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            "_model_module_version": "1.5.0",
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            "_view_module_version": "1.2.0",
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            "_view_module": "@jupyter-widgets/base",
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    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ayulockin/deepgenerativemodeling/blob/master/Autoencoder_and_Variational_Autoencoder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U4eqN-N5P45",
        "colab_type": "text"
      },
      "source": [
        "# Setups, Imports and Installations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2Pas_u35J5Q",
        "colab_type": "code",
        "outputId": "0b5002ba-32c1-47d9-facc-77b0733baa6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "from tensorflow import keras\n",
        "import tensorflow.keras.backend as K"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EdNuACCZ5sgz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%capture\n",
        "!pip install wandb\n",
        "\n",
        "import wandb\n",
        "from wandb.keras import WandbCallback\n",
        "\n",
        "!wandb login 69f60a7711ce6b8bbae91ac6d15e45d6b1f1430e"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcd2uWQJ5wRj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "from tqdm.notebook import tqdm"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ST4D3VC37ux8",
        "colab_type": "text"
      },
      "source": [
        "# Let's use MNIST Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A5mg4qlY7ncx",
        "colab_type": "code",
        "outputId": "62d48dfe-bf93-43ff-f532-0a3cb6b17593",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "source": [
        "from keras.datasets import mnist\n",
        "\n",
        "# MNIST dataset\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "image_size = x_train.shape[1]\n",
        "original_dim = image_size * image_size\n",
        "x_train = np.reshape(x_train, [-1, original_dim])\n",
        "x_test = np.reshape(x_test, [-1, original_dim])\n",
        "x_train = x_train.astype('float32') / 255\n",
        "x_test = x_test.astype('float32') / 255\n",
        "\n",
        "## Just dataset info\n",
        "print(\"X_train: \", x_train.shape)\n",
        "print(\"X_test: \", x_test.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 1s 0us/step\n",
            "X_train:  (60000, 784)\n",
            "X_test:  (10000, 784)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lPr559Q98N7w",
        "colab_type": "text"
      },
      "source": [
        "# Autoencoder "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9DLCOH2ZBub",
        "colab_type": "text"
      },
      "source": [
        "#### Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9_fN41-8MGo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Autoencoder:\n",
        "  def __init__(self, input_shape, original_dim, intermediate_dim, latent_space):\n",
        "    self.input_shape = input_shape\n",
        "    self.original_dim = original_dim\n",
        "    self.intermediate_dim = intermediate_dim\n",
        "    self.latent_space = latent_space\n",
        "    \n",
        "  def build_model(self):\n",
        "    ## Initialize encoder model\n",
        "    encoder_input, encoder_output = self.build_encoder()\n",
        "    ## Initialize decoder model\n",
        "    self.build_decoder()\n",
        "    ## Join encoder and decoder\n",
        "    decoder_output = self.decoder(encoder_output)\n",
        "    ## Build autoencoder model\n",
        "    return keras.models.Model(inputs=[encoder_input], outputs=[decoder_output])\n",
        "\n",
        "  def build_encoder(self):\n",
        "    ## ENCODER\n",
        "    encoder_input = keras.layers.Input(shape=self.input_shape)\n",
        "    x = keras.layers.Dense(self.intermediate_dim, activation='relu')(encoder_input)\n",
        "    ## This is latent space\n",
        "    encoder_output = keras.layers.Dense(self.latent_space)(x)\n",
        "    encoder_output = keras.layers.LeakyReLU(0.3)(encoder_output)\n",
        "    \n",
        "    self.encoder =  keras.models.Model(inputs=[encoder_input], outputs=[encoder_output])\n",
        "    return encoder_input, encoder_output\n",
        "\n",
        "  def build_decoder(self):\n",
        "    ## DECODER\n",
        "    decoder_input = keras.layers.Input(shape=self.latent_space)\n",
        "    x = keras.layers.Dense(self.intermediate_dim, activation='relu')(decoder_input)\n",
        "    ## This is reconstruction\n",
        "    decoder_output = keras.layers.Dense(self.original_dim, activation='sigmoid')(x)\n",
        "    \n",
        "    self.decoder = keras.models.Model(inputs=[decoder_input], outputs=[decoder_output])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1SNEC8SJoVI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keras.backend.clear_session()\n",
        "ae = Autoencoder(input_shape=(784), \n",
        "                 original_dim=(784), \n",
        "                 intermediate_dim=512,\n",
        "                 latent_space=2)\n",
        "model = ae.build_model()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9knZHOwJoTf",
        "colab_type": "code",
        "outputId": "2327de7b-4f37-464f-b42b-94e95f4b65d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        }
      },
      "source": [
        "tf.keras.utils.plot_model(\n",
        "      model, to_file='model.png', show_shapes=True, show_layer_names=True, expand_nested=True, dpi=96\n",
        "    )"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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Z3lHdvW/+85//wNjYGEDz+08bGhrg7+8Pe3t7AC1fLjAzM8OSJUuQmJgIgUCANWvWyK0f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\nJQBCCDFSXAAndR0EIYSQoff/+gfu+h9VGw0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HmKhYC-smWg8",
        "colab_type": "text"
      },
      "source": [
        "> *Must Read:* You may notice that we have three different models in the `Autoencoder`. They are `encoder` and `decoder`. Using `build_model` method you can build the entire `autoencoder model`. You will soon realize why I complicated things a bit. But it's always a good practice to wrap your model in a class. And have `intermediate` models if you have any. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WD6BCdwKZHXp",
        "colab_type": "text"
      },
      "source": [
        "#### Compile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdrJnSCwJoPk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile('adam', 'mean_squared_error')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mNrEr-wpZKUC",
        "colab_type": "text"
      },
      "source": [
        "#### WandB and Callbacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3UEJlm4ip0oJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class ReconstructionLogger(tf.keras.callbacks.Callback):\n",
        "  def __init__(self, batch_size):\n",
        "    self.batch_size = batch_size\n",
        "\n",
        "    super(ReconstructionLogger, self).__init__()\n",
        "\n",
        "  def on_epoch_end(self, logs, epoch):\n",
        "    sample_images = x_test[:self.batch_size]\n",
        "    \n",
        "    images = []\n",
        "    reconstructions = []\n",
        "    \n",
        "    for i in range(32):\n",
        "        reconstruction = self.model.predict(sample_images[i].reshape((1,)+sample_images[i].shape))\n",
        "\n",
        "        images.append(sample_images[i].reshape(28,28))\n",
        "        reconstructions.append(reconstruction.reshape(28,28))\n",
        "\n",
        "    wandb.log({\"images\": [wandb.Image(image)\n",
        "                          for image in images]})\n",
        "    wandb.log({\"reconstructions\": [wandb.Image(reconstruction)\n",
        "                          for reconstruction in reconstructions]})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VD2QgVshp0j4",
        "colab_type": "code",
        "outputId": "24c10479-82ca-49aa-e0d3-2d997b6aff0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "wandb.init(entity='ayush-thakur', project='keras-gan')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/ayush-thakur/keras-gan\" target=\"_blank\">https://app.wandb.ai/ayush-thakur/keras-gan</a><br/>\n",
              "                Run page: <a href=\"https://app.wandb.ai/ayush-thakur/keras-gan/runs/358ks2iw\" target=\"_blank\">https://app.wandb.ai/ayush-thakur/keras-gan/runs/358ks2iw</a><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "W&B Run: https://app.wandb.ai/ayush-thakur/keras-gan/runs/358ks2iw"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FY15cELvtCMn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "config = wandb.config\n",
        "config.batch_size = 32\n",
        "config.epochs = 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSmoIktbZO62",
        "colab_type": "text"
      },
      "source": [
        "#### Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUXxyIH0JoME",
        "colab_type": "code",
        "outputId": "fb935d8a-c0b8-4515-d317-0d4511ed8c4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "_ = model.fit(x_train, \n",
        "              x_train, \n",
        "              epochs=config.epochs, \n",
        "              batch_size=config.batch_size,\n",
        "              callbacks=[WandbCallback(),\n",
        "                         ReconstructionLogger(config.batch_size)])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0534\n",
            "Epoch 2/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0454\n",
            "Epoch 3/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0438\n",
            "Epoch 4/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0427\n",
            "Epoch 5/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0420\n",
            "Epoch 6/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0414\n",
            "Epoch 7/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0409\n",
            "Epoch 8/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0405\n",
            "Epoch 9/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0402\n",
            "Epoch 10/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0399\n",
            "Epoch 11/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0396\n",
            "Epoch 12/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0394\n",
            "Epoch 13/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0392\n",
            "Epoch 14/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0390\n",
            "Epoch 15/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0388\n",
            "Epoch 16/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0386\n",
            "Epoch 17/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0385\n",
            "Epoch 18/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0383\n",
            "Epoch 19/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0382\n",
            "Epoch 20/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0381\n",
            "Epoch 21/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0380\n",
            "Epoch 22/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0379\n",
            "Epoch 23/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0378\n",
            "Epoch 24/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0377\n",
            "Epoch 25/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0376\n",
            "Epoch 26/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0375\n",
            "Epoch 27/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0374\n",
            "Epoch 28/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0373\n",
            "Epoch 29/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0373\n",
            "Epoch 30/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0372\n",
            "Epoch 31/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0371\n",
            "Epoch 32/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0371\n",
            "Epoch 33/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0370\n",
            "Epoch 34/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0369\n",
            "Epoch 35/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0369\n",
            "Epoch 36/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0368\n",
            "Epoch 37/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0368\n",
            "Epoch 38/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0367\n",
            "Epoch 39/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0366\n",
            "Epoch 40/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0366\n",
            "Epoch 41/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0365\n",
            "Epoch 42/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0365\n",
            "Epoch 43/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0365\n",
            "Epoch 44/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0364\n",
            "Epoch 45/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0364\n",
            "Epoch 46/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0363\n",
            "Epoch 47/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0363\n",
            "Epoch 48/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0363\n",
            "Epoch 49/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0362\n",
            "Epoch 50/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0362\n",
            "Epoch 51/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0362\n",
            "Epoch 52/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0361\n",
            "Epoch 53/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0360\n",
            "Epoch 54/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0360\n",
            "Epoch 55/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0360\n",
            "Epoch 56/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0360\n",
            "Epoch 57/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0359\n",
            "Epoch 58/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0359\n",
            "Epoch 59/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0359\n",
            "Epoch 60/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0358\n",
            "Epoch 61/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0359\n",
            "Epoch 62/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0358\n",
            "Epoch 63/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0358\n",
            "Epoch 64/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0357\n",
            "Epoch 65/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0357\n",
            "Epoch 66/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0357\n",
            "Epoch 67/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0356\n",
            "Epoch 68/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0356\n",
            "Epoch 69/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0356\n",
            "Epoch 70/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0356\n",
            "Epoch 71/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0355\n",
            "Epoch 72/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0355\n",
            "Epoch 73/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0355\n",
            "Epoch 74/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0355\n",
            "Epoch 75/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0355\n",
            "Epoch 76/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0355\n",
            "Epoch 77/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0354\n",
            "Epoch 78/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0354\n",
            "Epoch 79/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0354\n",
            "Epoch 80/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0354\n",
            "Epoch 81/100\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0354\n",
            "Epoch 82/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0353\n",
            "Epoch 83/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0353\n",
            "Epoch 84/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0353\n",
            "Epoch 85/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0353\n",
            "Epoch 86/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0353\n",
            "Epoch 87/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0353\n",
            "Epoch 88/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0352\n",
            "Epoch 89/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0352\n",
            "Epoch 90/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0352\n",
            "Epoch 91/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0352\n",
            "Epoch 92/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0352\n",
            "Epoch 93/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0351\n",
            "Epoch 94/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0351\n",
            "Epoch 95/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0351\n",
            "Epoch 96/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0351\n",
            "Epoch 97/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0351\n",
            "Epoch 98/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0351\n",
            "Epoch 99/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0350\n",
            "Epoch 100/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0350\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jIrCRv5oZUrk",
        "colab_type": "text"
      },
      "source": [
        "#### Visualize **Latent Space**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qXsm9__MEJ1",
        "colab_type": "code",
        "outputId": "82892437-c0f2-40e4-a526-233042fd8157",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "3d90a3f33f3548f7847b5e5d195b7dfc",
            "0873c44292594a738e6b7200ab9f2a42",
            "7bf995499ff34dadbcb7ec4a9d2565b6",
            "78685c16c2394c7c9fa3bd1c8b2f6ff1",
            "322f53061e3a41608a90ddfaf7a40886",
            "b8921263415840719ae1f1c906f301c9",
            "60e9a4d9e99946bcb14fa05606b69c6e",
            "38e9b315dee341dd8ec9196642d8a775"
          ]
        }
      },
      "source": [
        "## Number of examples\n",
        "n_to_show = 5000\n",
        "\n",
        "example_idx = np.random.choice(range(len(x_test)), n_to_show)\n",
        "example_images = x_test[example_idx]\n",
        "example_labels = y_test[example_idx]\n",
        "\n",
        "latent_val1 = []\n",
        "latent_val2 = []\n",
        "\n",
        "for img, label in tqdm(zip(example_images, example_labels)):\n",
        "  latent_val = ae.encoder.predict(img.reshape((1,)+img.shape))[0]\n",
        "  latent_val1.append(latent_val[0])\n",
        "  latent_val2.append(latent_val[1])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3d90a3f33f3548f7847b5e5d195b7dfc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9-oKSXTvXxQ1",
        "colab_type": "text"
      },
      "source": [
        "> Notice the use of `intermediate` model `encoder`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSKfAhOZHak2",
        "colab_type": "code",
        "outputId": "442734c2-647d-4d3a-a163-ad60617e4c6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.scatter(latent_val1, latent_val2, cmap='rainbow' , c= example_labels\n",
        "            , alpha=0.5, s=2);\n",
        "plt.xlim(-10,20);\n",
        "plt.ylim(-20,10);"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PaLSVdaFKOr1SNg+lub99DbPXr2TJlFnk5JdwQ8kILxan6I7NZV26nDwrRWGw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\n8J7fDM0tsGkL3BhDabeuHl57C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TVGk57aesW3f2gqwb75ZcjN7gvglsMIpPgDUJAL5aWC\ntBQGzL7HS0OT5vBRzcJ5YkD6/IXNij37NfPnaJYujO+6CwQUDocZ4MYiPwcy0qG4QHDBakFbO5Sf\nvcrGpz3TOnj7g7XUN28k1TOb7IxVvY+Hwq20dLxBiqeaFHfF1J3gGUKK9LJMLuh7IDUVPvax2Bt3\nd5uCtfnzoWz6zPC01nQQJhU7UggyhZNbZQUWw6dPm1SAx9UxFpHJMlvPerXHYyqYMjJ4TTewUTdS\njqacUrwk2AXMnDi8+qrpZ1mwYPTtAdqOQs02CHb3BW9XOmRXm9n6JPTVT3c2rJMsmmc0wkfyuB4O\np0PhdIBlo9ft6yRuh8XP/iNKoFvT0m5xyTrIzU5Mxm7j84p395rjLl/Ut8/d+zQtrXDpRVbM1rHB\nvLNL8aNfKKqr4F8+GTt4V5RJPvXRvv9nnJ31uWcM07pVrMt3iEColvbudwc8Hgw3EQzVEwhO096I\nM5lXXoH77zdp9cF861tmjfzJJ0/tOfnbeGfPf/Pnti28ppt6H3YIqzetHos3VBNv0cLfqel7cO5c\n+OlP4e67SRMuskijmhI8TJJm5tGjxoP7u981A6N4yKmG6ktgRj/jHpsD5l0F1RedlX3jLpegvFSM\nK3B3dCm+8X3jzf3VL0BaytC/n9th0dFt8d+PKH77pyi19cP3Pe3ep3j6OYXPP3LNglKalBRNcYGm\nrJgB7Y4rlhhTke5uNcIe+mhs1nR2wbHjcW2e5AxgWs+8M1OXoHUUr6diwONeVwWWdOKwj9CLPI3x\n6wCtuo1ckY1dnEJTEaVMYHU44JJLxmeZOm8erFsHa2M4vr3yCjQ0wBtvwBVXTPx84yXQgRXsQoTT\nkDGS5N06Qohor3Wp0ppjdFMl0lisfSxkUPGYx8yw5+sMZlppOCczXZ6fD+ecA2lp4I5zgGB3Q/mq\n0bdLEhednUZvHAGhyNDAvf+QotsH5SWQlqLZuAm2vgX//pUoVeVDr43nX9LUNkBZiWnhqqnTRCLm\n/yc5UavYtEWz7yCgBb/7s2LFUsm6c42b2O8e0Bw7DpalWRxHQqa6EkqLISMNQmGNw54sQjvTmdbB\n22bzkJd1wZDHpbTjcU2flO1YOagOcZxaZlNNhUjg+9AaOjpM2jvWuldNDfzhD6aSZ8UKyByu4nkE\nCgvhi1+M/dz998MLL8B11419vxMho5S5rKfU5cUrBg7oolrzqDqKnyjXyTKyhJMafPxNHScVO3da\nM4dNqwshcE52p4DLBZ/5zOQeI8mIFBdKPvsJkALycgZ+b4IhzcN/04TD8KFbJTdfL3hhi0ZFITrM\n5PuSCwXHTkBFKTQ2RfnhzzUZafDxO63edrL//w+KXXugegbMrtYcPWbWvwEOHNZYEkqK4JrL48ui\n5OVK1p6ryEgT2Ee5q+/YabzD168Fh+Psy9KcKUyr4B0KN6NUBJlgYY/pRq7IJaCDZIlxBM+RePll\n+NnP4Jpr4JZbBjxVG4ngzc8n7frre9d0E055OXxwZH1JpTWCflX+IR9s+gEIC9Z9Fmym0Gq7amG7\nbmWDLCJf9M1It6sW9usOLpKFvTNphEBklhPVIXxESKEvmyEALzZj1dmzSpSBg3zcFAj3KdNaTjK9\nOWnPORiHHVYuFXR0mhY0h8Piu1+NEgpDdeXAgV0kotm1V1PboFk830iY/v6/NfsPQkF+b0KHYMhU\nrHu9cMNVksULBMdrjMouwPLFghSvpLJckJEW3/XpdAiuuTS+geZP/4+iswu8Hsnac+N6SZJpyLSK\nklpHz4rgnSdzyGMSDFeCQSP35PMNePhoOMzPWlvJsCy+cNNNEw9Y3R3wtz9AXjGsfU/c6XefjvCY\nOoYXG1fKEtO+5W+FznpAmCrqVBO8a7SPNkI06eCA4H1Ad3IMH80EyewxAGnVQV6K1nOEbjKFg5tl\nZa88qxSCq2XpAJ/tFGHnBqt8Yn+DJGcFQgjWnTvw+i4riR0kd+zU/Pb/Krq6oa1dcfO1FlXlkJUJ\nF19gAixAfQNoJZg/21jKWlJQXtK3H49bsHTh5A0qly2Eg0dg5jgsIuobNX95XLFonuD8c5Kz9qlk\nWkVJuz2TYLiRYDiKx1WenBWNlYsuMu1dBQOtO91C4BKCzGFaSMZMaxOcOAitjXDB1WbWHAdBonQT\nIYQiijbr0+nFsPIOkBak9hnRXCALmEtGn8FJDxfKApoJUtGvLeyw7mIXbUSAIjxD1r2lEDHXwsdK\nSCle1rUU42WWlWyOTTKQnGxT7e6ww8oeA471a234/RG2vWva0QrzJUUFsOFCQWY6cbW0HTlmZsoL\n5ibm+/vRD43/tt/SCs0tcPio5vxzEnI6ScbJNJNHXaYffuLLaDQF2ZcihY3GtpfxuMpJ886c6tM7\nrQlpjQUjVl/HjVJwaBekpkNeyejb96NRB3AgSReJa2fq1hF26lZK8ZIr3Il5jzF4KVrDQ3ovHiRf\ntc5LfN93NAL7njGDoZnrzYAmyWnPp/41QkOTcQO7asPYArAvoPjclxWhMPzPf5bMmWRv6hO1msf+\nrli5VLB8sTlWbb1m5x7N8iWCVK8xQsnNhvQ4U/pJRmc88qjTLO8hSfFU43VXYbM8NLZtprn9FWoa\nH53qE5t8IkHY8RDsfsoEx/HQ1GTajjZvHvKUQ4jEBTUpYcb8MQdugFzhGhK4/TrCg9FDPBk9jhrH\nYNIrbKyUuRRIz8jv8bXX4B//GPD33ac62KZaiMZx3BLcZABFcc7itdZjez+hLqOS1rgXwtPHpzxJ\nH1prWtp0r7d2PLz/JthwIeOaqdpsxgTF7YL0QbpP3T7N/kNjO5fRqG/U1DUYffaTvLxV8ewmzc7d\nGssSVFeKZOCeBkyrtDlARmqfa5jXXUF75zbcrrEHidOOYCe0HzezrmgI5DAKWVu2wG9+A3fcAWvW\nmArzPXtMhfnBg/D001BXB+dObSWK0po6/KRiJ3WUdjgfUdoJ002UTt0ntJJQfD74yU9MXUBlJTUV\n+bykGqjFj0NICoQbpTT1BFhERkyVqgorky/oNUhkXLPuLbqRfbqDK2UJuSIOxTNXOsy5ygyOnCmj\nbz8BTnQrLAEFnmk2fp9k9h1UPLlRc+H5gkXzxv7eDxyGvz6umFUN110RX2Zk1TIbq5aN+VAAOGyS\ne78Iquf3/mx8QbHtHbhig2DxPDheA8VFfWvr42HhPIHXI0hL0dTWawry4JzlArdbM2dWMmBPJ6Zd\n8O5PqmcGsys+N9WnMWm0BhV/OhBmcZbkvPxsmHeNEdsYSdry4EGorzf/rlkDhw/DffeZ0tX77oN7\n7jHqZxh3s126jXzhHlD0NVFadJAXVB2zRTrzZOy13xM97VjZOLnRqhhxf9nCyVWylOOqmwf0YZaQ\nxUqR4II+txuuvtpYmRYXc5R2jtNNLm6qRArZOLlPbaeLMFFZyfJhCgpdIn6982YdxEeETsLkEkfw\nFgJyxlFFNEaaA4p/3x7AEvD15W5Sz6Ke4LoGaG03qeBF88b+ekuaj2msuukTQUjB4I9IKU1+npmV\n5+XA5tc0m7ZozlslWL92/J+n3SaYNUPzy98bTfVbr5eUFktKJ8+uIMk4mdbB+0xnX4fihboINT7J\neQV2yKoY/UXXX2+EUmbPNv/PzDQFavn5xu3r2mt7Nz1CF8/pOgq1m5utyoSdd732c4QuLC2YN4xz\nWAo2vNjIGyFo1Sofh+lisciiQLhpFgGU1oQZ57LBSAgBN93U+9/FOosM6aBEeEnpyQzk4yKCGhJo\no1ojGbuJzXpZSBshCiZLnW2cOC1BhkPgkHC2tfmes0xQmC8oHsHxdiQqywUfu0OCUBw8oqgoHV5L\nPBHU1GkeeFixcN7AoPzkRsXeA3DL9ZKiAoHPp0hLid+XfCSEEORkQygEnul16SbpRzJ4TyELMy3u\nnOlgZvoYvvweDyxd2vf/jAz4zndiblqAmzmkUy4S6wldLdKwCTnibDJTOLldVo0Y8LbqJg7SSRp2\n5otM5ooMioSHNOIrZosoxTu0UoKXnEHLDCcHAcOpo7mFjTmib+ARikapwU83EWy675zrtJ8n1XHm\ninRWi7F5k3uEDc80/Iql2AX/ttSFEEx7r/lE43AIqic4jk1NEXzzB5p9BzXrLwC3W3PxBTIhtp2D\n6egEn9/Inw54vAtCYWOWAjB3lmTOTJ2QDp1AUHPt5QIQYzJ42fqGYt9BzVUbZK8YTZLJY/rdWc4i\n3DbB5aWTJ4+aIuxcZiU+32UXkpmM7po22o1kucgiUzsoF2Z9VwrR27sdD1to5BF1lNVPvc6Nr9bC\nxz9uFOCAzbqBPbqdK2QJRWJ0Q5EQUZoJEkVzDB9FPR7eXTpMgCitOhT3eZ0OWDJ5c50INhsg4Ohx\n8AehskJPSvCeNcNUqWcN0nO67kpJR4fxDj/JaN+3llaNlJCRPvx2NXWaPz+kmFkZv7rbSd7ZpTl2\nAuobGeBvnmRySAbvs5yIVnQSJgPH2EftkQh87Wvm93/7tzF7JJbKFEoZf2FWLi6cWMx+eQf+/fWE\njx0krSd4d+sIYTR+HSGe4vAUy8Ed0Rm0EGIFWb2PV4lUrhNlZMSZDUhydvDZj0maW0Bagje3KzIn\nyaFLShFzvdntErjH4Pra0an5/QMKKeHDH5AD7Ef7EwhAJAxdvphPj8h7LpfUN2pmJG6FLskITLM+\n7xX69ddfn+rTOD04cgQaG2HZstg65nHyYrSOnbRzoShgthzjHejdd+Gqq8zvTzzRWyg3Efaodvbo\nds6X+WTFURwWUQpZW8OD0cN0F+Ryrb2CLOEkqKN0EiYb55BBSZcO48TqVVybKhp1gK2qkYUik3I5\nudXlScaHz6959kVFcaFg6cKh10tXt+aXv1MoDR9+v5y2LVT+gOYPDyhsNrj9JjmscYnWmsZmSEsx\nTm1JTg3j6fNOzryniuZmSEkZah4cD1rDt79t9vGVr8TlA6205hXdSEhHOV/mY+sJXJaQSC2wxqNA\nNns2vO99fb8ngL26nQN0MkOnxRW8bVKiioqxqygW4d734RRWTFORJh3gMXWMTJxcK0unVMXvqO5i\nLx04tKR8AhmIJJNHTR28uc30PS9dOPR5p9PolkejjGkmfKpxuwR33d7znR9hHVsIQd4kKDcnSTzJ\n4D0V7Ntn2rqqq+FLXxp9+85O2L/fVJk7naZy+txzzX6KiuI6ZJAoO3UbUTSLyCKrZ215tchlscjC\nMx73LJvNvI+T+P0QDht7y+GIRIzz2NKlkJU15OnzZT4zdBozRGrcpyGF4BpZSlBFeSR6jAiKW2UV\njkmsAj5Jqw5yRHcxS6RTr/3U4meZyMYVRx/4XJGBA4uyBBcUJkkcZSVw6UXDBzS7TXDbDaeHEt5Y\nis+STH+SwXsSaI1GORQOM8/hwBUrgAw322trMxaRrkFD+N//Hp55Bu6+2/QqA3zgA2M6J7ewsV4U\nEkaR2W/9VgqBd5TLQGtNPX4cWMPPhsNhs+7d0QFf/zrkDVOZ/dOfwve/D6tWwYMPmixCv79HpnD2\nuYWNAZuQNIsg23QLCs1auqlg6AAgR7i4SVbgxErIrPsV1cgu2tHALt1OIwEKhJuqGMcG+nwkLRse\nYWNhop3lznJa2jROB3g9o3+2DY2Kjk5BVYVZW46Fwy5YvSL+6yQY0ry5TVNSBKXFww8elVI8/bwi\nGIQLzxOkp50eA4Ak04dk8J4EHunsZKvfz61paazzxphVVVebANb/uaNH4d57Tc/2178+MMDPnAk7\ndhjLzQlQKUefzfYWsGk7YudOSEujpTSfx9Vx7EjeJ6uGXytWygTjGHUU9drPa6qJleX55DscJmPw\n0EPw1FPwP/6HeY8TJBcnGygiRJQyhp/Npoyi+BbUUezIuNqo5okMtIZykUImDmrwU8Qw1e2RILz9\nAKBh8c102CQbVS2lwsNKmYAG3bOcphazrut2w923S2y24T+/za8qfvNnRW42fPgDFpVliTmHg4dN\nD3ZRIaYffBhq6+HPf4XOLtj2juZjd2rycpIz4yTxkwzek8A8p5PmaJRKxwgVyoNTxlr3Bb/BbNhg\nfsZKIGDS1Cnxr6e+pOrZSweXHvRT8e3vgdeL58c/JMNy4MU2/Nq43W4qz8PhmF7hh3Qne+kg5Zp1\n5F/4ukmtf+97Ro+9piYhwVtKySUyvmWE4WjUAR5XxyjBw4Y42uzKZUrvenWWcMac7feiohDpaczV\nilYi1OAjpBUrSQbviWKzgc1u1p5Hq0Wsb9S4nJCaAlkJbGsqKlDUN8LxWrhqg6K0WOLza2rqTAr+\nZKFYbi4snAf7D5w5QihKqUkVrEkykGTwngRWud2sco/xG1leDt/9rpHxTEQR1ck0dmenmcnnDFq0\n09qk6dPTB1SrO4SF0Jjty8uhsBC33cWNomL0Y/ZkEpp0ABuSjH4GJAtFFh5sVIgUyOh5/J574PLL\nzVr+BGnTIXbqNuaI9LgK3YYjhCKCppvohM9pCA4PLLml9/cipSjCjUAQ0lEciXYpm4YEgpqXt2qK\nC0m4Q1ZGmuAjH5BYcvQ+9svXS5YtgpKixK4Fuz0CKTVaQ3MrlBbDcy8pXn/brJ2v6UnBO2ySf7oL\nQBOOSFJTJmfWHQxpHPaxqwPGIhLRPPGMqay/+lKJvV9m4/W3Fb/4rWL1CsVdtyfDyqkg+VeeTsQo\n4Bo3Wht9w2jU/ACBnvapHJyIl16Cn//ctHrddlvvy1aLXJaILDyZNvjmN8d82FYd5FF1FDsWt8rK\n3hS7V9hYJAa9v/R0WLQoxl7GhlKKnbSxRTUQEooLrYLRXzQMRbh5ryyfPGU0V18xnxKaVh0ijKaD\nMDnjKRo8zTh2Al56RZOdBXMmweU3XlMOt0tQXpr44ztski9+2gTu9FTFQ09osjIhMx3y+42fd+xU\n3P9LRXUV/Msn+85ZKc27ezQeN8yomNjgZt9BxSNPalYuFaw7d/S/S1uHCfQed+xtfX7Ye8D83u0D\nn0/T7YPqSjhao+j2w6FjEzrlJGMgGbzPVBwOM+MOhSA7G4CtrdsItB9mTslaysNhE9RD/ZTDIhGk\n1njsI6wJh0Im1V1YGDND4MTCix0PNuTgFHvdMehqh6p5cfWmR5SigxBZwzisKaX4/9Rumglxq6hg\nmchm/jBGKfEihOitxJ9snMLicllCQEfIPkXHnGpKi+GCNYKi8Y+vpj3lpZLyUvjLY5o3tsNVGwSf\n+ujA672lDYIh0+15koNHFN/+kSliWzhP8M8f1ric458x+3qaPzo6R9fyaG7R/P6/FZ6eegHLEmit\nef5lTXOrkTxNSxXc+B4JGrwe+PUfFYEgfOgWydWXSHKzYHb1uE83yRiZUPAWQtwEfBWYC6zSWr/e\n77l/Be4GosCntNZ/n8ixkoyD1IHrr5UHX0O0H8PjmQEXXWR6s/PzzZN+P3z1qyagf+1rA4vp+vPL\nXxq/8E9/2lSMD8IjbNwkKxCYQFiv/TTpILOUB/sjvwJfF9zyT1A0ugzTr9U+9tPJzVSwXA7t1VFA\nPQFCKKJCc5Ecp9vEFFIsPHEpwJ0puJyCiy84Pd5wKBTlP3+jyUyHD94y9lvlBWskebmaRfOGvt8L\nVkOKF8pK+oJ6TV2PVrmG5YsFzgmK+i2cK8jLEWTH0dBgWcYxzWGn93qMRuHtHRp/AFYvN0sMlWXm\nSa01c2cah7aMdHA4JBesntj5JhkbE515vwPcAPy8/4NCiHnArcB8oAjYKISYpbWehIXE0wylTIFW\nUdGElNHGQ2nZhUSbD2LLmGFmzYWFULsDXClgz4X29r50+3DB2+Ew3/QRZuf9q7SfVbU0EcQti6ma\nsxSa6iBj9OKskI4SIIpG41cR6Pen6tJhXFjYpOQjzKKNEHNIoD6l3w/d3UPrBJKcVWx5DZ59EaSA\nSy+KUpA3tmWNvBwRs4JcKcV9/6GorYe7btc0NCrmzBKcuxKEkBQXQEXZxO8NUgoK8+PbNiO9p17A\n1lcvYLOZmXZ7px6SKRFCcPn6M3+ZZzozoeCttd4FMYshrgX+rLUOAoeEEPuBVcCWiRzvjODee+G3\nv4WPfAT+9V/H9tpo1PRGKwU33zxmU2GRNxtbXj8ltK462P+s8RBffY9JsytlbEaH4667jLVmjIpy\nwAT/xkazfm8zfczHdTd5wg3rro39mt6Xanbrdlp1kL26gxK8nEceucKJ1sYxqU77eEIdpwgPV1gl\nlMkUhuvyOSn9O+ZinW9/27TuffnLMGPG2F6b5Ixh8QKoKDUJrNzsxO77RK2ZZT/0hMblgts9gsoy\nyXlDk1mnjFhyqKXFgtKzKTV0GjFZa97FwCv9/n+857EkbW2mfau9feTtTpwwAbL/DLilBR591Px+\n8cV9Ke8xopsa2ZYBXR7NOYULsLvSQVrDC6v0x7KGD9wAr70G998Pa9fCRz/KApnJAmIPBo6pbt5R\nrZy/q5HUmgb8F6/lJVmPT0dwYOESird1C1E011NODi40oGFUx++wVjyhjhNBYdeSqNBcKUviUj6r\nzUtl8/IVLHNFSHosnL1kpFv86L7RtxsrUkr+5VPQ0AQ+v6KuYejg4Mgx4wBWWnxqAlfln7wAACAA\nSURBVOeJWsWPf6mYWQV3vz9ZCnU6MOqnJITYCMQqL/mS1vqRiZ6AEOKjwEcBysoSpJQwnfnWt+DG\nG2HlytjPNzTAs8/CY4/BrFmm3eskOTlGZU3r2IE2EoE77jAqZ7/7Xewg+6MfoX58P3VfuptDH7yB\nmTPPJ1+Mr9F0xJntyZ71fs9titZxAh9XyhLShYMH1WEO6U5Sn3mU81/eg7u4iOXzigiLKDNIw43F\nZhrpJtKrAld4tJFb/u+DuNZdBOeUmLetFQGiA8RXwijaCRFBodBIBH4iuOKo6D7xkfdzVNWTYU9N\nBu8ko9LtU/zoZwqvFz5xl8BuH/0aqyiTVJRB//WgTVsi7D9kWsoeeNQsPd/zoclrI+vP3gOmE6C9\nE+5+/6QfLkkCGDV4a60vGcd+TwD9GzFKeh6Ltf9fAL8A4yo2jmOdXrhccN55Qx5u0UF263bm/+5X\npD+50Ww3WFxFCDPjHo6mJtiyxQTxPXvgnHOGbBKtr+dEfhqepjbW6FxyGJ+bQlBHeUIdx4bgSlnS\na3TCypXwmc/Az34GgUZYUwSzL4eMYmrw0eFroNPXSnreMpaTRUgoiheuBEcRoqKSZYPctTYwSHRl\nxw5Sn3sRsME5awDYqGo43jMoOOnd7RE2rpGlKMwlFUHHLbs6355DKm6Kh1NKS5KkH0ePw+79pno8\nqjR33Do+b+8/PmiCZ3EhlJeAzTp1ZifnrTJlHhNda+/qUvz0V4rMTOOylhRtmTwmKz/yKPBHIcQP\nMAVrM4FXJ+lYp4SwVuzSbeQL97hnqv2JaIVE9BZ3bVMtvKGbERctZ019i5lhj1V1rKAAfvADI8wy\nzMz+4Fc+y4MHl9FeUcz/lF6sGLPmaI+WeRbOYdPMIRRthJCYWa7t5AxCCDPj7uyEA7thsQt8zZBR\nzBWhNDr3bKTIH4D0max1FbKWQtiwEOIRkGtqMn3hn/scLBxq8fRytJ5WQtwkK8iUznGLtbiFjdmJ\nLIBLckYzuxpuvhZefQtCIUF7J2SNUDZSUxflldc1yxdDeWnfLfjSi2HvPli2SJCbnfig19pm1tdj\neXk7HJKrLxv7MZXSHD4K6WmQnSXYdwje2W3U7t53w5jEHZOMkQn5eQshrgd+DOQCbcDbWuvLep77\nEnAXEAE+o7V+crT9TWc/7wO6k8fVMQpwc4s1sWRqhw7xqDpGOg6ukiVIIXghWsvLupH3iBIWWPGL\ntXSqEL9W+0nFzofkjFFHuiGl+Ks6jAcbV8uSmNvvVG08r+uYRRqXWMPLjTbqAAJj9jEApWDvXshO\nAysI6aVg2UArOPqa2aZs5egalv2pqYErrzQV+k8/PaAS/GTa/N+jO+giwi2ygtWyb1lBa81e3UEI\nxXyREZdm+bQn5IO9z4A3GyrOm7AqX1BHOUIXhXhIHUX7fUJ01IC0Q8r0k4MNhTXtHZCTNT5FsrYO\n8/qy4pFff9cnI9Q3GbnUb37JIitj4LahUJQ3tsGSBeB29w2egyHNa28ZdbrKMc6Q6xs0f/yLIj0N\n7rhNDmvEMlaOHOvb78fvlISjmkceV2Rlwvp1ybXzeDnlft5a64eAh4Z57pvA2CW6pin5uJhDOuUJ\nsG8MoQih6CLc+1gUTYZwoMd406jBzzG6sRD4iJLCyF9qh5TcKqtG3CYNO24sssTIjaa5g4P2SaSE\nOXOGPi4klA9N5cdFOGx+LMssC/TDJiQpSK6hhKN0s4iBg58AUV7U9UTQFAlPYgRRaneAvxXKV4M1\nwYbc8eBvgeaD0FEL5edOOHjv0u08r2uZSwaXxaHpPi58rbDjYXN9rLgD7NPLAPvvzyre3Q3vuVww\nb/bY/54ZaYKMEdxwe7fLgMYWyMuJrWv+w59rXn4V5s2Gb3257/HDR2Hj85qCPPjYnWM7N8tmalId\njqGXSiCgiar4nNiGvJd0yM81qX4hBA6b4KbrkqnyU0FyaBQnKcKesJtajnBxnSzD0c+56jyZzxwy\nyGdsKfmZpHI1JaRLBylybDOmTh0mQHRIEC6RXj6kq80wYPt2Dhek8NusDqpJ5XbbFLVOlZfD44+b\nG3+BqZ9s1yGO6m5miFQ8wsYqWx6xOm1cWCwTWQRQpJOAWaVScPBFYzKSVQUZJRPf51hJLYK5V4Ir\nNSF6AcXCQ6n2UikmMc9pd4MnC2wusCZxdj9OnA6BZRmJ0OFoaNK0tMLMGaPrpw/Hd78q6Owy1eyx\n8HhMgHUPGmOWFMHKZVBeMvbj5mQJ7vmgxGYbmBUIhTW/e8Coun3wFkl62tj2nZ4muOv2ZL/3VJAM\n3mMgoKMIjKzlRBm8HusU1vBWkiMgpWTdWJTFjh2D7m6is2bxqD6KrKlhQ/occtIGpjGlEPDuu3Df\nfdRcsYKO29dymK4xn99I+HWERgIU4ekreMOkubfoRlp1iItlAW7Rc5lWDlyueE01sZ1WAuSzUgwU\nVPlVZA91BPi4mEOW5WSZSJDgym9+A9u2wUduBK8FqVOk8ykl9O/ZnyC5wsV7rYqE7S8mdhcsvXVy\njzEBLlknOO8cMeIM9C+PKZpa4H3vlcyoGN9xLMsiY4SSijtvM6n3ZYNk/70ewVUbxn/vidXHDfSo\nIY57t0mmiGTwjhO/jvCQOooAbpDlsQP4W2/BoUNmbdaVoJSg1nDggNEnH0k8ZdTdaCKBLmxf/zoi\nGETcey/FgRpK7/shqQVV8O3vDP0G5+VBWRmrVCZSVoCCHaqF+WRMuIo0oKM8pI7SoP2sl4Us7Gda\nEkWzR7cTRNGiQxQLm/k7/OMfJnV+2WUgJbNEGn4dMU5l/QhFo7yB6Q9/m2YuHlyxPhHeecesv4e8\nMG9J4vabZMqRUuAdZfw8f47gyDFNTgI9hAbzzm7Bo3/X7D8In/vE5B0HjEXpB26RRKPjS5snmTqS\nwTtOjDCI6RcetsTvF7+A2lqoqIBlyxJz4J07TW94SYlR/honNbqOfdY+Fs0qIKs1iszMZF2bQDuz\nkenDDApyc+E738EGLFERvsF2wkrxYTmTmROsxt6uW9ivO3AghxiB2ITkclHMHtXOUxxnucpmSSvo\nX/8apaJYixdDUVGPutrQNK/DstgQKaIWH6uZ+Iy7VvnIwoFT2uALX4D6eliwYOw7euEFo9x2443G\n+jXJacfaNRLWTHw/gYDm6AlTtDbYfGT3Xk1jE+wEolFNcys89Lhi/lzB+eckfj15IuYnSaaOZPCO\nE4+wcYMsR8DwKl233WaqrGfNGnFfJ3ukHUgul8UDUsZDSE83/RbFE1tvjxBBW4K2z99NlqwwD6am\ncvgHX0c4nFQwsj+GDUkuLuPyNSjY7lHtHNCdLBNZpAj7ALGUWLwebWKrbqQUDxfKQorl0CLAQ3Tx\nOk0ILWgjDJl57LphPXWhDubmpVLY1GTEaKpiF99dbysf8Rzi5TXVxH+rQ5STwj/JuUbVbjzKdkoZ\nWdz2dliyJGar2xmBisLxt8DhhoL5U30205aXtmpe2qpZu2aoUcvqFZKNLygcNjhw2KgiNzSB54jm\n/HHWe8ZDt09xvAZmVpHszz4NSAbvMeARo/y51q41P6MQIEobQSSCCHrkD6GkBH7yE/D54PBhU7g1\njgWqUlFMtsjE029dvYMwG93tCAS3kULKCMVcNin5tJwX87l3dSuHdReH6SRV27lelo8YwN/RrRyh\nm3PIoVKmxtzGpyN4sbNUZLFMZoOQHLnuUo7QRbmKwL33si/bwRv//D4uyJgVcwCQKEQitJ2lhA9/\n2NQcjDK4O63pboTDL4G0Qc5Mo5ufZAglRZCTDcUxVnRmV0vuuQP2H9TkZEN6Ktx2g5x0n5wf/Kdi\n/0G4/Ua49KK+4N3UEuVzXzZyrT/6tiA9JVmgNh1IBu8pIF04uEqWYiHi0trGZoPvfx927zYuCbNn\nG6GSGMYkQR1lo6rFg8U6WdBbzS6FJIUUMwM8egQKCvA67MwiDYnAM4FLYa0soFJ1sYt2NAz18R7E\nCpFNmw5RKIZfYFwnC1hCFtk4e6tjL5aFtBMiVzihuJgTJQ7qXVCLn2ImJ3ivlDmU4CGLBAShc8+d\n+D6mO54cKFkBTu+0rCifLsyZKZkzggbT+efIAbPs6pG7OxOC22nGmB7PwFl3fSN0dQMCWlshPSm8\nMi2YkEhLopnOIi2jEYl0EVF+nPacuAQetNZE0QNT5sEgdHWZ4rTB/OQnxkc7EDACJfffH7MorkkH\n+Ks6gg3J+2UVjsGDg2efNWvn550H//t/m8fGkCJ7JHKUzTRwoyhnpTWwQj2sjY54PNX4Pm10xsct\nmKI1vmiQWitMCZ6EdAAkSXIqOVGrOXhEs3yxwOOe+nXnSETR2k5Mdbd/bIpit8Hac5Pfs8nglIu0\nnO0c6zLeViVeQWPbi0SVn9yMC3A6RleP2qTqOUQnV8oSY5cJ8L3vwf798L/+11Bp1E98wqRdjxwx\nxU7DVLNn4+QyWYwTqzdwt6kQG9UJ5stM5jY2Guev7dvh7383L3r44fgcxYDXaaKVEK/oRlYy8H3a\nx6CYNuoSxGgIgcfmYsY4tdmTJJlqNr6g2HcQ0lJg8YKpD942mxzW+nT92mTQnm4kg/c4qfUpbnm2\niyjwtYtDLLBnYoUFlhVfr3YHIYIo/ET7HhxpFiqlCdizR+7tFUJQPqgCe6tq4EUa2Kc6mXvJJUb9\nLBSCujqTeq+rizt43yaq2KwbuFIMLKDbq9p5Sp3gQvJZZEuw+XGSJGcga1YKMjI0FWWnNnArpREi\nfgnYmjrNgUOaZYtH7oFPcmpJBu9xIjEKS34dZisNeFMqOF/mx/2FuEQW0UmY3P4zx89/3lj7ZMXX\nRNquQ7QRohTv8OlnrVh0ZCd7M20sTis2KfnnnjPH2bsX/H5j9hEn86wM5jHUanSramQ/HTiRLOLU\nBe+wVjRoP22EqRIpfYIuMQjoKNt1C8V4JrXALUmSeJg1QzLrFAsWtndo/vRXRU4WvPcaGdf96h+b\nFLv3gddrTFNGQynF9p1GMtWyBA2NUF0JNlsy8CeSZPAejSeeMOIr99xj+p57yPdIHr7ES5MOcNiR\nzmyZPiYzA7ew4R7853c6zc8wBHR0gKTqRlVDHX6ulCVUErtqm0iIwrr9fLI2AksXmU/c5TI/a+Jr\nWD2iuqjFx1KRPeza8iWyCKeSAwxBThLWZnlhcFpdaz0uA4j+bFQ1vK6bsRBcIgtZJYZfsjhGN5t1\nA0V4uHkynboPH4YdO+Cii5K2SkmmFd0+U3ymFCgNloD2DsULm2HxfCgvHbr0tWalIC1NU1UR33f1\nxVfg//yXIj8XqqsENbWaW663mHsGN1lMBcngPQq/fasZX0s69xw4hMwdGBiyXZJsPMyOR9a0dge0\n18CMtUbjeTDNzfhVmI6cdPJwIbQ27WE9N/+jdQd4OqWbuZ58zhOmz7hUeIloTcZIldB2Fyy4DqJB\n8IxvRvyybqCRAHnCTdUwg4RC6eHmGIYnAR3lYXUEiTB67ifX4XWIJ9QxivBwkTUGedeTdHeD1tjc\nkjRsZAsn5aPochfhYQlZo243YX7zG9i61YhUr18/ucfqRyCo8fkYl5d0kvhobjG2msOlj7e8FuGv\nj8MNV8OaldPv9lqYb6RdPe4+bfbHn1Y8+hS8/hZ8/V+HBu/qSkn1GMa6WRnGAMXphP2HNFKYtrgk\niWX6XV3TiGNdiv+YdTXRSISV5amMqRQwGoUf/hBaWuCLX4Qjr4C/DfJmQdagb0JXF3zlKzyzYR61\nl63lclcl5b/4A7zyCvzLv0BWFsFf/ZLo+gX4l5zPyUz7KpnLKuKwVkyfmDzoOSKXWnwUxjBN6dJh\nOghTgDtm6r5FB2giiEtLXlWNzJeZZAon3UTo0GGCopOqaApbaKSKVAqkm1K8I8/IfT740pcgEuHi\nO+/kvLJiPLkDNcaDOsou3U6x8PQar3iFjQvHM1AYK5dfbgZd41Fhi4fWVqPkN2fOgE6BvzymqK2H\nm6+VlI3DvCLJyNQ1aP7rL4q0FLj79ti2mq+8DkdOmH/XrOx7vLFJ09JmWr7Ga2gSVZrH/67w+eH6\nq+S4lNGEEBQNkuNfNE/yxjbFiqXjOq0hLJwn+dG3oK5O85fHYUYF5GYnr8dEc9oEb5+O0EnYzEpP\nkYp+oQcuKXPRFdHMyR6jjWQ4bPqyg0Fzs519GXQ1QnoMByrLAo+HDF+EFmHDjQWdnWYfgQC43VS3\nhMn8/Qukv9kGH7rTLEAlCJ+OsFO3US5SYtp8zpCpzBhmxv20qqGRAFfI4phSpVt0I2GtyMHFVpqI\nKlhnFVCEmwycNGo/20QrNdFuDkXbyHZ4uUlWkDmabafWUFuLdcsteHJz4cUXTT98D4d1J8/pWsp1\nCjdYiVFbi5vVq83PZHH//cY05gtfgJV9EcLV06drO22+1acXDjs4bOD1DF9beusNkuIixQWrB85g\nH3xM0dwK77tBUlUxvuOHQ0ZxLRKBzi7zeSeChfMkP7h3bIpqh44oXnlDs+5cSVHB0D+Gx2Xe50c+\nqEfVi08yPk6bPu+/Ro/QROD/tXee4XFV1xp+95nRqHfJkm3Jktxt2bgb40K16dj0EtPSIIkJSci9\nIaRybxJIuemBEAghhEAoAUwP2JQQ022qK65ylWRZvU45+/5Yki3JI2mapBl5v37m0XjmlD1Ho7P2\nXuVbnG0VUBCBntqhcNBuJRFH4K03d++WVeKECX2rorndaG3jc7mk9rulBaqqRGFNKckOv/56Mer/\n8z9QGjnpyU/sGlbp/YwnjXMdhUHt+7qvnDIaOccqPKpTGsAH9iF26Aamqyx26kaOszIPl8a9aVey\nRddxmhpO21/uYXuSjV6yhMU5U/ouO2tokJK3z30Ohg+HN97osgqt127esg8yWqUwzmrXYW9uln0m\nTTqSXf/667BmjRwnf5A6hAXLAw/I57355i6d1nw+TZubqKgZHqq0uTVOhyRiBcNrb9iU7dGcf3bw\nbTc7s++Axu2B4sLAs8U7aHH7uPcBzfA8uOjc8GZ4z77k4633pBPbKQuNlGq4DOk670xcNOI5Oslr\ngNhlN3C3/SnJOLmZqTgDETYZNcrvy43awwFaGEXykQQwlwu1eTPODz6QrmTp6VDYyZC6XPD1r4u7\ntI9yscNs2QJPPQUXXQRjek5rHaWSmaIzmKiCbzayyMpjIXk9ZrvPsLKZ0Z59Poa0Lu+doHI5XuXi\nUAr21TOurAzmXQC5AVzb1FTp7uXzSSJht99HmnId3X/91Vfhjjskkey//1teW7UK1q0T0ZpYMd5X\nXQXLlx/1mR0ORZLpd9KvxLtCM7wnL4iMgRs5PHTD/+//wKpXxdG3+GRfWDKnC4+3GJajmTTeTBQH\ni5gx3idb+dggN/pBQtF7845AcGvN2/ZB1lPDiSqva5/pBx+EtWvFiJxyytE7T5smjz5o1l4UkPj6\n69JGMy9PJgN//au4WU86qcv26crF6d0NXYAoFbryt1KKw7ePm2+WlXGAZXKAZMzHxfnP6F67VkIW\nCxYceW3yZOn21lmm9AtfgEWLYO7cvs9n2yKWU1UFf/4zZBxdMjdgmMYRBuBP93vZvBW+dp1F8aje\nvxPTp8LwfEkeSwlzkpeRrpg70xjuwSRmjHeXG/0gUGyl8g1KScAR2KrbD++0tLCyoYHZyS5GJiQz\nvHuW+oUXips8iLrr7jRrL0+0Z3dftOw84ocNE2P16aey8qysPMp49yde2+YQbeQS33unoo7ytQ48\nHjHMvXHxxTBrlrjNO9BaYsI/+QmkpYnnoSPEUFICP/5x12MUFckjEKqqZKXu8Uhf74ULA9vPYOgn\n1m+C6lrYXgbF/h19h8kf5uCu/xuYcbk9mpXP2zgdsPRMy9R49wMxY7wHikOtNv/a6+Hk4XGMTO5q\nbHKs8KQ4q30+2mybJJ3EWQ4/LYJmzJBHmChEREbl5sKyZfJiSgqsWBG4y70PfFrTjJcUnL3G3h63\ny1hHFacynDMtP8l6/lizBu65B668EpYs6X3bTjFfQBL83nkHGw0pyVj+dOJDZdgwuO02WXXfcAP8\n8Y8B18r7xeOR7LJB9CYZwqdsj035QZgxVeGKG9jf5VevsyjbDYsi0GM8kjQ3Q9leuQ+1tEKqkTuI\nODFjvGvbNG9XepmR4yAvsf9chr/6pJXHdnlZU+7ljwsjmxh3WnIyk1wuRvS1ogyDJOXkIqsYBV2b\nkiQmSiy9N7SGf/1L0lnPOadX1+x/7Aq2UsdiNYIS1YNADLCHRhrwUoM7oPE3ag/rD6xnTFM1ueXl\nfY/3gw8gM/OIEU9MpPI7N/HWl5eSMGceZyRENo6tL72Uyof/Svy+JtI++QQrVOO9Z494AaZOlYmA\nIWZ5+l9SBpadofql+5fWmto6MYDdV7DjR1uMH4COY8GSka64dJmFpSA1xUxO+4OYMd4v7/fw0HY3\nZzbF8cWJEaqR8MOsHAf/LvdxfG7knfQupSh2iaCKV9tU00YW8V07iwWC1vLwY1yrdCvbdD2lKpP4\nYAMNhw7B3/8usd3Zs/Hm5/GmXUmCcjBHde+WpgmkTmFce5LaLBXYCrgeDx+dt4CK0tEsHX3i0fH0\nvXsljn/eeeLG/vnPxT1+552Ha6Q8pZM5aKdR0A9f7ya8vPTrm0navotTF1xIZqgHqqsToZn9+yM5\nPMMgsGCuYu8BzfB+ynf8dJtm5b80UybCOUtip0FIkdEa6FdixnjPznFS1mizIC+CX16fTxLDdu2C\np5+G6dO5sCSeC0tCmxy85j3ANuq50Coiy0qQUrGsLL8JVR/qat7TVczf08q01WtF2KMgALeybUv3\nsf37paVnN7fwOvsQ66khDotZyo9rvjeys+Gyy2TlnZdHLW42UodDK6aqzC6Z/gutPGaSTSq9exGW\nOEYym1y/Ai+A/A6efVY8A0uWkE8iZ8SXkDJpHMpPzTnf/jasWoX++GPUD34gQiWFhV16m49USVxm\nlUi9fIRJxsnUUVPxjppCei8ehz4pLZWSv0i69Q2DwsxpFjP7ziMNGWVJZCVWcxQrDtrsKIM506Vz\nmSEyxIzxLkq1uGlqhNs/+nyiQd3WBm+9BdOnh3W4VymnDjcTyGDRs6slZpuTIyVbjq6GJAkHTixy\nXnwN/vmcrBqvvbbPc+yw69k73Mmc7Y0k1tcfvvl7tM0L9l7qtJvjyGSMldbHkfygFCxdevi/WTqe\nE9Uw4nEc1fDDqSzSepNlbSdJOUnq7Wu2bx889JAk0rW0YC1dSnFv8qXz51Ndvpvnrjud4pQWFt16\nq9/NUlX/hCaUUkwPxItwaBd4miF/ck8H6rV8z2DoYNxoxZeuUTErdvKrP9rsPwBXX2ax5OTBHs3Q\nIWaMd2eaq6FyPYyaD1Y4n8DlkuSjjRvh858Pe1znMJIdNDKdTKitlclBU5PfbSdbmYzRacSffRHE\npfWdmNXOO6qayotOYeTJZzGmsATq62HNGtwzj+NQbhsoOMEaRkoEjJelFJNVyI7hwBgxgvLzz6Dm\nrdcZ//g/cZx4osSwe+IrX6Hxy1fTbO+jkrb+HVuouBvhjd/J7//Er0F2FAYlDTGDUoq0MJw8g83I\nfBGZHBaAknNP1NRqEhMgIcG44juIGYW1zjxyEexfCwu/DXO+PAADCwC7/Tp2ESt58UVJpBofQjud\npiaZVEya1MXtvru9w9f0jg5fK1fCXXfBkiUcvGkFgF+J02hFa83ffFtp3FfG0u1tFDpSpaFHJ9nP\n7thaU04LacRFZJIScbxueOWnsvI+6b8g5Uj4olq34cKKznEbBoXWVo3DCXFDuJzK67VDdpnvL9c8\n/KRNVgZcc3lgbUxjjSGtsNaZpFxwxMnPUGi1vdhAUljL9iO0aR/P2LtRKJZao45Ie55xRq/7VeoW\n6rSbMSrtaIWyJ56Axx6TWuarrz788igrpauG+IwZIjJyyimhGe2KCmmeMnGiuHKbayEhrfcAm9bS\nNcuyxMiG8ceklOI4K5vKgmSy6yvhwkslhPDvfx+RMO2GpRQjAunkFgC2lsS7iIr/OF2w+HuA3cU1\ndEi38ZS9mwQsLrNGD6rgkCE6qK7VPPiYTXoaXHmJ/2YnveGzNXV1kJkRvFzqQBJOrNvpBEuJo9Rw\nhJg03ufcCa3VkBRkPhZAs+3ll/Z6fGhuZJIklgXLAw9I05FvfANycvBg04QPkBaYVbSSQ0Kv+txa\na16w99GEl0TlpIBuZWkTJ4p4yMSJvY+lqEg6bIWC1lKuVFMD3/se6F2w6TkoPgFmfqbn/SoqqPn7\nX3h5yVTG1A9jRnpxaOdvZ4bVHkPOdUgMPzl5QPpge7XNM/Ye2rBZZhUeFdcPC8tCqlzb2bYN1wP3\nEf+5M0gtKAlbqc8wNPB5JbrS5iag6o3u/OctzdvrNEtOUsyaNjS/VcNyFNdfaxHnjO4JykATk8bb\nskIz3ABebDxofGg82MHtvHKluLJ37JBY84EDkJNDiopjqVWIQrFDN/CmXckUMljk6Ll2RCnFJJVO\npW4ly18HrTlzenUdu30+VuoyRqpkFjikv3eddvO6XcFolUKpFWCseuJE2LpVjOb+9aB90NbQ+z7Z\n2RxavIj9U3NxJGuCkZXZYtexXtdwos4h19nNQOfnw5tvyvMBSK31oWnE2/6dsHvKh48MW7eSuu4j\nLkvPwvrGoh614A3HFrk5imuvsHC5QmsV6nDIqtQxxJO4E02s+yhi0niHQ5rlYgUTsIE8K0jX6+rV\nUqJ13XXSDKNTZ6/M9o5a1ToOl7ZIt/r28cy1Qs/gWEMFL1OOU1vM8eXgcjg4SCs7aKBN+ygNpAJZ\nKVFd01qeZ54PeZMhq48Eq7g4ipct51yayO6rdWc3tul6NlZuJfulxzj5pEuguLjrBgNYDxOvHCy1\nCvGhSVP96JNbuxYefhiWLsV50UVGUc3QhYz0I9+HHWU2b7yjOWm+YlRB338LC+Yqjpsc2wlthtA4\n5ow3hGC0O7jpJiltOuGEHpsmj7ZSGaWSgxdeCZLxpJNFPDm4cLWXoRWRwulqiazbS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hsIl94205YNr8gDevt90oINVyyf/x8LI+gAKu0KNJ8ROv9mBjo/Ew8L3PL7GKOJ0RZFo9JN0d\nPAg//7mswL/4xV6PtUc3sjUzC0fqyZTEdyo5KyuTyU1iopRnJRydQ9Aj9fVS2tdTW9PO7N4NX/iC\njPUPfwj8HEuWwPvvQ01Nj1UHIaO1jOfHP4bMCAipNFbChmdg2CQoCfx7eZjEREiIl/BGhL04htjn\n+qstcjJtPtqgmDxRk5NlVrDHKrFvvIOgxm7jN/ZGFPBNSkm1XCTjZDxp/le37VxsFTOPXAr6Uh9z\nN4PDCf9aJcpgy5eLOlwYWJbVe7b8wYOScd3aKoaolzj2BJWOw7IoiE/qul1REVx0kay8ExIkkz2Q\n4sv334erroJRo6QUrK99PvxQXNO7d0tiViAGHySTviP/IZg4/VNPyfluvNG/tG5iohht245cglhL\nHbTWQv2+0Pb3tMDH/wRngiR/9Za93VIL5Rsl/JFkFNyOBSzLor5R09oGTc2Qk9X3PoahSfQb7//9\nXxHq+MMfwk7gsdHYaBSKDqexU1mc7BhOuW7mEG3k+slcd1oWxX0ltDXXwEePSfLOLbdLOdVvfhOa\n8bZtWU1/+KEkZl15Zc8rzkmTxKWck9PVsG3bJo9TToF4Mf6JyulfpjMuDi67TJ6XlcEll0g9+xNP\n9G5gDx2CtjZxOwfCmWdK2diYMYEb7g6CTa6zbbj5ZqkwGDdOJif+SOlBVa6lBSoqZGITzLmzx8Bx\nF0NSiLr23jZoaxQj7vP2brz3fQC73gBvC4wz3ciOFS5dZtHQCMOjpw+TYRCIfuO9ciVthw7CW28Q\nH6bxzrYS+BqTUUBGu9scoFa7edbeiwPFFdZoEnoqG+sVLQ+t4ZvfhK99Hc47L7SB1tZKLXNlpcR5\nZ86E44/3v61S/mPfd9whdeCZmT3v6489e8QoNzXJar4n4wbizn7gARFeCWSl7nLBddcFPpZwsCyZ\nhGzYACecEPz+99wDb74pVQrBNEWxLMgMQzQmMUOMv+WEuD7CF3mlYuTzp4R+PkPMkZqiSI0CJWPD\n4BL1xrv6d//HPVnVeEeX8A3bS1KYIhA51tE3xEQc5JBAPBZxhBhnTMqCmcvBEQez4uGaa0MfZFaW\nuN0//lhWzlNCuDmfcoqItgQ74Zk/X7wcubm9G+4OQjGM4WDbMrnJzJShguc0AAAa3ElEQVSJi8cj\nK2SvFz76SOq+Xe0Tsx/9KPTzZGSIxyLSMfZASAlQ8jV1GEw6u3/HYjAYopKoN95t8+dRb2/GgcKH\nHdpB6uslLjxhgt8VYrxycL4jAlnk8RGcDv/3f4e+78GDIkqTmhq88bGs6BY++ec/peb7C1+Ak0+G\nv/9d2oZu3iyeiltvlffC5aqrRNCnhza0BoPBMJhEfTrrcCuJL1sTWGFNPJwhHjR33CHtRN99N7KD\ni1a0lhWqzyfPA6BGt/Gkr4yP7eAk6A/j9Qa/T1WVSJs+eZTCbs+43fLZOuqgExNlwlFcLBOVSPXF\nVsoYboPBELVE/coboMAKs8d0YaHEf/vQJtda84E+RJ32sMAa1rNkajSybp0IjSxfLnXQP/2pJKK5\nApvwHKKN3TShteY4+khh9XjEuHUknb33Hvzxj7JSPTsIN+4zz4jwyksvibpZIFx+udSidzSIufRS\nSYRLT5fJSrCJcAaDwRCDDJ073aFDkpV+/PHSA7ozy5fDFVf02crSh+YDXY0bm0lk+FVea9Cy4ksN\nQL/co20UktEeED4vlL0l0qUjZ4iBbG0Vjffx43uvv169Gl59VbpxFRQEXbNcTApnWwXk9NXEpb5e\nvBhJSfLT5ZKYc0ODhCaC4bTT5LFwYeD7OJ0Si+/wKFiWxKc73jMYDIZjgOi/27nd4pLty4X5wgvw\nt79JD+svfanre0oF1IPaqSxOUyNowsMwPyVjzdrLyvY2oxdZxSSpni9fi/ay0t6NA8UFVlFgsqnN\nVbB3nWQa55eCM15cyo89JqVOy5f3vO/y5WK4Q9T8diqLcQRQ69zWBo2N8tPrFeN9xhlS/lVcHNxJ\nR42SXunBsHGjlNHNmQMrVgS3r8FgGDRq6zTvf6yZMkkxLMeIy4RLdBvv1lYpR2pshJUre+/bPH++\nGO4wG1YUWz0nnTlQuHAcft4bPjQebHwofOjAujon50LJQnAlieEGMXCZmT1/9jfegM99Dk48Ucqb\nQsHjETGTkpK+E9xyc6X23uk8MqGKi5N682DYv1/q4I8/vucabH90TOZaWoI7n8Fg8MvmrTYvvqJZ\nfJKidGL/pUF9uF7z8r81DU2aZWfGUEgySol+411bKzfsmprejffo0f3W17lFe/m3XU6miudCNQql\nVJ+u8BQVxwVWEQoCrxu3HFA4u+trCxbIxKQnoZCnn4Zdu6CuLrBz+OOVV+BPf5Ls7Rtv7Hv7SOhu\n790LW7fKJCAY4z1tmuiRb9smWfW5AZZVGQwGv5RXQm09HKiA0n7s+106UdHQqJk1LerzpGOC6Dbe\nGRlSGtTQANOnD9owanCzk0YO6BbmWDlYASpupao46c28ZT1MnCiZ0aHQ2/n+67/EiIXTVGPkSMjO\nhrFj+942UsycKQ1HCgv73rYzSkk99333iRqccZ0bDGFxwhxFwQhFwYj+PU9utuK8M8yKO1IoHWAp\n0UAwe/ZsvXbt2sEexlHYWrNN15Om4shXQZYPPfCAyIxecgl85jP9M8BI0IcuelSxbRs8+CAsWxa5\nSZ3bDc8/L2GKmTMjc0yDwWAIAKXUOq317L63PELs+S9sWx4DiKUU4630roZ7926Jx/flqh8zRly7\nJSWRHdShQ6LdfdddAddy90qsGG4QD8EPfxhZb8zmzZLweOedkbmeBoPB0I9Et9u8O263rLYOHZLV\nbB99nd+yK9mpGzjTKiBL9VEC1QeN2sML9l5ySeAkKx/1t7/Bf/4j9dU/+EHPUqLz58sj0lRVSdy4\noSE6V80+X0AZ/lHDmDGiLFdSEn3X0mAwGLoRW8a7uVninG1tcOBAn8b7gG6hFg/1uMnqq365Dxrw\nUIObNmw0oK66Cl5/HWbMCEwDPNKMHw+f/7zUfgfSFGQgWbVKSsC+9KXgmqIMJsnJffZDNxgMhmgh\ntox3Roa4NuvqpM63D5ZYI6jFzUj6iFO3tUnZktMp2dZxRxd25ZPI2VYByTglYa2oSJTBBou2Nnj0\nUflZVCRJZ9FCh0fgwIHBHonBYDAMSWLLeAPM7hrTb9Qetup6RqtU0lVXKdBUFUdqIBXWdXXSOtKy\npKbcjzqZUooCwpRpjSRxcZKpXVMzOCv/3rj8cpg7N+z+6wakv7fDZVz5BoOhCzGfbf62XcmbupJp\nZHGaI4xah40bJUY7YULoxxhodHv/8IFwm3/6Kbz4Ipx/fvDlXYbQOLAetr8GoxfBiGmDPRqDwdBP\nDM1s85degt/9TjS1/TBGpTGRDCaq9NDP0ZFcNdiu561b4StfEeGVQFCqq+Hevl3qvt97L/Jje+kl\naSSyZk3kj23wj7sRbC+4mwZ7JAaDIcqIfuO9cqUogG3bJjrfBQWScd7egjJXJXC2o4CR4XQee/NN\n+P73Q5cXjRR790J5uZQthcKdd4p4yW23RXZcICvuz31OGokYBobCOTDjchg1d7BHYjAYoozoj3mv\nWIFdVsaLU7LJv+9XHFdRjuOVV8TI9ZFtHjBZWRI3HtGPEkNtbfDhhyLj2pOk58KFEm8PtsFHB5de\nKpOcz3425GH2SEGBxLJjhX37xIOxZMnAKsdFEssBqfmDPQqDwRCFRNfKu7X16NdKS/GcdQYVDg+b\nV1xFy+mnySo5Uoa7/Rz86U9i/CJBWZl0OWvq5O5cswZ+/GP485973i8uToRHOlpcBsvxx8Mjj4gM\na1lZaMeIJSorpXzQH6+/Lp6aF14Y2DEZDAbDABBdK+8emmvEKwfnWYV4Jowg5bkwNLx7w095WKh4\n/nIPjnfXYrlcR9zMo0eLvvmsWRE7j1/WrpWyt2nTgnefb9ok16GnlerBg1KetmBBZNTNWlrgnXek\nZj1Yr8c778A118gk7qWXjk7aO/VUyWU48cTwx2kwGAxRRnQZbz8lWh1kB6uQprWIhViWGNABKrWp\n1w1sX1JIbkIDBZMnH3mjpAR+8Yv+H8C4cWJcTzghuP0qKuD22yVx7ze/gXQ/CYAffCBJa1VVYrxt\nW65rqNf2rbfgl78Uj8EPfhDcvk1NYpybekjmysuDK68MbVwGg8EQ5USX8Xa5+t4mUCor4a9/FcMy\nbdqAtY60UDSdMA11wnwKHBFonRkseXnSrStY0tKkTC4+/kif7u4cf7zE02fNklatP/oR5OSIvnso\nUqjjx8tj82aZFJx3XuD7nnoqPP44DBsWfQpzBoPB0M9El/EOgEbtoREveSSgelvx5eRIVrplSbvL\nASJFpTDXmoUjytIJ+iQxEb773d63SU+Hyy6T53v2iMZ8S4tk/odivAsKpNvaz34G774bnPEGOO64\n4M9pMBgMQ4CYE2l5zLeTGtycYxUyMtj2nAPNjtehajtMWQZJWYdf1lqjIeC+4FHL9u2ySh8ehofB\n45HmLkVFXY9je+HgVkjOgQO1Il0b6c5sBoPBEAWEItIScyvvbBJoxSaJGOhYVbsXWmrl0cl4v6Er\n2a4bOMcqIEclRPaclZVw7bUSJrjvvv51KY8ZE/4x4uJg3ryjX6/eBRufBUcq3PeOfI5f/1rc+4bo\nxOeGpkOQkmdCGQZDPxNzxvsUKz92Vq2Tz4WWGsjoKidapVtpwUcjXnIifc6NG0WnPS5OVOlCLTsL\nht5akm7aJEb3yiuDy/xOGQZZJZAyAkYdkFh8YmJkxuuPxoPgjIeEKJgc2F6o2gZJ2ZAyMLkaEWHn\nG7DvAxh7CoycMdijMRiGNDFnvJVSxIDZFhLS/BqD062R1OImn34wRieeCP/7v7LyHgjD/dFH8Pvf\nw0UXST/s7tx7rySWVVQEZ7wT0mDaxfL8f/qhH3pnmqrgo8cgLgFmXw3WIP9Z1JTBhmcgdRjMvmZw\nxxIM8SkyAXJFWaMcg2EIEtXG22vbfEodo0ghxeq9DtutfXygD5FHIsVW6gCNMDSSlJOk/rr0liX1\nzwPF3r1QXQ07d/p//8orpTXo1VcP3JiCxRkPcYmQkA4qCty9ybnidcgsGuyRBEfBbGmg4ohg1YjB\nYPBLVCWszZo9W6/rlLD2oncvq9jPWNL4knNir/vu0o08Y+8hAxdXOSIQi40CWrQXjRj7qMXjEdf4\n6NHR15o0GGxfe816FBhvg8FwTBHzXcXqtBu702QiHRdOLFxYbLRr8Wi7x33zSSRTO2nTPppt70AM\nt19p0z6etHfzuF1Gk/bzeRob4dlnB18GNS5OSrZi2XCD6Igbw20wGGKEqLpbteLjEG2H/z/POYwf\nWNNJxMFqvZ899NwaMUE52EIDG6nlY6oHYrj9ikJ+OVb786N4/nmpy/7d7wZ0XIYYxeeBun3gi/2J\nrcFgiDLjHYdFBl3jZUmWk6lWFmNJI5fey6rmM4zxpDEeSRJ7ylvG/3nXc8DuoXnFAFJru2m1vdKg\nZPFiePDBXrd3KQcXWEVcZBX7d5sfOCBNOSKoyR4z2LbE2Q2BU/YOfPgI7OmHXu8Gg2HAiSrjnari\ncPhZZ06xMjnbUUCq6t1QneUs4EvOiWRZYuQ/pIZ9NLONhtAGVF0NK1ZIx7EAeNOu5O++7VTrNjEw\ntrj5d9kN/ML+hN/am7Cfekpagz76aJ/Hi1cOElQP9eznnw/f+AZcf33AH6ffOXBAOnkdOtS/57nu\nOpg7F556qn/PM5RIzJDEvEQ/mvUGgyHmiKpMqDrcbNF1TFKRKXG63Cphl93AHEKUR129WozR6tXw\nxS/2KTxRrltowEP9rm1kXXilqIY98QQebGy0/LzhBqz0dFi6NLQxdVBUBF/5SnjHiDRPPCEGtbUV\nrrhCZFM/+ABGjRLN9UhRVSWJcgcPRu6YfWHbULcX0kcMfilZKAyfAnkTY3PsBoPhKKLqL1mhSIng\nkCZY6UywwlhpLF4sK9ypUwNSjFpijaAONyO2vSkCKTt2gNfLOFc6NzCJVOJwFrnge98LfUzRzKmn\niuHu6Gi2di389Kdy/X70o8icw+uG71wDB6+Asy6JzDEDYf2TsPUVGHU8zInisrfeMIbbYBgyRNVf\nczbxFFpRlLWclQV33hnw5qkqjlTipAXpH/4AhYWHO6WNtJL7a5TRw6RJ8uigoEC6hkWyh3nZW1D2\nL5GbtS6L3HH7okNBLhaU/QwGw5Anqoz3kLktWpZ/tbFjjYIC+PnPI3e8gwfhoVWQ0AKnTO57+0hS\nej4UzIH0QWjzajAYDN2IqoS1aMDbSy25YZDZvBneeh+qR8Lsqwb23JYFmYXG9WwwGKICcyfqxE67\ngVd0ObNUFtOt/usBrrVmhy6jTbcx3hqDM5oV1KKJWbMk+7+za95gMBiOQYzV6EQDHtz4qNOegLYv\nsxt5U1cyT+VSEoSeug8f+/R+rNYmWnxOUlNHD71Yal2dxP2Li+Ezn4nM50tKgnPOCf84BoPBEOMY\nt3knSlUm51ujOMEKrA3jAZqppJV9BCcC41ROShnP9A1bSfn4X9BQEcpwg8PdDOsehI3PQSRCA9qG\nA59A1faj3/N6Re/8o4/gtdekZWik8HoP188bkGtxcKv8fg0GwzFD1K+8t9v1vKErWaDyGNMf3cJq\naiSjfPx4HJdcwnCSAt51uspmmEpkeAitPbOtbEguBKqko1VjI9x+O0yZIjXSkcbdKL3FPU1yw3eE\nOW9rPAhbXwZHHGR8EZydlPHuuAPWrYMLL4TZswMqswuImhppd5qZKdKwjh4EbIYQa5qaSLEspvfU\ny/zTVbDhacgdByd+fWAHZzAYBo2oN94VtFKHh4O0MIZ+MN579sD778O+fXDxxUG5dxOUg9G9jeng\nQREtWbQIJkzo+p5SMPlcWZValiiu3XcfpKXBZZdFzuB1kJwLU88HZyI4IvBrT8qE/FJwpYoB74zb\nLZ+rtBTGjg3/XB00N4s73u2WFfgQN95rW1r4ZmUlFrBy5EjyXH5abcYlyHcprh96wxsMhqgl6o33\nLJXNSJVEfgir287YWlOPhzTisDob6NJSuOkmGD48cMO9Y4cY2rPPPiJI4o+334b775ftb7vt6Pe9\nXvjrX8HphDPPFJGTKVMib7hBPlt6QeDb23bv43C4YPwS/+/deCPU1kZWVQ1g5Ei49VZITIT4+Mge\nOwpJdziIUwqXUiT2NFEZcxIMnwoJaQM7OIPBMKhEvfGOVw6KCFO4Zc8etq9/i9cXjGNWyiimq06Z\n5A4HLFgQ3PE2bBD1sMzMrsb7lVdkJX/ppWJgRo2SFejOnaI8ltCtsUpVlcSElYJly+Dvfw/5I0aU\nF18UT8CKFTBzZt/bezzw8MOQnAwXXCCGNdKGu4NRo/rnuFHIOJeL5wsLsYCU3rwMSVkDNiaDwRAd\nRL3xjgj338+wf68mf8/JWPNOg+MWhXe8U0+V/tWlpUdes234+9/x1dVSNmMcmVNnkVlcLMY9K8t/\n96/8fNEndzplIhAt7N4tK+d9+wIz3gcOwHPPyWdcvFhc/4aIkDbEQwMGgyE0lI5kJnCYzJ49W69d\nuzbs4zRqD2vsCgpVMqVWJrz5Jvr++2k6sJfkiaWoSKp+deatt9i9bysrz57K6PgcznUUHsm0jqVS\nsOZmcfVPmBBYy1HbhpdeklKuRYti67MaDIYhgW3bbNoKRSMhJSW2CqmUUuu01rOD2WdIrrwraeVT\n6qnVHkrJhPnzUTNmkPLyyzBxYv+d+IQTSNEzKbIrGKfaV5+hGDK3W376S1CKFFrDli2QkSEegM4k\nJUnsPVAsS2L2HbS0wD/+ITHqM86IzHgNBoOhF55fZfPoUzB+DHzvm7FlvENhSBrvQpI5VQ0nV3WK\nMScmwrnn9vu5s1Q8yxxhxGXr68UQejzwzDNHG9ZIsXUr/OQnkJ4Ov/tdZJPkdu2CF16Q0MJpp0lY\nwGAwGPqRtFRJYUqNot5W/cmQvKvGKYspKopiyMHQ2CjxZp9PVq8gvcRTIvyNzMqC3FxRQIu0m3vs\nWLjySpl4GMNtMBgGgBPnOzmu1CblGGjgCEM05h3zfPyxuJ7vvhsaGkSYpD/c/VoPfHy6qkomDv1R\nDjeUaGuEzS9AyjApBzMYDEMWE/MOkme9u2nAw6WqGEc0ZfUed5z8TEkRYzduXP+cZwANd4P24Prl\nr4m/8y74/OdFIc3QMy21ULdXVPFGLwJlJjsGg+EIx6zxrvC18Dz7sNGMI43ZBKZnPqB0LkWLYeq0\nm5V2GbMrtjPJ68GqrBzsIUU/6SOgdKko2BnDbTAYunHMGu9MXJSQQjNexpEe3M4bN4pIy9Klkq1t\n2/Ctb0F5uXTSysjon0HHKBYKBxaf3voNJp55ae+qdAZBWZA9JjLHevJr0HwITvgyjA5SkMhgMEQl\nYRlvpdQvgPMAN7Ad+KzWurb9vVuAzwM+4Eat9YthjjWiuBwO/pupoe38j3+I8S4shFNOkQzxJ5+U\nDPF16yTDuh3btnnY3kW9t5mrf/UkSW1e+Pa3Jfv9GCFVxXGJVYwjSeE4rR9L9Qz+aakG2wtVW43x\nNhiGCOGuvFcBt2itvUqpnwG3ADcrpSYDlwOlwAhgtVJqvNbaF+b5ooNLLoGSEpg2Tf6fkQE//CHs\n3y8iJZ1oxeZjqrG1m4P1BymqaICmpmPKeIPI3BoGiYU3wsHNMO2SwR6JwWCIEBHLNldKXQBcrLVe\n3r7qRmt9e/t7LwK3aq3f6u0YQzXb/G1vJXW4OW2PG6etYUyE3KEGg8FgiHkGO9v8c8Aj7c9HAm93\nem9v+2vHJPOcw+RJyeCOo9/RWurUU1KOZLJXV8vPrCCaZ7jdUkpmasQNBoPBL33eHZVSqwF/Ml/f\n1Vo/1b7NdwEv8GCwA1BKXQdc1/7fNqXU+mCPcYySA1QN9iBiAHOdAsdcq8Aw1ykwzHUKnAnB7tCn\n8dZaL+7tfaXUtcC5wGn6iA9+H1DYabOC9tf8Hf9u4O72Y60N1nVwrGKuVWCY6xQ45loFhrlOgWGu\nU+AopYKOF4dVQKqUOhP4FrBUa93c6a2ngcuVUvFKqRJgHPBuOOcyGAwGg8EghBtU/AMQD6xSEuN8\nW2v9Ja31BqXUo8BGxJ2+YshkmhsMBoPBMMiEZby11mN7ee8nwE+CPOTd4YznGMNcq8Aw1ylwzLUK\nDHOdAsNcp8AJ+lpFVWMSg8FgMBgMfWNEkw0Gg8FgiDGiwngrpS5RSm1QStlKqdnd3rtFKbVNKbVF\nKXXGYI0x2lBK3aqU2qeU+rD9cfZgjynaUEqd2f692aaU+vZgjydaUUrtUkp90v49GnoqSWGglPqL\nUqqycwmrUipLKbVKKbW1/WfmYI4xGujhOpl7VDeUUoVKqVeVUhvbbd7X2l8P+jsVFcYbWA9cCLze\n+cVuMqtnAncqZXQ2O/FrrfX09sfzgz2YaKL9e3IHcBYwGbii/ftk8M8p7d8jU9rTlb8i957OfBt4\nWWs9Dni5/f/HOn/l6OsE5h7VHS/wTa31ZGAesKL9vhT0dyoqjLfWepPWeouft5YBD2ut27TWO4Ft\nwNyBHZ0hRpkLbNNa79Bau4GHke+TwRAwWuvXgepuLy8D7m9/fj9w/oAOKgrp4ToZuqG1PqC1fr/9\neQOwCVEfDfo7FRXGuxdGAns6/f+Ylln1ww1KqY/bXVbHvOuuG+a7EzgaeEkpta5d8dDQO3la6wPt\nz8uBvMEcTJRj7lE9oJQqBmYA7xDCd2rAjLdSarVSar2fh1kN9UAf1+yPwBhgOnAA+OWgDtYQyyzU\nWs9EQgwrlFInDvaAYoV2VUlTsuMfc4/qAaVUCvA48HWtdX3n9wL9Tg1Y54e+ZFZ7IGCZ1aFIoNdM\nKXUP8Gw/DyfWOKa/O8Ggtd7X/rNSKfUkEnJ4vfe9jmkqlFLDtdYHlFLDgcrBHlA0orWu6Hhu7lFH\nUErFIYb7Qa31E+0vB/2dina3uZFZ7YH2X3AHFyBJf4YjvAeMU0qVKKVcSOLj04M8pqhDKZWslErt\neA6cjvku9cXTwDXtz68BnhrEsUQt5h51NEqkSO8FNmmtf9XpraC/U1Eh0tLeC/z3QC5QC3yotT6j\n/b3vIu1GvYiL4YVBG2gUoZR6AHFHaWAXcH2nmIkBaC9N+Q3gAP7Srvpn6IRSajTwZPt/ncBD5jod\nQSn1D+BkpENWBfBDYCXwKDAKKAMu1Vof08laPVynkzH3qC4opRYC/wE+Aez2l7+DxL2D+k5FhfE2\nGAwGg8EQONHuNjcYDAaDwdANY7wNBoPBYIgxjPE2GAwGgyHGMMbbYDAYDIYYwxhvg8FgMBhiDGO8\nDQaDwWCIMYzxNhgMBoMhxjDG22AwGAyGGOP/ASpyn8Bzf0drAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5gEmbcTREN6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Log the same chart on W&B for future comparision. \n",
        "plt.figure(figsize=(8,8))\n",
        "plt.scatter(latent_val1, latent_val2, \n",
        "            cmap='rainbow', \n",
        "            c=example_labels, \n",
        "            alpha=0.5, \n",
        "            s=2);\n",
        "plt.xlim(-10,20);\n",
        "plt.ylim(-20,10);\n",
        "            \n",
        "wandb.log({\"ae_latent_space\": plt})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-eczKyjYOo_",
        "colab_type": "text"
      },
      "source": [
        "> `xlim` and `ylim` limits may change depending on how the latent representation is formed. You may have to experiment with the limits but the provided limit hsould hopefully work just fine. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_qhIuz2R9AMD",
        "colab_type": "text"
      },
      "source": [
        "# Variational Autoencoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A31-vPZnHlFl",
        "colab_type": "text"
      },
      "source": [
        "#### Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuQB601Oxley",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class VariationalAutoencoder:\n",
        "  def __init__(self, input_shape, original_dim, intermediate_dim, latent_dim):\n",
        "    self.input_shape = input_shape\n",
        "    self.original_dim = original_dim\n",
        "    self.intermediate_dim = intermediate_dim\n",
        "    self.latent_dim = latent_dim\n",
        "\n",
        "  def build_model(self):\n",
        "    # instantiate VAE model\n",
        "    self.build_encoder()\n",
        "    self.build_decoder()\n",
        "\n",
        "    self.outputs = self.decoder(self.encoder(self.inputs)[2])\n",
        "\n",
        "    self.vae = keras.models.Model(self.inputs, self.outputs, name='vae_mlp')\n",
        "    self.add_loss()\n",
        "\n",
        "    return self.vae\n",
        "\n",
        "  def sampling(self, args):\n",
        "      z_mean, z_log_var = args\n",
        "      batch = K.shape(z_mean)[0]\n",
        "      dim = K.int_shape(z_mean)[1]\n",
        "      # by default, random_normal has mean = 0 and std = 1.0\n",
        "      epsilon = K.random_normal(shape=(batch, dim))\n",
        "      return z_mean + K.exp(0.5 * z_log_var) * epsilon\n",
        "\n",
        "  def build_encoder(self):\n",
        "    self.inputs = keras.layers.Input(shape=self.input_shape, name='encoder_input')\n",
        "    x = keras.layers.Dense(self.intermediate_dim, activation='relu')(self.inputs)\n",
        "    self.z_mean = keras.layers.Dense(self.latent_dim, name='z_mean')(x)\n",
        "    self.z_log_var = keras.layers.Dense(self.latent_dim, name='z_log_var')(x)\n",
        "\n",
        "    self.z = keras.layers.Lambda(self.sampling, output_shape=(self.latent_dim,), name='z')([self.z_mean, self.z_log_var])\n",
        "\n",
        "    # instantiate encoder model\n",
        "    self.encoder = keras.models.Model(self.inputs, [self.z_mean, self.z_log_var, self.z], name='encoder')\n",
        "\n",
        "\n",
        "  def build_decoder(self):\n",
        "    latent_inputs = keras.layers.Input(shape=(self.latent_dim,), name='z_sampling')\n",
        "    x = keras.layers.Dense(self.intermediate_dim, activation='relu')(latent_inputs)\n",
        "    outputs = keras.layers.Dense(self.original_dim, activation='sigmoid')(x)\n",
        "\n",
        "    # instantiate decoder model\n",
        "    self.decoder = keras.models.Model(latent_inputs, outputs, name='decoder')\n",
        "\n",
        "  def add_loss(self):\n",
        "    # VAE loss = mse_loss or xent_loss + kl_loss\n",
        "    reconstruction_loss = keras.losses.mse(self.inputs, self.outputs)\n",
        "    reconstruction_loss *= self.original_dim\n",
        "\n",
        "    kl_loss = 1 + self.z_log_var - K.square(self.z_mean) - K.exp(self.z_log_var)\n",
        "    kl_loss = K.sum(kl_loss, axis=-1)\n",
        "    kl_loss *= -0.5\n",
        "\n",
        "    vae_loss = K.mean(reconstruction_loss + kl_loss)\n",
        "\n",
        "    self.vae.add_loss(vae_loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8UwHZRoy_C4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keras.backend.clear_session()\n",
        "vae = VariationalAutoencoder(input_shape=(784), \n",
        "                               original_dim=(784), \n",
        "                               intermediate_dim=512,\n",
        "                               latent_dim=2)\n",
        "model = vae.build_model()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avi2WqU_WLFq",
        "colab_type": "code",
        "outputId": "0aca67b3-14aa-4793-fa30-8267d59bbc26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        }
      },
      "source": [
        "tf.keras.utils.plot_model(\n",
        "      model, to_file='model.png', show_shapes=True, show_layer_names=True, expand_nested=True, dpi=96\n",
        "    )"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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7fF3H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\nn7MBAAAIleDg4ODgYKpTwEBiZWU1cuRIgiDc3d1FRPDdIADAECVGdQAAG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6nmz58v+HBK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obW39/vvvSxisVlm4cKG1tXVMTIzyZbNmzb788svBgwdXfga1H0l66tSp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\nz93dfc6cORUfn2XLlpmZmQkhmjZ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ICGA7BfQpNE2zHaEbbdq0KTIyct26dUKh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\nN2/erl27Nm/eX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AAAAAAArQjJIQAAAAAAAAAAgFaE5BAAAAAAoFHExcXZ2tru3bvX0IE0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\ntFqtdnNz27p1q2apf//733/7299UK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LCg/HMNO1j4+vqatJf/FR04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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SM8-hO_OEgBA",
        "colab_type": "text"
      },
      "source": [
        "#### Compile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qQYZmWlEQiG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile('adam')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6ugRDlrSGoF_"
      },
      "source": [
        "#### WandB and Callbacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XBlFoIhCGoGF",
        "colab": {}
      },
      "source": [
        "class ReconstructionLogger(tf.keras.callbacks.Callback):\n",
        "  def __init__(self, batch_size):\n",
        "    self.batch_size = batch_size\n",
        "\n",
        "    super(ReconstructionLogger, self).__init__()\n",
        "\n",
        "  def on_epoch_end(self, logs, epoch):\n",
        "    sample_images = x_test[:self.batch_size]\n",
        "    \n",
        "    images = []\n",
        "    reconstructions = []\n",
        "    \n",
        "    for i in range(32):\n",
        "        reconstruction = self.model.predict(sample_images[i].reshape((1,)+sample_images[i].shape))\n",
        "\n",
        "        images.append(sample_images[i].reshape(28,28))\n",
        "        reconstructions.append(reconstruction.reshape(28,28))\n",
        "\n",
        "    wandb.log({\"images\": [wandb.Image(image)\n",
        "                          for image in images]})\n",
        "    wandb.log({\"reconstructions\": [wandb.Image(reconstruction)\n",
        "                          for reconstruction in reconstructions]})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "9cf5724e-42ef-49e3-92fd-fbbfb811a09f",
        "id": "OhLGa6jJGoGV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "wandb.init(entity='ayush-thakur', project='keras-gan')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://app.wandb.ai/ayush-thakur/keras-gan\" target=\"_blank\">https://app.wandb.ai/ayush-thakur/keras-gan</a><br/>\n",
              "                Run page: <a href=\"https://app.wandb.ai/ayush-thakur/keras-gan/runs/2eski4mn\" target=\"_blank\">https://app.wandb.ai/ayush-thakur/keras-gan/runs/2eski4mn</a><br/>\n",
              "            "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "W&B Run: https://app.wandb.ai/ayush-thakur/keras-gan/runs/2eski4mn"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "T40Q6GZcGoGh",
        "colab": {}
      },
      "source": [
        "config = wandb.config\n",
        "config.batch_size = 32\n",
        "config.epochs = 100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KaXtVeioVicO"
      },
      "source": [
        "#### Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OsY3_bPaVicT",
        "colab": {}
      },
      "source": [
        "# _ = model.fit(x_train, \n",
        "#               x_train, \n",
        "#               epochs=config.epochs, \n",
        "#               batch_size=config.batch_size,\n",
        "#               callbacks=[WandbCallback(),\n",
        "#                          ReconstructionLogger(config.batch_size)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "06gnmC7QbHX7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3dedc54a-5968-4e66-f779-5fadf6ad37c3"
      },
      "source": [
        "_ = model.fit(x_train, \n",
        "              x_train, \n",
        "              epochs=config.epochs, \n",
        "              batch_size=config.batch_size)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 45.7282\n",
            "Epoch 2/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 41.9360\n",
            "Epoch 3/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 41.0426\n",
            "Epoch 4/100\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 40.4732\n",
            "Epoch 5/100\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 40.0294\n",
            "Epoch 6/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 39.6717\n",
            "Epoch 7/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 39.3761\n",
            "Epoch 8/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 39.1173\n",
            "Epoch 9/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 38.8496\n",
            "Epoch 10/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 38.6521\n",
            "Epoch 11/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 38.4523\n",
            "Epoch 12/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 38.2985\n",
            "Epoch 13/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 38.1358\n",
            "Epoch 14/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.9928\n",
            "Epoch 15/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.8643\n",
            "Epoch 16/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.7653\n",
            "Epoch 17/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.6785\n",
            "Epoch 18/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.5738\n",
            "Epoch 19/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.4967\n",
            "Epoch 20/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.4202\n",
            "Epoch 21/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.3527\n",
            "Epoch 22/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.2733\n",
            "Epoch 23/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.2011\n",
            "Epoch 24/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.1373\n",
            "Epoch 25/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.0733\n",
            "Epoch 26/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 37.0419\n",
            "Epoch 27/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.9787\n",
            "Epoch 28/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.8962\n",
            "Epoch 29/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.8630\n",
            "Epoch 30/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.8153\n",
            "Epoch 31/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.7799\n",
            "Epoch 32/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.6999\n",
            "Epoch 33/100\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 36.6988\n",
            "Epoch 34/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.6589\n",
            "Epoch 35/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.6237\n",
            "Epoch 36/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.5768\n",
            "Epoch 37/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.5499\n",
            "Epoch 38/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.5092\n",
            "Epoch 39/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.4592\n",
            "Epoch 40/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.4585\n",
            "Epoch 41/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.4114\n",
            "Epoch 42/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.3861\n",
            "Epoch 43/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.3583\n",
            "Epoch 44/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.3367\n",
            "Epoch 45/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.3150\n",
            "Epoch 46/100\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 36.2686\n",
            "Epoch 47/100\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 36.2666\n",
            "Epoch 48/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.2364\n",
            "Epoch 49/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.2105\n",
            "Epoch 50/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.1968\n",
            "Epoch 51/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.1417\n",
            "Epoch 52/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.1553\n",
            "Epoch 53/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.1279\n",
            "Epoch 54/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.0947\n",
            "Epoch 55/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.0758\n",
            "Epoch 56/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.0481\n",
            "Epoch 57/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.0408\n",
            "Epoch 58/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 36.0055\n",
            "Epoch 59/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.9907\n",
            "Epoch 60/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.9846\n",
            "Epoch 61/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.9598\n",
            "Epoch 62/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.9145\n",
            "Epoch 63/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.9383\n",
            "Epoch 64/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.9092\n",
            "Epoch 65/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.8861\n",
            "Epoch 66/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.8702\n",
            "Epoch 67/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.8472\n",
            "Epoch 68/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.8502\n",
            "Epoch 69/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.8310\n",
            "Epoch 70/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.8139\n",
            "Epoch 71/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.7995\n",
            "Epoch 72/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.7856\n",
            "Epoch 73/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.7668\n",
            "Epoch 74/100\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 35.7537\n",
            "Epoch 75/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.7192\n",
            "Epoch 76/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.7124\n",
            "Epoch 77/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.7243\n",
            "Epoch 78/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.6894\n",
            "Epoch 79/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.6768\n",
            "Epoch 80/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.6631\n",
            "Epoch 81/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.6381\n",
            "Epoch 82/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.6490\n",
            "Epoch 83/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.6252\n",
            "Epoch 84/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.6179\n",
            "Epoch 85/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.6221\n",
            "Epoch 86/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.5934\n",
            "Epoch 87/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.5649\n",
            "Epoch 88/100\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 35.5461\n",
            "Epoch 89/100\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 35.5436\n",
            "Epoch 90/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.5320\n",
            "Epoch 91/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.5239\n",
            "Epoch 92/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.5189\n",
            "Epoch 93/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.5061\n",
            "Epoch 94/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.5036\n",
            "Epoch 95/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.4886\n",
            "Epoch 96/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.4662\n",
            "Epoch 97/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.4533\n",
            "Epoch 98/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.4390\n",
            "Epoch 99/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.4360\n",
            "Epoch 100/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 35.4368\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wNtw8DbFXfbf"
      },
      "source": [
        "#### Visualize **Latent Space**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "13f8596f-7ddb-43ec-8981-4cade7a19c16",
        "id": "NOdazjLiXfbj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "132f5bcb592a4091bee5c5835ac7e951",
            "f746d91be8be41199725598b5b9b2483",
            "c8826edc6e2c48a08f7ec9ce6f225517",
            "813ff3c7df4e40fda340b06e0e74e19c",
            "1111b4f3763a46cda2a03ffc5189f948",
            "500964b7ba1e4b228c56f178cca46f8f",
            "b46e347facd04ce6afac1cd848bd4df6",
            "66130c9515c046d2b642f443773b2604"
          ]
        }
      },
      "source": [
        "## Number of examples\n",
        "n_to_show = 5000\n",
        "\n",
        "example_idx = np.random.choice(range(len(x_test)), n_to_show)\n",
        "example_images = x_test[example_idx]\n",
        "example_labels = y_test[example_idx]\n",
        "\n",
        "latent_val1 = []\n",
        "latent_val2 = []\n",
        "\n",
        "for img, label in tqdm(zip(example_images, example_labels)):\n",
        "  latent_val = vae.encoder.predict(img.reshape((1,)+img.shape))[2][0]\n",
        "  latent_val1.append(latent_val[0])\n",
        "  latent_val2.append(latent_val[1])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "132f5bcb592a4091bee5c5835ac7e951",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ohbK-nROXfbz"
      },
      "source": [
        "> Notice the use of `intermediate` model `encoder`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "5e598a69-ddac-4a22-d3d8-fc4faa9f1a1a",
        "id": "rQzA0QKlXfcA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.scatter(latent_val1, latent_val2, cmap='rainbow' , c= example_labels\n",
        "            , alpha=0.5, s=2);\n",
        "plt.xlim(-3,3);\n",
        "plt.ylim(-3,3);"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LpdPA7e77uE8+1ew9INxyg0Vu9uBCUnta2HdIY9uwaplFRrq6NKaPP9W8/5GQnqZ49H6L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\nCrKznejWMe6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V77vxTyxDHkb5UqkvFc099mOnQskCoRb2N0vDypsH1askw83JBsiCWdDd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9I48/svFjt2KNGOdevgttum/nqPB231WnqSJ+irDHNapkYH5TlzSEaCpOorWa2VIaVkt+wn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\nJWVjjDEmJCwpG2OMMSFhSdkYY4wJif8Hj5t0312apRgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4lzwmYmcXfb1",
        "colab": {}
      },
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.scatter(latent_val1, latent_val2, \n",
        "            cmap='rainbow', \n",
        "            c=example_labels, \n",
        "            alpha=0.5, \n",
        "            s=2);\n",
        "\n",
        "plt.xlim(-3,3);\n",
        "plt.ylim(-3,3);\n",
        "\n",
        "wandb.log({\"vae_latent_space\": plt})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "J4cOAA2DXfcL"
      },
      "source": [
        "> Note how the `xlim` and `ylim` were not actually necesarry by commenting it out and then running the cell. The latent points are bounded around a region.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2La-MOfmaCXK",
        "colab_type": "text"
      },
      "source": [
        "# Generate New Digits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmM-IAvum6lB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "ff59c4984a214d1eb6664b4de9ddd819",
            "8eed5df5553c44f0b2f8847ec50f24b6",
            "80be754213da47b5801a238d6ef0c888",
            "4829b8373ece4887a68bb194f42bf695",
            "639777b5cd374f37a454e8d649c1b75d",
            "fb4e6e2cb9744bfba66d2586595a0fc3",
            "9bfc77b6340e4ea09ea862ec7a76f21b",
            "7c0a52339ea949868a88757ddfbecf1b"
          ]
        },
        "outputId": "87f8ec79-c9d1-4b9c-fe06-514b249139a3"
      },
      "source": [
        "generated_images = []\n",
        "\n",
        "for i in tqdm(range(32)):\n",
        "  ## Sample from normal distribution \n",
        "  l_sample = np.random.normal(size=2)\n",
        "  l_sample = l_sample.reshape((1,)+l_sample.shape)\n",
        "  ## Pass the sample to the decoder\n",
        "  gen_img = vae.decoder(l_sample)\n",
        "  gen_img = gen_img.numpy()\n",
        "  generated_images.append(gen_img.reshape(28,28))\n",
        "\n",
        "wandb.log({\"generated_images\": [wandb.Image(image) for image in generated_images]})"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ff59c4984a214d1eb6664b4de9ddd819",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=32), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}