variational_autoencoder_mnist.ipynb

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      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Khaled-Mohammed-Abdelgaber/deep-learning-projects-/blob/main/variational_autoencoder_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#importing required libraries and modules\n",
        "we will use keras to implement variational autoencoder \n",
        "we need the following layers from keras.layers:\n",
        "* **Conv2D**: well known why.\n",
        "* **Conv2DTranspose**: needed in deconvolution\n",
        "* **Flatten**: to flate the output of last convolution layer before going to Desne layer\n",
        "* **Dense**: to process the feature to get laten feature vectore  \n",
        "* **Lambda**: used to make mathmatic operations to the output of neural layers after feeding it to following layers\n",
        "* **Reshape**: used to reshape the output of convolution layers\n"
      ],
      "metadata": {
        "id": "I4gSLHna6xpG"
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    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow==1.13.2\n",
        "!pip install q keras==2.0.8"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 1000
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        "outputId": "7d356b35-9c18-4d64-b20b-f2e3bb723324"
      },
      "execution_count": 29,
      "outputs": [
        {
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow==1.13.2\n",
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            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->keras-applications>=1.0.6->tensorflow==1.13.2) (1.5.2)\n",
            "Installing collected packages: mock, tensorflow-estimator, tensorboard, keras-applications, tensorflow\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.8.0\n",
            "    Uninstalling tensorflow-estimator-2.8.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.8.0\n",
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            "    Found existing installation: tensorboard 2.8.0\n",
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            "      Successfully uninstalled tensorboard-2.8.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.8.2+zzzcolab20220719082949\n",
            "    Uninstalling tensorflow-2.8.2+zzzcolab20220719082949:\n",
            "      Successfully uninstalled tensorflow-2.8.2+zzzcolab20220719082949\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "kapre 0.3.7 requires tensorflow>=2.0.0, but you have tensorflow 1.13.2 which is incompatible.\u001b[0m\n",
            "Successfully installed keras-applications-1.0.8 mock-4.0.3 tensorboard-1.13.1 tensorflow-1.13.2 tensorflow-estimator-1.13.0\n"
          ]
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting q\n",
            "  Downloading q-2.7-py2.py3-none-any.whl (10 kB)\n",
            "Collecting keras==2.0.8\n",
            "  Downloading Keras-2.0.8-py2.py3-none-any.whl (276 kB)\n",
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            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from keras==2.0.8) (6.0)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.7/dist-packages (from keras==2.0.8) (1.21.6)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.7/dist-packages (from keras==2.0.8) (1.7.3)\n",
            "Installing collected packages: q, keras\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.8.0\n",
            "    Uninstalling keras-2.8.0:\n",
            "      Successfully uninstalled keras-2.8.0\n",
            "Successfully installed keras-2.0.8 q-2.7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
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    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "mGJN7db7ylpu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b4e557f-6109-4402-b388-a74e072a975c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using TensorFlow backend.\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ]
        }
      ],
      "source": [
        "import keras \n",
        "import tensorflow as tf\n",
        "from keras.layers import * \n",
        "from keras.models import Model\n",
        "from keras.datasets import mnist\n",
        "from tensorflow.keras import backend as K\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#first to mount the google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uZ6JEq94iWV",
        "outputId": "642ea62b-8fc8-4078-e86d-7e2554e02db6"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading datasets and preprocessing "
      ],
      "metadata": {
        "id": "r-sejwWI9C3U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train , y_train) ,(X_test , y_test) = mnist.load_data()"
      ],
      "metadata": {
        "id": "kXI-cF_zytKp"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(X_train[0,0,0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TPTUKQdw9lnQ",
        "outputId": "4fc30ed2-d57a-4445-8500-ecf4ef1c1ac0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.uint8"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.astype('float32') # we convert training data from int to float so that we can normalize it to values from 0 to 1\n",
        "X_test = X_test.astype('float32')\n",
        "#note if train and test input was int as in their original case when we divide it to 255 to normalize it, the result will be zero or 1 \n",
        "#so that the first step to convert them to float\n",
        "X_train = X_train/255.0\n",
        "X_test = X_test/255.0"
      ],
      "metadata": {
        "id": "7qZrvw6_ytNx"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(X_train[0,0,0])"
      ],
      "metadata": {
        "id": "TctsVePFytRW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ed87fc1-f393-4d7f-8417-8be1089e651b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.float32"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CvsO812E_kGA",
        "outputId": "f5bff461-e1e4-4e4b-c039-50873f7270f2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#we need to reshape the datasets to (num_of_datasets,height,width,num_channel) because Conv2D deal with 3D tensor only if not an error will appear \n",
        "#the model accept images in sequence and first dimention which is 60000 in case of train will not considered as a dimention \n",
        "#in original case the mnist image shape is (28,28) we will convert it to (28,28,1)\n",
        "train_datasets_num = X_train.shape[0]\n",
        "img_height = X_train.shape[2]\n",
        "img_width = X_train.shape[1]\n",
        "num_channels = 1 #since mnist is grey images\n",
        "test_datasets_num = X_test.shape[0]\n",
        "\n",
        "\n",
        "X_train = X_train.reshape(train_datasets_num,img_height,img_width,num_channels)\n",
        "X_test = X_test.reshape(test_datasets_num,img_height,img_width,num_channels)\n",
        "\n",
        "input_shape = (img_height , img_width , num_channels) #represent the shape model will accept each time\n",
        "\n",
        "X_train.shape , X_test.shape , input_shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uxnLCsTB-KJl",
        "outputId": "2eb9be32-7662-49e4-9ae7-a531427fac3f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28, 1), (10000, 28, 28, 1), (28, 28, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Displaying some images of datasets"
      ],
      "metadata": {
        "id": "ysXXgAG1Bk3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in np.random.randint(0 , 60000 , 5):\n",
        "  plt.figure()\n",
        "  plt.imshow(X_train[i][:,:,0]) #note that imshow accept 2D not 3D so that we need to specify the channel\n",
        "  plt.title(\"number \" + str(y_train[i]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "co3GbmBQ-KM-",
        "outputId": "b88c1e8f-618c-4729-9cbf-7d5872362737"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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wA0kQdiAJwg4kQdiBJAg7kMT/A5QeRLrf+r7oAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building model "
      ],
      "metadata": {
        "id": "QnDjZQoHwt7A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "we can not use sequential model since variational autoencoder is not sequential for all layers in bottleneck there are mean and variance.\n",
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tm27du2aertOnTojR47U53SrV69++PChenvYsGEuLi569lPqypUrx44du3jx4s2bN5OTk5OTk/Py8hwdHatVq9a4ceN27dr17NnTyclJ1+Hx8fHiDXne3t7t2rXLy8tbs2bNpk2boqOjrays6tWrN3z48FGjRpmbm8sevnfv3uPHj9+6dSsxMTEjI8PZ2bl27dqtWrXq2rXre++9Z2RkVOynJtq4ceOdO3fU2x988IGrq6tC5YSEhI0bN164cOHq1av37t1zdnZu0qTJmDFjevfubWJiUtRTP3v2bOfOnSdPnhQvDAcHh5o1a7q7u3fu3FnhwlA7derUwYMH1dtjxoypWbOm8H8uts2bN9+5c+fBgwfm5uY1a9Zs2rRp9+7d+/fvL9tgUFDQgwcPBEG4f/++WLh169arV6+qt318fFq3bq3ePnToUFRUlHq7TZs2PXr0UH6Ohw8fXrFixe3bt1NSUtLS0qpVq1a3bt169ep5eXkNHDhQn3fw0aNHERERFy9evHLlSlJSUnJyckZGhr29vZOTU926ddu2bdutW7dmzZoV2g4AAMWQnp5uY2NT1r0AgP84I5VKVdZ9KDW3b98uKCgoYSOBgYGrV6/WKnR3d9+yZYtW4f79+2fMmCHbyPDhwxcsWKBV+N133wUFBUkrh4aGtmjRQqtwxowZ+/fvl228VatWK1as0Px15sWLFzNnzjx8+LC08qhRoz799FNp4enTp7UKN23a1LJlS9kzCoLg6+sbExOjVRgVFeXg4KBVuGfPntmzZ2sV+vn5zZ8/X1fjRWVvb29ra6t//ZSUlKNHjx49erR3796F/iENAACKpGrVqklJSertefPmLVq0SFfNx48fT58+fevWrS9fvpRtJzg42Nvb293dPTo6Wl24b9++Xr166WowOjp66NCh169f11XByspq3rx5M2fOLFeukPvV9u3b9/nnn1+6dEm5WqVKlaZOnfrll1/KBhI//vij+OVw0KBBf/zxh+bex48f29vbiw+zs7PFYCYgIGDZsmXq7XfeeUeMTBQcPXq0S5cu6u2GDRsqvAhaJk6cKP2uq2n06NFisBQaGjps2DD1dqdOnY4ePbpp06YZM2YkJyfLHmtpably5Up9srSDBw+OHTtWHY3IcnJy+t///jd+/HjprnXr1o0bN069Xb58+ZSUlELz1OTkZGdnZ/XcGOXLl09NTdXnzjmpM2fOLFiw4K+//lKuZmZmNnLkyMDAQNmORUVFdejQQb29dOnSgICA/v377927V6tajx49Dhw4oFmSn5//888/z58//8WLF7pO7ebm9vPPP4vtF1u3bt0OHTqk3g4ODtb1nt66dSsgICA8PFz2r7BGjRqFhoa6ublZWlpmZWWJhyjkpgcPHhw1apQYsko5OTl9++23o0aN0lXh+++//+STT9Tbx48f79ChQ1hY2OjRox8/fixbv1atWr/99puXl5dWeZs2bc6cOaPrLIIg/PTTT+IKBXPmzFm6dKl6e+rUqT/99JOuow4ePDhr1qx//vlHVwV3d/dvvvlG4W+W5OTkL7/88rfffsvOzlboniAInTp1WrZsmZubm+xec3Nz8Z/i2NjYOnXqKLcGAIBoxYoV9+/f79ChQ8eOHa2srPQ/MCEh4fnz56+uYwBg4GxsbBwdHfWszISZpa9du3bS3E4QhClTplSqVElafu/ePf0b9/Dw+P3337XuqrawsFi1apX0D05BELZv367wt/1/W1ZW1uHDh7/44otp06bt2rXryZMnZd0jAADeXomJiW3btg0JCZHN7QRBePjwYffu3VesWKFPawUFBYsXL27ZsqVyZJWRkTF37txevXrl5OQoVPvll1969+5daG4nCMKzZ88WLVrUq1ev0r37beLEieJ2bGzsxYsXCz1k48aN4rZCjFG6vv/+++HDh+vK7QRBeP78+ahRo/73v/8pNJKTkzN16tTu3bsr5HaCIDx69Mjf39/f31/6UmsO0MzOzt65c2ehPd+0aZM4p/2AAQOKl9sdOHCgQ4cOheZ2giC8fPly7dq1bdu2ffbsWaGVFy9eLM3tBEHo3bu35sOHDx927tx5xowZyt/to6Ojvby8pDcdvgpXrlxp167dn3/+qevuyevXr7ds2XL37t36tJadnT158uTu3bsr5HaCIDx69Gj06NGyF4asRYsW9enTR1duJwhCXFxct27dgoOD9WmtJPLz8z/77LMePXoo5HaCIFy6dMnHx0fXPRCJiYlt2rRZtWpVobmdIAjHjh1r3779yZMni9ljAAB0u3//fmho6MSJE7/99ttTp05pThUGACgVTJhZ+sS7L7WUL1/e1dVVnEpFVKRIafbs2cbG8oHrjBkzpHNyZmZmRkVFdevWTf9TvOny8/MvX7584sSJCxcuiLNIAQCAMpSamtqmTRvNeefatm3bqVMnDw+P58+fX7p06fDhw1euXBEEYcqUKcqz4akNGjRox44d4kNTU9PevXu7u7s3a9YsJSXln3/+OXbsmDh07+DBg76+vhEREbJfojZv3jxp0iTNksGDB7dr187Z2dnKyiolJSUpKWnbtm2a428iIiKCgoJkB4QVT4MGDdzd3cXsMCQkpHnz5gr1s7Ozt27dqt42MjIaM2aM/ueqX79+p06dBEGIiYlJTExUF9asWVMcc9OwYUPZA8+cOXPs2DHxpJ06dWrYsGHFihUfPHhw6tSp+Ph4seZXX301aNCgBg0aSBvJzc3t3Lnz33//LZZUqlRJ/d41atQoPj7+n3/+OXTokDjZw9q1awVBWLNmjWYjlpaWAwcOFMPLkJAQPz8/5WcdEhIibhcv6Tx37lyfPn00v1t26NDh/fffd3Z2trOzS0tLS0pK+uuvvzSnzbh69eoXX3wRGBio0Oz9+/e1np2aqanp0KFDxYfx8fEtWrRITU0VS2rVqtW9e3d3d/fq1avfuHHjypUr+/fvT0tLEwQhLy9v6NChKpXqww8/LMYz1dP169c7dOiQkZEhlvj4+LRq1ap58+bJyckXL14MDw+/e/dufn7+wIEDC50ZJScnp0OHDuqpXNWsrKx8fX3VF0ZcXJz6wrh9+7Z679q1a01MTH755RflZnfs2CEOgDMxMWnSpEmLFi2srKyio6NPnDgh9qqgoOCjjz7q3r171apVxWObN2+uTogvXbokPs3GjRuLk45Ur169sBfp/+Hn5xcaGio+rFSpkq+vb9OmTZs2bZqZmXnjxo1ly5Y9ffpUvfezzz5r2LBh//79tV6lzp073717VyxxcnKaPHlyjRo1qlat+uLFi6SkpJs3b65atUq8QyIrK8vPz+/mzZuFjjwGAKB4rly5cuXKFQsLi9atW3fs2FHXN0kAQFHxDb6UWVhYuLu769pbt25daXSnzy2Tau3bt1dYscDFxaVTp07iTyqiq1evviXR3d27d0+cOBEVFZWZmVnWfQEAAP/XnDlzNHO7+fPnf/311+LDESNG5ObmDhw4cM+ePYIgFDphwOLFizVzuwYNGuzYsaNJkyaadQoKCvz8/DZt2qR+eOjQoQULFkgHsjx79kzzpitbW9s9e/Z4enpqVQsICFixYsXMmTPF5GblypWlGN0JgjB69Ggxutu0adMPP/ygsN7V3r17xW873t7e0gnMFcyYMUM9q+ekSZPE5GPEiBGa74gs8Strr169li1bVq9ePXHX8+fPhwwZsm/fPvXDvLw8XRPFT5gwQTO3a9++/ebNm2vUqKFZJysrq2/fvuJsjWvXru3YsePw4cM164wePVqM7iIjI5OTkxVmHbl+/bo4kNHZ2Vl2pgplKpVq7Nix4rtvamq6YcOGIUOGaFWbNm3a9u3b/f39xTvzgoKCvv32WzMzM10t//LLL+pmmzZtOnLkyEaNGt25c+fYsWOVKlUSl5DJysry9fXVzO0mTpwYGBgohty+vr6CINy5c6dr167qBbBVKtW4cePc3NwaNWpU1Cerp7Fjx4qBVrly5YKDg8W5VdXS09N9fHz+/vtvfe6lGz16tGZup8+F8euvv7Zv317rwtAi5naDBw/+8ccfNZO5Bw8eTJw4UYxas7OzV6xYoflPxKpVq9Qb7dq1E9ca+PLLLwcMGFDo05E6cuSIZm7n4uKyf/9+rYlDJ02a1K9fP3GQ3NixY319fTXXO1y8eLHmEgaTJk36/vvvpfc6TJo0aejQoeLrGRsbe+jQIVYNAAC8Ui9evFAvVePg4NChQ4fOnTtrThQPACgGJswsZcor3leuXFlaqH9017lz52JU0Lwx8z8pLS0tLCxs5syZ8+fPDw8PJ7cDAMCgnD59Wlw7TRCEn376SZoSmZqa7ty5U/lXeLVHjx59/vnn4sMOHTpcuXJFK7cTBMHY2DgkJERc8koQhMWLF0vXkNu4caOYiJiYmOzatUua26lbmzp16kcffSSWXL58OSUlpdDe6m/EiBGmpqbq7eTkZDGfkKU5W6Y+C8uVopEjR+7du1cztxMEwdLSMiwsTHOkYFhYmPTY6Ojo9evXiw8//PDDEydOaMUzgiBUqFAhPDxcc8TYpEmTxJXS1Dp37uzs7Kzezs/PFzNaWRs2bBC3R40apZCJ6nL06FHNSQ5Xrlwpze3UBgwYoBn/PHv2TDOqlFLHWmPHjr1y5crMmTN79uw5ZcqU7du3a75QK1as0Dz76tWrf/nlF2lgU7du3XPnzr377rvqh1lZWZMnT9b3GRZRcHCw5srZ+/fv18rtBEGwsbGJjIzUZ9W906dPb968WXzo5+encGFoDkSbPHmy1oUhZWRktH379i1btmjmdoIgVK9ePSwsTHMdOF0LjZdcbm6uuDqjIAgdOnS4dOmS9M9GR0fHvXv3ik/86dOn27dvF/eqVCpxRUxBEHr16rVixQrZMcouLi7btm0T/z0RBEGfWV4BACgVKSkpO3fuVC8OfeTIEf1/8wQAaGHUXSlTvqlE865Jkf5rpdjZ2RWjgsLSDm+0nJycc+fOnThxQnm5CE379u07fvy4rr1ubm6DBg2S3bVz507NG4Flff755+KyK5oSExMLXbane/fu6qmrpH799VfNGaikrK2t58yZI7srOjpanE1LlxEjRui6HfvLL79UXhnIxcVl9OjRsrsiIiKkA0C1zJw5U/aKffr06ZIlS5SPbd++fa9evWR3bdy4UXndIxMTE11r8MTExGj+UiarX79+LVu2lN31/fffqyeq0sXZ2fnjjz+W3RUVFfXnn38qn3rixIk1a9aUlufl5Wn+ii2Ly1sLl7csLm9NpXV59+zZUzYKeqv88MMP4natWrV0XSrGxsarVq3as2eP8vJgv/76qziIx9LSMiQkRGFI05IlS/bv3//vv/8KgqBSqX7++WdxFI7agQMHxO2hQ4d27NhR4dSzZs1avny5+PD+/ftFGu6mzMbGpk+fPuJowpCQEF2zJqSnp4vdtrS0/OCDD0qrD4Xy8PAICgrSNXn7999/Lw5oS05OTk1N1fpirDl1ZNWqVX/99VddKZqJicmaNWv+/PNP9fyBz549W7NmzfTp08UKRkZG48eP//LLL9UPQ0JCAgICZJtSqVSaSadmgqI/zevE3d1decClv7//3LlzxRFpmuNNZVWpUuXXX3/VtbegoEDzquvevbu/v7+uyg4ODuo19tQPjxw5cv369Vcx8E7zrezRo4e3t7dsNQsLi6CgoIYNGyr/vaP5BKtWrbpq1SqFCyM4OPjQoUPqCyMjIyM4OFhrwlstY8aM0Zp2UmRkZDRz5swRI0aoH165ciU3N1cz8SotYWFhsbGx4sPly5frWm3R1tZ2yZIl4lypQUFBYiZ66tQpcTpNY2PjdevWKYTQtWvXHjBggBiIFnoRQhCES5cubdu2TbPkP/zlRBe+e2vhu7csLm8tb+HlPW/ePH3uYLt58+bNmzfV30w6dOjg6upajNunAOBtRnRXymTDOT33FqrQwea2trbSwlKJ7vLz80veSGl59uzZ1q1bz5w5o/z1SCotLU3h66/CehWPHz++d++ecuO6fpJ4+fJloceKf4dLPXz4UPlw2TddLTMzs9BTK9yqHB8frzxlmZWVla5dT548KfTUumZPys/PL/TYxo0b69qVnJysfLjCLzIvXrwo9NQKPygnJCQ8evRI+XBdMjIyCj21rgtepVIVeiyXtxYub1lc3ppK6/JWqPOWyM/P14w9PvvsM13BjyAIlSpVmjJlyjfffKPQoOZvK0OGDJH94UlkZGS0cOFC8QemTZs2aUV3rq6u5cqVu3fv3r1795QDAEEQatasaW5uLl6umqt8lYrRo0eL0d3OnTtXr14t+9tWSEiI+MVs6NChsnVekc8++0xhxawOHToYGRmJn6nk5GTN7655eXk7d+4UH06ZMkXhX1pBECpWrDh37txPP/1U/fCPP/7QjO4EQfD39//qq6/Up7t48WJsbOw777wjbefYsWMJCQnq7bZt24pL+hVJrVq1Bg4cqL5OpkyZolzZxMSkXr164hSdhV4nEyZMUHhVz5w58+DBA/HhwoULlVtr06aNt7f3wYMH1Q83btyo/IEqhsTERM075xYsWKBQuX79+v369dOc4VaL1oUREBCgK9ZSq1ix4qxZs8SThoaGKn9yFZJOQRC6dOkibqtUqqSkJOlov5Lbu3evuN27d29XV1eFyu+//76FhcWLFy/Kly+flJSUnp6unjq1oKBg5MiR6ouwRYsWTk5OyifVXGqo1P+x+k+Sfrf8D3850YXv3lr47i2Ly1vLW3h5F3peLadPnz59+rSNjU2nTp3atWtXpGMB4G3GhJmlTHbSEpHCXeH6ENcb18Xa2lpaWOg0MmrKN8MaVHRXqVKlAQMGfPDBB9WqVSvrvgAAACUnTpzQ/CqiXpRLwfTp0xW+TSUnJ6uH0Kn5+PgU2gFfX18xLHz8+LHW/cVff/31rl27Ll269OTJkzZt2hTamubPVbm5uYXWLxIfHx8xlEsSTAAAIABJREFU68rMzNy9e7dstbKaLdPIyKhr164KFcqVK6f53Sw5OVlz79mzZ58/fy4+1Oe907xP/Pz581oTLjk7O2vOFa85KaumUnm5Jk+evHXr1rNnzyYnJ+u6cV6rb+J2odeJ8sDcyMhIcdva2rp169aFnl3zdSv0Xv5iEBc1FATBysqq0N/glOPGU6dOaf6CrM+SbJr/jJw5c0ZhJi4LCwtdI1rUtP6aKHShzWJQqVS7du0SHxY6TNbCwuLChQsPHz7Mysq6fv26uORhhw4dgoODjx49eu/ePYUoVFSkixAAgFfHyMiocePGgwYN6t27t8I9fAAALYy6K2UlHFenLCkpSbmC7JAy5TuaRcrRXaGp4Wtmb2/fp0+fPn36xMXFnTx58uTJk0+ePCn0qAEDBiisF6gQrA4dOlTXTDsiXfe8V69evdBpHxR+o5w5c6aue6zUFL73eHh4FHrqihUr6tr1/fffK18VCjdh9enTR9fUSSLZpFkQBBsbm0K7rTDCwN/fv9g/TzRq1KjQUyvcCf7FF18UFBQoHGtiYqJrl5eXV6E/Gev6LJuamhbabS5vLVzesgzw8i5XrtybfnlXqFBBuZH/vMuXL4vb5ubmVapUUa7v4ODQqlUrXXmD5gpwJiYm+sQ/FhYWjRo1unbtmvrhsWPHijd/YFJS0vHjxzW/b5T6tyNjY+PRo0d/99136ochISHSBdViY2PPnj2r3q5Tp0779u1Ltw8KqlevrjwcShCEatWqiUPEtCZ+OHz4sLhtb2+vucaYLk2aNFEPPxIEIS8vLyoq6r333tOsMGrUqCNHjqi3f//9d+kaitnZ2eIwTVNTU30WUyyh9PT0qKgozVUVla8TY2Nj5ehO83Xz8fHRZ6YpDw8Pcfvs2bPZ2dmlOzRT80Ndt27dQuu/++67VapU0fWHjNaF0bRp00IbbNasmampqfo/xLy8vNOnT2sOntPk6upa6E+EVlZW4qC0ok7poY/4+HjNQW9a60TKKuEcp7m5uefPn9d8YQ3tTznD1KpVK61lU//DX0504bu3Fr57y+Ly1vIWXt4rVqwIDg4udHJU4f98WW3Xrp14J4o+v90BANSI7krZK524udDoTnZGhUqVKunTuPJfdJmZmfo08vrVqlWrVq1aH3744fXr10+ePHnmzBmFUYYVKlRQmCRBgaWlZaG/VelSrly54p1UTc/kVZa5uXlJsuTKlSsX+9gKFSoU+9dqY2Pjkrxiel7wskxNTUtyavHLaDGUL1++JD+rlaTbXN5FxeVdVCW5vI2MjN7Cy/s/RnPoVYMGDfQ5xNHRUdeumJgYcdvU1LRnz576NKj5DerGjRvKlVUq1aNHj+7evZuQkBAfH3///v34+PgbN25ojvZ7dcaOHStGd+Hh4U+ePNH69yo4OFjcHjNmzGvokkiffzk1J37UmrNB873Lz89XuJtKk+ZX6+vXr2tFd4MGDZo0aZJ6MN+DBw+ioqK0YrC9e/eKX2L79etX7H8QZD1+/Fi8TtSXSkxMTHR0tPKPrVrs7OyU/4XUfN3OnDmjz+um+a0+Ly/v9u3b7777rv5dKpTmXxz6BFGCIDg6Our6Q6Z4F0a5cuXEHxxv3LihK7ordLkB4f+98/JVTDSSmJio+VCfsFN/WVlZ4uWn3rh7967ySEToov93y7fzywnfvYuK795FxeVdVGV4edva2ipPKubk5NS+ffsOHToUOr0zAEAB0d2bpNDoTraC8howIoXoLj8/X2ESdkOgHn3fuHHjUaNGRUdHR0VFXbp0SfmWKwAA8BpoRnd6/matEN2lp6eL29nZ2cWYDFBzzkZNL1++PHz48O7du7dv366wMu6r1qBBg9atW585c0YQhPz8/NDQ0MmTJ2tWEKeFNDIyes3Rna6bvvWk+d49efKkVN678uXLDxkyZN26deqHmzZt0oruQkJCxO1SmVy0oKDg1KlTu3bt2rp1q+YSdMVW6A92mq/b3bt37969W9RT6Lrmi+3VfahL68IQKQySeG0ePnwobpubm2vOY1ls9+7d27Zt244dO9T/VgAAYAhsbGzatGnTvn370r1PBQDeWkR3b5KjR48qz3hz8uRJaaF02hnZSQYUwrlCI0NNsrMQvLal8kxNTT08PDw8PLKyss6ePRsVFXX9+nXluQsAAMCrk5KSIm7Xrl1bn0MUfuXXnHeueGQnEli7du2CBQtkZy8QVa5c2cvLKzIy8lXP8zNq1Cjx5/iQkBDN6O7kyZMJCQnq7S5dupRKBqC/Eq5N8oreu1GjRonR3R9//PHzzz+L01Klp6f/+eef6m0HB4fu3buXsAP79u2bOXPmrVu3FOpUqFChY8eOt2/fvn37tj5tFvqqlnzqi1KfPOON+FCrvdIJUfSk+ZfUO++8U8LWYmJiZs2atXfvXoU6xsbG7u7uNjY2mnNmAgDwilhYWHh4eHh6ejZp0sQQ/ucFgP8Mors3SWpq6ubNm0ePHi279+bNm7L3XTZr1kyrRPamac0/wrWIS6roQ/YHiCJNHFQqKlSo0Llz586dO6elpZ06derkyZNxcXGvuQ8AAEBz1Iuev9ErfG3QnIXJ0dGxGCtCaR2iUqmGDx8eGhqq63Tt27fv0qVLly5dPDw8jI2NXVxcXnV0N2LEiICAAPV8d3///XdsbKz4c//GjRvFaqUyhux10nzvatWqpWfko0n2EE9Pz9q1a9+7d08QhCdPnvz55599+/ZV7woJCRHvHhs1alQJo8cvv/xy4cKFsrtMTU1bt27dpUsXLy+vdu3amZmZ9enTR8/orlDian+CILi5uRVj7GNJZl2T9eo+1LVr165Vq1ZR+1OMQ14nzSWOxLeyeI4dO9a3b9+nT5/K7m3YsKH6InzvvfdsbGzWrVtHdAcAeHVMTEyaN2/evn17d3d3hfX8AADFRnT3hlmzZk23bt2qV68u3fXLL79IC+3t7aWLW8j+zX/ixIlx48ZJy7Oysn766Sf9e6i5xomo1Cfq0Z+tra2vr6+vr29CQgJTaAIA8JpVqVJF3NbzNhp1DCNLczWRhg0bHj16tCR9EwRh+fLlWrldjRo13N3d27Vr5+np2apVK61fIkr4y7s+LC0tP/jgg82bN6sfbty48YsvvhAEITc3948//hDrDBo06FX3pHRpvnddu3YVh8qV3Pjx4+fPn6/eDgkJ0YzuxDolnFz06NGjWrmdo6Ojm5tb27ZtPT0927VrpzXvZSleJ5UrVxZbmzlz5vDhw0ur5WJ7dR/qbt26rVmzpiR9M0CaTzAhIUGlUhVvREJmZuaQIUM0czsLC4tmzZq1atXK09OzY8eOmu+L8Fr+sQIAvLXee++9UaNGGcLE1ADwH1aim0/x+qWlpfXp0ycyMlKzMDc3d9asWQcOHJDWHzZsmPQW4/r160tr/v3339IWcnNz/f39NVdoKJTs/9w3btwQZ63Mzs4u+cQ4xVCtWjUDvycXAID/Hs1fk+Pj4/U5RCEM0PwR/Pz58yXpmCAIWVlZCxYsEB82atTo0v/H3n3HR1Xne/w/02dSJ5M26YQQkhBCJ/TeBAVp6qKiXNC1rXrXXct6Laz3yr37W/eurHdtq6ILXBErCCol9CY9tISSSsqkt0mmz/z+OD/nNzdlCCQhIbyef/CY+Z7v+c73nJnwILzn+/meOlVYWLhp06YXX3xx3LhxLb9BfHO2wVu2bJn78fr168UH27Ztc+8Kds8993ipoN4zeb53p0+f7sSRly1b5s5Ctm7dKiYWV69edZejGDFiRHJyckde4oknnnA/Dg0N/fHHH8vKyrZt27Zy5crp06e33K+uEz8nXXffbljX/VCfOnWqIxPrmTwv0Gazeal04mYwGFqu7l21apW79qZEInnzzTdra2uPHDnyt7/97d57722W2wk36y8rAMDtKTk5mdwOALoa0d2tp7Gx8Yknnpg/f/677767ffv2t956a8GCBd9//33LnsHBwUuXLm3ZPnny5FZHfvHFF//617+eP3/ebDYXFhZ+8803d9xxx7Fjx65req3W5Ll06dJTTz3117/+9ZFHHklPT9+xY8d1jQkAAG5RI0aMcD/Ozc1tzyleFuh4lgFvampq54BtWbt2rfvrRFKp9KeffhoyZIiX/gaD4eYsZJkxY4Z7H7vLly+fOXNGEISNGze6O9xy1TKF//venTt3rhOrqUdGRs6YMUN8bDKZvv32W0EQ3MsWhQ7frn379mVnZ7ufrl279o477vDS3+l0et8P77p43rfMzMzOGrYjPH+o27Pqzm63e/kiYLMLvPll9rtaamqq59OioqJrnvL666/rdLrIyMg5c+aIn2e73f7BBx+4Ozz++OMvv/yyUqn0MojnhxYAAKCLmEymoy1kZWV197yA3oDo7laVlZW1evXqp59++h//+Mfly5db7bNq1Sp/f/+W7cHBwYmJiS3bLRbL+++/v3DhwsGDB8+YMeMPf/iD+3fL9tetjoqKarU9IyPj/fff379/v8Vi8dytHQAA9GITJkxwV+q22+1bt2713v/s2bNeoruJEyeqVCr30/YUXTSZTNHR0YmJidOmTVu+fPmRI0fchzw3CR49enRsbKz3oXbu3On51GazXfPVb4xEIvHc2/i7775zuVybN28Wn0ZGRk6aNKlTXqXjg7TfrFmz3I+tVuunn356zVNKS0sDAwNTUlJmzZr161//uq1/8QqC0Ox2uf8UBEGhUHSwyKTn50Sn03leSKtOnTplNBrdTzv4OfF8ub1797ZnC73PP/88ODh46NChd99997PPPtvpFeMXLVrk/vCcO3fumgn6119/7eXonXfe6X5ss9k8N3RsS2FhoVarHTBgwB133PHrX/86JyenHbO+cR38SdHr9Z6/eX3zzTfXPOWHH34QBKG0tPTHH38U92vMzc31XEW3ZMmSaw6ye/du9+Ou+8sKAADc5q5evbq0hTfffLO753ULc7lcPaTYBrod0d2tZNq0ae3vvHjx4rZW1wmC8Nprr7X/t9CAgIDf//737eys1Wr79OnjvU9ZWVk7RwMAALc0mUw2d+5c91P3nmRtefHFF70clcvl999/v/vpn//852vW63vvvfeKi4uvXLmya9eutWvXxsfHuw81NTW5H7f6badmmqVNXbqHrucOxD/99NPBgwfde1w98sgjnZK6eYagN2E/4NjY2IkTJ7qfvvTSSw0NDd5P+Y//+I/6+vrs7Ozt27d//fXXXrLV+fPnuws/7NixQywkKD6dO3duqzUh2s/zc9Ke2lDNdk/s4L1duHChuyCnzWZ78sknvfd3uVyvvvpqdXX16dOnN2/enJeX1+pG1B0REhIyatQo8bHT6XzppZe8dHY6nX/4wx+8dIiNjR07dqz76fPPP+95w1v15ptv1tXVZWVlbdu27euvv+7qkvgd/0n51a9+5X68evVqd+XbVmVkZLi/PalUKsVos9k9uebfV/v27fP8riQbfgMAANwSzp8/v2jRotWrV3f3RNAjEN3dSu6///7/+q//arl3XUvLly/3/gWH9PT03/72t+15UY1G8/e//z0iIqK9sxSEe++913sHVt0BAHD78AwbMjMz//nPf7bV88CBA63u3evp+eefdz+22WxPPPGEl/+Vvnjx4sqVK91PFy9eHB4e7n7qWR7z1KlT3iv1ffHFFxkZGZ4tXVo8s0+fPuPHjxcfHzlyxDMN8twJryN8fX3dj71nCZ3F86tgFRUV3mPaXbt2eVYIfOKJJzwTlGbUarU7HamtrX311Vfduyx3vLio5+eksLCwsrLSS+fTp0+//fbbni0d/Jz4+/s/9thj7qc7duz48ssvvfR//fXXPVehPfvssx159bY8/vjj7sdffvml58LEZv7xj3/k5eV5H+16Pxiey22ffvrpTs8mm+n4T8pTTz3lnqTRaHzjjTfa6iluNO5+euedd2o0GkEQUlNTPYugeN/RwGq1rlixwrPl5lT6BQAAwA0rLy9/4YUXFi1adP78+e6eC3oKortbzIIFCzZu3BgTE9NWh8DAwPfee8/7b7yixx57bPXq1d4rYc6dOzcjIyM9Pf26JvnQQw95P4XoDgCA28eYMWM8s4cVK1a8//77Lbvt3Llz9uzZ1xwtJSXFMwv84YcfRowY0eqvN7m5uVOnTvVc2tUsxhg6dKj7cXl5+auvvtrqK9pstlWrVrUMzBobG685245wF4F0uVz/+Mc/xMcTJkzwXDjYEe61XIIg7Nu3r6svRxCEuXPnepZ/fO+992bMmHH16tWWPY8dOzZv3jyxVKAgCHK5/Omnn/Y+uGfNTPcHLDQ09K677urgtD0/J4IgeCYrnlwu15o1a6ZNm9YsA+74jX311VdDQ0PdT3/1q1/99re/bTWM+eCDD/793//d/TQlJeW6ina038MPP+xZ3mP69Olijcdm1qxZ89RTT11ztAULFnjWgP2f//mftj4YR48ebfbBeOKJJ6579tfJ8ydl27Zt7ldvv/DwcM/fzv76178+++yzLccxm83/+q//6i5AqlKp3N/FVCgUnnvmvfbaa21FyGfOnBkzZkyzwqo39iH89NNPJR7aU+oTAAAA18tisbz33nszZ87ctGmT+wuIgCAIXfsVxVvRmDFjWqZZkZGRLXv279//N7/5TbNGz43WWxowYEDLU4YPH35dM0xLS/v++++/+uqrzz77zPN32ri4uPnz5//qV7/S6XTtHOqOO+4YOXLk9u3bt2/fnpmZ6f6lztfXd/78+UuXLnX/31BCQkLLmXt+BdWTQqH49NNPMzIytm/fvnv3bs/dPgICAsaPH3/HHXe0c4YAAKAXeOuttzZt2iR+d8dutz/xxBMHDx5csGDBiBEjNBrNiRMndu/e/dZbb4mZh1KptFqtXkb7y1/+sm3bNvfSoszMzKFDhy5atGjmzJmzZ89WqVQFBQVff/31n//8Z4vF4j7rpZdeGj16tOc406ZNGzhw4Llz58Snq1atOnPmzDPPPNO/f/+IiIiKioq8vLwtW7Z89tln7m8d+fr6uv+91NUFwO+7776nnnrKbDYLHvXuOr6GzK1fv37ux1lZWdHR0YMHD7bb7bGxsc1KPnaitWvXJiUl1dTUiE937tw5YMCAe++9d+bMmTNmzLDb7Xl5eR9//PHHH3/sDsAkEsmnn37quVyyVenp6UlJSRcvXhQ8btcDDzzQnnoV3sXGxt5zzz3utW7ffvvtyJEj//CHPwwaNCg2Nra2tjY/P19cCubOSzr3cxIUFLR27Vr3v5+dTufbb7/97bff3nPPPTNnzhw7dmx1dXV2dvabb765d+9ez7O++OKLDr60F5988klKSor4I2Y0Gu+6667nnntu6tSpo0aNampqOnHixJYtW9zL4675Q/3555+npqZ6fjBSU1PFD8b06dPtdntOTs5HH320Zs0a939ntPOD0XGePynfffddfHx83759m5qa5s2b98orr7RzkJUrV/74448nT54Un/7tb387c+bMsmXLBg8enJiYeO7cuX379r399tslJSXuUz766KOUlBT309///vfuXRsNBsPAgQP/7d/+bdKkSXFxcVKp9OrVqydPnvzss88yMjLEW+T5ISwvL+/ADQAAAEBXMRgMS5Ys8fxHIOBGdNfcmDFjxowZ056eiYmJnluOt0dqaqrn9yVvmEajEbf9rK2tLSwsVCqVERERgYGBNzBUcHDwkiVLlixZ4nK5CgsLGxoawsLCgoODZTKZZ7d+/fpd88vOnmQy2cyZM2fOnOl0Og0GQ1lZWWBgYHBw8I1NEgAA3NL8/Px27tw5a9as4uJisWXdunXr1q1r2XPEiBFjxox55513vIymVqv37Nkzffp0MacRBMFms23YsGHDhg1tnfLSSy/953/+Z7NGuVz+1VdfDRkyRIzHBEHYsmXLli1bWh1BIpG88sorsbGxjz76qNjipUhgp/D19b333ns964t6loXsuClTpnj+535tba0Y/HTp//KHhobu2rVr5syZFRUVYovRaPzkk08++eSTVvtLJJKPPvrogQceaM/gK1aseOGFFzxbOivp/Oijjw4c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1D7T7nxAlAy+WWjK3WuO//GlC1bvOezdNVXeb8Xp1FlZ6rGoUSKVaWIGPwVQqlD4TYnqrqt2tRj17c2njrmiu+7aKo/dQQVBSFh2q2945KAT+sdoE2649VzuhkHfBYBLrMtg2lVQsWVfycEzNV4O0fgqnYndrJSxnDYKAAAuFqI7ALhAcHDwsmXLPv/8c+flxo0bRxXd1dXVHThwwPk6KyuroqKiq6vr4lcJAAAmB4WfRnvDLXFLbzT9sFNfWlx3KK/5ZEFHeYmpe7hfAKp2fpywfGXswuu9uYXdYm48frj1zMnWosK2okJDS6PHkXiCIAgOhzdTqQZbfOYflyC7cDNJQRB8w6cMGt3JVJ49RYnE4cWDSv5xWk2U54I/F6V/oF9ktPsZdQ6Ho7uu2uGwi6M55cE9h/tXeTJZd211YEKyQqNxb7cajfrSYolC6d5o6TN011S5Lnsb6gzNDR4ThqTOGOq0QqlSGZ29hOgOwIT74kTVtoOluXk674csmBHnPMpOIeerQgAAJh7/HgOAp1WrVrmiu/fff//111/3fs/MDRs2OP793dkDDzzw3HPPjUuJAABgMpHI5OqQsOjsJdHZS/pamw0tje0lZ5tPHWs9c1JfNsg3p6buruL3/jZkdOdwdNVUtRWfbj17sqOi1NDcYOzQGzv07ls7joEokbgfz+bePnBlm9xPM7CnIAgSuXzAeK9+TfINjwyaOm3o4gSfsIjOC3en7G2oc9hsgnQU/2k1dXpuXmozGQ+/9POid9fLVBesDLSZTX2tzd21nlFff3ur6/Wgh+35TokapgC/SNapAJgwZyqadxwuz91X3Kwf/FDVgVLiw7Mz4nMyteHBg/+1DwAAJgTRHQB4uuuuux555BHnnpl6vX7v3r3XX+/VQ/GC226Zoih+5StfIboDAOBq4xMW4RMWEZY+K/WetT31NbX5e4o2vNlRfs6jW1vxaWt/38Dlbn2tTafWv1p/aF9HheeQYXizq6QolQ21XGxgu2ywkM/LGw1K7us31N0FQRDsDlVAkEebsavDbrVKL1wYNzy7ZZB0s6+lqa+lycsZ3JdLWo39AzvI1MNtDjrwDxQAxlt9a8+ugorcfbozFc1eDokI0WRnaLMztCnxYeNaGwAAGBuiOwDwFBwcfOONN+7YscN5uXHjRi+ju4qKipMnTzpfL1q0KDw8fAx3t1gse/fu/eCDD06cONHY2Nja2hocHBwZGZmVlXXvvffeeOONMtmo/+o+f/78rl27Pvvss8rKytbW1paWFqlUGhYWFhYWlpmZeeeddy5fvlyhUAw/yb59+1zn9i1evFgikQiCYDQad+zY8f777xcXFzc2NnZ1dUVGRiYmJt5+++1r164NDQ0dw08AAIBJy9JnsPQZ7GaTzWIRRFHlH6gM9Ayc3Gmi46bf/7XwjKyd677Zc+Fejtb+fn35ufCZs90b24oKd//ov7vOVww6myiVKvz8fULD+tpaTV0XLC8b6gy5C/tIhtpXUzIgVJPIR/jFYLRsZtMw7zoEx5hDQXfDpYPeMXa0u157LNRzGn53UMfAvUwBYHwYzdZdBRUfHzi3q6DSyyFKhcyZ2C2YMfghoAAAYJIgugOAQaxevdoV3W3atOn111+XevFN0Ntvv+16/cADD4z2pr29vX/+859ffPHF5uYLHpZsbm5ubm4+derUX//618jIyCeffPKRRx5RqQZ/Ft6dzWbbsmXLyy+/7Dp+z111dXV1dfWxY8fefPPN0NDQp59++tvf/vYwueCNN95oNpudrw0Gg4+Pz8mTJ2+77bb6+vqB0+7du/fJJ598/PHHn376aY2GrVcAAFeCmn07dRvf6ag4Z+zQW/sNDptNHRJ2+z+2DXomnLvQ6RnTV3/tyG+fcW+026z60mL36K717KntD68ydXV6DBdFURUcGjZjdtT8HO31N8t9NWf/8cbJv7x8sT7XpWHsaLdbrZIhftMQRUmf206VTr7hU6QjPVrkYdCwTZRI5L4aiWzk3+UsBoPc18916RcZrQoMNnbq3fsMctCgG0Nzo9fFAsAYHTpb++mhstw8XZ/J242UZ6VEO4+y0/iMYikzAACYKER3ADCIe+655+GHH7ZYLIIg6PX6L774YtmyZSOO+uc//+l8IZVKV69ePao7NjQ0LFy4sKqqavhujY2N3/ve9954440vvvhi+DVtNpvt9ttv3759uzd3b2tre/zxx996662PP/44MjLSmyGbNm168MEHLYNtS+VkNptfeumljz766MCBA2FhbMMCALjsWQyG6r073Fv62lqq9+6Y+ZX/HnFscHKqKIoOt0VvEqm0r6XJYbc7V5sZO9oPPP+UR24nSiRJK++d+dDDwdOmu4decl/fL/thLjlzT3dHeUlI6oxB3xVF0XNPS1EMTEwWxdEtxVMHe/52FH3Noutf+JM6JGz41XL/uueFK/800XEK/wCP6G7gcXouDoejt6luNPUCwCicq2nbWVCRm6c73+j5kMdQEqKCszO02Zna2IjAca0NAABcXER3ADAIjUazfPnyTz75xHm5cePGEaO748ePV1T8a3ur5cuXBwUNt32Wh/Pnipe7XAAAIABJREFUzy9evLi2ttaj3c/PLy0traioqK+vz729qKho4cKF+/fvHya9e+ihhzxyO1EUQ0JCIiIi/Pz8iouLe3p6Bn6EZcuWnTp1asTNM0+ePPnQQw8Nk9u5lJWV3XzzzceOHRuxJwAAk1zkvByJTOax6Kpow1+n3XG/MmCEr0Tbik87Ltys0mF3aGLiXVlR69lTHofbiRJJzo9/OX311wbuJGnpM3i0fPmNIsdbT121vmzI6M7U09XbeEHoJZFI/WMTBFEc1V18p3g+gdSvbzf3dKtDwsawIWdI2ky5j2dK2nL6ePx1KwbtbzMaG48dHu1dAGB4bV19uwoqtu4/d6TI24cDgvzVzo0xZyZ59VwmAACYbC7CcQIAcEVatWqV6/UHH3xgs9mG779hwwbX61HtlmmxWO6880733E6tVv/+978/f/58T0/P0aNHu7u7jx49+uyzz/r4+Lj6nDt3bpi7bN682b0ehULx6KOPNjU1tba2nj179vDhw93d3TU1Nf/85z9nzpzpPlCn0/3f//3fiDXfe++9JtO/TqxZvXr122+/febMGYPBUFxc/MYbb8ybN8+98/Hjx995550R5wQAYJJTB4X4xyV4NHbXnD/22m+GH9hdU1X28SbPVlEInpbmuuqoOGcx9Lq/H5I6Y8rs+QMDJ4fN1lFW4jnZKCOuS89qNJZ8+A/HEIftnd/1ie3f+3L/iyiGpEwfbd4WkjrTY49NfWlxm+7MoJ0dDkfF9tzy7ZubTxVYjf0DO8iUqoE7cNbu39s/YG9Pp/ZzZ6t2fTKqggFgKA6HsPNoxbo/fLrgW3/5yZ93e5PbiaKYk6n9wUNL1z+9+uG7s8ntAAC4fBHdAcDg7rrrLrlc7nyt1+v37NkzTGeHw+FKp1Qq1V133eX9jX7wgx8UFha6LhcvXqzT6b773e/Gx8c7W6RS6bx5837605+ePn16zpw5rp67du363e9+N3BCq9W6bt0616VEItm2bdvrr78eHh7u3i02NnbNmjWnT59+/vnn3ds//PDDEWtuamoSBGH27NkFBQXvvffeQw89NGPGDB8fn7S0tG9961uHDh26//773fuvX79+xDkBAJjkRKk0cm7OwPaid98s/Nvrxo72QUd1VpUd+d2znVXlHu0+oeFBU6e5Lk3dXR4d7BaLRD7IOvimk0eaCz2Xs9u8WAo/4VpOHy/bunFgu6m78/Rbf/JoVGg0EbPmDew8vOBp06PmL/RoLP3ofY9Y1Klyx0df/PS7h379sy9+/NjWh27bfN8NR19+zqNPxKy5Hi1txYWn3nhl4Il3pu6uM+/8RRgimwQA7x0/1/Crd/IX/Ndf/vvFj7fuPzfyAEGYnjjlm3cuWP/06h999fpFsxOlo19nDAAAJhX+LQeAwWk0mptvvtl1uXHjIN80ueTn57e0tDhfr1y50s/Pz8u7VFRUvPbaa67LyMjIjz/+2BXaeZg6derhw4eXLFniann++ec99tIUBGHv3r01NTWuy2eeeebGG28cpoannnpq9uzZrsvDhw8P9US8u+nTp+fl5c2d6/l9liAIUql0/fr17klhfn5+V5fnN5IAAFx2Uu68X+GnGdh+5KVndvzP2rP/eKP17KnOqvKu6squ6sqO8nNn3vrTrnXfqtq5zXOAKMYtXS6RyV0NSo2/Rxd9ma5m306Pxv721jNv/2Xgqi+byTi2T3Qp2Uymgt//snz7ZqvxP9UaO/SFb77WUe753XTswmU+YRGjvYXSP2DqLZ5PUNXm7y549dfW/gt+ZWo9c/LYq7+2mYz97a1d1ZVtxafbik/XHcrzyN6m3nK3b4TnspUz7/z56O9+Yer6z6F3hpamgld+Wbnjo9EWDAAu5xs71289ftsT/1z1043rtx5v7/L8j95AUWEB9yzL+O337/jld265bXF6SIDPiEMAAMBlgbPuAGBIq1at2rp1q/P15s2b//SnP0mHOEjGfXfKNWvWeH+L119/3T0ne/fdd/39Pb+5cyeTyV544YUFCxY4L/V6/ZtvvvnYY4+599myZYvrtVKpfPzxx4evQRTF5cuXnzx50nlpsVja2trCwsKGH/Xcc89pNIN8d+nk6+v71a9+9cUXX3ReOhyOurq6gICA4ecEAGCSC8+ck3Tbvbr3/j7wMZeWwuMthccFQVBoAtQhoaauDnNP98C1WU5BU6dlfuM77i3+8YkDu534429FUZJ0690ypUqQSDrKSk7//Y/nd28f2NMjl5q0DC2NX/z48YQbdsQsvN4nJLSvteX87u3VX3zu0c0nLGLed58a2y1iFy4LTEjurCpzbzz7zl/054rT7nsobOZsmUrVWHDo5Bt/6KqudO8jkUrT7nvI43S9sPTMpJX3Fr75qsddTr/1p/oj++MW3xCds6T19MnKnR+3njk5toIBXOV6+ky7Cio/OVi690SVl0N81QrnUXZz0mLGtTYAADBRiO4AYEi33367XC63WCyCIOj1+l27dq1YsWJgN5vN9v777ztf+/n5rVy50vtbuMdsKSkpS5cuHXHI/Pnz09PTi4qKnJcbNmzwiO7WrFmTmZlZW1tbU1OTnJw8fBbopNVq3S/7+wc57sWdWq2+4447hu9z7bXXuqI7QRBaWlrS09NHrAQAgEnumh/+vKuqov7wvqE6mHu6zD3DrTVXBgQt+P7T6uBQ98aAuMTAxOTOygsCJ0tf7+EX/9/x118Iz5zb21jXdb5iqDn79e09dTWamDivP8eEsVvMFZ9uqfh0y1AdRKl0xtr/8osc4/fR6pDQ5DtWFfz+eY/2hqP7G47ul0hlDofdYbcPHDhlbnbSrfcMbM/82qO1+bv1pcUe7e0lZ9tLzp78y+/HVicA5J06/9nh8tx9OrNlhIPVXeZOj83O0OZkatVK+ci9AQDAZYvoDgCGpNFobr31Vle6tnHjxkGju88//7yj4187Jt17772uE/JG1NTUVFX1nycrFy9e7OXAa6+91hXdFRQUGI1GlUrl/u61117r5VROHsfgWUY6L2fu3LmSkY5PiI6OHtWcAABcFmQq9eJnX979g4dbTh8fw3Clf8B1v349bvENHu2BCUlJt95z7LXfDDwszdJnqD+U594ikclEUWKzmF0thuaGrpqqyRzdiVKpRCb3ZmPPaXesnn7/17/MvWZ/6/Gu8xWlW94b+JbdNvg6SN+IyGt//Cu5j+/At1TBIdf+5Je71n2rX9824q2j5l1r6TO0Fp0abc0Arh5nK1t2FVRs2VdS2+LtmQJJsaHOxC4ydOTnMgEAwBWAs+4AYDirVq1yvd60aZPNNsjjkO+++67r9QMPPOD95Lt373a/HFV053pttVqPHDni/U3dGQyGvXv3Pvfcc7/61a/c20eM2VJSUkac3GO1n9lsHqonAACXF01U7K3rNy3+xcsBg+1yOYzIeTl3/HP7wNxOEARRKs38xv/MfnidZKRngBJX3L7kuVcisua7N/a3tzYWHHBvkUg9H9NUBQUPbHSy9Bk8Wwy9g/YcWJ6526uvnsPSZ817/H+Dpg73K4RMqcp56vmFP3th0AMFJbJBihcHfZZIFJc+94cZa/9r0CEDaaLjbl2/KShpyNoi5+bc/cHumGuvG2YSURTnfffHOT/5pXjh/uqm7k5vagBwxWts73lnR+Gqn22848l3X/3giDe5XWig722L03/12K0vrbv9nmUZ5HYAAFw9WHUHAMO588471Wq1cwPJnp6ezz///Oabb3bvYDQaXcvygoKCli1b5v3kFRUXbHv18ssvr1+/3puB3d3d7pfl5eVLliwZfojVam1sbKypqSkrK9P9W2VlpX2wDaNGFBQUNGIfH58LzkgfNPUEAOAyJff1S73nQe31N5/fvb18e25vY113zXlB8Fww5+QTGh6UnBaTvSRt9VcUfkN+8SpVKOc8+sPoaxYVvPKr5pNHPd6VqX3CZsyavvpr0dmLRVG0mYxtRYXm3h7XWI/4LSg5TRkQZOr618YAMqUqbMYsn7CIQW8doE2S+/i6ZpAqlUFJqYP29IuM0UTH9tTXOi8lUlng1GmDJm2eA6dEaZfdknDjynOb36387GOPs+jCZsyaMnv+9Pu/HqCdOtQMqqCQsPRZtfv3uFqU/oGBCUmD9xbFnP99LnJutm7TOw1H8oc6dNA/Tpu88t70Nd9UBYUMX79vROSKV98u3bqxdv/utqLC3sZ693eDp02fv+6ncYuWOez2oKnTWs6ccK6eFKXS4JR0uRc/HwBXKrPFtrOgYsfh8u2HSr0cIpNJsjO0ORna7AztyL0BAMCViOgOAIajVqtvueWWDz/80Hm5ceNGj+ju448/7u3912Ppa9askV74nPXwOjsveAr7xIkTYyuyp6dnqLfq6+u3bNmybdu23bt3X8QtK/38/C7WVFckm9VmMpr6+039fUaH3S6RSv00PpoAP1EURVH0chKL2WKxWB12hyAKCqVCJpN6PxYAcGmogoJT712buOL27rrq/vbWvtbm3sa6vtYWm8kokcmkSpXSP0ATqw1OSg1MTB50M0YPEpksav61K157u72kqP3c2Z66Gmt/n0yl9ouKCc+cGzQ1RRX4r6dnkm+/Lygpxahvt9ttgiDIfXwD4i8IveIWLbv5zxv621qdW0TKlKrAxGTfKVGD3jfja9+OvfY607+P6FNqAkLTMwft6RMafuMf/t5TX+O8lMoVmph4dWj4oJ0v+GgKhSYmXhTFOf/zo5S71zSfOqYv0xk72tUhYZFzswMTknzCIyXD/h4l9/G99qe/7qwotdusDodDIpWqQ8KDk9OGHCCKCTeujJp/bWdVRUfFua7qSouhx2rsFxyCVKGQ+2kCE5Kj5l3rH+ftN+NSpTLtvodS713bWXGu6WRB0/HDzv0/Yxcvi85e4jclWhAEUSqd990fJ62819JncNhtMpVaEx2niYr18hYAriSHi+p2FVTk5uk6e0feK9gpIzkyO0Obk5kQ4KcauTcAALhyEd0BwAhWrVrliu4+/PDDv/zlL+6n2W3YsMH1elS7ZQoDorsx81iE59TT0/PEE0/8+c9/9maGyMjIxsbGi1IMBEHo7emrLK0uPVtefKrUbLb4+qnnLZy9aPk1zgTOmxkcdkdbi76tWW8ymqRSaXT8lMCQQJlMOuIRgwCAS0+h8Q9Nm3kRJ1QFBkdfsyj6mkXD9JEqlBGz5g3TQSKXh8/M8vKO6uBQ9YKFXnYOTZs5ts/rfAZFlEg00XGa6LEcy+cfq/WPHd0aFGVAUMSsuRGz5o7hdoMSRTEoKTUoKTXtvocG7eATFjHU6kYAV4Oy2vZdBZVbD5SU1rR7OSRuSlB2RnxOZkJ85Mi7mwAAgKsB0R0AjOC2227z2DPz1ltvdb7V29u7bds25+uYmBj3I+i8cbEWUckGnONy5syZO+64o6qqaphRfn5+WVlZK1euXL16dX5+/tq1ay9KMVc5i8Xaqe+qq2o4fay4srS6sb7ZZrUplIrgc+eDwwJjtFGRMSN/l1daVKErLO3u6u3tNlitVolEEhDkH6ONmpmVGhIefAk+BQAAAIBR0Xf37yyo+PRQWX5htZdD/H1V2RnanExt5rTBl0QDAICrFtEdAIxArVavXLly06ZNzsuNGze6orsPPvjAtQvlgw8+ONqZ3U+Mk0gkRqPRfT3fmDU2Ni5ZsqSjo8OjfcqUKfPnz58/f/7MmTMzMjK02v88tG4d4gAYjJbZZG6oaTpzQrd/15G68w12u8MhOERBFATBZrVlXyeOGN05HI5TR86+83+bHP8iiIIgSsTpmSnhU0KJ7gAAAIBJZdexyp1Hyzfn6ez2wU88HWjBzPicDG1Oplbu3Z4cAADgakN0BwAjW7VqlSu6y83NfeONNxQKhSAI77//vqvPaHfLFC6M7ux2e2Fh4dy5F2E3p0cffdQ9t8vKynr22WfnzJkTETFkaDQw58PYGPtMpUUVRSdK9G2dFst/AlGz2dJn6HdvGZShp6+zo7u9taO/z/M8DJPJZLPZHA4HJ94BAAAAE+5kaeOugsqP9pc0tg159LiHFG24M7ELC+LscAAAMByiOwAY2a233uq+Z+bOnTtvvfVW5wtnh6lTp2ZmZo522tTUVPfLEydOfPnorqGhYevWra7L2NjY3bt3BwYGDj/KI7qz2+1fsoyrVn+/sbSosriw1Gwyu7c77Hab1TbiD7anu7fufEOnfpDDCwVBcK7CEwXnKj4AAAAAl1pNc9fOgortB0tPlTV5OWRKiMa5MWZyXNi41gYAAK4YRHcAMDK1Wn3HHXe89957zssPP/zw1ltv/eCDD2w2m7Plq1/96himXbJkifvliRMnvBx49uzZvLy8mJiYmJiY2NjYsLAw10qs7du3u+dDTz755Ii5nSAIp0+fdr90OLzd6QXu7Ha7zWqzmC0Wi9Vjtxz/QE381JjAIP/hZ+ju7Kkqrda3XZCkiqIok0nlCrlEKhFFcjsAAADgUjP0m3cWVH5+tPyzI+VeDlEpZM7Ebl563LjWBgAArjxEdwDglVWrVrmiu82bN7/55ptbtmxxvbtmzZoxzBkaGpqYmFhZWem8fO+9937xi1+Eh4ePOPCHP/zhZ5995nwtkUiqq6tjYmKcl67ZnObPnz/ibEaj8fPPP3dv4ei7MXA4HP/K7cwW64CNMTUBmrjEGH8vorvK0hp96wXRnUQiKlVKlVopk0nZLRMAcFmw/fsw4P+0GD33ggaAy0J+YfWugoot+0p6+80j9xYEQRBmp0bnZGizMxP81IpxrQ0AAFypiO4AwCsrV6507ZnZ1dW1Z8+eHTt2ON+aO3fu1KlTxzbtgw8++Oyzzzpfd3V1ff3rX//kk0+GH3Ly5ElXbicIwu233+7K7QRBUKvV7p0NBsOINTz11FN9fX3uLWazt/8phYvD7jD09nV1dJvNnl9WCoLgp/GJjI3Q+PsOP0l3Z09VWbW+tdO9USaXh0YEh0eGKZX8zx8AcHmQq308W3w52AnA5aT4fOvOoxWfHCytqNd7OSQhOiQnIz47MyEmPGBcawMAAFc8ojsA8IpcLr/rrrveffdd5+X3vvc9V771wAMPjHnadevWvfTSS85EUBCE7du3v/DCCz/60Y+G6t/c3Hzvvfe6tzz22GPul1lZWe6XR48eXbp06TAF5Obm/v73v/dodNUD79nt9u7OnrYWvck4SPCp9lGFhgf7+KoHvuXkcAiCw2Ho6WtuaO3rveDnL1fIIqLComIilGrVxa8bAIBxEJ29uK+t2fLvR4jkPj7x1988sSUBgDea9b07Cyo/O1J28Eytl0OC/H1yMrTZmdoZU6eMa20AAODqQXQHAN5avXq1K7o7e/as84UoimvXrh3znEFBQS+88IJ7/Pbkk0/u27fv73//e2hoqEfnY8eO3X///e5bYqalpV1//fXufWbPnu1++fTTT2dkZNx0002D3v2VV15Zt27dwPaenp7RfhDYbLaWxrbayvq+3r6B70plMqVaKZMN+c+uw263Wm02m00YcM6gUqmITYjWJscNk/wBADCpTMlaMCVrwURXAQDestrsOwsqnBtjejlEIorZGdrsTG1OplbCtvYAAOCiIroDAG/dfPPNGo3GI9ZavHixN6fTDeM73/nOp59+un37dlfLJ598kp6evnz58vT09PT0dIvFUldXt2fPno8++sh9YGBgoOv4PZeoqKhVq1Zt3LjReWkymW6++ea1a9d+5zvfiY2NDQsLa2lpqa2t3bNnz7vvvltUVOTsplKpzGaz3W53XtbX13+ZT3R1slntzQ2t1RV1hp4LojuZXKZQyNVqpVwuk0glQw23WKyGHkN/n8nh8Mzu5Ap5eGRoZEy4iqMyAAAAgIvqaHH9roKKjw+ca+kY+awBp/SpU7IztDmZ2mB/z82BAQAALgqiOwDwllwuv/POO9955x33xjVr1nz5mTdt2rRmzRr3ZK6lpeUf//jHMEPCw8P37t07ffr0gW+98cYb+fn5jY2NrpZ//OMfw8yWmZn5/vvv33HHHefOnXO25OfnP/zww6P+GFc3q9XaVN9SVVbT23PB//nVPqqQsCBNoJ847KO4xj5jS1NbV0f3wOhOKpP6B2oCgvxlcvnFrxsAAAC4+lTU63cWVHx6qPxsZbOXQ6LDA5yJXWJ0yLjWBgAAQHQHAKOwatUq9+hOKpXed999X35aHx+fLVu2vPjii08//bTRaByx/9133/3iiy8mJiYO+q6/v//GjRvXrFlTWzvC8QwKheIHP/jBM888I5fLr7nmGld0t23bNpPJpFQqR/tBrloOh8Nms/d09Xa0d1osVve3NP5+8UmxoeHBw0d3BkNffXWTvq3DbveM7iSiqFQpVD5K6dCL9gAAAACMqLPHuLOgYmdBxe5jlSP3FgRBEHzVCmdil5UaM661AQAAuPAlIACMwooVKzQajevypptuCgoKuliTP/HEE2VlZQ888MAwc954440nTpz48MMPh8rtnBYuXFhWVva73/0uJGTwB0LVavXXvva18vLyX/7yl3K5XLhw+WBnZ6dry014w2F32Kw2i9lqMVscF2ZvgcH+KelTI6LCh4/uerv7qivqWpvaXduW/ocoSqVSqVQ6/AwAAAAAhrLneNWP/7Rr4bfX/+//7fQyt5s7PfbxBxa9+f/u/87qheR2AADgUmLVHQAI1dXVXvaUy+Xd3d3ez9zZ2TmqSmJiYt59911BEM6fP19SUtLU1NTQ0BAQEBAfHx8fH5+YmOjr6+vlVEqlct26dY8//nhdXV1VVVVVVVVdXZ3NZhNFMTMz86abblKpVO79ly9fPnCrRncmk2m0n2X4Ca8kRqOpt7vXbDIPXDPnq/GNSYgK/v/s3Xd8VFX+//E7vSeZ9F5JIAQSINQggoJgA1bsXVddXd1ddffrVrd93b7qrt9V17L627UX1oYUaQpSEzqEFNJ77zPJ9N8f2Y2TO5PJJJAivJ5/ZT73nHPPneRhZN4554QGCT5zN1O3qapMHN1JpVJDoD4sMkStVZPbAQAAACN1vKRhW17ppn1nKhv8/adZanzooszEnKykyBDD8K0BAADGANEdAExGiYmJiYmJZz+OTCbrj/2WLVt29qPBK5fLZTb1trd1Wfqsnle1ek1UbESg0TDMqrtuU2VpTXNjq9PxVXQnk8vCo0Ljk2N0es25nzcAAABwnqpp6tqWV7o1tyT3dK2fXcKM+v6NMaclho/p3AAAAIZFdAcAwNlqb+moLq/t6TZ5XpLLZRqtWqFSDNXX5XI5Hc7+zTYddof7JZlcFhkTnjglXm/wd7UlAAAAcMHqtdi25ZVuzyvbuK/Yzy4Kuaw/sVs4M2FM5wYAAOA/ojsAAM6KyyW0NrVXlFR3d/Z4XpXJZEqVUi4f8heu0+my2ewOu91zf1G5TBYZE56UGqcjugMAAACGtvdE1ba80o37itu6ev3skpkanZOVmJOVGKBTD98aAABgHBHdAQBwllxNDS2lheWd7YPOQdRo1YHBAcFhRplM6qOzzWprb+3s7OhxuG2V2U8qkwYYDSHhwSq10uVyORxOm9VmtVjlcrlGq5ZKpb7PzwMAAADOb4WVLdvySj87WFJQ0exnl/goY05mYk5WYnykcUznBgAAMGpEdwAAnBWXy9XS1FZ+pspqsbnXNVp1dGxESJhRJpP56G61WNua2zrbOx0Oh+iSVCoxBOiDQ41yhcxhd5hNvT3d5p6uHp1eK1fKFQq5VOIrFAQAAADOS83tpv6NMXcdq/CzS6Be3b8xZmZq9JjODQAA4OwR3QEAMHoup8thd9qtNqvF5hy8bE6r08QkRIVFhsjkvqI7U4+5srSmrrrRZrV5XJTIZFKZXCaRSJobW/ftzKsqq+0196VOT865ZK4xJEir15zrBwIAAAAmKYfTtT2vdFte6ad7i2128d+9DWXhzIScrMScrCS5z80wAAAAJg+iOwAARs/hcFgtVqvVZrfZRZc0Ok10fFRIeLDvDTPNpt7Kspr66kabddAISpVCq9MoVcr+7h2tnXl7jp48XNi/Z+aM2VN1Bp1WILoDcCEq/uidoo/eca9oQ8OXP/nSRM0HADDWDhXWbcsr3XLgTE1T1/CtBUEQhGmJ4f2JXWgQ50YDAICvGaI7AABGr9fc197a2ddr8byk1qgjokODQ4N8b5jZa+qrqahvrGt2D/8kEklUbMSU9GRjSKDQv7bP4bRZ7Xab3eVyyeQylUalUPJLHMAFqqumqj5vn3tFHxU7znMwNzUcfPp/5z3yM31kzDjfeoycf080nlwOx+l3/ykIQsYt90z0XIDzSnld+7a80m15ZUeK6vzsEhka0H+U3ZS40DGdGwAAwNjhUz8AAEavp8tUX9No6jZ7XlIo5PoAvUarlkglXvva7Y5eU29rU1tzfUtn++A/H5YIYZGhU9ITA40BTqfT2mftNfdZrTan0ymRSORyuUqtUij4JQ4AE8BhtZ7819+PvPC0va933sM/nejpnAPn3xONs/q8fV/++rGO8jNzv/vjiZ4LcJ7oNFn6N8bcllvqZxe1StGf2M2dHjemcwMAABgHfOoHAMDotTS3FZ0saWtu97zkdDqtFqvNZne5XF779pp6z5wuKz5dZjb1ii5JBElQcEB0XKROr7XbHG2tHa1NbVaLVSqVyBUKlVqpUinlRHcAMO4qdm7Z9/uf9tTVTPREzpnz74nGU3dt9YE//6J828aJnghw/vjiSPm2vLLN+4s7TV62tfBqzrTYnKzERVmJOrVyTOcGAAAwbvjUDwCA0Wtv7jhTUN7e1ul5ydRjri6rDTQGGAJ0oj0z7TZ7e2tHTUX9iUOnS7xFd4JEMATqw6NC1Vp1X5+lrKiyOL/U1G3W6jTh0WHhUaFKlUIq9XWEHgDg3DI3Nez44QOijTq/1s6/JxpnR1/8y5EXnnJYrRM9EeB8cLK0cVte6dbc0jPVrX52SY4J6U/sYsICx3RuAAAlP0scAAAgAElEQVQA44/oDgCA0Wtv6ywvruzu7PG81NLUlvvlEZlcFpcYrVR99SfALper19x36kjR8bxTRadKG2qber2suhO0Oq0xJFCtVra3dh7ae/zgrsOdHd1RseHZizLTMlLcBwQAjIOmU8fOs5Tr/HuicXb81WfJ7YCzVNvc3b8x5v5T1X52CQ7U5mQmLspKzEiOHNO5AQAATCCiOwAARsPlcrlcrj5zX0dbp81m92xg7jFXldcaAg36AF10XGRIuNHaZ+3ttXR1dDc3tp4+WlRSUN5Y12yxWHUGndPhNJnMTofzv4MLLU2txfllMrmsrbm9tKiitbldrpCHhBlTM1JiE6LkCpnnHQEAAIDJr89q355Xui2vbNO+YucQe8uLSKWS/sQuJzNJ4v0gaQAAgPMH0R0AAKPhcrmcDqfNZrdYrIK3DxysFltnW9fxvPzK0uoZc9JnL5zZ3tLR3NBaWlheVV7X19tn7bPa7HadXhsZE261WC1VVqvDOjD4mfwyc0+v3Wbv6TbX1zTKZFKtTh0SHpyQHBsWGSLagRMAAACY/Pafqt6WV/rZwZKGVi+7Vng1IyVyUVZiTlaS0aAZ07kBAABMHkR3AACMhtViM5t6+3r7XE7vfynscrnsdkdPV0+vuU8ul1v6LD3d5q6O7obapq6O7pAwY3xyTHhkaGhESHCY0dRtqq6oqy6rrSyrcdgdLpertbm9r8/isDvsNrvD7oiICZ8xe+qsBTODw4IUSsU4PywAAAAwakVVLdvzyrbllZ4sbfSzS2x44KKspJysxKTo4DGdGwAAwCREdAcAwGhY+iztrR29pj7fzRwOp9Npqyitrq6oc7lcLqfL6XSqNar45JjMudPnLMqKSYiUy2UdbV21lfV7duTWVjU47A5BELo6u7u7egSXIJPLdHptfHLMZWuXTZs5hdwOAEahr72tbMvHbSUFPXU13XXV3TWVgiBRBQapAgLVxtDI7IXxS5aHzZwtkUpFHXsaaov+/ZYgCB0VJZ7DnnztRaU+oP/r6PmLo+bleL27uamhfMemuoN7OivLTI319j6zNjRcFxEdm7M05cp1gQnJvidv6eo49frL7hVdROS0624fuFr22SflWzd0VVeaGuuUhsCAuITwzOy4JStic5Z6jnZOnkjEYbUee/kZUTEkfWbipZf77nhmw/quqnL3ijo4JOPmb3pt3F1TVbFzc/2hfV1V5T0N9U6bVRsaro+Kjb3okpQrrjHExPkz1QEup7Nm3xeNR3ObThzpaagzNzc67baA2Hh9dLwhJi5y9vyklVdL5V5+555642VLZ4cgCA6rRXSp7uAel8PR/7UqMGjGbff5mIC1u6ti5+amE4c7K8s6K8rMLU2a4FBdeGT4rLlJy6+MzF7o+dMo0nT8cPWene6VmIVLIrMXCoJQuunDoo/eaT51zOV06COjQzNmpa6+Pnr+4mHHBM6tlk5z/8aYXxwpH761IAiCoNeqFmUm5mQlzp4aM6ZzAwAAmMyI7gAAGI3O9q7SwoqWpjbPS2ERIbMWzAiLDHG5BJfT6XS6XC6XS3BJBIlEIpHKpCq1MjImPDouIiI6TKfXSiSSQKMgkUgWLs02hgTarPb+Xk6XUxAEmUym1WmiYiOi4yOVKhVnewDAiDQeyzv0tz/U5e0byFQG2Pt6TY31giDU5e458vcng9PSVzz1clBymnsbU0Pd4ef/PNTgJ197ceBriVTiNegqXP/G/j/9wmYatDVcd211d211w5GDh579U+ScBfMffTxyzoKh7mLp6hTNIWzG7GnX3S64XPnv/L+DT/2vvdc8cKm3tbm3tbnx2KGTr71oTJm65FdPikY++yfyJFMqa/Z93njskHtRFxGVeMkqYejfW/Ze8+5fPuqwDArAsu75jmdLl9N58vUX8575nahxV01lV01lXd7e3L/8Jv7iFdnf+WFYxix/Jly+fePBp/5XlBoKgtB2prDtTKEgCPlvvaL9Y/i0626becf9qkCje5tTb7zcVV3hddi63D11uXv6vw6ISxwquqvP23fsH/9Xu3+30zHorFxTY52psa7p5JFTr7+kj4rNvPvB9Otul6lUQz1F4/E80bdSKpdHZi/c+9uf5L/1ylcP1d3VdqbwzIb379hToAoIGmo04BxyuYTteaXb8kq3HCwx9Vr97DUvIz4nKzEnM1Gl5KMqAABwoeP/hwAAGI2Otq7i/LKWxlbxBYkQERN21fUrMmZNc7pcTofDbnf0B3ESqUQqlSoUcplcJhn8UaZGq9Zo1RHRYQsunuN0OB0Oh93u6F9+J5VKlGqVXM7hdgAwYi35xzfdd4PNbPKncVtxwfp1l6546uXE5Veck7tbujo+//FDVbu2+W7WcOTgJ7evzvrmQwt+8Ev/B3farFsevK1m3xc+2rSXFm2859orX3rPzwTubKTfcKcoujM11tce+DJm0cVDdSnd8rEoihMEIeMm8ZK73tbmbY98s+HIQd8TqNq9vWr39vnf//mse77ro5nL6Tzw51+6J5RDMbc0HXnh6YqdW9a+uVGh1Q3b3k/HX30u96+/8QySRXrqa/b97qcn/vn8FX9/2zhlqv/jn3rzH+653YC4xZeQ22EcHC6q698Ys7yu3c8uqfFhOVmJOVmJEcGGMZ0bAADA1wjRHQAAo9HV0V1RUtXW0uFelMvlAUH6kDCjUqkUJIJUIpEIMolE4nIJLsElEYT+VXcSn0vnJFKJTJBJJBKZXCb0d5Gy1A4ARqyjrPjTe671M7fr57RZt//gvnXvbQ1Om36Wd7d0tn9w/Yru2mo/2x9/9bne1pZlv/ubP42dNuuWh273ndv1c1itux5/+IZP90oVSj9nMjopV1yz7/c/s3Z3uReLP37HR3RX/NE7okr0/Iv00bHulZ76mg9vWNnb1uLnNHKffqKvrXXhY78aqsGuxx8u/vhdP0cTBKGt+PTGe6+7+tUP5GqN/728c7l2/PCB0k0f+t+jp67mwxsvW/XsGz7eRnftJUVl2z71emnKVev8vy8wUhX1Hf3L7A4V1vnZJdyoX5SVmJOVODUhfEznBgAA8HXETvcAAIxGT5eppqK+s23QZ5RyhSw4zBgaEaJUKyWS/2yPKVfIFUq5UqlQKBVyhVw63DEzA72USoVSqVD40QUA4Gn3L38gSpL6aUPDwzPnGGLiJDIvC5qdNmvJxg/O8tYuh2Pr9+7ymttJZfLQ6Zn66FjPnSSLP3635NP1/ozfWpRfs/fzgZfq4JCwGbMVOr3Xxl01laIT0caCTKlMW3ujqFi2dcNQ0Wl3TVX9of2iYvr1t7u/dFitWx681WtuJ1MqwzJm6SKiPd/GE/98vu7gl15veuqNl0eU2/VrOn746It/GWkvT0de/MuIcrt+9r6+bY/e07+z67BKNn3gtHnZnFCmUiVdtnqktwaG1W22fLir4MEnP13+vX/+/vUv/cntlArZ0uyUn3xz+Us/v+HuNfPJ7QAAALxi1R0AACPjcrmcDqfdZrdarPbBG14pVcqY+Mj45BiNVj1R0wMACILQXVPlucVieOaci3/9l+C09P6XDqu17LNPPI9bq9q9bf6jj/d/rYuMzn7wMUEQOipKPHOXmXfcr9QH9H8dNferTSlLN3/omUupg4wLf/hE8sqr5RqtIAhdVeUb771OFO/teeJHMTnLNMGhfj5myNSMnJ/8tn8/TJfDUfzJe3t/8yN7X5+oWfWXOxIuufxsnsgfGbfcc+qNl90rDoul7LNPpl5zs2fjog/fFlWUhoCklVe7V06/8//aigtEzXQR0Yt+/ET8xZfJ1WpBEFoKTm759i3m5kb3Njt//NANG/Yq9YM237N0th986teeMwlKSp155/2RsxcYYuPbSwrz33ql+JP3BZfLvc2pN/8x697v9YejM267z9LZIQjCsX8847AOysmi518UNXdR/9eqwEG7U9Yf2n/4uT95uXtyWuZd3w7PzNZHRrcUnKjdv/v4K8+KzsCzdndu/d5d17z7mWd3H7ThkdqwiPaSQofFknjpFT7OzANGYdexiu15ZdtyS5s7/F3ZnJUWnZOVlJOZaNDx0wgAADAMojsAAEbG5XJZ+qwWq81uszsdTvdLSqUiKjYiJiFKTXQHABOq4chBpd5g7ekeqGjDI6948R33475kSmXq6uu6a6sO/e0P7n3d4yJ9ZEz2Q48JglCxc4vXoEsfFSt4OP7/nhdV1MbgGzbsUxuDByoB8Unr3t/+7tU5fW1fHZtq7ek++a8XBoJD32IXX7Lqb68NRDISmWzqNTcrtLrt379X1LKjrOQsn8gfgQnJkXMWiBLT4o/f9RLduVyFH7wpqk1bd6tUrnBvk//mP0RtAmITrv3gc/f1haHpM9e9t+3dq3Nspp6BormpoXD965l3Pejet+D910VJmyAIWfd8Z/7DPxtYfxk2Y/ay3z0bEJd46NlBMZvN1HP63X9lffMhQRBm3HZff/Hkay+Io7sFF8154PvihxUEQRB2//L7LqdTVExeuXrZ7/7WH+UKghA9/6Lo+RclLFu15Tu3uf9UCILQfOpoyafrp1x9ndfBRQJiEy790wvhWdmCIDht1qKP3glMTPGnIzCsU2VN/RtjFlb6u41tQpQxJyspJysxLoLTFgEAAPxFdAcAI9bU1NTU1FRRUTHRE8FXXC5XjDQjKDZeEIQva852ozPfHHZHW0u7ucfsGvwn+YIgyJXy0IiQiOgwlWpsjxSaJKaFzA/TxAqC0FFp+bTT++E6OCf0en1KSkpERIRSeUH8aAFnL3XN9alrrjc11reXFLadKWg7Uxi/ZLl7bjcgLCPLs+iwWEa9Sqm7pqq18JSouPCxX7vndv1UgcZFj/3v5z95yL1YsWOzP9GdQqdf/uSLnpNMXrVGHxXbU1/jXjS3NArjIv2GO0XRXf2h/abGel1ElHux9uAec1ODuO+Nd7q/bDp1tKumUtRmya+f9twXVBseOfc7P9z/x1+4Fyt2bBZFd545pTY0fN7DP/XcN3X2/d8v2fhBR/l/8k65Wh2cNt3rRpR+ajp5pLOiVFQMnZ654i+veDYOz8pe9bfXPr71KlH94NO/Sb587aB00yuJZNVzbxinTO1/JVUo06+/Y5TzBv6rvrV7e17ZtrzSvSeq/OwSqNfkZCXmZCXOnBI1fGsAAAAMRnQHACNTWVn52Wcj27AI40MjCdBoAsbhRlarrbayvqO10+n0iO5ksgCjISg4UKG8UH7DhmpiBEGwmZ115uFPN8HZKC4uzs7Ozs7OnuiJAF8nuogoXURU7OJLfLTx/DsMQRDsfeZRR3d1uXs8i6lDrJeactW6Lx7/nstt++WO8jPmliZt6DDnP8VfvMJrEikIQnDadFF057BavLY855JXrdn72x8POmLQ5Sr64M053/4f92bFH4l3y4zMXhiYkOxeqcvdK2qj1BtiFi7xet+p624VRXeNx/KcNqtU8Z8/d7B2d7YW5Yt6zb7/UanMy+9riVSaefdDbUX5oRlZoemZxpQ0r8ci+q/o3295Fhf/7PdDtY+YNS/lim+Ubv7IvWhqrKvL3Rebs9T3vRKWrRrI7YCzZLU5tuWVbs8r25pb0me1D99BEARBWJSZmJOVmJOZKJNxWjMAAMAoXSgfLALAuVJV5e+fmuJ81ddrKTxZ0lDX5PTY+UoilSiVCqVKIZXyUQXOvbKyMqI74JxwWCwNR3Nr9u6s/HxrR/kZLw08dlb0X+1BcXSnj44dKvuRyGSBCSkdZcXuxbai/GGju+DU9KEu6SPFa1w8T78bIzKlcuo1t5x87QX3YtFH77hHd/Zec9nWDaKOnivD6g58KaoEJgy566NSb1AFGi2d7QMVl9PZVlwQ+t8llbUeowmCELNoyBhs2rW3DnVpFOoP7RNVtGEREbPm+eiSuvo6UXQnCELTicPDRneJl14+ihkCIgfya/o3xqxp6hq+tSAIgpCeFNGf2IUE6cZ0bgAAABcCojsAGJnOzs6JngImmNViLT9T1drY7h7dSaQSjVYdEBSg1qhkZ/e3+cBQ2tvbh28EYAhOh7355NHaA1/WHdzTeCzXdzh3NqusOstLRBWXw3n4uT8POTGPJXGthfm+VwoKgiDagtKdVCleL+hy+Ltc5uxNv+kuUXTXXVPVcORg5JwF/S9LNn7gsAx6ZKUhIHnVGtE4HRXit9Fq6vbxNkrl4n/bthblD0R3nvtVCoKgChyXk7dcrs6qclHNR/L63wbTPYtdVWXD3i0oOc3/qQEiZ6pb+zfGPF4i3tJ2KFGhAf0bY6bEho7p3AAAAC4oRHcAAIyMzWqrqazv7uxxuW2YKZPJQsKM0XERGq1mAucGABCxmU2VOzeXbPqoLnePvdfsZy+JdPTRndXULaqYGusOPz9k5uTJ1Dj8FsRyjXbISx7R3XgKTEiOmpdTnzdonVnxx+8ORHfFH70j6pK29kaZx0Getp4eUaWzonTUb+OgPTz/S2MM8X+0UTO3Nrtv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      ],
      "metadata": {
        "id": "Zopc6QG_CtnN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Encoder part"
      ],
      "metadata": {
        "id": "4S618o3wvcPY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "laten_dim = 2 #this represent number of dimension or components to which each image will incoded\n",
        "input_img = Input(shape = input_shape , name = 'encoder_input') #just instantiate a Keras tensor.\n",
        "x = Conv2D(32,3,activation = 'relu',padding = 'same')(input_img)#32 filter each of size 3 * 3 \n",
        "x = Conv2D(64,3,activation = 'relu',padding = 'same',strides=(2,2))(x)\n",
        "x = Conv2D(64 , 3 , activation = 'relu',padding  = 'same')(x)  #64 filter each of size 3*3 and activation is relu\n",
        "x = Conv2D(64 , 3 , activation = 'relu',padding  = 'same')(x)\n",
        "\n",
        "#we need to store the shape of features before conv2D layers which will be used latter in reshapping\n",
        "conv_shape = K.int_shape(x) #Returns the shape of tensor or variable as a tuple of int or None entries\n",
        "print('conv output shape is ',conv_shape)\n",
        "x = Flatten()(x) \n",
        "x = Dense(32 , activation = 'relu')(x)\n",
        "\n",
        "mu = Dense(laten_dim , name = 'laten_mean')(x)\n",
        "sigma = Dense(laten_dim , name = 'laten_variance')(x)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rMQ_uhkw-KPi",
        "outputId": "41e53047-4ca0-4d2d-d6db-621e0021d137"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/backend/tensorflow_backend.py:1205: calling reduce_prod_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "keep_dims is deprecated, use keepdims instead\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "conv output shape is  (None, 14, 14, 64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "K.int_shape(mu)[1] ,K.shape(mu)[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdMK6pncq2VG",
        "outputId": "5ace16df-b516-4e91-883c-516ae71bd129"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, <tf.Tensor 'strided_slice:0' shape=() dtype=int32>)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## sampling function\n",
        "there are a lot of math of which i do not care about for more information you can visit [this tutorial](https://www.borealisai.com/research-blogs/tutorial-5-variational-auto-encoders/) "
      ],
      "metadata": {
        "id": "5qB_ro1GpcEp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sampling(args):\n",
        "  z_mu,z_sigma = args\n",
        "   \n",
        "  eps = K.random_normal(shape=(K.shape(z_mu)[0], K.int_shape(z_mu)[1]))\n",
        "  return z_mu + K.exp(z_sigma /2) * eps"
      ],
      "metadata": {
        "id": "3ja9nQ8xnEem"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z = Lambda(sampling , output_shape=(laten_dim,),name = 'Z')([mu,sigma])\n",
        "encoder = Model(input_img , [mu ,sigma , z], name = 'encoder_output')\n",
        "encoder.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QJdn9yk8rDoI",
        "outputId": "25bed969-590d-465d-c402-e9155c15c6b2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "____________________________________________________________________________________________________\n",
            "Layer (type)                     Output Shape          Param #     Connected to                     \n",
            "====================================================================================================\n",
            "encoder_input (InputLayer)       (None, 28, 28, 1)     0                                            \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)                (None, 28, 28, 32)    320         encoder_input[0][0]              \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)                (None, 14, 14, 64)    18496       conv2d_1[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)                (None, 14, 14, 64)    36928       conv2d_2[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)                (None, 14, 14, 64)    36928       conv2d_3[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)              (None, 12544)         0           conv2d_4[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "dense_1 (Dense)                  (None, 32)            401440      flatten_1[0][0]                  \n",
            "____________________________________________________________________________________________________\n",
            "laten_mean (Dense)               (None, 2)             66          dense_1[0][0]                    \n",
            "____________________________________________________________________________________________________\n",
            "laten_variance (Dense)           (None, 2)             66          dense_1[0][0]                    \n",
            "____________________________________________________________________________________________________\n",
            "Z (Lambda)                       (None, 2)             0           laten_mean[0][0]                 \n",
            "                                                                   laten_variance[0][0]             \n",
            "====================================================================================================\n",
            "Total params: 494,244\n",
            "Trainable params: 494,244\n",
            "Non-trainable params: 0\n",
            "____________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils.vis_utils import plot_model\n",
        "plot_model(encoder,show_shapes=True ,to_file='Encoder.png')"
      ],
      "metadata": {
        "id": "BSaeJWgduZHV"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "SXTXIQdFS2u5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Decoder part\n",
        "first i want to take about Reshape layer and Conv2DTranspose layer \n",
        "* **Reshape layer:** used to reshape the output of previouse layer no any thing else. it is non-trainable layer. we can say that flatten is special case of reshape layer.\n",
        "* **Conv2DTranspose:** is reverse process of convolution (Deconvolution)\n",
        "we need this layer to upsample (trainable upsampling)\n",
        "* **note that:** \n",
        "  * **UpSampling2D** is just a simple scaling up of the image by using nearest neighbour or bilinear upsampling, so nothing smart. Advantage is it's cheap.\n",
        "just repeating in special sequence.\n",
        "  * **Conv2DTranspose** is a convolution operation whose kernel is learnt (just like normal conv2d operation) while training your model. Using Conv2DTranspose will also upsample its input but the key difference is the model should learn what is the best upsampling for the job.[source](https://stackoverflow.com/questions/53654310/what-is-the-difference-between-upsampling2d-and-conv2dtranspose-functions-in-ker)\n",
        "  * in Conv2DTranspose we make traditional convolution and upsampling. the main factor that affect on the size of output of Conv2DTranspose is strides value. \n",
        "  if strides is one shape will be equal to input shape.\n",
        "\n"
      ],
      "metadata": {
        "id": "HjSP3FQNw7Ms"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "decoder_input = Input(shape = (laten_dim ,) , name = 'decoder_input')\n",
        "x = Dense(conv_shape[1] * conv_shape[2] * conv_shape[3],activation='relu')(decoder_input)\n",
        "x = Reshape((conv_shape[1] , conv_shape[2] , conv_shape[3]))(x)#we can say that flatten is special case of reshape\n",
        "x = Conv2DTranspose(32,2,padding = 'same',strides = (2,2) , activation = 'relu')(x)\n",
        "x = Conv2DTranspose(32,2,padding = 'same', activation = 'relu')(x)\n",
        "#more layers will improve the output upto overfitting\n",
        "x = Conv2DTranspose(num_channels , 2 , padding = 'same',activation = 'sigmoid',name = 'decoder_output')(x) #this layer no stides added so strides = 1 \n",
        "decoder = Model(decoder_input , x , name = 'decoder')\n",
        "\n",
        "decoder.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "laWHALjjrDt4",
        "outputId": "07336afa-1860-494f-e740-20baf3d3d1f1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "decoder_input (InputLayer)   (None, 2)                 0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 12544)             37632     \n",
            "_________________________________________________________________\n",
            "reshape_3 (Reshape)          (None, 14, 14, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_5 (Conv2DTr (None, 28, 28, 32)        8224      \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_6 (Conv2DTr (None, 28, 28, 32)        4128      \n",
            "_________________________________________________________________\n",
            "decoder_output (Conv2DTransp (None, 28, 28, 1)         129       \n",
            "=================================================================\n",
            "Total params: 50,113\n",
            "Trainable params: 50,113\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(decoder,show_shapes=True ,to_file='Decoder.png')"
      ],
      "metadata": {
        "id": "yCe5NsEMnlYJ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "Fe2hUUX9Sotg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Note on autoencoder loss function:\n",
        "**Reconstruction Loss:** The loss function we use to train the autoencoder is highly dependent on the type of input and output we want the autoencoder to adapt to. If we are working with image data, the most popular loss functions for reconstruction are MSE Loss and L1 Loss. In case the inputs and outputs  are within the range [0,1], as in MNIST, we can also make use of Binary Cross Entropy as the reconstruction loss.[source](https://www.v7labs.com/blog/autoencoders-guide)\n"
      ],
      "metadata": {
        "id": "0OybzTt0gWME"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "z_decoded = decoder(z) #apply decoder laten sample\n",
        "class CustomLayer(keras.layers.Layer):\n",
        "  def vae_loss(self , x , z_decoded):\n",
        "    x = K.flatten(x)\n",
        "    z_decoded = K.flatten(z_decoded)\n",
        "\n",
        "\n",
        "    re_loss = keras.metrics.binary_crossentropy(x,z_decoded)\n",
        "    kl_loss = -5e-4 * K.mean(1+sigma - K.square(mu)-K.exp(sigma),axis = -1)\n",
        "    return K.mean(re_loss + kl_loss)\n",
        "  \n",
        "  def call(self,inputs):\n",
        "    x = inputs[0]\n",
        "    z_decoded = inputs[1]\n",
        "    loss = self.vae_loss(x,z_decoded)\n",
        "    self.add_loss(loss,inputs = inputs)\n",
        "    return x"
      ],
      "metadata": {
        "id": "ptBLjDVKnlhl"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z_decoded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTEYhLL_twGH",
        "outputId": "eed02375-71d1-4b33-c6cd-b15c8d982419"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'decoder_1/decoder_output/Sigmoid:0' shape=(?, ?, ?, 1) dtype=float32>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# apply the custom loss to the input images and the decoded latent distribution sample\n",
        "y = CustomLayer()([input_img , z_decoded])"
      ],
      "metadata": {
        "id": "IinLuIY1nlnI"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxtxBd8Ot3p4",
        "outputId": "d72a6269-3f0e-4f71-a25b-9f39d1a9fe01"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'custom_layer_1/Identity:0' shape=(?, 28, 28, 1) dtype=float32>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "VAE = Model(input_img , y, name = 'vae')\n",
        "VAE.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LKW3IaxfZbxo",
        "outputId": "c34d9db6-2516-4dc3-ec32-525ae29a3d16"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "____________________________________________________________________________________________________\n",
            "Layer (type)                     Output Shape          Param #     Connected to                     \n",
            "====================================================================================================\n",
            "encoder_input (InputLayer)       (None, 28, 28, 1)     0                                            \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)                (None, 28, 28, 32)    320         encoder_input[0][0]              \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)                (None, 14, 14, 64)    18496       conv2d_1[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)                (None, 14, 14, 64)    36928       conv2d_2[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)                (None, 14, 14, 64)    36928       conv2d_3[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)              (None, 12544)         0           conv2d_4[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "dense_1 (Dense)                  (None, 32)            401440      flatten_1[0][0]                  \n",
            "____________________________________________________________________________________________________\n",
            "laten_mean (Dense)               (None, 2)             66          dense_1[0][0]                    \n",
            "____________________________________________________________________________________________________\n",
            "laten_variance (Dense)           (None, 2)             66          dense_1[0][0]                    \n",
            "____________________________________________________________________________________________________\n",
            "Z (Lambda)                       (None, 2)             0           laten_mean[0][0]                 \n",
            "                                                                   laten_variance[0][0]             \n",
            "____________________________________________________________________________________________________\n",
            "decoder (Model)                  (None, 28, 28, 1)     50113       Z[0][0]                          \n",
            "____________________________________________________________________________________________________\n",
            "custom_layer_1 (CustomLayer)     [(None, 28, 28, 1), ( 0           encoder_input[0][0]              \n",
            "                                                                   decoder[1][0]                    \n",
            "====================================================================================================\n",
            "Total params: 544,357\n",
            "Trainable params: 544,357\n",
            "Non-trainable params: 0\n",
            "____________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(VAE,show_shapes=True ,to_file='/content/drive/MyDrive/Colab Notebooks/VAE/VAE.png')"
      ],
      "metadata": {
        "id": "e-HshBdwZb0t"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "8iHW5gd1Se60"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "VAE.compile(optimizer = 'adam' , loss = None)\n",
        "VAE.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jsWSqcpZb3l",
        "outputId": "2ed6573d-179e-4b2c-9283-8bb15b433404"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "____________________________________________________________________________________________________\n",
            "Layer (type)                     Output Shape          Param #     Connected to                     \n",
            "====================================================================================================\n",
            "encoder_input (InputLayer)       (None, 28, 28, 1)     0                                            \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)                (None, 28, 28, 32)    320         encoder_input[0][0]              \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)                (None, 14, 14, 64)    18496       conv2d_1[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)                (None, 14, 14, 64)    36928       conv2d_2[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)                (None, 14, 14, 64)    36928       conv2d_3[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)              (None, 12544)         0           conv2d_4[0][0]                   \n",
            "____________________________________________________________________________________________________\n",
            "dense_1 (Dense)                  (None, 32)            401440      flatten_1[0][0]                  \n",
            "____________________________________________________________________________________________________\n",
            "laten_mean (Dense)               (None, 2)             66          dense_1[0][0]                    \n",
            "____________________________________________________________________________________________________\n",
            "laten_variance (Dense)           (None, 2)             66          dense_1[0][0]                    \n",
            "____________________________________________________________________________________________________\n",
            "Z (Lambda)                       (None, 2)             0           laten_mean[0][0]                 \n",
            "                                                                   laten_variance[0][0]             \n",
            "____________________________________________________________________________________________________\n",
            "decoder (Model)                  (None, 28, 28, 1)     50113       Z[0][0]                          \n",
            "____________________________________________________________________________________________________\n",
            "custom_layer_1 (CustomLayer)     [(None, 28, 28, 1), ( 0           encoder_input[0][0]              \n",
            "                                                                   decoder[1][0]                    \n",
            "====================================================================================================\n",
            "Total params: 544,357\n",
            "Trainable params: 544,357\n",
            "Non-trainable params: 0\n",
            "____________________________________________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: Output \"custom_layer_1\" missing from loss dictionary. We assume this was done on purpose, and we will not be expecting any data to be passed to \"custom_layer_1\" during training.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "feoMl8BSvy-y",
        "outputId": "28f33212-5c84-4adc-b265-0754a4cf3739"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = VAE.fit(X_train ,None , epochs = 10 , batch_size = 32 , validation_split = 0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xbhdLXH9oHWj",
        "outputId": "5e43eba4-2af5-42e7-a0e8-43472474ed72"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train on 48000 samples, validate on 12000 samples\n",
            "Epoch 1/10\n",
            "48000/48000 [==============================] - 300s - loss: 0.1905 - val_loss: 0.1897\n",
            "Epoch 2/10\n",
            "48000/48000 [==============================] - 301s - loss: 0.1886 - val_loss: 0.1885\n",
            "Epoch 3/10\n",
            "48000/48000 [==============================] - 298s - loss: 0.1871 - val_loss: 0.1869\n",
            "Epoch 4/10\n",
            "48000/48000 [==============================] - 300s - loss: 0.1861 - val_loss: 0.1863\n",
            "Epoch 5/10\n",
            "48000/48000 [==============================] - 299s - loss: 0.1852 - val_loss: 0.1856\n",
            "Epoch 6/10\n",
            "48000/48000 [==============================] - 299s - loss: 0.1844 - val_loss: 0.1848\n",
            "Epoch 7/10\n",
            "48000/48000 [==============================] - 296s - loss: 0.1838 - val_loss: 0.1852\n",
            "Epoch 8/10\n",
            "48000/48000 [==============================] - 295s - loss: 0.1832 - val_loss: 0.1843\n",
            "Epoch 9/10\n",
            "48000/48000 [==============================] - 297s - loss: 0.1827 - val_loss: 0.1846\n",
            "Epoch 10/10\n",
            "48000/48000 [==============================] - 301s - loss: 0.1824 - val_loss: 0.1828\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "VAE.save(\"/content/drive/MyDrive/Colab Notebooks/VAE/VAE.h5\")"
      ],
      "metadata": {
        "id": "a1RB4ZKR6MpM"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred = VAE.predict(X_test)"
      ],
      "metadata": {
        "id": "hwSJGUJzu5kz"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pred.shape , X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ii6U0-gMRNMf",
        "outputId": "1998209d-bad3-422d-c543-af9b0b069ecd"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10000, 28, 28, 1), (10000, 28, 28, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(pred[10,:,:,0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "hwamJMlLRQrl",
        "outputId": "8f39f1f0-6107-4a51-fef4-211e843a32e5"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc089010190>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(X_test[10,:,:,0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "xYCLvFwPRjkM",
        "outputId": "2dd28262-d9f4-4a1f-bf10-42cdf4a35227"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc088fee750>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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gCMIOBEHYgSAIOxAEYQeCIOxAEIQdCOL/AQ36KWvFB8+AAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8KicVRnHRov0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}