01-CNN-Autoencoder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REDES NEURONALES PROFUNDAS\n",
    "Utilice el conjunto de datos Fashion-MNIST para construir un clasificador convolucional de imágenes de productos. Para la construcción del modelo utilice los dos esquemas que se describen a continuación y compare los resultados:\n",
    "\n",
    "1. Entrenar de un Autocodificador convolucional multicapa.\n",
    "2. Extraer la reducción de la dimensionalidad que el Autocodificador construye en el entrenamiento.\n",
    "\n",
    "Para los puntos 2) compruebe, y muestre con ejemplos, que las imágenes están bien reconstruidas.\n",
    "\n",
    "## OBJETIVO\n",
    "Aplicar el proceso de aprendizaje a partir de datos para resolver problemas de clasificación utilizando redes neuronales convolucionales profundas sobre la herramienta Keras.\n",
    "\n",
    "## DATOS\n",
    "Incluidos en Keras.\n",
    "También, existe otra fuente equivalente que se consigue en el siguiente URL https://www.kaggle.com/zalando-research/fashionmnist donde hay un resumen de estos datos en el archivo CVS y XLSX.\n",
    "\n",
    "la clasificación para el aprendizaje supervisado es:\n",
    "\n",
    "    Label \tClass\n",
    "    0 \t \tT-shirt/top\n",
    "    1 \t \tTrouser\n",
    "    2 \t \tPullover\n",
    "    3 \t \tDress\n",
    "    4 \t \tCoat\n",
    "    5 \t \tSandal\n",
    "    6 \t \tShirt\n",
    "    7 \t \tSneaker\n",
    "    8 \t \tBag\n",
    "    9 \t \tAnkle boot\n",
    "\n",
    "**Importante: Lea los comentarios y apuntes del Notebook para tener claridad de los pasos.**\n",
    "### Consideraciones\n",
    "- Utilice sólo los conjuntos de datos indicado.\n",
    "- El frameworks a utilizar es TensorFlow, Keras con Jupyter Notebbooks.\n",
    "\n",
    "### Enlaces de interés\n",
    "- documentación Keras, URL: https://keras.io/models/sequential/\n",
    "- documentación TensorFlow, URL: https://www.tensorflow.org/versions\n",
    "- Tutorial CNN basico, URL: https://www.kaggle.com/nhlr21/deep-keras-cnn-tutorial/notebook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importando Librerias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importando dependencias de trabajo\n",
    "# importando librerias basicas\n",
    "import os\n",
    "import sys\n",
    "import re\n",
    "import gc\n",
    "\n",
    "# importando modulos de analisis de datos, ML y graficas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow import keras\n",
    "from collections import OrderedDict\n",
    "from collections import Counter\n",
    "\n",
    "# importando dependencias para tensorflow\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "# importando para sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# importando para keras\n",
    "from keras.models import Model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Input\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Reshape\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import UpSampling2D\n",
    "from keras.layers import BatchNormalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones Utiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# funcion que recibe una lista numpy y recupera la forma de cada elemento, devuelve una lista con formas\n",
    "def get_shape(data):\n",
    "    # respuesta de la funcion\n",
    "    ans = list()\n",
    "\n",
    "    for d in data:\n",
    "        sp = d.shape\n",
    "        ans.append(sp)\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# funcion que transforma el entero de la clase a la palabra de la etiqueta, devuelve una lista de etiquetas\n",
    "def class2label(data, labels):\n",
    "    # respuesta de la funcion\n",
    "    ans = list()\n",
    "\n",
    "    for d in data:\n",
    "        d = int(d)\n",
    "        l = str(labels[d])\n",
    "        ans.append(l)\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# funcion que estandariza los datos en numpy de acuerdo a un valor min & max, devuelve un arreglo np flotante\n",
    "def std_data(data, minv, maxv):\n",
    "    rangev = maxv - minv\n",
    "    ans = data.astype(\"float32\")/float(rangev)\n",
    "    # ans = pd.Series(ans)\n",
    "    # respuesta de la funcion\n",
    "    return ans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cargar y Preparar los Datos\n",
    "\n",
    "Los pasos de esta sección son:\n",
    "\n",
    "1. Leer los datos desde MNIST.\n",
    "2. Formatear los datos para que los acepte el DataFrame de Pandas.\n",
    "2. Crear el DataFrame de Pandas con un esquema propio.\n",
    "2. Formatear los datos MNIST para pobrar el DataFrame de pandas.\n",
    "3. Revisar que todo este como debería estar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lista de nombres de las clasificaciones\n",
    "label_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# se carga el archivo de datos de trabajo por medio de Keras\n",
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nombres de columnas para el dataframe de pandas\n",
    "col_names = [\"img_data\", \"img_shape\", \"class\", \"label\", \"std_img_data\", \"cat_labels\"]#, \"ReshapeData\", \"Label\", \"Class\", \"DataSize\", \"ReshapeSize\", \"ResKeras\", \"ScoreKeras\"]\n",
    "# creando dataframe con columnas\n",
    "fashion_df = pd.DataFrame(columns=col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# integrando datos de mnist\n",
    "img_data = np.concatenate((x_train, x_test), axis = 0)\n",
    "class_data = np.concatenate((y_train, y_test), axis = 0)\n",
    "# recuperando forma de imagenes\n",
    "img_shape = get_shape(img_data)\n",
    "# recuperando etiquetas de las clases\n",
    "labels = class2label(class_data, label_names)\n",
    "# estandarizar los datos de la imagen\n",
    "std_img_data = std_data(img_data, 0, 255)\n",
    "# categorizando las clases a aprender\n",
    "cat_labels = to_categorical(class_data, len(label_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cambio de formato para utilizar el dataframe\n",
    "img_data = img_data.tolist()\n",
    "std_img_data = std_img_data.tolist()\n",
    "cat_labels = cat_labels.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definir arreglo basico de datos\n",
    "data_list = (img_data, img_shape, class_data, labels, std_img_data, cat_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# poblamdo las columnas del dataframe\n",
    "for col, data in zip(col_names, data_list):\n",
    "    fashion_df[col] = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 70000 entries, 0 to 69999\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   img_data      70000 non-null  object\n",
      " 1   img_shape     70000 non-null  object\n",
      " 2   class         70000 non-null  uint8 \n",
      " 3   label         70000 non-null  object\n",
      " 4   std_img_data  70000 non-null  object\n",
      " 5   cat_labels    70000 non-null  object\n",
      "dtypes: object(5), uint8(1)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "fashion_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>(28, 28)</td>\n",
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       "      <td>Ankle boot</td>\n",
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       "      <td>Sandal</td>\n",
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       "                                                img_data img_shape  class  \\\n",
       "0      [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  (28, 28)      9   \n",
       "1      [[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 41, 188, 103, ...  (28, 28)      0   \n",
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       "4      [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  (28, 28)      0   \n",
       "...                                                  ...       ...    ...   \n",
       "69995  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  (28, 28)      9   \n",
       "69996  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 31, 52, 98, 94, 8...  (28, 28)      1   \n",
       "69997  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 6...  (28, 28)      8   \n",
       "69998  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 164, 137, 1...  (28, 28)      1   \n",
       "69999  [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...  (28, 28)      5   \n",
       "\n",
       "             label                                       std_img_data  \\\n",
       "0       Ankle boot  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "1      T-shirt/top  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.00392156885936856...   \n",
       "2      T-shirt/top  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "3            Dress  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12...   \n",
       "4      T-shirt/top  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "...            ...                                                ...   \n",
       "69995   Ankle boot  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "69996      Trouser  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
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       "69998      Trouser  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "69999       Sandal  [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...   \n",
       "\n",
       "                                              cat_labels  \n",
       "0      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
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       "...                                                  ...  \n",
       "69995  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "69996  [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "69997  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, ...  \n",
       "69998  [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "69999  [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...  \n",
       "\n",
       "[70000 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fashion_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# libero memoria\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocesar los Datos\n",
    "\n",
    "Los pasos de esta sección son:\n",
    "\n",
    "1. Revisar que los datos están bien.\n",
    "2. Elegir la características o propiedades de aprendizaje.\n",
    "3. Elegir la variable objetivo del aprendizaje.\n",
    "4. Dividir el conjunto de datos entre las poblaciones de entrenamiento y pruebas.\n",
    "5. Formatear los datos de aprendizaje y objetivo acorde a la red neuronal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cchequeo la distribucion de datos\n",
    "sns.set()\n",
    "plt.figure(figsize=(20, 8))\n",
    "sns.histplot(fashion_df[col_names[3]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70000, 28, 28)\n",
      "(70000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "# seleccionando caracteristicas de aprendizaje y variables objetivo\n",
    "# recuperando la forma de las imagenes basado en el primer elemento de la lista\n",
    "\n",
    "# recuperando los valores y ajustando el tensor para la CNN\n",
    "A = fashion_df[col_names[4]]\n",
    "# recuperando los valores de la cateogoria\n",
    "b = fashion_df[col_names[5]].values\n",
    "\n",
    "# fortateo de datos numpy\n",
    "X = np.array([np.array(i, dtype=\"object\") for i in A], dtype=\"object\")\n",
    "y = np.array([np.array(j, dtype=\"object\") for j in b], dtype=\"object\")\n",
    "\n",
    "print(X.shape)\n",
    "# forma basica general de las imagenes\n",
    "imgsh = X[0].shape\n",
    "# ajuste de forma para el modelo CNN\n",
    "X = X.reshape(fashion_df.shape[0], imgsh[0], imgsh[1], 1)\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# semilla para el random\n",
    "rseed = 42\n",
    "\n",
    "# en tamanho de la muestra para pruebas esta entre 0.2 y 0.3\n",
    "train_pop = 0.8\n",
    "test_pop = 1.0 - train_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# distribuir los datos entre entrenamiento vs. pruebas\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, X, test_size = test_pop, random_state = rseed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# formateo para keras y tensorflow\n",
    "X_train = tf.convert_to_tensor(X_train, dtype=\"float64\")\n",
    "y_train = tf.convert_to_tensor(y_train, dtype=\"float64\")\n",
    "X_test = tf.convert_to_tensor(X_test, dtype=\"float64\")\n",
    "y_test = tf.convert_to_tensor(y_test, dtype=\"float64\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definir Modelo CNN\n",
    "\n",
    "Los pasos de esta sección son:\n",
    "\n",
    "1. Definir las variables topológicas de la red neuronal.\n",
    "2. Definir los parámetros de optimización y aprendizaje del modelo.\n",
    "3. Definir la topología (capas) del modelo.\n",
    "4. Definir las condiciones de entrenamiento para el modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defino parametros necesarios para el modelo Autoencoder\n",
    "# parametros para las capas\n",
    "filters = 16\n",
    "mid_neurons = 7*7\n",
    "outn = 1\n",
    "mid_reshape = (7,7,1)\n",
    "ksize = (3,3)\n",
    "psize = (2,2)\n",
    "categories = len(label_names)\n",
    "act = \"relu\"\n",
    "out = \"relu\"\n",
    "pad = \"same\"\n",
    "ldrop = 0.2\n",
    "\n",
    "# forma del kernel de entrada y de las capas intermedias\n",
    "inshape = X[0].shape\n",
    "\n",
    "# parametros de optimizacion del modelo\n",
    "# l = \"binary_crossentropy\"\n",
    "l = \"mse\"\n",
    "opti = \"adam\"\n",
    "met = [\"accuracy\"]\n",
    "\n",
    "# parametros de operacion/aprendizaje del modelo\n",
    "ver = 1\n",
    "epo = 50\n",
    "bs = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definicion de las capas para el Autoencoder\n",
    "layers = (\n",
    "    # capa de entrada\n",
    "    Input(shape = inshape, name = \"LayIn\"),\n",
    "    # capa convolucional intermedia con regularizacion\n",
    "    Conv2D(filters, ksize, activation = act, padding = pad, name = \"EnConv1\"),\n",
    "    MaxPooling2D(psize, padding = pad, name = \"EnPool1\"),\n",
    "    # BatchNormalization(name = \"EnNorm1\"),\n",
    "    Dropout(ldrop, name = \"EnDrop1\"),\n",
    "\n",
    "    # capa convolucional intermedia con regularizacion\n",
    "    Conv2D(int(filters/2), ksize, activation=act, padding = pad, name = \"EnConv2\"),\n",
    "    MaxPooling2D(psize, padding = pad, name = \"EnPool2\"),\n",
    "    # BatchNormalization(name = \"EnNorm2\"),\n",
    "    # Dropout(ldrop, name = \"EnDrop2\"),\n",
    "    \n",
    "    # capa intermedia de 2D a 1D\n",
    "    Flatten(name = \"LayFlat\"),\n",
    "    # # # capa intermedia densamente poblada con regularizacion\n",
    "    Dense(mid_neurons, activation = act, name = \"DenseMid\"),\n",
    "    Dropout(ldrop, name = \"MidDrop\"),\n",
    "    # # # capa intermedia de 1D a 2D\n",
    "    Reshape(mid_reshape, name = \"layReshape\"),\n",
    "    \n",
    "    # capa convolucional intermedia con regularizacion\n",
    "    Conv2D(int(filters/2), ksize, activation = act, padding = pad, name = \"DeConv1\"),\n",
    "    UpSampling2D(psize, name = \"DeUpsam1\"),\n",
    "    # BatchNormalization(name = \"DeNorm1\"),\n",
    "    # Dropout(ldrop, name = \"DeDrop1\"),\n",
    "\n",
    "    # capa convolucional intermedia con regularizacion\n",
    "    Conv2D(filters, ksize, activation = act, padding = pad, name = \"DeConv2\"),\n",
    "    UpSampling2D(psize, name = \"DeUpsam2\"),\n",
    "    # BatchNormalization(name = \"DeNorm2\"),\n",
    "    Dropout(ldrop, name = \"DeDrop2\"),\n",
    "    # capa de salida\n",
    "    # Reshape(inshape),\n",
    "    Conv2D(outn, ksize, activation = out, padding = pad, name = \"LayOut\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definiendo el modelo CNN en Keras\n",
    "cnn_model = Sequential(layers)\n",
    "cnn_model.model_name = \"DCNN Autoencoder\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compilando las condiciones de optimizacion y ajuste del Modelo CNN\n",
    "cnn_model.compile(loss = l, optimizer = opti, metrics = met)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "EnConv1 (Conv2D)             (None, 28, 28, 16)        160       \n",
      "_________________________________________________________________\n",
      "EnPool1 (MaxPooling2D)       (None, 14, 14, 16)        0         \n",
      "_________________________________________________________________\n",
      "EnDrop1 (Dropout)            (None, 14, 14, 16)        0         \n",
      "_________________________________________________________________\n",
      "EnConv2 (Conv2D)             (None, 14, 14, 8)         1160      \n",
      "_________________________________________________________________\n",
      "EnPool2 (MaxPooling2D)       (None, 7, 7, 8)           0         \n",
      "_________________________________________________________________\n",
      "LayFlat (Flatten)            (None, 392)               0         \n",
      "_________________________________________________________________\n",
      "DenseMid (Dense)             (None, 49)                19257     \n",
      "_________________________________________________________________\n",
      "MidDrop (Dropout)            (None, 49)                0         \n",
      "_________________________________________________________________\n",
      "layReshape (Reshape)         (None, 7, 7, 1)           0         \n",
      "_________________________________________________________________\n",
      "DeConv1 (Conv2D)             (None, 7, 7, 8)           80        \n",
      "_________________________________________________________________\n",
      "DeUpsam1 (UpSampling2D)      (None, 14, 14, 8)         0         \n",
      "_________________________________________________________________\n",
      "DeConv2 (Conv2D)             (None, 14, 14, 16)        1168      \n",
      "_________________________________________________________________\n",
      "DeUpsam2 (UpSampling2D)      (None, 28, 28, 16)        0         \n",
      "_________________________________________________________________\n",
      "DeDrop2 (Dropout)            (None, 28, 28, 16)        0         \n",
      "_________________________________________________________________\n",
      "LayOut (Conv2D)              (None, 28, 28, 1)         145       \n",
      "=================================================================\n",
      "Total params: 21,970\n",
      "Trainable params: 21,970\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# resumen de la topologia del modelo\n",
    "cnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# condiciones de parada temprana\n",
    "cnn_earlystop_acc = EarlyStopping(monitor = \"val_accuracy\", min_delta = 0.001, patience = 7, verbose = ver, mode = \"max\", restore_best_weights = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entrenar Modelo\n",
    "\n",
    "Los pasos de esta sección son:\n",
    "\n",
    "1. Entrenar el modelo con el conjunto de entrenamiento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1750/1750 [==============================] - 43s 25ms/step - loss: 0.4420 - accuracy: 0.4979 - val_loss: 0.3859 - val_accuracy: 0.5050\n",
      "Epoch 2/50\n",
      "1750/1750 [==============================] - 55s 31ms/step - loss: 0.3785 - accuracy: 0.4998 - val_loss: 0.3803 - val_accuracy: 0.5055\n",
      "Epoch 3/50\n",
      "1750/1750 [==============================] - 51s 29ms/step - loss: 0.3809 - accuracy: 0.4996 - val_loss: 0.3707 - val_accuracy: 0.5057\n",
      "Epoch 4/50\n",
      "1750/1750 [==============================] - 48s 28ms/step - loss: 0.3724 - accuracy: 0.5001 - val_loss: 0.3713 - val_accuracy: 0.5053\n",
      "Epoch 5/50\n",
      "1750/1750 [==============================] - 48s 28ms/step - loss: 0.3596 - accuracy: 0.5003 - val_loss: 0.3669 - val_accuracy: 0.5061\n",
      "Epoch 6/50\n",
      "1750/1750 [==============================] - 49s 28ms/step - loss: 0.3551 - accuracy: 0.5004 - val_loss: 0.3652 - val_accuracy: 0.5061\n",
      "Epoch 7/50\n",
      "1750/1750 [==============================] - 52s 30ms/step - loss: 0.3548 - accuracy: 0.5003 - val_loss: 0.3532 - val_accuracy: 0.5061\n",
      "Epoch 8/50\n",
      "1750/1750 [==============================] - 54s 31ms/step - loss: 0.3536 - accuracy: 0.5002 - val_loss: 0.3475 - val_accuracy: 0.5064\n",
      "Epoch 9/50\n",
      "1750/1750 [==============================] - 55s 31ms/step - loss: 0.3550 - accuracy: 0.5004 - val_loss: 0.3530 - val_accuracy: 0.5064\n",
      "Epoch 10/50\n",
      "1750/1750 [==============================] - 90s 52ms/step - loss: 0.3546 - accuracy: 0.5003 - val_loss: 0.3557 - val_accuracy: 0.5063\n",
      "Epoch 11/50\n",
      "1750/1750 [==============================] - 69s 40ms/step - loss: 0.3592 - accuracy: 0.4997 - val_loss: 0.3462 - val_accuracy: 0.5058\n",
      "Epoch 12/50\n",
      "1750/1750 [==============================] - 82s 47ms/step - loss: 0.3516 - accuracy: 0.5003 - val_loss: 0.3464 - val_accuracy: 0.5067\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00012: early stopping\n"
     ]
    }
   ],
   "source": [
    "# ajustando el modelo MLP Keras\n",
    "cnn_log = cnn_model.fit(\n",
    "    x = X_train,#np.array(X_trainB), \n",
    "    y = X_train,#to_categorical(np.array(y_trainB), categories), \n",
    "    batch_size = bs,\n",
    "    epochs = epo, \n",
    "    verbose = ver,\n",
    "    callbacks = [cnn_earlystop_acc],\n",
    "    workers = 8,\n",
    "    shuffle = False,\n",
    "    use_multiprocessing = True,\n",
    "    validation_data = (X_test, X_test)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probar Modelo\n",
    "\n",
    "Los pasos de esta sección son:\n",
    "\n",
    "1. Probar el modelo con el conjunto de pruebas.\n",
    "2. Evaluar globalmente los resultados.\n",
    "3. Guardar el modelo entrenado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "438/438 [==============================] - 6s 13ms/step - loss: 0.3669 - accuracy: 0.5061\n"
     ]
    }
   ],
   "source": [
    "cnn_eval = cnn_model.evaluate(x = X_test, y = y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perdida promedio:  0.3669230341911316\n",
      "Precision promedio:  0.5060509443283081\n"
     ]
    }
   ],
   "source": [
    "# resultados generales\n",
    "print(\"Perdida promedio: \", cnn_eval[0])\n",
    "print(\"Precision promedio: \", cnn_eval[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "438/438 [==============================] - 5s 10ms/step\n"
     ]
    }
   ],
   "source": [
    "# pruebas sobre el modelo\n",
    "cnn_predictions = cnn_model.predict(X_test, verbose = ver)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El modelo entrenado esta en: Models\\hdig_cnn_autoencoder.h5\n"
     ]
    }
   ],
   "source": [
    "# guardar el modelo entrenado\n",
    "wdir = os.getcwd()\n",
    "folder_models = \"Models\"\n",
    "model_fname = \"hdig_cnn_autoencoder.h5\"\n",
    "model_fpn = os.path.join(folder_models, model_fname)\n",
    "print(\"El modelo entrenado esta en:\", model_fpn)\n",
    "cnn_model.save(model_fpn)\n",
    "# tf.keras.models.save_model(cnn_autoencoder, model_fpn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mostrar Resultados\n",
    "\n",
    "Los pasos de esta sección son:\n",
    "\n",
    "1. Mostrar las curvas de aprendizaje.\n",
    "2. Mostrar la reconstrucción del Autoencoder.\n",
    "3. Mostrar la abstracción del Autoencoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Felipe\\AppData\\Local\\Temp/ipykernel_63588/1089549431.py:20: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reporte del aprendizaje\n",
    "# base de la figura\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, figsize = (16,8))\n",
    "\n",
    "# datos de la figura en de perdida y precision\n",
    "ax1.plot(cnn_log.history[\"loss\"], 'red', label = \"Train Loss\")\n",
    "ax1.plot(cnn_log.history[\"val_loss\"], 'darkorange', label = \"Test Loss\")\n",
    "ax2.plot(cnn_log.history[\"accuracy\"], 'red', label = \"Train Accuracy\")\n",
    "ax2.plot(cnn_log.history[\"val_accuracy\"], 'royalblue', label = \"Test Accuracy\")\n",
    "\n",
    "# leyenda de la grafica\n",
    "fig.suptitle(\"LEARNING BEHAVIOR\")\n",
    "ax1.grid(True)\n",
    "ax2.grid(True)\n",
    "ax1.set_title(\"Loss\")\n",
    "ax2.set_title(\"Accuracy\")\n",
    "ax1.set(xlabel = \"Epoch [cycle]\", ylabel = \"loss [%]\")\n",
    "ax2.set(xlabel = \"Epoch [cycle]\", ylabel = \"Acc [%]\")\n",
    "fig.legend()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prueba funcional del autoencoder\n",
    "max_img = 10\n",
    "random_test_img = np.random.randint(len(X_test), size = max_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "middle_layer = \"LayFlat\"\n",
    "cnn_abstraction = Model(inputs = cnn_model.input, outputs = cnn_model.get_layer(middle_layer).output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x288 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# desplegando pruebas\n",
    "plt.figure(figsize=(20, 4))\n",
    "og_shape = fashion_df[col_names[1]][0]\n",
    "\n",
    "for i, img_id in enumerate(random_test_img):\n",
    "    # imagen original\n",
    "    ax = plt.subplot(3, max_img, i + 1)\n",
    "    temp_X = np.array(X_test[img_id])\n",
    "    plt.imshow(temp_X.reshape(og_shape))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "    \n",
    "    # imagen abstracta\n",
    "    ax = plt.subplot(3, max_img, max_img + i + 1)\n",
    "    temp_abstract = cnn_abstraction(temp_X)\n",
    "    temp_abstract = np.array(temp_abstract)\n",
    "    plt.imshow(temp_abstract)\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # imagen reconstruida\n",
    "    ax = plt.subplot(3, max_img, 2*max_img + i + 1)\n",
    "    temp_pre = np.array(cnn_predictions[img_id])\n",
    "    plt.imshow(temp_pre.reshape(og_shape))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  }
 ],
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