PROYECTO U3 Versión Final.ipynb

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    "# Proyecto U3: Compound eye mapping of the ﬂy Musca domestica\n",
    "\n",
    "### By: Jessica Arelys Rojas Palomino, Oscar David Hincapie Garcia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este notebook posee los siguientes temas principales:\n",
    "    - 1. Planteamiento:\n",
    "        - 1.1 Contexto del problema.\n",
    "        - 1.2 Metodología\n",
    "        - 1.3 Análisis \n",
    "        - 1.4 Trabajos futuros\n",
    "    - 2. Modelo:\n",
    "        - 2.1 Descarga de las imágenes y organización de la base de datos\n",
    "        - 2.2 Evaluación del modelo A (modelo simple)\n",
    "        - 2.3 Evaluación del modelo B (modelo complejo)"
   ]
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   "source": [
    "## 1.1 Contexto del problema\n",
    "\n",
    "Un ojo compuesto es un órgano visual que se encuentra en ciertos artrópodos como insectos y crustáceos. Estos ojos están compuestos por un gran número de unidades estructurales denominadas omatidias. Actualmente, en el departamento de Ciencia e Ingeniería de la Universidad de Groningen en Holanda, se está desarrollando un proyecto para determinar un método que permita realizar la evaluación cuantitativa de la distribución de los ejes omatidios de los ojos compuestos de artrópodos para medir con exactitud las coordenadas angulares (Ángulo azimutal y de elevación) de cada ommatidia a partir de un conjunto de imágenes tomadas desde diferentes coordenadas.\n",
    "\n",
    "A continuación se muestran las imágenes del procedimiento aplicado a un ROI del ojo compuesto, este procesamiento se puede dividir en dos pasos:\n",
    "1 Correlación cruzada: Para alinear 3 imágenes.\n",
    "2 Interpolación con coordenadas barycentricas: para obtener las coordenadas angulares de las omatidias dentro un triángulo formado por el centro de las 3 imágenes."
   ]
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   "source": [
    "![alt text](./imagen.JPG)"
   ]
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   "source": [
    "Cabe resaltar que las anotaciones que tiene cada imagen (coordenadas angulares azimuth, elevación y el número de la imagen) se conocen desde la adquisición. Sin embargo, por cantidad de las imágenes que se van a adquirir, el procesamiento debe ser automático. Es por ello que se plantea una estrategia de solución implementando técnicas de Deep Learning para el procesamiento de las imágenes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para el proyecto U3 (redes convolucionales) se propone implementar una estrategia de clasificación que permita identificar los tipos de ángulos azimutales de cada imagen obtenida, debido a que este ángulo es conocido desde el inicio de la adquisición de las imágenes. El dataset con el que se trabajará cuenta con 108 imágenes que fue adiquirido a través de un sistema óptico diseño por el Departamento de Superficie y Películas Delgadas de la universidad mencionada."
   ]
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   "source": [
    "## 1.2 Metodología"
   ]
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   "source": [
    "#### Dataset\n",
    "Se cuenta con 108 imágenes divididas en 7 ángulos azimutales diferentes: 0, 300, 310, 320, 330, 340 y 350.\n",
    "#### Métrica de evaluación \n",
    "Se hará uso de las redes neurales convolucionales (CNN) para clasificar cada imagen según la clase a la que corresponda, en este caso la clase estará determinada por el ángulo azimutal.\n",
    "#### Estrategia de resolución\n",
    "Se realizará un pre-procesado de las imágenes y se evaluarán diferentes arquitecturas de red para evaluar la precisión del modelo para la clasificación.\n",
    "#### Flujo de trabajo\n",
    "- Para la versión final, inicialmente se importan las librerías que serán necesarias para la correcta ejecución del proyecto.\n",
    "- Posteriormente, se realiza un código sencillo para organizar el dataset según las clases que se trabajarán, en esta versión final, se trabajó únicamente con tres clases correspondientes a los ángulos azimutales: 0, 310 y 350.\n",
    "- Debido a que para poder empezar el entrenamiento debemos tener las imagenes preprocesadas, se diseña el generador que contiene la información para saber cómo se va a realizar el preprocesado, se reescalan las imagenes y cada pixel estarán entre 0 y 1 para hacer más eficiente el entrenamiento en vez de ir de 0-255, se modifica el tamaño de las imagenes y  se define el numero de imagenes que le vamos a enviar a nuestra computadora a procesar en cada uno de los pasos (batch_size).\n",
    "- Posteriormente, ya que la clasificación que haremos será categórica, se selecciona class_mode='binary' y finalmente, se imprimimen las imagenes correspondientes a las clases que vamos a trabajar.\n",
    "- Separamos nuestras imagenes con respecto a los datos de entrenamiento y a los de validación e imprimimos la distribución en las clases de entrenamiento y en las clases para realizar la validación. \n",
    "- Posteriormente creamos nuestra red neuronal convolucional CNN, tomando los conceptos aprendidos de la teoría de clase: https://github.com/rramosp/20191.DL/blob/master/U3.01%20-%20Convolutional%20Neural%20Networks.ipynb definimos la primera capa de entrada que recibe toda la información de pixeles de nuestra imagen, pasaremos nuestra información a las capas ocultas, estaremos utilizando Maxpooling: para agrupar y reducir la dimension de la estructura quedandonos sólamente con los pixeles de mayor valor lo que hará que sea menos pesado realizar el procesamiento de todas las imagenes ya que nos quedaremos con una matriz más pequeña, también tendremos capas que se encargarán de realizar las convoluciones, las cuales permitirán pasarle filtros a nuestra imagen para detectar ciertos patrones de interés, durante la primera convolución nuestra imagen tendrá una profundidad de 32 y posteriormente de 64. Los filtros que vamos a estar usando en las convoluciones tendrán altura y longitud de (3,3) y (2,2) respectivamente.\n",
    "- Utilizamos capas Flatten para empezar nuestra clasificación, esa imagen que ahora es muy profunda pero pequeña, la haremos plana, le pasaremos esa información a capas normales \"densas\" y posteriormente utilizaremos Dropout: le apagaremos un % aleatoriamente de la neuronas cada paso, para evitar sobreajustar. Así nuestro modelo aprenderá caminos diferentes. Nuestras capas convolucionales y densas tendrán funciones de acitvación 'relu' y sólo la capa de salida le aplicaremos softmax, precisamente porque estamos trabajando con un problema de clasificación.\n",
    "- Entrenamos el modelo e imprimos diferentes gráficos para determinar la precisión de nuestro modelo."
   ]
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    "## 2.1 Descarguemos las imágenes\n",
    "- Descargamos las imágenes desde google Drive.\n",
    "- Procesamos las imágenes obtenidas para crear sub-folders con las clases:\n",
    "    - Las clases hacen referencia al angulo azimutal, estos son 0,300,310,320,330,340,350.\n",
    "    - Como se tienen 7 clases y esto genera complejidad en el modelo, se utilizarán 3 clases para el proyecto. Estas serán 0,310,350"
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      "  inflating: Dataset/Frame010_Elevation40_Azimuth0.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame010_Elevation40_Azimuth0.tif  \n",
      "  inflating: Dataset/Frame096_Elevation50_Azimuth310.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame096_Elevation50_Azimuth310.tif  \n",
      "  inflating: Dataset/Frame098_Elevation70_Azimuth310.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame098_Elevation70_Azimuth310.tif  \n",
      "  inflating: Dataset/Frame_059_Elevation_Azimuth330.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame_059_Elevation_Azimuth330.tif  \n",
      "  inflating: Dataset/Frame077_Elevation-10_Azimuth320.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame077_Elevation-10_Azimuth320.tif  \n",
      "  inflating: Dataset/Frame062_Elevation-30_Azimuth330.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame062_Elevation-30_Azimuth330.tif  \n",
      "  inflating: Dataset/Frame045_Elevation-30_Azimuth340.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame045_Elevation-30_Azimuth340.tif  \n",
      "  inflating: Dataset/Frame004_Elevation20_Azimuth0.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame004_Elevation20_Azimuth0.tif  \n",
      "  inflating: Dataset/Frame002_Elevation40_Azimuth0.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame002_Elevation40_Azimuth0.tif  \n",
      "  inflating: Dataset/Frame022_Elevation 20_Azimuth350.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame022_Elevation 20_Azimuth350.tif  \n",
      "  inflating: Dataset/Frame042_Elevation0_Azimuth340.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame042_Elevation0_Azimuth340.tif  \n",
      "  inflating: Dataset/Frame031_Elevation60_Azimuth350.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame031_Elevation60_Azimuth350.tif  \n",
      "  inflating: Dataset/Frame038_Elevation40_Azimuth340.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame038_Elevation40_Azimuth340.tif  \n",
      "  inflating: Dataset/Frame044_Elevation-20_Azimuth340.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame044_Elevation-20_Azimuth340.tif  \n",
      "  inflating: Dataset/Frame064_Elevation50_Azimuth330.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame064_Elevation50_Azimuth330.tif  \n",
      "  inflating: Dataset/Frame066_Elevation70_Azimuth330.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame066_Elevation70_Azimuth330.tif  \n",
      "  inflating: Dataset/Frame012_Elevation50_Azimuth0.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame012_Elevation50_Azimuth0.tif  \n",
      "  inflating: Dataset/Frame043_Elevation-10_Azimuth340.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame043_Elevation-10_Azimuth340.tif  \n",
      "  inflating: Dataset/Frame081_Elevation60_Azimuth320.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame081_Elevation60_Azimuth320.tif  \n",
      "  inflating: Dataset/Frame106_Elevation20_Azimuth300.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame106_Elevation20_Azimuth300.tif  \n",
      "  inflating: Dataset/Frame008_Elevation20_Azimuth0.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame008_Elevation20_Azimuth0.tif  \n",
      "  inflating: Dataset/Frame108_Elevation0_Azimuth300.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame108_Elevation0_Azimuth300.tif  \n",
      "  inflating: Dataset/Frame073_Elevation30_Azimuth320.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame073_Elevation30_Azimuth320.tif  \n",
      "  inflating: Dataset/Frame014_Elevation70_Azimuth0.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame014_Elevation70_Azimuth0.tif  \n",
      "  inflating: Dataset/Frame087_Elevation50_Azimuth310.tif  \n",
      "  inflating: __MACOSX/Dataset/._Frame087_Elevation50_Azimuth310.tif  \n",
      "  inflating: __MACOSX/._Dataset      \n",
      "unzip:  cannot find or open DataSet.zip, DataSet.zip.zip or DataSet.zip.ZIP.\n"
     ]
    }
   ],
   "source": [
    "!wget https://www.dropbox.com/s/qe502c4trqn4tcc/DataSet.zip?dl=0\n",
    "!unzip DataSet.zip?dl=0\n",
    "!unzip DataSet.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "from time import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import mlutils\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import itertools\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from __future__ import division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.python.keras.preprocessing.image  import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "from tensorflow.python.keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Frame089_Elevation30_Azimuth310.tif',\n",
       " 'Frame081_Elevation60_Azimuth320.tif',\n",
       " 'Frame037_Elevation50_Azimuth340.tif',\n",
       " 'Frame048_Elevation60_Azimuth340.tif',\n",
       " 'Frame088_Elevation40_Azimuth310.tif',\n",
       " 'Frame091_Elevation10_Azimuth310.tif',\n",
       " 'Frame012_Elevation50_Azimuth0.tif',\n",
       " 'Frame002_Elevation40_Azimuth0.tif',\n",
       " 'Frame077_Elevation-10_Azimuth320.tif',\n",
       " 'Frame047_Elevation50_Azimuth340.tif',\n",
       " 'Frame020_Elevation40_Azimuth350.tif',\n",
       " 'Frame041_Elevation10_Azimuth340.tif',\n",
       " 'Frame073_Elevation30_Azimuth320.tif',\n",
       " 'Frame084_Elevation90_Azimuth320.tif',\n",
       " 'Frame016_Elevation90_Azimuth0.tif',\n",
       " 'Frame042_Elevation0_Azimuth340.tif',\n",
       " 'Frame104_Elevation40_Azimuth300.tif',\n",
       " 'Frame030_Elevation50_Azimuth350.tif',\n",
       " 'Frame087_Elevation50_Azimuth310.tif',\n",
       " 'Frame008_Elevation20_Azimuth0.tif',\n",
       " 'Frame099_Elevation80_Azimuth310.tif',\n",
       " 'Frame051_Elevation90_Azimuth340.tif',\n",
       " 'Frame003_Elevation30_Azimuth0.tif',\n",
       " 'Frame045_Elevation-30_Azimuth340.tif',\n",
       " 'Frame096_Elevation50_Azimuth310.tif',\n",
       " 'Frame072_Elevation40_Azimuth320.tif',\n",
       " 'Frame005_Elevation10_Azimuth0.tif',\n",
       " 'Frame078_Elevation-20_Azimuth320.tif',\n",
       " 'Frame067_Elevation80_Azimuth330.tif',\n",
       " 'Frame038_Elevation40_Azimuth340.tif',\n",
       " 'Frame007_Elevation10_Azimuth0.tif',\n",
       " 'Frame092_Elevation0_Azimuth310.tif',\n",
       " 'Frame015_Elevation80_Azimuth0.tif',\n",
       " 'Frame094_Elevation20_Azimuth310.tif',\n",
       " 'Frame025_Elevation-10_Azimuth350.tif',\n",
       " 'Frame098_Elevation70_Azimuth310.tif',\n",
       " 'Frame056_Elevation30_Azimuth330.tif',\n",
       " 'Frame055_Elevation40_Azimuth330.tif',\n",
       " 'Frame050_Elevation80_Azimuth340.tif',\n",
       " 'Frame093_Elevation-10_Azimuth310.tif',\n",
       " 'Frame019_Elevation50_Azimuth350.tif',\n",
       " 'Frame009_Elevation30_Azimuth0.tif',\n",
       " 'Frame022_Elevation 20_Azimuth350.tif',\n",
       " 'Frame108_Elevation0_Azimuth300.tif',\n",
       " 'Frame064_Elevation50_Azimuth330.tif',\n",
       " 'Frame_059_Elevation_Azimuth330.tif',\n",
       " 'Frame062_Elevation-30_Azimuth330.tif',\n",
       " 'Frame040_Elevation20_Azimuth340.tif',\n",
       " 'Frame004_Elevation20_Azimuth0.tif',\n",
       " 'Frame082_Elevation70_Azimuth320.tif',\n",
       " 'Frame106_Elevation20_Azimuth300.tif',\n",
       " 'Frame031_Elevation60_Azimuth350.tif',\n",
       " 'Frame043_Elevation-10_Azimuth340.tif',\n",
       " 'Frame103,Elevation50_Azimuth300.tif',\n",
       " 'Frame058_Elevation10_ Azimuth330.tif',\n",
       " 'Frame021_Elevation30_Azimuth350.tif',\n",
       " 'Frame010_Elevation40_Azimuth0.tif',\n",
       " 'Frame027_Elevation-30_Azimuth350.tif',\n",
       " 'Frame044_Elevation-20_Azimuth340.tif',\n",
       " 'Frame023_Elevation10_Azimuth350.tif',\n",
       " 'Frame049_Elevation70_Azimuth340.tif',\n",
       " 'Frame006_Elevation0_Azimuth0.tif',\n",
       " 'Frame024_Elevation0_Azimuth350.tif',\n",
       " 'Frame065_Elevation60_Azimuth330.tif',\n",
       " 'Frame054_Elevation50_Azimuth330.tif',\n",
       " 'Frame 001_Elevation50_Azimuth0.tif',\n",
       " 'Frame105_Elevation30_Azimuth300.tif',\n",
       " 'Frame107_Elevation10_Azimuth300.tif',\n",
       " 'Frame014_Elevation70_Azimuth0.tif',\n",
       " 'Frame075_Elevation10_Azimuth320.tif',\n",
       " 'Frame039_Elevation30_Azimuth340.tif',\n",
       " 'Frame026_Elevation-20_Azimuth350.tif',\n",
       " 'Frame013_Elevation60_Azimuth0.tif',\n",
       " 'Frame074_Elevation20_Azimuth320.tif',\n",
       " 'Frame080_Elevation50_Azimuth320.tif',\n",
       " 'Frame066_Elevation70_Azimuth330.tif',\n",
       " 'Frame061_Elevation-20_Azimuth330.tif',\n",
       " 'Frame083_Elevation80_Azimuth320.tif',\n",
       " 'Frame060_Elevation-10_Azimuth330.tif',\n",
       " 'Frame033_Elevation80_Azimuth350.tif',\n",
       " 'Frame090_Elevation20_Azimuth310.tif',\n",
       " 'Frame100_Elevation90_Azimuth310.tif',\n",
       " 'Frame057_Elevation20_Azimuth330.tif',\n",
       " 'Frame028_Elevation-40_Azimuth350.tif',\n",
       " 'Frame032_Elevation70_Azimuth350.tif',\n",
       " 'Frame068_Elevation90_Azimuth330.tif',\n",
       " 'Frame076_Elevation0_Azimuth320.tif',\n",
       " 'Frame071_Elevation50_Azimuth320.tif',\n",
       " 'Frame034_Elevation90_Azimuth350.tif',\n",
       " 'Frame097_Elevation60_Azimuth310.tif']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Path='./Dataset'\n",
    "Files=os.listdir(Path)\n",
    "Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "des_organized_data=1\n",
    "if des_organized_data==1:\n",
    "    for fil in Files: \n",
    "        if fil.endswith('.tif'):\n",
    "            Nam = fil.split('.')[0].split('_')\n",
    "            Nam_class = Nam[len(Nam)-1].replace(' ','')  \n",
    "            if Nam_class=='Azimuth0' or Nam_class=='Azimuth310' or Nam_class=='Azimuth350':\n",
    "                if not os.path.exists(Path+'/'+Nam_class):\n",
    "                      os.mkdir(Path+'/'+Nam_class)\n",
    "                os.rename(Path+'/'+fil,Path+'/'+Nam_class+'/'+fil)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definamos el siguiente código para buscar repetibilidad a la hora de ejecutar el código"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['PYTHONHASHSEED']='0'\n",
    "np.random.seed(111)\n",
    "sesion_conf=tf.ConfigProto(intra_op_parallelism_threads=1,inter_op_parallelism_threads=1)\n",
    "tf.set_random_seed(111)\n",
    "sess=tf.Session(graph=tf.get_default_graph(),config=sesion_conf)\n",
    "K.set_session(sess)\n",
    "#aug.seed(111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos las imágenes con ImageDataGenerator para realizar el preprocesamiento de las imágenes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(rescale=1./255., fill_mode='nearest')\n",
    "img_size = 100\n",
    "train_generator_fn = lambda: datagen.flow_from_directory(\n",
    "        Path,  # this is the target directory\n",
    "        target_size=(img_size, img_size),  # all images will be resized to 150x150\n",
    "        batch_size=100,\n",
    "        interpolation = \"bilinear\",\n",
    "        class_mode='binary') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 43 images belonging to 3 classes.\n",
      "(43,) (43, 100, 100, 3) [0. 0. 2. 2. 2. 2. 2. 1. 0. 0. 2. 0. 1. 2. 1. 1. 1. 2. 0. 1. 0. 2. 0. 0.\n",
      " 2. 1. 2. 0. 2. 1. 1. 0. 0. 0. 2. 0. 1. 0. 1. 1. 2. 2. 1.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdc4178dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for imgs,labels in train_generator_fn():\n",
    "    if np.random.random()>.5:\n",
    "        break\n",
    "print labels.shape,imgs.shape,labels\n",
    "mlutils.show_labeled_image_mosaic(imgs, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43, 100, 100, 3) (43,)\n"
     ]
    }
   ],
   "source": [
    "print imgs.shape,labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dividimos el dataset en datos de validación y entrenamiento. 75% de los datos para entrenar y 25% de los datos para validar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 100, 100, 3) (32,) (11, 100, 100, 3) (11,)\n",
      "\n",
      "distribution of train classes\n",
      "2.0    12\n",
      "1.0    11\n",
      "0.0     9\n",
      "dtype: int64\n",
      "\n",
      "distribution of test classes\n",
      "0.0    6\n",
      "2.0    3\n",
      "1.0    2\n",
      "dtype: int64\n",
      "\n",
      "num classes 3\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(imgs, labels, test_size=.25)\n",
    "num_classes = len(np.unique(y_train))\n",
    "print x_train.shape, y_train.shape, x_test.shape, y_test.shape\n",
    "print \"\\ndistribution of train classes\"\n",
    "print pd.Series(y_train).value_counts()\n",
    "print \"\\ndistribution of test classes\"\n",
    "print pd.Series(y_test).value_counts()\n",
    "print \"\\nnum classes\", num_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definimos el modelo A (modelo simple)\n",
    "def get_conv_model_A(num_classes, img_size=100):\n",
    "    \n",
    "    tf.reset_default_graph()\n",
    "    tf.keras.backend.clear_session()\n",
    "    \n",
    "    inputs = tf.keras.Input(shape=(img_size,img_size,3),name='Input')\n",
    "    \n",
    "    layers = tf.keras.layers.Convolution2D(32, (3,3), padding =\"same\", activation='relu')(inputs)\n",
    "    layers = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(layers)\n",
    "\n",
    "    layers = tf.keras.layers.BatchNormalization()(layers)\n",
    "    \n",
    "    layers = tf.keras.layers.Convolution2D(64, (2,2), padding =\"same\", activation='relu')(layers)\n",
    "    layers = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(layers)\n",
    "\n",
    "    #layers = tf.keras.layers.BatchNormalization()(layers)\n",
    "    \n",
    "    layers = tf.keras.layers.Flatten()(layers)\n",
    "    \n",
    "    layers = tf.keras.layers.Dense(256, activation='relu')(layers)\n",
    "    layers = tf.keras.layers.Dense(16, activation='relu')(layers)\n",
    "    layers = tf.keras.layers.Dropout(0.5)(layers)\n",
    "    \n",
    "    predictions = tf.keras.layers.Dense(num_classes, activation='softmax',name='Output')(layers)\n",
    "\n",
    "    model = tf.keras.Model(inputs = inputs, outputs=predictions)\n",
    "    \n",
    "    model.compile(optimizer = tf.keras.optimizers.Adam(lr=0.0001),\n",
    "                  loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definimos el modelo B (modelo complejo)\n",
    "def get_conv_model_B(num_classes, img_size=100):\n",
    "    \n",
    "    tf.reset_default_graph()\n",
    "    tf.keras.backend.clear_session()\n",
    "    \n",
    "    inputs = tf.keras.Input(shape=(img_size,img_size,3),name='Input')\n",
    "    \n",
    "    layers = tf.keras.layers.Convolution2D(32, (3,3), padding =\"same\", activation='relu')(inputs)\n",
    "    layers = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(layers)\n",
    "    layers = tf.keras.layers.BatchNormalization()(layers)\n",
    "    layers = tf.keras.layers.Dropout(0.3)(layers)\n",
    "    \n",
    "    layers = tf.keras.layers.Convolution2D(64, (2,2), padding =\"same\", activation='relu')(layers)\n",
    "    layers = tf.keras.layers.MaxPooling2D(pool_size=(2,2))(layers)\n",
    "    layers = tf.keras.layers.BatchNormalization()(layers)\n",
    "    layers = tf.keras.layers.Dropout(0.3)(layers)\n",
    "    \n",
    "    layers = tf.keras.layers.Flatten()(layers)\n",
    "    \n",
    "    layers = tf.keras.layers.Dense(256, activation='relu')(layers)\n",
    "    layers = tf.keras.layers.Dense(36, activation='relu')(layers)\n",
    "    layers = tf.keras.layers.Dense(16, activation='relu')(layers)\n",
    "    layers = tf.keras.layers.Dropout(0.3)(layers)\n",
    "    \n",
    "    predictions = tf.keras.layers.Dense(num_classes, activation='softmax',name='Output')(layers)\n",
    "\n",
    "    model = tf.keras.Model(inputs = inputs, outputs=predictions)\n",
    "    \n",
    "    model.compile(optimizer = tf.keras.optimizers.Adam(lr=0.0001),\n",
    "                  loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, x_train, y_train, x_test, y_test, batch_size, epochs, model_name=\"\"):\n",
    "    model.reset_states()\n",
    "    model.fit(x_train, y_train, epochs=epochs,\n",
    "              batch_size=batch_size,\n",
    "              validation_data=(x_test, y_test))\n",
    "    metrics = model.evaluate(x_test, y_test)\n",
    "    return {k:v for k,v in zip (model.metrics_names, metrics)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_confusion_matrix(y_test,y_pred,num_classes,class_names,batch,epoc,save):\n",
    "    if not os.path.exists('/home/user/20191.DL-master/Proyecto U3 v2/ConfusionMatrix'):\n",
    "              os.mkdir('/home/user/20191.DL-master/Proyecto U3 v2/ConfusionMatrix') \n",
    "    cm=confusion_matrix(y_test,y_pred)\n",
    "    cm = cm/np.sum(cm)\n",
    "    cmap=plt.cm.Blues\n",
    "    tick_marks = np.arange(num_classes)\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title('Normalized confusion matrix')\n",
    "    plt.colorbar()\n",
    "    plt.xticks(tick_marks, class_names, rotation=0)\n",
    "    plt.yticks(tick_marks, class_names)\n",
    "    if save==1:\n",
    "        plt.savefig('/home/user/20191.DL-master/Proyecto U3 v2/ConfusionMatrix/'+'Model_B'+str(batch)+'_E'+str(epoc)+'.png',\n",
    "                   bbox_inches='tight',format='png',dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Plot_acuracys(model):\n",
    "    plt.plot(model.history.history[\"acc\"], label=\"acc\")\n",
    "    plt.plot(model.history.history[\"val_acc\"], label=\"val_acc\")\n",
    "    plt.grid()\n",
    "    plt.legend();\n",
    "    plt.xlabel(\"training step\")\n",
    "    plt.ylabel(\"accuracy\")\n",
    "#    plt.ylim(0.4,1.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Creemos un modelo simple (Modelo A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "Input (InputLayer)           (None, 100, 100, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 100, 100, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 50, 50, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 50, 50, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 50, 50, 64)        8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 25, 25, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 40000)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               10240256  \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 16)                4112      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "Output (Dense)               (None, 3)                 51        \n",
      "=================================================================\n",
      "Total params: 10,253,699\n",
      "Trainable params: 10,253,635\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = get_conv_model_A(num_classes)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 32 samples, validate on 11 samples\n",
      "Epoch 1/200\n",
      "32/32 [==============================] - 3s 86ms/step - loss: 1.0987 - acc: 0.3125 - val_loss: 1.1533 - val_acc: 0.1818\n",
      "Epoch 2/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 1.9997 - acc: 0.3750 - val_loss: 1.1053 - val_acc: 0.1818\n",
      "Epoch 3/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 1.1214 - acc: 0.4688 - val_loss: 1.0877 - val_acc: 0.5455\n",
      "Epoch 4/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 1.1665 - acc: 0.4062 - val_loss: 1.0950 - val_acc: 0.5455\n",
      "Epoch 5/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 1.0210 - acc: 0.3750 - val_loss: 1.0975 - val_acc: 0.2727\n",
      "Epoch 6/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.9815 - acc: 0.5625 - val_loss: 1.0989 - val_acc: 0.2727\n",
      "Epoch 7/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.9420 - acc: 0.5000 - val_loss: 1.0996 - val_acc: 0.2727\n",
      "Epoch 8/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.8483 - acc: 0.5938 - val_loss: 1.0996 - val_acc: 0.2727\n",
      "Epoch 9/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.8663 - acc: 0.5312 - val_loss: 1.0993 - val_acc: 0.2727\n",
      "Epoch 10/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.7916 - acc: 0.6250 - val_loss: 1.1012 - val_acc: 0.3636\n",
      "Epoch 11/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.8741 - acc: 0.5000 - val_loss: 1.1022 - val_acc: 0.2727\n",
      "Epoch 12/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.8472 - acc: 0.5312 - val_loss: 1.1017 - val_acc: 0.3636\n",
      "Epoch 13/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.6607 - acc: 0.7500 - val_loss: 1.0996 - val_acc: 0.2727\n",
      "Epoch 14/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.8316 - acc: 0.5000 - val_loss: 1.0983 - val_acc: 0.3636\n",
      "Epoch 15/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.7234 - acc: 0.6250 - val_loss: 1.0968 - val_acc: 0.2727\n",
      "Epoch 16/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.6234 - acc: 0.7188 - val_loss: 1.0962 - val_acc: 0.2727\n",
      "Epoch 17/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.8370 - acc: 0.5312 - val_loss: 1.0983 - val_acc: 0.3636\n",
      "Epoch 18/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.7410 - acc: 0.7500 - val_loss: 1.1003 - val_acc: 0.3636\n",
      "Epoch 19/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.8339 - acc: 0.5625 - val_loss: 1.1007 - val_acc: 0.3636\n",
      "Epoch 20/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.7960 - acc: 0.6250 - val_loss: 1.1015 - val_acc: 0.2727\n",
      "Epoch 21/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.7464 - acc: 0.6875 - val_loss: 1.1016 - val_acc: 0.2727\n",
      "Epoch 22/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.6514 - acc: 0.7188 - val_loss: 1.1015 - val_acc: 0.2727\n",
      "Epoch 23/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.6104 - acc: 0.8125 - val_loss: 1.1013 - val_acc: 0.2727\n",
      "Epoch 24/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.7137 - acc: 0.6875 - val_loss: 1.1017 - val_acc: 0.2727\n",
      "Epoch 25/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.6787 - acc: 0.7500 - val_loss: 1.1013 - val_acc: 0.2727\n",
      "Epoch 26/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.5475 - acc: 0.7188 - val_loss: 1.0983 - val_acc: 0.2727\n",
      "Epoch 27/200\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.6589 - acc: 0.6562 - val_loss: 1.0962 - val_acc: 0.2727\n",
      "Epoch 28/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.6047 - acc: 0.7500 - val_loss: 1.0947 - val_acc: 0.2727\n",
      "Epoch 29/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.6964 - acc: 0.6562 - val_loss: 1.0944 - val_acc: 0.2727\n",
      "Epoch 30/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.7023 - acc: 0.7188 - val_loss: 1.0934 - val_acc: 0.2727\n",
      "Epoch 31/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.5934 - acc: 0.7188 - val_loss: 1.0927 - val_acc: 0.2727\n",
      "Epoch 32/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.5947 - acc: 0.8125 - val_loss: 1.0934 - val_acc: 0.2727\n",
      "Epoch 33/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.6639 - acc: 0.6875 - val_loss: 1.0970 - val_acc: 0.3636\n",
      "Epoch 34/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.7252 - acc: 0.5938 - val_loss: 1.1014 - val_acc: 0.3636\n",
      "Epoch 35/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.6545 - acc: 0.6875 - val_loss: 1.1047 - val_acc: 0.2727\n",
      "Epoch 36/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.5942 - acc: 0.7500 - val_loss: 1.1055 - val_acc: 0.2727\n",
      "Epoch 37/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.5791 - acc: 0.8438 - val_loss: 1.1045 - val_acc: 0.2727\n",
      "Epoch 38/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.5831 - acc: 0.8125 - val_loss: 1.1032 - val_acc: 0.3636\n",
      "Epoch 39/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.6175 - acc: 0.7188 - val_loss: 1.1024 - val_acc: 0.3636\n",
      "Epoch 40/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.5680 - acc: 0.7500 - val_loss: 1.1018 - val_acc: 0.3636\n",
      "Epoch 41/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.5124 - acc: 0.7500 - val_loss: 1.1006 - val_acc: 0.3636\n",
      "Epoch 42/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.4746 - acc: 0.8438 - val_loss: 1.0984 - val_acc: 0.2727\n",
      "Epoch 43/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.5720 - acc: 0.7500 - val_loss: 1.0961 - val_acc: 0.2727\n",
      "Epoch 44/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.6008 - acc: 0.6875 - val_loss: 1.0943 - val_acc: 0.2727\n",
      "Epoch 45/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.4170 - acc: 0.8438 - val_loss: 1.0925 - val_acc: 0.2727\n",
      "Epoch 46/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.4222 - acc: 0.7812 - val_loss: 1.0905 - val_acc: 0.2727\n",
      "Epoch 47/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.5862 - acc: 0.6875 - val_loss: 1.0889 - val_acc: 0.2727\n",
      "Epoch 48/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.5731 - acc: 0.7188 - val_loss: 1.0882 - val_acc: 0.2727\n",
      "Epoch 49/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.5274 - acc: 0.6875 - val_loss: 1.0893 - val_acc: 0.2727\n",
      "Epoch 50/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.5540 - acc: 0.7188 - val_loss: 1.0897 - val_acc: 0.2727\n",
      "Epoch 51/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.4282 - acc: 0.8438 - val_loss: 1.0902 - val_acc: 0.2727\n",
      "Epoch 52/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.4946 - acc: 0.7500 - val_loss: 1.0901 - val_acc: 0.2727\n",
      "Epoch 53/200\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.3652 - acc: 0.8438 - val_loss: 1.0903 - val_acc: 0.2727\n",
      "Epoch 54/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.5488 - acc: 0.6250 - val_loss: 1.0904 - val_acc: 0.2727\n",
      "Epoch 55/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.3733 - acc: 0.7812 - val_loss: 1.0899 - val_acc: 0.2727\n",
      "Epoch 56/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3489 - acc: 0.8438 - val_loss: 1.0886 - val_acc: 0.2727\n",
      "Epoch 57/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.4296 - acc: 0.8750 - val_loss: 1.0874 - val_acc: 0.2727\n",
      "Epoch 58/200\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.4956 - acc: 0.7812 - val_loss: 1.0866 - val_acc: 0.2727\n",
      "Epoch 59/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.4872 - acc: 0.8125 - val_loss: 1.0853 - val_acc: 0.3636\n",
      "Epoch 60/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.5370 - acc: 0.7812 - val_loss: 1.0855 - val_acc: 0.3636\n",
      "Epoch 61/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.2998 - acc: 0.8750 - val_loss: 1.0866 - val_acc: 0.2727\n",
      "Epoch 62/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.5199 - acc: 0.7188 - val_loss: 1.0881 - val_acc: 0.2727\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 63/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.5004 - acc: 0.7812 - val_loss: 1.0908 - val_acc: 0.2727\n",
      "Epoch 64/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.6626 - acc: 0.6250 - val_loss: 1.0894 - val_acc: 0.2727\n",
      "Epoch 65/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.3992 - acc: 0.8438 - val_loss: 1.0883 - val_acc: 0.2727\n",
      "Epoch 66/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.4801 - acc: 0.7812 - val_loss: 1.0878 - val_acc: 0.2727\n",
      "Epoch 67/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4931 - acc: 0.7500 - val_loss: 1.0871 - val_acc: 0.2727\n",
      "Epoch 68/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.4738 - acc: 0.8125 - val_loss: 1.0854 - val_acc: 0.2727\n",
      "Epoch 69/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.3457 - acc: 0.8438 - val_loss: 1.0834 - val_acc: 0.2727\n",
      "Epoch 70/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.4677 - acc: 0.8125 - val_loss: 1.0832 - val_acc: 0.2727\n",
      "Epoch 71/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.3328 - acc: 0.8125 - val_loss: 1.0836 - val_acc: 0.2727\n",
      "Epoch 72/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3042 - acc: 0.8750 - val_loss: 1.0845 - val_acc: 0.2727\n",
      "Epoch 73/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.5647 - acc: 0.7188 - val_loss: 1.0863 - val_acc: 0.2727\n",
      "Epoch 74/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.4420 - acc: 0.8125 - val_loss: 1.0889 - val_acc: 0.2727\n",
      "Epoch 75/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.5607 - acc: 0.6562 - val_loss: 1.0917 - val_acc: 0.2727\n",
      "Epoch 76/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.4081 - acc: 0.7812 - val_loss: 1.0952 - val_acc: 0.2727\n",
      "Epoch 77/200\n",
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      "Epoch 78/200\n",
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      "Epoch 79/200\n",
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      "Epoch 80/200\n",
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      "Epoch 81/200\n",
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      "Epoch 82/200\n",
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      "Epoch 83/200\n",
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      "Epoch 84/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3425 - acc: 0.8750 - val_loss: 1.0940 - val_acc: 0.3636\n",
      "Epoch 85/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.3537 - acc: 0.8438 - val_loss: 1.0932 - val_acc: 0.3636\n",
      "Epoch 86/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4051 - acc: 0.7812 - val_loss: 1.0926 - val_acc: 0.3636\n",
      "Epoch 87/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4374 - acc: 0.7188 - val_loss: 1.0909 - val_acc: 0.3636\n",
      "Epoch 88/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3464 - acc: 0.8438 - val_loss: 1.0900 - val_acc: 0.3636\n",
      "Epoch 89/200\n",
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      "Epoch 90/200\n",
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      "Epoch 91/200\n",
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      "Epoch 92/200\n",
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      "Epoch 93/200\n",
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      "Epoch 94/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.3223 - acc: 0.8438 - val_loss: 1.0921 - val_acc: 0.3636\n",
      "Epoch 95/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.3007 - acc: 0.8438 - val_loss: 1.0911 - val_acc: 0.3636\n",
      "Epoch 96/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.3070 - acc: 0.8750 - val_loss: 1.0884 - val_acc: 0.2727\n",
      "Epoch 97/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.3895 - acc: 0.7812 - val_loss: 1.0862 - val_acc: 0.2727\n",
      "Epoch 98/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.4405 - acc: 0.8125 - val_loss: 1.0844 - val_acc: 0.2727\n",
      "Epoch 99/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.4595 - acc: 0.7812 - val_loss: 1.0840 - val_acc: 0.2727\n",
      "Epoch 100/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.5206 - acc: 0.7812 - val_loss: 1.0838 - val_acc: 0.2727\n",
      "Epoch 101/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.5035 - acc: 0.6562 - val_loss: 1.0821 - val_acc: 0.2727\n",
      "Epoch 102/200\n",
      "32/32 [==============================] - 1s 29ms/step - loss: 0.3194 - acc: 0.7812 - val_loss: 1.0797 - val_acc: 0.2727\n",
      "Epoch 103/200\n",
      "32/32 [==============================] - 1s 42ms/step - loss: 0.3335 - acc: 0.8438 - val_loss: 1.0761 - val_acc: 0.2727\n",
      "Epoch 104/200\n",
      "32/32 [==============================] - 1s 30ms/step - loss: 0.4103 - acc: 0.7812 - val_loss: 1.0719 - val_acc: 0.2727\n",
      "Epoch 105/200\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.4035 - acc: 0.8125 - val_loss: 1.0683 - val_acc: 0.2727\n",
      "Epoch 106/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.4394 - acc: 0.7188 - val_loss: 1.0662 - val_acc: 0.2727\n",
      "Epoch 107/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.4581 - acc: 0.7500 - val_loss: 1.0673 - val_acc: 0.2727\n",
      "Epoch 108/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.3996 - acc: 0.8438 - val_loss: 1.0684 - val_acc: 0.2727\n",
      "Epoch 109/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.4198 - acc: 0.8438 - val_loss: 1.0706 - val_acc: 0.2727\n",
      "Epoch 110/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.4817 - acc: 0.7188 - val_loss: 1.0765 - val_acc: 0.2727\n",
      "Epoch 111/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.3820 - acc: 0.8125 - val_loss: 1.0830 - val_acc: 0.2727\n",
      "Epoch 112/200\n",
      "32/32 [==============================] - 1s 34ms/step - loss: 0.3988 - acc: 0.7500 - val_loss: 1.0865 - val_acc: 0.3636\n",
      "Epoch 113/200\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.3457 - acc: 0.8438 - val_loss: 1.0899 - val_acc: 0.3636\n",
      "Epoch 114/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.2904 - acc: 0.8750 - val_loss: 1.0923 - val_acc: 0.3636\n",
      "Epoch 115/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.3926 - acc: 0.7500 - val_loss: 1.0932 - val_acc: 0.3636\n",
      "Epoch 116/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.4413 - acc: 0.7812 - val_loss: 1.0922 - val_acc: 0.3636\n",
      "Epoch 117/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.3338 - acc: 0.8750 - val_loss: 1.0897 - val_acc: 0.3636\n",
      "Epoch 118/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4573 - acc: 0.7188 - val_loss: 1.0854 - val_acc: 0.3636\n",
      "Epoch 119/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3091 - acc: 0.7812 - val_loss: 1.0817 - val_acc: 0.3636\n",
      "Epoch 120/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4065 - acc: 0.7500 - val_loss: 1.0790 - val_acc: 0.2727\n",
      "Epoch 121/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4775 - acc: 0.6562 - val_loss: 1.0775 - val_acc: 0.3636\n",
      "Epoch 122/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.2438 - acc: 0.8438 - val_loss: 1.0764 - val_acc: 0.2727\n",
      "Epoch 123/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.2813 - acc: 0.8750 - val_loss: 1.0753 - val_acc: 0.2727\n",
      "Epoch 124/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.3461 - acc: 0.8438 - val_loss: 1.0763 - val_acc: 0.3636\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 125/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4525 - acc: 0.7500 - val_loss: 1.0790 - val_acc: 0.3636\n",
      "Epoch 126/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.2722 - acc: 0.9062 - val_loss: 1.0826 - val_acc: 0.3636\n",
      "Epoch 127/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.4786 - acc: 0.6562 - val_loss: 1.0852 - val_acc: 0.3636\n",
      "Epoch 128/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3520 - acc: 0.8750 - val_loss: 1.0876 - val_acc: 0.3636\n",
      "Epoch 129/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.3876 - acc: 0.7812 - val_loss: 1.0881 - val_acc: 0.3636\n",
      "Epoch 130/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.3752 - acc: 0.7812 - val_loss: 1.0871 - val_acc: 0.3636\n",
      "Epoch 131/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.3672 - acc: 0.7500 - val_loss: 1.0858 - val_acc: 0.3636\n",
      "Epoch 132/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.3941 - acc: 0.8125 - val_loss: 1.0856 - val_acc: 0.3636\n",
      "Epoch 133/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.5612 - acc: 0.6875 - val_loss: 1.0839 - val_acc: 0.3636\n",
      "Epoch 134/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.2943 - acc: 0.8438 - val_loss: 1.0820 - val_acc: 0.3636\n",
      "Epoch 135/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.3855 - acc: 0.8438 - val_loss: 1.0762 - val_acc: 0.3636\n",
      "Epoch 136/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.2319 - acc: 0.9375 - val_loss: 1.0723 - val_acc: 0.3636\n",
      "Epoch 137/200\n",
      "32/32 [==============================] - 1s 28ms/step - loss: 0.2731 - acc: 0.8125 - val_loss: 1.0692 - val_acc: 0.2727\n",
      "Epoch 138/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.4207 - acc: 0.7188 - val_loss: 1.0676 - val_acc: 0.2727\n",
      "Epoch 139/200\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.3435 - acc: 0.7812 - val_loss: 1.0716 - val_acc: 0.2727\n",
      "Epoch 140/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.2739 - acc: 0.8750 - val_loss: 1.0755 - val_acc: 0.3636\n",
      "Epoch 141/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4404 - acc: 0.6562 - val_loss: 1.0793 - val_acc: 0.3636\n",
      "Epoch 142/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.2882 - acc: 0.8125 - val_loss: 1.0825 - val_acc: 0.3636\n",
      "Epoch 143/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.3066 - acc: 0.9062 - val_loss: 1.0876 - val_acc: 0.3636\n",
      "Epoch 144/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.5313 - acc: 0.6250 - val_loss: 1.0881 - val_acc: 0.3636\n",
      "Epoch 145/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.4142 - acc: 0.7500 - val_loss: 1.0859 - val_acc: 0.3636\n",
      "Epoch 146/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.2868 - acc: 0.9062 - val_loss: 1.0832 - val_acc: 0.3636\n",
      "Epoch 147/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.2422 - acc: 0.8438 - val_loss: 1.0800 - val_acc: 0.3636\n",
      "Epoch 148/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.2942 - acc: 0.8750 - val_loss: 1.0782 - val_acc: 0.3636\n",
      "Epoch 149/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3392 - acc: 0.8438 - val_loss: 1.0780 - val_acc: 0.3636\n",
      "Epoch 150/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.3452 - acc: 0.7812 - val_loss: 1.0770 - val_acc: 0.3636\n",
      "Epoch 151/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4114 - acc: 0.7188 - val_loss: 1.0737 - val_acc: 0.3636\n",
      "Epoch 152/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.2999 - acc: 0.8438 - val_loss: 1.0684 - val_acc: 0.3636\n",
      "Epoch 153/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.2971 - acc: 0.7812 - val_loss: 1.0642 - val_acc: 0.3636\n",
      "Epoch 154/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.3769 - acc: 0.7500 - val_loss: 1.0600 - val_acc: 0.3636\n",
      "Epoch 155/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.2316 - acc: 0.8438 - val_loss: 1.0597 - val_acc: 0.2727\n",
      "Epoch 156/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.2666 - acc: 0.8438 - val_loss: 1.0603 - val_acc: 0.3636\n",
      "Epoch 157/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.3786 - acc: 0.7500 - val_loss: 1.0625 - val_acc: 0.3636\n",
      "Epoch 158/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.3290 - acc: 0.8438 - val_loss: 1.0683 - val_acc: 0.3636\n",
      "Epoch 159/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.4150 - acc: 0.8125 - val_loss: 1.0766 - val_acc: 0.3636\n",
      "Epoch 160/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3409 - acc: 0.8125 - val_loss: 1.0786 - val_acc: 0.3636\n",
      "Epoch 161/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.2332 - acc: 0.9062 - val_loss: 1.0802 - val_acc: 0.3636\n",
      "Epoch 162/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.2679 - acc: 0.8438 - val_loss: 1.0807 - val_acc: 0.3636\n",
      "Epoch 163/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3543 - acc: 0.7500 - val_loss: 1.0809 - val_acc: 0.3636\n",
      "Epoch 164/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4454 - acc: 0.7812 - val_loss: 1.0807 - val_acc: 0.3636\n",
      "Epoch 165/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.4519 - acc: 0.6250 - val_loss: 1.0819 - val_acc: 0.3636\n",
      "Epoch 166/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.2089 - acc: 0.9062 - val_loss: 1.0800 - val_acc: 0.3636\n",
      "Epoch 167/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.2776 - acc: 0.8438 - val_loss: 1.0783 - val_acc: 0.3636\n",
      "Epoch 168/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3436 - acc: 0.7812 - val_loss: 1.0769 - val_acc: 0.3636\n",
      "Epoch 169/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3238 - acc: 0.8125 - val_loss: 1.0751 - val_acc: 0.3636\n",
      "Epoch 170/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.4395 - acc: 0.7188 - val_loss: 1.0750 - val_acc: 0.3636\n",
      "Epoch 171/200\n",
      "32/32 [==============================] - 1s 30ms/step - loss: 0.4598 - acc: 0.6250 - val_loss: 1.0762 - val_acc: 0.3636\n",
      "Epoch 172/200\n",
      "32/32 [==============================] - 1s 32ms/step - loss: 0.4428 - acc: 0.7500 - val_loss: 1.0786 - val_acc: 0.3636\n",
      "Epoch 173/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.3448 - acc: 0.8750 - val_loss: 1.0807 - val_acc: 0.3636\n",
      "Epoch 174/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.3852 - acc: 0.7500 - val_loss: 1.0821 - val_acc: 0.3636\n",
      "Epoch 175/200\n",
      "32/32 [==============================] - 1s 28ms/step - loss: 0.3449 - acc: 0.8438 - val_loss: 1.0830 - val_acc: 0.3636\n",
      "Epoch 176/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.3636 - acc: 0.8125 - val_loss: 1.0827 - val_acc: 0.3636\n",
      "Epoch 177/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.3858 - acc: 0.7500 - val_loss: 1.0835 - val_acc: 0.3636\n",
      "Epoch 178/200\n",
      "32/32 [==============================] - 1s 34ms/step - loss: 0.3123 - acc: 0.8125 - val_loss: 1.0847 - val_acc: 0.3636\n",
      "Epoch 179/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.3291 - acc: 0.8750 - val_loss: 1.0861 - val_acc: 0.3636\n",
      "Epoch 180/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.2808 - acc: 0.8438 - val_loss: 1.0876 - val_acc: 0.3636\n",
      "Epoch 181/200\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.3354 - acc: 0.8125 - val_loss: 1.0884 - val_acc: 0.3636\n",
      "Epoch 182/200\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.2845 - acc: 0.8438 - val_loss: 1.0883 - val_acc: 0.3636\n",
      "Epoch 183/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.3108 - acc: 0.8125 - val_loss: 1.0863 - val_acc: 0.3636\n",
      "Epoch 184/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.2835 - acc: 0.8438 - val_loss: 1.0812 - val_acc: 0.3636\n",
      "Epoch 185/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.4609 - acc: 0.6562 - val_loss: 1.0753 - val_acc: 0.3636\n",
      "Epoch 186/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3006 - acc: 0.7812 - val_loss: 1.0714 - val_acc: 0.3636\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 187/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.2714 - acc: 0.8438 - val_loss: 1.0661 - val_acc: 0.3636\n",
      "Epoch 188/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.2550 - acc: 0.9062 - val_loss: 1.0598 - val_acc: 0.3636\n",
      "Epoch 189/200\n",
      "32/32 [==============================] - 0s 15ms/step - loss: 0.4158 - acc: 0.7500 - val_loss: 1.0608 - val_acc: 0.4545\n",
      "Epoch 190/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3246 - acc: 0.7812 - val_loss: 1.0639 - val_acc: 0.4545\n",
      "Epoch 191/200\n",
      "32/32 [==============================] - 1s 16ms/step - loss: 0.3185 - acc: 0.8438 - val_loss: 1.0686 - val_acc: 0.4545\n",
      "Epoch 192/200\n",
      "32/32 [==============================] - 0s 16ms/step - loss: 0.4449 - acc: 0.7500 - val_loss: 1.0710 - val_acc: 0.4545\n",
      "Epoch 193/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.3481 - acc: 0.8125 - val_loss: 1.0713 - val_acc: 0.3636\n",
      "Epoch 194/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.3055 - acc: 0.7500 - val_loss: 1.0708 - val_acc: 0.3636\n",
      "Epoch 195/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.1582 - acc: 0.8750 - val_loss: 1.0671 - val_acc: 0.3636\n",
      "Epoch 196/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4738 - acc: 0.7500 - val_loss: 1.0533 - val_acc: 0.3636\n",
      "Epoch 197/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.3493 - acc: 0.8438 - val_loss: 1.0458 - val_acc: 0.3636\n",
      "Epoch 198/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.2125 - acc: 0.8750 - val_loss: 1.0411 - val_acc: 0.3636\n",
      "Epoch 199/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.4442 - acc: 0.7500 - val_loss: 1.0408 - val_acc: 0.4545\n",
      "Epoch 200/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.3492 - acc: 0.7812 - val_loss: 1.0528 - val_acc: 0.4545\n",
      "11/11 [==============================] - 0s 5ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'acc': 0.4545454680919647, 'loss': 1.0528228282928467}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train(model, x_train, y_train, x_test, y_test, batch_size=100, epochs=200, model_name=\"model_A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Accuracy = ', 45.45454545454545, '%')\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "y_pred = np.argmax(y_pred,axis=1)\n",
    "class_names=['0','310','350']\n",
    "Accuracy = np.mean(y_pred == y_test)\n",
    "print('Accuracy = ', Accuracy*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd54171410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix(y_test,y_pred,num_classes,class_names,100,200,save=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd544610d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_acuracys(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Evaluemos un modelo más complejo (Model B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "Input (InputLayer)           (None, 100, 100, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 100, 100, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 50, 50, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 50, 50, 32)        128       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 50, 50, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 50, 50, 64)        8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 25, 25, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 25, 25, 64)        256       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 25, 25, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 40000)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               10240256  \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 36)                9252      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 16)                592       \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "Output (Dense)               (None, 3)                 51        \n",
      "=================================================================\n",
      "Total params: 10,259,687\n",
      "Trainable params: 10,259,495\n",
      "Non-trainable params: 192\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_2 = get_conv_model_B(num_classes)\n",
    "model_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 32 samples, validate on 11 samples\n",
      "Epoch 1/200\n",
      "32/32 [==============================] - 3s 97ms/step - loss: 1.9415 - acc: 0.4375 - val_loss: 1.1251 - val_acc: 0.1818\n",
      "Epoch 2/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 1.4111 - acc: 0.3750 - val_loss: 1.1213 - val_acc: 0.1818\n",
      "Epoch 3/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 1.2546 - acc: 0.4375 - val_loss: 1.1168 - val_acc: 0.1818\n",
      "Epoch 4/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.9117 - acc: 0.5625 - val_loss: 1.1148 - val_acc: 0.1818\n",
      "Epoch 5/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.9172 - acc: 0.6250 - val_loss: 1.1129 - val_acc: 0.1818\n",
      "Epoch 6/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.9095 - acc: 0.6562 - val_loss: 1.1130 - val_acc: 0.1818\n",
      "Epoch 7/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.7660 - acc: 0.7812 - val_loss: 1.1128 - val_acc: 0.1818\n",
      "Epoch 8/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.8916 - acc: 0.5938 - val_loss: 1.1136 - val_acc: 0.1818\n",
      "Epoch 9/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.6399 - acc: 0.7500 - val_loss: 1.1139 - val_acc: 0.1818\n",
      "Epoch 10/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.7298 - acc: 0.7188 - val_loss: 1.1135 - val_acc: 0.1818\n",
      "Epoch 11/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.6997 - acc: 0.6875 - val_loss: 1.1119 - val_acc: 0.1818\n",
      "Epoch 12/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.5248 - acc: 0.7812 - val_loss: 1.1122 - val_acc: 0.1818\n",
      "Epoch 13/200\n",
      "32/32 [==============================] - 1s 28ms/step - loss: 0.6314 - acc: 0.7500 - val_loss: 1.1127 - val_acc: 0.1818\n",
      "Epoch 14/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.3894 - acc: 0.8750 - val_loss: 1.1118 - val_acc: 0.1818\n",
      "Epoch 15/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.3438 - acc: 0.9062 - val_loss: 1.1111 - val_acc: 0.1818\n",
      "Epoch 16/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.4642 - acc: 0.7812 - val_loss: 1.1108 - val_acc: 0.0909\n",
      "Epoch 17/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.3793 - acc: 0.8125 - val_loss: 1.1110 - val_acc: 0.0909\n",
      "Epoch 18/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.3281 - acc: 0.8438 - val_loss: 1.1121 - val_acc: 0.0909\n",
      "Epoch 19/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.2347 - acc: 0.9375 - val_loss: 1.1136 - val_acc: 0.0909\n",
      "Epoch 20/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.3165 - acc: 0.8750 - val_loss: 1.1165 - val_acc: 0.1818\n",
      "Epoch 21/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.3370 - acc: 0.8750 - val_loss: 1.1202 - val_acc: 0.1818\n",
      "Epoch 22/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.2177 - acc: 0.9062 - val_loss: 1.1261 - val_acc: 0.1818\n",
      "Epoch 23/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.2248 - acc: 0.9062 - val_loss: 1.1337 - val_acc: 0.1818\n",
      "Epoch 24/200\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.1008 - acc: 0.9688 - val_loss: 1.1409 - val_acc: 0.1818\n",
      "Epoch 25/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1295 - acc: 0.9688 - val_loss: 1.1463 - val_acc: 0.1818\n",
      "Epoch 26/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.2051 - acc: 0.9062 - val_loss: 1.1475 - val_acc: 0.1818\n",
      "Epoch 27/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.3802 - acc: 0.7812 - val_loss: 1.1470 - val_acc: 0.1818\n",
      "Epoch 28/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.2420 - acc: 0.8750 - val_loss: 1.1452 - val_acc: 0.1818\n",
      "Epoch 29/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.3018 - acc: 0.8750 - val_loss: 1.1437 - val_acc: 0.1818\n",
      "Epoch 30/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1922 - acc: 0.8750 - val_loss: 1.1438 - val_acc: 0.1818\n",
      "Epoch 31/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.2162 - acc: 0.8438 - val_loss: 1.1469 - val_acc: 0.2727\n",
      "Epoch 32/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1697 - acc: 0.9062 - val_loss: 1.1487 - val_acc: 0.4545\n",
      "Epoch 33/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.2970 - acc: 0.8438 - val_loss: 1.1517 - val_acc: 0.2727\n",
      "Epoch 34/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1431 - acc: 0.9375 - val_loss: 1.1571 - val_acc: 0.2727\n",
      "Epoch 35/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.1581 - acc: 0.9062 - val_loss: 1.1616 - val_acc: 0.2727\n",
      "Epoch 36/200\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.1137 - acc: 0.9375 - val_loss: 1.1668 - val_acc: 0.2727\n",
      "Epoch 37/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1895 - acc: 0.9062 - val_loss: 1.1737 - val_acc: 0.2727\n",
      "Epoch 38/200\n",
      "32/32 [==============================] - 1s 34ms/step - loss: 0.1571 - acc: 0.8750 - val_loss: 1.1802 - val_acc: 0.2727\n",
      "Epoch 39/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1255 - acc: 0.9375 - val_loss: 1.1875 - val_acc: 0.2727\n",
      "Epoch 40/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.1298 - acc: 0.9062 - val_loss: 1.1962 - val_acc: 0.2727\n",
      "Epoch 41/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.2285 - acc: 0.8438 - val_loss: 1.2073 - val_acc: 0.2727\n",
      "Epoch 42/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.1976 - acc: 0.8750 - val_loss: 1.2167 - val_acc: 0.2727\n",
      "Epoch 43/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1414 - acc: 0.9062 - val_loss: 1.2258 - val_acc: 0.2727\n",
      "Epoch 44/200\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.1707 - acc: 0.9062 - val_loss: 1.2320 - val_acc: 0.2727\n",
      "Epoch 45/200\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.1192 - acc: 0.9375 - val_loss: 1.2375 - val_acc: 0.2727\n",
      "Epoch 46/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.2080 - acc: 0.8750 - val_loss: 1.2409 - val_acc: 0.2727\n",
      "Epoch 47/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1044 - acc: 0.9375 - val_loss: 1.2432 - val_acc: 0.2727\n",
      "Epoch 48/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.2528 - acc: 0.8750 - val_loss: 1.2455 - val_acc: 0.2727\n",
      "Epoch 49/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.1163 - acc: 0.9062 - val_loss: 1.2485 - val_acc: 0.2727\n",
      "Epoch 50/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.1066 - acc: 0.9688 - val_loss: 1.2514 - val_acc: 0.2727\n",
      "Epoch 51/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.1783 - acc: 0.8750 - val_loss: 1.2529 - val_acc: 0.2727\n",
      "Epoch 52/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.0839 - acc: 0.9688 - val_loss: 1.2555 - val_acc: 0.2727\n",
      "Epoch 53/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1406 - acc: 0.8750 - val_loss: 1.2579 - val_acc: 0.2727\n",
      "Epoch 54/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1063 - acc: 0.9375 - val_loss: 1.2599 - val_acc: 0.2727\n",
      "Epoch 55/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.1913 - acc: 0.9062 - val_loss: 1.2593 - val_acc: 0.2727\n",
      "Epoch 56/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1755 - acc: 0.9062 - val_loss: 1.2612 - val_acc: 0.3636\n",
      "Epoch 57/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.2253 - acc: 0.8750 - val_loss: 1.2641 - val_acc: 0.4545\n",
      "Epoch 58/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.2163 - acc: 0.8750 - val_loss: 1.2715 - val_acc: 0.3636\n",
      "Epoch 59/200\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.2723 - acc: 0.8125 - val_loss: 1.2784 - val_acc: 0.2727\n",
      "Epoch 60/200\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.2049 - acc: 0.8438 - val_loss: 1.2863 - val_acc: 0.1818\n",
      "Epoch 61/200\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.1026 - acc: 0.9375 - val_loss: 1.2949 - val_acc: 0.1818\n",
      "Epoch 62/200\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.1613 - acc: 0.8750 - val_loss: 1.3021 - val_acc: 0.1818\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 63/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.1153 - acc: 0.9062 - val_loss: 1.3088 - val_acc: 0.1818\n",
      "Epoch 64/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.1768 - acc: 0.8750 - val_loss: 1.3170 - val_acc: 0.1818\n",
      "Epoch 65/200\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.1550 - acc: 0.9062 - val_loss: 1.3270 - val_acc: 0.1818\n",
      "Epoch 66/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.2490 - acc: 0.8125 - val_loss: 1.3373 - val_acc: 0.1818\n",
      "Epoch 67/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1308 - acc: 0.9062 - val_loss: 1.3421 - val_acc: 0.2727\n",
      "Epoch 68/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1269 - acc: 0.9062 - val_loss: 1.3475 - val_acc: 0.2727\n",
      "Epoch 69/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1495 - acc: 0.9062 - val_loss: 1.3568 - val_acc: 0.2727\n",
      "Epoch 70/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0987 - acc: 0.9375 - val_loss: 1.3689 - val_acc: 0.1818\n",
      "Epoch 71/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1871 - acc: 0.9062 - val_loss: 1.3843 - val_acc: 0.1818\n",
      "Epoch 72/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1273 - acc: 0.9062 - val_loss: 1.4031 - val_acc: 0.1818\n",
      "Epoch 73/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1010 - acc: 0.9062 - val_loss: 1.4221 - val_acc: 0.1818\n",
      "Epoch 74/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1353 - acc: 0.9062 - val_loss: 1.4323 - val_acc: 0.1818\n",
      "Epoch 75/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.2128 - acc: 0.8438 - val_loss: 1.4287 - val_acc: 0.1818\n",
      "Epoch 76/200\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.1024 - acc: 0.9062 - val_loss: 1.4281 - val_acc: 0.1818\n",
      "Epoch 77/200\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.1418 - acc: 0.8750 - val_loss: 1.4297 - val_acc: 0.2727\n",
      "Epoch 78/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.0957 - acc: 0.9375 - val_loss: 1.4354 - val_acc: 0.2727\n",
      "Epoch 79/200\n",
      "32/32 [==============================] - 1s 32ms/step - loss: 0.1977 - acc: 0.8438 - val_loss: 1.4444 - val_acc: 0.4545\n",
      "Epoch 80/200\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.0678 - acc: 1.0000 - val_loss: 1.4548 - val_acc: 0.4545\n",
      "Epoch 81/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1254 - acc: 0.9375 - val_loss: 1.4659 - val_acc: 0.4545\n",
      "Epoch 82/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0639 - acc: 0.9688 - val_loss: 1.4699 - val_acc: 0.3636\n",
      "Epoch 83/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1336 - acc: 0.9062 - val_loss: 1.4740 - val_acc: 0.3636\n",
      "Epoch 84/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1399 - acc: 0.8750 - val_loss: 1.4773 - val_acc: 0.3636\n",
      "Epoch 85/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0717 - acc: 0.9375 - val_loss: 1.4843 - val_acc: 0.3636\n",
      "Epoch 86/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.0800 - acc: 0.9688 - val_loss: 1.4927 - val_acc: 0.3636\n",
      "Epoch 87/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1604 - acc: 0.9062 - val_loss: 1.5109 - val_acc: 0.3636\n",
      "Epoch 88/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.1945 - acc: 0.8438 - val_loss: 1.5379 - val_acc: 0.4545\n",
      "Epoch 89/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1099 - acc: 0.9062 - val_loss: 1.5637 - val_acc: 0.4545\n",
      "Epoch 90/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0833 - acc: 0.9375 - val_loss: 1.5911 - val_acc: 0.4545\n",
      "Epoch 91/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1909 - acc: 0.8438 - val_loss: 1.6217 - val_acc: 0.4545\n",
      "Epoch 92/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0823 - acc: 0.9375 - val_loss: 1.6488 - val_acc: 0.4545\n",
      "Epoch 93/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.1530 - acc: 0.9375 - val_loss: 1.6746 - val_acc: 0.4545\n",
      "Epoch 94/200\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.0877 - acc: 0.9688 - val_loss: 1.7020 - val_acc: 0.4545\n",
      "Epoch 95/200\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.1101 - acc: 0.9375 - val_loss: 1.7164 - val_acc: 0.4545\n",
      "Epoch 96/200\n",
      "32/32 [==============================] - 1s 28ms/step - loss: 0.0746 - acc: 0.9375 - val_loss: 1.7308 - val_acc: 0.4545\n",
      "Epoch 97/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0535 - acc: 0.9688 - val_loss: 1.7454 - val_acc: 0.2727\n",
      "Epoch 98/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0807 - acc: 0.9688 - val_loss: 1.7576 - val_acc: 0.2727\n",
      "Epoch 99/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.1446 - acc: 0.9375 - val_loss: 1.7698 - val_acc: 0.2727\n",
      "Epoch 100/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0734 - acc: 0.9688 - val_loss: 1.7781 - val_acc: 0.1818\n",
      "Epoch 101/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1291 - acc: 0.9688 - val_loss: 1.7929 - val_acc: 0.1818\n",
      "Epoch 102/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0462 - acc: 0.9688 - val_loss: 1.8085 - val_acc: 0.1818\n",
      "Epoch 103/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0980 - acc: 0.9375 - val_loss: 1.8181 - val_acc: 0.1818\n",
      "Epoch 104/200\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.0790 - acc: 0.9375 - val_loss: 1.8308 - val_acc: 0.1818\n",
      "Epoch 105/200\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.1644 - acc: 0.9375 - val_loss: 1.8162 - val_acc: 0.1818\n",
      "Epoch 106/200\n",
      "32/32 [==============================] - 1s 35ms/step - loss: 0.1054 - acc: 0.9688 - val_loss: 1.8061 - val_acc: 0.1818\n",
      "Epoch 107/200\n",
      "32/32 [==============================] - 1s 37ms/step - loss: 0.0997 - acc: 0.9375 - val_loss: 1.7979 - val_acc: 0.1818\n",
      "Epoch 108/200\n",
      "32/32 [==============================] - 1s 29ms/step - loss: 0.1242 - acc: 0.9062 - val_loss: 1.7913 - val_acc: 0.1818\n",
      "Epoch 109/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.2302 - acc: 0.7812 - val_loss: 1.7921 - val_acc: 0.1818\n",
      "Epoch 110/200\n",
      "32/32 [==============================] - 1s 29ms/step - loss: 0.1014 - acc: 0.9375 - val_loss: 1.7918 - val_acc: 0.1818\n",
      "Epoch 111/200\n",
      "32/32 [==============================] - 1s 27ms/step - loss: 0.1079 - acc: 0.9688 - val_loss: 1.8000 - val_acc: 0.1818\n",
      "Epoch 112/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.0747 - acc: 0.9375 - val_loss: 1.8140 - val_acc: 0.1818\n",
      "Epoch 113/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1377 - acc: 0.9375 - val_loss: 1.8428 - val_acc: 0.1818\n",
      "Epoch 114/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0398 - acc: 0.9688 - val_loss: 1.8699 - val_acc: 0.1818\n",
      "Epoch 115/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0984 - acc: 0.9375 - val_loss: 1.8698 - val_acc: 0.1818\n",
      "Epoch 116/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0749 - acc: 0.9688 - val_loss: 1.8693 - val_acc: 0.1818\n",
      "Epoch 117/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1699 - acc: 0.9062 - val_loss: 1.8671 - val_acc: 0.1818\n",
      "Epoch 118/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0991 - acc: 0.9062 - val_loss: 1.8680 - val_acc: 0.1818\n",
      "Epoch 119/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0441 - acc: 0.9688 - val_loss: 1.8710 - val_acc: 0.1818\n",
      "Epoch 120/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0959 - acc: 0.9375 - val_loss: 1.8807 - val_acc: 0.1818\n",
      "Epoch 121/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1554 - acc: 0.8750 - val_loss: 1.8818 - val_acc: 0.1818\n",
      "Epoch 122/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.1093 - acc: 0.9688 - val_loss: 1.8890 - val_acc: 0.1818\n",
      "Epoch 123/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1169 - acc: 0.9062 - val_loss: 1.9072 - val_acc: 0.1818\n",
      "Epoch 124/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1334 - acc: 0.8750 - val_loss: 1.9282 - val_acc: 0.1818\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 125/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0940 - acc: 0.9375 - val_loss: 1.9419 - val_acc: 0.1818\n",
      "Epoch 126/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1711 - acc: 0.9062 - val_loss: 1.9600 - val_acc: 0.1818\n",
      "Epoch 127/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0359 - acc: 1.0000 - val_loss: 1.9776 - val_acc: 0.1818\n",
      "Epoch 128/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1245 - acc: 0.8750 - val_loss: 1.9942 - val_acc: 0.1818\n",
      "Epoch 129/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1094 - acc: 0.9062 - val_loss: 2.0104 - val_acc: 0.1818\n",
      "Epoch 130/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1885 - acc: 0.8750 - val_loss: 2.0320 - val_acc: 0.1818\n",
      "Epoch 131/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0592 - acc: 0.9688 - val_loss: 2.0551 - val_acc: 0.1818\n",
      "Epoch 132/200\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.0784 - acc: 0.9375 - val_loss: 2.0780 - val_acc: 0.1818\n",
      "Epoch 133/200\n",
      "32/32 [==============================] - 1s 29ms/step - loss: 0.0158 - acc: 1.0000 - val_loss: 2.0933 - val_acc: 0.1818\n",
      "Epoch 134/200\n",
      "32/32 [==============================] - 1s 32ms/step - loss: 0.1081 - acc: 0.9062 - val_loss: 2.1073 - val_acc: 0.1818\n",
      "Epoch 135/200\n",
      "32/32 [==============================] - 1s 29ms/step - loss: 0.1004 - acc: 0.9062 - val_loss: 2.1232 - val_acc: 0.1818\n",
      "Epoch 136/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1717 - acc: 0.8438 - val_loss: 2.1427 - val_acc: 0.1818\n",
      "Epoch 137/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1099 - acc: 0.9062 - val_loss: 2.1572 - val_acc: 0.1818\n",
      "Epoch 138/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.1175 - acc: 0.9375 - val_loss: 2.1822 - val_acc: 0.1818\n",
      "Epoch 139/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0975 - acc: 0.9375 - val_loss: 2.2258 - val_acc: 0.1818\n",
      "Epoch 140/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0551 - acc: 0.9688 - val_loss: 2.2717 - val_acc: 0.1818\n",
      "Epoch 141/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.0649 - acc: 0.9375 - val_loss: 2.3158 - val_acc: 0.1818\n",
      "Epoch 142/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0409 - acc: 0.9688 - val_loss: 2.3583 - val_acc: 0.1818\n",
      "Epoch 143/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0546 - acc: 0.9375 - val_loss: 2.3991 - val_acc: 0.1818\n",
      "Epoch 144/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0650 - acc: 0.9375 - val_loss: 2.4332 - val_acc: 0.1818\n",
      "Epoch 145/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1933 - acc: 0.9062 - val_loss: 2.4524 - val_acc: 0.1818\n",
      "Epoch 146/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1120 - acc: 0.9375 - val_loss: 2.4742 - val_acc: 0.1818\n",
      "Epoch 147/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0644 - acc: 0.9688 - val_loss: 2.4860 - val_acc: 0.1818\n",
      "Epoch 148/200\n",
      "32/32 [==============================] - 1s 17ms/step - loss: 0.0064 - acc: 1.0000 - val_loss: 2.5065 - val_acc: 0.1818\n",
      "Epoch 149/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1198 - acc: 0.9375 - val_loss: 2.4682 - val_acc: 0.1818\n",
      "Epoch 150/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.1109 - acc: 0.9062 - val_loss: 2.3621 - val_acc: 0.1818\n",
      "Epoch 151/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.1312 - acc: 0.9375 - val_loss: 2.2428 - val_acc: 0.1818\n",
      "Epoch 152/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.1301 - acc: 0.9375 - val_loss: 2.1112 - val_acc: 0.1818\n",
      "Epoch 153/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.1071 - acc: 0.9062 - val_loss: 2.0104 - val_acc: 0.1818\n",
      "Epoch 154/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.1135 - acc: 0.9062 - val_loss: 1.9435 - val_acc: 0.1818\n",
      "Epoch 155/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.0999 - acc: 0.9375 - val_loss: 1.8966 - val_acc: 0.1818\n",
      "Epoch 156/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0464 - acc: 0.9688 - val_loss: 1.8666 - val_acc: 0.1818\n",
      "Epoch 157/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1630 - acc: 0.9062 - val_loss: 1.8587 - val_acc: 0.1818\n",
      "Epoch 158/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0646 - acc: 0.9375 - val_loss: 1.8601 - val_acc: 0.1818\n",
      "Epoch 159/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1689 - acc: 0.8750 - val_loss: 1.8750 - val_acc: 0.1818\n",
      "Epoch 160/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.1015 - acc: 0.9375 - val_loss: 1.8976 - val_acc: 0.1818\n",
      "Epoch 161/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.1509 - acc: 0.8750 - val_loss: 1.9290 - val_acc: 0.1818\n",
      "Epoch 162/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1320 - acc: 0.9375 - val_loss: 1.9728 - val_acc: 0.1818\n",
      "Epoch 163/200\n",
      "32/32 [==============================] - 1s 26ms/step - loss: 0.1129 - acc: 0.9375 - val_loss: 2.0457 - val_acc: 0.1818\n",
      "Epoch 164/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1756 - acc: 0.9062 - val_loss: 2.1224 - val_acc: 0.1818\n",
      "Epoch 165/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0793 - acc: 0.9688 - val_loss: 2.1960 - val_acc: 0.1818\n",
      "Epoch 166/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.1356 - acc: 0.9062 - val_loss: 2.2825 - val_acc: 0.1818\n",
      "Epoch 167/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0483 - acc: 0.9688 - val_loss: 2.3698 - val_acc: 0.1818\n",
      "Epoch 168/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0683 - acc: 0.9688 - val_loss: 2.4593 - val_acc: 0.1818\n",
      "Epoch 169/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.2099 - acc: 0.8750 - val_loss: 2.5438 - val_acc: 0.1818\n",
      "Epoch 170/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0034 - acc: 1.0000 - val_loss: 2.6267 - val_acc: 0.1818\n",
      "Epoch 171/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.0441 - acc: 0.9688 - val_loss: 2.7049 - val_acc: 0.1818\n",
      "Epoch 172/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0685 - acc: 0.9375 - val_loss: 2.7816 - val_acc: 0.1818\n",
      "Epoch 173/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1934 - acc: 0.9375 - val_loss: 2.8092 - val_acc: 0.1818\n",
      "Epoch 174/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0564 - acc: 0.9688 - val_loss: 2.8297 - val_acc: 0.1818\n",
      "Epoch 175/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0364 - acc: 1.0000 - val_loss: 2.8473 - val_acc: 0.1818\n",
      "Epoch 176/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1198 - acc: 0.9688 - val_loss: 2.8576 - val_acc: 0.1818\n",
      "Epoch 177/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0807 - acc: 0.9375 - val_loss: 2.8681 - val_acc: 0.1818\n",
      "Epoch 178/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0795 - acc: 0.9375 - val_loss: 2.8732 - val_acc: 0.1818\n",
      "Epoch 179/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1748 - acc: 0.8750 - val_loss: 2.8777 - val_acc: 0.1818\n",
      "Epoch 180/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.1487 - acc: 0.8438 - val_loss: 2.8952 - val_acc: 0.1818\n",
      "Epoch 181/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1345 - acc: 0.9062 - val_loss: 2.9057 - val_acc: 0.1818\n",
      "Epoch 182/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0780 - acc: 0.9688 - val_loss: 2.9219 - val_acc: 0.1818\n",
      "Epoch 183/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.0723 - acc: 0.9375 - val_loss: 2.9410 - val_acc: 0.1818\n",
      "Epoch 184/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0431 - acc: 0.9688 - val_loss: 2.9726 - val_acc: 0.1818\n",
      "Epoch 185/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0737 - acc: 1.0000 - val_loss: 2.9946 - val_acc: 0.1818\n",
      "Epoch 186/200\n",
      "32/32 [==============================] - 1s 24ms/step - loss: 0.0586 - acc: 0.9375 - val_loss: 3.0276 - val_acc: 0.1818\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 187/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.1409 - acc: 0.8750 - val_loss: 3.0579 - val_acc: 0.1818\n",
      "Epoch 188/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0404 - acc: 0.9688 - val_loss: 3.0911 - val_acc: 0.1818\n",
      "Epoch 189/200\n",
      "32/32 [==============================] - 1s 18ms/step - loss: 0.0882 - acc: 0.9375 - val_loss: 3.1206 - val_acc: 0.1818\n",
      "Epoch 190/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0266 - acc: 0.9688 - val_loss: 3.1459 - val_acc: 0.1818\n",
      "Epoch 191/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1272 - acc: 0.9375 - val_loss: 3.1751 - val_acc: 0.1818\n",
      "Epoch 192/200\n",
      "32/32 [==============================] - 1s 23ms/step - loss: 0.0500 - acc: 0.9688 - val_loss: 3.2036 - val_acc: 0.1818\n",
      "Epoch 193/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.0924 - acc: 0.9062 - val_loss: 3.2176 - val_acc: 0.1818\n",
      "Epoch 194/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.0316 - acc: 1.0000 - val_loss: 3.2352 - val_acc: 0.1818\n",
      "Epoch 195/200\n",
      "32/32 [==============================] - 1s 19ms/step - loss: 0.1554 - acc: 0.9062 - val_loss: 3.2521 - val_acc: 0.1818\n",
      "Epoch 196/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.1260 - acc: 0.8750 - val_loss: 3.2611 - val_acc: 0.1818\n",
      "Epoch 197/200\n",
      "32/32 [==============================] - 1s 22ms/step - loss: 0.1112 - acc: 0.8750 - val_loss: 3.2702 - val_acc: 0.1818\n",
      "Epoch 198/200\n",
      "32/32 [==============================] - 1s 21ms/step - loss: 0.0796 - acc: 0.9688 - val_loss: 3.2797 - val_acc: 0.1818\n",
      "Epoch 199/200\n",
      "32/32 [==============================] - 1s 25ms/step - loss: 0.1816 - acc: 0.8750 - val_loss: 3.3000 - val_acc: 0.1818\n",
      "Epoch 200/200\n",
      "32/32 [==============================] - 1s 20ms/step - loss: 0.1997 - acc: 0.8438 - val_loss: 3.3283 - val_acc: 0.1818\n",
      "11/11 [==============================] - 0s 7ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'acc': 0.1818181872367859, 'loss': 3.3283066749572754}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train(model_2, x_train, y_train, x_test, y_test, batch_size=100, epochs=200, model_name=\"model_B\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Accuracy = ', 18.181818181818183, '%')\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_2.predict(x_test)\n",
    "y_pred = np.argmax(y_pred,axis=1)\n",
    "class_names=['0','310','350']\n",
    "Accuracy = np.mean(y_pred == y_test)\n",
    "print('Accuracy = ', Accuracy*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd5762cad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_confusion_matrix(y_test,y_pred,num_classes,class_names,100,200,save=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd55f17510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot_acuracys(model_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 ANÁLISIS \n",
    "\n",
    "Durante el desarrollo de este proyecto se realizaron tres versiones diferentes de notebooks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Análisis PROYECTO U3 Versión 1\n",
    "Este fue el primer notebook probado, se utilizó una arquitectura empleada en clase, utilizando 3 épocas,  trabajando con tres clases, batch_size = 10 y un dropout de 0.2. El inconveniente de trabajar con esta arquitectura fue que en el momento de imprimir los gráficos de barras para visualizar las predicciones no se podía distinguir de manera precisa la relación de la imagen con la precisión. Cabe resaltar que estas imagenes que se procesaron en este proyecto NO son de fácil clasificación debido que no se puede determinar una clasificación a simple vista como lo haría un programa con un dataset de \"perros\" o \"gatos\". Estas imagenes contienen caracterísitcas diferenciables muy poco notorias, además el color de las imagenes puede hacer  que el sistema prediga erróneamente cuando se pasa los filtros durante la convolución.\n",
    "\n",
    "#### Análisis PROYECTO U3 Versión 2\n",
    "Debido al resultado anterior, se plantea otra estrategia implementando un nuevo diseño de arquitectura de red y una forma distinta de visualización, en este caso se emplea un dataset más pequeño de 2 clases con 3 imagenes por clase. Se realiza una arquitectura de CNN secuencial, con un Dropout de 0.5, batch_size=6, epocas=5, pero en esta ocasión al finalizar el entrenamiento lo que se hace es que se guarda nuestro modelo en un archivo para que no tengamos que entrenar nuestro modelo cada vez que queramos hacer una predicción. \n",
    "\n",
    "\n",
    "Luego de que entrenamos, llamamos a nuestra carperta generada llamada modelo y grabamos la arquitectura del modelo en un archivo y los pesos en otro archivo. Luego hacemos la predicción y en lugar de imprimir todas las imagenes, verificamos la predicción en la clasificación de cada imagen de manera individual debido a que la ingresamos por consola. A partir de ese momento visualizamos que el sistema no se equivoca, es decir, cuando le damos la dirección de la imagen (que contiene en el nombre el ángulo anotado de la clase a la que pertenece), el sistema procesa la imagen y efectivamente la clasifica en la clase a la que corresponde. Es por ello que visualizamos en este modelo la precisión y tenemos un acc: 1.0000, un val_loss: 4.5895e-06 y un val_acc: 1 por lo que determinamos que el modelo está sobre-entrenado.\n",
    "     \n",
    "#### Análisis PROYECTO U3 Versión 3\n",
    "En este momento, luego de realizar las predicciones anteriores, implementamos el mismo flujo de trabajo mencionado en la metodología pero en este caso establecemos 3 clases: ['Azimuth310', 'Azimuth350', 'Azimuth0'] y dentro de la función para el modelo de la CNN definimos un lr=0.0001, hay 3 clases, dropout de 0.5. Se evaluaron diferentes valores para las epocas y para el batch_size construyendo dos gráficos que permiten determinar cómo afectan estos dos parámetros la precisión del modelo.\n",
    "Desafortunadamente, los valores presentados en estos gráficos varían cada que se ejecuta el código.\n",
    "Para esta versión del proyecto la cantidad de imágenes usadas fueron 43, mientras que en las dos primeras versiones se usaron 18 y 12 imágenes respectivamente.\n",
    "\n",
    "#### Análisis de la versión final. PROYECTO U3 Versión Final\n",
    "En esta versión se buscó darle más compleijdad al modelo, por lo que se definió un modelo B que tiene más capas y se le cambiaron algunos parámetros. El modelo complejo presenta diferencias grandes y poca presición a la hora de validar el modelo. Esto puede ser porque sea necesario cambiar algunos parámetros del modelo o porque la complejidad de las imágenes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 TRABAJO A FUTURO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Debido a la complejidad de las imagenes, no se obtiene una predicción que indique que el sistema está realizando de manera óptima la clasificación de las imágenes, esto puede darse por muchos factores, inicialmente del dataset que contiene 108 imagenes se trabaja sólo con 90 de ellas, por lo cual, se puede considerar que son pocos datos para realizar el entrenamiento, es decir, el dataset es pequeño. Para una futura ejecución es aconsejable obtener más imagenes que pertenezcan a las clases analizadas.\n",
    "- Por otro lado, las caracterísitcas de cada imagen, independiente de que tengan elevación o un ángulo azimutal completamente diferente, presentan el mismo patrón de colores con características similares. La variación de estos ángulos es indistingible al ojo humano, por lo cual en el momento de realizar el proceso de convolución pueden existir errores de precisión que alteren las predicciones. En este caso como no es posible cambiar las características del dataset debido al objetivo del proyecto, se puede aconsejar trabajar con un mayor número de épocas, aumentar el tamaño de las imágenes, es decir, modificar los parámetros del algorirtmo dependiendo de los valores obtenidos para un dataset de mayor tamaño.\n",
    "- Adicionalmente, es importante mencionar que no sólo se espera clasificar los ángulos azimutales, sino también la elevación de estos con respecto a la clase perteneciente como se explicó en el contexto de este proyecto. Por lo anterior se espera no sólo trabajar con tres clases sino con un total de clases mayor y a su vez, realizar la clasificación del ángulo de elevación que tiene cada imagen. En conclusión trabajar con un número mayor de clases y sus respectivas elevaciones con el fin de obtener a cabalidad las coordenadas angulares que son de interés en el proyecto.\n",
    "- Si no es posible obtener con precisión el resultado de clasificación de estas coordenadas, es aconsejable utilizar otra estrategia para realizar el procesamiento de las imágenes que no involucre el uso de redes convolucionales mediante Deep Learning debido a la naturaleza de las imágenes."
   ]
  }
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