diff --git a/GPS_Panel/Classifier/.gitignore b/GPS_Panel/Classifier/.gitignore
new file mode 100644
index 0000000..3ebbdde
--- /dev/null
+++ b/GPS_Panel/Classifier/.gitignore
@@ -0,0 +1,6 @@
+
+dataset_panel/
+dataset_prueba_1/
+model/
+preview/
+
diff --git a/GPS_Panel/Classifier/.ipynb_checkpoints/Train_Classifier_AllDeep-checkpoint.ipynb b/GPS_Panel/Classifier/.ipynb_checkpoints/Train_Classifier_AllDeep-checkpoint.ipynb
new file mode 100644
index 0000000..f398615
--- /dev/null
+++ b/GPS_Panel/Classifier/.ipynb_checkpoints/Train_Classifier_AllDeep-checkpoint.ipynb
@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the necessary packages\n",
+    "from imutils import paths\n",
+    "import numpy as np\n",
+    "import argparse\n",
+    "import imutils\n",
+    "import pickle\n",
+    "import cv2\n",
+    "import os\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.svm import SVC\n",
+    "from imutils.video import VideoStream\n",
+    "from imutils.video import FPS\n",
+    "import time\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.layers import *\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Set the directory you want to start from\n",
+    "rootDir = '.'\n",
+    "for dirName, subdirList, fileList in os.walk(rootDir):\n",
+    "    print('Found directory: %s' % dirName)\n",
+    "    for fname in fileList:\n",
+    "        print('\\t%s' % fname)\n",
+    "        \n",
+    "\n",
+    "# load our serialized face detector from disk\n",
+    "# fix seed for reproducible results (only works on CPU, not GPU)\n",
+    "seed = 9\n",
+    "np.random.seed(seed=seed)\n",
+    "tf.random.set_seed(seed=seed)\n",
+    "\n",
+    "# hyper parameters for model\n",
+    "nb_classes = 10  # number of classes\n",
+    "based_model_last_block_layer_number = 126  # value is based on based model selected.\n",
+    "img_width, img_height = 80, 80  # change based on the shape/structure of your images\n",
+    "batch_size = 8  # try 4, 8, 16, 32, 64, 128, 256 dependent on CPU/GPU memory capacity (powers of 2 values).\n",
+    "nb_epoch = 500  # number of iteration the algorithm gets trained.\n",
+    "learn_rate = 1e-4  # sgd learning rate\n",
+    "momentum = .9  # sgd momentum to avoid local minimum\n",
+    "transformation_ratio = .1  # how aggressive will be the data augmentation/transformation\n",
+    "patience = 10\n",
+    "\n",
+    "train_data_dir = './dataset_panel/'  # Inside, each class should have it's own folder\n",
+    "#validation_data_dir = './dataset/'  # each class should have it's own folder\n",
+    "model_path = './model/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "base_model = tf.keras.applications.Xception(input_shape=(img_width, img_height, 3), weights='imagenet', include_top=False)\n",
+    "# Top Model Block\n",
+    "x = base_model.output\n",
+    "x = GlobalAveragePooling2D()(x)\n",
+    "predictions = Dense(nb_classes, activation='softmax')(x)\n",
+    "\n",
+    "# add your top layer block to your base model\n",
+    "model = tf.keras.Model(base_model.input, predictions)\n",
+    "\n",
+    "\n",
+    "# # let's visualize layer names and layer indices to see how many layers/blocks to re-train\n",
+    "# # uncomment when choosing based_model_last_block_layer\n",
+    "# for i, layer in enumerate(model.layers):\n",
+    "#     print(i, layer.name)\n",
+    "\n",
+    "# first: train only the top layers (which were randomly initialized)\n",
+    "# i.e. freeze all layers of the based model that is already pre-trained.\n",
+    "for layer in base_model.layers:\n",
+    "    layer.trainable = False\n",
+    "\n",
+    "print(model.summary())\n",
+    "# Read Data and Augment it: Make sure to select augmentations that are appropriate to your images.\n",
+    "# To save augmentations un-comment save lines and add to your flow parameters.\n",
+    "train_datagen = ImageDataGenerator(rescale=1. / 255,\n",
+    "                                   rotation_range=transformation_ratio,\n",
+    "                                   shear_range=transformation_ratio,\n",
+    "                                   zoom_range=transformation_ratio,\n",
+    "                                   cval=transformation_ratio,\n",
+    "                                   horizontal_flip=True,\n",
+    "                                   vertical_flip=True,\n",
+    "                                   validation_split=0.1)\n",
+    "\n",
+    "\n",
+    "\n",
+    "train_generator = train_datagen.flow_from_directory(train_data_dir,\n",
+    "                                                    target_size=(img_height, img_width),\n",
+    "                                                    batch_size=batch_size,\n",
+    "                                                    class_mode='binary',\n",
+    "                                                    subset='training') # set as training data\n",
+    "\n",
+    "validation_generator = train_datagen.flow_from_directory(train_data_dir, # same directory as training data\n",
+    "                                                        target_size=(img_height, img_width),\n",
+    "                                                        batch_size=batch_size,\n",
+    "                                                        class_mode='binary',\n",
+    "                                                        subset='validation') # set as validation data\n",
+    "\n",
+    "model.compile(optimizer='nadam',\n",
+    "              loss='categorical_crossentropy',  # categorical_crossentropy if multi-class classifier\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "# save weights of best training epoch: monitor either val_loss or val_acc\n",
+    "\n",
+    "top_weights_path = os.path.join(os.path.abspath(model_path), 'top_model_weights.h5')\n",
+    "callbacks_list = [\n",
+    "    ModelCheckpoint(top_weights_path, monitor='val_accuracy', verbose=1, save_best_only=True),\n",
+    "    EarlyStopping(monitor='val_accuracy', patience= patience, verbose=0)\n",
+    "]\n",
+    "\n",
+    "# Train Simple CNN\n",
+    "hist = model.fit(train_generator,\n",
+    "                    steps_per_epoch= int(train_generator.samples/batch_size*.8),\n",
+    "                    epochs=int (nb_epoch / 5),\n",
+    "                    validation_data=validation_generator,\n",
+    "                    validation_steps = int(validation_generator.samples /batch_size*.8),\n",
+    "                    validation_freq=1,\n",
+    "                    callbacks=callbacks_list)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# verbose\n",
+    "print(\"\\nStarting to Fine Tune Model\\n\")\n",
+    "\n",
+    "# add the best weights from the train top model\n",
+    "# at this point we have the pre-train weights of the base model and the trained weight of the new/added top model\n",
+    "# we re-load model weights to ensure the best epoch is selected and not the last one.\n",
+    "model.load_weights(top_weights_path)\n",
+    "\n",
+    "print(model.summary())\n",
+    "# based_model_last_block_layer_number points to the layer in your model you want to train.\n",
+    "# For example if you want to train the last block of a 19 layer VGG16 model this should be 15\n",
+    "# If you want to train the last Two blocks of an Inception model it should be 172\n",
+    "# layers before this number will used the pre-trained weights, layers above and including this number\n",
+    "# will be re-trained based on the new data.\n",
+    "for layer in model.layers[:based_model_last_block_layer_number]:\n",
+    "    layer.trainable = False\n",
+    "for layer in model.layers[based_model_last_block_layer_number:]:\n",
+    "    layer.trainable = True\n",
+    "\n",
+    "# compile the model with a SGD/momentum optimizer\n",
+    "# and a very slow learning rate.\n",
+    "model.compile(optimizer='nadam',\n",
+    "              loss='categorical_crossentropy',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "# save weights of best training epoch: monitor either val_loss or val_acc\n",
+    "final_weights_path = os.path.join(os.path.abspath(model_path), 'model_weights.h5')\n",
+    "callbacks_list = [\n",
+    "    ModelCheckpoint(final_weights_path, monitor='val_accuracy', verbose=1, save_best_only=True),\n",
+    "    EarlyStopping(monitor='val_accuracy', patience=patience, verbose=0)\n",
+    "]\n",
+    "\n",
+    "# fine-tune the model\n",
+    "hist = model.fit(train_generator,\n",
+    "            steps_per_epoch=train_generator.samples/batch_size * .8,\n",
+    "            epochs=nb_epoch,\n",
+    "            validation_data=validation_generator,\n",
+    "            validation_steps = validation_generator.samples/batch_size*.8,\n",
+    "            callbacks=callbacks_list)\n",
+    "\n",
+    "# save model\n",
+    "model_json = model.to_json()\n",
+    "with open(os.path.join(os.path.abspath(model_path), 'model.json'), 'w') as json_file:\n",
+    "    json_file.write(model_json)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "json_file = open(os.path.join(os.path.abspath(model_path), 'model.json'), 'r')\n",
+    "loaded_model_json = json_file.read()\n",
+    "json_file.close()\n",
+    "loaded_model = tf.keras.models.model_from_json(loaded_model_json)\n",
+    "\n",
+    "\n",
+    "#Confution Matrix and Classification Report\n",
+    "Y_pred = model.predict_generator(validation_generator, validation_generator.samples // batch_size+1)\n",
+    "y_pred = np.argmax(Y_pred, axis=1)\n",
+    "print('Confusion Matrix')\n",
+    "print(confusion_matrix(validation_generator.classes, y_pred))\n",
+    "print('Classification Report')\n",
+    "target_names = train_generator.class_indices.keys()\n",
+    "print(classification_report(validation_generator.classes, y_pred, target_names=target_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/GPS_Panel/Classifier/.ipynb_checkpoints/Train_Classifier_DEEP_SVC-checkpoint.ipynb b/GPS_Panel/Classifier/.ipynb_checkpoints/Train_Classifier_DEEP_SVC-checkpoint.ipynb
new file mode 100644
index 0000000..d281190
--- /dev/null
+++ b/GPS_Panel/Classifier/.ipynb_checkpoints/Train_Classifier_DEEP_SVC-checkpoint.ipynb
@@ -0,0 +1,26075 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the necessary packages\n",
+    "from imutils import paths\n",
+    "import numpy as np\n",
+    "\n",
+    "import pickle\n",
+    "import cv2\n",
+    "import os\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "import time\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "\n",
+    "from sklearn.metrics import classification_report,plot_confusion_matrix,confusion_matrix\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
+    "\n",
+    "tf.keras.backend.clear_session()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found directory: .\n",
+      "\tTrain_Classifier_AllDeep.ipynb\n",
+      "\tTrain_Classifier_DEEP_SVC.ipynb\n",
+      "Found directory: ./.ipynb_checkpoints\n",
+      "\tTrain_Classifier_AllDeep-checkpoint.ipynb\n",
+      "\tTrain_Classifier_DEEP_SVC-checkpoint.ipynb\n",
+      "Found directory: ./dataset_prueba_1\n",
+      "Found directory: ./dataset_prueba_1/model_SVM\n",
+      "\tembeddings.pickle\n",
+      "\tmodel_SVM.h5\n",
+      "\tle.pickle\n",
+      "\trecognizer.pickle\n",
+      "Found directory: ./dataset_prueba_1/7- Soiling\n",
+      "\tSoling 19 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 6 El Aguila M1 2019.jpg\n",
+      "\tSoling 2 El Aguila M1 2019.jpg\n",
+      "\tSoling 3 El Aguila M1 2019.jpg\n",
+      "\tSoling 15 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 12 Pampa Camarones M2 2019.jpg\n",
+      "\tSoling 1 El Aguila M1 2019.jpg\n",
+      "\tSoling 20 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 5 El Aguila M1 2019.jpg\n",
+      "\tSoling 10 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 9 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 21 Cerro Dominador M1 2019.jpg\n",
+      "\tSoling 13 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 4 El Aguila M1 2019.jpg\n",
+      "\tSoling 18 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 16 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 7 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 14 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 8 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 22 Valle de la Luna M1 2019.jpg\n",
+      "\tSoling 17 Pampa Camarones M4 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/1- Una-Mas Celdas\n",
+      "\tCinco-Mas Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M1 2018.jpg\n",
+      "\tUna Celda 22 LLano de LLampos M4 2019.jpg\n",
+      "\tCinco-Mas 4 Cerro Demonidor M2 2019.jpg\n",
+      "\tUna Celda 10 Cerro Dominador M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 5 Llano de LLampos M5 2019.jpg\n",
+      "\tDos-Cuatro Celdas 10 LLano de LLampos M3 2019.jpg\n",
+      "\tUna Celda 17 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 7 Pampa Camarones M2 2019.jpg\n",
+      "\tCinco-Mas Celdas 19 Maria Elena M6 2018.jpg\n",
+      "\tUna Celda 14 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 15 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 7 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 17 Maria Elena M6 2018.jpg\n",
+      "\tDos-Cuatro Celdas 4 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 12 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 11 LLano de LLampos M5 2019.jpg\n",
+      "\tDos-Cuatro Celdas 2 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 8 Pampa Camarones M2 2019.jpg\n",
+      "\tCinco-Mas Celdas 20 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 11 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 18 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 15 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 3 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 21 Maria Elena M7 2018.jpg\n",
+      "\tUna Celda 2 - El Aguila M1 2019.jpg\n",
+      "\tUna Celda 3 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 14 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M2 2018.jpg\n",
+      "\tUna Celda 18 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 15 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 19 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 9 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 7 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 6 Maria Elena M1 2018.jpg\n",
+      "\tUna Celda 1 - Citrino.jpg\n",
+      "\tUna Celda 23 Valle de la Luna M1 2019.jpg\n",
+      "\tUna Celda 21 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 20 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 9 Pampa Camarones M4 2019.jpg\n",
+      "\tUna Celda 5 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 13 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 16 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 13 Maria Elena M1 2018.jpg\n",
+      "\tUna Celda 4 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 6 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 16 Maria Elena M6 2018.jpg\n",
+      "\tUna Celda 11 Cerro Dominador M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 8 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 3 LLano de LLampos M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 9 Pampa Camarones M3 2019.jpg\n",
+      "\tUna Celda 6 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 13 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 19 Maria Elena M2 2018.jpg\n",
+      "\tUna Celda 8 Pampa Camarones M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 16 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 18 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 20 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 2 LLano de LLampos M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 5 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 10 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 17 Maria Elena M2 2018.jpg\n",
+      "Found directory: ./dataset_prueba_1/4- Falla Diodos\n",
+      "\tUn Diodo 25  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 7 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 19  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 14  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 18  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 26  Lllano de LLampos M4 2019.jpg\n",
+      "\tDos Diodos 2  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 11  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 6 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 22  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 1 El Aguila M1 2019.jpg\n",
+      "\tUn Diodo 23  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 24  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 21  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 3 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 9  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 16  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 15  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 4 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 13  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 12  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 1 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 8  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 20  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 10  Pampa Camarones M2 2019.jpg\n",
+      "\tDos Diodos 1  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 17  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 5 Pampa Camarones M2 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/9- No es Panel\n",
+      "\tNo es panel 12 Casuto M1 2019.jpg\n",
+      "\tNo es panel 5 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 16 Llano de Llampos M1 2019.jpg\n",
+      "\tNo es panel 9 Casuto M1 2019.jpg\n",
+      "\tNo es panel 13 Casuto M1 2019.jpg\n",
+      "\tNo es panel 3 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 7 Casuto M1 2019.jpg\n",
+      "\tNo es panel 15 Casuto M1 2019.jpg\n",
+      "\tNo es panel 1 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 2 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 6 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 11 Casuto M1 2019.jpg\n",
+      "\tNo es panel 10 Casuto M1 2019.jpg\n",
+      "\tNo es panel 14 Casuto M1 2019.jpg\n",
+      "\tNo es panel 4 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 17 Cerro Dominador M1 2019.jpg\n",
+      "\tNo es panel 8 Casuto M1 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/8- Tracker dañado\n",
+      "\tTracker dañado 8 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 20 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 9 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 11 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 2 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 19 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 10 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 4 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 15 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 17 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 7 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 5 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 6 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 18 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 16 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 12 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 1 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 3 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/0- Normal\n",
+      "\tNormal 30 Valle de La Luna M1 2019.jpg\n",
+      "\tNormal 16 Tiltil M2 2019.jpg\n",
+      "\tNormal 3 Citrino.jpg\n",
+      "\tNormal 20 Casuto M1 2019.jpg\n",
+      "\tNormal 12 Tiltil M1 2019.jpg\n",
+      "\tNormal 2 Citrino.jpg\n",
+      "\tNormal 15 Tiltil M1 2019.jpg\n",
+      "\tNormal 11 Pampa Camarones M3 2019.jpg\n",
+      "\tNormal 1 Citrino.jpg\n",
+      "\tNormal 17 Casuto M1 2019.jpg\n",
+      "\tNormal 21 Casuto M1 2019.jpg\n",
+      "\tNormal 5 El Aguila M1 2019.jpg\n",
+      "\tNormal 14 Tiltil M1 2019.jpg\n",
+      "\tNormal 6 El Aguila M1 2019.jpg\n",
+      "\tNormal 25 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 10 Pampa Camarones M1 2019.jpg\n",
+      "\tNormal 9 El Aguila M1 2019.jpg\n",
+      "\tNormal 27 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 19 Casuto M1 2019.jpg\n",
+      "\tNormal 18 Casuto M1 2019.jpg\n",
+      "\tNormal 8 El Aguila M1 2019.jpg\n",
+      "\tNormal 28 Cerro Dominado M1 2019.jpg\n",
+      "\tNormal 22 Casuto M1 2019.jpg\n",
+      "\tNormal 4 Citrino.jpg\n",
+      "\tNormal 24 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 23 Casuto M1 2019.jpg\n",
+      "\tNormal 26 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 13 Tiltil M1 2019.jpg\n",
+      "\tNormal 7 El Aguila M1 2019.jpg\n",
+      "Found directory: ./dataset_catdog\n",
+      "Found directory: ./dataset_catdog/train\n",
+      "\tcat.2414.jpg\n",
+      "\tcat.6120.jpg\n",
+      "\tcat.8346.jpg\n",
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+      "\tdog.11425.jpg\n",
+      "\tdog.6299.jpg\n",
+      "\tdog.3415.jpg\n",
+      "\tdog.8006.jpg\n",
+      "\tdog.1397.jpg\n",
+      "\tdog.7001.jpg\n",
+      "\tdog.2927.jpg\n",
+      "\tdog.2833.jpg\n",
+      "\tdog.871.jpg\n",
+      "\tdog.7849.jpg\n",
+      "\tdog.8072.jpg\n",
+      "\tdog.10186.jpg\n",
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+      "\tdog.7808.jpg\n",
+      "\tdog.111.jpg\n",
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+      "\tdog.7904.jpg\n",
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+      "\tdog.2611.jpg\n",
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+      "\tdog.5757.jpg\n",
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+      "\tdog.3024.jpg\n",
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+      "\tdog.1207.jpg\n",
+      "\tdog.4153.jpg\n",
+      "\tdog.1428.jpg\n",
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+      "\tdog.3959.jpg\n",
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+      "\tdog.6784.jpg\n",
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+      "\tdog.4020.jpg\n",
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+      "\tdog.12069.jpg\n",
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+      "\tdog.3059.jpg\n",
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+      "\tdog.9973.jpg\n",
+      "\tdog.2088.jpg\n",
+      "\tdog.10896.jpg\n",
+      "\tdog.6539.jpg\n",
+      "\tdog.1277.jpg\n",
+      "\tdog.11121.jpg\n",
+      "\tdog.4356.jpg\n",
+      "\tdog.2545.jpg\n",
+      "\tdog.1688.jpg\n",
+      "\tdog.5144.jpg\n",
+      "\tdog.12056.jpg\n",
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+      "\tdog.5934.jpg\n",
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+      "\tdog.6586.jpg\n",
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+      "\tdog.11684.jpg\n",
+      "\tdog.302.jpg\n",
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+      "\tdog.4595.jpg\n",
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+      "\tdog.6329.jpg\n",
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+      "\tdog.12136.jpg\n",
+      "\tdog.6816.jpg\n",
+      "\tdog.799.jpg\n",
+      "\tdog.6398.jpg\n",
+      "\tdog.10815.jpg\n",
+      "\tdog.12010.jpg\n",
+      "\tdog.4269.jpg\n",
+      "\tdog.6655.jpg\n",
+      "\tdog.6898.jpg\n",
+      "\tdog.11852.jpg\n",
+      "\tdog.2675.jpg\n",
+      "\tdog.5310.jpg\n",
+      "\tdog.12016.jpg\n",
+      "\tdog.832.jpg\n",
+      "\tdog.4333.jpg\n",
+      "\tdog.4337.jpg\n",
+      "\tdog.3396.jpg\n",
+      "\tdog.9239.jpg\n",
+      "\tdog.8733.jpg\n",
+      "\tdog.11481.jpg\n",
+      "\tdog.7807.jpg\n",
+      "\tdog.546.jpg\n",
+      "\tdog.11055.jpg\n",
+      "\tdog.7260.jpg\n",
+      "\tdog.10339.jpg\n",
+      "\tdog.9606.jpg\n",
+      "\tdog.295.jpg\n",
+      "\tdog.1563.jpg\n",
+      "\tdog.11805.jpg\n",
+      "\tdog.4655.jpg\n",
+      "\tdog.9585.jpg\n",
+      "\tdog.11972.jpg\n",
+      "\tdog.6313.jpg\n",
+      "\tdog.5010.jpg\n",
+      "\tdog.479.jpg\n",
+      "\tdog.2158.jpg\n",
+      "\tdog.734.jpg\n",
+      "\tdog.3707.jpg\n",
+      "\tdog.6569.jpg\n",
+      "\tdog.3490.jpg\n",
+      "\tdog.12161.jpg\n",
+      "\tdog.1144.jpg\n",
+      "\tdog.4674.jpg\n",
+      "\tdog.829.jpg\n",
+      "\tdog.7518.jpg\n",
+      "\tdog.299.jpg\n",
+      "\tdog.5397.jpg\n",
+      "\tdog.11094.jpg\n",
+      "\tdog.9994.jpg\n",
+      "\tdog.4891.jpg\n",
+      "\tdog.9693.jpg\n",
+      "\tdog.11367.jpg\n",
+      "\tdog.8106.jpg\n",
+      "\tdog.4244.jpg\n",
+      "\tdog.534.jpg\n",
+      "\tdog.4178.jpg\n",
+      "\tdog.10256.jpg\n",
+      "\tdog.3175.jpg\n",
+      "\tdog.2365.jpg\n",
+      "\tdog.10919.jpg\n",
+      "\tdog.8267.jpg\n",
+      "\tdog.4646.jpg\n",
+      "\tdog.11457.jpg\n",
+      "\tdog.7537.jpg\n",
+      "\tdog.9476.jpg\n",
+      "\tdog.7911.jpg\n",
+      "\tdog.2413.jpg\n",
+      "\tdog.4517.jpg\n",
+      "\tdog.4863.jpg\n",
+      "\tdog.1190.jpg\n",
+      "\tdog.8223.jpg\n",
+      "\tdog.12270.jpg\n",
+      "\tdog.1322.jpg\n",
+      "\tdog.9113.jpg\n",
+      "\tdog.3318.jpg\n",
+      "\tdog.197.jpg\n",
+      "\tdog.8450.jpg\n",
+      "\tdog.3731.jpg\n",
+      "\tdog.7586.jpg\n",
+      "\tdog.10403.jpg\n",
+      "\tdog.3053.jpg\n",
+      "\tdog.1983.jpg\n",
+      "\tdog.1511.jpg\n",
+      "\tdog.2008.jpg\n",
+      "\tdog.9762.jpg\n",
+      "\tdog.72.jpg\n",
+      "\tdog.6078.jpg\n",
+      "\tdog.10987.jpg\n",
+      "\tdog.5352.jpg\n",
+      "\tdog.3828.jpg\n",
+      "\tdog.3388.jpg\n",
+      "\tdog.10960.jpg\n",
+      "\tdog.4928.jpg\n",
+      "\tdog.1392.jpg\n",
+      "\tdog.1501.jpg\n",
+      "\tdog.1140.jpg\n",
+      "\tdog.4921.jpg\n",
+      "\tdog.2314.jpg\n",
+      "\tdog.3527.jpg\n",
+      "\tdog.1230.jpg\n",
+      "\tdog.12123.jpg\n",
+      "\tdog.6664.jpg\n",
+      "\tdog.3924.jpg\n",
+      "\tdog.3456.jpg\n",
+      "\tdog.4471.jpg\n",
+      "\tdog.10751.jpg\n",
+      "\tdog.1419.jpg\n",
+      "\tdog.8386.jpg\n",
+      "\tdog.3390.jpg\n",
+      "\tdog.7533.jpg\n",
+      "\tdog.330.jpg\n",
+      "\tdog.231.jpg\n",
+      "\tdog.1311.jpg\n",
+      "\tdog.8615.jpg\n",
+      "\tdog.3758.jpg\n",
+      "\tdog.7063.jpg\n",
+      "\tdog.2122.jpg\n",
+      "\tdog.10511.jpg\n",
+      "\tdog.6269.jpg\n",
+      "\tdog.7980.jpg\n",
+      "\tdog.4587.jpg\n",
+      "\tdog.287.jpg\n",
+      "\tdog.8757.jpg\n",
+      "\tdog.12188.jpg\n",
+      "\tdog.4317.jpg\n",
+      "\tdog.1513.jpg\n",
+      "\tdog.7958.jpg\n",
+      "\tdog.8786.jpg\n",
+      "\tdog.5603.jpg\n",
+      "\tdog.5989.jpg\n",
+      "\tdog.28.jpg\n",
+      "\tdog.4685.jpg\n",
+      "\tdog.4070.jpg\n",
+      "\tdog.4462.jpg\n",
+      "\tdog.12287.jpg\n",
+      "\tdog.2803.jpg\n",
+      "\tdog.6015.jpg\n",
+      "\tdog.5893.jpg\n",
+      "\tdog.11156.jpg\n",
+      "\tdog.1455.jpg\n",
+      "\tdog.6807.jpg\n",
+      "\tdog.9068.jpg\n",
+      "Found directory: ./dataset_panel\n",
+      "\tembeddings.pickle\n",
+      "\tle.pickle\n",
+      "\trecognizer.pickle\n",
+      "Found directory: ./dataset_panel/2- Dos-Cuatro Celdas\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 10 LLano de LLampos M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 15 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 7 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 4 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 11 LLano de LLampos M5 2019.jpg\n",
+      "\tDos-Cuatro Celdas 2 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 8 Pampa Camarones M2 2019.jpg\n",
+      "\tDos-Cuatro Celdas 3 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 21 Maria Elena M7 2018.jpg\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 13 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 6 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 9 Pampa Camarones M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 19 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 16 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 18 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 20 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 5 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 17 Maria Elena M2 2018.jpg\n",
+      "Found directory: ./dataset_panel/6- Caja Conexion\n",
+      "\tCaja Conexion 2 Pampa Camarones M3 2019.jpg\n",
+      "\tCaja Conexion 3 Pampa Camarones M3 2019.jpg\n",
+      "\tCaja Conexion 1 El Aguila M1 2019.jpg\n",
+      "\tCaja Conexión 11 Pampa Camarones M1 2019.jpg\n",
+      "\tCaja Conexión 12 Llano de Llampos M2 2019.jpg\n",
+      "Found directory: ./dataset_panel/7- Soiling\n",
+      "\tSoling 19 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 6 El Aguila M1 2019.jpg\n",
+      "\tSoling 2 El Aguila M1 2019.jpg\n",
+      "\tSoling 3 El Aguila M1 2019.jpg\n",
+      "\tSoling 15 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 12 Pampa Camarones M2 2019.jpg\n",
+      "\tSoling 1 El Aguila M1 2019.jpg\n",
+      "\tSoling 20 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 5 El Aguila M1 2019.jpg\n",
+      "\tSoling 10 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 9 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 21 Cerro Dominador M1 2019.jpg\n",
+      "\tSoling 13 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 4 El Aguila M1 2019.jpg\n",
+      "\tSoling 18 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 16 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 7 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 14 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 8 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 22 Valle de la Luna M1 2019.jpg\n",
+      "\tSoling 17 Pampa Camarones M4 2019.jpg\n",
+      "Found directory: ./dataset_panel/4- Falla Diodos\n",
+      "\tUn Diodo 25  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 7 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 19  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 14  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 18  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 26  Lllano de LLampos M4 2019.jpg\n",
+      "\tDos Diodos 2  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 11  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 6 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 22  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 1 El Aguila M1 2019.jpg\n",
+      "\tUn Diodo 23  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 24  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 21  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 3 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 9  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 16  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 15  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 4 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 13  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 12  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 1 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 8  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 20  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 10  Pampa Camarones M2 2019.jpg\n",
+      "\tDos Diodos 1  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 17  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 5 Pampa Camarones M2 2019.jpg\n",
+      "Found directory: ./dataset_panel/3- Cinco-Mas Celdas\n",
+      "\tCinco-Mas Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas 4 Cerro Demonidor M2 2019.jpg\n",
+      "\tCinco-Mas Celdas 5 Llano de LLampos M5 2019.jpg\n",
+      "\tCinco-Mas Celdas 19 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 17 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 20 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 11 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 18 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 15 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 14 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 9 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 7 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 6 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 16 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 8 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 3 LLano de LLampos M3 2019.jpg\n",
+      "\tCinco-Mas Celdas 13 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 2 LLano de LLampos M3 2019.jpg\n",
+      "\tCinco-Mas Celdas 10 Maria Elena M1 2018.jpg\n",
+      "Found directory: ./dataset_panel/1- Una Celda\n",
+      "\tUna Celda 22 LLano de LLampos M4 2019.jpg\n",
+      "\tUna Celda 10 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 17 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 7 Pampa Camarones M2 2019.jpg\n",
+      "\tUna Celda 14 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 12 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 2 - El Aguila M1 2019.jpg\n",
+      "\tUna Celda 3 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 18 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 15 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 19 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 1 - Citrino.jpg\n",
+      "\tUna Celda 23 Valle de la Luna M1 2019.jpg\n",
+      "\tUna Celda 21 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 20 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 9 Pampa Camarones M4 2019.jpg\n",
+      "\tUna Celda 5 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 13 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 16 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 4 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 11 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 6 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 8 Pampa Camarones M3 2019.jpg\n",
+      "Found directory: ./dataset_panel/9- No es Panel\n",
+      "\tNo es panel 12 Casuto M1 2019.jpg\n",
+      "\tNo es panel 5 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 16 Llano de Llampos M1 2019.jpg\n",
+      "\tNo es panel 9 Casuto M1 2019.jpg\n",
+      "\tNo es panel 13 Casuto M1 2019.jpg\n",
+      "\tNo es panel 3 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 7 Casuto M1 2019.jpg\n",
+      "\tNo es panel 15 Casuto M1 2019.jpg\n",
+      "\tNo es panel 1 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 2 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 6 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 11 Casuto M1 2019.jpg\n",
+      "\tNo es panel 10 Casuto M1 2019.jpg\n",
+      "\tNo es panel 14 Casuto M1 2019.jpg\n",
+      "\tNo es panel 4 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 17 Cerro Dominador M1 2019.jpg\n",
+      "\tNo es panel 8 Casuto M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/5- Panel Desconectado\n",
+      "\tPanel Desconectado 24 Llano de LLampos M3 2019.jpg\n",
+      "\tPanel Desconectado 6 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 19 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 9 Pampa Camarones M2 2019.jpg\n",
+      "\tPanel Desconectado 12 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 10 Pampa Camarones M2 2019.jpg\n",
+      "\tPanel Desconectado 21 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 4 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 25 Llano de LLampos M3 2019.jpg\n",
+      "\tPanel Desconectado 23 Llano de LLampos M2 2019.jpg\n",
+      "\tPanel Desconectado 20 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 11 Pampa Camarones M2 2019.jpg\n",
+      "\tPanel Desconectado 15 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 1 El Aguila M2 2019.jpg\n",
+      "\tPanel Desconectado 16 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 14 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 18 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 22 Tiltil M2 2019.jpg\n",
+      "\tPanel Desconectado 13 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 17 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 3 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 5 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 7 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 2 El Aguila M2 2019.jpg\n",
+      "\tPanel Desconectado 8 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/8- Tracker dañado\n",
+      "\tTracker dañado 8 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 20 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 9 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 11 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 2 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 19 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 10 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 4 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 15 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 17 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 7 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 5 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 6 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 18 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 16 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 12 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 1 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 3 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/10- Reflejos\n",
+      "\tReflejo 3 Citrino.jpg\n",
+      "\tReflejo 10 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 4 Citrino.jpg\n",
+      "\tReflejo 11 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 23 Maria Elena M3 2018.jpg\n",
+      "\tReflejo 22 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 18 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 5 El Aguila M1 2019.jpg\n",
+      "\tReflejo 21 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 7 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 2 Citrino.jpg\n",
+      "\tReflejo 16 Pampa Camarones M2 2019.jpg\n",
+      "\tReflejo 6 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 8 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 19 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 15 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 9 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 13 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 17 Pampa Camarones M2 2019.jpg\n",
+      "\tReflejo 1 Citrino.jpg\n",
+      "\tReflejo 14 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 20 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 12 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/0- Normal\n",
+      "\tNormal 30 Valle de La Luna M1 2019.jpg\n",
+      "\tNormal 16 Tiltil M2 2019.jpg\n",
+      "\tNormal 3 Citrino.jpg\n",
+      "\tNormal 20 Casuto M1 2019.jpg\n",
+      "\tNormal 12 Tiltil M1 2019.jpg\n",
+      "\tNormal 2 Citrino.jpg\n",
+      "\tNormal 15 Tiltil M1 2019.jpg\n",
+      "\tNormal 11 Pampa Camarones M3 2019.jpg\n",
+      "\tNormal 1 Citrino.jpg\n",
+      "\tNormal 17 Casuto M1 2019.jpg\n",
+      "\tNormal 21 Casuto M1 2019.jpg\n",
+      "\tNormal 5 El Aguila M1 2019.jpg\n",
+      "\tNormal 14 Tiltil M1 2019.jpg\n",
+      "\tNormal 6 El Aguila M1 2019.jpg\n",
+      "\tNormal 25 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 10 Pampa Camarones M1 2019.jpg\n",
+      "\tNormal 9 El Aguila M1 2019.jpg\n",
+      "\tNormal 27 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 19 Casuto M1 2019.jpg\n",
+      "\tNormal 18 Casuto M1 2019.jpg\n",
+      "\tNormal 8 El Aguila M1 2019.jpg\n",
+      "\tNormal 28 Cerro Dominado M1 2019.jpg\n",
+      "\tNormal 22 Casuto M1 2019.jpg\n",
+      "\tNormal 4 Citrino.jpg\n",
+      "\tNormal 24 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 23 Casuto M1 2019.jpg\n",
+      "\tNormal 26 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 13 Tiltil M1 2019.jpg\n",
+      "\tNormal 7 El Aguila M1 2019.jpg\n",
+      "Found directory: ./model\n",
+      "\tmodel.json\n",
+      "\ttop_model_weights.h5\n",
+      "\tmodel_weights.h5\n",
+      "Found directory: ./preview\n"
+     ]
+    }
+   ],
+   "source": [
+    "img_width, img_height = 224, 224\n",
+    "#base_model = tf.keras.applications.MobileNetV2(input_shape=(img_width, img_height, 3), weights='imagenet', include_top=False)\n",
+    "#img_width, img_height =  \n",
+    "#base_model = tf.keras.applications.NASNetMobile(input_shape=(img_width, img_height, 3), weights='imagenet', include_top=False)\n",
+    "base_model = tf.keras.applications.Xception(input_shape=(img_width, img_height, 3), weights='imagenet', include_top=False)\n",
+    "\n",
+    "#\n",
+    "\n",
+    "# Set the directory you want to start from\n",
+    "rootDir = '.'\n",
+    "for dirName, subdirList, fileList in os.walk(rootDir):\n",
+    "    print('Found directory: %s' % dirName)\n",
+    "    for fname in fileList:\n",
+    "        print('\\t%s' % fname)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INFO] processing image 1/180\n",
+      "[INFO] processing image 2/180\n",
+      "[INFO] processing image 3/180\n",
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+      "[INFO] processing image 180/180\n",
+      "[INFO] serializing 720 encodings...\n"
+     ]
+    }
+   ],
+   "source": [
+    "path_dataset = \"./dataset_prueba_1/\"\n",
+    "embeddings_output = path_dataset + \"model_SVM/embeddings.pickle\"\n",
+    "imagePaths = list(paths.list_images(path_dataset))\n",
+    "# initialize our lists of extracted facial embeddings and\n",
+    "# corresponding people names\n",
+    "knownEmbeddings = []\n",
+    "knownNames = []\n",
+    "\n",
+    "# initialize the total number of faces processed\n",
+    "total = 0\n",
+    "\n",
+    "# loop over the image paths\n",
+    "for (i, imagePath) in enumerate(imagePaths):\n",
+    "    print(\"[INFO] processing image {}/{}\".format(i + 1,\n",
+    "    len(imagePaths)))\n",
+    "    name = imagePath.split(os.path.sep)[-2]\n",
+    "    Im = cv2.resize(cv2.imread(imagePath), (img_width, img_height))\n",
+    "    for im in [Im, cv2.flip(Im, 0), cv2.flip(Im, 1), cv2.flip(Im, -1)]:\n",
+    "        vec = base_model.predict(tf.keras.backend.expand_dims(im,0))\n",
+    "        # add the name of the person + corresponding face\n",
+    "        # embedding to their respective lists\n",
+    "        knownNames.append(name)\n",
+    "        knownEmbeddings.append(vec.flatten())\n",
+    "        total += 1\n",
+    "        \n",
+    "print(\"[INFO] serializing {} encodings...\".format(total))\n",
+    "data = {\"embeddings\": knownEmbeddings, \"names\": knownNames}\n",
+    "f = open(embeddings_output, \"wb\")\n",
+    "f.write(pickle.dumps(data))\n",
+    "f.close()           "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INFO] loading face embeddings...\n",
+      "[INFO] encoding labels...\n",
+      "[INFO] training model...\n",
+      "Confusion matrix, without normalization\n",
+      "[[13  2  2  2  0  0]\n",
+      " [ 1 41  3  0  0  0]\n",
+      " [ 2  2 19  0  0  0]\n",
+      " [ 3  4  1 11  0  0]\n",
+      " [ 0  0  0  0 17  0]\n",
+      " [ 0  3  0  1  2 15]]\n",
+      "Normalized confusion matrix\n",
+      "[[0.68421053 0.10526316 0.10526316 0.10526316 0.         0.        ]\n",
+      " [0.02222222 0.91111111 0.06666667 0.         0.         0.        ]\n",
+      " [0.08695652 0.08695652 0.82608696 0.         0.         0.        ]\n",
+      " [0.15789474 0.21052632 0.05263158 0.57894737 0.         0.        ]\n",
+      " [0.         0.         0.         0.         1.         0.        ]\n",
+      " [0.         0.14285714 0.         0.04761905 0.0952381  0.71428571]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification Report\n",
+      "                   precision    recall  f1-score   support\n",
+      "\n",
+      "        0- Normal       0.68      0.68      0.68        19\n",
+      "1- Una-Mas Celdas       0.79      0.91      0.85        45\n",
+      "  4- Falla Diodos       0.76      0.83      0.79        23\n",
+      "       7- Soiling       0.79      0.58      0.67        19\n",
+      "8- Tracker dañado       0.89      1.00      0.94        17\n",
+      "   9- No es Panel       1.00      0.71      0.83        21\n",
+      "\n",
+      "         accuracy                           0.81       144\n",
+      "        macro avg       0.82      0.79      0.79       144\n",
+      "     weighted avg       0.81      0.81      0.80       144\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_recognizer = path_dataset + \"model_SVM/recognizer.pickle\"\n",
+    "output_label = path_dataset + \"model_SVM/le.pickle\"\n",
+    "embeddings_output = path_dataset + \"model_SVM/embeddings.pickle\"\n",
+    "\n",
+    "# load the face embeddings\n",
+    "print(\"[INFO] loading face embeddings...\")\n",
+    "data = pickle.loads(open(embeddings_output, \"rb\").read())\n",
+    "# encode the labels\n",
+    "print(\"[INFO] encoding labels...\")\n",
+    "le = LabelEncoder()\n",
+    "labels = le.fit_transform(data[\"names\"])\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(data[\"embeddings\"], labels, random_state=0, test_size=0.2)\n",
+    "\n",
+    "# train the model used to accept the 128-d embeddings of the face and\n",
+    "# then produce the actual face recognition\n",
+    "print(\"[INFO] training model...\")\n",
+    "classifier = SVC(C=2.0, kernel=\"linear\", probability=True, class_weight = 'balanced')\n",
+    "classifier.fit(X_train, y_train)\n",
+    "# write the actual face recognition model to disk\n",
+    "f = open(output_recognizer, \"wb\")\n",
+    "f.write(pickle.dumps(classifier))\n",
+    "f.close()\n",
+    "# write the label encoder to disk\n",
+    "f = open(output_label, \"wb\")\n",
+    "f.write(pickle.dumps(le))\n",
+    "f.close()\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Plot non-normalized confusion matrix\n",
+    "titles_options = [(\"Confusion matrix, without normalization\", None),\n",
+    "                  (\"Normalized confusion matrix\", 'true')]\n",
+    "for title, normalize in titles_options:\n",
+    "    disp = plot_confusion_matrix(classifier,X_test, y_test,\n",
+    "                                 display_labels=np.unique(data[\"names\"]),\n",
+    "                                 cmap=plt.cm.Blues,\n",
+    "                                 normalize=normalize)\n",
+    "    disp.ax_.set_title(title)\n",
+    "\n",
+    "    print(title)\n",
+    "    print(disp.confusion_matrix)\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "y_pred = classifier.predict(X_test)\n",
+    "print('Classification Report')\n",
+    "target_names = np.unique(data[\"names\"])\n",
+    "print(classification_report(y_test, y_pred, target_names=target_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion matrix, without normalization\n",
+      "[[12  1  1  5  0  0]\n",
+      " [ 1 36  4  4  0  0]\n",
+      " [ 1  1 20  1  0  0]\n",
+      " [ 4  2  0 13  0  0]\n",
+      " [ 0  0  0  0 17  0]\n",
+      " [ 0  4  0  0  3 14]]\n",
+      "Normalized confusion matrix\n",
+      "[[0.63157895 0.05263158 0.05263158 0.26315789 0.         0.        ]\n",
+      " [0.02222222 0.8        0.08888889 0.08888889 0.         0.        ]\n",
+      " [0.04347826 0.04347826 0.86956522 0.04347826 0.         0.        ]\n",
+      " [0.21052632 0.10526316 0.         0.68421053 0.         0.        ]\n",
+      " [0.         0.         0.         0.         1.         0.        ]\n",
+      " [0.         0.19047619 0.         0.         0.14285714 0.66666667]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification Report\n",
+      "                   precision    recall  f1-score   support\n",
+      "\n",
+      "        0- Normal       0.67      0.63      0.65        19\n",
+      "1- Una-Mas Celdas       0.82      0.80      0.81        45\n",
+      "  4- Falla Diodos       0.80      0.87      0.83        23\n",
+      "       7- Soiling       0.57      0.68      0.62        19\n",
+      "8- Tracker dañado       0.85      1.00      0.92        17\n",
+      "   9- No es Panel       1.00      0.67      0.80        21\n",
+      "\n",
+      "         accuracy                           0.78       144\n",
+      "        macro avg       0.78      0.78      0.77       144\n",
+      "     weighted avg       0.79      0.78      0.78       144\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "KNN = KNeighborsClassifier(n_neighbors=1)\n",
+    "KNN.fit(X_train, y_train)\n",
+    "\n",
+    "# Plot non-normalized confusion matrix\n",
+    "titles_options = [(\"Confusion matrix, without normalization\", None),\n",
+    "                  (\"Normalized confusion matrix\", 'true')]\n",
+    "for title, normalize in titles_options:\n",
+    "    disp = plot_confusion_matrix(KNN,X_test, y_test,\n",
+    "                                 display_labels=np.unique(data[\"names\"]),\n",
+    "                                 cmap=plt.cm.Blues,\n",
+    "                                 normalize=normalize)\n",
+    "    disp.ax_.set_title(title)\n",
+    "\n",
+    "    print(title)\n",
+    "    print(disp.confusion_matrix)\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "y_pred = KNN.predict(X_test)\n",
+    "print('Classification Report')\n",
+    "target_names = np.unique(data[\"names\"])\n",
+    "print(classification_report(y_test, y_pred, target_names=target_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "# load the actual face recognition model along with the label encoder\n",
+    "recognizer = pickle.loads(open(output_recognizer, \"rb\").read())\n",
+    "le = pickle.loads(open(output_label, \"rb\").read())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/GPS_Panel/Classifier/Train_Classifier_AllDeep.ipynb b/GPS_Panel/Classifier/Train_Classifier_AllDeep.ipynb
new file mode 100644
index 0000000..5d28fb5
--- /dev/null
+++ b/GPS_Panel/Classifier/Train_Classifier_AllDeep.ipynb
@@ -0,0 +1,26590 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the necessary packages\n",
+    "from imutils import paths\n",
+    "import numpy as np\n",
+    "import argparse\n",
+    "import imutils\n",
+    "import pickle\n",
+    "import cv2\n",
+    "import os\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.svm import SVC\n",
+    "from imutils.video import VideoStream\n",
+    "from imutils.video import FPS\n",
+    "import time\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.layers import *\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
+    "\n",
+    "tf.keras.backend.clear_session()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found directory: .\n",
+      "\tTrain_Classifier_AllDeep.ipynb\n",
+      "\tTrain_Classifier_DEEP_SVC.ipynb\n",
+      "Found directory: ./.ipynb_checkpoints\n",
+      "\tTrain_Classifier_AllDeep-checkpoint.ipynb\n",
+      "\tTrain_Classifier_DEEP_SVC-checkpoint.ipynb\n",
+      "Found directory: ./dataset_prueba_1\n",
+      "Found directory: ./dataset_prueba_1/model_SVM\n",
+      "\tembeddings.pickle\n",
+      "\tmodel_SVM.h5\n",
+      "\tle.pickle\n",
+      "\trecognizer.pickle\n",
+      "Found directory: ./dataset_prueba_1/7- Soiling\n",
+      "\tSoling 19 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 6 El Aguila M1 2019.jpg\n",
+      "\tSoling 2 El Aguila M1 2019.jpg\n",
+      "\tSoling 3 El Aguila M1 2019.jpg\n",
+      "\tSoling 15 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 12 Pampa Camarones M2 2019.jpg\n",
+      "\tSoling 1 El Aguila M1 2019.jpg\n",
+      "\tSoling 20 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 5 El Aguila M1 2019.jpg\n",
+      "\tSoling 10 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 9 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 21 Cerro Dominador M1 2019.jpg\n",
+      "\tSoling 13 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 4 El Aguila M1 2019.jpg\n",
+      "\tSoling 18 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 16 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 7 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 14 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 8 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 22 Valle de la Luna M1 2019.jpg\n",
+      "\tSoling 17 Pampa Camarones M4 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/1- Una-Mas Celdas\n",
+      "\tCinco-Mas Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M1 2018.jpg\n",
+      "\tUna Celda 22 LLano de LLampos M4 2019.jpg\n",
+      "\tCinco-Mas 4 Cerro Demonidor M2 2019.jpg\n",
+      "\tUna Celda 10 Cerro Dominador M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 5 Llano de LLampos M5 2019.jpg\n",
+      "\tDos-Cuatro Celdas 10 LLano de LLampos M3 2019.jpg\n",
+      "\tUna Celda 17 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 7 Pampa Camarones M2 2019.jpg\n",
+      "\tCinco-Mas Celdas 19 Maria Elena M6 2018.jpg\n",
+      "\tUna Celda 14 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 15 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 7 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 17 Maria Elena M6 2018.jpg\n",
+      "\tDos-Cuatro Celdas 4 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 12 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 11 LLano de LLampos M5 2019.jpg\n",
+      "\tDos-Cuatro Celdas 2 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 8 Pampa Camarones M2 2019.jpg\n",
+      "\tCinco-Mas Celdas 20 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 11 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 18 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 15 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 3 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 21 Maria Elena M7 2018.jpg\n",
+      "\tUna Celda 2 - El Aguila M1 2019.jpg\n",
+      "\tUna Celda 3 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 14 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M2 2018.jpg\n",
+      "\tUna Celda 18 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 15 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 19 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 9 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 7 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 6 Maria Elena M1 2018.jpg\n",
+      "\tUna Celda 1 - Citrino.jpg\n",
+      "\tUna Celda 23 Valle de la Luna M1 2019.jpg\n",
+      "\tUna Celda 21 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 20 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 9 Pampa Camarones M4 2019.jpg\n",
+      "\tUna Celda 5 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 13 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 16 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 13 Maria Elena M1 2018.jpg\n",
+      "\tUna Celda 4 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 6 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 16 Maria Elena M6 2018.jpg\n",
+      "\tUna Celda 11 Cerro Dominador M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 8 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 3 LLano de LLampos M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 9 Pampa Camarones M3 2019.jpg\n",
+      "\tUna Celda 6 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 13 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 19 Maria Elena M2 2018.jpg\n",
+      "\tUna Celda 8 Pampa Camarones M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 16 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 18 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 20 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 2 LLano de LLampos M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 5 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 10 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 17 Maria Elena M2 2018.jpg\n",
+      "Found directory: ./dataset_prueba_1/4- Falla Diodos\n",
+      "\tUn Diodo 25  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 7 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 19  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 14  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 18  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 26  Lllano de LLampos M4 2019.jpg\n",
+      "\tDos Diodos 2  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 11  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 6 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 22  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 1 El Aguila M1 2019.jpg\n",
+      "\tUn Diodo 23  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 24  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 21  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 3 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 9  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 16  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 15  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 4 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 13  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 12  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 1 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 8  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 20  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 10  Pampa Camarones M2 2019.jpg\n",
+      "\tDos Diodos 1  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 17  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 5 Pampa Camarones M2 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/9- No es Panel\n",
+      "\tNo es panel 12 Casuto M1 2019.jpg\n",
+      "\tNo es panel 5 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 16 Llano de Llampos M1 2019.jpg\n",
+      "\tNo es panel 9 Casuto M1 2019.jpg\n",
+      "\tNo es panel 13 Casuto M1 2019.jpg\n",
+      "\tNo es panel 3 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 7 Casuto M1 2019.jpg\n",
+      "\tNo es panel 15 Casuto M1 2019.jpg\n",
+      "\tNo es panel 1 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 2 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 6 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 11 Casuto M1 2019.jpg\n",
+      "\tNo es panel 10 Casuto M1 2019.jpg\n",
+      "\tNo es panel 14 Casuto M1 2019.jpg\n",
+      "\tNo es panel 4 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 17 Cerro Dominador M1 2019.jpg\n",
+      "\tNo es panel 8 Casuto M1 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/8- Tracker dañado\n",
+      "\tTracker dañado 8 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 20 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 9 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 11 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 2 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 19 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 10 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 4 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 15 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 17 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 7 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 5 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 6 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 18 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 16 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 12 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 1 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 3 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/0- Normal\n",
+      "\tNormal 30 Valle de La Luna M1 2019.jpg\n",
+      "\tNormal 16 Tiltil M2 2019.jpg\n",
+      "\tNormal 3 Citrino.jpg\n",
+      "\tNormal 20 Casuto M1 2019.jpg\n",
+      "\tNormal 12 Tiltil M1 2019.jpg\n",
+      "\tNormal 2 Citrino.jpg\n",
+      "\tNormal 15 Tiltil M1 2019.jpg\n",
+      "\tNormal 11 Pampa Camarones M3 2019.jpg\n",
+      "\tNormal 1 Citrino.jpg\n",
+      "\tNormal 17 Casuto M1 2019.jpg\n",
+      "\tNormal 21 Casuto M1 2019.jpg\n",
+      "\tNormal 5 El Aguila M1 2019.jpg\n",
+      "\tNormal 14 Tiltil M1 2019.jpg\n",
+      "\tNormal 6 El Aguila M1 2019.jpg\n",
+      "\tNormal 25 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 10 Pampa Camarones M1 2019.jpg\n",
+      "\tNormal 9 El Aguila M1 2019.jpg\n",
+      "\tNormal 27 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 19 Casuto M1 2019.jpg\n",
+      "\tNormal 18 Casuto M1 2019.jpg\n",
+      "\tNormal 8 El Aguila M1 2019.jpg\n",
+      "\tNormal 28 Cerro Dominado M1 2019.jpg\n",
+      "\tNormal 22 Casuto M1 2019.jpg\n",
+      "\tNormal 4 Citrino.jpg\n",
+      "\tNormal 24 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 23 Casuto M1 2019.jpg\n",
+      "\tNormal 26 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 13 Tiltil M1 2019.jpg\n",
+      "\tNormal 7 El Aguila M1 2019.jpg\n",
+      "Found directory: ./dataset_catdog\n",
+      "Found directory: ./dataset_catdog/train\n",
+      "\tcat.2414.jpg\n",
+      "\tcat.6120.jpg\n",
+      "\tcat.8346.jpg\n",
+      "\tcat.6718.jpg\n",
+      "\tcat.9209.jpg\n",
+      "\tcat.2699.jpg\n",
+      "\tcat.1814.jpg\n",
+      "\tcat.2472.jpg\n",
+      "\tcat.9271.jpg\n",
+      "\tcat.5866.jpg\n",
+      "\tcat.55.jpg\n",
+      "\tcat.4382.jpg\n",
+      "\tcat.3649.jpg\n",
+      "\tcat.8661.jpg\n",
+      "\tcat.4525.jpg\n",
+      "\tcat.6162.jpg\n",
+      "\tcat.689.jpg\n",
+      "\tcat.8321.jpg\n",
+      "\tcat.11710.jpg\n",
+      "\tcat.7951.jpg\n",
+      "\tcat.8011.jpg\n",
+      "\tcat.2297.jpg\n",
+      "\tcat.4187.jpg\n",
+      "\tcat.268.jpg\n",
+      "\tcat.12015.jpg\n",
+      "\tcat.4449.jpg\n",
+      "\tcat.3808.jpg\n",
+      "\tcat.2971.jpg\n",
+      "\tcat.12217.jpg\n",
+      "\tcat.5753.jpg\n",
+      "\tcat.3950.jpg\n",
+      "\tcat.5132.jpg\n",
+      "\tcat.3786.jpg\n",
+      "\tcat.11662.jpg\n",
+      "\tcat.9685.jpg\n",
+      "\tcat.1068.jpg\n",
+      "\tcat.1717.jpg\n",
+      "\tcat.5256.jpg\n",
+      "\tcat.3040.jpg\n",
+      "\tcat.6080.jpg\n",
+      "\tcat.8411.jpg\n",
+      "\tcat.1190.jpg\n",
+      "\tcat.12414.jpg\n",
+      "\tcat.4720.jpg\n",
+      "\tcat.11929.jpg\n",
+      "\tcat.8915.jpg\n",
+      "\tcat.2170.jpg\n",
+      "\tcat.4857.jpg\n",
+      "\tcat.2312.jpg\n",
+      "\tcat.3928.jpg\n",
+      "\tcat.10757.jpg\n",
+      "\tcat.2400.jpg\n",
+      "\tcat.4264.jpg\n",
+      "\tcat.10033.jpg\n",
+      "\tcat.5450.jpg\n",
+      "\tcat.144.jpg\n",
+      "\tcat.10.jpg\n",
+      "\tcat.2984.jpg\n",
+      "\tcat.6299.jpg\n",
+      "\tcat.4620.jpg\n",
+      "\tcat.7448.jpg\n",
+      "\tcat.5180.jpg\n",
+      "\tcat.8305.jpg\n",
+      "\tcat.1563.jpg\n",
+      "\tcat.4375.jpg\n",
+      "\tcat.2358.jpg\n",
+      "\tcat.10055.jpg\n",
+      "\tcat.2270.jpg\n",
+      "\tcat.544.jpg\n",
+      "\tcat.6472.jpg\n",
+      "\tcat.6512.jpg\n",
+      "\tcat.9375.jpg\n",
+      "\tcat.7585.jpg\n",
+      "\tcat.7062.jpg\n",
+      "\tcat.3956.jpg\n",
+      "\tcat.384.jpg\n",
+      "\tcat.7499.jpg\n",
+      "\tcat.5544.jpg\n",
+      "\tcat.1445.jpg\n",
+      "\tcat.11277.jpg\n",
+      "\tcat.3336.jpg\n",
+      "\tcat.8702.jpg\n",
+      "\tcat.10202.jpg\n",
+      "\tcat.10122.jpg\n",
+      "\tcat.8009.jpg\n",
+      "\tcat.8465.jpg\n",
+      "\tcat.1083.jpg\n",
+      "\tcat.11966.jpg\n",
+      "\tcat.10627.jpg\n",
+      "\tcat.6393.jpg\n",
+      "\tcat.9705.jpg\n",
+      "\tcat.7335.jpg\n",
+      "\tcat.6119.jpg\n",
+      "\tcat.5896.jpg\n",
+      "\tcat.7025.jpg\n",
+      "\tcat.5989.jpg\n",
+      "\tcat.20.jpg\n",
+      "\tcat.12239.jpg\n",
+      "\tcat.10489.jpg\n",
+      "\tcat.8048.jpg\n",
+      "\tcat.1085.jpg\n",
+      "\tcat.10984.jpg\n",
+      "\tcat.1064.jpg\n",
+      "\tcat.10922.jpg\n",
+      "\tcat.1050.jpg\n",
+      "\tcat.3968.jpg\n",
+      "\tcat.7189.jpg\n",
+      "\tcat.3060.jpg\n",
+      "\tcat.9293.jpg\n",
+      "\tcat.3005.jpg\n",
+      "\tcat.11680.jpg\n",
+      "\tcat.1668.jpg\n",
+      "\tcat.2467.jpg\n",
+      "\tcat.7714.jpg\n",
+      "\tcat.12258.jpg\n",
+      "\tcat.6352.jpg\n",
+      "\tcat.3168.jpg\n",
+      "\tcat.3700.jpg\n",
+      "\tcat.8460.jpg\n",
+      "\tcat.7440.jpg\n",
+      "\tcat.3036.jpg\n",
+      "\tcat.7207.jpg\n",
+      "\tcat.10602.jpg\n",
+      "\tcat.8688.jpg\n",
+      "\tcat.6112.jpg\n",
+      "\tcat.9637.jpg\n",
+      "\tcat.12461.jpg\n",
+      "\tcat.2688.jpg\n",
+      "\tcat.9570.jpg\n",
+      "\tcat.2948.jpg\n",
+      "\tcat.5434.jpg\n",
+      "\tcat.3901.jpg\n",
+      "\tcat.11626.jpg\n",
+      "\tcat.6117.jpg\n",
+      "\tcat.7639.jpg\n",
+      "\tcat.7188.jpg\n",
+      "\tcat.2659.jpg\n",
+      "\tcat.11525.jpg\n",
+      "\tcat.5116.jpg\n",
+      "\tcat.10468.jpg\n",
+      "\tcat.5249.jpg\n",
+      "\tcat.2575.jpg\n",
+      "\tcat.2747.jpg\n",
+      "\tcat.3142.jpg\n",
+      "\tcat.9219.jpg\n",
+      "\tcat.5744.jpg\n",
+      "\tcat.11772.jpg\n",
+      "\tcat.337.jpg\n",
+      "\tcat.6685.jpg\n",
+      "\tcat.6650.jpg\n",
+      "\tcat.1674.jpg\n",
+      "\tcat.5986.jpg\n",
+      "\tcat.11994.jpg\n",
+      "\tcat.4807.jpg\n",
+      "\tcat.4329.jpg\n",
+      "\tcat.12089.jpg\n",
+      "\tcat.5514.jpg\n",
+      "\tcat.7892.jpg\n",
+      "\tcat.9272.jpg\n",
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+      "\tdog.4405.jpg\n",
+      "\tdog.538.jpg\n",
+      "\tdog.3024.jpg\n",
+      "\tdog.1508.jpg\n",
+      "\tdog.1947.jpg\n",
+      "\tdog.1207.jpg\n",
+      "\tdog.4153.jpg\n",
+      "\tdog.1428.jpg\n",
+      "\tdog.317.jpg\n",
+      "\tdog.3309.jpg\n",
+      "\tdog.1210.jpg\n",
+      "\tdog.3959.jpg\n",
+      "\tdog.11108.jpg\n",
+      "\tdog.6784.jpg\n",
+      "\tdog.10236.jpg\n",
+      "\tdog.1939.jpg\n",
+      "\tdog.9212.jpg\n",
+      "\tdog.9451.jpg\n",
+      "\tdog.5281.jpg\n",
+      "\tdog.9492.jpg\n",
+      "\tdog.12075.jpg\n",
+      "\tdog.3732.jpg\n",
+      "\tdog.9249.jpg\n",
+      "\tdog.8145.jpg\n",
+      "\tdog.11460.jpg\n",
+      "\tdog.6028.jpg\n",
+      "\tdog.4306.jpg\n",
+      "\tdog.1535.jpg\n",
+      "\tdog.3788.jpg\n",
+      "\tdog.11280.jpg\n",
+      "\tdog.3929.jpg\n",
+      "\tdog.2915.jpg\n",
+      "\tdog.5480.jpg\n",
+      "\tdog.9253.jpg\n",
+      "\tdog.11958.jpg\n",
+      "\tdog.9392.jpg\n",
+      "\tdog.11641.jpg\n",
+      "\tdog.1765.jpg\n",
+      "\tdog.1566.jpg\n",
+      "\tdog.6260.jpg\n",
+      "\tdog.11288.jpg\n",
+      "\tdog.2345.jpg\n",
+      "\tdog.4578.jpg\n",
+      "\tdog.11900.jpg\n",
+      "\tdog.6066.jpg\n",
+      "\tdog.3821.jpg\n",
+      "\tdog.9889.jpg\n",
+      "\tdog.11472.jpg\n",
+      "\tdog.11406.jpg\n",
+      "\tdog.10619.jpg\n",
+      "\tdog.4052.jpg\n",
+      "\tdog.1153.jpg\n",
+      "\tdog.6248.jpg\n",
+      "\tdog.11274.jpg\n",
+      "\tdog.9118.jpg\n",
+      "\tdog.1139.jpg\n",
+      "\tdog.1492.jpg\n",
+      "\tdog.1633.jpg\n",
+      "\tdog.2730.jpg\n",
+      "\tdog.4736.jpg\n",
+      "\tdog.4020.jpg\n",
+      "\tdog.11902.jpg\n",
+      "\tdog.3604.jpg\n",
+      "\tdog.3536.jpg\n",
+      "\tdog.12044.jpg\n",
+      "\tdog.6244.jpg\n",
+      "\tdog.18.jpg\n",
+      "\tdog.3494.jpg\n",
+      "\tdog.5987.jpg\n",
+      "\tdog.9687.jpg\n",
+      "\tdog.4148.jpg\n",
+      "\tdog.6152.jpg\n",
+      "\tdog.11681.jpg\n",
+      "\tdog.3899.jpg\n",
+      "\tdog.6255.jpg\n",
+      "\tdog.3021.jpg\n",
+      "\tdog.2473.jpg\n",
+      "\tdog.10156.jpg\n",
+      "\tdog.3517.jpg\n",
+      "\tdog.517.jpg\n",
+      "\tdog.1101.jpg\n",
+      "\tdog.4194.jpg\n",
+      "\tdog.6427.jpg\n",
+      "\tdog.8888.jpg\n",
+      "\tdog.8769.jpg\n",
+      "\tdog.5433.jpg\n",
+      "\tdog.5554.jpg\n",
+      "\tdog.8412.jpg\n",
+      "\tdog.5616.jpg\n",
+      "\tdog.6492.jpg\n",
+      "\tdog.12069.jpg\n",
+      "\tdog.4842.jpg\n",
+      "\tdog.1002.jpg\n",
+      "\tdog.11509.jpg\n",
+      "\tdog.3059.jpg\n",
+      "\tdog.425.jpg\n",
+      "\tdog.9973.jpg\n",
+      "\tdog.2088.jpg\n",
+      "\tdog.10896.jpg\n",
+      "\tdog.6539.jpg\n",
+      "\tdog.1277.jpg\n",
+      "\tdog.11121.jpg\n",
+      "\tdog.4356.jpg\n",
+      "\tdog.2545.jpg\n",
+      "\tdog.1688.jpg\n",
+      "\tdog.5144.jpg\n",
+      "\tdog.12056.jpg\n",
+      "\tdog.5115.jpg\n",
+      "\tdog.2527.jpg\n",
+      "\tdog.5934.jpg\n",
+      "\tdog.3563.jpg\n",
+      "\tdog.5279.jpg\n",
+      "\tdog.83.jpg\n",
+      "\tdog.11412.jpg\n",
+      "\tdog.6586.jpg\n",
+      "\tdog.7798.jpg\n",
+      "\tdog.4797.jpg\n",
+      "\tdog.11684.jpg\n",
+      "\tdog.302.jpg\n",
+      "\tdog.8056.jpg\n",
+      "\tdog.4595.jpg\n",
+      "\tdog.12409.jpg\n",
+      "\tdog.6329.jpg\n",
+      "\tdog.2753.jpg\n",
+      "\tdog.8875.jpg\n",
+      "\tdog.12136.jpg\n",
+      "\tdog.6816.jpg\n",
+      "\tdog.799.jpg\n",
+      "\tdog.6398.jpg\n",
+      "\tdog.10815.jpg\n",
+      "\tdog.12010.jpg\n",
+      "\tdog.4269.jpg\n",
+      "\tdog.6655.jpg\n",
+      "\tdog.6898.jpg\n",
+      "\tdog.11852.jpg\n",
+      "\tdog.2675.jpg\n",
+      "\tdog.5310.jpg\n",
+      "\tdog.12016.jpg\n",
+      "\tdog.832.jpg\n",
+      "\tdog.4333.jpg\n",
+      "\tdog.4337.jpg\n",
+      "\tdog.3396.jpg\n",
+      "\tdog.9239.jpg\n",
+      "\tdog.8733.jpg\n",
+      "\tdog.11481.jpg\n",
+      "\tdog.7807.jpg\n",
+      "\tdog.546.jpg\n",
+      "\tdog.11055.jpg\n",
+      "\tdog.7260.jpg\n",
+      "\tdog.10339.jpg\n",
+      "\tdog.9606.jpg\n",
+      "\tdog.295.jpg\n",
+      "\tdog.1563.jpg\n",
+      "\tdog.11805.jpg\n",
+      "\tdog.4655.jpg\n",
+      "\tdog.9585.jpg\n",
+      "\tdog.11972.jpg\n",
+      "\tdog.6313.jpg\n",
+      "\tdog.5010.jpg\n",
+      "\tdog.479.jpg\n",
+      "\tdog.2158.jpg\n",
+      "\tdog.734.jpg\n",
+      "\tdog.3707.jpg\n",
+      "\tdog.6569.jpg\n",
+      "\tdog.3490.jpg\n",
+      "\tdog.12161.jpg\n",
+      "\tdog.1144.jpg\n",
+      "\tdog.4674.jpg\n",
+      "\tdog.829.jpg\n",
+      "\tdog.7518.jpg\n",
+      "\tdog.299.jpg\n",
+      "\tdog.5397.jpg\n",
+      "\tdog.11094.jpg\n",
+      "\tdog.9994.jpg\n",
+      "\tdog.4891.jpg\n",
+      "\tdog.9693.jpg\n",
+      "\tdog.11367.jpg\n",
+      "\tdog.8106.jpg\n",
+      "\tdog.4244.jpg\n",
+      "\tdog.534.jpg\n",
+      "\tdog.4178.jpg\n",
+      "\tdog.10256.jpg\n",
+      "\tdog.3175.jpg\n",
+      "\tdog.2365.jpg\n",
+      "\tdog.10919.jpg\n",
+      "\tdog.8267.jpg\n",
+      "\tdog.4646.jpg\n",
+      "\tdog.11457.jpg\n",
+      "\tdog.7537.jpg\n",
+      "\tdog.9476.jpg\n",
+      "\tdog.7911.jpg\n",
+      "\tdog.2413.jpg\n",
+      "\tdog.4517.jpg\n",
+      "\tdog.4863.jpg\n",
+      "\tdog.1190.jpg\n",
+      "\tdog.8223.jpg\n",
+      "\tdog.12270.jpg\n",
+      "\tdog.1322.jpg\n",
+      "\tdog.9113.jpg\n",
+      "\tdog.3318.jpg\n",
+      "\tdog.197.jpg\n",
+      "\tdog.8450.jpg\n",
+      "\tdog.3731.jpg\n",
+      "\tdog.7586.jpg\n",
+      "\tdog.10403.jpg\n",
+      "\tdog.3053.jpg\n",
+      "\tdog.1983.jpg\n",
+      "\tdog.1511.jpg\n",
+      "\tdog.2008.jpg\n",
+      "\tdog.9762.jpg\n",
+      "\tdog.72.jpg\n",
+      "\tdog.6078.jpg\n",
+      "\tdog.10987.jpg\n",
+      "\tdog.5352.jpg\n",
+      "\tdog.3828.jpg\n",
+      "\tdog.3388.jpg\n",
+      "\tdog.10960.jpg\n",
+      "\tdog.4928.jpg\n",
+      "\tdog.1392.jpg\n",
+      "\tdog.1501.jpg\n",
+      "\tdog.1140.jpg\n",
+      "\tdog.4921.jpg\n",
+      "\tdog.2314.jpg\n",
+      "\tdog.3527.jpg\n",
+      "\tdog.1230.jpg\n",
+      "\tdog.12123.jpg\n",
+      "\tdog.6664.jpg\n",
+      "\tdog.3924.jpg\n",
+      "\tdog.3456.jpg\n",
+      "\tdog.4471.jpg\n",
+      "\tdog.10751.jpg\n",
+      "\tdog.1419.jpg\n",
+      "\tdog.8386.jpg\n",
+      "\tdog.3390.jpg\n",
+      "\tdog.7533.jpg\n",
+      "\tdog.330.jpg\n",
+      "\tdog.231.jpg\n",
+      "\tdog.1311.jpg\n",
+      "\tdog.8615.jpg\n",
+      "\tdog.3758.jpg\n",
+      "\tdog.7063.jpg\n",
+      "\tdog.2122.jpg\n",
+      "\tdog.10511.jpg\n",
+      "\tdog.6269.jpg\n",
+      "\tdog.7980.jpg\n",
+      "\tdog.4587.jpg\n",
+      "\tdog.287.jpg\n",
+      "\tdog.8757.jpg\n",
+      "\tdog.12188.jpg\n",
+      "\tdog.4317.jpg\n",
+      "\tdog.1513.jpg\n",
+      "\tdog.7958.jpg\n",
+      "\tdog.8786.jpg\n",
+      "\tdog.5603.jpg\n",
+      "\tdog.5989.jpg\n",
+      "\tdog.28.jpg\n",
+      "\tdog.4685.jpg\n",
+      "\tdog.4070.jpg\n",
+      "\tdog.4462.jpg\n",
+      "\tdog.12287.jpg\n",
+      "\tdog.2803.jpg\n",
+      "\tdog.6015.jpg\n",
+      "\tdog.5893.jpg\n",
+      "\tdog.11156.jpg\n",
+      "\tdog.1455.jpg\n",
+      "\tdog.6807.jpg\n",
+      "\tdog.9068.jpg\n",
+      "Found directory: ./dataset_panel\n",
+      "\tembeddings.pickle\n",
+      "\tle.pickle\n",
+      "\trecognizer.pickle\n",
+      "Found directory: ./dataset_panel/2- Dos-Cuatro Celdas\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 10 LLano de LLampos M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 15 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 7 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 4 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 11 LLano de LLampos M5 2019.jpg\n",
+      "\tDos-Cuatro Celdas 2 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 8 Pampa Camarones M2 2019.jpg\n",
+      "\tDos-Cuatro Celdas 3 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 21 Maria Elena M7 2018.jpg\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 13 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 6 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 9 Pampa Camarones M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 19 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 16 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 18 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 20 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 5 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 17 Maria Elena M2 2018.jpg\n",
+      "Found directory: ./dataset_panel/6- Caja Conexion\n",
+      "\tCaja Conexion 2 Pampa Camarones M3 2019.jpg\n",
+      "\tCaja Conexion 3 Pampa Camarones M3 2019.jpg\n",
+      "\tCaja Conexion 1 El Aguila M1 2019.jpg\n",
+      "\tCaja Conexión 11 Pampa Camarones M1 2019.jpg\n",
+      "\tCaja Conexión 12 Llano de Llampos M2 2019.jpg\n",
+      "Found directory: ./dataset_panel/7- Soiling\n",
+      "\tSoling 19 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 6 El Aguila M1 2019.jpg\n",
+      "\tSoling 2 El Aguila M1 2019.jpg\n",
+      "\tSoling 3 El Aguila M1 2019.jpg\n",
+      "\tSoling 15 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 12 Pampa Camarones M2 2019.jpg\n",
+      "\tSoling 1 El Aguila M1 2019.jpg\n",
+      "\tSoling 20 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 5 El Aguila M1 2019.jpg\n",
+      "\tSoling 10 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 9 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 21 Cerro Dominador M1 2019.jpg\n",
+      "\tSoling 13 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 4 El Aguila M1 2019.jpg\n",
+      "\tSoling 18 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 16 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 7 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 14 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 8 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 22 Valle de la Luna M1 2019.jpg\n",
+      "\tSoling 17 Pampa Camarones M4 2019.jpg\n",
+      "Found directory: ./dataset_panel/4- Falla Diodos\n",
+      "\tUn Diodo 25  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 7 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 19  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 14  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 18  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 26  Lllano de LLampos M4 2019.jpg\n",
+      "\tDos Diodos 2  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 11  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 6 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 22  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 1 El Aguila M1 2019.jpg\n",
+      "\tUn Diodo 23  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 24  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 21  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 3 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 9  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 16  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 15  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 4 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 13  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 12  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 1 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 8  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 20  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 10  Pampa Camarones M2 2019.jpg\n",
+      "\tDos Diodos 1  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 17  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 5 Pampa Camarones M2 2019.jpg\n",
+      "Found directory: ./dataset_panel/3- Cinco-Mas Celdas\n",
+      "\tCinco-Mas Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas 4 Cerro Demonidor M2 2019.jpg\n",
+      "\tCinco-Mas Celdas 5 Llano de LLampos M5 2019.jpg\n",
+      "\tCinco-Mas Celdas 19 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 17 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 20 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 11 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 18 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 15 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 14 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 9 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 7 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 6 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 16 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 8 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 3 LLano de LLampos M3 2019.jpg\n",
+      "\tCinco-Mas Celdas 13 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 2 LLano de LLampos M3 2019.jpg\n",
+      "\tCinco-Mas Celdas 10 Maria Elena M1 2018.jpg\n",
+      "Found directory: ./dataset_panel/1- Una Celda\n",
+      "\tUna Celda 22 LLano de LLampos M4 2019.jpg\n",
+      "\tUna Celda 10 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 17 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 7 Pampa Camarones M2 2019.jpg\n",
+      "\tUna Celda 14 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 12 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 2 - El Aguila M1 2019.jpg\n",
+      "\tUna Celda 3 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 18 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 15 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 19 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 1 - Citrino.jpg\n",
+      "\tUna Celda 23 Valle de la Luna M1 2019.jpg\n",
+      "\tUna Celda 21 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 20 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 9 Pampa Camarones M4 2019.jpg\n",
+      "\tUna Celda 5 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 13 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 16 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 4 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 11 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 6 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 8 Pampa Camarones M3 2019.jpg\n",
+      "Found directory: ./dataset_panel/9- No es Panel\n",
+      "\tNo es panel 12 Casuto M1 2019.jpg\n",
+      "\tNo es panel 5 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 16 Llano de Llampos M1 2019.jpg\n",
+      "\tNo es panel 9 Casuto M1 2019.jpg\n",
+      "\tNo es panel 13 Casuto M1 2019.jpg\n",
+      "\tNo es panel 3 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 7 Casuto M1 2019.jpg\n",
+      "\tNo es panel 15 Casuto M1 2019.jpg\n",
+      "\tNo es panel 1 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 2 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 6 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 11 Casuto M1 2019.jpg\n",
+      "\tNo es panel 10 Casuto M1 2019.jpg\n",
+      "\tNo es panel 14 Casuto M1 2019.jpg\n",
+      "\tNo es panel 4 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 17 Cerro Dominador M1 2019.jpg\n",
+      "\tNo es panel 8 Casuto M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/5- Panel Desconectado\n",
+      "\tPanel Desconectado 24 Llano de LLampos M3 2019.jpg\n",
+      "\tPanel Desconectado 6 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 19 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 9 Pampa Camarones M2 2019.jpg\n",
+      "\tPanel Desconectado 12 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 10 Pampa Camarones M2 2019.jpg\n",
+      "\tPanel Desconectado 21 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 4 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 25 Llano de LLampos M3 2019.jpg\n",
+      "\tPanel Desconectado 23 Llano de LLampos M2 2019.jpg\n",
+      "\tPanel Desconectado 20 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 11 Pampa Camarones M2 2019.jpg\n",
+      "\tPanel Desconectado 15 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 1 El Aguila M2 2019.jpg\n",
+      "\tPanel Desconectado 16 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 14 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 18 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 22 Tiltil M2 2019.jpg\n",
+      "\tPanel Desconectado 13 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 17 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 3 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 5 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 7 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 2 El Aguila M2 2019.jpg\n",
+      "\tPanel Desconectado 8 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/8- Tracker dañado\n",
+      "\tTracker dañado 8 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 20 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 9 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 11 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 2 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 19 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 10 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 4 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 15 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 17 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 7 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 5 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 6 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 18 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 16 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 12 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 1 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 3 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/10- Reflejos\n",
+      "\tReflejo 3 Citrino.jpg\n",
+      "\tReflejo 10 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 4 Citrino.jpg\n",
+      "\tReflejo 11 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 23 Maria Elena M3 2018.jpg\n",
+      "\tReflejo 22 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 18 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 5 El Aguila M1 2019.jpg\n",
+      "\tReflejo 21 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 7 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 2 Citrino.jpg\n",
+      "\tReflejo 16 Pampa Camarones M2 2019.jpg\n",
+      "\tReflejo 6 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 8 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 19 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 15 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 9 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 13 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 17 Pampa Camarones M2 2019.jpg\n",
+      "\tReflejo 1 Citrino.jpg\n",
+      "\tReflejo 14 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 20 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 12 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/0- Normal\n",
+      "\tNormal 30 Valle de La Luna M1 2019.jpg\n",
+      "\tNormal 16 Tiltil M2 2019.jpg\n",
+      "\tNormal 3 Citrino.jpg\n",
+      "\tNormal 20 Casuto M1 2019.jpg\n",
+      "\tNormal 12 Tiltil M1 2019.jpg\n",
+      "\tNormal 2 Citrino.jpg\n",
+      "\tNormal 15 Tiltil M1 2019.jpg\n",
+      "\tNormal 11 Pampa Camarones M3 2019.jpg\n",
+      "\tNormal 1 Citrino.jpg\n",
+      "\tNormal 17 Casuto M1 2019.jpg\n",
+      "\tNormal 21 Casuto M1 2019.jpg\n",
+      "\tNormal 5 El Aguila M1 2019.jpg\n",
+      "\tNormal 14 Tiltil M1 2019.jpg\n",
+      "\tNormal 6 El Aguila M1 2019.jpg\n",
+      "\tNormal 25 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 10 Pampa Camarones M1 2019.jpg\n",
+      "\tNormal 9 El Aguila M1 2019.jpg\n",
+      "\tNormal 27 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 19 Casuto M1 2019.jpg\n",
+      "\tNormal 18 Casuto M1 2019.jpg\n",
+      "\tNormal 8 El Aguila M1 2019.jpg\n",
+      "\tNormal 28 Cerro Dominado M1 2019.jpg\n",
+      "\tNormal 22 Casuto M1 2019.jpg\n",
+      "\tNormal 4 Citrino.jpg\n",
+      "\tNormal 24 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 23 Casuto M1 2019.jpg\n",
+      "\tNormal 26 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 13 Tiltil M1 2019.jpg\n",
+      "\tNormal 7 El Aguila M1 2019.jpg\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found directory: ./model\n",
+      "\tmodel.json\n",
+      "\ttop_model_weights.h5\n",
+      "\tmodel_weights.h5\n",
+      "Found directory: ./preview\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Set the directory you want to start from\n",
+    "rootDir = '.'\n",
+    "for dirName, subdirList, fileList in os.walk(rootDir):\n",
+    "    print('Found directory: %s' % dirName)\n",
+    "    for fname in fileList:\n",
+    "        print('\\t%s' % fname)\n",
+    "        \n",
+    "\n",
+    "# load our serialized face detector from disk\n",
+    "# fix seed for reproducible results (only works on CPU, not GPU)\n",
+    "seed = 1\n",
+    "np.random.seed(seed=seed)\n",
+    "tf.random.set_seed(seed=seed)\n",
+    "\n",
+    "# hyper parameters for model\n",
+    "nb_classes = 5  # number of classes\n",
+    "based_model_last_block_layer_number = 126  # value is based on based model selected.\n",
+    "img_width, img_height = 224, 224  # change based on the shape/structure of your images\n",
+    "batch_size = 2  # try 4, 8, 16, 32, 64, 128, 256 dependent on CPU/GPU memory capacity (powers of 2 values).\n",
+    "nb_epoch = 100  # number of iteration the algorithm gets trained.\n",
+    "learn_rate = 1e-4  # sgd learning rate\n",
+    "momentum = .9  # sgd momentum to avoid local minimum\n",
+    "transformation_ratio = .0001  # how aggressive will be the data augmentation/transformation\n",
+    "patience = 10\n",
+    "\n",
+    "train_data_dir = './dataset_prueba_1/'  # Inside, each class should have it's own folder\n",
+    "#validation_data_dir = './dataset/'  # each class should have it's own folder\n",
+    "model_path = './model/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1 (Conv2D)           (None, 111, 111, 32) 864         input_1[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1_bn (BatchNormaliza (None, 111, 111, 32) 128         block1_conv1[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1_act (Activation)   (None, 111, 111, 32) 0           block1_conv1_bn[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2 (Conv2D)           (None, 109, 109, 64) 18432       block1_conv1_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2_bn (BatchNormaliza (None, 109, 109, 64) 256         block1_conv2[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2_act (Activation)   (None, 109, 109, 64) 0           block1_conv2_bn[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv1 (SeparableConv2 (None, 109, 109, 128 8768        block1_conv2_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv1_bn (BatchNormal (None, 109, 109, 128 512         block2_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2_act (Activation (None, 109, 109, 128 0           block2_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2 (SeparableConv2 (None, 109, 109, 128 17536       block2_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2_bn (BatchNormal (None, 109, 109, 128 512         block2_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d (Conv2D)                 (None, 55, 55, 128)  8192        block1_conv2_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_pool (MaxPooling2D)      (None, 55, 55, 128)  0           block2_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization (BatchNorma (None, 55, 55, 128)  512         conv2d[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "add (Add)                       (None, 55, 55, 128)  0           block2_pool[0][0]                \n",
+      "                                                                 batch_normalization[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1_act (Activation (None, 55, 55, 128)  0           add[0][0]                        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1 (SeparableConv2 (None, 55, 55, 256)  33920       block3_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1_bn (BatchNormal (None, 55, 55, 256)  1024        block3_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2_act (Activation (None, 55, 55, 256)  0           block3_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2 (SeparableConv2 (None, 55, 55, 256)  67840       block3_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2_bn (BatchNormal (None, 55, 55, 256)  1024        block3_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_1 (Conv2D)               (None, 28, 28, 256)  32768       add[0][0]                        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_pool (MaxPooling2D)      (None, 28, 28, 256)  0           block3_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_1 (BatchNor (None, 28, 28, 256)  1024        conv2d_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_1 (Add)                     (None, 28, 28, 256)  0           block3_pool[0][0]                \n",
+      "                                                                 batch_normalization_1[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1_act (Activation (None, 28, 28, 256)  0           add_1[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1 (SeparableConv2 (None, 28, 28, 728)  188672      block4_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1_bn (BatchNormal (None, 28, 28, 728)  2912        block4_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2_act (Activation (None, 28, 28, 728)  0           block4_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2 (SeparableConv2 (None, 28, 28, 728)  536536      block4_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2_bn (BatchNormal (None, 28, 28, 728)  2912        block4_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_2 (Conv2D)               (None, 14, 14, 728)  186368      add_1[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_pool (MaxPooling2D)      (None, 14, 14, 728)  0           block4_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_2 (BatchNor (None, 14, 14, 728)  2912        conv2d_2[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_2 (Add)                     (None, 14, 14, 728)  0           block4_pool[0][0]                \n",
+      "                                                                 batch_normalization_2[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1_act (Activation (None, 14, 14, 728)  0           add_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2_act (Activation (None, 14, 14, 728)  0           block5_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3_act (Activation (None, 14, 14, 728)  0           block5_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_3 (Add)                     (None, 14, 14, 728)  0           block5_sepconv3_bn[0][0]         \n",
+      "                                                                 add_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1_act (Activation (None, 14, 14, 728)  0           add_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block6_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block6_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2_act (Activation (None, 14, 14, 728)  0           block6_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block6_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block6_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3_act (Activation (None, 14, 14, 728)  0           block6_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block6_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block6_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_4 (Add)                     (None, 14, 14, 728)  0           block6_sepconv3_bn[0][0]         \n",
+      "                                                                 add_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1_act (Activation (None, 14, 14, 728)  0           add_4[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block7_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block7_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2_act (Activation (None, 14, 14, 728)  0           block7_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block7_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block7_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3_act (Activation (None, 14, 14, 728)  0           block7_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block7_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block7_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_5 (Add)                     (None, 14, 14, 728)  0           block7_sepconv3_bn[0][0]         \n",
+      "                                                                 add_4[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1_act (Activation (None, 14, 14, 728)  0           add_5[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block8_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block8_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2_act (Activation (None, 14, 14, 728)  0           block8_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block8_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block8_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3_act (Activation (None, 14, 14, 728)  0           block8_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block8_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block8_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_6 (Add)                     (None, 14, 14, 728)  0           block8_sepconv3_bn[0][0]         \n",
+      "                                                                 add_5[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1_act (Activation (None, 14, 14, 728)  0           add_6[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block9_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block9_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2_act (Activation (None, 14, 14, 728)  0           block9_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block9_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block9_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3_act (Activation (None, 14, 14, 728)  0           block9_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block9_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block9_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_7 (Add)                     (None, 14, 14, 728)  0           block9_sepconv3_bn[0][0]         \n",
+      "                                                                 add_6[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_7[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block10_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block10_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2_act (Activatio (None, 14, 14, 728)  0           block10_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2 (SeparableConv (None, 14, 14, 728)  536536      block10_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2_bn (BatchNorma (None, 14, 14, 728)  2912        block10_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3_act (Activatio (None, 14, 14, 728)  0           block10_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3 (SeparableConv (None, 14, 14, 728)  536536      block10_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3_bn (BatchNorma (None, 14, 14, 728)  2912        block10_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_8 (Add)                     (None, 14, 14, 728)  0           block10_sepconv3_bn[0][0]        \n",
+      "                                                                 add_7[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_8[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block11_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block11_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2_act (Activatio (None, 14, 14, 728)  0           block11_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2 (SeparableConv (None, 14, 14, 728)  536536      block11_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2_bn (BatchNorma (None, 14, 14, 728)  2912        block11_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3_act (Activatio (None, 14, 14, 728)  0           block11_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3 (SeparableConv (None, 14, 14, 728)  536536      block11_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3_bn (BatchNorma (None, 14, 14, 728)  2912        block11_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_9 (Add)                     (None, 14, 14, 728)  0           block11_sepconv3_bn[0][0]        \n",
+      "                                                                 add_8[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_9[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block12_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block12_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2_act (Activatio (None, 14, 14, 728)  0           block12_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2 (SeparableConv (None, 14, 14, 728)  536536      block12_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2_bn (BatchNorma (None, 14, 14, 728)  2912        block12_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3_act (Activatio (None, 14, 14, 728)  0           block12_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3 (SeparableConv (None, 14, 14, 728)  536536      block12_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3_bn (BatchNorma (None, 14, 14, 728)  2912        block12_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_10 (Add)                    (None, 14, 14, 728)  0           block12_sepconv3_bn[0][0]        \n",
+      "                                                                 add_9[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_10[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block13_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block13_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2_act (Activatio (None, 14, 14, 728)  0           block13_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2 (SeparableConv (None, 14, 14, 1024) 752024      block13_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2_bn (BatchNorma (None, 14, 14, 1024) 4096        block13_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_3 (Conv2D)               (None, 7, 7, 1024)   745472      add_10[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_pool (MaxPooling2D)     (None, 7, 7, 1024)   0           block13_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_3 (BatchNor (None, 7, 7, 1024)   4096        conv2d_3[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_11 (Add)                    (None, 7, 7, 1024)   0           block13_pool[0][0]               \n",
+      "                                                                 batch_normalization_3[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1 (SeparableConv (None, 7, 7, 1536)   1582080     add_11[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1_bn (BatchNorma (None, 7, 7, 1536)   6144        block14_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1_act (Activatio (None, 7, 7, 1536)   0           block14_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2 (SeparableConv (None, 7, 7, 2048)   3159552     block14_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2_bn (BatchNorma (None, 7, 7, 2048)   8192        block14_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2_act (Activatio (None, 7, 7, 2048)   0           block14_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "global_average_pooling2d (Globa (None, 2048)         0           block14_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "dense (Dense)                   (None, 5)            10245       global_average_pooling2d[0][0]   \n",
+      "==================================================================================================\n",
+      "Total params: 20,871,725\n",
+      "Trainable params: 20,817,197\n",
+      "Non-trainable params: 54,528\n",
+      "__________________________________________________________________________________________________\n",
+      "None\n",
+      "Found 146 images belonging to 7 classes.\n",
+      "Found 34 images belonging to 7 classes.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 58 steps, validate for 13 steps\n",
+      "Epoch 1/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.7914 - accuracy: 0.0614\n",
+      "Epoch 00001: val_loss improved from inf to 16.76773, saving model to /home/dlsaavedra/Desktop/Rentadrone.cl-ai-test&SomeCode/model-definition/GPS_Panel/Classifier/model/top_model_weights.h5\n",
+      "58/58 [==============================] - 50s 862ms/step - loss: 15.5191 - accuracy: 0.0603 - val_loss: 16.7677 - val_accuracy: 0.0000e+00\n",
+      "Epoch 2/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.5472 - accuracy: 0.0088\n",
+      "Epoch 00002: val_loss did not improve from 16.76773\n",
+      "58/58 [==============================] - 49s 837ms/step - loss: 15.7657 - accuracy: 0.0086 - val_loss: 22.9064 - val_accuracy: 0.0000e+00\n",
+      "Epoch 3/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.3652 - accuracy: 0.0175\n",
+      "Epoch 00003: val_loss did not improve from 16.76773\n",
+      "58/58 [==============================] - 50s 871ms/step - loss: 15.2392 - accuracy: 0.0172 - val_loss: 157.1759 - val_accuracy: 0.1154\n",
+      "Epoch 4/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.9721 - accuracy: 0.0439\n",
+      "Epoch 00004: val_loss did not improve from 16.76773\n",
+      "58/58 [==============================] - 52s 895ms/step - loss: 15.7661 - accuracy: 0.0431 - val_loss: 1889.7077 - val_accuracy: 0.0000e+00\n",
+      "Epoch 5/20\n",
+      "57/58 [============================>.] - ETA: 1s - loss: 16.8935 - accuracy: 0.0175\n",
+      "Epoch 00005: val_loss did not improve from 16.76773\n",
+      "58/58 [==============================] - 73s 1s/step - loss: 16.7411 - accuracy: 0.0172 - val_loss: 139.8746 - val_accuracy: 0.0000e+00\n",
+      "Epoch 6/20\n",
+      "57/58 [============================>.] - ETA: 1s - loss: 14.6952 - accuracy: 0.0088\n",
+      "Epoch 00006: val_loss did not improve from 16.76773\n",
+      "58/58 [==============================] - 76s 1s/step - loss: 14.5113 - accuracy: 0.0086 - val_loss: 60.4487 - val_accuracy: 0.0000e+00\n",
+      "Epoch 7/20\n",
+      "57/58 [============================>.] - ETA: 1s - loss: 15.6087 - accuracy: 0.0000e+00\n",
+      "Epoch 00007: val_loss did not improve from 16.76773\n",
+      "58/58 [==============================] - 74s 1s/step - loss: 15.5478 - accuracy: 0.0000e+00 - val_loss: 16.9469 - val_accuracy: 0.1538\n",
+      "Epoch 8/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 14.4803 - accuracy: 0.0088\n",
+      "Epoch 00008: val_loss did not improve from 16.76773\n",
+      "58/58 [==============================] - 57s 978ms/step - loss: 14.5081 - accuracy: 0.0086 - val_loss: 22.4798 - val_accuracy: 0.0769\n",
+      "Epoch 9/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.5354 - accuracy: 0.0088\n",
+      "Epoch 00009: val_loss did not improve from 16.76773\n",
+      "58/58 [==============================] - 55s 955ms/step - loss: 15.6145 - accuracy: 0.0086 - val_loss: 17.0278 - val_accuracy: 0.0000e+00\n",
+      "Epoch 10/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.6774 - accuracy: 0.0439\n",
+      "Epoch 00010: val_loss improved from 16.76773 to 16.71888, saving model to /home/dlsaavedra/Desktop/Rentadrone.cl-ai-test&SomeCode/model-definition/GPS_Panel/Classifier/model/top_model_weights.h5\n",
+      "58/58 [==============================] - 57s 977ms/step - loss: 15.5459 - accuracy: 0.0431 - val_loss: 16.7189 - val_accuracy: 0.8846\n",
+      "Epoch 11/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.9579 - accuracy: 0.0702\n",
+      "Epoch 00011: val_loss improved from 16.71888 to 16.71781, saving model to /home/dlsaavedra/Desktop/Rentadrone.cl-ai-test&SomeCode/model-definition/GPS_Panel/Classifier/model/top_model_weights.h5\n",
+      "58/58 [==============================] - 53s 914ms/step - loss: 16.0992 - accuracy: 0.0690 - val_loss: 16.7178 - val_accuracy: 0.0000e+00\n",
+      "Epoch 12/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 16.5248 - accuracy: 0.1228\n",
+      "Epoch 00012: val_loss did not improve from 16.71781\n",
+      "58/58 [==============================] - 51s 871ms/step - loss: 16.3787 - accuracy: 0.1207 - val_loss: 16.7196 - val_accuracy: 0.0000e+00\n",
+      "Epoch 13/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.2509 - accuracy: 0.0439\n",
+      "Epoch 00013: val_loss did not improve from 16.71781\n",
+      "58/58 [==============================] - 51s 885ms/step - loss: 15.3349 - accuracy: 0.0431 - val_loss: 16.7209 - val_accuracy: 0.0000e+00\n",
+      "Epoch 14/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.8867 - accuracy: 0.0351\n",
+      "Epoch 00014: val_loss did not improve from 16.71781\n",
+      "58/58 [==============================] - 59s 1s/step - loss: 15.7515 - accuracy: 0.0345 - val_loss: 17.0744 - val_accuracy: 0.0000e+00\n",
+      "Epoch 15/20\n",
+      "57/58 [============================>.] - ETA: 1s - loss: 15.5322 - accuracy: 0.0088    \n",
+      "Epoch 00015: val_loss did not improve from 16.71781\n",
+      "58/58 [==============================] - 64s 1s/step - loss: 15.4726 - accuracy: 0.0086 - val_loss: 18.1466 - val_accuracy: 0.0000e+00\n",
+      "Epoch 16/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.5316 - accuracy: 0.0000e+00\n",
+      "Epoch 00016: val_loss did not improve from 16.71781\n",
+      "58/58 [==============================] - 58s 993ms/step - loss: 15.7495 - accuracy: 0.0000e+00 - val_loss: 17.3060 - val_accuracy: 0.0000e+00\n",
+      "Epoch 17/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.5314 - accuracy: 0.0000e+00\n",
+      "Epoch 00017: val_loss did not improve from 16.71781\n",
+      "58/58 [==============================] - 51s 881ms/step - loss: 15.6105 - accuracy: 0.0000e+00 - val_loss: 18.0962 - val_accuracy: 0.0000e+00\n",
+      "Epoch 18/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.3194 - accuracy: 0.0000e+00\n",
+      "Epoch 00018: val_loss did not improve from 16.71781\n",
+      "58/58 [==============================] - 54s 935ms/step - loss: 15.2634 - accuracy: 0.0000e+00 - val_loss: 22.3874 - val_accuracy: 0.0000e+00\n",
+      "Epoch 19/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 15.8838 - accuracy: 0.0175\n",
+      "Epoch 00019: val_loss did not improve from 16.71781\n",
+      "58/58 [==============================] - 54s 932ms/step - loss: 16.0262 - accuracy: 0.0259 - val_loss: 20.3104 - val_accuracy: 0.0000e+00\n",
+      "Epoch 20/20\n",
+      "57/58 [============================>.] - ETA: 0s - loss: 16.5897 - accuracy: 0.0175\n",
+      "Epoch 00020: val_loss did not improve from 16.71781\n",
+      "58/58 [==============================] - 55s 951ms/step - loss: 16.5119 - accuracy: 0.0172 - val_loss: 16.8419 - val_accuracy: 0.0769\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "base_model = tf.keras.applications.Xception(input_shape=(img_width, img_height, 3), weights='imagenet', include_top=False)\n",
+    "#base_model = tf.keras.applications.MobileNetV2(input_shape=(img_width, img_height, 3), weights='imagenet', include_top=False)\n",
+    "#base_model = tf.keras.applications.NASNetMobile(input_shape=(img_width, img_height, 3), weights='imagenet', include_top=False)\n",
+    "\n",
+    "\n",
+    "# Top Model Block\n",
+    "x = base_model.output\n",
+    "x = GlobalAveragePooling2D()(x)\n",
+    "predictions = Dense(nb_classes, activation='softmax')(x)\n",
+    "\n",
+    "# add your top layer block to your base model\n",
+    "model = tf.keras.Model(base_model.input, predictions)\n",
+    "\n",
+    "\n",
+    "# # let's visualize layer names and layer indices to see how many layers/blocks to re-train\n",
+    "# # uncomment when choosing based_model_last_block_layer\n",
+    "# for i, layer in enumerate(model.layers):\n",
+    "#     print(i, layer.name)\n",
+    "\n",
+    "# first: train only the top layers (which were randomly initialized)\n",
+    "# i.e. freeze all layers of the based model that is already pre-trained.\n",
+    "for layer in base_model.layers:\n",
+    "    layer.trainable = True\n",
+    "\n",
+    "print(model.summary())\n",
+    "# Read Data and Augment it: Make sure to select augmentations that are appropriate to your images.\n",
+    "# To save augmentations un-comment save lines and add to your flow parameters.\n",
+    "train_datagen = ImageDataGenerator(rescale=1. / 255,\n",
+    "                                   rotation_range=transformation_ratio,\n",
+    "                                   shear_range=transformation_ratio,\n",
+    "                                   zoom_range=transformation_ratio,\n",
+    "                                   cval=transformation_ratio,\n",
+    "                                   horizontal_flip=True,\n",
+    "                                   vertical_flip=True,\n",
+    "                                   validation_split=0.2)\n",
+    "\n",
+    "\n",
+    "\n",
+    "train_generator = train_datagen.flow_from_directory(train_data_dir,\n",
+    "                                                    target_size=(img_height, img_width),\n",
+    "                                                    batch_size=batch_size,\n",
+    "                                                    class_mode='binary',\n",
+    "                                                    subset='training') # set as training data\n",
+    "\n",
+    "validation_generator = train_datagen.flow_from_directory(train_data_dir, # same directory as training data\n",
+    "                                                        target_size=(img_height, img_width),\n",
+    "                                                        batch_size=batch_size,\n",
+    "                                                        class_mode='binary',\n",
+    "                                                        subset='validation') # set as validation data\n",
+    "\n",
+    "model.compile(optimizer='adam',\n",
+    "              loss='categorical_crossentropy',  # categorical_crossentropy if multi-class classifier\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "# save weights of best training epoch: monitor either val_loss or val_acc\n",
+    "\n",
+    "top_weights_path = os.path.join(os.path.abspath(model_path), 'top_model_weights.h5')\n",
+    "callbacks_list = [\n",
+    "    ModelCheckpoint(top_weights_path, monitor='val_loss', verbose=1, save_best_only=True),\n",
+    "    EarlyStopping(monitor='val_loss', patience= patience, verbose=0)\n",
+    "]\n",
+    "\n",
+    "# Train Simple CNN\n",
+    "hist = model.fit(train_generator,\n",
+    "                    steps_per_epoch= int(train_generator.samples/batch_size*.8),\n",
+    "                    epochs=int (nb_epoch / 5),\n",
+    "                    validation_data=validation_generator,\n",
+    "                    validation_steps = int(validation_generator.samples /batch_size*.8),\n",
+    "                    validation_freq=1,\n",
+    "                    callbacks=callbacks_list)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Starting to Fine Tune Model\n",
+      "\n",
+      "Model: \"model\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1 (Conv2D)           (None, 111, 111, 32) 864         input_1[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1_bn (BatchNormaliza (None, 111, 111, 32) 128         block1_conv1[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1_act (Activation)   (None, 111, 111, 32) 0           block1_conv1_bn[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2 (Conv2D)           (None, 109, 109, 64) 18432       block1_conv1_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2_bn (BatchNormaliza (None, 109, 109, 64) 256         block1_conv2[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2_act (Activation)   (None, 109, 109, 64) 0           block1_conv2_bn[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv1 (SeparableConv2 (None, 109, 109, 128 8768        block1_conv2_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv1_bn (BatchNormal (None, 109, 109, 128 512         block2_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2_act (Activation (None, 109, 109, 128 0           block2_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2 (SeparableConv2 (None, 109, 109, 128 17536       block2_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2_bn (BatchNormal (None, 109, 109, 128 512         block2_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d (Conv2D)                 (None, 55, 55, 128)  8192        block1_conv2_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_pool (MaxPooling2D)      (None, 55, 55, 128)  0           block2_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization (BatchNorma (None, 55, 55, 128)  512         conv2d[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "add (Add)                       (None, 55, 55, 128)  0           block2_pool[0][0]                \n",
+      "                                                                 batch_normalization[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1_act (Activation (None, 55, 55, 128)  0           add[0][0]                        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1 (SeparableConv2 (None, 55, 55, 256)  33920       block3_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1_bn (BatchNormal (None, 55, 55, 256)  1024        block3_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2_act (Activation (None, 55, 55, 256)  0           block3_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2 (SeparableConv2 (None, 55, 55, 256)  67840       block3_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2_bn (BatchNormal (None, 55, 55, 256)  1024        block3_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_1 (Conv2D)               (None, 28, 28, 256)  32768       add[0][0]                        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_pool (MaxPooling2D)      (None, 28, 28, 256)  0           block3_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_1 (BatchNor (None, 28, 28, 256)  1024        conv2d_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_1 (Add)                     (None, 28, 28, 256)  0           block3_pool[0][0]                \n",
+      "                                                                 batch_normalization_1[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1_act (Activation (None, 28, 28, 256)  0           add_1[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1 (SeparableConv2 (None, 28, 28, 728)  188672      block4_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1_bn (BatchNormal (None, 28, 28, 728)  2912        block4_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2_act (Activation (None, 28, 28, 728)  0           block4_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2 (SeparableConv2 (None, 28, 28, 728)  536536      block4_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2_bn (BatchNormal (None, 28, 28, 728)  2912        block4_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_2 (Conv2D)               (None, 14, 14, 728)  186368      add_1[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_pool (MaxPooling2D)      (None, 14, 14, 728)  0           block4_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_2 (BatchNor (None, 14, 14, 728)  2912        conv2d_2[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_2 (Add)                     (None, 14, 14, 728)  0           block4_pool[0][0]                \n",
+      "                                                                 batch_normalization_2[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1_act (Activation (None, 14, 14, 728)  0           add_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2_act (Activation (None, 14, 14, 728)  0           block5_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3_act (Activation (None, 14, 14, 728)  0           block5_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block5_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block5_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_3 (Add)                     (None, 14, 14, 728)  0           block5_sepconv3_bn[0][0]         \n",
+      "                                                                 add_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1_act (Activation (None, 14, 14, 728)  0           add_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block6_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block6_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2_act (Activation (None, 14, 14, 728)  0           block6_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block6_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block6_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3_act (Activation (None, 14, 14, 728)  0           block6_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block6_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block6_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_4 (Add)                     (None, 14, 14, 728)  0           block6_sepconv3_bn[0][0]         \n",
+      "                                                                 add_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1_act (Activation (None, 14, 14, 728)  0           add_4[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block7_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block7_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2_act (Activation (None, 14, 14, 728)  0           block7_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block7_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block7_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3_act (Activation (None, 14, 14, 728)  0           block7_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block7_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block7_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_5 (Add)                     (None, 14, 14, 728)  0           block7_sepconv3_bn[0][0]         \n",
+      "                                                                 add_4[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1_act (Activation (None, 14, 14, 728)  0           add_5[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block8_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block8_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2_act (Activation (None, 14, 14, 728)  0           block8_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block8_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block8_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3_act (Activation (None, 14, 14, 728)  0           block8_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block8_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block8_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_6 (Add)                     (None, 14, 14, 728)  0           block8_sepconv3_bn[0][0]         \n",
+      "                                                                 add_5[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1_act (Activation (None, 14, 14, 728)  0           add_6[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1 (SeparableConv2 (None, 14, 14, 728)  536536      block9_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1_bn (BatchNormal (None, 14, 14, 728)  2912        block9_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2_act (Activation (None, 14, 14, 728)  0           block9_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2 (SeparableConv2 (None, 14, 14, 728)  536536      block9_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2_bn (BatchNormal (None, 14, 14, 728)  2912        block9_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3_act (Activation (None, 14, 14, 728)  0           block9_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3 (SeparableConv2 (None, 14, 14, 728)  536536      block9_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3_bn (BatchNormal (None, 14, 14, 728)  2912        block9_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_7 (Add)                     (None, 14, 14, 728)  0           block9_sepconv3_bn[0][0]         \n",
+      "                                                                 add_6[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_7[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block10_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block10_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2_act (Activatio (None, 14, 14, 728)  0           block10_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2 (SeparableConv (None, 14, 14, 728)  536536      block10_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2_bn (BatchNorma (None, 14, 14, 728)  2912        block10_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3_act (Activatio (None, 14, 14, 728)  0           block10_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3 (SeparableConv (None, 14, 14, 728)  536536      block10_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3_bn (BatchNorma (None, 14, 14, 728)  2912        block10_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_8 (Add)                     (None, 14, 14, 728)  0           block10_sepconv3_bn[0][0]        \n",
+      "                                                                 add_7[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_8[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block11_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block11_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2_act (Activatio (None, 14, 14, 728)  0           block11_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2 (SeparableConv (None, 14, 14, 728)  536536      block11_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2_bn (BatchNorma (None, 14, 14, 728)  2912        block11_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3_act (Activatio (None, 14, 14, 728)  0           block11_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3 (SeparableConv (None, 14, 14, 728)  536536      block11_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3_bn (BatchNorma (None, 14, 14, 728)  2912        block11_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_9 (Add)                     (None, 14, 14, 728)  0           block11_sepconv3_bn[0][0]        \n",
+      "                                                                 add_8[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_9[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block12_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block12_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2_act (Activatio (None, 14, 14, 728)  0           block12_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2 (SeparableConv (None, 14, 14, 728)  536536      block12_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2_bn (BatchNorma (None, 14, 14, 728)  2912        block12_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3_act (Activatio (None, 14, 14, 728)  0           block12_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3 (SeparableConv (None, 14, 14, 728)  536536      block12_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3_bn (BatchNorma (None, 14, 14, 728)  2912        block12_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_10 (Add)                    (None, 14, 14, 728)  0           block12_sepconv3_bn[0][0]        \n",
+      "                                                                 add_9[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1_act (Activatio (None, 14, 14, 728)  0           add_10[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1 (SeparableConv (None, 14, 14, 728)  536536      block13_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1_bn (BatchNorma (None, 14, 14, 728)  2912        block13_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2_act (Activatio (None, 14, 14, 728)  0           block13_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2 (SeparableConv (None, 14, 14, 1024) 752024      block13_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2_bn (BatchNorma (None, 14, 14, 1024) 4096        block13_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_3 (Conv2D)               (None, 7, 7, 1024)   745472      add_10[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_pool (MaxPooling2D)     (None, 7, 7, 1024)   0           block13_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_3 (BatchNor (None, 7, 7, 1024)   4096        conv2d_3[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_11 (Add)                    (None, 7, 7, 1024)   0           block13_pool[0][0]               \n",
+      "                                                                 batch_normalization_3[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1 (SeparableConv (None, 7, 7, 1536)   1582080     add_11[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1_bn (BatchNorma (None, 7, 7, 1536)   6144        block14_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1_act (Activatio (None, 7, 7, 1536)   0           block14_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2 (SeparableConv (None, 7, 7, 2048)   3159552     block14_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2_bn (BatchNorma (None, 7, 7, 2048)   8192        block14_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2_act (Activatio (None, 7, 7, 2048)   0           block14_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "global_average_pooling2d (Globa (None, 2048)         0           block14_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "dense (Dense)                   (None, 5)            10245       global_average_pooling2d[0][0]   \n",
+      "==================================================================================================\n",
+      "Total params: 20,871,725\n",
+      "Trainable params: 20,817,197\n",
+      "Non-trainable params: 54,528\n",
+      "__________________________________________________________________________________________________\n",
+      "None\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "WARNING:tensorflow:sample_weight modes were coerced from\n",
+      "  ...\n",
+      "    to  \n",
+      "  ['...']\n",
+      "Train for 58.400000000000006 steps, validate for 13.600000000000001 steps\n",
+      "Epoch 1/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 16.7909 - accuracy: 0.1207\n",
+      "Epoch 00001: val_loss improved from inf to 17.16391, saving model to /home/dlsaavedra/Desktop/Rentadrone.cl-ai-test&SomeCode/model-definition/GPS_Panel/Classifier/model/model_weights.h5\n",
+      "59/58 [==============================] - 75s 1s/step - loss: 16.5745 - accuracy: 0.1186 - val_loss: 17.1639 - val_accuracy: 0.4286\n",
+      "Epoch 2/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 16.1648 - accuracy: 0.0948\n",
+      "Epoch 00002: val_loss did not improve from 17.16391\n",
+      "59/58 [==============================] - 56s 948ms/step - loss: 16.1636 - accuracy: 0.0932 - val_loss: 17.1654 - val_accuracy: 0.1429\n",
+      "Epoch 3/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 15.5401 - accuracy: 0.0690\n",
+      "Epoch 00003: val_loss did not improve from 17.16391\n",
+      "59/58 [==============================] - 57s 959ms/step - loss: 15.4813 - accuracy: 0.0678 - val_loss: 17.1791 - val_accuracy: 0.0714\n",
+      "Epoch 4/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 15.4705 - accuracy: 0.0690\n",
+      "Epoch 00004: val_loss did not improve from 17.16391\n",
+      "59/58 [==============================] - 56s 950ms/step - loss: 15.3447 - accuracy: 0.0678 - val_loss: 17.1779 - val_accuracy: 0.0000e+00\n",
+      "Epoch 5/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 16.3031 - accuracy: 0.0517\n",
+      "Epoch 00005: val_loss did not improve from 17.16391\n",
+      "59/58 [==============================] - 57s 960ms/step - loss: 16.7087 - accuracy: 0.0508 - val_loss: 17.2318 - val_accuracy: 0.0000e+00\n",
+      "Epoch 6/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 16.1641 - accuracy: 0.0776\n",
+      "Epoch 00006: val_loss did not improve from 17.16391\n",
+      "59/58 [==============================] - 55s 933ms/step - loss: 15.9583 - accuracy: 0.0847 - val_loss: 17.1775 - val_accuracy: 0.0000e+00\n",
+      "Epoch 7/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 15.4702 - accuracy: 0.0172\n",
+      "Epoch 00007: val_loss did not improve from 17.16391\n",
+      "59/58 [==============================] - 56s 947ms/step - loss: 15.2762 - accuracy: 0.0254 - val_loss: 17.1652 - val_accuracy: 0.0000e+00\n",
+      "Epoch 8/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 15.8864 - accuracy: 0.0086\n",
+      "Epoch 00008: val_loss improved from 17.16391 to 17.16194, saving model to /home/dlsaavedra/Desktop/Rentadrone.cl-ai-test&SomeCode/model-definition/GPS_Panel/Classifier/model/model_weights.h5\n",
+      "59/58 [==============================] - 71s 1s/step - loss: 15.6854 - accuracy: 0.0085 - val_loss: 17.1619 - val_accuracy: 0.1429\n",
+      "Epoch 9/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 15.8172 - accuracy: 0.0517\n",
+      "Epoch 00009: val_loss did not improve from 17.16194\n",
+      "59/58 [==============================] - 57s 973ms/step - loss: 15.6855 - accuracy: 0.0508 - val_loss: 17.8925 - val_accuracy: 0.1786\n",
+      "Epoch 10/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 15.3315 - accuracy: 0.0517\n",
+      "Epoch 00010: val_loss did not improve from 17.16194\n",
+      "59/58 [==============================] - 56s 957ms/step - loss: 15.2081 - accuracy: 0.0508 - val_loss: 17.3912 - val_accuracy: 0.4643\n",
+      "Epoch 11/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 15.2621 - accuracy: 0.0517\n",
+      "Epoch 00011: val_loss did not improve from 17.16194\n",
+      "59/58 [==============================] - 58s 978ms/step - loss: 15.1398 - accuracy: 0.0508 - val_loss: 17.1633 - val_accuracy: 0.1429\n",
+      "Epoch 12/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 15.5397 - accuracy: 0.0345\n",
+      "Epoch 00012: val_loss did not improve from 17.16194\n",
+      "59/58 [==============================] - 56s 953ms/step - loss: 15.6173 - accuracy: 0.0339 - val_loss: 18.1601 - val_accuracy: 0.1429\n",
+      "Epoch 13/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 15.4702 - accuracy: 0.0690\n",
+      "Epoch 00013: val_loss did not improve from 17.16194\n",
+      "59/58 [==============================] - 58s 986ms/step - loss: 15.4808 - accuracy: 0.0678 - val_loss: 17.4696 - val_accuracy: 0.0000e+00\n",
+      "Epoch 14/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 15.1233 - accuracy: 0.1034\n",
+      "Epoch 00014: val_loss did not improve from 17.16194\n",
+      "59/58 [==============================] - 55s 931ms/step - loss: 15.0034 - accuracy: 0.1017 - val_loss: 19.5240 - val_accuracy: 0.1429\n",
+      "Epoch 15/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 15.8864 - accuracy: 0.1293\n",
+      "Epoch 00015: val_loss did not improve from 17.16194\n",
+      "59/58 [==============================] - 58s 985ms/step - loss: 15.6854 - accuracy: 0.1271 - val_loss: 17.6684 - val_accuracy: 0.0357\n",
+      "Epoch 16/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 16.1640 - accuracy: 0.1293\n",
+      "Epoch 00016: val_loss did not improve from 17.16194\n",
+      "59/58 [==============================] - 57s 963ms/step - loss: 16.2992 - accuracy: 0.1271 - val_loss: 17.4060 - val_accuracy: 1.0000\n",
+      "Epoch 17/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 16.3026 - accuracy: 0.0517\n",
+      "Epoch 00017: val_loss did not improve from 17.16194\n",
+      "59/58 [==============================] - 57s 960ms/step - loss: 16.0945 - accuracy: 0.0508 - val_loss: 17.9671 - val_accuracy: 0.1071\n",
+      "Epoch 18/100\n",
+      "58/58 [============================>.] - ETA: 0s - loss: 16.0946 - accuracy: 0.0948\n",
+      "Epoch 00018: val_loss did not improve from 17.16194\n",
+      "59/58 [==============================] - 56s 955ms/step - loss: 16.0946 - accuracy: 0.0932 - val_loss: 17.1750 - val_accuracy: 0.0000e+00\n"
+     ]
+    }
+   ],
+   "source": [
+    "# verbose\n",
+    "print(\"\\nStarting to Fine Tune Model\\n\")\n",
+    "\n",
+    "# add the best weights from the train top model\n",
+    "# at this point we have the pre-train weights of the base model and the trained weight of the new/added top model\n",
+    "# we re-load model weights to ensure the best epoch is selected and not the last one.\n",
+    "model.load_weights(top_weights_path)\n",
+    "\n",
+    "print(model.summary())\n",
+    "# based_model_last_block_layer_number points to the layer in your model you want to train.\n",
+    "# For example if you want to train the last block of a 19 layer VGG16 model this should be 15\n",
+    "# If you want to train the last Two blocks of an Inception model it should be 172\n",
+    "# layers before this number will used the pre-trained weights, layers above and including this number\n",
+    "# will be re-trained based on the new data.\n",
+    "for layer in model.layers[:based_model_last_block_layer_number]:\n",
+    "    layer.trainable = True\n",
+    "for layer in model.layers[based_model_last_block_layer_number:]:\n",
+    "    layer.trainable = True\n",
+    "\n",
+    "# compile the model with a SGD/momentum optimizer\n",
+    "# and a very slow learning rate.\n",
+    "model.compile(optimizer='nadam',\n",
+    "              loss='categorical_crossentropy',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "# save weights of best training epoch: monitor either val_loss or val_acc\n",
+    "final_weights_path = os.path.join(os.path.abspath(model_path), 'model_weights.h5')\n",
+    "callbacks_list = [\n",
+    "    ModelCheckpoint(final_weights_path, monitor='val_loss', verbose=1, save_best_only=True),\n",
+    "    EarlyStopping(monitor='val_loss', patience=patience, verbose=0)\n",
+    "]\n",
+    "\n",
+    "# fine-tune the model\n",
+    "hist = model.fit(train_generator,\n",
+    "            steps_per_epoch=train_generator.samples/batch_size * .8,\n",
+    "            epochs=nb_epoch,\n",
+    "            validation_data=validation_generator,\n",
+    "            validation_steps = validation_generator.samples/batch_size*.8,\n",
+    "            callbacks=callbacks_list)\n",
+    "\n",
+    "# save model\n",
+    "model_json = model.to_json()\n",
+    "with open(os.path.join(os.path.abspath(model_path), 'model.json'), 'w') as json_file:\n",
+    "    json_file.write(model_json)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-5-94ce6e05c1eb>:10: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use Model.predict, which supports generators.\n",
+      "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 18 batches). You may need to use the repeat() function when building your dataset.\n",
+      "Confusion Matrix\n",
+      "[[0 4 1 0 0 0]\n",
+      " [0 7 5 1 0 0]\n",
+      " [0 2 1 1 1 0]\n",
+      " [0 1 3 0 0 0]\n",
+      " [0 2 0 0 2 0]\n",
+      " [0 2 0 1 0 0]]\n",
+      "Classification Report\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Number of classes, 6, does not match size of target_names, 7. Try specifying the labels parameter",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-94ce6e05c1eb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Classification Report'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mtarget_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclass_indices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/envs/new/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m                           FutureWarning)\n\u001b[1;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/new/lib/python3.7/site-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mclassification_report\u001b[0;34m(y_true, y_pred, labels, target_names, sample_weight, digits, output_dict, zero_division)\u001b[0m\n\u001b[1;32m   1951\u001b[0m                 \u001b[0;34m\"Number of classes, {0}, does not match size of \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1952\u001b[0m                 \u001b[0;34m\"target_names, {1}. Try specifying the labels \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1953\u001b[0;31m                 \u001b[0;34m\"parameter\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1954\u001b[0m             )\n\u001b[1;32m   1955\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtarget_names\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Number of classes, 6, does not match size of target_names, 7. Try specifying the labels parameter"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "\n",
+    "json_file = open(os.path.join(os.path.abspath(model_path), 'model.json'), 'r')\n",
+    "loaded_model_json = json_file.read()\n",
+    "json_file.close()\n",
+    "loaded_model = tf.keras.models.model_from_json(loaded_model_json)\n",
+    "\n",
+    "\n",
+    "#Confution Matrix and Classification Report\n",
+    "Y_pred = model.predict_generator(validation_generator, validation_generator.samples // batch_size+1)\n",
+    "y_pred = np.argmax(Y_pred, axis=1)\n",
+    "print('Confusion Matrix')\n",
+    "print(confusion_matrix(validation_generator.classes, y_pred))\n",
+    "print('Classification Report')\n",
+    "target_names = train_generator.class_indices.keys()\n",
+    "print(classification_report(validation_generator.classes, y_pred, target_names=target_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/GPS_Panel/Classifier/Train_Classifier_DEEP_SVC.ipynb b/GPS_Panel/Classifier/Train_Classifier_DEEP_SVC.ipynb
new file mode 100644
index 0000000..d281190
--- /dev/null
+++ b/GPS_Panel/Classifier/Train_Classifier_DEEP_SVC.ipynb
@@ -0,0 +1,26075 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import the necessary packages\n",
+    "from imutils import paths\n",
+    "import numpy as np\n",
+    "\n",
+    "import pickle\n",
+    "import cv2\n",
+    "import os\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "import time\n",
+    "import tensorflow as tf\n",
+    "\n",
+    "\n",
+    "from sklearn.metrics import classification_report,plot_confusion_matrix,confusion_matrix\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
+    "\n",
+    "tf.keras.backend.clear_session()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found directory: .\n",
+      "\tTrain_Classifier_AllDeep.ipynb\n",
+      "\tTrain_Classifier_DEEP_SVC.ipynb\n",
+      "Found directory: ./.ipynb_checkpoints\n",
+      "\tTrain_Classifier_AllDeep-checkpoint.ipynb\n",
+      "\tTrain_Classifier_DEEP_SVC-checkpoint.ipynb\n",
+      "Found directory: ./dataset_prueba_1\n",
+      "Found directory: ./dataset_prueba_1/model_SVM\n",
+      "\tembeddings.pickle\n",
+      "\tmodel_SVM.h5\n",
+      "\tle.pickle\n",
+      "\trecognizer.pickle\n",
+      "Found directory: ./dataset_prueba_1/7- Soiling\n",
+      "\tSoling 19 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 6 El Aguila M1 2019.jpg\n",
+      "\tSoling 2 El Aguila M1 2019.jpg\n",
+      "\tSoling 3 El Aguila M1 2019.jpg\n",
+      "\tSoling 15 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 12 Pampa Camarones M2 2019.jpg\n",
+      "\tSoling 1 El Aguila M1 2019.jpg\n",
+      "\tSoling 20 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 5 El Aguila M1 2019.jpg\n",
+      "\tSoling 10 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 9 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 21 Cerro Dominador M1 2019.jpg\n",
+      "\tSoling 13 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 4 El Aguila M1 2019.jpg\n",
+      "\tSoling 18 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 16 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 7 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 14 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 8 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 22 Valle de la Luna M1 2019.jpg\n",
+      "\tSoling 17 Pampa Camarones M4 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/1- Una-Mas Celdas\n",
+      "\tCinco-Mas Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M1 2018.jpg\n",
+      "\tUna Celda 22 LLano de LLampos M4 2019.jpg\n",
+      "\tCinco-Mas 4 Cerro Demonidor M2 2019.jpg\n",
+      "\tUna Celda 10 Cerro Dominador M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 5 Llano de LLampos M5 2019.jpg\n",
+      "\tDos-Cuatro Celdas 10 LLano de LLampos M3 2019.jpg\n",
+      "\tUna Celda 17 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 7 Pampa Camarones M2 2019.jpg\n",
+      "\tCinco-Mas Celdas 19 Maria Elena M6 2018.jpg\n",
+      "\tUna Celda 14 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 15 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 7 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 17 Maria Elena M6 2018.jpg\n",
+      "\tDos-Cuatro Celdas 4 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 12 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 11 LLano de LLampos M5 2019.jpg\n",
+      "\tDos-Cuatro Celdas 2 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 8 Pampa Camarones M2 2019.jpg\n",
+      "\tCinco-Mas Celdas 20 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 11 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 18 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 15 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 3 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 21 Maria Elena M7 2018.jpg\n",
+      "\tUna Celda 2 - El Aguila M1 2019.jpg\n",
+      "\tUna Celda 3 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 14 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M2 2018.jpg\n",
+      "\tUna Celda 18 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 15 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 19 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 9 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 7 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 6 Maria Elena M1 2018.jpg\n",
+      "\tUna Celda 1 - Citrino.jpg\n",
+      "\tUna Celda 23 Valle de la Luna M1 2019.jpg\n",
+      "\tUna Celda 21 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 20 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 9 Pampa Camarones M4 2019.jpg\n",
+      "\tUna Celda 5 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 13 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 16 Cerro Dominador M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 13 Maria Elena M1 2018.jpg\n",
+      "\tUna Celda 4 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 6 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 16 Maria Elena M6 2018.jpg\n",
+      "\tUna Celda 11 Cerro Dominador M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 8 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 3 LLano de LLampos M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 9 Pampa Camarones M3 2019.jpg\n",
+      "\tUna Celda 6 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 13 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 19 Maria Elena M2 2018.jpg\n",
+      "\tUna Celda 8 Pampa Camarones M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 16 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 18 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 20 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 2 LLano de LLampos M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 5 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas Celdas 10 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 17 Maria Elena M2 2018.jpg\n",
+      "Found directory: ./dataset_prueba_1/4- Falla Diodos\n",
+      "\tUn Diodo 25  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 7 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 19  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 14  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 18  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 26  Lllano de LLampos M4 2019.jpg\n",
+      "\tDos Diodos 2  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 11  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 6 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 22  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 1 El Aguila M1 2019.jpg\n",
+      "\tUn Diodo 23  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 24  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 21  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 3 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 9  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 16  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 15  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 4 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 13  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 12  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 1 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 8  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 20  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 10  Pampa Camarones M2 2019.jpg\n",
+      "\tDos Diodos 1  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 17  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 5 Pampa Camarones M2 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/9- No es Panel\n",
+      "\tNo es panel 12 Casuto M1 2019.jpg\n",
+      "\tNo es panel 5 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 16 Llano de Llampos M1 2019.jpg\n",
+      "\tNo es panel 9 Casuto M1 2019.jpg\n",
+      "\tNo es panel 13 Casuto M1 2019.jpg\n",
+      "\tNo es panel 3 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 7 Casuto M1 2019.jpg\n",
+      "\tNo es panel 15 Casuto M1 2019.jpg\n",
+      "\tNo es panel 1 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 2 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 6 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 11 Casuto M1 2019.jpg\n",
+      "\tNo es panel 10 Casuto M1 2019.jpg\n",
+      "\tNo es panel 14 Casuto M1 2019.jpg\n",
+      "\tNo es panel 4 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 17 Cerro Dominador M1 2019.jpg\n",
+      "\tNo es panel 8 Casuto M1 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/8- Tracker dañado\n",
+      "\tTracker dañado 8 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 20 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 9 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 11 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 2 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 19 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 10 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 4 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 15 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 17 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 7 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 5 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 6 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 18 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 16 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 12 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 1 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 3 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_prueba_1/0- Normal\n",
+      "\tNormal 30 Valle de La Luna M1 2019.jpg\n",
+      "\tNormal 16 Tiltil M2 2019.jpg\n",
+      "\tNormal 3 Citrino.jpg\n",
+      "\tNormal 20 Casuto M1 2019.jpg\n",
+      "\tNormal 12 Tiltil M1 2019.jpg\n",
+      "\tNormal 2 Citrino.jpg\n",
+      "\tNormal 15 Tiltil M1 2019.jpg\n",
+      "\tNormal 11 Pampa Camarones M3 2019.jpg\n",
+      "\tNormal 1 Citrino.jpg\n",
+      "\tNormal 17 Casuto M1 2019.jpg\n",
+      "\tNormal 21 Casuto M1 2019.jpg\n",
+      "\tNormal 5 El Aguila M1 2019.jpg\n",
+      "\tNormal 14 Tiltil M1 2019.jpg\n",
+      "\tNormal 6 El Aguila M1 2019.jpg\n",
+      "\tNormal 25 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 10 Pampa Camarones M1 2019.jpg\n",
+      "\tNormal 9 El Aguila M1 2019.jpg\n",
+      "\tNormal 27 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 19 Casuto M1 2019.jpg\n",
+      "\tNormal 18 Casuto M1 2019.jpg\n",
+      "\tNormal 8 El Aguila M1 2019.jpg\n",
+      "\tNormal 28 Cerro Dominado M1 2019.jpg\n",
+      "\tNormal 22 Casuto M1 2019.jpg\n",
+      "\tNormal 4 Citrino.jpg\n",
+      "\tNormal 24 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 23 Casuto M1 2019.jpg\n",
+      "\tNormal 26 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 13 Tiltil M1 2019.jpg\n",
+      "\tNormal 7 El Aguila M1 2019.jpg\n",
+      "Found directory: ./dataset_catdog\n",
+      "Found directory: ./dataset_catdog/train\n",
+      "\tcat.2414.jpg\n",
+      "\tcat.6120.jpg\n",
+      "\tcat.8346.jpg\n",
+      "\tcat.6718.jpg\n",
+      "\tcat.9209.jpg\n",
+      "\tcat.2699.jpg\n",
+      "\tcat.1814.jpg\n",
+      "\tcat.2472.jpg\n",
+      "\tcat.9271.jpg\n",
+      "\tcat.5866.jpg\n",
+      "\tcat.55.jpg\n",
+      "\tcat.4382.jpg\n",
+      "\tcat.3649.jpg\n",
+      "\tcat.8661.jpg\n",
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+      "\tcat.6162.jpg\n",
+      "\tcat.689.jpg\n",
+      "\tcat.8321.jpg\n",
+      "\tcat.11710.jpg\n",
+      "\tcat.7951.jpg\n",
+      "\tcat.8011.jpg\n",
+      "\tcat.2297.jpg\n",
+      "\tcat.4187.jpg\n",
+      "\tcat.268.jpg\n",
+      "\tcat.12015.jpg\n",
+      "\tcat.4449.jpg\n",
+      "\tcat.3808.jpg\n",
+      "\tcat.2971.jpg\n",
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+      "\tcat.5753.jpg\n",
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+      "\tcat.3786.jpg\n",
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+      "\tcat.9685.jpg\n",
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+      "\tcat.12414.jpg\n",
+      "\tcat.4720.jpg\n",
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+      "\tcat.8915.jpg\n",
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+      "\tcat.753.jpg\n",
+      "\tcat.5707.jpg\n",
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+      "\tcat.8884.jpg\n",
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+      "\tdog.231.jpg\n",
+      "\tdog.1311.jpg\n",
+      "\tdog.8615.jpg\n",
+      "\tdog.3758.jpg\n",
+      "\tdog.7063.jpg\n",
+      "\tdog.2122.jpg\n",
+      "\tdog.10511.jpg\n",
+      "\tdog.6269.jpg\n",
+      "\tdog.7980.jpg\n",
+      "\tdog.4587.jpg\n",
+      "\tdog.287.jpg\n",
+      "\tdog.8757.jpg\n",
+      "\tdog.12188.jpg\n",
+      "\tdog.4317.jpg\n",
+      "\tdog.1513.jpg\n",
+      "\tdog.7958.jpg\n",
+      "\tdog.8786.jpg\n",
+      "\tdog.5603.jpg\n",
+      "\tdog.5989.jpg\n",
+      "\tdog.28.jpg\n",
+      "\tdog.4685.jpg\n",
+      "\tdog.4070.jpg\n",
+      "\tdog.4462.jpg\n",
+      "\tdog.12287.jpg\n",
+      "\tdog.2803.jpg\n",
+      "\tdog.6015.jpg\n",
+      "\tdog.5893.jpg\n",
+      "\tdog.11156.jpg\n",
+      "\tdog.1455.jpg\n",
+      "\tdog.6807.jpg\n",
+      "\tdog.9068.jpg\n",
+      "Found directory: ./dataset_panel\n",
+      "\tembeddings.pickle\n",
+      "\tle.pickle\n",
+      "\trecognizer.pickle\n",
+      "Found directory: ./dataset_panel/2- Dos-Cuatro Celdas\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 10 LLano de LLampos M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 15 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 7 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 4 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 11 LLano de LLampos M5 2019.jpg\n",
+      "\tDos-Cuatro Celdas 2 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 8 Pampa Camarones M2 2019.jpg\n",
+      "\tDos-Cuatro Celdas 3 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 21 Maria Elena M7 2018.jpg\n",
+      "\tDos-Cuatro Celdas 14 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 13 Maria Elena M1 2018.jpg\n",
+      "\tDos-Cuatro Celdas 6 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 9 Pampa Camarones M3 2019.jpg\n",
+      "\tDos-Cuatro Celdas 19 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 16 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 18 Maria Elena M2 2018.jpg\n",
+      "\tDos-Cuatro Celdas 20 Maria Elena M3 2018.jpg\n",
+      "\tDos-Cuatro Celdas 5 El Aguila M1 2019.jpg\n",
+      "\tDos-Cuatro Celdas 17 Maria Elena M2 2018.jpg\n",
+      "Found directory: ./dataset_panel/6- Caja Conexion\n",
+      "\tCaja Conexion 2 Pampa Camarones M3 2019.jpg\n",
+      "\tCaja Conexion 3 Pampa Camarones M3 2019.jpg\n",
+      "\tCaja Conexion 1 El Aguila M1 2019.jpg\n",
+      "\tCaja Conexión 11 Pampa Camarones M1 2019.jpg\n",
+      "\tCaja Conexión 12 Llano de Llampos M2 2019.jpg\n",
+      "Found directory: ./dataset_panel/7- Soiling\n",
+      "\tSoling 19 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 6 El Aguila M1 2019.jpg\n",
+      "\tSoling 2 El Aguila M1 2019.jpg\n",
+      "\tSoling 3 El Aguila M1 2019.jpg\n",
+      "\tSoling 15 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 12 Pampa Camarones M2 2019.jpg\n",
+      "\tSoling 1 El Aguila M1 2019.jpg\n",
+      "\tSoling 20 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 5 El Aguila M1 2019.jpg\n",
+      "\tSoling 10 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 9 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 21 Cerro Dominador M1 2019.jpg\n",
+      "\tSoling 13 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 4 El Aguila M1 2019.jpg\n",
+      "\tSoling 18 Pampa Camarones M4 2019.jpg\n",
+      "\tSoling 16 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 7 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 14 Pampa Camarones M3 2019.jpg\n",
+      "\tSoling 8 Pampa Camarones M1 2019.jpg\n",
+      "\tSoling 22 Valle de la Luna M1 2019.jpg\n",
+      "\tSoling 17 Pampa Camarones M4 2019.jpg\n",
+      "Found directory: ./dataset_panel/4- Falla Diodos\n",
+      "\tUn Diodo 25  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 7 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 19  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 14  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 18  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 26  Lllano de LLampos M4 2019.jpg\n",
+      "\tDos Diodos 2  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 11  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 6 Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 22  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 1 El Aguila M1 2019.jpg\n",
+      "\tUn Diodo 23  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 24  Casuto M4 2019.jpg\n",
+      "\tUn Diodo 21  Tiltil M4 2019.jpg\n",
+      "\tUn Diodo 3 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 9  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 16  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 15  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 4 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 13  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 12  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 1 Pampa Camarones M1 2019.jpg\n",
+      "\tUn Diodo 8  Pampa Camarones M2 2019.jpg\n",
+      "\tUn Diodo 20  Pampa Camarones M4 2019.jpg\n",
+      "\tUn Diodo 10  Pampa Camarones M2 2019.jpg\n",
+      "\tDos Diodos 1  Tiltil M2 2019.jpg\n",
+      "\tUn Diodo 17  Pampa Camarones M3 2019.jpg\n",
+      "\tUn Diodo 5 Pampa Camarones M2 2019.jpg\n",
+      "Found directory: ./dataset_panel/3- Cinco-Mas Celdas\n",
+      "\tCinco-Mas Celdas 1 El Aguila M1 2019.jpg\n",
+      "\tCinco-Mas 4 Cerro Demonidor M2 2019.jpg\n",
+      "\tCinco-Mas Celdas 5 Llano de LLampos M5 2019.jpg\n",
+      "\tCinco-Mas Celdas 19 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 17 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 20 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 11 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 18 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 15 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 14 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 9 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 7 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 6 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas Celdas 16 Maria Elena M6 2018.jpg\n",
+      "\tCinco-Mas Celdas 8 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 3 LLano de LLampos M3 2019.jpg\n",
+      "\tCinco-Mas Celdas 13 Maria Elena M3 2018.jpg\n",
+      "\tCinco-Mas Celdas 12 Maria Elena M1 2018.jpg\n",
+      "\tCinco-Mas 2 LLano de LLampos M3 2019.jpg\n",
+      "\tCinco-Mas Celdas 10 Maria Elena M1 2018.jpg\n",
+      "Found directory: ./dataset_panel/1- Una Celda\n",
+      "\tUna Celda 22 LLano de LLampos M4 2019.jpg\n",
+      "\tUna Celda 10 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 17 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 7 Pampa Camarones M2 2019.jpg\n",
+      "\tUna Celda 14 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 12 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 2 - El Aguila M1 2019.jpg\n",
+      "\tUna Celda 3 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 18 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 15 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 19 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 1 - Citrino.jpg\n",
+      "\tUna Celda 23 Valle de la Luna M1 2019.jpg\n",
+      "\tUna Celda 21 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 20 Cerro Dominador M2 2019.jpg\n",
+      "\tUna Celda 9 Pampa Camarones M4 2019.jpg\n",
+      "\tUna Celda 5 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 13 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 16 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 4 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 11 Cerro Dominador M1 2019.jpg\n",
+      "\tUna Celda 6 El Aguila M1 2019.jpg\n",
+      "\tUna Celda 8 Pampa Camarones M3 2019.jpg\n",
+      "Found directory: ./dataset_panel/9- No es Panel\n",
+      "\tNo es panel 12 Casuto M1 2019.jpg\n",
+      "\tNo es panel 5 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 16 Llano de Llampos M1 2019.jpg\n",
+      "\tNo es panel 9 Casuto M1 2019.jpg\n",
+      "\tNo es panel 13 Casuto M1 2019.jpg\n",
+      "\tNo es panel 3 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 7 Casuto M1 2019.jpg\n",
+      "\tNo es panel 15 Casuto M1 2019.jpg\n",
+      "\tNo es panel 1 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 2 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 6 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 11 Casuto M1 2019.jpg\n",
+      "\tNo es panel 10 Casuto M1 2019.jpg\n",
+      "\tNo es panel 14 Casuto M1 2019.jpg\n",
+      "\tNo es panel 4 El Aguila M1 2019.jpg\n",
+      "\tNo es panel 17 Cerro Dominador M1 2019.jpg\n",
+      "\tNo es panel 8 Casuto M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/5- Panel Desconectado\n",
+      "\tPanel Desconectado 24 Llano de LLampos M3 2019.jpg\n",
+      "\tPanel Desconectado 6 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 19 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 9 Pampa Camarones M2 2019.jpg\n",
+      "\tPanel Desconectado 12 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 10 Pampa Camarones M2 2019.jpg\n",
+      "\tPanel Desconectado 21 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 4 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 25 Llano de LLampos M3 2019.jpg\n",
+      "\tPanel Desconectado 23 Llano de LLampos M2 2019.jpg\n",
+      "\tPanel Desconectado 20 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 11 Pampa Camarones M2 2019.jpg\n",
+      "\tPanel Desconectado 15 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 1 El Aguila M2 2019.jpg\n",
+      "\tPanel Desconectado 16 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 14 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 18 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 22 Tiltil M2 2019.jpg\n",
+      "\tPanel Desconectado 13 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 17 Pampa Camarones M3 2019.jpg\n",
+      "\tPanel Desconectado 3 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 5 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 7 Pampa Camarones M1 2019.jpg\n",
+      "\tPanel Desconectado 2 El Aguila M2 2019.jpg\n",
+      "\tPanel Desconectado 8 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/8- Tracker dañado\n",
+      "\tTracker dañado 8 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 20 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 9 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 11 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 2 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 19 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 10 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 4 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 15 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 17 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 7 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 5 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 6 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 18 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 13 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 16 Maria Elena M13 2018.jpg\n",
+      "\tTracker dañado 12 Maria Elena M3 2018.jpg\n",
+      "\tTracker dañado 1 Pampa Camarones M1 2019.jpg\n",
+      "\tTracker dañado 3 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/10- Reflejos\n",
+      "\tReflejo 3 Citrino.jpg\n",
+      "\tReflejo 10 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 4 Citrino.jpg\n",
+      "\tReflejo 11 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 23 Maria Elena M3 2018.jpg\n",
+      "\tReflejo 22 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 18 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 5 El Aguila M1 2019.jpg\n",
+      "\tReflejo 21 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 7 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 2 Citrino.jpg\n",
+      "\tReflejo 16 Pampa Camarones M2 2019.jpg\n",
+      "\tReflejo 6 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 8 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 19 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 15 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 9 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 13 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 17 Pampa Camarones M2 2019.jpg\n",
+      "\tReflejo 1 Citrino.jpg\n",
+      "\tReflejo 14 Pampa Camarones M1 2019.jpg\n",
+      "\tReflejo 20 LLano de LLampos M2 2019.jpg\n",
+      "\tReflejo 12 Pampa Camarones M1 2019.jpg\n",
+      "Found directory: ./dataset_panel/0- Normal\n",
+      "\tNormal 30 Valle de La Luna M1 2019.jpg\n",
+      "\tNormal 16 Tiltil M2 2019.jpg\n",
+      "\tNormal 3 Citrino.jpg\n",
+      "\tNormal 20 Casuto M1 2019.jpg\n",
+      "\tNormal 12 Tiltil M1 2019.jpg\n",
+      "\tNormal 2 Citrino.jpg\n",
+      "\tNormal 15 Tiltil M1 2019.jpg\n",
+      "\tNormal 11 Pampa Camarones M3 2019.jpg\n",
+      "\tNormal 1 Citrino.jpg\n",
+      "\tNormal 17 Casuto M1 2019.jpg\n",
+      "\tNormal 21 Casuto M1 2019.jpg\n",
+      "\tNormal 5 El Aguila M1 2019.jpg\n",
+      "\tNormal 14 Tiltil M1 2019.jpg\n",
+      "\tNormal 6 El Aguila M1 2019.jpg\n",
+      "\tNormal 25 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 10 Pampa Camarones M1 2019.jpg\n",
+      "\tNormal 9 El Aguila M1 2019.jpg\n",
+      "\tNormal 27 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 19 Casuto M1 2019.jpg\n",
+      "\tNormal 18 Casuto M1 2019.jpg\n",
+      "\tNormal 8 El Aguila M1 2019.jpg\n",
+      "\tNormal 28 Cerro Dominado M1 2019.jpg\n",
+      "\tNormal 22 Casuto M1 2019.jpg\n",
+      "\tNormal 4 Citrino.jpg\n",
+      "\tNormal 24 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 23 Casuto M1 2019.jpg\n",
+      "\tNormal 26 Llano de Llampos M1 2019.jpg\n",
+      "\tNormal 13 Tiltil M1 2019.jpg\n",
+      "\tNormal 7 El Aguila M1 2019.jpg\n",
+      "Found directory: ./model\n",
+      "\tmodel.json\n",
+      "\ttop_model_weights.h5\n",
+      "\tmodel_weights.h5\n",
+      "Found directory: ./preview\n"
+     ]
+    }
+   ],
+   "source": [
+    "img_width, img_height = 224, 224\n",
+    "#base_model = tf.keras.applications.MobileNetV2(input_shape=(img_width, img_height, 3), weights='imagenet', include_top=False)\n",
+    "#img_width, img_height =  \n",
+    "#base_model = tf.keras.applications.NASNetMobile(input_shape=(img_width, img_height, 3), weights='imagenet', include_top=False)\n",
+    "base_model = tf.keras.applications.Xception(input_shape=(img_width, img_height, 3), weights='imagenet', include_top=False)\n",
+    "\n",
+    "#\n",
+    "\n",
+    "# Set the directory you want to start from\n",
+    "rootDir = '.'\n",
+    "for dirName, subdirList, fileList in os.walk(rootDir):\n",
+    "    print('Found directory: %s' % dirName)\n",
+    "    for fname in fileList:\n",
+    "        print('\\t%s' % fname)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INFO] processing image 1/180\n",
+      "[INFO] processing image 2/180\n",
+      "[INFO] processing image 3/180\n",
+      "[INFO] processing image 4/180\n",
+      "[INFO] processing image 5/180\n",
+      "[INFO] processing image 6/180\n",
+      "[INFO] processing image 7/180\n",
+      "[INFO] processing image 8/180\n",
+      "[INFO] processing image 9/180\n",
+      "[INFO] processing image 10/180\n",
+      "[INFO] processing image 11/180\n",
+      "[INFO] processing image 12/180\n",
+      "[INFO] processing image 13/180\n",
+      "[INFO] processing image 14/180\n",
+      "[INFO] processing image 15/180\n",
+      "[INFO] processing image 16/180\n",
+      "[INFO] processing image 17/180\n",
+      "[INFO] processing image 18/180\n",
+      "[INFO] processing image 19/180\n",
+      "[INFO] processing image 20/180\n",
+      "[INFO] processing image 21/180\n",
+      "[INFO] processing image 22/180\n",
+      "[INFO] processing image 23/180\n",
+      "[INFO] processing image 24/180\n",
+      "[INFO] processing image 25/180\n",
+      "[INFO] processing image 26/180\n",
+      "[INFO] processing image 27/180\n",
+      "[INFO] processing image 28/180\n",
+      "[INFO] processing image 29/180\n",
+      "[INFO] processing image 30/180\n",
+      "[INFO] processing image 31/180\n",
+      "[INFO] processing image 32/180\n",
+      "[INFO] processing image 33/180\n",
+      "[INFO] processing image 34/180\n",
+      "[INFO] processing image 35/180\n",
+      "[INFO] processing image 36/180\n",
+      "[INFO] processing image 37/180\n",
+      "[INFO] processing image 38/180\n",
+      "[INFO] processing image 39/180\n",
+      "[INFO] processing image 40/180\n",
+      "[INFO] processing image 41/180\n",
+      "[INFO] processing image 42/180\n",
+      "[INFO] processing image 43/180\n",
+      "[INFO] processing image 44/180\n",
+      "[INFO] processing image 45/180\n",
+      "[INFO] processing image 46/180\n",
+      "[INFO] processing image 47/180\n",
+      "[INFO] processing image 48/180\n",
+      "[INFO] processing image 49/180\n",
+      "[INFO] processing image 50/180\n",
+      "[INFO] processing image 51/180\n",
+      "[INFO] processing image 52/180\n",
+      "[INFO] processing image 53/180\n",
+      "[INFO] processing image 54/180\n",
+      "[INFO] processing image 55/180\n",
+      "[INFO] processing image 56/180\n",
+      "[INFO] processing image 57/180\n",
+      "[INFO] processing image 58/180\n",
+      "[INFO] processing image 59/180\n",
+      "[INFO] processing image 60/180\n",
+      "[INFO] processing image 61/180\n",
+      "[INFO] processing image 62/180\n",
+      "[INFO] processing image 63/180\n",
+      "[INFO] processing image 64/180\n",
+      "[INFO] processing image 65/180\n",
+      "[INFO] processing image 66/180\n",
+      "[INFO] processing image 67/180\n",
+      "[INFO] processing image 68/180\n",
+      "[INFO] processing image 69/180\n",
+      "[INFO] processing image 70/180\n",
+      "[INFO] processing image 71/180\n",
+      "[INFO] processing image 72/180\n",
+      "[INFO] processing image 73/180\n",
+      "[INFO] processing image 74/180\n",
+      "[INFO] processing image 75/180\n",
+      "[INFO] processing image 76/180\n",
+      "[INFO] processing image 77/180\n",
+      "[INFO] processing image 78/180\n",
+      "[INFO] processing image 79/180\n",
+      "[INFO] processing image 80/180\n",
+      "[INFO] processing image 81/180\n",
+      "[INFO] processing image 82/180\n",
+      "[INFO] processing image 83/180\n",
+      "[INFO] processing image 84/180\n",
+      "[INFO] processing image 85/180\n",
+      "[INFO] processing image 86/180\n",
+      "[INFO] processing image 87/180\n",
+      "[INFO] processing image 88/180\n",
+      "[INFO] processing image 89/180\n",
+      "[INFO] processing image 90/180\n",
+      "[INFO] processing image 91/180\n",
+      "[INFO] processing image 92/180\n",
+      "[INFO] processing image 93/180\n",
+      "[INFO] processing image 94/180\n",
+      "[INFO] processing image 95/180\n",
+      "[INFO] processing image 96/180\n",
+      "[INFO] processing image 97/180\n",
+      "[INFO] processing image 98/180\n",
+      "[INFO] processing image 99/180\n",
+      "[INFO] processing image 100/180\n",
+      "[INFO] processing image 101/180\n",
+      "[INFO] processing image 102/180\n",
+      "[INFO] processing image 103/180\n",
+      "[INFO] processing image 104/180\n",
+      "[INFO] processing image 105/180\n",
+      "[INFO] processing image 106/180\n",
+      "[INFO] processing image 107/180\n",
+      "[INFO] processing image 108/180\n",
+      "[INFO] processing image 109/180\n",
+      "[INFO] processing image 110/180\n",
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+      "[INFO] processing image 153/180\n",
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+      "[INFO] processing image 158/180\n",
+      "[INFO] processing image 159/180\n",
+      "[INFO] processing image 160/180\n",
+      "[INFO] processing image 161/180\n",
+      "[INFO] processing image 162/180\n",
+      "[INFO] processing image 163/180\n",
+      "[INFO] processing image 164/180\n",
+      "[INFO] processing image 165/180\n",
+      "[INFO] processing image 166/180\n",
+      "[INFO] processing image 167/180\n",
+      "[INFO] processing image 168/180\n",
+      "[INFO] processing image 169/180\n",
+      "[INFO] processing image 170/180\n",
+      "[INFO] processing image 171/180\n",
+      "[INFO] processing image 172/180\n",
+      "[INFO] processing image 173/180\n",
+      "[INFO] processing image 174/180\n",
+      "[INFO] processing image 175/180\n",
+      "[INFO] processing image 176/180\n",
+      "[INFO] processing image 177/180\n",
+      "[INFO] processing image 178/180\n",
+      "[INFO] processing image 179/180\n",
+      "[INFO] processing image 180/180\n",
+      "[INFO] serializing 720 encodings...\n"
+     ]
+    }
+   ],
+   "source": [
+    "path_dataset = \"./dataset_prueba_1/\"\n",
+    "embeddings_output = path_dataset + \"model_SVM/embeddings.pickle\"\n",
+    "imagePaths = list(paths.list_images(path_dataset))\n",
+    "# initialize our lists of extracted facial embeddings and\n",
+    "# corresponding people names\n",
+    "knownEmbeddings = []\n",
+    "knownNames = []\n",
+    "\n",
+    "# initialize the total number of faces processed\n",
+    "total = 0\n",
+    "\n",
+    "# loop over the image paths\n",
+    "for (i, imagePath) in enumerate(imagePaths):\n",
+    "    print(\"[INFO] processing image {}/{}\".format(i + 1,\n",
+    "    len(imagePaths)))\n",
+    "    name = imagePath.split(os.path.sep)[-2]\n",
+    "    Im = cv2.resize(cv2.imread(imagePath), (img_width, img_height))\n",
+    "    for im in [Im, cv2.flip(Im, 0), cv2.flip(Im, 1), cv2.flip(Im, -1)]:\n",
+    "        vec = base_model.predict(tf.keras.backend.expand_dims(im,0))\n",
+    "        # add the name of the person + corresponding face\n",
+    "        # embedding to their respective lists\n",
+    "        knownNames.append(name)\n",
+    "        knownEmbeddings.append(vec.flatten())\n",
+    "        total += 1\n",
+    "        \n",
+    "print(\"[INFO] serializing {} encodings...\".format(total))\n",
+    "data = {\"embeddings\": knownEmbeddings, \"names\": knownNames}\n",
+    "f = open(embeddings_output, \"wb\")\n",
+    "f.write(pickle.dumps(data))\n",
+    "f.close()           "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INFO] loading face embeddings...\n",
+      "[INFO] encoding labels...\n",
+      "[INFO] training model...\n",
+      "Confusion matrix, without normalization\n",
+      "[[13  2  2  2  0  0]\n",
+      " [ 1 41  3  0  0  0]\n",
+      " [ 2  2 19  0  0  0]\n",
+      " [ 3  4  1 11  0  0]\n",
+      " [ 0  0  0  0 17  0]\n",
+      " [ 0  3  0  1  2 15]]\n",
+      "Normalized confusion matrix\n",
+      "[[0.68421053 0.10526316 0.10526316 0.10526316 0.         0.        ]\n",
+      " [0.02222222 0.91111111 0.06666667 0.         0.         0.        ]\n",
+      " [0.08695652 0.08695652 0.82608696 0.         0.         0.        ]\n",
+      " [0.15789474 0.21052632 0.05263158 0.57894737 0.         0.        ]\n",
+      " [0.         0.         0.         0.         1.         0.        ]\n",
+      " [0.         0.14285714 0.         0.04761905 0.0952381  0.71428571]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification Report\n",
+      "                   precision    recall  f1-score   support\n",
+      "\n",
+      "        0- Normal       0.68      0.68      0.68        19\n",
+      "1- Una-Mas Celdas       0.79      0.91      0.85        45\n",
+      "  4- Falla Diodos       0.76      0.83      0.79        23\n",
+      "       7- Soiling       0.79      0.58      0.67        19\n",
+      "8- Tracker dañado       0.89      1.00      0.94        17\n",
+      "   9- No es Panel       1.00      0.71      0.83        21\n",
+      "\n",
+      "         accuracy                           0.81       144\n",
+      "        macro avg       0.82      0.79      0.79       144\n",
+      "     weighted avg       0.81      0.81      0.80       144\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_recognizer = path_dataset + \"model_SVM/recognizer.pickle\"\n",
+    "output_label = path_dataset + \"model_SVM/le.pickle\"\n",
+    "embeddings_output = path_dataset + \"model_SVM/embeddings.pickle\"\n",
+    "\n",
+    "# load the face embeddings\n",
+    "print(\"[INFO] loading face embeddings...\")\n",
+    "data = pickle.loads(open(embeddings_output, \"rb\").read())\n",
+    "# encode the labels\n",
+    "print(\"[INFO] encoding labels...\")\n",
+    "le = LabelEncoder()\n",
+    "labels = le.fit_transform(data[\"names\"])\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(data[\"embeddings\"], labels, random_state=0, test_size=0.2)\n",
+    "\n",
+    "# train the model used to accept the 128-d embeddings of the face and\n",
+    "# then produce the actual face recognition\n",
+    "print(\"[INFO] training model...\")\n",
+    "classifier = SVC(C=2.0, kernel=\"linear\", probability=True, class_weight = 'balanced')\n",
+    "classifier.fit(X_train, y_train)\n",
+    "# write the actual face recognition model to disk\n",
+    "f = open(output_recognizer, \"wb\")\n",
+    "f.write(pickle.dumps(classifier))\n",
+    "f.close()\n",
+    "# write the label encoder to disk\n",
+    "f = open(output_label, \"wb\")\n",
+    "f.write(pickle.dumps(le))\n",
+    "f.close()\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Plot non-normalized confusion matrix\n",
+    "titles_options = [(\"Confusion matrix, without normalization\", None),\n",
+    "                  (\"Normalized confusion matrix\", 'true')]\n",
+    "for title, normalize in titles_options:\n",
+    "    disp = plot_confusion_matrix(classifier,X_test, y_test,\n",
+    "                                 display_labels=np.unique(data[\"names\"]),\n",
+    "                                 cmap=plt.cm.Blues,\n",
+    "                                 normalize=normalize)\n",
+    "    disp.ax_.set_title(title)\n",
+    "\n",
+    "    print(title)\n",
+    "    print(disp.confusion_matrix)\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "y_pred = classifier.predict(X_test)\n",
+    "print('Classification Report')\n",
+    "target_names = np.unique(data[\"names\"])\n",
+    "print(classification_report(y_test, y_pred, target_names=target_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion matrix, without normalization\n",
+      "[[12  1  1  5  0  0]\n",
+      " [ 1 36  4  4  0  0]\n",
+      " [ 1  1 20  1  0  0]\n",
+      " [ 4  2  0 13  0  0]\n",
+      " [ 0  0  0  0 17  0]\n",
+      " [ 0  4  0  0  3 14]]\n",
+      "Normalized confusion matrix\n",
+      "[[0.63157895 0.05263158 0.05263158 0.26315789 0.         0.        ]\n",
+      " [0.02222222 0.8        0.08888889 0.08888889 0.         0.        ]\n",
+      " [0.04347826 0.04347826 0.86956522 0.04347826 0.         0.        ]\n",
+      " [0.21052632 0.10526316 0.         0.68421053 0.         0.        ]\n",
+      " [0.         0.         0.         0.         1.         0.        ]\n",
+      " [0.         0.19047619 0.         0.         0.14285714 0.66666667]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification Report\n",
+      "                   precision    recall  f1-score   support\n",
+      "\n",
+      "        0- Normal       0.67      0.63      0.65        19\n",
+      "1- Una-Mas Celdas       0.82      0.80      0.81        45\n",
+      "  4- Falla Diodos       0.80      0.87      0.83        23\n",
+      "       7- Soiling       0.57      0.68      0.62        19\n",
+      "8- Tracker dañado       0.85      1.00      0.92        17\n",
+      "   9- No es Panel       1.00      0.67      0.80        21\n",
+      "\n",
+      "         accuracy                           0.78       144\n",
+      "        macro avg       0.78      0.78      0.77       144\n",
+      "     weighted avg       0.79      0.78      0.78       144\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "KNN = KNeighborsClassifier(n_neighbors=1)\n",
+    "KNN.fit(X_train, y_train)\n",
+    "\n",
+    "# Plot non-normalized confusion matrix\n",
+    "titles_options = [(\"Confusion matrix, without normalization\", None),\n",
+    "                  (\"Normalized confusion matrix\", 'true')]\n",
+    "for title, normalize in titles_options:\n",
+    "    disp = plot_confusion_matrix(KNN,X_test, y_test,\n",
+    "                                 display_labels=np.unique(data[\"names\"]),\n",
+    "                                 cmap=plt.cm.Blues,\n",
+    "                                 normalize=normalize)\n",
+    "    disp.ax_.set_title(title)\n",
+    "\n",
+    "    print(title)\n",
+    "    print(disp.confusion_matrix)\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "y_pred = KNN.predict(X_test)\n",
+    "print('Classification Report')\n",
+    "target_names = np.unique(data[\"names\"])\n",
+    "print(classification_report(y_test, y_pred, target_names=target_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "# load the actual face recognition model along with the label encoder\n",
+    "recognizer = pickle.loads(open(output_recognizer, \"rb\").read())\n",
+    "le = pickle.loads(open(output_label, \"rb\").read())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}