Deep_Learning.ipynb

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        "\n",
        "<image src=\"https://i.imgur.com/8tD3MOI.png\" width=100%>\n",
        "\n",
        "### By: Fabian Perez & Henry Mantilla"
      ]
    },
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      "source": [
        "## **Contenido**\n",
        "\n",
        "[**1. Deep Learning**](#eje0)\n",
        "\n",
        "[**2. Problema de clasificacion de digitos**](#eje1)\n",
        "\n",
        "[**3. Problema de denoising**](#eje2)\n",
        "\n",
        "[**4. Modelos Convoluciones**](#eje3)"
      ]
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      "source": [
        "# **Taller Aprendizaje Profundo**\n",
        "\n",
        "\n",
        "**<font color='#bdaaed'>Inteligencia Artificial:</font>**\n",
        "\n",
        "Es la combinación de algoritmos con el propósito de crear máquinas que presenten las mismas capacidades que el ser humano.\n",
        "\n",
        "**<font color='#62dedd'>Aprendizaje de máquina:</font>**\n",
        "\n",
        "El Aprendizaje de máquina (_Machine Learning_) es una rama de la IA que conlleva la creación de algoritmos capaces de modificarse a sí mismos, sin intervención humana. Se trata de un método de análisis de datos, basado en la idea de que los sistemas pueden aprender a partir de esos datos, identificar patrones y tomar decisiones sin la ayuda de un ser humano.\n",
        "\n",
        "**<font color='#e7c299'>Aprendizaje Profundo:</font>**\n",
        "\n",
        "El Aprendizaje Profundo (_Deep Learning_) utiliza una clase específica de algoritmos, llamados redes neuronales. Los algoritmos se crean y funcionan de manera similar al _Machine Learning_. Sin embargo, existen numerosas capas de estos algoritmos, y cada una de ellas proporciona una interpretación diferente de los datos. Las redes neuronales funcionan en un intento de imitar la función de las redes neuronales del cerebro humano.\n",
        "\n",
        "\n",
        "<div align=\"center\"><img src=\"https://i.imgur.com/L1BRu6Z.png\"  width=\"300\" height=\"300\"></div>"
      ]
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      "source": [
        "## <font color='#e7c299'>1. Deep Learning</font> <a id=\"eje0\">\n",
        "\n",
        "\n"
      ]
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        "La unidad fundamental de procesamiento en las redes neuronales es **el Perceptrón**, el cual puede ser utilizado como un algoritmo de inteligencia artificial en determinados casos. Este componente se basa en la manera en que las neuronas biológicas operan y fue concebido por Frank Rosenblatt en 1957.\n",
        "\n",
        "\n",
        "<div align=\"center\"><img src=\"https://i.imgur.com/zgZNocs.png\" ></div>\n",
        "\n",
        "Cada neurona biológica consta de varias partes, entre ellas, el cuerpo celular que contiene la mayoría de los componentes complejos de la célula y el núcleo, además de extensiones ramificadas denominadas dendritas, así como una extensión larga llamada axón. En el extremo del axón, se bifurca en telodendrias y, en sus puntas, se encuentran estructuras diminutas llamadas terminales sinápticos o sinapsis, las cuales se conectan con otras neuronas en sus dendritas o cuerpos celulares. Las neuronas biológicas generan breves impulsos eléctricos conocidos como potenciales de acción o señales que recorren el axón, produciendo la liberación de neurotransmisores en las sinapsis. Si una neurona recibe suficientes neurotransmisores en un lapso de tiempo breve, dispara sus propios impulsos eléctricos. Pese a que las neuronas individuales parecen actuar de manera sencilla, pueden conectarse con miles de otras neuronas para crear redes complejas capaces de llevar a cabo tareas complicadas.\n",
        "\n",
        "\n",
        "<div align=\"center\"><img src=\"https://i.imgur.com/1WcBilL.png\" height=\"500\" width=\"500\"></div>  \n",
        "\n",
        "Asi como en las neuronas biologicas los perceptrones tienen entradas, conexiones y salidas"
      ]
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      "source": [
        "### **Componentes de una red neuronal (Perceptron)**"
      ]
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        "####  <font color=\"#2C6E49\">***Capas***</font>\n",
        "\n",
        "*  Capas de entrada: es la información de entrada, esta debe ser un vector\n",
        "\n",
        "*  Capas ocultas: son las conexiones internas en la red, donde a cada neurona se le asignara un peso para calcular la salida\n",
        "\n",
        "*  Capas de salida: es la informacion de salida calcula a partir de la red.\n",
        "\n",
        "<div align=\"center\"><img src=\"https://www.atriainnovation.com/wp-content/uploads/2019/10/Redes_neuronales_esquema.png\" ></div>\n",
        "\n",
        "El numero de capas a usar depende de la complejidad del problema a resolver, por ejemplo, si se quiere clasificar imagenes de perros y gatos, se necesitan muchas capas para poder identificar las caracteristicas de cada una de las clases.\n",
        "\n",
        "### <font color=\"#2C6E49\">***Neuronas***</font>\n",
        "\n",
        "son las unidades de procesamiento fundamental, cada neurona tiene una entrada, una salida y una función de activación asociada.\n",
        "\n",
        "Para calcular la salida de una neurona se realiza la siguiente operación:\n",
        "\n",
        "$$ \\color{orange} { \\hat{y} = \\sum_{i=1}^{n} w_i x_i + b}$$\n",
        "\n",
        "donde $w_i$ es el peso de la entrada $x_i$, $b$ es el sesgo y $n$ es el numero de entradas.\n",
        "\n",
        "Esto tambien se puede ver como una multiplicación de matrices:\n",
        "\n",
        "$$ \\color{orange} {\\hat{y} = \\mathbf{W} \\cdot \\mathbf{X} + \\mathbf{b}}$$\n",
        "\n",
        "\n",
        "<div align=\"center\"><img src=\"https://i.imgur.com/a4b7UBT.png\" width=\"400\" ></div>\n",
        "\n",
        "donde $\\mathbf{W}$ es la matriz de pesos, $\\mathbf{X}$ es el vector de entradas y $\\mathbf{b}$ es el vector de sesgos.\n",
        "\n",
        "### <font color=\"#2C6E49\">***Funciónes de activación***</font>\n",
        "\n",
        "Es la función que se aplica a la salida de la neurona para obtener la salida final de la red, esta función debe ser no lineal para poder resolver problemas complejos.\n",
        "\n",
        "<font color='#A792CE'>¿Por qué no lineal?</font>\n",
        "\n",
        "Si la función de activación es lineal, la red neuronal se comporta como una regresión lineal, es decir, la salida de la red es una combinación lineal de las entradas, por lo que no se puede resolver problemas complejos.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
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        "#@markdown *  **Sigmoid**: transforma los valores introducidos a una escala (0,1), donde los valores altos tienden de manera asintótica a 1 y los valores muy bajos tienden de manera asintótica a 0. \n",
        "#@markdown $$ \\color{orange}  {y = \\frac{1}{1+e^{-x}}} $$\n",
        "x_plot = np.linspace(-10, 10, 100)\n",
        "y_plot = activations.sigmoid(x_plot)\n",
        "plt.plot(x_plot,y_plot)\n",
        "plt.grid()\n",
        "plt.title('Sigmoide')\n",
        "plt.show()"
      ]
    },
    {
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      "source": [
        "#@markdown *  **Tangente hiperbólica**: transforma los valores introducidos a una escala (-1,1), donde los valores altos tienden de manera asintótica a 1 y los valores muy bajos tienden de manera asintótica a -1.\n",
        "#@markdown $$\\color{orange} { y = \\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}}$$\n",
        "\n",
        "x_plot = \n",
        "y_plot = \n",
        "plt.plot(x_plot,y_plot)\n",
        "plt.grid()\n",
        "plt.title('Tanh')\n",
        "plt.show()"
      ]
    },
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        "#@markdown *  **ReLU**: transforma los valores introducidos anulando los valores negativos y dejando los positivos tal y como entran.\n",
        "#@markdown $$\\color{orange}  {y = \\mathrm{max}(0,x)}$$\n",
        "\n",
        "x_plot = \n",
        "y_plot = \n",
        "plt.plot(x_plot,y_plot)\n",
        "plt.grid()\n",
        "plt.title('ReLU')\n",
        "plt.show()"
      ]
    },
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        "#@markdown *  **Leaky ReLU**: transforma los valores introducidos multiplicando los negativos por un coeficiente rectificativo y dejando los positivos según entran.\n",
        "#@markdown  $$ \\color{orange} {y = \\mathrm{max}(0.1x,x)}$$\n",
        "x_plot =\n",
        "plt.plot(x_plot,y_plot)\n",
        "plt.grid()\n",
        "plt.title('Leaky ReLU')\n",
        "plt.show()"
      ]
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        "#@markdown *  **Softmax**: transforma las salidas a una representación en forma de probabilidades, de tal manera que la sumatoria de todas las probabilidades de las salidas dé 1.\n",
        "#@markdown  $$ \\color{orange} { \\sigma (z_{i}) = \\frac{e^{z_{i}}}{\\sum_{j=1}^{N}e^{z_{j}}}}$$\n",
        "x_plot = \n",
        "y_plot = \n",
        "plt.plot(x_plot,y_plot(x_plot).numpy())\n",
        "plt.grid()\n",
        "plt.title('Softmax')\n",
        "plt.show()"
      ]
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      "source": [
        "El resultado de la suma de todas las salidas de una capa con funcion de activacion softmax es una **distribución de probabilidad**, es decir, la suma de todas las salidas es igual a 1."
      ]
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        "np.sum(y_plot(x_plot))"
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      "source": [
        "### **Practica Funciones de activación**\n",
        "\n",
        "A continuacion, se comparte el link del *playground* para que puedan experimentar con las funciones de activación.\n",
        "\n",
        "https://playground.tensorflow.org/\n",
        "\n",
        "<font color='#A792CE'>*¿Como resolver este problema solo con una neurona?*</font>\n",
        "\n",
        "<div align=\"center\">\n",
        "    <img src=\"https://i.imgur.com/XLsU64U.png\">\n",
        "</div>"
      ]
    },
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      "source": [
        "### **Practica Perceptron Feed-Forward**"
      ]
    },
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          "text": [
            "[[0.57907137]]\n"
          ]
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      "source": [
        "# forward-pass of a 3-layer neural network:\n",
        "import numpy as np\n",
        "\n",
        "f = lambda x: 1/(1+np.exp(-x)) # sigmoid function\n",
        "X = np.random.randn(1, 6) # random input (6x1)\n",
        "\n",
        "W1 = np.random.randn(6, 4) # random weights layer 1 (6x4)\n",
        "b1 = np.random.randn(1, 4) # random bias layer 1 (1x4)\n",
        "\n",
        "W2 = np.random.randn(4, 3) # random weights layer 2 (4x3)\n",
        "b2 = np.random.randn(1, 3) # random bias layer 2 (1x3)\n",
        "\n",
        "W3 = np.random.randn(3, 1) # random weights layer 3 (3x1)\n",
        "b3 = np.random.randn(1, 1) # random bias layer 3 (1x1)\n",
        "\n",
        "# forward pass\n",
        "h1 = f(X.dot(W1) + b1)\n",
        "h2 = f(h1.dot(W2) + b2)\n",
        "y = f(h2.dot(W3) + b3)\n",
        "\n",
        "print(y)"
      ]
    },
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      "source": [
        "### **Funciones de pérdida**\n",
        "\n",
        "Valor verdadero $ = \\mathbf{y}$\n",
        "\n",
        "Valor predicho $ =\\mathbf{\\hat{y}}$\n",
        "\n",
        "*  **Error cuadrático medio (MSE)**: calcula la cantidad de error que hay entre dos conjuntos de datos.\n",
        "\n",
        "$$ \\color{orange}  {MSE = \\frac{1}{N}\\sum_{i=1}^{N}(y_{i}-\\hat{y_{i}})^2}$$\n",
        "\n",
        "\n",
        "*  **Binary Crossentropy** : calcula la pérdida de entropía cruzada entre etiquetas verdaderas y etiquetas predichas. Se usa para aplicaciones de clasificación binaria (0 o 1). \n",
        "\n",
        "$$ \\color{orange} { BC = -\\frac{1}{N}\\sum_{i=1}^{N}[y_{i}\\cdot log(\\hat{y_{i}})+(1-y_{i})\\cdot log(1-\\hat{y_{i}})]}$$\n"
      ]
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        "### **Backpropagation**\n",
        "\n",
        "El backpropagation es un algoritmo que permite calcular los gradientes de la función de pérdida con respecto a los pesos de la red neuronal, esto permite actualizar los pesos de la red para minimizar la función de pérdida.\n",
        "\n",
        "en la red queremos **minimizar** la función de pérdida:\n",
        "\n",
        "$$ \\color{orange}  {W* = argmin_{W} \\mathcal{L}(W)}$$\n",
        "\n",
        "para esto se calcula el gradiente de la función de pérdida con respecto a los pesos de la red, esto se hace de manera recursiva:\n",
        "\n",
        "se calcula el gradiente de la función de pérdida con respecto a los pesos de la última capa, luego se calcula el gradiente de la función de pérdida con respecto a los pesos de la penúltima capa y así sucesivamente hasta llegar a la primera capa.\n",
        "\n",
        "el gradiente se calcula de la siguiente manera:\n",
        "\n",
        "$$ \\color{orange}  {\\nabla_{W}  = \\frac{\\partial \\mathcal{L}(W)}{\\partial W}}$$\n",
        "\n",
        "donde $W$ es la matriz de pesos de la red.\n",
        "\n",
        "\n"
      ]
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      "source": [
        "### **Optimizadores**\n",
        "\n",
        "Los optimizadores son los encargados de actualizar los pesos de la red, para esto se utiliza el gradiente de la función de pérdida con respecto a los pesos de la red.\n",
        "\n",
        "Para este taller utilizaremos el optimizador **SGD** (Stochastic Gradient Descent), es el optimizador más simple, actualiza los pesos de la red de la siguiente manera:\n",
        "\n",
        "$$ \\color{orange}  { W_{t+1} = W_{t} - \\alpha \\nabla_{W}}$$\n",
        "\n",
        "donde $W_{t}$ son los pesos de la red en el paso $t$, $\\alpha$ es el learning rate y $\\nabla_{W} es el gradiente de la función de pérdida con respecto a los pesos de la red.\n"
      ]
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      "source": [
        "### **Métricas de evaluación**\n",
        "\n",
        "Las métricas de evaluación son las encargadas de medir el desempeño de la red neuronal, para este taller utilizaremos las siguientes métricas:\n",
        "\n",
        "*  **Accuracy**: es la cantidad de predicciones correctas que hace la red.\n",
        "\n",
        "$$ \\color{orange}  {Accuracy = \\frac{TP+TN}{TP+TN+FP+FN}}$$\n",
        "\n",
        "*  **Precision**: es la cantidad de predicciones positivas que son correctas.\n",
        "\n",
        "$$ \\color{orange}  {Precision = \\frac{TP}{TP+FP}}$$\n",
        "\n",
        "*  **Recall**: es la cantidad de predicciones positivas que se hicieron correctamente.\n",
        "\n",
        "$$ \\color{orange}  {Recall = \\frac{TP}{TP+FN}}$$\n",
        "\n",
        "TP = True Positive (predicción positiva correcta)\n",
        "\n",
        "TN = True Negative (predicción negativa correcta)\n",
        "\n",
        "FP = False Positive (predicción positiva incorrecta)\n",
        "\n",
        "FN = False Negative (predicción negativa incorrecta)"
      ]
    },
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      "source": [
        "##  <font color='#e7c299'>2. Problema de clasificación usando los datos MNIST </font> <a id='eje1'>\n"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "\n",
        "La base de datos MNIST es una extensa colección de dígitos escritos a mano que se emplea habitualmente para entrenar diversos sistemas de procesamiento de imágenes. Asimismo, se suele utilizar esta base de datos para el desarrollo y prueba de algoritmos de aprendizaje automático."
      ]
    },
    {
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      "metadata": {
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      "source": [
        "#@title **Código:** Importando librerías.\n",
        "#@markdown Sobre las librerías importadas:\n",
        "#@markdown + `Numpy`: librería de soporte sobre vectores y arreglos numéricas.\n",
        "#@markdown + `Matplotlib`: librería para la generación de gráficos.\n",
        "#@markdown + `Keras`: librería de soporte a redes neuronales.\n",
        "\n",
        "from tensorflow import keras\n",
        "from keras.datasets import mnist\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import keras\n",
        "from keras import layers\n",
        "from keras import activations\n",
        "import tensorflow as tf\n",
        "from PIL import Image\n",
        "import cv2\n"
      ]
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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title **Código:** Importar el dataset MNIST.\n",
        "#@markdown Al importar los datos la librería nos asigna una selección para los datos de entrenamiento `x_train`, `y_train` y los datos de prueba `x_test`, `y_test`.\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
        "\n",
        "n = np.random.randint(0, x_train.shape[0])\n",
        "\n",
        "plt.figure()\n",
        "plt.imshow(x_train[n,:,:], cmap='gray')\n",
        "plt.colorbar()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3VpdJIblLRHG",
        "outputId": "06feda10-230b-483e-b123-1fb7675fc6ce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "((60000, 28, 28), (60000,))"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train.shape , y_train.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x8us9LMUY5XM"
      },
      "source": [
        "### Normalizar los datos.\n",
        "\n",
        "Se sugiere llevar a cabo una etapa de preprocesamiento de los datos con el fin de estandarizar el rango de los valores. En este sentido, se utilizó una técnica de normalización que permitió escalar los datos al intervalo de valores $[0,1]$, utilizando la siguiente fórmula:\n",
        "\n",
        "$$\n",
        "\\color{orange}{\\mathbf{X}_{normalized} = \\frac{\\mathbf{X}}{\\mathbf{X}_{max}}} \n",
        "$$\n",
        "\n",
        "donde $\\mathbf{X}_{max}$ corresponde con el valor máximo presente en los datos.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f5T4w1v2Y5XN"
      },
      "outputs": [],
      "source": [
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "x_train = np.expand_dims(x_train, -1)\n",
        "x_test = np.expand_dims(x_test, -1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "K2uxEgAZ-y0R",
        "outputId": "e60c2e37-17a2-4751-e9a4-c687bfde2105"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x1a81486e7c0>"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "n = np.random.randint(0, x_train.shape[0])\n",
        "\n",
        "plt.figure()\n",
        "plt.imshow(x_train[n,:,:,0], cmap='gray')\n",
        "plt.colorbar()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xjh4eLxEY5XO"
      },
      "source": [
        "### Inspección de los datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "Gt_k1HbIwvMJ",
        "outputId": "8149f2ff-12a3-41c9-d809-cd2c70c954af"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 750x400 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "num_row = 2\n",
        "num_col = 5\n",
        "fig, axes = plt.subplots(num_row, num_col, figsize=(1.5*num_col,2*num_row))\n",
        "for i in range(10):\n",
        "    ax = axes[i//num_col, i%num_col]\n",
        "    ax.imshow(x_train[i], cmap='gray')\n",
        "    ax.set_title('Label: {}'.format(y_train[i]))\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UWA488mtEEtC"
      },
      "source": [
        "### **Ejercicio de clasificación** <a name=\"eje2\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnfxGpbWIi2n",
        "outputId": "6e9e9919-a76f-487a-fa68-9991c0a405ef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 120)               94200     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 84)                10164     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                5440      \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 24)                792       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 10)                250       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 112,926\n",
            "Trainable params: 112,926\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#@title **Código:** Definir Modelo con arquitectura Perceptrón Multicapa (MLP)\n",
        "#@markdown <img src=\"https://i.ibb.co/HnxBr6d/rsta20190163f02.jpg\" width=\"800\">\n",
        "\n",
        "input_img = keras.Input(shape=(28, 28, 1))\n",
        "nclasses = 10\n",
        "\n",
        "x = layers.Flatten()(input_img)\n",
        "\n",
        "#x = layers.Dense(120,activation='relu')(x)\n",
        "\n",
        "\n",
        "classes = layers.Dense(nclasses, activation='softmax')(x)\n",
        "\n",
        "MLP = keras.Model(input_img, classes)\n",
        "\n",
        "MLP.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZ1r7mkBVT33",
        "outputId": "35b78ff3-ec86-4501-db4a-d669a0d58ee5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "600/600 [==============================] - 4s 5ms/step - loss: 0.1057 - accuracy: 0.9694 - val_loss: 0.1223 - val_accuracy: 0.9619\n",
            "Epoch 2/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.1001 - accuracy: 0.9710 - val_loss: 0.1184 - val_accuracy: 0.9640\n",
            "Epoch 3/20\n",
            "600/600 [==============================] - 3s 6ms/step - loss: 0.0940 - accuracy: 0.9727 - val_loss: 0.1171 - val_accuracy: 0.9632\n",
            "Epoch 4/20\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 0.0890 - accuracy: 0.9745 - val_loss: 0.1252 - val_accuracy: 0.9606\n",
            "Epoch 5/20\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 0.0843 - accuracy: 0.9756 - val_loss: 0.1104 - val_accuracy: 0.9656\n",
            "Epoch 6/20\n",
            "600/600 [==============================] - 3s 5ms/step - loss: 0.0802 - accuracy: 0.9771 - val_loss: 0.1154 - val_accuracy: 0.9636\n",
            "Epoch 7/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0759 - accuracy: 0.9781 - val_loss: 0.1082 - val_accuracy: 0.9661\n",
            "Epoch 8/20\n",
            "600/600 [==============================] - 3s 4ms/step - loss: 0.0717 - accuracy: 0.9792 - val_loss: 0.1105 - val_accuracy: 0.9658\n",
            "Epoch 9/20\n",
            "600/600 [==============================] - 3s 4ms/step - loss: 0.0687 - accuracy: 0.9806 - val_loss: 0.1063 - val_accuracy: 0.9676\n",
            "Epoch 10/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0647 - accuracy: 0.9818 - val_loss: 0.1071 - val_accuracy: 0.9669\n",
            "Epoch 11/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0618 - accuracy: 0.9824 - val_loss: 0.1060 - val_accuracy: 0.9679\n",
            "Epoch 12/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0591 - accuracy: 0.9830 - val_loss: 0.1026 - val_accuracy: 0.9690\n",
            "Epoch 13/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0558 - accuracy: 0.9840 - val_loss: 0.1040 - val_accuracy: 0.9685\n",
            "Epoch 14/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0539 - accuracy: 0.9846 - val_loss: 0.1073 - val_accuracy: 0.9681\n",
            "Epoch 15/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0514 - accuracy: 0.9854 - val_loss: 0.1014 - val_accuracy: 0.9693\n",
            "Epoch 16/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0488 - accuracy: 0.9862 - val_loss: 0.1057 - val_accuracy: 0.9689\n",
            "Epoch 17/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0465 - accuracy: 0.9874 - val_loss: 0.1002 - val_accuracy: 0.9699\n",
            "Epoch 18/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0442 - accuracy: 0.9878 - val_loss: 0.1076 - val_accuracy: 0.9683\n",
            "Epoch 19/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0415 - accuracy: 0.9883 - val_loss: 0.1028 - val_accuracy: 0.9709\n",
            "Epoch 20/20\n",
            "600/600 [==============================] - 2s 4ms/step - loss: 0.0401 - accuracy: 0.9890 - val_loss: 0.1060 - val_accuracy: 0.9710\n"
          ]
        }
      ],
      "source": [
        "#@title **Código:** Entrenamos el modelo MLP\n",
        "MLP.compile(optimizer='sgd',  #AdamOptimizer(), \n",
        "              loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history_mlp = MLP.fit(x_train, y_train,\n",
        "                epochs=20,\n",
        "                batch_size=100,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test, y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9PRczUFY5XR"
      },
      "source": [
        "#### test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "2BLVV67PWQK4",
        "outputId": "9d7a97c5-56b7-4df1-d4d6-85cb74e834e2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title **Código:** Curvas de convergencia de la red MLP\n",
        "plt.plot(history_mlp.history['loss'])\n",
        "plt.plot(history_mlp.history['accuracy'])\n",
        "plt.plot(history_mlp.history['val_loss'])\n",
        "plt.plot(history_mlp.history['val_accuracy'])\n",
        "plt.legend(['Loss de Entrenamiento','Precisión de entrenamiento','Loss de Validación', 'Precisión de validación'])\n",
        "plt.xlabel('Epocas')\n",
        "plt.title('Entrenamiento MLP')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQe4lMNuWRs2",
        "outputId": "8baaf791-ebee-436b-996e-41b71775dbbb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.1291 - accuracy: 0.9591\n",
            "Test de precisión: 0.9591000080108643\n"
          ]
        }
      ],
      "source": [
        "#@title **Código:** Evaluamos la precisión del modelo MLP\n",
        "test_loss, test_acc = MLP.evaluate(x_test, y_test)\n",
        "\n",
        "print('Test de precisión:', test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "7p0l7fOO7_QN",
        "outputId": "4c7ed447-c473-4599-9587-e2df5cc3dbf3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 34ms/step\n",
            "Predicción:  9\n",
            "Etiqueta:  9\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# predict in model\n",
        "\n",
        "n = np.random.randint(0, x_test.shape[0])\n",
        "\n",
        "plt.figure()\n",
        "plt.imshow(x_test[n,:,:,0], cmap='gray')\n",
        "plt.colorbar()\n",
        "\n",
        "y_pred = MLP.predict(x_test[n:n+1,:,:,:])\n",
        "\n",
        "print('Predicción: ', np.argmax(y_pred))\n",
        "print('Etiqueta: ', y_test[n])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSrlyvUHY5XT"
      },
      "source": [
        "### Nuestros propios digitos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18sFrv84Y5XU"
      },
      "source": [
        "Para realizar esta parte de la practica, cada participante debe crear un digito a mano,entrando a esta pagina:\n",
        "\n",
        "[link](http://www-cs-students.stanford.edu/~blynn//mnist/)\n",
        "\n",
        "dar click derecho en la imagen y guardarla como un archivo .png, luego cargar la imagen en el siguiente codigo y ejecutarlo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kZ_Rj9WWY5XU",
        "outputId": "321df88d-dc3b-469b-d441-8f3d7766601b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x1a8262b9580>"
            ]
          },
          "execution_count": 125,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "img = Image.open('digito.png')\n",
        "\n",
        "img = np.array(img)[:,:,0]\n",
        "\n",
        "img = img.astype('float32') / 255.\n",
        "\n",
        "img = cv2.resize(img, (28,28), interpolation=cv2.INTER_CUBIC)\n",
        "\n",
        "\n",
        "plt.figure()\n",
        "plt.imshow(img, cmap='gray')\n",
        "plt.colorbar()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "csIlir4WY5XV",
        "outputId": "e8e20247-929b-424a-f7a4-c59b9cd53c91"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "img.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LdIVqxP-Y5XX",
        "outputId": "6d92aeb6-f96d-4e45-e490-d4176e099ec6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(1, 28, 28, 1)\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "Predicción:  5\n"
          ]
        }
      ],
      "source": [
        "img = np.expand_dims(img, -1)\n",
        "img = np.expand_dims(img, 0)\n",
        "\n",
        "y_pred = MLP.predict(img)\n",
        "\n",
        "print('Predicción: ', np.argmax(y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWE1pqbZrZ1v"
      },
      "source": [
        "##  <font color='#e7c299'> 3. Ejercicio básico de denoising </font>  <a name=\"eje2\"> \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "cWlMzBPVwhPR",
        "outputId": "fcd16f12-8e03-4de0-dd75-cf773335300e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2000x200 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title **Código:** Agregar ruido a los datos\n",
        "#@markdown Aplicamos un ruido con una distribución normal a la base de datos, con un factor de 0.5.\n",
        "#@markdown Imprimimos los primeros 10 valores de la base de datos una vez aplicado el ruido.\n",
        "noise_factor = 0.3\n",
        "x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) \n",
        "x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) \n",
        "\n",
        "x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n",
        "x_test_noisy = np.clip(x_test_noisy, 0., 1.)\n",
        "\n",
        "n = 10\n",
        "plt.figure(figsize=(20, 2))\n",
        "for i in range(1, n + 1):\n",
        "    ax = plt.subplot(1, n, i)\n",
        "    plt.imshow(x_test_noisy[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3b5VaKT3EzzX",
        "outputId": "86515eff-d238-4e9b-b74a-64d76b4c7982"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 128)               100480    \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 64)                2112      \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 128)               8320      \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 784)               101136    \n",
            "                                                                 \n",
            " reshape (Reshape)           (None, 28, 28, 1)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 222,384\n",
            "Trainable params: 222,384\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#@title **Código:** Definir modelo con arquitectura autoencoder\n",
        "#@markdown <img src=\"https://i.ibb.co/XjHpsyD/0-uq2-Zip-B9-Tq-I9-G-k.png\" width=\"900\">\n",
        "input_img = keras.Input(shape=(28,28,1))\n",
        "\n",
        "input = layers.Flatten()(input_img)\n",
        "encoded = layers.Dense(128, activation='relu')(input)\n",
        "encoded = layers.Dense(64, activation='relu')(encoded)\n",
        "\n",
        "encoded = layers.Dense(32, activation='relu')(encoded)\n",
        "\n",
        "decoded = layers.Dense(64, activation='relu')(encoded)\n",
        "decoded = layers.Dense(128, activation='relu')(decoded)\n",
        "decoded = layers.Dense(784, activation='sigmoid')(decoded)\n",
        "\n",
        "out = layers.Reshape((28, 28, 1), input_shape=(784,))(decoded)\n",
        "\n",
        "autoencoder = keras.Model(input_img, out)\n",
        "\n",
        "autoencoder.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Db0UXRIKwH-O",
        "outputId": "ea8814a6-d177-4b9a-c80c-9e192a62e686"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "235/235 [==============================] - 5s 13ms/step - loss: 0.2517 - val_loss: 0.1844\n",
            "Epoch 2/50\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.1674 - val_loss: 0.1545\n",
            "Epoch 3/50\n",
            "235/235 [==============================] - 3s 15ms/step - loss: 0.1498 - val_loss: 0.1434\n",
            "Epoch 4/50\n",
            "235/235 [==============================] - 5s 21ms/step - loss: 0.1406 - val_loss: 0.1355\n",
            "Epoch 5/50\n",
            "235/235 [==============================] - 4s 15ms/step - loss: 0.1346 - val_loss: 0.1307\n",
            "Epoch 6/50\n",
            "235/235 [==============================] - 3s 14ms/step - loss: 0.1294 - val_loss: 0.1257\n",
            "Epoch 7/50\n",
            "235/235 [==============================] - 3s 13ms/step - loss: 0.1249 - val_loss: 0.1219\n",
            "Epoch 8/50\n",
            "235/235 [==============================] - 3s 13ms/step - loss: 0.1218 - val_loss: 0.1197\n",
            "Epoch 9/50\n",
            "235/235 [==============================] - 3s 14ms/step - loss: 0.1194 - val_loss: 0.1172\n",
            "Epoch 10/50\n",
            "235/235 [==============================] - 3s 15ms/step - loss: 0.1175 - val_loss: 0.1165\n",
            "Epoch 11/50\n",
            "235/235 [==============================] - 4s 16ms/step - loss: 0.1160 - val_loss: 0.1143\n",
            "Epoch 12/50\n",
            "235/235 [==============================] - 5s 20ms/step - loss: 0.1148 - val_loss: 0.1133\n",
            "Epoch 13/50\n",
            "235/235 [==============================] - 4s 15ms/step - loss: 0.1138 - val_loss: 0.1123\n",
            "Epoch 14/50\n",
            "235/235 [==============================] - 4s 16ms/step - loss: 0.1128 - val_loss: 0.1113\n",
            "Epoch 15/50\n",
            "235/235 [==============================] - 5s 20ms/step - loss: 0.1121 - val_loss: 0.1112\n",
            "Epoch 16/50\n",
            "235/235 [==============================] - 4s 19ms/step - loss: 0.1113 - val_loss: 0.1109\n",
            "Epoch 17/50\n",
            "235/235 [==============================] - 4s 17ms/step - loss: 0.1107 - val_loss: 0.1096\n",
            "Epoch 18/50\n",
            "235/235 [==============================] - 3s 13ms/step - loss: 0.1100 - val_loss: 0.1095\n",
            "Epoch 19/50\n",
            "235/235 [==============================] - 3s 13ms/step - loss: 0.1094 - val_loss: 0.1088\n",
            "Epoch 20/50\n",
            "235/235 [==============================] - 5s 20ms/step - loss: 0.1087 - val_loss: 0.1078\n",
            "Epoch 21/50\n",
            "235/235 [==============================] - 4s 16ms/step - loss: 0.1082 - val_loss: 0.1074\n",
            "Epoch 22/50\n",
            "235/235 [==============================] - 3s 13ms/step - loss: 0.1076 - val_loss: 0.1072\n",
            "Epoch 23/50\n",
            "235/235 [==============================] - 3s 14ms/step - loss: 0.1071 - val_loss: 0.1065\n",
            "Epoch 24/50\n",
            "235/235 [==============================] - 5s 20ms/step - loss: 0.1067 - val_loss: 0.1063\n",
            "Epoch 25/50\n",
            "235/235 [==============================] - 5s 20ms/step - loss: 0.1063 - val_loss: 0.1061\n",
            "Epoch 26/50\n",
            "235/235 [==============================] - 3s 14ms/step - loss: 0.1058 - val_loss: 0.1052\n",
            "Epoch 27/50\n",
            "235/235 [==============================] - 4s 15ms/step - loss: 0.1054 - val_loss: 0.1051\n",
            "Epoch 28/50\n",
            "235/235 [==============================] - 3s 13ms/step - loss: 0.1050 - val_loss: 0.1046\n",
            "Epoch 29/50\n",
            "235/235 [==============================] - 4s 18ms/step - loss: 0.1047 - val_loss: 0.1046\n",
            "Epoch 30/50\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.1043 - val_loss: 0.1039\n",
            "Epoch 31/50\n",
            "235/235 [==============================] - 4s 15ms/step - loss: 0.1040 - val_loss: 0.1044\n",
            "Epoch 32/50\n",
            "235/235 [==============================] - 3s 13ms/step - loss: 0.1037 - val_loss: 0.1036\n",
            "Epoch 33/50\n",
            "235/235 [==============================] - 3s 15ms/step - loss: 0.1035 - val_loss: 0.1037\n",
            "Epoch 34/50\n",
            "235/235 [==============================] - 3s 15ms/step - loss: 0.1031 - val_loss: 0.1034\n",
            "Epoch 35/50\n",
            "235/235 [==============================] - 3s 14ms/step - loss: 0.1029 - val_loss: 0.1028\n",
            "Epoch 36/50\n",
            "235/235 [==============================] - 4s 16ms/step - loss: 0.1027 - val_loss: 0.1028\n",
            "Epoch 37/50\n",
            "235/235 [==============================] - 4s 16ms/step - loss: 0.1024 - val_loss: 0.1025\n",
            "Epoch 38/50\n",
            "235/235 [==============================] - 3s 14ms/step - loss: 0.1021 - val_loss: 0.1027\n",
            "Epoch 39/50\n",
            "235/235 [==============================] - 4s 15ms/step - loss: 0.1019 - val_loss: 0.1020\n",
            "Epoch 40/50\n",
            "235/235 [==============================] - 4s 16ms/step - loss: 0.1016 - val_loss: 0.1016\n",
            "Epoch 41/50\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.1015 - val_loss: 0.1016\n",
            "Epoch 42/50\n",
            "235/235 [==============================] - 4s 16ms/step - loss: 0.1013 - val_loss: 0.1011\n",
            "Epoch 43/50\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.1011 - val_loss: 0.1017\n",
            "Epoch 44/50\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.1010 - val_loss: 0.1013\n",
            "Epoch 45/50\n",
            "235/235 [==============================] - 3s 12ms/step - loss: 0.1007 - val_loss: 0.1011\n",
            "Epoch 46/50\n",
            "235/235 [==============================] - 4s 16ms/step - loss: 0.1006 - val_loss: 0.1009\n",
            "Epoch 47/50\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 0.1005 - val_loss: 0.1006\n",
            "Epoch 48/50\n",
            "235/235 [==============================] - 3s 11ms/step - loss: 0.1004 - val_loss: 0.1010\n",
            "Epoch 49/50\n",
            "235/235 [==============================] - 3s 13ms/step - loss: 0.1003 - val_loss: 0.1008\n",
            "Epoch 50/50\n",
            "235/235 [==============================] - 4s 18ms/step - loss: 0.1001 - val_loss: 0.1004\n"
          ]
        }
      ],
      "source": [
        "#@title **Código:** Entrenar el modelo\n",
        "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
        "\n",
        "history = autoencoder.fit(x_train_noisy, x_train,\n",
        "                epochs=50,\n",
        "                batch_size=256,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test_noisy, x_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "MIk3qhtFYT-I",
        "outputId": "b331772a-a9c9-49ec-c97d-b8c8da7e6202"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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vLlPeffv2WRxb3rDbnTt3ivvuu084ODgIPz8/8e6778rDkm9O17NnT9G2bdsy5QkMDBRPPvlkudc7atSoMmUqHdZ8c5mioqKEs7Oz0Gg0omnTpmLYsGFi//79cpro6Gih0+nKnGPy5Mllhgb//fffokuXLkKlUpW551u2bBHdu3cXDg4OwsnJSfTp00ccPXq0TL63MplM4pNPPhGBgYFCrVaLTp06ifXr14vo6GgRGBhokfby5cuif//+QqvVCldXV/HKK6+Iw4cPlxnWXNHnL4R5yPfbb78t/Pz8hL29vWjevLmYNWuWxRD1UmvWrBH333+/0Ol0QqfTiVatWolRo0aJxMREOU1Fn1155S8oKBDvv/++CA4OFvb29sLHx0c8++yz4vTp01aX79q1a2L06NHC3d1d6HQ60adPH3H+/Plyh56npaWJUaNGiYCAAPm8vXr1El9//bWcpvT7++OPP5a5FiJrSELUce8tIiIiottgHxYiIiKq9xiwEBERUb3HgIWIiIjqPQYsREREVO8xYCEiIqJ6jwELERER1XsNZuI4k8mES5cuwdHR0erp04mIiKhuCCGQm5sLPz8/KBQV16M0mIDl0qVLZZ4+S0RERHeH8+fPo1GjRhXubzABS+kDvc6fP1/mEfdERERUP+Xk5CAgIMDiwZzlaTABS2kzkJOTEwMWIiKiu8ztunOw0y0RERHVewxYiIiIqN5jwEJERET1XoPpw0JEloxGI4qLi+u6GER0j7O3t4dSqbzjfBiwEDUwQgikpqYiKyurrotCRAQAcHFxgY+Pzx3Nk8aAhaiBKQ1WvLy8oNVqOZEiEdUZIQQKCgqQnp4OAPD19a12XgxYiBoQo9EoByvu7u51XRwiIjg4OAAA0tPT4eXlVe3mIXa6JWpASvusaLXaOi4JEdENpX+T7qRfHQMWogaIzUBEVJ/UxN8kBixERFXwzTffYMuWLXVdjBqTkJCAWbNmoaSkpK6LQlQlDFiIiAAsXboULi4u5e7773//i3nz5qFr1661cu6goCDMnTu3VvIuz9WrV9G/f3+0bt0adnbWd2Xcvn07JEmSR6JVdu9KTZkyBR07drS+sFRlVfkc7mYMWIioXhg2bBgkSSqznDp1yibnHzBgAE6cOFFme2JiIj788EP89ttvDeI5ZUIIDB06FO+99x569+5dI3lWdO/uFg8++CDGjBlT18W4Y7XxOdwanNYljhK6jYy8IuQVlsDPxQEqO8Z3RLXpsccew5IlSyy2eXp62uTcDg4O8miGm7Vs2RLHjh2zSRlsQZIkrF+/vkppDQYDVCrVbdNVdO8aEiEEjEZjtWqkbKWhfw78Bb6NyDk78ODs7Th7Jb+ui0LU4KnVavj4+FgsSqUSw4YNQ9++fS3SjhkzBg8++KC8/uCDD2L06NF499134ebmBh8fH0yZMsXimKysLLzyyivw9vaGRqNBu3bt5B/v8qrTFy5ciKZNm0KlUqFly5ZYtmyZxX5JkvDNN9+gX79+0Gq1aN68OX755ZdKrzE9PR19+vSBg4MDgoODsXz58jJpsrKy8NJLL8HT0xNOTk54+OGHcfDgwQrzTE5OhiRJWLlyJbp16yZf244dOyzSHT58GI8//jj0ej28vb0xZMgQZGRkWNzDN954A2PGjIGHhweioqIAAL///jtatGgBBwcHPPTQQ0hOTrbIt7x7N2PGDHh7e8PR0REjRoxAYWGhxf59+/bhkUcegYeHB5ydndGzZ0/Ex8dXeu/Kc7t7VdoUtWzZMgQFBcHZ2RkDBw5Ebm4uAHPN3o4dO/DFF1/ItXrJyclyzcKGDRvQpUsXqNVq/PXXXzCZTJg+fTqCg4Ph4OCAkJAQrF69Wj5f6XGxsbEIDQ2FVqtFt27dkJiYKKc5ffo0nn76aXh7e0Ov1yMsLKxM/6igoCB89NFHGDp0KPR6PQIDA/HLL7/g8uXLePrpp6HX69GhQwfs37+/0s/h559/RufOnaHRaNCkSRNMnTrVot9SZd/h5ORkPPTQQwAAV1dXSJKEYcOGAQCKioowevRoeHl5QaPR4P7778e+ffus/vysIhqI7OxsAUBkZ2fXaL7dpseKwPfWi/izV2s0X6LacO3aNXH06FFx7do1eZvJZBL5RcU2X0wmk1Vlj46OFk8//XSV97311luiZ8+e8nrPnj2Fk5OTmDJlijhx4oT47rvvhCRJYtOmTUIIIYxGo7jvvvtE27ZtxaZNm8Tp06fFr7/+Kn7//XchhBBLliwRzs7Ocn5r164V9vb2YsGCBSIxMVF89tlnQqlUiq1bt8ppAIhGjRqJFStWiJMnT4rRo0cLvV4vrly5UuF1Pv744yIkJETs2rVL7N+/X3Tr1k04ODiIzz//XE4TGRkp+vTpI/bt2ydOnDgh3nnnHeHu7l5hvklJSXJZVq9eLY4ePSpeeukl4ejoKDIyMoQQQmRmZgpPT08xYcIEcezYMREfHy8eeeQR8dBDD1ncQ71eL8aNGyeOHz8ujh8/Ls6dOyfUarWIiYkRx48fF//5z3+Et7e3ACAyMzPLvXerVq0SarVafPPNN+L48ePi/fffF46OjiIkJEROExsbK5YtWyaOHTsmjh49KkaMGCG8vb1FTk6Oxed+82dcntvdq8mTJwu9Xi+eeeYZcejQIfG///1P+Pj4iP/3//6fEEKIrKwsERERIUaOHClSUlJESkqKKCkpEdu2bRMARIcOHcSmTZvEqVOnxJUrV8RHH30kWrVqJTZu3ChOnz4tlixZItRqtdi+fbsQQsjHhYeHi+3bt4sjR46IHj16iG7dusllTkhIEIsWLRKHDh0SJ06cEB988IHQaDTi7NmzcprAwEDh5uYmFi1aJE6cOCFee+014eTkJB577DHxww8/iMTERNG3b1/RunVr+d/arZ/D//73P+Hk5CSWLl0qTp8+LTZt2iSCgoLElClT5DSVfYdLSkrEmjVrBACRmJgoUlJSRFZWlhBCiNGjRws/Pz/x+++/iyNHjojo6Gjh6upa4Xe0vL9Npar6+82A5TYenbNDBL63Xvx18nKN5ktUG8r7o5BfVCwC31tv8yW/qNiqskdHRwulUil0Op28PPvss/K+qgQs999/v0WasLAw8d577wkhhPjjjz+EQqEQiYmJ5Z7/1j/23bp1EyNHjrRI89xzz4knnnhCXgcgPvjgA3k9Ly9PABAbNmwo9xyJiYkCgNi7d6+87dixYwKAHLD8+eefwsnJSRQWFloc27RpU/HVV1+Vm29pwDJjxgx5W3FxsWjUqJGYOXOmEEKIadOmiUcffdTiuPPnz8s/RkKY72GnTp0s0kyYMEG0adPGYtt7771XacASEREhXn/9dYtjwsPDLQKWWxmNRuHo6Ch+/fVXedv48ePFkCFDKjymKvdq8uTJQqvVWgRC48aNE+Hh4fJ6z549xVtvvWWRR2ng8dNPP8nbCgsLhVarFX///bdF2hEjRohBgwZZHLdlyxZ5/2+//SYAlPtjXapt27Zi3rx58npgYKD417/+Ja+npKQIAGLixInytl27dgkAIiUlRQhR9nPo1auX+OSTTyzOs2zZMuHr6yuv3+47XHo9pZ91aRp7e3uxfPlyeZvBYBB+fn7i008/Lff6aiJgqb+NcfWEVm2ekS+viEP/iGrbQw89hIULF8rrOp3OquM7dOhgse7r6ytPCZ6QkIBGjRqhRYsWVcrr2LFjePnlly22de/eHV988UWF59TpdHBycpLPWV6ednZ26NKli7ytVatWFtX4Bw8eRF5eXpmZiq9du4bTp09XWuaIiAj5vZ2dHUJDQ+X+NwcPHsS2bdug1+vLHHf69Gn5vtxcttIyh4eHV3ie8hw7dgyvvvpqmWO2bdsmr6elpeGDDz7A9u3bkZ6eDqPRiIKCApw7d05OM3369ErPU9V7FRQUBEdHR3n95u/F7YSGhsrvT506hYKCAjzyyCMWaQwGAzp16mSx7ebvRel09Onp6WjcuDHy8vIwZcoU/Pbbb0hJSUFJSQmuXbtmce235uHt7Q0AaN++fZlt6enp8PHxKVP2gwcPYufOnfj444/lbUajEYWFhSgoKJAnc7PmOwyYvy/FxcXo3r27vM3e3h5du3at1f5eDFhuQ68236J8Bix0l3KwV+Loh1F1cl5r6XQ6NGvWrMx2hUIB838Gbyhvxkx7e3uLdUmSYDKZzOWppc6IlZ2zOvLy8uDr64vt27eX2XcnQ1bz8vLQp08fzJw5s8y+m5/vYm2QWF3R0dG4cuUKvvjiCwQGBkKtViMiIgIGg6HKeVT1Xt3JZ3Tz/cjLywMA/Pbbb/D397dIp1arLdZvPmfppGml5xw7diw2b96M2bNno1mzZnBwcMCzzz5b5trLy6OyfG+Vl5eHqVOn4plnnimzT6PRlHue0nzv5DtcWxiw3IZWZf6jm28w1nFJiKpHkiRoVXf3P3VPT08cPnzYYltCQkKZP7SV6dChAy5cuIATJ05UqZaldevW2LlzJ6Kjo+VtO3fuRJs2bape8Fu0atUKJSUliIuLQ1hYGADzsOmbh4x27twZqampsLOzQ1BQkFX57969Gw888AAAyOd544035HzXrFmDoKAgq0a6tG7dukxH4t27d9/2mD179mDo0KEVHrNz5078+9//xhNPPAEAOH/+vEUH4Kq4k3t1M5VKBaPx9n/j27RpA7VajXPnzqFnz57VPt/OnTsxbNgw9OvXD4A5sLi1I3NN6Ny5MxITE8v9T0BVlY4Su/n+lHZE37lzJwIDAwGY/wOxb9++Wh0ezlFCt6FjDQtRnXv44Yexf/9+fP/99zh58iQmT55cJoC5nZ49e+KBBx5A//79sXnzZiQlJWHDhg3YuHFjuenHjRuHpUuXYuHChTh58iTmzJmDtWvXYuzYsdW+jpYtW+Kxxx7DK6+8gj179iAuLg4vvfSSRe1PZGQkIiIi0LdvX2zatAnJycn4+++/8f7771uMCCnPggULsG7dOhw/fhyjRo1CZmYmXnzxRQDAqFGjcPXqVQwaNAj79u3D6dOn8ccff2D48OGV/li/+uqrOHnyJMaNG4fExESsWLECS5curbQcb731Fr799lssWbIEJ06cwOTJk3HkyBGLNM2bN8eyZctw7Ngx7NmzB4MHDy5TCzZhwgSLoOdWd3KvbhYUFIQ9e/YgOTkZGRkZFdYuODo6YuzYsXj77bfx3Xff4fTp04iPj8e8efPw3XffVfl8zZs3x9q1a5GQkICDBw/ihRdeqJUajUmTJuH777/H1KlTceTIERw7dgwrV67EBx98UOU8AgMD5aHwly9fRl5eHnQ6HV577TWMGzcOGzduxNGjRzFy5EgUFBRgxIgRNX4dpRiw3EZpk1ABAxaiOhMVFYWJEyfi3XffRVhYGHJzcyv9IavImjVrEBYWhkGDBqFNmzZ49913K/yx7tu3L7744gvMnj0bbdu2xVdffYUlS5ZYDKWujiVLlsDPzw89e/bEM888g5dffhleXl7yfkmS8Pvvv+OBBx7A8OHD0aJFCwwcOBBnz56V+yxUZMaMGZgxYwZCQkLw119/4ZdffoGHhwcAwM/PDzt37oTRaMSjjz6K9u3bY8yYMXBxcYFCUfFPQePGjbFmzRr89NNPCAkJwaJFi/DJJ59UWo4BAwbIn1eXLl1w9uxZvPbaaxZp/u///g+ZmZno3LkzhgwZIg+RvVlKSkqZfh03u5N7dbOxY8dCqVSiTZs28PT0rPSc06ZNw8SJEzF9+nS0bt0ajz32GH777TcEBwdX+Xxz5syBq6srunXrhj59+iAqKgqdO3eu8vFVFRUVhfXr12PTpk0ICwvDfffdh88//1yuFakKf39/TJ06FePHj4e3t7dcYzdjxgz0798fQ4YMQefOnXHq1Cn88ccfcHV1rfHrKCWJWxuG71I5OTlwdnZGdnZ2jc5GOWPDcSzacRovdg/GpD7VrwomsoXCwkIkJSUhODjYoo2aGrbk5GQEBwfjwIEDnP6e6qXK/jZV9febNSy3ob8+SohNQkRERHWHActtlHZWzDcwYCEiIqord/fQARvgsGYiqu+CgoLKDPsmamhYw3Ib8ighDmsmIiKqMwxYbkPLPixERER1jgHLbbBJiIiIqO4xYLkNznRLRERU9xiw3AZrWIiIiOoeA5bbKO10W2AwwmRiL3wiIqK6wIDlNnQ3PTSuoJjNQkT3ksLCQnz88cc4depUXRelxvz000/473//W9fFoNsQQmDOnDlWPROpoWPAchsaewUU5id4s1mIqBYJIfDyyy/Dzc0NkiQhISHBJucdNmwY+vbtW+6+0aNH49SpU3f0tNuKJCcn2/Q6AfMTk0ePHo2IiIhqHT9lyhSLqf8ru3elHnzwwVp9gm91LV26FC4uLvL6rddWnqpcrzUqO+f06dOxceNGhISE1Nj57nacOO42JEmCTm2H3MISBixEtWjjxo1YunQptm/fjiZNmsgP7attX3zxRbmTri1fvhzJycn47bffbFKO2nblyhWMGDECP/30E4KCgmokz4ru3d1o7NixePPNN+vFOf/3v/9h9erV2L59O+zt7W1apvqMAUsV6FSlAQubhIhqy+nTp+Hr64tu3brZ9LzOzs7lbh88eDAGDx5s07LUJnd3dxw5cqRKaYuLi6v0Q1nRvbsb6fV66PX6enHOBx54APHx8TYty92ATUJVoLs+eVwea1iIasWwYcPw5ptv4ty5c5AkSa4BCAoKwty5cy3SduzYEVOmTJHXJUnCN998g379+kGr1aJ58+b45ZdfLI45cuQIevfuDScnJzg6OqJHjx44ffq0fO6bq/mLioowevRoeHl5QaPR4P7778e+ffvk/du3b4ckSYiNjUVoaCi0Wi26deuGxMTESq9x79696NSpEzQaDUJDQ3HgwIEyaQ4fPozHH38cer0e3t7eGDJkCDIyMirMs7RZ46effkLz5s2h0WgQFRWF8+fPW6T7+eef0blzZ2g0GjRp0gRTp05FScmNv2eSJGHhwoV46qmnoNPp8PHHHwMAZsyYAW9vbzg6OmLEiBEoLCy0yPfWe5efn4+hQ4dCr9fD19cXn332WZkyL1u2DKGhoXB0dISPjw9eeOEFpKenV3rvbmYymdCoUSMsXLjQYvuBAwegUChw9uxZAMCcOXPQvn176HQ6BAQE4PXXX0deXl6F+d7aPGM0GhETEwMXFxe4u7vj3XffLVObtHHjRtx///1ymt69e8vfq1IXLlzAoEGD4ObmBp1Oh9DQUOzZs6fcc5pMJnz44Ydo1KgR1Go1OnbsiI0bN8r7S5sR165di4ceegharRYhISHYtWtXle/f3axaAcuCBQsQFBQEjUaD8PBw7N27t8K0ixcvRo8ePeDq6gpXV1dERkZWmv7VV1+FJEll/kjVpRsjhRiw0F1ICMCQb/vFiqaCL774Qv5DnZKSYhEgVMXUqVPx/PPP459//sETTzyBwYMH4+rVqwCAixcv4oEHHoBarcbWrVsRFxeHF1980eIH+2bvvvsu1qxZg++++w7x8fFo1qwZoqKi5PxKvf/++/jss8+wf/9+2NnZ4cUXX6ywfHl5eejduzfatGmDuLg4TJkyBWPHjrVIk5WVhYcffhidOnXC/v37sXHjRqSlpeH555+v9NoLCgrw8ccf4/vvv8fOnTuRlZWFgQMHyvv//PNPDB06FG+99RaOHj2Kr776CkuXLpWDklJTpkxBv379cOjQIbz44ov44YcfMGXKFHzyySfYv38/fH198e9//7vSsowbNw47duzAzz//jE2bNmH79u1lagqKi4sxbdo0HDx4ED/99BOSk5MxbNgwizRBQUEWQenNFAoFBg0ahBUrVlhsX758Obp3747AwEA53ZdffokjR47gu+++w9atW/Huu+9WWv6bffbZZ1i6dCm+/fZb/PXXX7h69SrWrVtnkSY/Px8xMTHYv38/YmNjoVAo0K9fP5hMJgDmz71nz564ePEifvnlFxw8eBDvvvuuvP9WX3zxBT777DPMnj0b//zzD6KiovDUU0/h5MmTFunef/99jB07FgkJCWjRogUGDRpU4fe5QRFWWrlypVCpVOLbb78VR44cESNHjhQuLi4iLS2t3PQvvPCCWLBggThw4IA4duyYGDZsmHB2dhYXLlwok3bt2rUiJCRE+Pn5ic8//9yqcmVnZwsAIjs729pLuq2BX+0Sge+tFz8dKFtmovrk2rVr4ujRo+LatWs3NhblCTHZyfZLUZ5VZf/8889FYGCgxbbAwMAyfwtCQkLE5MmT5XUA4oMPPpDX8/LyBACxYcMGIYQQEyZMEMHBwcJgMJR73ujoaPH000/Lx9rb24vly5fL+w0Gg/Dz8xOffvqpEEKIbdu2CQBiy5YtcprffvtNALC87zf56quvhLu7u8X+hQsXCgDiwIEDQgghpk2bJh599FGL486fPy8AiMTExHLzXbJkiQAgdu/eLW87duyYACD27NkjhBCiV69e4pNPPrE4btmyZcLX11deByDGjBljkSYiIkK8/vrrFtvCw8NFSEiIvH7zvcvNzRUqlUr88MMP8v4rV64IBwcH8dZbb5VbfiGE2LdvnwAgcnNz5W0PP/ywmDdvXoXHHDhwQEiSJM6ePSuEEMJoNAp/f3+xcOHCCo/58ccfhbu7u7y+ZMkS4ezsLK9PnjzZ4tp8fX3lz1wIIYqLi0WjRo3k6y3P5cuXBQBx6NAhIYT5c3d0dBRXrlwpN/2t5/Tz8xMff/yxRZqwsDD5c0hKShIAxDfffCPvP3LkiAAgjh07VmG56oNy/zZdV9Xfb6trWObMmYORI0di+PDhaNOmDRYtWgStVotvv/223PTLly/H66+/jo4dO6JVq1b45ptvYDKZEBsba5Hu4sWLePPNN7F8+fJ618no5rlYiKj+6dChg/xep9PByclJbmZISEhAjx49qvR35fTp0yguLkb37t3lbfb29ujatSuOHTtW4Tl9fX0BoMKmjWPHjqFDhw7QaDTytltH6hw8eBDbtm2T+zXo9Xq0atVKLldF7OzsEBYWJq+3atUKLi4ucnkPHjyIDz/80CLfkSNHIiUlBQUFBfJxoaGhZcocHh5usa2y0UWnT5+GwWCwOMbNzQ0tW7a0SBcXF4c+ffqgcePGcHR0RM+ePQEA586dk9PExsbijTfeqPBcHTt2ROvWreValh07diA9PR3PPfecnGbLli3o1asX/P394ejoiCFDhuDKlSsW11yR7OxspKSkWFyLnZ1dmXt08uRJDBo0CE2aNIGTk5PclFl6LQkJCejUqRPc3Nxue86cnBxcunTJ4rsHAN27d7+j715DYlWnW4PBgLi4OEyYMEHeplAoEBkZWeU2tIKCAhQXF1t8gCaTCUOGDMG4cePQtm3bKuVTVFSEoqIieT0nJ6eKV2E9HR+ASHczey3w/y7VzXnvkEKhKNNvoLi4uOypbglGJEmSq90dHBzuuBzlufmckmSe+6Ciqv6qyMvLQ58+fTBz5swy+0p/lKqb79SpU/HMM8+U2XdzAKXT6ap9jqrKz89HVFQUoqKisHz5cnh6euLcuXOIioqCwWCwKq/BgwdjxYoVGD9+PFasWIHHHnsM7u7uAMx9PXr37o3XXnsNH3/8Mdzc3PDXX39hxIgRMBgM0Grv/LsJAH369EFgYCAWL14MPz8/mEwmtGvXTr6Wu+W7d7ewqoYlIyMDRqMR3t7eFtu9vb2RmppapTzee+89+Pn5ITIyUt42c+ZM2NnZYfTo0VUuy/Tp0+Hs7CwvAQEBVT7WWqU1LOx0S3clSQJUOtsv1/+Q3glPT0+kpKTI6zk5OUhKSrIqjw4dOuDPP/8sN9C5VdOmTaFSqbBz5055W3FxMfbt24c2bdpYdd6btW7dGv/8849Fp9Xdu3dbpOncuTOOHDmCoKAgNGvWzGKpLJgoKSmxmFwsMTERWVlZaN26tZxvYmJimTybNWsGhaLin4DWrVvLnUMrKvPNmjZtCnt7e4tjMjMzceLECXn9+PHjuHLlCmbMmIEePXqgVatW1a4ZeOGFF3D48GHExcVh9erVFiO64uLiYDKZ8Nlnn+G+++5DixYtcOlS1YN2Z2dn+Pr6WlxLSUkJ4uLi5PUrV64gMTERH3zwAXr16oXWrVsjMzPTIp8OHTogISGhTP+n8jg5OcHPz8/iuwcAO3fuvKPvXkNi01FCM2bMwMqVK7Fu3To5so+Li8MXX3yBpUuXypFiVUyYMAHZ2dnycmuv+Jqku/4ARDYJEdnWww8/jGXLluHPP//EoUOHEB0dDaVSaVUeb7zxBnJycjBw4EDs378fJ0+exLJly8od1aPT6fDaa69h3Lhx2LhxI44ePYqRI0eioKAAI0aMqPZ1vPDCC5AkCSNHjsTRo0fx+++/Y/bs2RZpRo0ahatXr2LQoEHYt28fTp8+jT/++APDhw+H0Vjx3x57e3u8+eab2LNnD+Li4jBs2DDcd9996Nq1KwBg0qRJ+P777zF16lQcOXIEx44dw8qVK/HBBx9UWua33noL3377LZYsWYITJ05g8uTJlQ6L1uv1GDFiBMaNG4etW7fi8OHDGDZsmEVQ1LhxY6hUKsybNw9nzpzBL7/8gmnTppXJq1evXpg/f36l5QsKCkK3bt0wYsQIGI1GPPXUU/K+Zs2aobi4WD7PsmXLsGjRokrzK+/6Z8yYgZ9++gnHjx/H66+/jqysLHm/q6sr3N3d8fXXX+PUqVPYunUrYmJiLPIYNGgQfHx80LdvX+zcuRNnzpzBmjVrKmyRGDduHGbOnIlVq1YhMTER48ePR0JCAt566y2ryt5QWRWweHh4QKlUIi0tzWJ7WloafHx8Kj129uzZmDFjBjZt2mTR/vbnn38iPT0djRs3hp2dHezs7HD27Fm88847lU5upFar4eTkZLHUFtawENWNCRMmoGfPnujduzeefPJJ9O3bF02bNrUqD3d3d2zdulUesdGlSxcsXry4wj4tM2bMQP/+/TFkyBB07twZp06dwh9//AFXV9dqX4der8evv/6KQ4cOoVOnTnj//ffLNP2U/u/aaDTi0UcfRfv27TFmzBi4uLhUWhOi1Wrx3nvv4YUXXkD37t2h1+uxatUqeX9UVBTWr1+PTZs2ISwsDPfddx8+//xzeTRNRQYMGICJEyfi3XffRZcuXXD27Fm89tprlR4za9Ys9OjRA3369EFkZCTuv/9+dOnSRd7v6emJpUuX4scff0SbNm0wY8aMMoEbYO4PU9lw7lKDBw/GwYMH0a9fP4vml5CQEMyZMwczZ85Eu3btsHz5ckyfPv22+d3snXfewZAhQxAdHY2IiAg4OjqiX79+8n6FQoGVK1ciLi4O7dq1w9tvv41Zs2ZZ5KFSqbBp0yZ4eXnhiSeeQPv27TFjxowKg+7Ro0cjJiYG77zzDtq3b4+NGzfil19+QfPmza0qe0MliVsbiG8jPDwcXbt2xbx58wCY280aN26MN954A+PHjy/3mE8//RQff/wx/vjjD9x3330W+65cuWJR5QuY/4ENGTIEw4cPL9NhqyI5OTlwdnZGdnZ2jQcv3/x5Bh/9dgx9O/ph7sBONZo3UU0qLCxEUlISgoODLfonUMO0dOlSjBkzxuJ//kT1UWV/m6r6+231TLcxMTGIjo5GaGgounbtirlz5yI/Px/Dhw8HAAwdOhT+/v5yNDtz5kxMmjQJK1asQFBQkNzXpbS3uru7u9xRqpS9vT18fHyqHKzUNq2qtIaFTUJERER1weqAZcCAAbh8+TImTZqE1NRUeSa+0o64586ds6i+XLhwIQwGA5599lmLfCZPnlzhxED1DUcJERER1S2rm4Tqq9psEtpyNA0vfb8fIY2c8fMb99do3kQ1iU1CRFQf1USTEJ8lVAXsdEtERFS3GLBUgZ4z3dJdpoFUnBJRA1ETf5MYsFSBlk9rprtE6VDdqkw/TkRkK6V/k+7k0TtWd7q9F5XWsOQXlUAIYdUEd0S2pFQq4eLiIs8eqtVq+X0lojojhEBBQQHS09Ph4uJi9cSPN2PAUgWlfVhMAigqMUFjX/0bTlTbSidxvBcehkZEdwcXF5fbTjB7OwxYqkB7U4CSV1TCgIXqNUmS4OvrCy8vryo9P4eIqDbZ29vfUc1KKQYsVaBQSNCqlCgwGFFQZAT0dV0iottTKpU18keCiKg+YKfbKrox2y073hIREdkaA5Yq0pfOdmtgwEJERGRrDFiqSHfTSCEiIiKyLQYsVaRTlQYsnDyOiIjI1hiwVJGOTUJERER1hgFLFWnZJERERFRnGLBUkV7FgIWIiKiuMGCpIrnTLR+ASEREZHMMWKpI7sPCGhYiIiKbY8BSRaU1LJw4joiIyPYYsFSRTmWuYSngsGYiIiKbY8BSRTf6sLCGhYiIyNYYsFQRZ7olIiKqOwxYqogz3RIREdUdBixVVDpKiJ1uiYiIbI8BSxWVNgkVsA8LERGRzTFgqaIbfVjYJERERGRrDFiqqHRqfoPRBEOJqY5LQ0REdG9hwFJF2ut9WAA2CxEREdkaA5YqslcqoLIz3y52vCUiIrItBixW0Msdb9mPhYiIyJYYsFhBq+LQZiIiorrAgMUKcg0LRwoRERHZFAMWK7CGhYiIqG4wYLECnydERERUNxiwWEHP2W6JiIjqRLUClgULFiAoKAgajQbh4eHYu3dvhWkXL16MHj16wNXVFa6uroiMjLRIX1xcjPfeew/t27eHTqeDn58fhg4dikuXLlWnaLVKe33yuDz2YSEiIrIpqwOWVatWISYmBpMnT0Z8fDxCQkIQFRWF9PT0ctNv374dgwYNwrZt27Br1y4EBATg0UcfxcWLFwEABQUFiI+Px8SJExEfH4+1a9ciMTERTz311J1dWS3QX588jjUsREREtiUJIYQ1B4SHhyMsLAzz588HAJhMJgQEBODNN9/E+PHjb3u80WiEq6sr5s+fj6FDh5abZt++fejatSvOnj2Lxo0bV6lcOTk5cHZ2RnZ2NpycnKp+QVaYufE4Fm4/jeHdgzC5T9taOQcREdG9pKq/31bVsBgMBsTFxSEyMvJGBgoFIiMjsWvXrirlUVBQgOLiYri5uVWYJjs7G5IkwcXFpcI0RUVFyMnJsVhqm56dbomIiOqEVQFLRkYGjEYjvL29LbZ7e3sjNTW1Snm899578PPzswh6blZYWIj33nsPgwYNqjTSmj59OpydneUlICCg6hdSTbrrw5rzOdMtERGRTdl0lNCMGTOwcuVKrFu3DhqNpsz+4uJiPP/88xBCYOHChZXmNWHCBGRnZ8vL+fPna6vYMi1rWIiIiOqEnTWJPTw8oFQqkZaWZrE9LS0NPj4+lR47e/ZszJgxA1u2bEGHDh3K7C8NVs6ePYutW7feth+KWq2GWq22pvh3jDPdEhER1Q2ralhUKhW6dOmC2NhYeZvJZEJsbCwiIiIqPO7TTz/FtGnTsHHjRoSGhpbZXxqsnDx5Elu2bIG7u7s1xbIZznRLRERUN6yqYQGAmJgYREdHIzQ0FF27dsXcuXORn5+P4cOHAwCGDh0Kf39/TJ8+HQAwc+ZMTJo0CStWrEBQUJDc10Wv10Ov16O4uBjPPvss4uPjsX79ehiNRjmNm5sbVCpVTV3rHZM73XJYMxERkU1ZHbAMGDAAly9fxqRJk5CamoqOHTti48aNckfcc+fOQaG4UXGzcOFCGAwGPPvssxb5TJ48GVOmTMHFixfxyy+/AAA6duxokWbbtm148MEHrS1irbkxNT+bhIiIiGzJ6nlY6itbzMNy7koBHpi1DQ72Shyb9litnIOIiOheUivzsNzrdNdnur1WbITR1CDiPCIiorsCAxYrlDYJAZyen4iIyJYYsFhBbaeAUiEBYD8WIiIiW2LAYgVJkm6a7ZY1LERERLbCgMVKOs52S0REZHMMWKzEoc1ERES2x4DFSqxhISIisj0GLFZiHxYiIiLbY8BiJTYJERER2R4DFivJNSxsEiIiIrIZBixWKq1h4RObiYiIbIcBi5VKn9jMmW6JiIhshwGLlbSq0hoW9mEhIiKyFQYsVip9ACJrWIiIiGyHAYuVOA8LERGR7TFgsRI73RIREdkeAxYr6eUmIfZhISIishUGLFa60emWNSxERES2woDFSvKwZo4SIiIishkGLFZip1siIiLbY8BipZsffiiEqOPSEBER3RsYsFiptIbFJIDCYlMdl4aIiOjewIDFSg72Svk9O94SERHZBgMWKykUktwsxNluiYiIbIMBSzVw8jgiIiLbYsBSDTdGCnFoMxERkS0wYKmG0gcg5rNJiIiIyCYYsFRD6Wy3nIuFiIjINhiwVANnuyUiIrItBizVwE63REREtsWApRrk2W4ZsBAREdkEA5ZqkEcJGdgkREREZAsMWKqBNSxERES2Va2AZcGCBQgKCoJGo0F4eDj27t1bYdrFixejR48ecHV1haurKyIjI8ukF0Jg0qRJ8PX1hYODAyIjI3Hy5MnqFM0mbtSwMGAhIiKyBasDllWrViEmJgaTJ09GfHw8QkJCEBUVhfT09HLTb9++HYMGDcK2bduwa9cuBAQE4NFHH8XFixflNJ9++im+/PJLLFq0CHv27IFOp0NUVBQKCwurf2W16MbEcQxYiIiIbEESQghrDggPD0dYWBjmz58PADCZTAgICMCbb76J8ePH3/Z4o9EIV1dXzJ8/H0OHDoUQAn5+fnjnnXcwduxYAEB2dja8vb2xdOlSDBw4sErlysnJgbOzM7Kzs+Hk5GTNJVlt3YELeHvVQdzfzAP/eSm8Vs9FRETUkFX199uqGhaDwYC4uDhERkbeyEChQGRkJHbt2lWlPAoKClBcXAw3NzcAQFJSElJTUy3ydHZ2Rnh4eJXztDWdik1CREREtmRnTeKMjAwYjUZ4e3tbbPf29sbx48erlMd7770HPz8/OUBJTU2V87g1z9J95SkqKkJRUZG8npOTU6Xz1wQ2CREREdmWTUcJzZgxAytXrsS6deug0WjuKK/p06fD2dlZXgICAmqolLfHhx8SERHZllUBi4eHB5RKJdLS0iy2p6WlwcfHp9JjZ8+ejRkzZmDTpk3o0KGDvL30OGvznDBhArKzs+Xl/Pnz1lzKHdHz4YdEREQ2ZVXAolKp0KVLF8TGxsrbTCYTYmNjERERUeFxn376KaZNm4aNGzciNDTUYl9wcDB8fHws8szJycGePXsqzVOtVsPJyclisRU+/JCIiMi2rOrDAgAxMTGIjo5GaGgounbtirlz5yI/Px/Dhw8HAAwdOhT+/v6YPn06AGDmzJmYNGkSVqxYgaCgILlfil6vh16vhyRJGDNmDD766CM0b94cwcHBmDhxIvz8/NC3b9+au9IaVNokVGwUMJSYoLLj/HtERES1yeqAZcCAAbh8+TImTZqE1NRUdOzYERs3bpQ7zZ47dw4KxY0f8IULF8JgMODZZ5+1yGfy5MmYMmUKAODdd99Ffn4+Xn75ZWRlZeH+++/Hxo0b77ifS20pnekWMNeyqOxUdVgaIiKihs/qeVjqK1vOwwIALT/YgKISE/589yEEuGlr/XxEREQNUa3Mw0I36K83CxXwAYhERES1jgFLNWmvjxTKY8dbIiKiWseApZpKZ7st4NBmIiKiWseApZr0nO2WiIjIZhiwVJP2esCSx9luiYiIah0Dlmoqne2WTUJERES1jwFLNZXOdstOt0RERLWPAUs1ycOa2SRERERU6xiwVJOOw5qJiIhshgFLNfEBiERERLbDgKWaONMtERGR7TBgqSatik1CREREtsKApZpu1LAwYCEiIqptDFiqSceJ44iIiGyGAUs1lY4SYqdbIiKi2seApZp0bBIiIiKyGQYs1aTjTLdEREQ2w4ClmkprWAqLTTCaRB2XhoiIqGFjwFJNpX1YACCfzUJERES1igFLNamUCtgpJADseEtERFTbGLBUkyRJcrNQPoc2ExER1SoGLHdAr+bzhIiIiGyBAcsdKJ2en31YiIiIahcDljvAJiEiIiLbYMByBzjbLRERkW0wYLkDpZPHsUmIiIiodjFguQPsdEtERGQbDFgqIwSw/Dngy05AXnqZ3Vq5SYh9WIiIiGoTA5bKSBKQfhy4ega4mlRmt441LERERDbBgOV23ILMr1fPlNnFPixERES2wYDldtyamF/LC1g4rJmIiMgmGLDcTmnAklm2SUjPYc1EREQ2wYDldlyDza/l1LBo2SRERERkEwxYbkduEiqvhoVNQkRERLZQrYBlwYIFCAoKgkajQXh4OPbu3Vth2iNHjqB///4ICgqCJEmYO3dumTRGoxETJ05EcHAwHBwc0LRpU0ybNg1CiOoUr2a5Bplfr10FrmVZ7JKfJcQmISIiolpldcCyatUqxMTEYPLkyYiPj0dISAiioqKQnl52nhIAKCgoQJMmTTBjxgz4+PiUm2bmzJlYuHAh5s+fj2PHjmHmzJn49NNPMW/ePGuLV/PUekDvbX5/Sz8WudMtm4SIiIhqldUBy5w5czBy5EgMHz4cbdq0waJFi6DVavHtt9+Wmz4sLAyzZs3CwIEDoVary03z999/4+mnn8aTTz6JoKAgPPvss3j00UcrrbmxqQr6sbBJiIiIyDasClgMBgPi4uIQGRl5IwOFApGRkdi1a1e1C9GtWzfExsbixIkTAICDBw/ir7/+wuOPP17hMUVFRcjJybFYak0FQ5vlmW4NJfWj+YqIiKiBsrMmcUZGBoxGI7y9vS22e3t74/jx49UuxPjx45GTk4NWrVpBqVTCaDTi448/xuDBgys8Zvr06Zg6dWq1z2kVOWBJtthcWsMiBHCt2CiPGiIiIqKaVS9GCf3www9Yvnw5VqxYgfj4eHz33XeYPXs2vvvuuwqPmTBhArKzs+Xl/PnztVdAt/KbhBzslZAk8/s8drwlIiKqNVZVCXh4eECpVCItLc1ie1paWoUdaqti3LhxGD9+PAYOHAgAaN++Pc6ePYvp06cjOjq63GPUanWFfWJqXGnAckunW0mSoFPZIa+oBAVFRsDRNsUhIiK611hVw6JSqdClSxfExsbK20wmE2JjYxEREVHtQhQUFEChsCyKUqmEyWSqdp41qrTTbW4KYCiw2KW73o+FNSxERES1x+pOFzExMYiOjkZoaCi6du2KuXPnIj8/H8OHDwcADB06FP7+/pg+fToAc0fdo0ePyu8vXryIhIQE6PV6NGvWDADQp08ffPzxx2jcuDHatm2LAwcOYM6cOXjxxRdr6jrvjNYN0LgAhVlAZjLg3UbeZX4AYhHnYiEiIqpFVgcsAwYMwOXLlzFp0iSkpqaiY8eO2Lhxo9wR99y5cxa1JZcuXUKnTp3k9dmzZ2P27Nno2bMntm/fDgCYN28eJk6ciNdffx3p6enw8/PDK6+8gkmTJt3h5dUgt2Dg0gFzP5abA5brHW8LDBzaTEREVFsk0UDG4+bk5MDZ2RnZ2dlwcnKq+ROsfhE4vAZ4ZBrQfbS8eeDXu7D7zFXMG9QJfUL8av68REREDVhVf7/rxSihu0IFT23WqUprWNgkREREVFsYsFRVBbPdljYJ5XG2WyIiolrDgKWqKnhqc+koIXa6JSIiqj0MWKqqdC6W7PNAiUHeXNokxAcgEhER1R4GLFWl9wbstYAwmYOW6+QnNrOGhYiIqNYwYKkqSSq3H0tpk1AB+7AQERHVGgYs1ijnmUI3Ot2yhoWIiKi2MGCxRjkdb9mHhYiIqPYxYLFGJTUs+WwSIiIiqjUMWKxRzuRxHNZMRERU+xiwWKO0021mMmAy16jcmOmWNSxERES1hQGLNZwbAQp7wGgAci4BYKdbIiIiW2DAYg2FEnANNL+/3o9Ff9M8LA3kOZJERET1DgMWa8kjhcwBi/Z6H5YSk4DBaKqrUhERETVoDFisdUvH29I+LABHChEREdUWBizWumW2W6VCgsbefBs5UoiIiKh2MGCxltwklCxvkvuxcPI4IiKiWsGAxVo3Tx53vZMtH4BIRERUuxiwWMulMSApgOJ8IC8dAKBVcbZbIiKi2sSAxVp2asCpkfn99Y63es52S0REVKsYsFTHLc8UKm0Syr5WXFclIiIiatAYsFTHLU9tbuapBwAcuphdVyUiIiJq0BiwVMctNSxhwW4AgP3JmXVVIiIiogaNAUt13DJ5XGigKwAgMS0XWQWGuioVERFRg8WApTpumTzOXa9GE08dACDuLGtZiIiIahoDluoobRK6lmleAHQNMjcL7U2+WlelIiIiarAYsFSHSgfovc3vr3e8DQ1iPxYiIqLawoClum55anNpDcs/F7JQWMwJ5IiIiGoSA5bquqXjbYCbA7wc1Sg2Chw8n1V35SIiImqAGLBUl9zx1hywSJJ0Y3gzO94SERHVKAYs1eVmGbAAQNj14c17k9jxloiIqCYxYKmuWyaPA250vI0/mwmjSdRFqYiIiBokBizVVdqHJS8VMOQDAFr7OkGvtkNuUQmOp+bUYeGIiIgalmoFLAsWLEBQUBA0Gg3Cw8Oxd+/eCtMeOXIE/fv3R1BQECRJwty5c8tNd/HiRfzrX/+Cu7s7HBwc0L59e+zfv786xbMNB1dA42J+n5kMAFAqJHS+3izE4c1EREQ1x+qAZdWqVYiJicHkyZMRHx+PkJAQREVFIT09vdz0BQUFaNKkCWbMmAEfH59y02RmZqJ79+6wt7fHhg0bcPToUXz22WdwdXW1tni2dcvQZgDoGmQu8z5OIEdERFRj7Kw9YM6cORg5ciSGDx8OAFi0aBF+++03fPvttxg/fnyZ9GFhYQgLCwOAcvcDwMyZMxEQEIAlS5bI24KDg60tmu25NQEuxVt0vC3tx7Iv+SqEEJAkqa5KR0RE1GBYVcNiMBgQFxeHyMjIGxkoFIiMjMSuXbuqXYhffvkFoaGheO655+Dl5YVOnTph8eLFlR5TVFSEnJwci8Xmyul42zHABfZKCWk5RbiQec32ZSIiImqArApYMjIyYDQa4e3tbbHd29sbqamp1S7EmTNnsHDhQjRv3hx//PEHXnvtNYwePRrfffddhcdMnz4dzs7O8hIQEFDt81fbLZPHAYDGXon2/s4AOLyZiIioptSLUUImkwmdO3fGJ598gk6dOuHll1/GyJEjsWjRogqPmTBhArKzs+Xl/PnzNizxda5la1gAIKz0uUJnGbAQERHVBKsCFg8PDyiVSqSlpVlsT0tLq7BDbVX4+vqiTZs2Fttat26Nc+fOVXiMWq2Gk5OTxWJzpTUs2ReAEoO8uTRgYQ0LERFRzbAqYFGpVOjSpQtiY2PlbSaTCbGxsYiIiKh2Ibp3747ExESLbSdOnEBgYGC187QJvRdgrwOECci6EVx1uT60+fTlfFzJK6qr0hERETUYVjcJxcTEYPHixfjuu+9w7NgxvPbaa8jPz5dHDQ0dOhQTJkyQ0xsMBiQkJCAhIQEGgwEXL15EQkICTp06Jad5++23sXv3bnzyySc4deoUVqxYga+//hqjRo2qgUusRZJUbsdbV50KLbz1APhcISIioppg9bDmAQMG4PLly5g0aRJSU1PRsWNHbNy4Ue6Ie+7cOSgUN+KgS5cuoVOnTvL67NmzMXv2bPTs2RPbt28HYB76vG7dOkyYMAEffvghgoODMXfuXAwePPgOL88G3IKBtMMWHW8B8/DmE2l52J98FVFtq99cRkRERIAkhGgQD73JycmBs7MzsrOzbdufZdNE4O8vgfBXgcdnypvXHbiAt1cdRMcAF/w0qrvtykNERHQXqervd70YJXRXk2e7taxhKe14e/hiNgoMJbYuFRERUYPCgOVOldOHBQD8XRzg66xBiUkg4XyW7ctFRETUgDBguVPy5HHJgMkob5YkSa5l2ZfEjrdERER3ggHLnXLyBxT2gKkYyLlosSvs+oMQOYEcERHRnWHAcqcUSsA1yPz+lmah0gchxp/NRInRZOOCERERNRwMWGqCezPz64X9FptbejvCUWOHfIMRx1Jy66BgREREDQMDlprQurf59eBK4KZR4gqFhNDrs97uS2azEBERUXUxYKkJbZ4G7LXAlZNlalnCgq93vGXAQkREVG0MWGqC2hFo/ZT5/cEVFrvkkULJmWggc/QRERHZHAOWmtLxBfProTVAcaG8uUMjZ6jsFMjIK0LylYI6KhwREdHdjQFLTQnqATgHAEXZQOJv8ma1nRIhjZwBsFmIiIiouhiw1BSFAggZaH6fYNksFCpPIMeAhYiIqDoYsNSkkEHm19NbgZwUeXPX6wHL/rOc8ZaIiKg6GLDUJPemQOMIQJiAf1bJmzsHukKSgKSMfKTnFlaSAREREZWHAUtNK+18m7BCnpPF2cEeLb0dAQBxyaxlISIishYDlprWpi9g5wBkJAIX4+XNpcOb/3fych0VjIiI6O7FgKWmaZyA1n3M7xOWy5sfb+8DAFh34CIy8w11UTIiIqK7FgOW2lDaLHR4tTwnS0QTd7T1c0JhsQn/2X22DgtHRER092HAUhuCHwCcGgGF2cCJDQAASZIwskcTAMB3u86isNhYlyUkIiK6qzBgqQ0KZblzsjzZwRe+zhpk5BXh54SLdVQ4IiKiuw8DltpSOifLqS1AbioAwF6pwIvdgwEAi/9MgsnEZwsRERFVBQOW2uLRDAgILzMny4CuAdCr7XAqPQ87TnDEEBERUVUwYKlN5czJ4qSxx6CuAQCAr/93pq5KRkREdFdhwFKb2vYD7DTA5ePApQPy5mHdg6FUSNh15goOX8yuwwISERHdHRiw1CaNM9Cqt/n9TZ1v/V0c0LuDLwBg8Z+sZSEiIrodBiy1rbRZ6NCPQEmRvLl0iPP6f1JwKetaXZSMiIjorsGApbY1eRBw9AMKs4DEDfLmdv7OiGjiDqNJYMnOpDorHhER0d2AAUttq2BOFgB4+QFzLct/955HTmGxrUtGRER012DAYgulzUKntgC5afLmni080cxLj7yiEqzae76OCkdERFT/MWCxBY/mQKMwQBiBgzdqWRQKCSN7mCeS+3ZnEoqNproqIRERUb3GgMVWOkebX/+eDxTlypuf7ugPD70KKdmF+P1QSh0VjoiIqH5jwGIrIQMBtyZAQQawe6G8WWOvRHREEADzRHJCcLp+IiKiWzFgsRWlPfDQ++b3f88DCq7Ku/51XyA09gocuZSDXWeu1FEBiYiI6q9qBSwLFixAUFAQNBoNwsPDsXfv3grTHjlyBP3790dQUBAkScLcuXMrzXvGjBmQJAljxoypTtHqt7bPAN7tgaIc4K858mZXnQrPdTFP17+Y0/UTERGVYXXAsmrVKsTExGDy5MmIj49HSEgIoqKikJ6eXm76goICNGnSBDNmzICPj0+lee/btw9fffUVOnToYG2x7g4KBdBrkvn93sVAziV514j7gyFJwLbEyziZlltBBkRERPcmqwOWOXPmYOTIkRg+fDjatGmDRYsWQavV4ttvvy03fVhYGGbNmoWBAwdCrVZXmG9eXh4GDx6MxYsXw9XV1dpi3T2aPwI0jgBKCoEdM+XNQR46PNrGGwCwcMfpuiodERFRvWRVwGIwGBAXF4fIyMgbGSgUiIyMxK5du+6oIKNGjcKTTz5pkXeDJElAr8nm9/HLgCs3gpNXezYFAKyNv4gdJy7XRemIiIjqJasCloyMDBiNRnh7e1ts9/b2RmpqarULsXLlSsTHx2P69OlVPqaoqAg5OTkWy10jMAJo/qh5XpZtH8ubOzV2xbBuQQCAd1cfRFaBoY4KSEREVL/U+Sih8+fP46233sLy5cuh0WiqfNz06dPh7OwsLwEBAbVYylrw8ETz6+E1QMo/8ub3HmuFJh46pOUUYdLPR+qocERERPWLVQGLh4cHlEol0tLSLLanpaXdtkNtReLi4pCeno7OnTvDzs4OdnZ22LFjB7788kvY2dnBaDSWe9yECROQnZ0tL+fP32VT2/t2ANr1N7/fOk3e7KBSYs6AjlAqJPxy8BJ+PXipggyIiIjuHVYFLCqVCl26dEFsbKy8zWQyITY2FhEREdUqQK9evXDo0CEkJCTIS2hoKAYPHoyEhAQolcpyj1Or1XBycrJY7joPvQ9ISuDkJuDs3/LmjgEuGPVQMwDABz8dRlpOYV2VkIiIqF6wukkoJiYGixcvxnfffYdjx47htddeQ35+PoYPHw4AGDp0KCZMmCCnNxgMciBiMBhw8eJFJCQk4NSpUwAAR0dHtGvXzmLR6XRwd3dHu3btaugy6yn3pkDnIeb3W6YCN81y++bDzdDO3wnZ14rx7up/OAMuERHd06wOWAYMGIDZs2dj0qRJ6NixIxISErBx40a5I+65c+eQknLjmTiXLl1Cp06d0KlTJ6SkpGD27Nno1KkTXnrppZq7irtZz/cAOw1wfre5puU6e6UCnz/fESo7BXacuIzle87VYSGJiIjqliQayH/dc3Jy4OzsjOzs7LuveWjTB+bp+r3bA6/8zzzB3HX/91cSpq0/Cgd7JTa81QNBHro6LCgREVHNqurvd52PEiIA98cAaicg7RBwZK3FruHdgnBfEzdcKzYi5ocEGE0NIr4kIiKyCgOW+kDrBnR70/x+60eAsVjepVBImP1cCBzVdog/l4VFnAWXiIjuQQxY6ov7Xge0HkBmEhD/vcWuRq5aTH6qLQBg7pYTOHIpuy5KSEREVGcYsNQXaj3wwDjz+82TgNRDFrv7d/bHo228UWwUiFl1EEUl5c9PQ0RE1BAxYKlPwkYAQT0AQx6wYgCQc2O0lSRJ+OSZ9vDQq5CYlos5m07UYUGJiIhsiwFLfaK0BwYsAzxaADkXgRXPA0V58m4PvRqf9GsPAPjqf2ew+WhaRTkRERE1KAxY6hsHV+CFH8z9WVL/Ada8BJhuNP882tYH0RGBAIC3VyXgVHpeRTkRERE1GAxY6iO3YGDQSvOEcic2AH/8P4vdH/Rug65BbsgrKsEry/Yjt7C4goyIiIgaBgYs9VVAGNDvK/P7PYuA3YvkXfZKBRYM7gwfJw1OX85HzA8HYeL8LERE1IAxYKnP2vYFIqea3/8xAUjcIO/ydFRj0ZAuUNkpsPloGuZtPVU3ZSQiIrIBBiz1Xfe3gM7RgDABq18ELiXIuzoGuOCjvuYHRH6+5QRij7ETLhERNUwMWOo7SQKe/Axo8hBQXGAe7px9Qd79fGgAhtxn7oQ7ZmUCTl9mJ1wiImp4GLDcDZT2wPPfAZ6tgbxUc9BSlCvvnti7DcKCXJFbVIKXv2cnXCIiangYsNwtNM7A4B8AvTeQdhj4cThgLAEAqOwsO+G+w064RETUwDBguZu4NL4+3NkBOLXZPIX/dV6OGiz8V2eolApsOpqGBdvYCZeIiBoOBix3G//OQL/rQ5x3L7B4UGKnxq6Y1tf8kMQ5W05g63F2wiUiooaBAcvdqG1f4MHrk8mtjwHO/i3vGhDWGP+6rzGEAN5amYDE1Nzy8yAiIrqLMGC5W/V8F2jbDzAVA6v+BWSelXdN6t3W3Am3sARDv92DC5kFdVhQIiKiO8eA5W4lScDT/wZ8Q4CCK8B/B8kjh1R2CiweGooW3nqk5RRh6P/txZW8ojouMBERUfUxYLmbqbTAwP+aRw6lHwHWvgyYTAAAF60K378YDn8XB5zJyMfwpfuQV1RSxwUmIiKqHgYsdztnf2DgCkCpBhJ/B7ZOk3f5OGvw/YiucNOp8M+FbLy6LA6GElMdFpaIiKh6GLA0BI1Cgafnm9//NQc4uEre1dRTjyXDwqBVKfHXqQzE/JDAOVqIiOiuw4CloejwPHB/jPn9L28C5/fJu0ICXPDVkC6wV0pY/08Kpv56BEIwaCEiorsHA5aG5OGJQMsnAWMRsPIFi2cO9Wjuic+e7whJAr7bdRbz+XRnIiK6izBgaUgUCuCZrwHvdkB+unnk0LVMefdTIX6Y3LsNAOCzzSewfM/ZinIiIiKqVxiwNDRqPTDov4DWA0j9B1jyBJB9Ud49rHsw3ny4GQBg4k+HseFQSl2VlIiIqMoYsDRELo2B6F8AR18g/Sjwf48ClxPl3TGPtMCgro1huj4b7s8JFyvJjIiIqO4xYGmovNsCIzYB7s2BnAvmoOXcHgCAJEn4qG87PN7OBwajCW+tTMCEtf+gsNhYx4UmIiIqHwOWhsylsTloaRQGFGYB3z8NJG4AACgVEua/0BmjezWHJAH/3XsefRfsxOnLeXVbZiIionIwYGnotG7A0J+B5lFAyTXz6KG47wCYg5aYR1rg+xe7wl2nwvHUXDw17y82ERERUb3DgOVeoNIBA5cDHf8FCBPw62hgxyzg+lwsPZp74ve3euC+Jm7INxjZRERERPUOA5Z7hdLePBtuj7Hm9W0fAb+9A5jMQYm3kwbLX7qPTURERFQvMWC5l0gS0Gsi8PgsABKw//+AH6OBwmwAbCIiIqL6iwHLvSj8ZeC5JYBSBRz7FfiiI7BrAVBSBKD8JqLXl8fh/NWCui03ERHds6oVsCxYsABBQUHQaDQIDw/H3r17K0x75MgR9O/fH0FBQZAkCXPnzi2TZvr06QgLC4OjoyO8vLzQt29fJCYmls2Mak7bfubOuB4tgGtXgT/+HzAv1PzgRJPpRhPRw82gkIDfD6Wi15wd+HTjceQVldR16YmI6B5jdcCyatUqxMTEYPLkyYiPj0dISAiioqKQnp5ebvqCggI0adIEM2bMgI+PT7lpduzYgVGjRmH37t3YvHkziouL8eijjyI/P9/a4pE1ArsBr+0C+nxpnmQu+xyw7mXgqweAk1uglICYR1vit9E9ENHEHYYSE/69/TQemr0dP+4/z6c+ExGRzUjCysf2hoeHIywsDPPnzwcAmEwmBAQE4M0338T48eMrPTYoKAhjxozBmDFjKk13+fJleHl5YceOHXjggQeqVK6cnBw4OzsjOzsbTk5OVTqGbmIoAPYsAv6aCxSZ+7QgqAfwyFTAvwuEENh8NA0f/34MZ6+Ym4ba+ztjUp82CAtyq7tyExHRXa2qv99W1bAYDAbExcUhMjLyRgYKBSIjI7Fr167ql/YW2dnmH0w3t4p/CIuKipCTk2Ox0B1QaYEeMcBbCUDEG+b+Lcl/AosfBn6IhpSZjEfb+mDT2w9gwuOtoFfb4dDFbDy3aBdGrYjHhUz2byEiotpjVcCSkZEBo9EIb29vi+3e3t5ITU2tkQKZTCaMGTMG3bt3R7t27SpMN336dDg7O8tLQEBAjZz/nqd1A6I+Bt6MA0IGAZCAoz8BC8KBbdOhFga80rMpto19EIO6NoYkAb/9k4Jen+3AR+uP4mLWtbq+AiIiaoDq3SihUaNG4fDhw1i5cmWl6SZMmIDs7Gx5OX/+vI1KeI9waQz0WwS8+hcQ3BMwFgE7ZpgDl+O/w1OvwvRn2uO3N82jiYpKTPjmryQ88Ok2jP7vAfxzIauur4CIiBoQqwIWDw8PKJVKpKWlWWxPS0ursEOtNd544w2sX78e27ZtQ6NGjSpNq1ar4eTkZLFQLfBpZx5N9NxSwNEPyDoLrBwErHgeuHoGbfyc8N+R92HJ8DB0a+oOo0ngl4OX8NT8nXj+q13YfDSNnXOJiOiOWRWwqFQqdOnSBbGxsfI2k8mE2NhYREREVLsQQgi88cYbWLduHbZu3Yrg4OBq50W1QJLMw6Df2Ad0HwMo7IGTm4AF9wFbP4ZUfA0PtfTCipH3Yf2b96NfJ3/YKSTsTbqKkd/vR+ScHfjP7rO4ZuBU/0REVD1WjxJatWoVoqOj8dVXX6Fr166YO3cufvjhBxw/fhze3t4YOnQo/P39MX36dADmjrpHjx4FADzxxBMYPHgwBg8eDL1ej2bNmgEAXn/9daxYsQI///wzWrZsKZ/L2dkZDg4OVSoXRwnZUMZJ4PdxwJlt5nXnxsBj04FWT5qDGwAp2dew9O9krNhzDrmF5nlbXLX2GNi1MZ7r0ghNPPV1VXoiIqpHqvr7bXXAAgDz58/HrFmzkJqaio4dO+LLL79EeHg4AODBBx9EUFAQli5dCgBITk4ut8akZ8+e2L59u7kQ13/kbrVkyRIMGzasSmViwGJjQgDHfgE2/j8g54J5m3NjwLst4N0G8DIveY7B+CE+Fd/uTMKFzBsdcjs3dkH/Lo3Qu4MfnB3s6+giiIiortVqwFIfMWCpI4Z84M/PgL/nAUZD2f0Ke8CjBUxerXESjbEyowm+O+uG0m4tKjsFHm3jjf5dGqFHMw/YKetdP3AiIqpFDFjItq5lAWmHgbSjQPqR66/HAENumaRFwZFY7zECX5/QIzHtxn5PRzWe6eSPfp390dLbscKaNyIiajgYsFDdEwLIOgekHzUvlw4Ax38HhLnzrWj7DE62eRMrTqvxc8JFZBYUy4f6uzjg4VZeeLi1FyKauENjr6yrqyAiolrEgIXqp4xTwPZPgMNrzOuSAuj4Agzdx2FbmgZr4i5g+4nLMJSY5EM09gp0b+qBh1t74aGWXvBzqVpHbCIiqv8YsFD9lnoI2PoxcGKDeV2pAroMB3q8g2tqD/x9OgOxx9Ox7Xg6UrILLQ5t5eOIh1p5ITzYDZ0DXeGkYaddIqK7FQMWujuc3wds/RBI+p953V5rDlyaPwIEhEPYO+B4ai62Hk/H1uPpOHAuEzfPQydJQCsfJ4QFuSI0yA1hQa7wdWYNDBHR3YIBC91dzmwHYqcBF/ff2KZUAf6hQHAP85OjG4XhqkGBHSfS8efJDMSdzZSfHH0zfxcHhN4UwLTwcoRCwQ68RET1EQMWuvsIAZz4AziyFkj6E8i9ZLlfqQYCupqDl+AegH8XpBcI7D+biX3JV7E/ORNHU3JgvOVRAM4O9ggNdEVYsBvCgtzQ3t8ZKjsOnyYiqg8YsNDdTQjg6hkg+U8g+S9zAJN3yxPBlWqgURgQ2M28BHRFvlAj4XwW9iVfxb7kqzhwLgsFtzwSQGOvQMcAF3QNckNYsBs6+LvAWct+MEREdYEBCzUsQgBXTpkDmKTrQUx+umUahR3g1+l6ANMdCAhHscoJRy/lYF/yVexNuor9ZzNxNb/sBHe+zhq08nFESx8ntPZ1REsfRzTx0LMmhoioljFgoYZNCODKaeDsTuDs3+bX7PO3JJLMjwhoFGpuSmoUBuHeDKczCrA36Xoz0tmrOH/1WrmnsFdKaOqpR0sfR7S6Hsi09nWCl6Oak9oREdUQBix078k6dyN4Sd4JXD1dNo3G2dyRt1EYEBAG+HdBjqTHidRcHE/NxfHUHCSm5uJ4Si5yi0rKPY2bTmUOXnyc0MrXHMg089JDbcfJ7YiIrMWAhSg3Dbiw78ZyMR4oKac2ReUIqLSAvYN5WLW9A4S9A4okDbJL7HHVoER6kT3OFahwtsAemSY9sqFDttDJr/kKPfw8XNHUyxFNPHVo6qlHE089mnjqOE8MEVElGLAQ3cpYDKQduRHAnN8LZCbVWPZXhCO2GLtggykMf5vawQBzoOLpqEZTTx2aeOrR1FOPFt56tPR2hCebloiIGLAQVUnBVeBaJlBcABRfu/FqyL++fg0ozgeKcs0PeCzMsny9lglRmA1JWI5EKpC02IFQ/FTUBTtMHVAIdZlTu2jt0cLbES29HdHCW29+7+MIF63KBhdORFQ/MGAhshUhzAFNSgJw9Bfg2K8WQ7CNdg5I9bwfB/QPYEtJR/yTYUJyRj5MFfzL83RUo4mHDkHuOgR56BDkrkWQhw6B7lpoVXa2uSYiIhthwEJUV0wmc5PTsV/MAUz2uVsSSBAKOwhJCaOkRIlQokRIKDIpYBASioUdzgtPnBZ+OCX8za8mf6TDBd5OGgS6m4OYYA9zH5kmHjo0dtey0y8R3ZUYsBDVB0LcVPPyi3kumWrKEQ44I/xwWvjhtMkPB0UTxJlaoBBqKCQgwE2LYA8dmnjoEeypQ1MPcw2Nj5OGjyYgonqLAQtRfSOEub+MsRgwlZgXYQRMxhvrphKguNA8y2/GiRvL1SRz2lsUww7/iGb4y9gau01tEG9qjiJY9oFR2SnQ2E2LIHctGrvpEOShRWM3LQLddWjk6gB7JSfHI6K6w4CFqCEpKTIHLRmJ5gAm/ThwbheQc9EimVGyxwVdWxxUtse2ohbYmuOPbJOmwmyVCgl+LhoEuukQ4KZFoLsWgW5a+b0jh2QTUS1jwELU0AlhHpZd+qyl5D+B3JQyyUz2ehQ6eCHHzh2XJXdcLHHGmSInJObrcL7EBZfhjHzhgDw4yEOxS7npVAhwM9fI+Dlr4OOsga+zBj7ODvB11sBDr4aSzU1EdAcYsBDda259YGTyX+UGMJUpgR0KJAfkCQ1yTBrkQ4M84YBs6HBOeCHJ5Isk4YMzwhdZcISdQoK3kzmQ8XHWwMfp+nLTureThs9kIqIKMWAhIqAoD8hNNQcupUtOCpB7ybw9JwUoyDDPP2OlLKFDkjAHMEkmHyQLH1yBE3KEFjnQIUdokQstjFDCXae6EcA4a+DlqIa3k+WrO2triO5JDFiIqOqMJYAhz7wUlb7m3ngtuGquvblyyvzQyZwLVc46X6jlACYHWuQJB+TDAbnCAfnQyO8LoIFC4wh7rTOEkz/g2RLeLo7wc9HA93oTlI+zhp2EiRqYqv5+cxYqIgKUdoCDi3mpCkOBuf/MlVM3gpirSeZRUIXZ5qU4HwCgk4qgQxF8pau3z9cIINe8FFxQ45AIxgFTM2wwNUOCqSnSJHd46tXwdXEot0+Nj5MGXk5qzklD1ACxhoWIaoex2Fw7U5h1I4gpzDZvKyqtwTG/NxXlwlCQjZKCXJgKc6DJSYLKmF8my1ThioTrwUuiCEAhVCgS9jDADgaYX4uECnqdFq5Oerg6O8HVUQ8PvRoeehXc9Wp46NXwdFTBQ6+Gs4M9n+dEVMfYJEREdy+TyTx8++J+4MJ+4OJ+iLSjZZ7ZVBUZwgkpwg2XhAcuCXeL95cVHjBpveGo08BVq4Kr1g6uWnu4apVwc7CHi4MdXB2UcHGwg7uLK7ycNdDYs/aGqCYxYCGihsWQD6QclAMYZCYDJQagpBAwGoCSIgijeV0yGqqcrUlIMEGCAgIKqeI/h3lCg1PCD+cVAUjXBCFH3wRFLs1h7xEML2ctvJ00cNer4Kixh15tB0eVBF3RZShyLgDZ54Gsc+ZXYzHgGwL4hwI+7QC7sg/GJLqXMGAhonuXEOYgxpBvnlwv+4J5kd9fhMg+D+SmQDKV3NGpioQ9zghfnBJ+MMAe/lIG/JEBH+kq7KXKa4SMkj0ynVoh37MjSnw7wy6gC/S+LeGi44gpuncwYCEiuh2TEcjPAIQJkBTmRaEEJOnGumQelSSyzqPg0jEUXDwCkX4c9pkn4ZiXBDtTUYXZFwslUoQbLgpPXIQHLgoPCAAdpDPoqDgFNymvzDGZQo/jpsZQKCRoFEZopBKopRKoJCNUKIYKxbATxbCDESV2WpSoXSE0rpC0rrDTu0Pl6A47nTvg4GpenP0Bt6ZV71BNZGMMWIiIapvJaG7quZwIXD5ufhaUSyCEcyMU6f2Ra+eBXIMJuYUlyC0sQV5RMbIKipFZUIzM/CIg6yzcM/+BX/4RNCk6jmamM1CjuFaKmqt0wRV1I2Q7NEaurjEK9EEwOAeh2KUJHHTOcNGa++y4aJRwUQMapbj+fCujuRlLoQQc3MwjyohqEAMWIqK7TYkBJSmHcC3lGK6VANeMdigwKpFnUiK/RIG8EgVyixXIKVEg1yBQlJ8DUXAV0rVMKIuyoDJkwRl5cJHy4II8uEm5aCRdhqeUXelpC4U97GCEnWSqNJ0JEq7ZOaNQ5QaD2h1GrQeEzhNKvRfsnLygcXSHAwphX5IPFOVcHxF2y2LIB5wbAd5tAa825sWtCQOhexgDFiKie4wQAjmFJcjMN+BqgQGZ+QbkFZXAkJ8Nu6wkOOQmQ5t3Fk4FZ+Fy7TzcDRfgaKw8mAGAEqG4bTBzJ0wKFQpdmqPYoxXg1QZ2vu2g8QyG0tEL0LiYm+iowWLAQkREt3ct0zwvjtIeUNhBSErklwBZRSZkFQpkF5qQdc2I7PwCFOVmwJibDuRdhqIgA3aFGdAUXYFDcSYcSzKhFfkoEBrkXZ+9OA8OyLv+mnv9vQH2CJTS0FI6jxaK82ghXYRWqrgfUAmUyJKckat0RYG9q0XtjqRxgp2dHezt7GBvb29e7OyhUtlBZa+CWmUPewc91K4BsHMNAFTaqt8XYzGQeRa4chLIOAlknQVUesDJz7w4+gFOvoDOi7VDd6hWZ7pdsGABZs2ahdTUVISEhGDevHno2rVruWmPHDmCSZMmIS4uDmfPnsXnn3+OMWPG3FGeRERUQ0o7514nAdBfXxqVSdy00qyMJoG8whLkFBZf77dTjJzrr7k3veYVlWBnUQn+KCxBXqEB+oIL8C48g4DiJAQaz6I5zsNbyoSTVAA7GOEhrsKj5CpQAuBa9S/1KhyRLnkiQ+mJK3Y+yLL3Qq7aB0a1C3xFGvxKLsDbcB4eRefgXHgRCnH7EWRCUgB6b0iOvuZAxsHVHNio9YBKZ35vse54/fWmxU5TO7VIxmIgL908dF7jbA5K72JWByyrVq1CTEwMFi1ahPDwcMydOxdRUVFITEyEl5dXmfQFBQVo0qQJnnvuObz99ts1kicREdU/SoUEZ609nLV39sNYVGJEbmEJknJzkZ+ZhqKcdJRkp8GYlw7kZ0BxLQOqaxlQlhTAZDICphIIk9HcQdhkBIT5VRJG6HANftIV6KVCuCEXbiIXKDljDn4KYX4URAUKhFp+OvlZ4Q0dCuEjXYWPlAlv6Sq8kAU7mG48WPRSfLWuV0ABo50DjHY6mOwcYLTXQah0MGrcIDQu5iBI6wZJ6w6Fzg1KnTvs9O6w0+ihzE+/acj+RfNzvnIumd/npQG4qRFFpTcHLhoX86gxjYt53cEF0LqZa4v0XtdfPc2v9ppqXVNtsLpJKDw8HGFhYZg/fz4AwGQyISAgAG+++SbGjx9f6bFBQUEYM2ZMmRqWO8mzFJuEiIjoZkIIFJWYcK2oBNfyMlFy9SyMWReArHNQ5FyEXd5FqPMvwb4oE9lqX2SoGyPVPgCX7BrhnOSPFJML8osF8g1GFBSVoLDEiKJiEwqLjSgsMaGkpATuyIaPlAkf6aq5Vgj50EuF0KLwxiuuQSsVQYdr0KMQWqkQWhTBQar6BIfVZYQSSlg/Q7R8vL0jSrQeEFpzAGPfZzaUzr41WMJaahIyGAyIi4vDhAkT5G0KhQKRkZHYtWtXtQpa3TyLiopQVHSj3TMnJ6da5yciooZJkiRo7JXQ2CvhqvcBfHwAhJeb1hlAYyvzN5kEDMbrAcz1QCbfUCIPY88tLEZGUQmSLJrJSlBYbESJ0QSjsQTKkkLYGQtgZ7wG++uvatM1qIz50BtzoDPlwNGUA0eRCyeRCxfkwgV5cJXyoEUhLsMFKcINKcL9+qMn3OX1FOGOK3CEAgKOKICzlA9n5MNZyofTLetuyIGHlH1jQTZUkhHK4lwos3OB7CQAQFrBDHg739HHUm1WBSwZGRkwGo3w9va22O7t7Y3jx49XqwDVzXP69OmYOnVqtc5JRER0p8yT+ylt+nwpk0mg2GSCocSE3BIThMEIJ4MRdoYSuBuMaGowosBQggKDEflFJbhmMKLYJGAyCZSYBIwmE4wmmF+FQJZJIMMocMxowjWDueao0GBEYXEJlIYcaIuvQFecCaeSq3A0ZmK0o6fNrvVWd23X5gkTJiAmJkZez8nJQUBAQB2WiIiIqHYpFBLUCiXUdko41nVhbMyqgMXDwwNKpRJpaWkW29PS0uDj41OtAlQ3T7VaDbWaDw0jIiK6FyisSaxSqdClSxfExsbK20wmE2JjYxEREVGtAtRGnkRERNSwWN0kFBMTg+joaISGhqJr166YO3cu8vPzMXz4cADA0KFD4e/vj+nTpwMwd6o9evSo/P7ixYtISEiAXq9Hs2bNqpQnERER3dusDlgGDBiAy5cvY9KkSUhNTUXHjh2xceNGudPsuXPnoFDcqLi5dOkSOnXqJK/Pnj0bs2fPRs+ePbF9+/Yq5UlERET3Nk7NT0RERHWmqr/fVvVhISIiIqoLDFiIiIio3mPAQkRERPUeAxYiIiKq9xiwEBERUb3HgIWIiIjqPQYsREREVO8xYCEiIqJ6jwELERER1XtWT81fX5VO2JuTk1PHJSEiIqKqKv3dvt3E+w0mYMnNzQUABAQE1HFJiIiIyFq5ublwdnaucH+DeZaQyWTCpUuX4OjoCEmSaizfnJwcBAQE4Pz583xGkQ3wftsW77dt8X7bFu+3bVX3fgshkJubCz8/P4uHJ9+qwdSwKBQKNGrUqNbyd3Jy4hfehni/bYv327Z4v22L99u2qnO/K6tZKcVOt0RERFTvMWAhIiKieo8By22o1WpMnjwZarW6rotyT+D9ti3eb9vi/bYt3m/bqu373WA63RIREVHDxRoWIiIiqvcYsBAREVG9x4CFiIiI6j0GLERERFTvMWC5jQULFiAoKAgajQbh4eHYu3dvXRepQfjf//6HPn36wM/PD5Ik4aeffrLYL4TApEmT4OvrCwcHB0RGRuLkyZN1U9i73PTp0xEWFgZHR0d4eXmhb9++SExMtEhTWFiIUaNGwd3dHXq9Hv3790daWlodlfjut3DhQnTo0EGeQCsiIgIbNmyQ9/N+154ZM2ZAkiSMGTNG3sb7XbOmTJkCSZIsllatWsn7a+t+M2CpxKpVqxATE4PJkycjPj4eISEhiIqKQnp6el0X7a6Xn5+PkJAQLFiwoNz9n376Kb788kssWrQIe/bsgU6nQ1RUFAoLC21c0rvfjh07MGrUKOzevRubN29GcXExHn30UeTn58tp3n77bfz666/48ccfsWPHDly6dAnPPPNMHZb67taoUSPMmDEDcXFx2L9/Px5++GE8/fTTOHLkCADe79qyb98+fPXVV+jQoYPFdt7vmte2bVukpKTIy19//SXvq7X7LahCXbt2FaNGjZLXjUaj8PPzE9OnT6/DUjU8AMS6devkdZPJJHx8fMSsWbPkbVlZWUKtVov//ve/dVDChiU9PV0AEDt27BBCmO+tvb29+PHHH+U0x44dEwDErl276qqYDY6rq6v45ptveL9rSW5urmjevLnYvHmz6Nmzp3jrrbeEEPx+14bJkyeLkJCQcvfV5v1mDUsFDAYD4uLiEBkZKW9TKBSIjIzErl276rBkDV9SUhJSU1Mt7r2zszPCw8N572tAdnY2AMDNzQ0AEBcXh+LiYov73apVKzRu3Jj3uwYYjUasXLkS+fn5iIiI4P2uJaNGjcKTTz5pcV8Bfr9ry8mTJ+Hn54cmTZpg8ODBOHfuHIDavd8N5uGHNS0jIwNGoxHe3t4W2729vXH8+PE6KtW9ITU1FQDKvfel+6h6TCYTxowZg+7du6Ndu3YAzPdbpVLBxcXFIi3v9505dOgQIiIiUFhYCL1ej3Xr1qFNmzZISEjg/a5hK1euRHx8PPbt21dmH7/fNS88PBxLly5Fy5YtkZKSgqlTp6JHjx44fPhwrd5vBixE95BRo0bh8OHDFu3NVDtatmyJhIQEZGdnY/Xq1YiOjsaOHTvqulgNzvnz5/HWW29h8+bN0Gg0dV2ce8Ljjz8uv+/QoQPCw8MRGBiIH374AQ4ODrV2XjYJVcDDwwNKpbJMz+a0tDT4+PjUUanuDaX3l/e+Zr3xxhtYv349tm3bhkaNGsnbfXx8YDAYkJWVZZGe9/vOqFQqNGvWDF26dMH06dMREhKCL774gve7hsXFxSE9PR2dO3eGnZ0d7OzssGPHDnz55Zews7ODt7c373ctc3FxQYsWLXDq1Kla/X4zYKmASqVCly5dEBsbK28zmUyIjY1FREREHZas4QsODoaPj4/Fvc/JycGePXt476tBCIE33ngD69atw9atWxEcHGyxv0uXLrC3t7e434mJiTh37hzvdw0ymUwoKiri/a5hvXr1wqFDh5CQkCAvoaGhGDx4sPye97t25eXl4fTp0/D19a3d7/cdddlt4FauXCnUarVYunSpOHr0qHj55ZeFi4uLSE1Nreui3fVyc3PFgQMHxIEDBwQAMWfOHHHgwAFx9uxZIYQQM2bMEC4uLuLnn38W//zzj3j66adFcHCwuHbtWh2X/O7z2muvCWdnZ7F9+3aRkpIiLwUFBXKaV199VTRu3Fhs3bpV7N+/X0RERIiIiIg6LPXdbfz48WLHjh0iKSlJ/PPPP2L8+PFCkiSxadMmIQTvd227eZSQELzfNe2dd94R27dvF0lJSWLnzp0iMjJSeHh4iPT0dCFE7d1vBiy3MW/ePNG4cWOhUqlE165dxe7du+u6SA3Ctm3bBIAyS3R0tBDCPLR54sSJwtvbW6jVatGrVy+RmJhYt4W+S5V3nwGIJUuWyGmuXbsmXn/9deHq6iq0Wq3o16+fSElJqbtC3+VefPFFERgYKFQqlfD09BS9evWSgxUheL9r260BC+93zRowYIDw9fUVKpVK+Pv7iwEDBohTp07J+2vrfktCCHFndTREREREtYt9WIiIiKjeY8BCRERE9R4DFiIiIqr3GLAQERFRvceAhYiIiOo9BixERERU7zFgISIionqPAQvRXSo5ORmSJCEhIaGui0JEVOsYsBDVQ8OGDYMkSfLi7u6Oxx57DP/884+cJiAgACkpKWjXrl0dlvTesnTpUri4uNR1MYjuSQxYiOqpxx57DCkpKUhJSUFsbCzs7OzQu3dveb9SqYSPjw/s7OxqtRwGg6FW87f1eWyhIV0LUX3BgIWonlKr1fDx8YGPjw86duyI8ePH4/z587h8+TKAsk1C27dvhyRJiI2NRWhoKLRaLbp164bExEQ5z9OnT+Ppp5+Gt7c39Ho9wsLCsGXLFovzBgUFYdq0aRg6dCicnJzw8ssv4+GHH8Ybb7xhke7y5ctQqVQWT2W91a+//oqwsDBoNBp4eHigX79+lZ4HANasWYO2bdtCrVYjKCgIn332mUWe//73v9G8eXNoNBp4e3vj2WeflfetXr0a7du3h4ODA9zd3REZGYn8/Hx5/zfffIPWrVtDo9GgVatW+Pe//y3vK72fa9euxUMPPQStVouQkBDs2rVLvr/Dhw9Hdna2XPM1ZcqUO7qW0uMGDRoEnU4Hf39/LFiwQN7/4osvWgSpAFBcXAwvLy/83//9X4X3nahBuuOnERFRjYuOjhZPP/20vJ6bmyteeeUV0axZM2E0GoUQQiQlJQkA4sCBA0KIGw+UDA8PF9u3bxdHjhwRPXr0EN26dZPzSUhIEIsWLRKHDh0SJ06cEB988IHQaDTyU7KFECIwMFA4OTmJ2bNni1OnTolTp06J5cuXC1dXV1FYWCinmzNnjggKChImk6nca1i/fr1QKpVi0qRJ4ujRoyIhIUF88sknlZ5n//79QqFQiA8//FAkJiaKJUuWCAcHB/lBjfv27RNKpVKsWLFCJCcni/j4ePHFF18IIYS4dOmSsLOzE3PmzJGfkrxgwQKRm5srhBDiP//5j/D19RVr1qwRZ86cEWvWrBFubm5i6dKlFvezVatWYv369SIxMVE8++yzIjAwUBQXF4uioiIxd+5c4eTkJD/xujTv6lxL6XGOjo5i+vTpIjExUXz55ZdCqVTKD0rcuXOnUCqV4tKlS/Ixa9euFTqdTj430b2CAQtRPRQdHS2USqXQ6XRCp9MJAMLX11fExcXJaSoKWLZs2SKn+e233wQAce3atQrP1bZtWzFv3jx5PTAwUPTt29cizbVr14Srq6tYtWqVvK1Dhw5iypQpFeYbEREhBg8eXOH+8s7zwgsviEceecRi27hx40SbNm2EEEKsWbNGODk5iZycnDL5xcXFCQAiOTm53PM1bdpUrFixwmLbtGnT5Mfel97Pb775Rt5/5MgRAUAcO3ZMCCHEkiVLhLOzc41cS+lxjz32mEWaAQMGiMcff1xeb9OmjZg5c6a83qdPHzFs2LByr5GoIWOTEFE99dBDDyEhIQEJCQnYu3cvoqKi8Pjjj+Ps2bOVHtehQwf5va+vLwAgPT0dAJCXl4exY8eidevWcHFxgV6vx7Fjx3Du3DmLPEJDQy3WNRoNhgwZgm+//RYAEB8fj8OHD2PYsGEVliMhIQG9evWqtKy3nufYsWPo3r27xbbu3bvj5MmTMBqNeOSRRxAYGIgmTZpgyJAhWL58OQoKCgAAISEh6NWrF9q3b4/nnnsOixcvRmZmJgAgPz8fp0+fxogRI6DX6+Xlo48+wunTp6t8/2ryWkpFRERYpImIiMCxY8fk9ZdeeglLliwBAKSlpWHDhg148cUXb1seooaGAQtRPaXT6dCsWTM0a9YMYWFh+Oabb5Cfn4/FixdXepy9vb38XpIkAIDJZAIAjB07FuvWrcMnn3yCP//8EwkJCWjfvn2ZTqI6na5Mvi+99BI2b96MCxcuYMmSJXj44YcRGBhYYTkcHByqdI3WcHR0RHx8PP773//C19cXkyZNQkhICLKysqBUKrF582Zs2LABbdq0wbx589CyZUskJSUhLy8PALB48WI5CExISMDhw4exe/dui3NUdv9q8lqqaujQoThz5gx27dqF//znPwgODkaPHj1q5VxE9RkDFqK7hCRJUCgUuHbtWrXz2LlzJ4YNG4Z+/fqhffv28PHxQXJycpWObd++PUJDQ7F48WKsWLHitv/L79ChQ6UdcsvTunVr7Ny5s0yZW7RoAaVSCQCws7NDZGQkPv30U/zzzz9ITk7G1q1bAZjvUffu3TF16lQcOHAAKpUK69atg7e3N/z8/HDmzBk5CCxdgoODq1w+lUplUTtyp9cCoEzAtHv3brRu3Vped3d3R9++fbFkyRIsXboUw4cPr3J5iRqS2h0PSUTVVlRUhNTUVABAZmYm5s+fj7y8PPTp06faeTZv3hxr165Fnz59IEkSJk6cWKXag1IvvfQS3njjDeh0OosRP+WZPHkyevXqhaZNm2LgwIEoKSnB77//jvfee6/CY9555x2EhYVh2rRpGDBgAHbt2oX58+fLo3nWr1+PM2fO4IEHHoCrqyt+//13mEwmtGzZEnv27EFsbCweffRReHl5Yc+ePbh8+bL84z916lSMHj0azs7OeOyxx1BUVIT9+/cjMzMTMTExVbr+oKAg5OXlITY2FiEhIdBqtdBqtdW6llI7d+7Ep59+ir59+2Lz5s348ccf8dtvv1mkeemll9C7d28YjUZER0dXqaxEDU5dd6IhorKio6MFAHlxdHQUYWFhYvXq1XKaijrdZmZmymkOHDggAIikpCT5mIceekg4ODiIgIAAMX/+fNGzZ0/x1ltvyccEBgaKzz//vNxy5ebmCq1WK15//fUqXceaNWtEx44dhUqlEh4eHuKZZ5657XlWr14t2rRpI+zt7UXjxo3FrFmz5H1//vmn6Nmzp3B1dRUODg6iQ4cOckfgo0ePiqioKOHp6SnUarVo0aKFRWdiIYRYvny5XB5XV1fxwAMPiLVr15Z7P4UQIjMzUwAQ27Ztk7e9+uqrwt3dXQAQkydPrva1lB43depU8dxzzwmtVit8fHzkUU83M5lMIjAwUDzxxBPl3meie4EkhBB1Fi0R0V0lOTkZTZs2xb59+9C5c+e6Ls5dLygoCGPGjMGYMWMqTZeXlwd/f38sWbIEzzzzjG0KR1TPsEmIiG6ruLgYV65cwQcffID77ruPwYqNmEwmZGRk4LPPPoOLiwueeuqpui4SUZ1hwEJEt7Vz50489NBDaNGiBVavXl3XxblnnDt3DsHBwWjUqBGWLl1a649hIKrP2CRERERE9R6HNRMREVG9x4CFiIiI6j0GLERERFTvMWAhIiKieo8BCxEREdV7DFiIiIio3mPAQkRERPUeAxYiIiKq9xiwEBERUb33/wH1ZZ04H0w5hgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title **Código:** Curvas de convergencia\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.legend(['Función de pérdida: entrenamiento','función de pérdida: validación'])\n",
        "plt.xlabel('Épocas')\n",
        "plt.xlabel('Binary crossentropy')\n",
        "plt.title('Entrenamiento autoencoder')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "PKtb9dm10Wvy",
        "outputId": "8815d05f-fc95-4ee9-a24f-6f9e7e1e8c2a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 3ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x200 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x200 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 2000x200 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title **Código:** Resultados obtenidos de los datos reconstruidos.\n",
        "#@markdown Imprimimos los primeros 10 valores de los datos orignales, los datos con ruido, y los datos reconstruidos.\n",
        "\n",
        "decoded_imgs = autoencoder.predict(x_test_noisy)\n",
        "\n",
        "n = 10\n",
        "plt.figure(figsize=(20, 2))\n",
        "for i in range(1, n + 1):\n",
        "    ax = plt.subplot(1, n, i)\n",
        "    plt.imshow(x_test[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(20, 2))\n",
        "for i in range(1, n + 1):\n",
        "    ax = plt.subplot(1, n, i)\n",
        "    plt.imshow(x_test_noisy[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(20, 2))\n",
        "for i in range(1, n + 1):\n",
        "    ax = plt.subplot(1, n, i)\n",
        "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "q6focyGrC8fQ",
        "outputId": "280ae6fe-99c6-43c4-e938-4b56a2301717"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Métricas de evaluación de la calidad de la reconstrucción\n",
            "MSE = 0.02478\n",
            "PSNR = 16.05866\n",
            "SNR = 4.96568\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title **Código:** Función para las métricas.\n",
        "def mse(vref, vcmp):\n",
        "    if np.iscomplexobj(vref) or np.iscomplexobj(vcmp):\n",
        "        dtype = np.complex128\n",
        "    else:\n",
        "        dtype = np.float64\n",
        "    r = np.asarray(vref, dtype=dtype).ravel()\n",
        "    c = np.asarray(vcmp, dtype=dtype).ravel()\n",
        "    return np.mean(np.abs(r - c)**2)\n",
        "\n",
        "def psnr(vref, vcmp, rng=None):\n",
        "    if rng is None:\n",
        "        rng = np.abs(vref.max() - vref.min())\n",
        "    dv = (rng + 0.0)**2\n",
        "    with np.errstate(divide='ignore'):\n",
        "        rt = dv / mse(vref, vcmp)\n",
        "    return 10.0 * np.log10(rt)\n",
        "\n",
        "def snr(vref, vcmp):\n",
        "    dv = np.var(vref)\n",
        "    with np.errstate(divide='ignore'):\n",
        "        rt = dv / mse(vref, vcmp)\n",
        "    return 10.0 * np.log10(rt)\n",
        "\n",
        "print('Métricas de evaluación de la calidad de la reconstrucción')\n",
        "print('MSE =', np.round(mse(x_test[7,:,:], decoded_imgs[7,:,:]),5))\n",
        "print('PSNR =', np.round(psnr(x_test[7,:,:], decoded_imgs[7,:,:]),5))\n",
        "print('SNR =', np.round(snr(x_test[7,:,:], decoded_imgs[7,:,:]),5))\n",
        "\n",
        "plt.imshow(decoded_imgs[7,:,:,0]);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iVtuB1pvY5Xg"
      },
      "source": [
        "## <font color='#e7c299'> 4. Modelos Convolucionales </font> <a name=\"eje3\"> "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6-6Jj_gVY5Xg"
      },
      "outputs": [],
      "source": [
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
        "\n",
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "x_train = np.expand_dims(x_train, -1)\n",
        "x_test = np.expand_dims(x_test, -1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1uS10pwLaqt",
        "outputId": "60887f35-68c7-4b94-83cc-489219503c88"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_4 (InputLayer)        [(None, 28, 28, 1)]       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 28, 28, 16)        160       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 14, 14, 16)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 14, 14, 16)        2320      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 7, 7, 16)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 784)               0         \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 10)                7850      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 10,330\n",
            "Trainable params: 10,330\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#@title **Código:** Definir Modelo con arquitectura de Red Neuronal Convolucional (CNN)\n",
        "#@markdown <img src=\"https://research.binus.ac.id/airdc/wp-content/uploads/sites/18/2018/03/ConvNet-1-640x288.png\" width=\"900\">\n",
        "\n",
        "\n",
        "\n",
        "input_img = keras.Input(shape=(28, 28, 1))\n",
        "nclasses = 10\n",
        "\n",
        "x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
        "x = layers.MaxPooling2D((2, 2), padding='same')(x)\n",
        "\n",
        "x = layers.Conv2D(16, (3, 3), activation='relu', padding='same')(x)\n",
        "x = layers.MaxPooling2D((2, 2), padding='same')(x)\n",
        "\n",
        "x = layers.Flatten()(x)\n",
        "classes = layers.Dense(nclasses, activation='softmax')(x)\n",
        "\n",
        "CNN = keras.Model(input_img, classes)\n",
        "CNN.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AOypEJm84pvB"
      },
      "source": [
        "**Convoluciones**\n",
        "\n",
        "Estas consisten en tomar «grupos de pixeles cercanos» de la imagen de entrada e ir operando matemáticamente (producto escalar) contra una pequeña matriz que se llama kernel.\n",
        "\n",
        "<img src=\"https://i.stack.imgur.com/9OZKF.gif\" width=\"900\">"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yPXFaTNOHi6-",
        "outputId": "a9f34bad-980d-4378-d4b3-7e369fb8665b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "600/600 [==============================] - 19s 31ms/step - loss: 0.1908 - accuracy: 0.9443 - val_loss: 0.1013 - val_accuracy: 0.9696\n",
            "Epoch 2/10\n",
            "600/600 [==============================] - 21s 35ms/step - loss: 0.0954 - accuracy: 0.9709 - val_loss: 0.0730 - val_accuracy: 0.9750\n",
            "Epoch 3/10\n",
            "600/600 [==============================] - 23s 39ms/step - loss: 0.0704 - accuracy: 0.9783 - val_loss: 0.0603 - val_accuracy: 0.9803\n",
            "Epoch 4/10\n",
            "600/600 [==============================] - 28s 46ms/step - loss: 0.0578 - accuracy: 0.9822 - val_loss: 0.0548 - val_accuracy: 0.9824\n",
            "Epoch 5/10\n",
            "600/600 [==============================] - 22s 37ms/step - loss: 0.0516 - accuracy: 0.9838 - val_loss: 0.0535 - val_accuracy: 0.9818\n",
            "Epoch 6/10\n",
            "600/600 [==============================] - 23s 38ms/step - loss: 0.0462 - accuracy: 0.9855 - val_loss: 0.0480 - val_accuracy: 0.9846\n",
            "Epoch 7/10\n",
            "600/600 [==============================] - 22s 37ms/step - loss: 0.0412 - accuracy: 0.9869 - val_loss: 0.0431 - val_accuracy: 0.9857\n",
            "Epoch 8/10\n",
            "600/600 [==============================] - 21s 35ms/step - loss: 0.0387 - accuracy: 0.9875 - val_loss: 0.0529 - val_accuracy: 0.9818\n",
            "Epoch 9/10\n",
            "600/600 [==============================] - 23s 38ms/step - loss: 0.0340 - accuracy: 0.9891 - val_loss: 0.0510 - val_accuracy: 0.9830\n",
            "Epoch 10/10\n",
            "600/600 [==============================] - 27s 46ms/step - loss: 0.0319 - accuracy: 0.9896 - val_loss: 0.0432 - val_accuracy: 0.9865\n"
          ]
        }
      ],
      "source": [
        "#@title **Código:** Entrenamos el modelo CNN\n",
        "CNN.compile(optimizer='adam',  #AdamOptimizer(), \n",
        "              loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history_CNN = CNN.fit(x_train, y_train,\n",
        "                epochs=10,\n",
        "                batch_size=100,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test, y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "JdU0Ss9JVX7P",
        "outputId": "9b40677e-f594-40de-e010-766cbf84b66b"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title **Código:** Curvas de convergencia de la red CNN\n",
        "plt.plot(history_CNN.history['loss'])\n",
        "plt.plot(history_CNN.history['accuracy'])\n",
        "plt.plot(history_CNN.history['val_loss'])\n",
        "plt.plot(history_CNN.history['val_accuracy'])\n",
        "plt.legend(['Loss de Entrenamiento','Precisión de entrenamiento','Loss de Validación', 'Precisión de validación'])\n",
        "plt.xlabel('Epocas')\n",
        "plt.title('Entrenamiento CNN')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yb7Uq-ZWSKAB",
        "outputId": "60f3ab71-a915-48d8-e1ec-dc1c6a57cece"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 2s 6ms/step - loss: 0.0432 - accuracy: 0.9865\n",
            "CNN: Test de precisión: 0.9865000247955322\n"
          ]
        }
      ],
      "source": [
        "#@title **Código:** Evaluamos la precisión del modelo CNN\n",
        "test_loss, test_acc = CNN.evaluate(x_test, y_test)\n",
        "\n",
        "print('CNN: Test de precisión:', test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "xTzolps38ATT",
        "outputId": "e8108678-1f96-4f1b-f75c-59e3e4346951"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n",
            "313/313 [==============================] - 2s 5ms/step\n",
            "Valor predicho: 2\n",
            "Probabilidad: 0.999938\n",
            "Valor verdadero:  2\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title **Código:** Imprimimos el resultado de la reconstrucción con el autoencoder y clasificación usando CNN\n",
        "\n",
        "pred_autoencoder = autoencoder.predict(x_test_noisy)\n",
        "pred = CNN.predict(pred_autoencoder)\n",
        "\n",
        "image_index = 1\n",
        "\n",
        "print(\"Valor predicho:\",  np.argmax(pred[image_index]))\n",
        "print(\"Probabilidad:\", np.max(pred[image_index]))\n",
        "print(\"Valor verdadero: \", y_test[image_index] )\n",
        "\n",
        "plt.imshow(pred_autoencoder[image_index,:,:,0], cmap='gray')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "Gd36adKdfGpv",
        "tWE1pqbZrZ1v",
        "iVtuB1pvY5Xg"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}