Autocodificadores/Cuadernos/ae_Eliminacion_ruido_MNIST.ipynb

{
 "cells": [
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   "id": "28b60d55-20fa-40d4-8a5b-ad71542da383",
   "metadata": {
    "tags": []
   },
   "source": [
    "<figure>\n",
    "<img src=\"../Imagenes/logo-final-ap.png\"  width=\"80\" height=\"80\" align=\"left\"/> \n",
    "</figure>\n",
    "\n",
    "# <span style=\"color:blue\"><left>Aprendizaje Profundo</left></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8843923f-cbce-49c7-be36-eb8a730b3de3",
   "metadata": {
    "id": "8843923f-cbce-49c7-be36-eb8a730b3de3"
   },
   "source": [
    "# <span style=\"color:red\"><center>Eliminación de Ruido: Ejemplo MNIST</center></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe40abf9-3244-480b-bb87-5b9a035dafa7",
   "metadata": {
    "id": "fe40abf9-3244-480b-bb87-5b9a035dafa7"
   },
   "source": [
    "##   <span style=\"color:blue\">Profesores</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692d9778-8422-4cdd-90f8-a6d94ff1e642",
   "metadata": {
    "id": "692d9778-8422-4cdd-90f8-a6d94ff1e642"
   },
   "source": [
    "1. Alvaro Mauricio Montenegro Díaz, ammontenegrod@unal.edu.co\n",
    "2. Daniel Mauricio Montenegro Reyes, dextronomo@gmail.com \n",
    "3. Campo Elías Pardo Turriago, cepardot@unal.edu.co "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d7b3ff-a399-4e88-814f-11f277c7a1b8",
   "metadata": {
    "id": "b3d7b3ff-a399-4e88-814f-11f277c7a1b8"
   },
   "source": [
    "##   <span style=\"color:blue\">Asesora Medios y Marketing digital</span>\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e155b5-086d-4454-9849-e5107145c418",
   "metadata": {
    "id": "55e155b5-086d-4454-9849-e5107145c418"
   },
   "source": [
    "4. Maria del Pilar Montenegro, pmontenegro88@gmail.com "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4485802-7f4f-4c43-b74a-9ebb9fc36481",
   "metadata": {
    "id": "e4485802-7f4f-4c43-b74a-9ebb9fc36481"
   },
   "source": [
    "## <span style=\"color:blue\">Asistentes</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b160e4-7209-40fd-8bab-90c8711d1c16",
   "metadata": {
    "id": "17b160e4-7209-40fd-8bab-90c8711d1c16"
   },
   "source": [
    "5. Oleg Jarma, ojarmam@unal.edu.co \n",
    "6. Laura Lizarazo, ljlizarazore@unal.edu.co "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2013fd2-1aea-4603-be27-13a595967673",
   "metadata": {
    "id": "a2013fd2-1aea-4603-be27-13a595967673"
   },
   "source": [
    "## <span style=\"color:blue\">Contenido</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f2374c-555c-4a8d-a541-d21c3d606e05",
   "metadata": {
    "id": "b4f2374c-555c-4a8d-a541-d21c3d606e05"
   },
   "source": [
    "* [Introducción](#Introducción)\n",
    "* [Autocodificador o autoencoder](#Autocodificador-o-autoencoder)\n",
    "* [Importa módulos](#Importa-módulos)\n",
    "* [Descargar Datos de la Fuente](#Descargar-Datos-de-la-Fuente)\n",
    "* [Parámetros de la red](#Parámetros-de-la-red)\n",
    "* [Codificador](#Codificador)\n",
    "* [Decodificador](#Decodificador)\n",
    "* [Auto-codificador](#Auto-codificador)\n",
    "* [Entrenamiento](#Compila)\n",
    "* [Recuperación de imágenes](#Recuperación-de-imágenes)\n",
    "* [Representación latente](#Representación-latente)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03d420f7-1f6a-404d-8b08-9b95ca394c93",
   "metadata": {
    "id": "03d420f7-1f6a-404d-8b08-9b95ca394c93"
   },
   "source": [
    "[[Volver al Inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff7772ae-e6d9-4cba-a260-1087e8d218fa",
   "metadata": {
    "id": "ff7772ae-e6d9-4cba-a260-1087e8d218fa"
   },
   "source": [
    "## <span style=\"color:blue\">Introducción</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cee0aeb-1b69-4683-b6a7-f745b5e87517",
   "metadata": {
    "id": "4cee0aeb-1b69-4683-b6a7-f745b5e87517"
   },
   "source": [
    "En este cuaderno se implementa un Autoencoder Convolucional para la eliminación de ruido usando el conjunto de datos MNIST.\n",
    "\n",
    "Basado en \n",
    "\n",
    "- [Convolutional Autoencoders for Image Noise Reduction](https://towardsdatascience.com/convolutional-autoencoders-for-image-noise-reduction-32fce9fc1763)\n",
    "\n",
    "- [Convolutional Autoencoder For Image Denoising](https://keras.io/examples/vision/autoencoder/) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00a849d-35bf-474f-903a-9da0fe01fdbe",
   "metadata": {
    "id": "f00a849d-35bf-474f-903a-9da0fe01fdbe"
   },
   "source": [
    "[[Volver al Inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef33ed6a-d912-4195-8742-686b53a3824d",
   "metadata": {
    "id": "ef33ed6a-d912-4195-8742-686b53a3824d"
   },
   "source": [
    "## <span style=\"color:blue\">Autocodificador o autoencoder</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd666002-4898-43fc-a57c-878f36a4bb84",
   "metadata": {
    "id": "fd666002-4898-43fc-a57c-878f36a4bb84"
   },
   "source": [
    "Recordemos un poco el concepto gráfico de lo que significa un autoenconder:\n",
    "\n",
    "![CAE](https://miro.medium.com/max/2400/1*VYH3i2-2CZ6Fyd7Bv9UHFw.png)\n",
    "\n",
    "Fuente: [Super-Resolution Using Autoencoders and TF2.0](https://medium.com/analytics-vidhya/super-resolution-using-autoencoders-and-tf2-0-505215c1674)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5197c685-65ef-41ad-b149-22df53c2c715",
   "metadata": {
    "id": "5197c685-65ef-41ad-b149-22df53c2c715"
   },
   "source": [
    "## <span style=\"color:blue\">Importa módulos</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d6ef6636-88f5-4658-9de1-7f57ffe155ef",
   "metadata": {
    "id": "d6ef6636-88f5-4658-9de1-7f57ffe155ef"
   },
   "outputs": [],
   "source": [
    "# Módulos especificos de tensorflow\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "#from tensorflow.keras.layers import Input, Dense, Activation\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.losses import MeanSquaredError\n",
    "\n",
    "# arreglos\n",
    "import numpy as np\n",
    "\n",
    "# Manejo de gráficos\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ace4e081-bb74-41f2-9528-968d03f9aa94",
   "metadata": {
    "id": "91c44791-a667-4cd5-981a-474781d47166"
   },
   "outputs": [],
   "source": [
    "np.set_printoptions(linewidth=125)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3c4bea8-18aa-4650-9440-b62515a1099d",
   "metadata": {
    "id": "91c44791-a667-4cd5-981a-474781d47166"
   },
   "source": [
    "## <span style=\"color:blue\">Lectura de los datos</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e6a07e8b-2506-4152-bf33-eb4daef44be2",
   "metadata": {
    "id": "15878970-2896-49b8-9ff2-0604bb23979d"
   },
   "outputs": [],
   "source": [
    "# Data\n",
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "# normaliza los datos\n",
    "x_train = x_train.reshape(60000, 784).astype(\"float32\") / 255\n",
    "x_test = x_test.reshape(10000, 784).astype(\"float32\") / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01950e4a-005e-49e4-8400-62179c8e9664",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5364b030-1a56-48ba-ab9a-2477f2d8ec6b",
    "outputId": "7ad129cf-220e-4c83-eefb-1a84f0ff5b53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784) (60000,)\n",
      "(10000, 784) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape,y_train.shape)\n",
    "print(x_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3676199-6b55-42d2-b649-314d06e6efab",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5364b030-1a56-48ba-ab9a-2477f2d8ec6b",
    "outputId": "7ad129cf-220e-4c83-eefb-1a84f0ff5b53"
   },
   "outputs": [],
   "source": [
    "x_train = np.reshape(x_train, newshape=(-1,28,28,1))\n",
    "x_test = np.reshape(x_test, newshape=(-1,28,28,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b5ab90c-fd5a-40f9-a4ea-0938ab64b2e5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5364b030-1a56-48ba-ab9a-2477f2d8ec6b",
    "outputId": "7ad129cf-220e-4c83-eefb-1a84f0ff5b53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1) (60000,)\n",
      "(10000, 28, 28, 1) (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape,y_train.shape)\n",
    "print(x_test.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8455cfa7-2910-4c27-a908-c0dabe7cf836",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 757
    },
    "id": "8455cfa7-2910-4c27-a908-c0dabe7cf836",
    "outputId": "9b0689ca-4837-4238-ff3d-cd10b1ff855a"
   },
   "outputs": [
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      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]\n",
      " [0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.        ]] \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOcAAAD3CAYAAADmIkO7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAALeklEQVR4nO3df2yVZxnG8et2OA1b3daaUUjrkgnTgYuYRZnDZSZuxoDTgmZlTmSKwNgfpokihqAiIBECKOII2M6poPsV7AzriLIQf8zhNFZc2BYqy3BzZcTicPzoWIDbP85L8tL0PKftAXq35/tJmnF6nee8z9vs6vP2PDnnmLsLQDxvGewJAOgd5QSCopxAUJQTCIpyAkFRTiAoylmEmW00s28O9jxQuSqmnGa238y6zexo7utHWXaXmT2Zv7+73+3uyy7AvH5qZsvLGL/EzLb08n03s7HlzW7wmdnVZvaYmR0xsy4zWzXYc7pQRgz2BC6w29z9icGeBPrGzC6WtEPSvZIaJZ2SdM2gTuoCqpiVsxgzu1bSRkkfzlbTw9n3z1rRzGyBmR0ws04z+1J+ZTKz35nZl3P3PWslNrP3mtkOM/uvme01s9uz78+VdKekr2fH3pZ9/xtm9kK2WjxnZtPKPMee5/JRM/t37vb+7PyeMbNjZnafmY0ys+3ZHJ4wsyty93/EzF41s/+Z2R/MbEKPY91rZm3Z2KfN7N25fJ2ZvWxmr5vZ38zspsTU75LU6e5r3f2Yu7/h7s+U87MYSiq+nO7+vKS7Je1y90vd/fKe9zGzT0j6mqRbJY2TdEtfH9/MLlHht/8vJV0p6Q5JG8xsgrv/WNIvJK3Kjn1bNuwFSTdJukzSdyRtMbPRAzzFvvqMCud3jaTbJG2XtEjSO1X4/+QruftuV+HncKWk9uwc8u5QYd5XSNon6bu57K+SJkqqVuFn8oiZvV2SzOwjZ345Zm6QtD/7JdGV/RK8ruwzHSIqrZyPmtnh3NecPo67XdL97r7H3Y9JWtKPY35S0n53v9/dT7p7u6Stkj5bbIC7P+Lune5+2t0fkvRPSR9Kza/HeR3ux/zOWO/uB939FUl/lPS0u//d3U9IapX0gdz8fuLuR7JsiaT3m9llucf6lbv/xd1PqlDcibmxW9z9UPazWCPpbZLek2VP9vjlWCdphqQfShojqU3Sr7PL3WGv0srZ4O6X576a+zhujKSXc7f/1Y9jXiVpUo/i3CmpttgAM/uCme3O3f99KqxgxTzc47wu78f8zjiY+3d3L7cvzeZ2kZl9L7vsfl3S/uw++fm9mvv38TNjs/FfNbPns0viwypcHRQ7t25JT7r7dnd/U9JqSTWSru3vyQ1FlfaEUDGlXppzQFJ97va7euTHJI3M3c4X72VJv3f3W/tybDO7SlKzpI+pcKl9ysx2S7ISc0xJza+/Pifp0ypc2u9XoVyvqQ/zy/6+XKjCuT3r7qfNLDX2GUmTy5jrkFZpK2cxByXVJS6XHpZ0l5mNN7ORkr7dI98tabqZjcyeJJqdyx6TdI2ZzTSzt2ZfH8yeiDpz7Ktz979EhcL+R5LM7IsqrJzl2C1piplVm1mtpKYyHqtK0glJh1Qo/Ip+jj2pwrmNMLNvSXpH4v5bJN1gZreY2UUqzLtL0vMDmPeQU2nl3GZn73O2Zt/fKelZSa+aWVfPQe6+XdIPsvvty/6b931Jb6pQtJ8p9wSJux+R9HEV/nbqVOGSb6UKf2tJ0n2SxmeXsI+6+3OS1kjalT3edZL+VOZ5b5b0DxVWut9KeqiMx/q5Cpf1r0h6TtKf+zH2Nyo8mdSRPcYbyv25YGY3mdnRM7fdfa+kz6vwbPprKqzYn8oucYc948XWA2NmLmmcu+8b7LlgeKq0lRMYMignEBSXtUBQrJxAUMl9zuxJDwDnkbv3us/LygkERTmBoCgnEBTlBIKinEBQlBMIinICQVFOICjKCQRFOYGgKCcQFOUEgqKcQFCUEwiKcgJB8b61OEtVVVXRbPPmzcmxDQ0N53g2lY2VEwiKcgJBUU4gKMoJBEU5gaAoJxAUWykVpqamJpm3trYWzS6+uCI+szYMVk4gKMoJBEU5gaAoJxAU5QSCopxAUJQTCIp9zgqzadOmZH7jjTcWzRYsWHCup4MEVk4gKMoJBEU5gaAoJxAU5QSCopxAUJQTCIp9zmFmzpw5yXzKlCnJvL29vWjW0tIyoDlhYFg5gaAoJxAU5QSCopxAUJQTCIpyAkFRTiAo9jmHmPr6+mS+cePGsh7/8ccfL5odOXKkrMdG/7ByAkFRTiAoygkERTmBoCgnEBTlBIJiKyWYMWPGJPPZs2cnc3dP5rt27UrmK1euTOa4cFg5gaAoJxAU5QSCopxAUJQTCIpyAkFRTiAo9jmDWbx4cTKfN29eMi+1z7lmzZpk3t3dncxx4bByAkFRTiAoygkERTmBoCgnEBTlBIKinEBQ7HMGM3Xq1LLGNzc3J/PUW18iFlZOICjKCQRFOYGgKCcQFOUEgqKcQFCUEwiKfc5BsH79+qJZXV1dcuzevXuT+fz58wc0p74YMSL9v0t1dXUyN7NkfujQoaLZyZMnk2OHI1ZOICjKCQRFOYGgKCcQFOUEgqKcQFCUEwiKfc7z4Oabb07mjY2NRbNS7zu7dOnSAc2pr1KfDzp37tzk2HvuuaesY2/YsKFotmTJkrIeeyhi5QSCopxAUJQTCIpyAkFRTiAoygkEZamn7s0s/bx+hRo5cmQy37FjRzKfNGlS0ayjoyM5dvz48cm8lPr6+mTe1tZWNJswYUJybKltoHLMnDkzmT/wwAPn7djnm7v3+lo6Vk4gKMoJBEU5gaAoJxAU5QSCopxAUJQTCIqXjA1ATU1NMk/tY5aS2mc8F1paWpJ5ah+1s7MzOXbatGnJvLW1NZmPHj26aFZbW5scOxyxcgJBUU4gKMoJBEU5gaAoJxAU5QSCopxAUOxzDkBTU1MyL/VRd8ePHy+arV27Njm21OsxS72WdNy4cck8tZfZ0NCQHLtnz55kfvDgwWSeelvOo0ePJscOR6ycQFCUEwiKcgJBUU4gKMoJBEU5gaAoJxAU+5y9SO23SdLUqVOTean3b21ubi6aXX/99cmxq1evTuZjx45N5lu3bk3ms2bNKpp1d3cnxy5btiyZT5w4MZmn9lhTP7PhipUTCIpyAkFRTiAoygkERTmBoCgnEBTlBIJin7MXjY2NybzUayJL7XM+9dRTRbMVK1aUdeyurq5kvmjRomSe2sscNWpUcuy8efOSeSkPPvhgWeOHG1ZOICjKCQRFOYGgKCcQFOUEgqKcQFBspZwHBw4cSOapl1aV2iop9RaR06dPT+b79u1L5inz589P5tXV1cm8o6Mjma9bt67fcxrOWDmBoCgnEBTlBIKinEBQlBMIinICQVFOIChLvbzJzNKvfRqmXnrppWRe6mP4Tp8+PeBjl9rHXLhwYTJva2tL5jNmzEjmixcvLppVVVUlx544cSKZT548OZm3t7cn8+HK3Xv9zEhWTiAoygkERTmBoCgnEBTlBIKinEBQlBMIitdz9qLUW1uW2scsNT5l6dKlyby2tjaZv/jiiwM+diml9jGbmpqSeaXuYw4UKycQFOUEgqKcQFCUEwiKcgJBUU4gKMoJBMU+5yDYuXNn0azUR/itWrUqmZezxyql31N327ZtybHsY55brJxAUJQTCIpyAkFRTiAoygkERTmBoNhK6UWpLYG6urqyHj+1HTJr1qyyHrvUxw8uX748mbe0tBTNTp06NaA5YWBYOYGgKCcQFOUEgqKcQFCUEwiKcgJBUU4gKD4CEBhkfAQgMMRQTiAoygkERTmBoCgnEBTlBIKinEBQlBMIinICQVFOICjKCQRFOYGgKCcQFOUEgqKcQFCUEwiKcgJBUU4gKMoJBEU5gaAoJxAU5QSCopxAUJQTCIpyAkFRTiAoygkERTmBoCgnEBTlBIKinEBQlBMIinICQVFOICjKCQRFOYGgKCcQFOUEgqKcQFCUEwiKcgJBUU4gKMoJBEU5gaAoJxAU5QSCMncf7DkA6AUrJxAU5QSCopxAUJQTCIpyAkFRTiCo/wPPULSU0Il4bgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "azar=np.random.randint(len(x_train))\n",
    "print(x_train[azar,:,:,0],'\\n')\n",
    "plt.imshow(x_train[azar,:,:,0],cmap='gray')\n",
    "plt.title('Etiqueta Humana:'+str(y_train[azar]))\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa67f448-c455-4486-b61d-864da422c63e",
   "metadata": {
    "id": "fa67f448-c455-4486-b61d-864da422c63e"
   },
   "source": [
    "## <span style=\"color:blue\">Añade Ruido</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9b8c72fe-2cc2-4e1c-9c00-f17d322accfb",
   "metadata": {
    "id": "9b8c72fe-2cc2-4e1c-9c00-f17d322accfb"
   },
   "outputs": [],
   "source": [
    "def noise(array):\n",
    "    \"\"\"\n",
    "    Añande ruido a la imagen \n",
    "    \"\"\"\n",
    "    noise_factor = 0.4\n",
    "    noisy_array = array + noise_factor * np.random.normal(\n",
    "        loc=0.0, scale=1.0, size=array.shape\n",
    "    )\n",
    "\n",
    "    return np.clip(noisy_array, 0.0, 1.0) # clip para dejar los valores en [0.0, 1.0]\n",
    "\n",
    "# Crea una copia de los datos con ruido agregado\n",
    "noisy_x_train = noise(x_train)\n",
    "noisy_x_test = noise(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5544c0a5-14ec-4cb8-95e0-ca4854d2aa95",
   "metadata": {
    "id": "5544c0a5-14ec-4cb8-95e0-ca4854d2aa95"
   },
   "source": [
    "## <span style=\"color:blue\">Despliega Imágenes </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9cc491e7-6c6d-444b-910c-1c2d603ae6dd",
   "metadata": {
    "id": "9cc491e7-6c6d-444b-910c-1c2d603ae6dd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display(array1, array2):\n",
    "    \"\"\"\n",
    "    Despliega diez imágenes aleatoriamente, de los conjuntos de datos recibidos.\n",
    "    \"\"\"\n",
    "\n",
    "    n = 10\n",
    "\n",
    "    indices = np.random.randint(len(array1), size=n)\n",
    "    images1 = array1[indices, :]\n",
    "    images2 = array2[indices, :]\n",
    "\n",
    "    plt.figure(figsize=(20, 4))\n",
    "    for i, (image1, image2) in enumerate(zip(images1, images2)):\n",
    "        ax = plt.subplot(2, n, i + 1)\n",
    "        plt.imshow(image1.reshape(28, 28))\n",
    "        plt.gray()\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "        ax = plt.subplot(2, n, i + 1 + n)\n",
    "        plt.imshow(image2.reshape(28, 28))\n",
    "        plt.gray()\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "# despliega algunos datos origonales y su versión con ruido\n",
    "display(x_train, noisy_x_train)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0091fdc2-ac85-493e-89da-14a649a71ae7",
   "metadata": {
    "id": "0091fdc2-ac85-493e-89da-14a649a71ae7"
   },
   "source": [
    "## <span style=\"color:blue\">Autoenconder Convolucional </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aab46bde-ac26-4fa2-946f-4b57c383ca28",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aab46bde-ac26-4fa2-946f-4b57c383ca28",
    "outputId": "0ea7f4a6-dc25-41d6-8328-ba7893c6f4e0"
   },
   "outputs": [],
   "source": [
    "inputs = layers.Input(shape=(28, 28, 1))\n",
    "\n",
    "# Encoder\n",
    "x = layers.Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")(inputs)\n",
    "x = layers.MaxPooling2D((2, 2), padding=\"same\")(x)\n",
    "x = layers.Conv2D(32, (3, 3), activation=\"relu\", padding=\"same\")(x)\n",
    "x = layers.MaxPooling2D((2, 2), padding=\"same\")(x)\n",
    "\n",
    "# Decoder\n",
    "x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)\n",
    "x = layers.Conv2DTranspose(32, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)\n",
    "x = layers.Conv2D(1, (3, 3), activation=\"sigmoid\", padding=\"same\")(x)\n",
    "\n",
    "# Autoencoder\n",
    "autoencoder = Model(inputs, x)\n",
    "autoencoder.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b363c5c3-0861-4f4c-841d-3ca611c634cb",
   "metadata": {
    "id": "b363c5c3-0861-4f4c-841d-3ca611c634cb"
   },
   "source": [
    "## Entrenar Autoencoder "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "651322d4-93b7-4d88-a748-7f02c5e8f75c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "651322d4-93b7-4d88-a748-7f02c5e8f75c",
    "outputId": "ea063b1d-e286-49d4-fcc9-53f1fdd35ed0"
   },
   "outputs": [],
   "source": [
    "history=autoencoder.fit(\n",
    "    x=x_train,\n",
    "    y=x_train,\n",
    "    epochs=20,\n",
    "    batch_size=128,\n",
    "    shuffle=True,\n",
    "    validation_data=(x_test, x_test),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ZP_tf_u7TpT3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "ZP_tf_u7TpT3",
    "outputId": "4a9d17a8-15e3-40f1-ae3a-73fba61e69b3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'][1:],'bo-')\n",
    "plt.plot(history.history['val_loss'][-20:],'ro-')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "511a395c-8077-4aa2-bc3c-0ca21a6675ab",
   "metadata": {
    "id": "511a395c-8077-4aa2-bc3c-0ca21a6675ab"
   },
   "source": [
    "## Mostrar Recuperación "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a277183d-1c2b-4503-88cb-e21632a9ad96",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "id": "a277183d-1c2b-4503-88cb-e21632a9ad96",
    "outputId": "42885d30-569f-463e-9cae-45cf6886bd33"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = autoencoder.predict(x_test)\n",
    "display(x_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc8b19df-6e5c-4263-aaf2-53cdb3a965ef",
   "metadata": {
    "id": "cc8b19df-6e5c-4263-aaf2-53cdb3a965ef"
   },
   "source": [
    "## Entrenar Sobre imágenes ruidosas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5KtweSkpUbzQ",
   "metadata": {
    "id": "5KtweSkpUbzQ"
   },
   "outputs": [],
   "source": [
    "autoencoder2 = Model(inputs, x)\n",
    "autoencoder2.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "273ddbee-4580-4e4d-ac59-056b3895fa58",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "273ddbee-4580-4e4d-ac59-056b3895fa58",
    "outputId": "495bd38f-1977-44f4-e537-70be0e053235"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.1002 - val_loss: 0.0935\n",
      "Epoch 2/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0928 - val_loss: 0.0916\n",
      "Epoch 3/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0912 - val_loss: 0.0900\n",
      "Epoch 4/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0903 - val_loss: 0.0893\n",
      "Epoch 5/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0896 - val_loss: 0.0887\n",
      "Epoch 6/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0891 - val_loss: 0.0883\n",
      "Epoch 7/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0887 - val_loss: 0.0880\n",
      "Epoch 8/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0884 - val_loss: 0.0876\n",
      "Epoch 9/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0881 - val_loss: 0.0874\n",
      "Epoch 10/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0878 - val_loss: 0.0873\n",
      "Epoch 11/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0876 - val_loss: 0.0869\n",
      "Epoch 12/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0874 - val_loss: 0.0867\n",
      "Epoch 13/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0872 - val_loss: 0.0867\n",
      "Epoch 14/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0870 - val_loss: 0.0866\n",
      "Epoch 15/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0869 - val_loss: 0.0869\n",
      "Epoch 16/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0867 - val_loss: 0.0863\n",
      "Epoch 17/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0866 - val_loss: 0.0863\n",
      "Epoch 18/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0865 - val_loss: 0.0861\n",
      "Epoch 19/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0864 - val_loss: 0.0859\n",
      "Epoch 20/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0863 - val_loss: 0.0858\n",
      "Epoch 21/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0862 - val_loss: 0.0858\n",
      "Epoch 22/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0861 - val_loss: 0.0857\n",
      "Epoch 23/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0860 - val_loss: 0.0858\n",
      "Epoch 24/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0860 - val_loss: 0.0856\n",
      "Epoch 25/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0859 - val_loss: 0.0855\n",
      "Epoch 26/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0858 - val_loss: 0.0857\n",
      "Epoch 27/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0858 - val_loss: 0.0854\n",
      "Epoch 28/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0857 - val_loss: 0.0854\n",
      "Epoch 29/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0857 - val_loss: 0.0853\n",
      "Epoch 30/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0856 - val_loss: 0.0854\n",
      "Epoch 31/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0856 - val_loss: 0.0855\n",
      "Epoch 32/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0855 - val_loss: 0.0852\n",
      "Epoch 33/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0855 - val_loss: 0.0854\n",
      "Epoch 34/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0855 - val_loss: 0.0851\n",
      "Epoch 35/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0854 - val_loss: 0.0850\n",
      "Epoch 36/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0854 - val_loss: 0.0850\n",
      "Epoch 37/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0853 - val_loss: 0.0850\n",
      "Epoch 38/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0853 - val_loss: 0.0849\n",
      "Epoch 39/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0852 - val_loss: 0.0850\n",
      "Epoch 40/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0852 - val_loss: 0.0850\n",
      "Epoch 41/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0852 - val_loss: 0.0850\n",
      "Epoch 42/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0851 - val_loss: 0.0848\n",
      "Epoch 43/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0851 - val_loss: 0.0849\n",
      "Epoch 44/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0851 - val_loss: 0.0848\n",
      "Epoch 45/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0850 - val_loss: 0.0847\n",
      "Epoch 46/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0850 - val_loss: 0.0851\n",
      "Epoch 47/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0850 - val_loss: 0.0847\n",
      "Epoch 48/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0849 - val_loss: 0.0847\n",
      "Epoch 49/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0849 - val_loss: 0.0848\n",
      "Epoch 50/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0849 - val_loss: 0.0846\n",
      "Epoch 51/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0848 - val_loss: 0.0847\n",
      "Epoch 52/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0848 - val_loss: 0.0848\n",
      "Epoch 53/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0848 - val_loss: 0.0846\n",
      "Epoch 54/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0848 - val_loss: 0.0845\n",
      "Epoch 55/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0847 - val_loss: 0.0845\n",
      "Epoch 56/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0847 - val_loss: 0.0845\n",
      "Epoch 57/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0847 - val_loss: 0.0845\n",
      "Epoch 58/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0847 - val_loss: 0.0844\n",
      "Epoch 59/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0846 - val_loss: 0.0845\n",
      "Epoch 60/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0846 - val_loss: 0.0844\n",
      "Epoch 61/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0846 - val_loss: 0.0844\n",
      "Epoch 62/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0846 - val_loss: 0.0844\n",
      "Epoch 63/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0846 - val_loss: 0.0846\n",
      "Epoch 64/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0846 - val_loss: 0.0844\n",
      "Epoch 65/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0845 - val_loss: 0.0844\n",
      "Epoch 66/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0845 - val_loss: 0.0844\n",
      "Epoch 67/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0845 - val_loss: 0.0844\n",
      "Epoch 68/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0845 - val_loss: 0.0844\n",
      "Epoch 69/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0845 - val_loss: 0.0843\n",
      "Epoch 70/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0845 - val_loss: 0.0843\n",
      "Epoch 71/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0844 - val_loss: 0.0843\n",
      "Epoch 72/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0844 - val_loss: 0.0843\n",
      "Epoch 73/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0844 - val_loss: 0.0843\n",
      "Epoch 74/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0844 - val_loss: 0.0842\n",
      "Epoch 75/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0844 - val_loss: 0.0843\n",
      "Epoch 76/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0844 - val_loss: 0.0844\n",
      "Epoch 77/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0844 - val_loss: 0.0844\n",
      "Epoch 78/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0844 - val_loss: 0.0843\n",
      "Epoch 79/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0844 - val_loss: 0.0845\n",
      "Epoch 80/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0843 - val_loss: 0.0843\n",
      "Epoch 81/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0843 - val_loss: 0.0843\n",
      "Epoch 82/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0843 - val_loss: 0.0843\n",
      "Epoch 83/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0843 - val_loss: 0.0843\n",
      "Epoch 84/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0843 - val_loss: 0.0842\n",
      "Epoch 85/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0843 - val_loss: 0.0843\n",
      "Epoch 86/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0843 - val_loss: 0.0842\n",
      "Epoch 87/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0843 - val_loss: 0.0842\n",
      "Epoch 88/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0843 - val_loss: 0.0842\n",
      "Epoch 89/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0843 - val_loss: 0.0842\n",
      "Epoch 90/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - val_loss: 0.0842\n",
      "Epoch 91/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - val_loss: 0.0842\n",
      "Epoch 92/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - val_loss: 0.0842\n",
      "Epoch 93/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - val_loss: 0.0841\n",
      "Epoch 94/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - val_loss: 0.0841\n",
      "Epoch 95/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - val_loss: 0.0841\n",
      "Epoch 96/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - val_loss: 0.0842\n",
      "Epoch 97/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - val_loss: 0.0843\n",
      "Epoch 98/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - val_loss: 0.0842\n",
      "Epoch 99/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - val_loss: 0.0841\n",
      "Epoch 100/100\n",
      "469/469 [==============================] - 2s 4ms/step - loss: 0.0842 - val_loss: 0.0842\n"
     ]
    }
   ],
   "source": [
    "history2=autoencoder2.fit(\n",
    "    x=noisy_x_train,\n",
    "    y=x_train,\n",
    "    epochs=100,\n",
    "    batch_size=128,\n",
    "    shuffle=True,\n",
    "    validation_data=(noisy_x_test, x_test),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "EhTpNpdNTg5f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "EhTpNpdNTg5f",
    "outputId": "14f31679-1898-4fb1-b5f4-76eeeb402a64"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history2.history['loss'][-60:],'bo-')\n",
    "plt.plot(history2.history['val_loss'][-60:],'ro-')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80600cc3-f43f-45e9-bbe4-0dd92426f1ae",
   "metadata": {
    "id": "80600cc3-f43f-45e9-bbe4-0dd92426f1ae"
   },
   "source": [
    "## Ver Predicciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "46209796-25bc-4882-9911-ff7a690cbbe1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "id": "46209796-25bc-4882-9911-ff7a690cbbe1",
    "outputId": "95b228b1-8803-4ac2-c4d4-47b92e32ff37"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = autoencoder.predict(noisy_x_test)\n",
    "display(noisy_x_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b710eec-270e-4779-9e37-f3e7c63fc70e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "ae_Eliminacion_ruido_MNIST.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}