Denoising_using_auto_encoder.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Denoising using auto-encoder.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNg2nI7+gClFkgktQWKaK2K"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LJOcxzkCIsA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras import layers, losses\n",
        "from keras.datasets import mnist\n",
        "from tensorflow.keras.models import Model"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uR7EScGREAP5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "41dd4e98-b208-49c4-b2b0-612172da0f71"
      },
      "source": [
        "(x_train, _), (x_test, _) = mnist.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I71RLzGZELlY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "\n",
        "\n",
        "x_train = x_train[..., tf.newaxis]\n",
        "x_test = x_test[..., tf.newaxis]"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Soe0ujFELoq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "b0570372-1af7-4e27-b6c3-3fe3bf159ec3"
      },
      "source": [
        "print (x_train.shape)\n",
        "print (x_test.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bC537rF6FntE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "noise_factor = 0.2\n",
        "x_train_noisy = x_train + noise_factor * tf.random.normal(shape=x_train.shape) \n",
        "x_test_noisy = x_test + noise_factor * tf.random.normal(shape=x_test.shape) \n",
        "\n",
        "x_train_noisy = tf.clip_by_value(x_train_noisy, clip_value_min=0., clip_value_max=1.)\n",
        "x_test_noisy = tf.clip_by_value(x_test_noisy, clip_value_min=0., clip_value_max=1.)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NDsHhRNFn0a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "outputId": "02844bf0-d767-4bb8-ef8e-53b411315d00"
      },
      "source": [
        "n = 10\n",
        "plt.figure(figsize=(20, 2))\n",
        "for i in range(n):\n",
        "    ax = plt.subplot(1, n, i + 1)\n",
        "    plt.title(\"original + noise\")\n",
        "    plt.imshow(tf.squeeze(x_test_noisy[i]))\n",
        "    plt.gray()\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dsQ+HDBlSlE+G9a8T+jS8ilyobO566VPhkp/85CfJa34yyxE0+lcDfqKrobjcX/ivXvzXno7gfsCMHDkyec1/CVeuv/76hr+vI9iPAwYMKKr+msJ87WtfS15X/UWKxy+Q/0suR8bwtdUn8Y1G+fBfpzg6K0fuLyZLLLFE8lqfqpecf/75yWsODdW/rJWvu/vwW+fT8jrrX+H4ryQPPfRQcozHKf9lSn3IUTjrrLNOcoz/iv/FL34x2jo+qv6i+MQTTySvNcKhCv0tDK8buoZw2P23v/3taD/zzDPJefyX51zYfXdhP/bv378oo1Jy4ecaCcV9uKqPAmlos0bVdSXiJxdKznO7RkHw2pr7y10u7JuvD//FcNFFF03O47lX+3Vd6FhsJNJHowmrJKxPP53+AZXH7I477pgc+/znP913c6TwWmutlZx37bXXRjvXtzfYYINo33777ckxjXwu0eiYXGQjR9xxG++5557kvKuuuqryMzT6oTuoH8t/1z0kz+mN/uWZo0XbPj/auiflOYrHtq6LTz31VEPfneOb3/xmtNlXKtXtSpQP712BfF9bf/31AQCXXnppp7+HYR8OHz68KCNCOoiwTeCoWe7nuq/mKFGNCuD3cZSPRogxvBaut956yTGOStEIEFYTMD//+c+T1yeeeGLld/N8xNGHGqXH9ISMiT4rGYtVe9Oq+0ggVQPwvkKjcHie0+hhhud1Hfff//73o83RcroWcJQp73mBNGqMfax9hucEnZv++9//RpujfLh9QHo/pO3IRZt1hqq9ja5Heh/EcHQT3z/k5t2llloqec0RqbwX0X0J7484Yl33VBrlw3BUMa/pmtaD1Q96bNSoUe1+Nu+vAeDiiy+Ott5bNRoN3J2In9cALEKvF277N9Nc2I/Nj33YGtiPzY992BrYj82Pfdga2I/Nj33YGtiPLUB3Hvw8BGDJEMLiIYRBALYFcE09zTK9iP3Y/NiHrYH92PzYh62B/dj82Ietgf3Y/NiHrYH92AJ0WepVFMXUEMLeAG4C0B/AX4qi6H4MqulV7Mfmxz5sDezH5sc+bA3sx+bHPmwN7Mfmxz5sDezH1qDL5dy7wvDhw4sy47pWZdp///2jfdJJJ1V+BmtSObM7kOqRNccG6445h4BqxRst8ccaRNUMsk5ay60xrHlUvS7rCTnniFZxyOU3KfN8vPnmm5UlpDsLa+A5Yz6Q5svQvDVcxYc1lapf5Fw7moeFr/Oee+4Z7dNOOy05j/PS5HSruZKQPckuu+ySvOaqBKqp59wt06dPf6QoivSid5FcmUXOJaI6Y9bMc24JzTHDOnPNn1SlOT/ggAOS18cff3y0pax9ch7PCaqt5uoc11zz6R8mGs0BloPzUQEz56SqoqqEdGdptGytVuhoNA8S65a1tCWPRdYta84SrmDHeVe0X+l83Qi8ZgDpuqF6b849wzkycjm7OiiFWttYHDRoUFHmq9HqSI2WBGa233775PWFF15YeS6vY12pFsVzADBz+6vgtU/HDeek09xPPLdzLjetasfruObGKHXx77//fmXZ2s7CY1HXvq6sLZovj/cUuQpIja59nBOJ80N0Bq66xfmWgJmrcDFltVEgzSGkc0yj/R01jkX2o/a9F154ofJ9XGmF83nkqm5pVchcTqNG0NwrvD5pZU3OK8K5XbTiHa+1mm+Qqwcxun/neXrvvfdOjp1yyinRrmtdrCNnWqNo3hjO+cN7f93f870Lr01aDZfnSc1vV1YKBdI1TSuNcbVdzR3G9z+8F+9MHqkvfelL0f7Pf/5T21icZ555ijXWWANAmrMQSPOv6J6j0bx1m266abRz+WYYbQfnme1Krsmepqv5wXpij8oVIYG0Wqvmu+G1oKvVBKvymOX2LH/961+j/bvf/S45j9dTHrNAuv/ge5+DDjqo4fbyvoHnZN2X8bqhe1TOk5e7X+yO1MsYY4wxxhhjjDHG9GH84McYY4wxxhhjjDGmRelVqdewYcOKZZddFsDMZY05JFNDLjm8LlfCncOt3n///eQYh7xxuJuW2+RjHHKeu065cLrFFlss2iw7AYBHHnkk2hruzpKVPfbYI9oavs2f8etf/zo5Vpb1Gz16NCZNmtSr8pIcLP/Q0GL2G8vDgLSPcBifyowOOeSQaHMItvLDH/4w2iw/A4Azzjij8n0Mhy9y6GJnyJW355KLb731Vm1htHPOOWdRhvZq+WQut56jKxKfHFxKEUjLMTIqD2QpJJc/BdIywzyeOyN/4nBrLtnJ5amBmcu7M2U/vO222/Dee+/VPhY1zJ/DwDUclEti5kpz8+/WsucqtyvJlbetGy1lyZKajz/+uEufySG2b7/9du7UHpGXsLwOSPtYv37p32kalYY0Kmnl8Vc19jqi0fmQx7CWaOU1c8kll0yO3XrrrQ21g+UNKnMp15yPPvoI06ZNq2UsDh8+vCh/u/Z5/q06x/G45T1AKYkv4fLFjc6TKp3Kyc6ZnXbaKdp///vfk2Ncypr7ageyyASWUOXkU/w7VZLJv/ndd9/tdQm0ykl4/cv1bZa55foypybg9wDAtdde2+57WOYHpLIeLd3Mc/uNN94YbZXcssRQS72znJbXxVw/U0lgud/54IMPapNdzjHHHEU5h6ifcvLB3D6M4bmFf7e+j6+PyjN5Lme5le6juLy7+ob3/iyTV4kKzzH6u1j2kpPp5u6thF4ZiwyXZQeA++67L9rsb+2/kydPjnau7Dmj8kDeZ3EZdU4/AKRrmkpreW5TmV4VvE8Bqvcq66+/fvKax/rGG2+cHCtltx9//HFt6yL78OSTT06O7bfffpXvq5pD1Yc8FlXizpIrhu8XgLQfcN/OzWMHH3xw8vqYY45p19bzjj766GgfeuihlZ/PqLxw8cUXj7bK4Fh6+txzz1nqZYwxxhhjjDHGGDO74Qc/xhhjjDHGGGOMMS2KH/wYY4wxxhhjjDHGtCi9muNnjjnmKEq9muYr4BJlWn6Wtcs33HBD5edzCVTVPLIWkPPk8L8DqS6a9ciqvdxnn32irZprLr/G+trctdbS9Jr7pkTLR2p+lvbOnTp1KqZPn16LZpNzw4wePTo5xvr/HPwbtP25vDHbbrtttKs0uArnImEttX6Xlts84YQTon3FFVc09F2qE37xxRejzfrpb3wjlV3efPPN0dbcM5zb4eOPP65NP73AAgsU2223HQDg9NNPT4599NFH0c6V3eaS6Lly6Jq3hPNX8XflYN/l+hlr4oFUd835fzR3xaOPPhptLZ/YlZLXXOoY+HS+e+yxx/Dhhx/Wrp/OlfDmXFYAcN5557X7edovubTsOuuskxy75ZZbGmoj5+LiPFp6/Tk/2amnnpocY40z53/jcsVAmv9N857wd3NeN+23nchVUmu+rbKPdaakMI8/zjN32WWXJefx2qLrypZbbhntRuc5RtdxLp2uOZh4jed2aN461tw3iur2+Xpcc801le+rq2xtv379inKO0t/N+TE0B9Zpp50Wbe7PmvMvR1UOp4UXXjg5b9y4cdHOzbs812p+JM67x/lNeJwDafncXG7DL3/5y9HO5bxR//Jvu+uuu2obi3PPPXdRrsGao0pzrDBVpYMVHgO5HF25Mcssuuii0daxyLkg7rjjjuRYVZ/RtXq11VaLtl6PqrwinJ8IaHyNr2ssDh06tOB+xXDfU/i38pykuT5yOZy4LDXnntT28LrTaP6c3D0Cjw/ds/FcormyOO/hKqusEm3tL8xhhx2WvD7zzDOj/eabb/ZKjh/uY5q3qOqeiHOLAfn8Ypyjh/erm222WXIe+5/7ieZ0YnjNBdJ5ma/7VlttlZw3ZsyYaOtYZJ/84Q9/iLZei9xYLHOqvf3225g8eXItY3GuueYqynWN762BdI+m/ZLnSb4/0hy/vM/m3E45tHQ8+22NNdaIdm4M5OAcs5p3l/sVz8FA+qyAc0dpHjHe2+jzEN6n33LLLc7xY4wxxhhjjDHGGDO74Qc/xhhjjDHGGGOMMS1Kr0q9QghFGeqkkiUOWdXQpiq0jDqHwuXK+bIk7I033mjouzbddNPk9XXXXRdtDgUEgF122SXaXBpbS0bnSkY2Cks8NMTzoYceAjAjJHTChAl9ppw7s+CCCyavuYTr/vvvnxxTOUuJSs44XJZlHRoyzSHOKrHisntHHHFEu98L5OVOHDLPv1PDQDnUV8svXnrppfyyR8JotTw6hyNqaCiXotxjjz2iraH4fP3Gjx+fHOOSmCxtyckYWQbBobFAGm570UUXJcd+8IMfRHvUqFHR1rDputFrym2sK6Q9NxZzZdoZ7vcaos+hrrnfI+GlyXn8vrvuuivaWpKa52E9xnA/0DB7Dqcv574Slgc2Wk5a1xcuj/3AAw/0Skg7S9RUlsxyF5YzcZg0kIZAa3ljlvdxeVENi19mmWWqmlgJr4MAcP/990ebw755jgZSiaF+L4/hOqhrLM4zzzzFmmuuCQB46qmnkmPcT3muB9L1gyVC3/nOd5Lzbrvttmh3tUx7FSpN4/n1Jz/5SXLspJNOijaHpg8ZMiQ5jyUkjaL9m+ctlmwDM5Whrm0ssmRP5Qc8P/JeE0jlqCpHYHhO0ZLPLA0/8sgjo62l2HlcsbTyt7/9bXIelzPeeuutk2PXX399tHmOUZkll3x+5plnkmMsjVJ5bhVaDrv8zZMnT64tHQHfZ+jefLnllou2ymUahX2tfZblgCyT5PELpGkfGl2PdD98zz33RJvnTIXLyvP9iNJoOfsO0k3UNhZHjBhRlNJ9Te3AkhzeVwCpT3idOf7445PzDjjggGirHJ79cPfdd0db5bO8x9c5gWEp1ve+973k2JJLLhltLs+tPuDfdeeddybHyrUHAM4555xol+kcSngO03mF9wa9sUfNwekVeK7n1Bd6HksVgep0IDqvs0SWx6/uS3idVNkay7b4mN4X5foIs/zyy0db5an8mSoN5XX4lVdesdTLGGOMMcYYY4wxZnbDD36MMcYYY4wxxhhjWpRel3p15X0bbbRRtDlUUcOJucqAZjPn8EQObdUqBo1Kv+qgKnM8kFaJ4DblQsU22WST5DVXG+vt0D0OqQXSKgn8u3MVmjSEk0P0WKa14YYbJudxiDiHr2rVixNPPDHaWvGLQ/A5pFnDBFl6kqs4x3B4IpBWitLQTJEu9Yi8hMNEgTSMlK8fkMokeUypNCRXoYXhEEmVdXDYK9sqaeTqGSw/A6qlCZ2BQ6xZysRh9sDM4Z9MOR6effZZTJw4sfaxqPMYS/RUOpWTInQXrVp09tlnR5vlGhy+C6T954wzzkiOsU9zVXGqKtXkULkFz8Na0UiqDNY2FocOHVqU843K8riva5g+hyJzxTqVE7HcTsPHOVSYry1XUNRj3Le08mJuTeNxNHLkyGjvtddeyXncFzQsnuF5WUPAN9hgg2jrvFzKae+77z588MEHtYzFESNGFOU6pFVJuYrIBx98kBzjvQjL0znUG0jlYvoZdcPf/ec//zk5puHvJTq3soxA5YUsY+N5PSdD0TWT55I6q13m9jdVEhIg3xerWGuttZLX3Nf33nvvaGvVWK6SlpPx7LvvvtH+4x//mByrqkyl6/0222wT7aOOOio5xuP0s5/9bLS1+hl/l35+uZ+ts/LsZz7zmaLcC6v0m+dT3fdUSUN0b8h7Vl2rdt9992jzXKgy6l/96lfRZqnPlVde2VCbFE4loPM/y+m7WnGtap4CUrl4b0mgmdx+OgdX4W30OivcnwYNGhRt7Xfc11S2qukuSlSKyPdUOsZ0T1wF90ndv5Z94+OPP65tLA4fPrwo0ypotUNeS/TerErGrtXxctLvRmWdXZm7FU7RceONNzb0nsUWWyx5zesk33+qjDNX+VrS2FjqZYwxxhhjjDHGGDO74Qc/xhhjjDHGGGOMMS2KH/wYY4wxxhhjjDHGtCi9muOnX79+RanxPeSQQ5JjrMPXsuesW+McBb/4xS+S88rSf8DMpbVZA8m5BjQnxemnn95u27W0L5d0bFTT1xN86UtfivYOO+yQHOPyvHXl+JljjjmK8rdrOeWu8LOf/Sx5/cQTT0T74IMPTo5xfh3uP6qB55xQXOpW+xznGlCNKecN4jLwmsumK8igbXIAACAASURBVJpQ7Uucm2rs2LHJMdHi94h+mvMEAGkuA82j0hX02qq2uETzJ3G+E9Yma6njl19+OdqsZwfS0sScVyaXW0rzU1Xpg7kUNpAvqVrmc3j44Ycxfvz42nP8aJlRzp3B+ReAtL9xeV3NsXT55Zc31I5VV1012rvuumtybIUVVog2X3/Nn8Mlx1977bXkGPuXc8PwXAEApZa8vc9odK5qtKQteijflsL+0b7NuZs4pw2XcwWArbbaKtqa3+m///1vtHmsa+4j7uucA2b++edPzms0zxn3hf322y85xjlMtPzzXHPNFe3x48c39F2a16Kcf+ouIV3a++yzT3KMc5Cde+65yTHO78C5H3JzJucMAtI1js/LlVjPXcfTTjst2poz7dFHH233u3Qu7Aqaq4Pzyxx33HG5t/bIWNR5k/NLcInnHPqbOFeKlullf/Geksd2Wxujzft33cs22kbONcFzLZDuxXX+4ZwUBx54YLR5zwKk/VrXqbIvP/nkk5gwYUItY3HAgAFFmfOO864AaZ/VPKBV8wmXcQbSdfJf//pXcozzhfA9xxFHHJGcx3suvj58rwPk83nwteT1fuutt07O41yZmm+rCs0XynlG9Zry2o1eWheZ3DzH6N6d7500RxmvY+x/ziMIpPcvfN+huZM4Z4vCe5Phw4dHW8utc44cLjcPpHk5eT3V/fuRRx5Z2Q6mrvvFueeeuyjLrGtOG84DyusRkN5zcY6ttddeOznv+uuvjzbnHAVmHt8lmqeOy6Pzfuiwww5Lzrv//vujzfdIQLpXPPXUU6Otc/IxxxzTbpsUXjc0ZxX798UXX0yOca7HTz75xDl+jDHGGGOMMcYYY2Y3/ODHGGOMMcYYY4wxpkWZZeXcc7IOlcJwqDSH8akshkPHNCSM4ZA5DT/j0EoukcySESANu9RQrGeeeSbaHPbNIewAcPXVV0eby8EBqWxEQzwbpSfK87EPtWwklwbOSU8YLUPNshG+jkB1GWqWeABpmCDDIXhAKhlSudNmm20WbS4nyHJCIC/zYynG66+/XnleJ+iVMFouz6llt3mM5coK8/vUPxyeyej44PK0PD5OPvnk5DwOv7399tuTYxxi3agMRakqfatjlucp7ascQl9XGC37cM8990yOcYjvxhtvnBzj/sxSHZ13L7vssobawWuIjj31fYnOB7fddlu0tXwut5+v68UXX5ycd9ZZZ7XbJiANxdWS6QxLbFRSyGG1dcou55lnnqIs96ph9Nyfda5UWUYj6LXlUGQu+fy3v/0tOe/www+P9je/+c1oswQASP2tpYk5fJllMzoun3rqqWhrqXeWLUgZYVTB5wGflsJ95ZVX8PHHH9cyFuedd96ilHPq7+HyxzrHsYyUS/RyqWEgXVs1fH+77baLNvcRLTXN6zV/HvsTSCXi6667bnKM5UMsseYQeWDm/lPFoosuWvkZjPb9b33rW9G+4YYbemRdVGkIj02VPTHrrLNOtLXs+9e+9rVoX3DBBckxPpcldio5/c9//tPu9+akxzoGcuOF4XlUffqb3/wm2o8//nhDn6eU1/jVV1/FJ598Uvu6qFJR3kur7In7IsuAeM+j5CSZnAaAPxtI72N43Ouay3OywntUvpfgfSeQ9mOVo2tp9pJciXQ9xuvp+PHje2Qs8rgB8v2NJZq8v+F1BUjnw6OPPjo5xvv1E044Ido6FnlcsRRI7035mMr+DjrooGjz2qHyJ5Zwq5zthz/8YbR5Xpk2bRoapfy+CRMmYNq0abWPRZ2DWCaZew7BPtTrWiV7BVLZPsvmWJIHpL4+++yzoz1mzJjkPL43veOOO5Jj5doPACeddFK0Oa0MANxyyy3RVikat4v3vJxWBkj7weTJk5HBUi9jjDHGGGOMMcaY2Q0/+DHGGGOMMcYYY4xpUfzgxxhjjDHGGGOMMaZFmWU5fuqAtZFAqq/l8nZAWjb1e9/7XrR32mmnys/nHCOaA4bzm2h51So0rwXnwWHtH5CWqucSj10tHV9XXpGhQ4cWZX4GLUfKaJnfRnNs5OByfZybafHFF0/O4zwgF154YbS5DLGi2l3Wy48aNardfwdSnXhVDqLuwDkzHnvssdr00yNGjCjKfEWcXwVIdbS5/Emsn9frlyufzWOR9e36GSuttFK0tWwq89e//rXdzwaAHXbYod326ljkMpqaO4VzCHEpyFyuDc3zUerJeyqXAX83AFx00UXR1nxnrNfP+YnzsGlJbM4pwGNC8yawtprL1uq6w7kGNO8A54PJUZYNBRrPYaE5rLgMvGrsuYTnAw88UGuOnzJniZao5+uueUU45wjrw7Vf8jrDuncAGDlyZLQ5j4KWt20U/nwdizzmuNTx7rvvnpz361//utvtYLRE8nLLLQdgRq6/999/v/axqGV4OffXxIkTG/o8zjECpFp+zSnI5YY5F6HmN+H+w3kDNJeN7p2qyPmGyxevscYayTHOj8DtbbTUNJCu5Y888kiP5BXRvCy8/9DfXjWPavlnzluoOR54vmW70T36LrvskrzmXBCaJ6Jqr6Lfxftc/jwgLWW98847R5vzmQBpDg0tmV7Ov2PHjsXkyZNrH4ta4nnzzTePdqN5qJRczhHet3PeGB33muO0RMt083dpbhK+7+B8W1tssUVyHo+x3NjmvVL//v0r28V5voCZysr3yFjk/CpAmg9M93K8Bumekqnam7TTjmhrWXZ+H+9lde3jnC2cR0bh/Emch6291wznI9T9fFfoiTyUvMcG0nVL1zSG12/NKchrl46dcp0HgEMOOSTaPAcovHfS83iO5zyZALDhhhtGm3OC7b333sl57ENdMzj/lubsrEJz33E/GDlypHP8GGOMMcYYY4wxxsxudPjgJ4TwlxDC2BDCk/Rv84UQbgkhjG77/7y5zzCzHvuxJfiCfdj8eCy2BB6LLYDHYkvgsdgCeCy2BB6LLYDHYmvTodQrhLA6gAkAzi+K4qtt/3Y8gHeLojg2hHAQgHmLojiwwy8LoShDCHNl5nKlBLsKl13kUECVaXE4GoeObbDBBsl5HAaskicuj8phr1pqesiQIe22CUhlCxz+r5IzDmXk0rDtsAZq8GP//v2LMrxMw3ZzcIjkk08+mTmzGi7T+Pbbb1eex9KB008/PdpaapqlHBy2DFRLWbTkLF8DlYZwWB+HNeqYy4WZ8lh46aWXngPwA9Q0FkubJSxAKnfRku0cts+llRWWGahEheUr/Hk5GSOPqXvvvTc5xtdTwyf32Wefdj9/tdVWS87j0qtzzDFHcoylR1tuuWW0r7jiisr2KmW5zWuvvRbjxo2rZSwOGTKkKOcDvcYc7q2lJzn0nsNhc/OHhp7++c9/jjZLbLW8LY8BnsdVesAlpG+66abK7yrliQBwzz33JOdxSDaXe1Y4HP/yyy9PjnViremRsahwOLpKYapC2rU8N4egH3/88ckxlskus8wy0VapEZeQ5vBqLSvLsjyVfPA8yuH5L774IqpQmRDPHdpPGJ6LP/zww+SYzL+1jEX2IfdRIJ1DG5UgKrwOfPLJJ8mxeef9dP+t5cOr+OIXvxjtbbfdNjn205/+NNoqleFrfuyxx0Zby0SzFFulswz7ieciIC9L4TD8q666qraxOHjw4KKUaeicxzKt888/PznG0hje25555pnJeSussEK75wFpGXg+pvusnPSBYRnAt7/97eQYpzFgKfApp5xS+Xks6QHSuYPX5JzEUD9DZDq1j0W+pkCaUmG//fZLjvF1Zimcysx1PmH+8Y9/RJvvGW6++ebkvEcffTTaBx746c9RaRLP8Sr14XHfKHofw5J0Pvbggw8m57FcTOGy6O+//35tY7F///5FOd/nJLI5H/Nv0jL3m2yySbQ33XTT5Nhpp50WbZ4PtW/zPQWvmbxe9gQ5iXoOvg9TSXK577333nvxwQcf9Pi6yGtJLqUEt1nlXOw3llQDqZST9/R/+ctfkvO23nrraPPekO8dO4L7J7eJPxtI11OVoPMczcc0zQLPmXrPKWtF16VeRVHcDUCTsmwKoEys8VcAm3X0OWbWYj+2BBNgHzY9HostgcdiC+Cx2BJ4LLYAHostgcdiC+Cx2NoM6PiUdlmgKIryTw1vAlig6sQQwm4Aduvi95iepSE/sg/rSLZpasVjsTXo9FjUv8abWY7HYmvQ6bFo+hxdGoua1NbMcjwWm58ujUXfa/Q5PBZbhK4++IkURVHkQtWLojgTwJnAjLCvKokXh+s99NBD3W1WNtyUK2SoZIglXGuttVa0tUoPZ8/WMOfHH3882vvuu2+0WdoFpBIMDdvksE4OFdcQzCWWWAJ1kPMj+3DAgAFFGX6qE3NONlgl79LPYIlGTtLDaJggh8KxL/S8I488Mtos7VLYv3qzvf3220f73HPPTY6xLI9DqxUOydZwRQ7D59D69ujsWCz/XeUBXN2Hrx+Qyru40sJ9992XnMfjTTfTLK9stEodt3H//fdPjrE8Rz+v6vNVcsFVNrTqHEtUWN614447Judx+L/2tVISw7KoKhodiyGEQuWLJVzVS+GQZ5b5qYSEw4e1Mhij8i7muuuuizZXOdGKOSwz0ypDXOWJw4U1VJZDly+++OLk2A9+8INoc+h+Z2TEXDVHK9woXR2LCkuPVerFsgCuHqTj+Ygjjog2V9MC0vWEfcIVw5Qll1wy2iq3yslWR48eHW2e57l9QCr70woyVTcDvB4DaWUTliIA+eooSqNjceDAgUX5PVwFEkivCUuggLTP6trOsERI5YkcZt7omsOh71xxBshLSC655JJo87qo1W5Y0qMVbnj85SqxMio91Mp1OTo7Fks59+GHH56cl6veynMgr3cqK/jRj34UbZb4AKkcnKXIWvGVpbqMygr4emo6gu9///vRPuGEE6LNFTKBVAKvcp9111032rxXU9kxoxUay71PI/Nwo2Nx+PDhRSkbzMmyuOIrkO57VB7dKCyr5arDKrHlY+wLHl9AuudVCTrvbdZff/1o51Jl6H0Mw/1MP4P7psqY6ppP2463e6+RQ2VgLH/h9YPXLSBfXevKK6+MNkv9db/F8x7vR/Q+l+dATisApPcNvPZptTJOW/CnP/0pOcZzOO+5FL4P03uyUiKeS8NS0pk9avnvOWm2Xldes3OpQXgfwVXpgHS88P2JfhffT3QmfQnDKSbYb0899VRyHt9L6NzEv5Ofh3CKEyCdm3QsSrXLyvZ2tarXWyGEhQCg7f9jOzjf9E3sx+bHPmwN7Mfmxz5sDezH5sc+bA3sx+bHPmwN7McWoasPfq4BsFObvROAkfU0x/Qy9mPzYx+2BvZj82Mftgb2Y/NjH7YG9mPzYx+2BvZji9BIOfdLANwPYKkQwqshhF0AHAtgnRDCaABrt702fRj7sSVYHPZh0+Ox2BJ4LLYAHostgcdiC+Cx2BJ4LLYAHoutTYfl3Gv9MtL7qV715z//ebQ5p4My//zzR1u1+5yj4NZbb02Ocfk7zl0xePDg5DzW03HOC80FtOKKK0Y7p83PXd+jjjoq2qp/5NwqmvelCtYPAp/mFnjrrbcwefLkWjKlzTHHHEWp3x8zZkxyLFfOkDX/7EP9bfw+1Wyy3nfkyE8fNv/kJz9Jztt7772jzfkKfve73yXnsYY/Vy4yB+f8mTJlSuV5yy67bLQ1HwLnY8nps5Epz9dZ+vXrV5R5PDS3Sw723dixn0Z66jjiz9QcEnytG9XSc84uniuANPdKLiEg56nR+aEOvvKVr0Rby+xy3qCiKGoZizyfcn4CIJ0/NJ9OmcOiI3g+yeVd45wy119/fXLsgAMOaPc9XEoVSHXvWqLy61//erQ5z4bm1MrBORB4/s/px1daaaXkNfetBx54oNaxWM5tuTmkUTQPGZdc15wJ/Pu5nLvCJUU/+uijyvN4/srl7eAcTJojgudvzYnFubjYP5o3gXP+5PLB9MRYVHhe0HK0Vddy5ZVXTl5zPh0t+VxVOl33A3we563QfBFcplvpSsJV/v1A2gc5n5fmY+Nro3mHDjvssGjvv//+tY3FwYMHF2UONM1rxjlB1D+c44HzvPz+979Pzvvud78bbc55BaQ5mThXA+eDAdK9LI8d3UtxDj7OAQbMnFerRPODbLXVVtHWUuBcupzXe85LBuTz7DB1jcVhw4YV5X5Lcw9y3k5dgy677LJoc15NzVnE15LzMwLAiSeeGG2ek3Klob/4xS9Gm3PRAWkeSoXbyOXHDz744Mr36D6Bc5Dkcrtsvvnm0db5lMfwCy+8UNtYnG+++YpyHtf7Re7bmj+pat3Re7Hbb7892pojjuE9h+5NXnnllWjzNdJcSjzf8h4aSPdnPAfstNNOyXmcQ1Jz9em6UgXnpdT71nJfNHHiREybNq2WsThgwICivH6aO5PnOPYnkK5JnDtJrx3n6+G1DwDuueeeaHN+Sb035XycnJ+H53EAKPOGtfddnNft1FNPjfaWW26ZnPfee+9FW68Hz/M333xztFdfffXkPJ6ju3q/2FWplzHGGGOMMcYYY4zp4/jBjzHGGGOMMcYYY0yL0u1y7p1h8ODBWGSRRQDMXJo2J++SUMLK8ziES0tvshSFQ8w0PLaqBKqGmHFJeC0vnZN+MRympmXfuF258tocgq9l5cuQ20bK83WGMlSb5TdAGhKrEg8Ox+RwOi5nq1xwwQXJa5aScTidlpOuKkerEiGGPw+Y2acl2q849JPD+IA0lI9L/ObkHFrutDPlpjvDPPPME0sGqkyCpTBaMpslTByimpMP6XhgiRiXt+VQayCV57AEc/vtt6/8LoX9xfIuLpcIzFwyl+Gxz6Hi2l4O/9R5pQzJrCrF21XKvq/zAtOotEvhkH31IUtd2Z867lnWwfPQtddem5zH8keVX7H0hMPMWT4JpPODhg5XlRIdMmRI8pplUVqWm8O166QoioYkXiqnLMuvAmn5cp3vcyVxWd7FEpLNNtssOY/nxyrZNJCGTesY4FLRPG9yOWMAOOWUU6KtcznPVbx+cvlrIO0nGg5d9pMHHngAPYHOmby259ZFbqe2jWVtXA4dSPcijO4H+DWHvqsE4sUXX2zXBtI1v9zLAfn5U6WmXGKZxxiXKwZSGbDujw455JDK7+sO06ZNi9IklQQwjfadM888M3nN6Ql0DeL1juFS6UCadoDHkc4PPCeobPWKK66INssRtJw7z3kqTaiSiKu0i+cplXXmpLZdZeLEiVHipTJD7lMs0wFSuR6jspqqku1AujfR8VcFfy/fmyg6r7PULifvYnQc8d6G9+J6f8bzqcquGpUZdYVSQnPhhRcm/86yUO2XvA9gqbBKd8pUGO1RpkEAUkkd7/+A6nQgulfgcapyO96XrrfeetHmdVDREt9MlfQXSK8b79t6imnTpkX/8G9TVG7Me0++59C1lTnuuOOS11tssUW0f/WrX0Vb5co8rnh/welJgHRsq9ya+5LK0xnuL0sssURy7P777482l5VX+S1fA52beCzkStM74scYY4wxxhhjjDGmRfGDH2OMMcYYY4wxxpgWpVerevXr168owwS1mgVXoGg0RJKz4QPVoZpt3x1trgyi4X/MUkstFW2WAABpeJjKN7iyDYdGc7USIA011VBNrjjFcpVHHnkkOS8XnlmG+E+dOrW2igkDBw4synBlvXacGZ/DjIH0WnJYnIbTsbRJK21x+B+HD3PIq3LGGWdEmyuBAWlVDZYvKHfddVflsTo4+eSTo3311Vcnx6TqVW0VE7gKzYgRI5JjLDnQ/lYH3J95XKmsg0NRf/vb30Zbx8quu+4a7csvvzw5xlU3WFKiYb4856jck8cwSzX22GOP5DyWXGjltrJfT5o0qbaKCQMHDixK3+nYZzQ8lkNdc9KzBRdcMNrXXHNNcozDVHls65zAUr6zzjor2lr5hcOf9bdwCDX3Ea00pmGvzDPPPBNtrmzBciEgnY9UZsnjZNy4cbWNxTnnnLMo17Kc9EH7FJ/LFWpURsdr/AknnJAcq6q6pvPhkksuGW2W/+iYZSnLnXfemRzj6oVcvUblEhzWr1KyOijlaM899xwmTZpUe1UvnU/HjRsXbZUKs4zgnHPOiTZXKmznu5LXXFGEx6VKWVh+8OMf/zjaf/zjH5PzeAxrha9tttkm2jymtHpSjjXWWCPaPIZZXgEAK6ywQrRzUjL00LqYQ+WD3O95TtHzWMqtcw9X8TnmmGOirXsClguw3FilXixHU8kec95550V75513rjxP4bHPMneWewIz/06mXFtfeuklfPTRR7WMxeHDhxdlBR6WFXYGlgvp+sn7QZWpcrVCvkfgcQOk+yqWoWy99dbJeTyHckU4he+fcns2TVlRJffRfstzWAdV2np9LOp8y2sVV69TuSOPWX4PAGy00UbR5n0AVwID0r0Ey6FzlVRVos5rHEvPVaLP877et/P9LUuNVLLN98i6zyo//91338WUKVNqXxe1CncuhQtLQvleT+W3vGZq9eAqdC/Le6dGq1Y++uijyWuuPMtt1zmZv1sl23xPcskll1R+N8v8tNq44KpexhhjjDHGGGOMMbMbfvBjjDHGGGOMMcYY06L4wY8xxhhjjDHGGGNMi9KrOX769+9flOXHNX9Ljs033zzaXFZQ8wtwyTwtY8dwfgnVq7LOmktbcv4aAHjiiSeizRpdANhuu+2izflCNthgg+Q8LkF7ww03JMe4TPu3vvWtaGvpwkbzIdWV44c1m6pVZW2jlifmXCKci0N1n7k8Tfx9rKUeOHBgch7rdVmzyflegJnLDTMrr7xytFkrr7kvcjkoWCfO2lQt28p5DlRPzhrssWPH9op+mvXd6p+cXr8KLanKZRa5jCPnCQDS3DGch2LddddNzuPxoaVvGc4Jpjm7Gi2znis7zvps1vwCaZnIusbigAEDYr4tLenM85rmBmCd8dChQ6Otc/Kxxx4bbS09XOZQABrXRXN+Ac0twBp1zlcApPpv/gzVSPNv0TaxPziXCL8HSHNCaPlc7j/PP/98bWNxyJAhRdnXdXzl+lsVmvvu8ccfjzavK0A6vjl3SK50Pa9bvOYCaWlaXQN4budcNDrncW6pMWPGJMe4b3COGS2hyrlz1Me8htY1FocPH16UbdPcRqzz598GpGsXt7kD7X4Cl1fO+Y3hfsbzFlC9fgJpOWPJP5fA/UzzOVblVNDcC7xe61q99NJLR/vpp5+ubSzONddc0Y+ah4zb12h+ybXXXjt5zeOjUXgdBNK1Kpdvhfu95mDisanzbRWar6KqnLu2l/fAnF8T+HSOnTBhAqZOnVrLWJxzzjmLcg+j68z+++8fbc7/CADLLLNMtHl/r31PS9Iz3EcWWWSRaGveoyqOP/745PVJJ50Ubc1px/Mml4Lefffdk/N4/ec1HcivyQzP87p3ktLWtY7FFVdcEcDMORn5vlXzw/AcyO/jfKidQXPmMXw/ynO2rmk8Fv/whz8kx3hO5bGoudeqxhtQPVdqribOV8S54YBP73kee+wxTJgwoZaxOGzYsKLs+7wH5u8DZt7785zBeYq4tDuQ7gE5TySQ3idw3iu9h+P1j+fWnN81/yPPazxWOJchkK5bhx56aHKsKnePriG8n9P9BB+Dc/wYY4wxxhhjjDHGzH74wY8xxhhjjDHGGGNMi9KrUq+hQ4cWZfnGf//735XnaVlPlTF0BQ6J0rDBKji0VcPsttxyy2hzuWcAOProo6PNoV5aoo1L/rE8DEjLeXKJ4VdffbWhtit1hbQPGjSoKMNDVTaQk2lxOPp6661X+Z6nnnoq2quvvnpyjEPEf/7zn1d+F8uH9t1332ivueaayXkaks9wH+TSjhzaqWiIM4dcDhgwINpaqvfMM8+MtvpX+mCvyEs4xF3L+UooYbvtBNIQfh3P48ePj3ZVSVggDVMdPXp0tPlaAmmopsoKqkJgVfbHY0zllBw6zG3vjHSQqWss9u/fvyhDiFWWwPPOiSeemBzj0G8OTefymkAa6sohqgqHyDcqR+LwZiCVjWio+tlnnx1tlvJxqDCQ9pGbbropOcZzgpYEraIDOUevl61tFJW+slyK5QdAKhXh0GOWDgBpaVmeR3VOZRknlwVXOFRa27vttts29Bk5ttpqq2hffvnllef1hARaZR2jRo2K9mKLLZYcY3kl+6JRyZbC11Kldkxu3/fLX/4y2rwPUXgMa+l4Ll992223JcdYOs2wrBFI55Jf/OIXybETTjiBX/bIWNR1i+fNK664oqHP03LfPEc9++yzybGq9UPlGiw1YpvXMCCVCJx77rnJMV7XeSzqno7H0YMPPpgc47WWy33r7+B5VNvIe7C6xmLuPoPXOC1nzfIc/m26v+drx2uTcsEFF0R7++23rzyP1xUtK87ztfT5ZF/KexGV7rHMRefa3BxRhaas4PL2kyZNqm0sDhs2rCjnBL1n43vCz33uc8kx/r0s2dP5hX2sqUHU51Vw/+Wy47pHzcHzwN///vdoX3nllcl5L774YrQ1fQLvnfkeStMi8P2FXo+//e1v0a5rLC6wwAJFuRdViRvPjbny5Tn4N+j9F+8duI82yumnn5685pQzuvfn/sky9jrISWfVhyzDP+OMMyz1MsYYY4wxxhhjjJnd8IMfY4wxxhhjjDHGmBbFD36MMcYYY4wxxhhjWpRezfHD+mnNGfH0009HW/OFsPaW829wvg0g1baythNIc/Sw/lK1eqyZZm21lm/bcccdo81l3xXOdaI5NLgEnJapY/2xlEtMYE2flstj6swrUuZUUS03l2TW8rqsN+d8HrvuumtyHpfY3HnnnZNjf/nLX9ptE5fzBqpLemv5edbC6jWuKkfLuYoAoCw3CcycO4T9zeVUc7lhtIQg5zVCjbkM5p133qLM0aB5i7jdWiqT4XKQyy+/fHKMr5OWBGZNdk7by/7mvnDYYYcl5/H441wknYHbyHmHgHReufnmStkvhAAAIABJREFUm6OtOSC4L2ipcdbS1zUWhwwZUpQ5AbTEKucN4HYBad/mPqv5HS699NLK72b/slZb8yPpvNYIq666avKa5zWeF3PlWfW3VOVM0e9i3994443JsZVWWina9913X21jcfDgwUWZ+0LzCnF/05xGnCuFfay5OHju3W233ZJj3Id5ndU+w2VOOZ8LXxMgvc46VzK8nq6xxhrJMc53l8sxw+T2E0qZN+PNN9/E5MmTa8/xk0NzfZTlrAHgtddei/bdd9+dnJfL3cN7kfPPP7/d9wDALbfcEm2+5nqNR44cGW2dxzhnBPfNHOobzgejeVaY3P5LqG0sDhw4sCjnAM0xyDkj5plnnuQY72l++MMfRlv3Qf/4xz+ivddeeyXHDjrooGjzfkTnMv5M3tOoH7mNWkadv+uyyy6Ldmfya3IeFM1XxHD7tU+W7Xr99dfxySef1DYWyz2+7rVGjBgRbV2reH7lvazuX66//vpo5+6fuN//9Kc/TY5xfp6vf/3r0dbcLZx7Rsutcx6/3/zmN9HW+xHOIcm/C0jnST623377JedxaXHOmwmkJefRh3Pfad4enmN1vuVcdYzOvTx/59Y7Xj95fgWAPffcM9rsb86TBqS5i3L3hI3Omzqv8H1OXXvUueaaqyj3zzxHdkRVHsocuj/i+zvOn6fw/chxxx0Xbc27y31E9yycg45ze/GeCkjHm86tnEOIyeWVVT7zmc9E+5133nGOH2OMMcYYY4wxxpjZDT/4McYYY4wxxhhjjGlRelXqNXz48KIsdVolx2kPLh94ww038Ocl5+XCuRgOwdSQ8EMOOSTaLHXgcDwgDd28+uqrk2O//vWvo83heTk0TI1D9LhUHJfXBNJQaS3B2xPyklwYLcvkFC47xyFuKsFgqY6W92bZEcuRVJrDfP7zn4+2ltJl+dVnP/vZ5Njbb79d+ZmNwlIoDgmda665Ks9T+QqX87zlllt6JIxWw9ZzfmQ4NPhLX/pSciwX+p1pU/KaJT4c0q4Sg6oQSaBastcZuK9xn68qbd8RPVFCWuHQfg1p5z7GJUg1BDlX6pXliRxeypJahcPs1ddcQnrDDTdMjmk5yxKVibCMib8LSCVnQ4cOjTaXxgQaL0ePGkPa+/XrV5QSC71+iy66aLRzMkaWXOl14bVEw915nJ511lnR1hLcPI9ySPs555yTnMflyVW2ymHsm222WbQ1HJpli9w/gdRfvM7+6U9/Ss7j/q9h/ExdYzHnw97kRz/6UbQ1tJ5LiXN5W5XrnXjiidHW67rCCitEmyUvnWH99dePNssSRdacldALtY7FUo70s5/9LDnGpbs1zQDPlSwd4PLJHcF7EJ6/GpVRcTlmANh4440r38dlw1kWrLA0UUvYV8lnlW233TbaOr+W60idUq8hQ4YU5X5EpcYsl8pJQ+aee+5oqw9ZDqeSWKYz9zhV5OR0hx9+eLSPOOKIaOs15nQKKt1keF3kebwjJN1EreXcy30f79WVddZZJ3nNklaGJVCKSqLK+1S1+Zp39JkMrwmayqSKxx57LHnNksCuwmv3pptumhxjyWdd6+LQoUOL8n5b76lY8lumDynhccpjgOctYOY9RhW50vEs6ee1UO+LcvDY4f2L9g+WiD300EPJMb63P/LIIxv6XpUkXnnllfzSUi9jjDHGGGOMMcaY2Q0/+DHGGGOMMcYYY4xpUWZZVS+lShajcAgsh4opmh2fq9fwd2m4KlcnWmKJJaLNYXDK3//+9+R1VUZ4hSu4fO9730uOHX/88Q19BochahhcGbr6wgsv4KOPPqotdO/LX/4ygMZlbF1F++aYMWOizb5hqQmQ9otDDz002hdffHHl57H0AEjDn3PyDw4X5io7QBoqzuGwGla6//77R1vDVh999NFojxs3rkekXiwnA9LQZj3W1apZDIdTcugxV7oAgAMPPDDaLHf8f//v/yXnaXU5JucfZsEFF4y2VihjSQPLBVm6omgFuVGjRkW7rjDaOeecsyhDSbsiresIDjfVSmoMVzT4/e9/nxyrmstVxskyPJZK6DGWNqiEjecEnf9Z/sRjW6tocT/L9Sv0UPUSDXku51oglVsB1dUu1ltvveT17bffHu2cfI8/nytMKRx6rbJQDiXXNbNKFqGyP/axVIxJKmawFJTXdCDtd3pNy5D5xx57DBMmTKhlLC6yyCJFWQmHxwOQzgUqT69DDsLSYZYgaYUmlhtwiLyGnOcqqXE1mSoJPgDssMMO0WafKTz+cpIjReb1HhmL6isO/efKdkBahYbXSJ6TgDQUX2X7LIFnSaPO7VVVe3J7ed5/AGnVW06RoNKEDqqptYvux3iezknCe6OSEM8tek14PuE5Q+8zDj744Gj/+c9/To598MEH0W50r5CD5zWtEMf+zvn+hBNOiPYBBxxQeR5XO9aqUSyd1fmU55zx48fXNhYHDBhQlJI7vq5A+nv1uvA14zlQ92S8dukY4/tM3gdoSgieB/h+gqsTA8DDDz8cbZUacZ/k+z6tgMdyPt2Xs3yf9zo6TzUq5+uJdAQqcePfkJv7eX/AEkwg9aFeL97rLLfcctHW8cySOp7jVca++OKLR1v3Jdddd120ee+p17jR+ZTXRe37nUhDYqmXMcYYY4wxxhhjzOxGhw9+QgiLhBDuCCE8HUJ4KoSwX9u/zxdCuCWEMLrt/9UZds0spSgK2IctwUD7sfmxD1sCj8UmZ/r06R6LrYHHYgtgH7YEHostgH3Y2jQS8TMVwM+KolgawMoAfhxCWBrAQQBuK4piSQC3tb02fRf7sDWwH5sf+7A1sB+bH/uwNbAfmx/7sDWwH5sf+7CF6XSOnxDCSACntP23ZlEUb4QQFgJwZ1EU7dfd/fS98cu23HLL5NhNN90U7W9/+9vJMc450mheGc41AKT5C1iDrOUNuXzsWmutFe2jjjoqOY9LyV544YXJMS0R3AhaUpXbxRp+LQXO5aVV67ruuusCmJFrYezYsVEo2R0fzjnnnEWpW9fcDJwL52tf+1pyjPMLNHp9cn2TdZpauplhbS1r6AFg1VVXjbaWQuV8MKwr3XvvvZPzct/Nek7WzmvuEPahlOPDXnvtFe2xY8cmms3u+HHEiBFFmaNBdaicr4FLtgNpaXP+HZrLhXWuOU0ta8l1zHJuF+4zWpKac5rwPAKkuXs4f4vCJcO1HDNr2DWvDMN6cu1rDOunu+PDhRZaqNh5550BzJyng0tYa1sazauxyy67RPt3v/tdcozHxDPPPBNtzWnBczfPXdpfuKTmjjvumBzjPGysh99nn32S80455ZRoa6lS7j+se9ccb/y71NcLL7xwtF999dXaxuKwYcOKr371q+XnJMd4ftG1j8dfLh8R6//5+gHAO++809BndIU111wzea35Nkq0TK2Wsa2C528ds3ytdF4p9f6jRo1Kcvx0x4dcBpzzCQD53GKco49/dzmuS84999zKz+BcNJyvZZtttknOu/TSS6PNOV44Pw3QeB4CXhu4jwFpGW0tK875ELqK5DWqbSzm8sN0tdw1M//880eb1zcg9V2jHH300dE+5JBDKs8r55cSLl3eKJp/kNeV+++/P9paBplLM+eoa13M5RLlscI5OwDg8ccfjzbvB3S/yteS86kAaf4Nvs9QX/N44ZyCnAsQSHOTKmuvvXa0eV7X3CScl0bXF6YqdxSQrn2aJ+5Xv/pVtPfdd9/axuLAgQOLsi/xOtURvD/sau7Dcg4A0nsIzVFVdS+juTBff/31aOseifcgnPdG/cg5FzV3D8NtzPWfHHWNxYEDBxblOND709w+tNE5g8uj33XXXckx3svx/kBz5PD+ld+juSZ5POt9H/s3B+9Ldc9e9Rna5ziXL+cfBYB55/00COu9996rzPEzoL1/rCKE8AUAXwfwLwALFEVRru5vAlig4j27AditM99jeo7u+lATaJlZQ3f9qAn6TO/TXR/qQ2Aza+iuHzUJr+l9vLdpDbrrR01Aanofj8XWoLt+1D86mt7HPmxNGvZKCGEYgCsB/KQoivF8rJgRmtHuU/aiKM4siuIbdWV6N12nDh9q9RTT+9ThR/0ruOld6vChH97Neurwox+mz1rq8GHuL+mmd6jDj34IO2vxfUZrUIcf/dBg1mIfti4NRfyEEAZiRge4qCiKsnb5WyGEhSjsq8PYrAUWWADbb789AOD8889PjrHcZeTIkZWfsf7660eb5QFAWo5QQ/i51B7bCodAn3XWWZXfxdIxDZ+vgsOwgPQv9io541KQfGOgv4tDGbXcbxm635bEshYfTpkyJYZx52QvWr6SS9KJZKLyMzhcHEjLbXJ4rIbIcUgeh2wqLGnaeuutk2Mchsilp3PSLoXP3XzzzaOtIfhXXXVVtFVyxmHFY8eOrc2P7777Li666CIAM5d/5rDI7bbbLjlWFY6ooeMcAq2hwauvvnq0F1100Whz2VGFx6WiY4fJybsYbmNXStgC6W/poDxlLT586623cOKJJwJIZS9AGoavY4BDl/lGR0u4chipyqq4vCuXHFcZh8o8SrSkKYfd52RrBx54YLRZiqvoGsLh3xxqzeH9QBreq/Ob9uO6/Dhx4kQ8+OCDANI1DEjD+SdNmpQca1SaxbIUlc9yGDGvH1zuO8f++++fvOYyp6XUuISlLCwL5vUASCVPuRKt9913X0Nt1H5SSq8+/vjj2nzYr1+/uIfJSbsU/q28SVZp19JLLx1tnYO5pC2fxxJVIJ0LeT7l9a0jdt9992hfdtll0VZZH3+mlnrnMvBcYvnYY49tuB362+ry44cffhglXiz3AVKZs/4mhqVO7Ju2dkZb59tGJSosR8hJB3jPpeszlzIv5x5g5j0kyyJuueWW5BhLg/g3NyrTAD5df6ZMmVKbDxdaaKEoU+aUDEA6B+ncz2keVJbC8NylVJVwZ8k5kK6fl19+eeXncen4iy++ODnG8z/LVXQNYdQ3K6+8crRfeOGFaPN+FUj3qMq+++6bvK7Lj1OnTo0SL00dwVJx7dtVY0fv01h+t8kmmyTH7r333mjzGNO95m233RZtnst4L9jea4bn5UMPPTTaLF0H0jL1uXWxUXkX+x5I1606fVi1B+e+yKkhAGCVVVaJNu/lVEaqEm+Gpbk8F/KzBgBYYYUVoj169Oho77ZbGkDI36VrMPdPnVcY/i0rrrhicqzq3kr9yeNb+5VKDKtopKpXAHAOgGeKovg9HboGwE5t9k4Aqp/WmFlK22bfPmwN7Mfmxz5sDezHJsbrYkthPzYxHosthf3Y/NiHLUwjET+rAdgBwBMhhPLPU78EcCyAv4UQdgHwMoCtK95vZjFtf1myD5ufYbAfWwH7sPnxWGwN7MPmx2OxNbAPmx+PxdbAPmxhOnzwUxTFvQCqBOzfqbc5picYOHBgkqVdsA+bhwn2Y/NjH7YEHotNTgjBY7E18FhsckIImD59un3Y/HgstgD2YWvTqape3eWtt96aqSxwZ9FcO0yVpg9IyydqCUaGNdmc6+Ppp59OzmPtbaPlkrWEsebBaeSYlhNnHajmfCi1gZoXqDsMGTIklqtdZpllkmOc30GvP5PL68NoOT3ORfOb3/wm2ppAjEvhcmldLanJuQYU1vLefvvt0f7lL3+ZnPfiiy9GWzWbXDr06quvjrZqQLlU5Xe+k86rnIQ5py3vDloCncdRmQeohPNUsfZZddaaD4XhnC2s7dXShPyZWoKR4THL5XKBdExwX3j00UeT81jfrnp81iiztpqvE5DP61Pm3uDv6S79+/ePuQK0lCXrwVXzX1VCmnNjAenvLnOzlWy44YbRPu6446KtOX04lwhr7LlsKZBWmWOdNQD84he/iDaPh84kRa7S/atGmnNf6HjLrRvdIYQQqwlttdVWyTEuk6z9kvOc5Eq4ch4nzfHAr3WNq4LX0lzyRu1P3Ed57dD5MJfHj7+P83AcdNBByXm8z+A5GuhcTptGmWOOOaIPNEdYo3nGeOxcc801yTH2jVbz47wxnAdK1yruP9yXdd3iXDGao+avf/1rtHlfoWOW0dwanKuPc+Vw2WkgnfN5TwWk+XG6mpOtI7RUM+cyUzjvjl4zhudi3Y/wHMU5I7Sc7xVXXBFtzs9T5rUpOeaYY6LNZd8BYIsttog2553gfS2Qrqda/pznR86Po3MMz7Gag0L3znUwceLEmMdOc4Iw6ifO69IonIMRSHOo8fqkcwDnR8rBPlQ4RwvnZ+H8QdomHWO8DvO8mOvDCu9zOYdSnejapPmxGkHHG6P7VV5beHzw2gekex++N9P8OTyONMcs527inFSat5FzwIwbN66dX9ExvPfTXHTlWOkpHyo8x+m8wHt/zit3wQUXJOfx/lXh8ddo3iPO48N7LyA/l7z22mvR5n0Pz7MAcMYZZ0Sb526g+rmBjmfu+3rdfvazn0U796zFKbeNMcYYY4wxxhhjWhQ/+DHGGGOMMcYYY4xpUYKWd+1JBg0aVJQhbxxSD6RlrFUGwKFNLDXR8D8Oo9Xyfxy6yHILDbvjUm8c6qUh5//+97+jzaUUgTRUjn+nhmVx+KeGGrIMiaU4uTKTGg7N4WIZzWanmG+++YpSjsQhx0Aa0qsSgKpwbJVrcDgdh7ADqbSBZXM5WRGH3bGkqqto2cdc2ePvfve70dbQfYbDhbmkKzDTNX6kKIrq+vSdIIQQB77KeC688MJos+wCSK8nl3flktFAGlKqIdQ8xvjzTjvttOQ8lqioBK4KlXpx2Dkf03D0Rssx5siV8WXqGovswxxchhhIxxFLbjQUmkOBc9eVJSpawpvHHM9dOj8cddRR0WZJHpDOtTlYAqPlnllSqLJEhmWiPA6AmeaZHhmLyhFHHBFtlsgA6djheYJlJ0Bavl7Dx1mey+j6+cYbb0SbJTj82UAa4s6yIAC48soro81j/c4772y3DcDMofU8V3Ip3c5Qzj+TJk3CtGnTah+LPG/pa5VjV7Hnnnsmr/l66eezXIbHuspEtt7601ycZ511VrQPPPDAyvYqLBWpQ3q81lprRVsleCyl1b4v9MpY5L6u+0aG282SWCBfuruKww8/PHl95JFHRpulDlxOGkjHlY7n6667Lto//vGPo639c4011oi2rvGlnKoz6LxfrpkffPABpk6dWvtY5PsFIN2jjho1qvIzeAxwaWlgZlk1w5I33vfkSmcz66yzTvI6t/ax3Ce3vvGcydJrIJ2Tt9xyy2jrdWMJDMtJgfQe55///GevjEW+12H5N9D4vLTUUktFW9NhcKoN3vvoHqlKFrXZZpslr3lPc9hhhyXHeL1mX+mcx3sT3nsD1ekzciXsc9S1R5177rmLsn9wygwgvUaXXXZZciy3V69C5ZN/+tOfos17VJ3j+D7/kUceifYf/vCH5DyWR+vzC5aTV5VlB/KyV4b7i+6B+P5Efwtfg1GjRlWORUf8GGOMMcYYY4wxxrQofvBjjDHGGGOMMcYY06L0alWvoiiibIulXUAq+XnllVcqP+PQQw+N9vnnn58c4/BJDf/jEEwO2VUZBMNVi84+++zkGIdpabjfQgstFO0OQpQjKpHgahcMV7YCulaJoDtMnz49CftkOIy30UobK620UvKaw1S1ws2ZZ57Z0GdyRSOWstxxxx2V7+GQcyANn2cfqrSLQ/e0Ctl9993X7nkKV1vRSlE9xcCBA6NUJFch4dZbb01e87l8zThEEkj7pVaV4jBqDj3VKms8Nnk8a7g4V1vRdjBcHUV/M4dPshQNSMMpOXR2+eWXT85jeRf3QeDTUGkNk+4On/vc52LFA65yB6T9l0OagXRuyY2Jb3zj0yjR3HhmuQFXwwNSWRBX7lI5L9OotEthedfCCy+cHMvJu5hzzz032tpve4oBAwZEyYPKUdmvKmllKSxLtvUz+Lo3ilYwq6qqp1XqWALzgx/8IDnG8gGWjmmINo+/XGU1Hn8jRoxIzrvnnnuireHt5ftyc0V30EpFjVYu4j6rsteNNtoo2lr9kPsp+2ObbbZJzjvvvPOi/a1vfSvaKrHiClAsNQTSfsbo+snzClf9AdL9Um7+4TlC1wbuB12dLzoL9+0DDjggOXb88cdHmyVRWoGN0T0q70t5vrrkkkuS81hux5Jq3WuynGuPPfZIjnGFrpz8kMeiSpzWXnvtaOs+oQqWjgF5/3eVQYMGxb2Yrlvc17VKIsvhbr755mjnUgTstttuyesqmbimg6hKQaDrJ/clrSrHUi/uLyrv4TlUfahVh0p0TuDxp/cmjd7j1AnP/SoTqqq+pPL7Rqt08r5eq0PxPQqvkSq90r0iwxWQtfIzw3uTXDXNRlNMqJyvlJzlpEqdZfz48bG/aCoS9RvDcxKnv1DJKj9H0ErYLO/idVGrjfL44/v6XP/I9XmW6+n+hT8/Vz2YZai5PsHPGvR9ORzxY4wxxhhjjDHGGNOi+MGPMcYYY4wxxhhjTIviBz/GGGOMMcYYY4wxLUqvlnMPIRSlHj2XV0TL0bIGkrX8mhuA8wZo7h7OOXLqqadGWzW6rLNjrazm0GANIpdGBdKcCpy7iH+HomV2OT9MVynLwI0bNw5TpkyprVRmqS9VvSXn+uCcSkBa7u61116Ltmq+WRPJ5WeBNO/BYostFm3VdnJ+FdZHHnzwwcl5f/zjH6Ot+mkuVcq/RccLl4FUbXUd+ZekTGePlMrMlXzU8vLPPvtsu5+Xy4uTY8UVV4z2gw8+2NB7VCv8/PPPR1u1z6znbbQspLL55ptHm9v4+c9/PjmP57Rc3oneKOf+/e9/P9qaI6IKzTmxyy67RJvnTIXngaocB3XB+mkd95w7RHOCVOVMU7iMpur5pU/3StnaHNtuu220L7300srzeG3JrSvcn3mOBqrn1BycawCo1p+rHxn2N5DmyOL8Kbz2dIa6xuKwYcOKMp9cVanm3oDzBFWV+AXS3DC6Vp9yyimd/l7Nb8f7HvXvTjvtFG0uS6y5JThHUS4fBGoci/PNN19R5q7ROWPw4MHR1rxpnKNR8yI1Cufu4XwhmoeFc1PxdeccZwDw9a9/PdoPPfRQcozXcV7frrrqquQ8zt3U1Xw8fN2q8kMCvT8WNQ8f77V4ntH8Zjz/6T7nwAMPjPZxxx0X7RNOOCE5j/O1PP3009HWHI98L6TjY/3114/2jTfeGG0tHc/jj++RAOCmm26KNuepuu2225Lz+F5Lc6TwXhk1jsVBgwYV5ZjQ+0Xey2n+TM6jsvrqq0ebxxeQjgnNacTv43n0iSeeSM6bPn16/kd0A86xCKTXWfsJ54LiY5r7rtE8rHWNxUGDBhXlfWhuPdJ8rjyvnXzyyQ19l5Y953LsvFfisQLMvFcs4ZxrQDr+NG8gw2OW10FFc4dxHj/eR2ueuEbzVSIzFh3xY4wxxhhjjDHGGNOi+MGPMcYYY4wxxhhjTIvS61KvqmO5coFc1pNlBVrWNMfSSy8dbQ5f5bBjIA1F5ZKnnYFDs7iEtIREJnBpXiAtRcclxOeff/7kvJx8pWzHhAkTMG3atFpC9+acc86iDIHrRMhZAkt1VFLAZQ9VKsUh6XxM5QAstcvJh1jawpItIA3lfu6556KtYa4c1qehhvzbONxWJTV8PVRadcUVV/DL2sJoBwwYUJRtf/fdd5NjHA6qoaxV4egsvQPS6/LKK68kx5ZddtloP/roo9FWKSRfz1xpRR7b2p84FLtRiUpXYXkpSxuBVCLYG1KvHXfcMdo8fwBpCCuHOLNUCkjnFh1jWqayEVhKpOONSwOrrIOlAnvttVe0TzrppOQ8nhuXXHLJ5BiX36zqw4qWc5dw89rG4tChQ4uyD+t8zmHrjaISBu73O+ywQ3LsggsuiDbPPSrpZGkQy6pU4pMrBctyGC6XrHDY9+WXX95QOzSknUsd56hrLA4dOrQor5/KPHPlXXnO4GuZkwlrGV6VKTQC76nYLwBwzjnntHseMPNcXgWXcNd5pWp90XVR54gMtY3FueaaqyilMip3qVvWwbJSIF2HWYbB+xkg3QPzOFWZM3+eStN4T8OsttpqyeueLtU9aNAgAMCUKVMwffr0Hl8XWQalsideg1i6rlITLsOsfZb3PY2WVma0FDlLRaZMmZIc47nwzjvvjLZKmniN0/l65513jrams2gUkfLVNhaHDx9elGPk7rvvTo7tvvvu0T7jjDO69Pn77rtvtDntA1C9zmgKES4TzpLMK6+8Mjlvzz33jDanrADS68f7aC33zddA0zPwfSu3kdMgKLvuumvy+vzzzwfQe2NR5z9mvvnmizbfc6l0k+Wteh/DaVw4bYH2JV5rWc6q0tnc/ojnC163ciltcmk0GpXY6nzB1+2FF16w1MsYY4wxxhhjjDFmdsMPfowxxhhjjDHGGGNaFD/4McYYY4wxxhhjjGlR+kyOH4Z1akCq5zzmmGMq31elywRS7W1Z6rG981gzx/bhhx9e2SbV8bFWdp999qlsb9X3AmmeINZ6as4a1tKrLzm/SV25DOaee+5ilVVWAZCWggTSko96TbiUX6nrBoDJkycn56kWnanSpfP3AvkyhQznwlDtM7dfteDdRcs0Lr744u1+L5D6+7XXXqs1x0/Zd95///3kWC63DueD4BKSmguAc1SpP1iLy3kJuIQqkJbzVV10o3DuD851or+ZNdMLLbRQcox13DnNPeuzcyWqeyLHj+YI4zmu0RLrmtOG0dxiXDaaNeWaM4fzZKyxxhrRvuuuuyq/S3NV8Djla85tANLyxZrjhf2W++5O0Ovl3LnsMpCWXq7KSQbk860wrIPnPC85Nt100+T1PffcE20t0cq5GHJ9gde7N998s/K7eZxy/igAeOaZZ6LN8xTwqbb+pZdewkcffVTLWMzlTOM5aM0110yOaS6IKvgzcvmLeB7QUrK81nIuPS4P3hE8FruaM433d3ytOlO+mH3//PPP98hY3G+//ZJjjZYVZjgvIZD251wq0L2aAAAFu0lEQVT+Dd7Lasnw3PsYzqGheac222yzaF999dXR5pLCQON5NLk/qa+4H2repDLnzhNPPIEJEyb0eF6RHDxP8pzBpb2BNJ+OjjF+rfsZhvPsjRkzJtqdybHEezHuE7qn5r33IYcckhzjexWek/k3KpxjCphpf9EjY1H3ZF3Zk3MOJwC49dZbK8/lcct5znQsav7EEm0v57XUcuIM57PRdYSvu5YT5zWf1wru00C6F9exXt5rvPfee5gyZUotY3Hw4MFFeW/FuYy0zY3S1fx2vL/Xa3fYYYdF+6ijjoq27kNzOd6qcjjp/mjkyJGVn7Hddtu1++8XXXRRQ98LpPP1f//7X+f4McYYY4wxxhhjjJnd8IMfY4wxxhhjjDHGmBalt6VebwN4GcBnALzTa1/cPn2hDUDvtGOxoig+2/FpHdPHfAjMXu2o248TMftcu0ZoRh96LM5MM/rRYzGlGX3osTgzzehHj8WUZvShx+LMNKMfPRZTmtGHHouzpg2VfuzVBz/xS0N4uC4daDO3oS+1o7P0lXa7HV2nr7TZ7egefaXdbkfX6Sttdju6R19pt9vRdfpKm92O7tFX2u12dJ2+0ma3o3v0lXb3hXb0hTZY6mWMMcYYY4wxxhjTovjBjzHGGGOMMcYYY0yLMqse/Jw5i76X6QttAPpOOzpLX2m329F1+kqb3Y7u0Vfa7XZ0nb7SZreje/SVdrsdXaevtNnt6B59pd1uR9fpK212O7pHX2l3X2jHLG/DLMnxY4wxxhhjjDHGGGN6Hku9jDHGGGOMMcYYY1oUP/gxxhhjjDHGGGOMaVF69cFPCGH9EMJzIYTnQwgH9eL3/iWEMDaE8CT923whhFtCCKPb/j9vL7RjkRDCHSGEp0MIT4UQ9ptVbekOs7Mf7cNuf699WBOzyodt320/1oTHon3Yze+2H2vCY9E+7OZ324814bFoH3bzu+3HKoqi6JX/APQHMAbA/wAYBGAUgKV76btXB7AcgCfp344HcFCbfRCA43qhHQsBWK7NHg7gPwCWnhVtsR/tQ/vQPrQfZ18/2ofN70P7sTX8aB82vw/tx9bwo33Y/D60HztoVy86YRUAN9HrgwEc3Ivf/wXpAM8BWIic81xvXvi27x0JYJ2+0Bb70T60D+1D+3H28qN92Pw+tB9bw4/2YfP70H5sDT/ah83vQ/ux+r/elHp9HsAr9PrVtn+bVSxQFMUbbfabABbozS8PIXwBwNcB/GtWt6WT2I9t2Ie1YR92nr7mQ8B+7Ap9zY/2Yefpaz4E7Meu0Nf8aB92nr7mQ8B+7Ap9zY/2Yefpaz4E7EcATu4MAChmPHYreuv7QgjDAFwJ4CdFUYyflW1pJXrz2tmHPYN92BrYj82Pfdga2I/Nj33YGtiPzY992BrMzn7szQc/rwFYhF4v3PZvs4q3QggLAUDb/8f2xpeGEAZiRge4qCiKv8/KtnSR2d6P9mHt2Iedp6/5ELAfu0Jf86N92Hn6mg8B+7Er9DU/2oedp6/5ELAfu0Jf86N92Hn6mg8B+xFA7z74eQjAkiGExUMIgwBsC+CaXvx+5RoAO7XZO2GG9q5HCSEEAOcAeKYoit/PyrZ0g9naj/Zhj2Afdp6+5kPAfuwKfc2P9mHn6Ws+BOzHrtDX/Ggfdp6+5kPAfuwKfc2P9mHn6Ws+BOzHGfRmQiEAG2JGVusxAH7Vi997CYA3AEzBDJ3hLgBGALgNwGgAtwKYrxfa8U3MCOl6HMBjbf9tOCvaYj/ah/ahfWg/zvr/PBbtQ/uxb/znsWgf2o994z+PRfvQfuyZ/0Jb44wxxhhjjDHGGGNMi+HkzsYYY4wxxhhjjDEtih/8GGOMMcYYY4wxxrQofvBjjDHGGGOMMcYY06L4wY8xxhhjjDHGGGNMi+IHP8YYY4wxxhhjjDEtih/8GGOMMcYYY4wxxrQofvBjjDHGGGOMMcYY06L8f+bLUooez6qwAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x144 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojEDMt80Fn78",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Denoise(Model):\n",
        "  def __init__(self):\n",
        "    super(Denoise, self).__init__()\n",
        "    self.encoder = tf.keras.Sequential([\n",
        "      layers.Input(shape=(28, 28, 1)), \n",
        "      layers.Conv2D(16, (3,3), activation='relu', padding='same', strides=2),\n",
        "      layers.Conv2D(8, (3,3), activation='relu', padding='same', strides=2)])\n",
        "    \n",
        "    self.decoder = tf.keras.Sequential([\n",
        "      layers.Conv2DTranspose(8, kernel_size=3, strides=2, activation='relu', padding='same'),\n",
        "      layers.Conv2DTranspose(16, kernel_size=3, strides=2, activation='relu', padding='same'),\n",
        "      layers.Conv2D(1, kernel_size=(3,3), activation='sigmoid', padding='same')])\n",
        "    \n",
        "  def call(self, x):\n",
        "    encoded = self.encoder(x)\n",
        "    decoded = self.decoder(encoded)\n",
        "    return decoded\n",
        "\n",
        "autoencoder = Denoise()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EbXCe1UsFoCL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "autoencoder.compile(optimizer='adam', loss=losses.MeanSquaredError())"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TVeJpmz6GGm7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "8bc89e9d-ab87-44c2-a1a8-7175c976f306"
      },
      "source": [
        "autoencoder.fit(x_train_noisy, x_train,\n",
        "                epochs=10,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test_noisy, x_test))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0170 - val_loss: 0.0041\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0039 - val_loss: 0.0037\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0036 - val_loss: 0.0035\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0035 - val_loss: 0.0034\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0035 - val_loss: 0.0034\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0034 - val_loss: 0.0033\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0034 - val_loss: 0.0033\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0033 - val_loss: 0.0034\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0033 - val_loss: 0.0033\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0033 - val_loss: 0.0032\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f682c7295f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TOaJiRCGGp4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "5f5e9955-3280-4acc-f470-420e70154108"
      },
      "source": [
        "autoencoder.encoder.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 14, 14, 16)        160       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 7, 7, 8)           1160      \n",
            "=================================================================\n",
            "Total params: 1,320\n",
            "Trainable params: 1,320\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dwKR8a_GGwW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "encoded_imgs = autoencoder.encoder(x_test).numpy()\n",
        "decoded_imgs = autoencoder.decoder(encoded_imgs).numpy()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F6mqkLKpGG1A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "outputId": "a2018b30-8cff-4828-eac5-7128375a423b"
      },
      "source": [
        "n = 10\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "\n",
        "  \n",
        "    ax = plt.subplot(2, n, i + 1)\n",
        "    plt.title(\"original + noise\")\n",
        "    plt.imshow(tf.squeeze(x_test_noisy[i]))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "\n",
        "   \n",
        "    bx = plt.subplot(2, n, i + n + 1)\n",
        "    plt.title(\"reconstructed\")\n",
        "    plt.imshow(tf.squeeze(decoded_imgs[i]))\n",
        "    plt.gray()\n",
        "    bx.get_xaxis().set_visible(False)\n",
        "    bx.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}