)  0           block7_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 19, 19, 728)  0           block7_sepconv3_bn[0][0]         \n",
      "                                                                 add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1_act (Activation (None, 19, 19, 728)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2_act (Activation (None, 19, 19, 728)  0           block8_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3_act (Activation (None, 19, 19, 728)  0           block8_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 19, 19, 728)  0           block8_sepconv3_bn[0][0]         \n",
      "                                                                 add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1_act (Activation (None, 19, 19, 728)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2_act (Activation (None, 19, 19, 728)  0           block9_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3_act (Activation (None, 19, 19, 728)  0           block9_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 19, 19, 728)  0           block9_sepconv3_bn[0][0]         \n",
      "                                                                 add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2_act (Activatio (None, 19, 19, 728)  0           block10_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3_act (Activatio (None, 19, 19, 728)  0           block10_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 19, 19, 728)  0           block10_sepconv3_bn[0][0]        \n",
      "                                                                 add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2_act (Activatio (None, 19, 19, 728)  0           block11_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3_act (Activatio (None, 19, 19, 728)  0           block11_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 19, 19, 728)  0           block11_sepconv3_bn[0][0]        \n",
      "                                                                 add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2_act (Activatio (None, 19, 19, 728)  0           block12_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3_act (Activatio (None, 19, 19, 728)  0           block12_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 19, 19, 728)  0           block12_sepconv3_bn[0][0]        \n",
      "                                                                 add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block13_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block13_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2_act (Activatio (None, 19, 19, 728)  0           block13_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2 (SeparableConv (None, 19, 19, 1024) 752024      block13_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2_bn (BatchNorma (None, 19, 19, 1024) 4096        block13_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 10, 10, 1024) 745472      add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_pool (MaxPooling2D)     (None, 10, 10, 1024) 0           block13_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 10, 10, 1024) 4096        conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 10, 10, 1024) 0           block13_pool[0][0]               \n",
      "                                                                 batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1 (SeparableConv (None, 10, 10, 1536) 1582080     add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1_bn (BatchNorma (None, 10, 10, 1536) 6144        block14_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1_act (Activatio (None, 10, 10, 1536) 0           block14_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2 (SeparableConv (None, 10, 10, 2048) 3159552     block14_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2_bn (BatchNorma (None, 10, 10, 2048) 8192        block14_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2_act (Activatio (None, 10, 10, 2048) 0           block14_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 2048)         0           block14_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 1001)         2051049     global_average_pooling2d_1[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 4)            4008        dense_1[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 22,916,537\n",
      "Trainable params: 22,862,009\n",
      "Non-trainable params: 54,528\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "125/125 [==============================] - 98s 781ms/step - loss: 0.5343 - acc: 0.7744 - val_loss: 0.7401 - val_acc: 0.6480\n",
      "Epoch 2/2\n",
      "125/125 [==============================] - 90s 721ms/step - loss: 0.3927 - acc: 0.8242 - val_loss: 0.8773 - val_acc: 0.6404\n"
     ]
    }
   ],
   "source": [
    "#TRANSFERENCIA DE APRENDIZAJE\n",
    "\n",
    "nb_epoch = 2\n",
    "setup_to_transfer_learn(model, conv_base)\n",
    "#model.summary()\n",
    "\n",
    "history_tl = model.fit_generator(\n",
    "    train_generator,\n",
    "    epochs=nb_epoch,\n",
    "    steps_per_epoch=nb_train//batch_size,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps= nb_validation//batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#FINE TUNNING\n",
    "#NB_IV3_LAYERS_TO_FREEZE=249\n",
    "#NB_IV3_LAYERS_TO_FREEZE=172\n",
    "NB_IV3_LAYERS_TO_FREEZE=-0\n",
    "#NB_IV3_LAYERS_TO_FREEZE=-4\n",
    "setup_to_finetune(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "125/125 [==============================] - 97s 780ms/step - loss: 0.7421 - acc: 0.7208 - val_loss: 0.5723 - val_acc: 0.7880\n",
      "Epoch 2/30\n",
      "125/125 [==============================] - 93s 744ms/step - loss: 0.6048 - acc: 0.7742 - val_loss: 0.5160 - val_acc: 0.8100\n",
      "Epoch 3/30\n",
      "125/125 [==============================] - 93s 745ms/step - loss: 0.5313 - acc: 0.7975 - val_loss: 0.4621 - val_acc: 0.8336\n",
      "Epoch 4/30\n",
      "125/125 [==============================] - 93s 741ms/step - loss: 0.4865 - acc: 0.8160 - val_loss: 0.4213 - val_acc: 0.8514\n",
      "Epoch 5/30\n",
      "125/125 [==============================] - 93s 742ms/step - loss: 0.4606 - acc: 0.8320 - val_loss: 0.3940 - val_acc: 0.8615\n",
      "Epoch 6/30\n",
      "125/125 [==============================] - 93s 745ms/step - loss: 0.4294 - acc: 0.8342 - val_loss: 0.3699 - val_acc: 0.8775\n",
      "Epoch 7/30\n",
      "125/125 [==============================] - 93s 742ms/step - loss: 0.4036 - acc: 0.8545 - val_loss: 0.3489 - val_acc: 0.8818\n",
      "Epoch 8/30\n",
      "125/125 [==============================] - 93s 743ms/step - loss: 0.3933 - acc: 0.8518 - val_loss: 0.3270 - val_acc: 0.8877\n",
      "Epoch 9/30\n",
      "125/125 [==============================] - 93s 743ms/step - loss: 0.3564 - acc: 0.8715 - val_loss: 0.3140 - val_acc: 0.8910\n",
      "Epoch 10/30\n",
      "125/125 [==============================] - 93s 745ms/step - loss: 0.3364 - acc: 0.8760 - val_loss: 0.2977 - val_acc: 0.8936\n",
      "Epoch 11/30\n",
      "125/125 [==============================] - 93s 745ms/step - loss: 0.3349 - acc: 0.8785 - val_loss: 0.2884 - val_acc: 0.8978\n",
      "Epoch 12/30\n",
      "125/125 [==============================] - 94s 749ms/step - loss: 0.3037 - acc: 0.8892 - val_loss: 0.2784 - val_acc: 0.8978\n",
      "Epoch 13/30\n",
      "125/125 [==============================] - 94s 752ms/step - loss: 0.3049 - acc: 0.8838 - val_loss: 0.2759 - val_acc: 0.9029\n",
      "Epoch 14/30\n",
      "125/125 [==============================] - 94s 749ms/step - loss: 0.2990 - acc: 0.8885 - val_loss: 0.2587 - val_acc: 0.9003\n",
      "Epoch 15/30\n",
      "125/125 [==============================] - 94s 748ms/step - loss: 0.2816 - acc: 0.8978 - val_loss: 0.2519 - val_acc: 0.9037\n",
      "Epoch 16/30\n",
      "125/125 [==============================] - 94s 748ms/step - loss: 0.2768 - acc: 0.9015 - val_loss: 0.2514 - val_acc: 0.9046\n",
      "Epoch 17/30\n",
      "125/125 [==============================] - 94s 749ms/step - loss: 0.2711 - acc: 0.8972 - val_loss: 0.2517 - val_acc: 0.9054\n",
      "Epoch 18/30\n",
      "125/125 [==============================] - 93s 743ms/step - loss: 0.2603 - acc: 0.9000 - val_loss: 0.2388 - val_acc: 0.9122\n",
      "Epoch 19/30\n",
      "125/125 [==============================] - 93s 744ms/step - loss: 0.2561 - acc: 0.9070 - val_loss: 0.2322 - val_acc: 0.9139\n",
      "Epoch 20/30\n",
      "125/125 [==============================] - 93s 744ms/step - loss: 0.2461 - acc: 0.9113 - val_loss: 0.2232 - val_acc: 0.9172\n",
      "Epoch 21/30\n",
      "125/125 [==============================] - 93s 741ms/step - loss: 0.2358 - acc: 0.9135 - val_loss: 0.2233 - val_acc: 0.9181\n",
      "Epoch 22/30\n",
      "125/125 [==============================] - 93s 744ms/step - loss: 0.2254 - acc: 0.9175 - val_loss: 0.2192 - val_acc: 0.9189\n",
      "Epoch 23/30\n",
      "125/125 [==============================] - 93s 743ms/step - loss: 0.2248 - acc: 0.9210 - val_loss: 0.2184 - val_acc: 0.9172\n",
      "Epoch 24/30\n",
      "125/125 [==============================] - 93s 747ms/step - loss: 0.2222 - acc: 0.9147 - val_loss: 0.2130 - val_acc: 0.9231\n",
      "Epoch 25/30\n",
      "125/125 [==============================] - 93s 746ms/step - loss: 0.2129 - acc: 0.9195 - val_loss: 0.2054 - val_acc: 0.9248\n",
      "Epoch 26/30\n",
      "125/125 [==============================] - 93s 745ms/step - loss: 0.1978 - acc: 0.9310 - val_loss: 0.2003 - val_acc: 0.9265\n",
      "Epoch 27/30\n",
      "125/125 [==============================] - 93s 744ms/step - loss: 0.2100 - acc: 0.9195 - val_loss: 0.2013 - val_acc: 0.9291\n",
      "Epoch 28/30\n",
      "125/125 [==============================] - 93s 748ms/step - loss: 0.1924 - acc: 0.9275 - val_loss: 0.1943 - val_acc: 0.9307\n",
      "Epoch 29/30\n",
      "125/125 [==============================] - 93s 743ms/step - loss: 0.1893 - acc: 0.9310 - val_loss: 0.1933 - val_acc: 0.9291\n",
      "Epoch 30/30\n",
      "125/125 [==============================] - 93s 745ms/step - loss: 0.1898 - acc: 0.9305 - val_loss: 0.1894 - val_acc: 0.9324\n"
     ]
    }
   ],
   "source": [
    "nb_epoch = 30 \n",
    "history_ft = model.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=nb_train//batch_size,\n",
    "    epochs=nb_epoch,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=nb_validation//batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#model.save(output_model_file)\n",
    "model.save('Xception_FT_model_1.h5')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#Ejecutar desde inicio hasta transfer learning y luego fine tunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graficas Transferencia de aprendizaje de Inception v3 a nuestro Clasificador\n"
     ]
    },
    {
     "data": {
      "image/png": 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4A5gFLJQ0K98mIi6NiNkRMRv4FvD3uepXe+oi4qxc+deBayLiWOB54MJBzsXMzAahyBHCHKAjIrZFxB5gJbCgj/YLgVv66lCSgA8Cq1LRD4CzC4zFzMyGSJFAmARsz213prK3kDQNmAHckyseJ6ksaZ2kng/9o4BdEbGvQJ+L0/7lrq6uAsM1M7OBaK5zf23AqojozpVNi4gdko4G7pH0MPBC0Q4jYgWwAqBUKkVdR2tmZq8rcoSwA5iS256cyqppo2K5KCJ2pK/bgDXAScCzwBGSegKprz7NzGwYFAmE9cDMdFVQC9mHfntlI0nHA+OBtbmy8ZLGpucTgJOBzRERwL3AOanpBcCPBzMRMzMbnJqBkNb5lwCrgX8FbouITZKWScpfNdQGrEwf9j1OAMqSfkkWAH8ZEZtT3eXAZyV1kJ1T+P7gp2NmZgOlN39+799KpVKUy+VGD8PMbESRtCEiSrXa+TeVzcwMcCCYmVniQDAzM8CBYGZmiQPBzMwAB4KZmSUOBDMzAxwIZmaWOBDMzAxwIJiZWeJAMDMzwIFgZmaJA8HMzAAHgpmZJQ4EMzMDHAhmZpY4EMzMDHAgmJlZUigQJM2XtEVSh6SlVeqvkbQxPbZK2pXKZ0taK2mTpIck/UlunxskPZbbb3b9pmVmZv3VXKuBpCZgOXAa0Amsl9QeEZt72kTEpbn2lwAnpc1XgE9ExKOS3glskLQ6Inal+ssiYlWd5mJmZoNQ5AhhDtAREdsiYg+wEljQR/uFwC0AEbE1Ih5Nz58EngZaBzdkMzMbCkUCYRKwPbfdmcreQtI0YAZwT5W6OUAL8Ktc8VfTUtI1ksb20udiSWVJ5a6urgLDNTOzgaj3SeU2YFVEdOcLJU0EbgL+NCJeS8VXAMcD7wWOBC6v1mFErIiIUkSUWlt9cGFmNlSKBMIOYEpue3Iqq6aNtFzUQ9JhwJ3AFyJiXU95ROyMzG7gerKlKTMza5AigbAemClphqQWsg/99spGko4HxgNrc2UtwO3AjZUnj9NRA5IEnA08MtBJmJnZ4NW8yigi9klaAqwGmoDrImKTpGVAOSJ6wqENWBkRkdv9XOCPgKMkLUpliyJiI/AjSa2AgI3Ap+oyIzMzGxC9+fN7/1YqlaJcLjd6GGZmI4qkDRFRqtXOv6lsZmaAA8HMzBIHgpmZAQ4EMzNLHAhmZgY4EMzMLHEgmJkZ4EAwM7PEgWBmZoADwczMEgeCmZkBDgQzM0scCGZmBjgQzMwscSCYmRngQDAzs8SBYGZmgAPBzMySQoEgab6kLZI6JC2tUn+NpI3psVXSrlzdBZIeTY8LcuW/L+nh1Oc3Jak+UzIzs4FortVAUhOwHDgN6ATWS2qPiM09bSLi0lz7S4CT0vMjgS8DJSCADWnf54HvABcDDwA/AeYDd9VpXmZm1k9FjhDmAB0RsS0i9gArgQV9tF8I3JKefwj4WUQ8l0LgZ8B8SROBwyJiXUQEcCNw9oBnYWZmg1YkECYB23PbnansLSRNA2YA99TYd1J6XqTPxZLKkspdXV0FhmtmZgNR75PKbcCqiOiuV4cRsSIiShFRam1trVe3ZmZWoUgg7ACm5LYnp7Jq2nhjuaivfXek50X6NDOzYVAkENYDMyXNkNRC9qHfXtlI0vHAeGBtrng1cLqk8ZLGA6cDqyNiJ/CipPelq4s+Afx4kHMxM7NBqHmVUUTsk7SE7MO9CbguIjZJWgaUI6InHNqAlekkcc++z0n6ClmoACyLiOfS888ANwAHk11d5CuMzMwaSLnP7/1eqVSKcrnc6GGYmY0okjZERKlWO/+mspmZAQ4EMzNLHAhmZgY4EMzMLHEgmJkZ4EAwM7PEgWBmZoADwczMEgeCmZkBDgQzM0scCGZmBjgQzMwscSCYmRngQDAzs8SBYGZmgAPBzMwSB4KZmQEOBDMzSwoFgqT5krZI6pC0tJc250raLGmTpJtT2Qckbcw9fivp7FR3g6THcnWz6zctMzPrr+ZaDSQ1AcuB04BOYL2k9ojYnGszE7gCODkinpf0DoCIuBeYndocCXQAP811f1lErKrXZMzMbOCKHCHMAToiYltE7AFWAgsq2lwMLI+I5wEi4ukq/ZwD3BURrwxmwGZmNjSKBMIkYHtuuzOV5R0HHCfpfknrJM2v0k8bcEtF2VclPSTpGkljq724pMWSypLKXV1dBYZrZmYDUa+Tys3ATGAesBC4VtIRPZWSJgInAqtz+1wBHA+8FzgSuLxaxxGxIiJKEVFqbW2t03DNzKxSkUDYAUzJbU9OZXmdQHtE7I2Ix4CtZAHR41zg9ojY21MQETsjsxu4nmxpyszMGqRIIKwHZkqaIamFbOmnvaLNHWRHB0iaQLaEtC1Xv5CK5aJ01IAkAWcDjwxg/GZmVic1rzKKiH2SlpAt9zQB10XEJknLgHJEtKe60yVtBrrJrh56FkDSdLIjjH+q6PpHkloBARuBT9VnSmZmNhCKiEaPobBSqRTlcrnRwzAzG1EkbYiIUq12/k1lMzMDHAhmZpY4EMzMDHAgmJlZ4kAwMzPAgWBmZokDwczMAAeCmZklDgQzMwMcCGZmljgQzMwMcCCYmVniQDAzM8CBYGZmiQPBzMwAB4KZmSUOBDMzAwoGgqT5krZI6pC0tJc250raLGmTpJtz5d2SNqZHe658hqQHUp+3pvs1m5lZg9QMBElNwHLgDGAWsFDSrIo2M4ErgJMj4l3An+eqX42I2elxVq7868A1EXEs8Dxw4eCmYmZmg1HkCGEO0BER2yJiD7ASWFDR5mJgeUQ8DxART/fVoSQBHwRWpaIfAGf3Z+BmZlZfRQJhErA9t92ZyvKOA46TdL+kdZLm5+rGSSqn8p4P/aOAXRGxr48+zcxsGDXXsZ+ZwDxgMnCfpBMjYhcwLSJ2SDoauEfSw8ALRTuWtBhYDDB16tSBjW7vXmhuBmlg+5uZHQCKBMIOYEpue3Iqy+sEHoiIvcBjkraSBcT6iNgBEBHbJK0BTgL+DjhCUnM6SqjWJ2m/FcAKgFKpFEUn9iYf/SjceSeMGQNjx7710dJSvXw46g/yhV5mtn8oEgjrgZmSZpB9aLcB51W0uQNYCFwvaQLZEtI2SeOBVyJidyo/GfjvERGS7gXOITsncQHw47rMqJrzz4dSCXbv7v2xZ0/29dVXYdeu3ut374bu7vqNrbl5eEKpP3001+vA0cxGkpr/8yNin6QlwGqgCbguIjZJWgaUI6I91Z0uaTPQDVwWEc9K+vfA9yS9Rna+4i8jYnPq+nJgpaSrgAeB79d9dj3a2urbX3d334FRK3QGUv/ii33X79tXe9xFHXRQY46k+mrjJT+zIaeIga3CNEKpVIpyudzoYeyfXntt6EOpv/V79tRvflLjQ6myvqXFIWUjgqQNEVGq1c5rA6PFQQfBuHHZY38RUT1AhjKUah1J7d5d3zlWC4/hDKVq5T4vZQPkQLCh0/NT/dixjR7JGyKyq86G+kgp3+bll+G55/ru47XX6jfHMWOGNnQGUt/UVL/52ZBxINiBRco+qFpa4JBDGj2aN+zbN7ShVPnouXiirz7qefFEU1PjQ6naeSl7E78jZvuD5ubs8fa3N3okb+juHtpQqlb/0kt91+/dW7/59Vw80chQqmwzZkxDz0s5EMysuqYmOPjg7LG/qHbxxFBfTFHrSKreF0/0dl7qH/4Bjjmmfq9VhQPBzEaO/fXiib17h/5IahiC2YFgZjYY+fNShx7a6NEMiq9PMzMzwIFgZmaJA8HMzAAHgpmZJQ4EMzMDHAhmZpY4EMzMDHAgmJlZMqLuhyCpC/i3Ae4+AXimjsMZCTznA4PnPPoNdr7TIqK1VqMRFQiDIalc5AYRo4nnfGDwnEe/4Zqvl4zMzAxwIJiZWXIgBcKKRg+gATznA4PnPPoNy3wPmHMIZmbWtwPpCMHMzPrgQDAzM2AUBoKk+ZK2SOqQtLRK/VhJt6b6ByRNH/5R1leBOX9W0mZJD0m6W9K0RoyznmrNOdfuY5JC0oi+RLHIfCWdm77PmyTdPNxjrLcC/66nSrpX0oPp3/aZjRhnPUm6TtLTkh7ppV6Svpnek4ckvaeuA4iIUfMAmoBfAUcDLcAvgVkVbT4DfDc9bwNubfS4h2HOHwDelp5/+kCYc2p3KHAfsA4oNXrcQ/w9ngk8CIxP2+9o9LiHYc4rgE+n57OAxxs97jrM+4+A9wCP9FJ/JnAXIOB9wAP1fP3RdoQwB+iIiG0RsQdYCSyoaLMA+EF6vgo4RZKGcYz1VnPOEXFvRLySNtcBk4d5jPVW5PsM8BXg68Bvh3NwQ6DIfC8GlkfE8wAR8fQwj7Heisw5gMPS88OBJ4dxfEMiIu4DnuujyQLgxsisA46QNLFerz/aAmESsD233ZnKqraJiH3AC8BRwzK6oVFkznkXkv2EMZLVnHM6lJ4SEXcO58CGSJHv8XHAcZLul7RO0vxhG93QKDLnK4HzJXUCPwEuGZ6hNVR//7/3S3O9OrL9n6TzgRLwx40ey1CSdBDwDWBRg4cynJrJlo3mkR0B3ifpxIjY1dBRDa2FwA0R8T8kzQVukvTuiHit0QMbqUbbEcIOYEpue3Iqq9pGUjPZoeazwzK6oVFkzkg6FfgCcFZE7B6msQ2VWnM+FHg3sEbS42Rrre0j+MRyke9xJ9AeEXsj4jFgK1lAjFRF5nwhcBtARKwFxpH9EbjRrND/94EabYGwHpgpaYakFrKTxu0VbdqBC9Lzc4B7Ip2tGaFqzlnSScD3yMJgpK8tQ405R8QLETEhIqZHxHSy8yZnRUS5McMdtCL/ru8gOzpA0gSyJaRtwznIOisy5yeAUwAknUAWCF3DOsrh1w58Il1t9D7ghYjYWa/OR9WSUUTsk7QEWE12lcJ1EbFJ0jKgHBHtwPfJDi07yE7etDVuxINXcM5/BRwC/G06f/5ERJzVsEEPUsE5jxoF57saOF3SZqAbuCwiRuyRb8E5fw64VtKlZCeYF43wH+6QdAtZsE9I50a+DIwBiIjvkp0rORPoAF4B/rSurz/C3z8zM6uT0bZkZGZmA+RAMDMzwIFgZmaJA8HMzAAHgpmZJQ4EMzMDHAhmZpb8f6qwbsR6QxmUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e42192b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e4277630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graficas Fine Tunning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a063c4a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e5d5a0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graficas Transferencia de Aprendizaje\n",
    "print('Graficas Transferencia de aprendizaje de Inception v3 a nuestro Clasificador')\n",
    "plot_training(history_tl)\n",
    "#Graficas Fine Tunning\n",
    "print('Graficas Fine Tunning')\n",
    "plot_training(history_ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e5d0a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f19e5dbd3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Mostramos otros graficos \n",
    "acc = history_ft.history['acc']\n",
    "val_acc = history_ft.history['val_acc']\n",
    "loss = history_ft.history['loss']\n",
    "val_loss = history_ft.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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.11651783064007759, 0.9625]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evluamos datos generados del conjunto de datos test\n",
    "#test_generator = testn_datagen.flow_from_directory(\n",
    "#    test_dir,   #debemos indicar el directorio de los datos de test\n",
    "#    target_size = (IM_WIDTH, IM_HEIGHT),\n",
    "#    batch_size = batch_size, #40 x 50 epocas = 2000\n",
    "#    class_mode = 'categorical')\n",
    "\n",
    "model.evaluate_generator(test_generator, steps = 5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}