VQ_VAE_Keras_MNIST_Example.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/IvanHalimP/HalimLab/blob/master/VQ_VAE_Keras_MNIST_Example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qmCU0kpMcnN1"
      },
      "source": [
        "# VQ-VAE Keras MNIST Example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FoYw_2tfdQm6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "1326d2aa-dae4-4ed5-a4e4-96a786c5cc6a"
      },
      "source": [
        "# Imports.\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Layer, Activation, Dense, Flatten, Dropout, Lambda, Conv2D, MaxPooling2D, UpSampling2D, Conv2DTranspose, SpatialDropout2D\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras import losses\n",
        "from keras import backend as K\n",
        "from keras.utils import to_categorical\n",
        "from keras.datasets import mnist, fashion_mnist"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXcLTn3scqfp"
      },
      "source": [
        "# Load data.\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "# Normalize data.\n",
        "x_test = x_test / np.max(x_train)\n",
        "x_train = x_train / np.max(x_train)\n",
        "\n",
        "# Add input channel dimension.\n",
        "x_train = x_train.reshape(-1, 28, 28, 1)\n",
        "x_test = x_test.reshape(-1, 28, 28, 1)\n",
        "\n",
        "# Convert labels to categorical.\n",
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)\n",
        "\n",
        "# Target dictionary.\n",
        "target_dict = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKR8CWkRU1Jp"
      },
      "source": [
        "# VQ layer.\n",
        "class VQVAELayer(Layer):\n",
        "    def __init__(self, embedding_dim, num_embeddings, commitment_cost,\n",
        "                 initializer='uniform', epsilon=1e-10, **kwargs):\n",
        "        self.embedding_dim = embedding_dim\n",
        "        self.num_embeddings = num_embeddings\n",
        "        self.commitment_cost = commitment_cost\n",
        "        self.initializer = initializer\n",
        "        super(VQVAELayer, self).__init__(**kwargs)\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        # Add embedding weights.\n",
        "        self.w = self.add_weight(name='embedding',\n",
        "                                  shape=(self.embedding_dim, self.num_embeddings),\n",
        "                                  initializer=self.initializer,\n",
        "                                  trainable=True)\n",
        "\n",
        "        # Finalize building.\n",
        "        super(VQVAELayer, self).build(input_shape)\n",
        "\n",
        "    def call(self, x):\n",
        "        # Flatten input except for last dimension.\n",
        "        flat_inputs = K.reshape(x, (-1, self.embedding_dim))\n",
        "\n",
        "        # Calculate distances of input to embedding vectors.\n",
        "        distances = (K.sum(flat_inputs**2, axis=1, keepdims=True)\n",
        "                     - 2 * K.dot(flat_inputs, self.w)\n",
        "                     + K.sum(self.w ** 2, axis=0, keepdims=True))\n",
        "\n",
        "        # Retrieve encoding indices.\n",
        "        encoding_indices = K.argmax(-distances, axis=1)\n",
        "        encodings = K.one_hot(encoding_indices, self.num_embeddings)\n",
        "        encoding_indices = K.reshape(encoding_indices, K.shape(x)[:-1])\n",
        "        quantized = self.quantize(encoding_indices)\n",
        "\n",
        "        # Metrics.\n",
        "        #avg_probs = K.mean(encodings, axis=0)\n",
        "        #perplexity = K.exp(- K.sum(avg_probs * K.log(avg_probs + epsilon)))\n",
        "        \n",
        "        return quantized\n",
        "\n",
        "    @property\n",
        "    def embeddings(self):\n",
        "        return self.w\n",
        "\n",
        "    def quantize(self, encoding_indices):\n",
        "        w = K.transpose(self.embeddings.read_value())\n",
        "        return tf.nn.embedding_lookup(w, encoding_indices, validate_indices=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKvlm7tIdy_d"
      },
      "source": [
        "# Calculate vq-vae loss.\n",
        "def vq_vae_loss_wrapper(data_variance, commitment_cost, quantized, x_inputs):\n",
        "    def vq_vae_loss(x, x_hat):\n",
        "        recon_loss = losses.mse(x, x_hat) / data_variance\n",
        "        \n",
        "        e_latent_loss = K.mean((K.stop_gradient(quantized) - x_inputs) ** 2)\n",
        "        q_latent_loss = K.mean((quantized - K.stop_gradient(x_inputs)) ** 2)\n",
        "        loss = q_latent_loss + commitment_cost * e_latent_loss\n",
        "        \n",
        "        return recon_loss + loss #* beta\n",
        "    return vq_vae_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gg96GlGBeJiH"
      },
      "source": [
        "# Hyper Parameters.\n",
        "epochs = 1000 # MAX\n",
        "batch_size = 64\n",
        "validation_split = 0.1\n",
        "\n",
        "# VQ-VAE Hyper Parameters.\n",
        "embedding_dim = 32 # Length of embedding vectors.\n",
        "num_embeddings = 128 # Number of embedding vectors (high value = high bottleneck capacity).\n",
        "commitment_cost = 0.25 # Controls the weighting of the loss terms.\n",
        "\n",
        "# EarlyStoppingCallback.\n",
        "esc = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=1e-4,\n",
        "                                    patience=5, verbose=0, mode='auto',\n",
        "                                    baseline=None, restore_best_weights=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrwSsrBIcqYp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "efd42681-0e7f-49b5-8b50-669ac2adc754"
      },
      "source": [
        "# Encoder\n",
        "input_img = Input(shape=(28, 28, 1))\n",
        "x = Conv2D(32, (3, 3), activation='relu')(input_img)\n",
        "#x = BatchNormalization()(x)\n",
        "x = Conv2D(32, (3, 3), activation='relu')(x)\n",
        "#x = BatchNormalization()(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "#x = Dropout(0.2)(x)\n",
        "x = Conv2D(64, (3, 3), activation='relu')(x)\n",
        "#x = BatchNormalization()(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)\n",
        "#x = Dropout(0.3)(x)\n",
        "x = Conv2D(64, (3, 3), activation='relu')(x)\n",
        "#x = BatchNormalization()(x)\n",
        "#x = Dropout(0.4)(x)\n",
        "\n",
        "# VQVAELayer.\n",
        "enc = Conv2D(embedding_dim, kernel_size=(1, 1), strides=(1, 1), name=\"pre_vqvae\")(x)\n",
        "enc_inputs = enc\n",
        "enc = VQVAELayer(embedding_dim, num_embeddings, commitment_cost, name=\"vqvae\")(enc)\n",
        "x = Lambda(lambda enc: enc_inputs + K.stop_gradient(enc - enc_inputs), name=\"encoded\")(enc)\n",
        "data_variance = np.var(x_train)\n",
        "loss = vq_vae_loss_wrapper(data_variance, commitment_cost, enc, enc_inputs)\n",
        "\n",
        "# Decoder.\n",
        "x = Conv2DTranspose(64, (3, 3), activation='relu')(x)\n",
        "x = UpSampling2D()(x)\n",
        "x = Conv2DTranspose(32, (3, 3), activation='relu')(x)\n",
        "x = UpSampling2D()(x)\n",
        "x = Conv2DTranspose(32, (3, 3), activation='relu')(x)\n",
        "x = Conv2DTranspose(1, (3, 3))(x)\n",
        "\n",
        "# Autoencoder.\n",
        "vqvae = Model(input_img, x)\n",
        "vqvae.compile(loss=loss, optimizer='adam')\n",
        "vqvae.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0817 17:38:45.713055 140434068273024 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0817 17:38:45.731148 140434068273024 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0817 17:38:45.738662 140434068273024 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0817 17:38:45.771762 140434068273024 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "W0817 17:38:46.324547 140434068273024 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
            "\n",
            "W0817 17:38:46.441968 140434068273024 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 24, 24, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 10, 10, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 3, 3, 64)          36928     \n",
            "_________________________________________________________________\n",
            "pre_vqvae (Conv2D)           (None, 3, 3, 32)          2080      \n",
            "_________________________________________________________________\n",
            "vqvae (VQVAELayer)           (None, 3, 3, 32)          4096      \n",
            "_________________________________________________________________\n",
            "encoded (Lambda)             (None, 3, 3, 32)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTr (None, 5, 5, 64)          18496     \n",
            "_________________________________________________________________\n",
            "up_sampling2d_1 (UpSampling2 (None, 10, 10, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_2 (Conv2DTr (None, 12, 12, 32)        18464     \n",
            "_________________________________________________________________\n",
            "up_sampling2d_2 (UpSampling2 (None, 24, 24, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_3 (Conv2DTr (None, 26, 26, 32)        9248      \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_4 (Conv2DTr (None, 28, 28, 1)         289       \n",
            "=================================================================\n",
            "Total params: 117,665\n",
            "Trainable params: 117,665\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6zjzMnb4cqVQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "outputId": "fd4d8f91-35c0-4801-bd6a-2078aa8e7e66"
      },
      "source": [
        "history = vqvae.fit(x_train, x_train,\n",
        "                    batch_size=batch_size, epochs=epochs,\n",
        "                    validation_split=validation_split,\n",
        "                    callbacks=[esc])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0817 17:38:46.809742 140434068273024 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1205: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "W0817 17:38:46.977737 140434068273024 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "W0817 17:38:47.273711 140434068273024 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 54000 samples, validate on 6000 samples\n",
            "Epoch 1/1000\n",
            "54000/54000 [==============================] - 20s 372us/step - loss: 7.5939 - val_loss: 3.5004\n",
            "Epoch 2/1000\n",
            "54000/54000 [==============================] - 17s 315us/step - loss: 1.9320 - val_loss: 1.2357\n",
            "Epoch 3/1000\n",
            "54000/54000 [==============================] - 17s 317us/step - loss: 1.1093 - val_loss: 0.9911\n",
            "Epoch 4/1000\n",
            "54000/54000 [==============================] - 17s 313us/step - loss: 0.9190 - val_loss: 0.8181\n",
            "Epoch 5/1000\n",
            "54000/54000 [==============================] - 17s 313us/step - loss: 0.8127 - val_loss: 0.7412\n",
            "Epoch 6/1000\n",
            "54000/54000 [==============================] - 17s 313us/step - loss: 0.7535 - val_loss: 0.7133\n",
            "Epoch 7/1000\n",
            "54000/54000 [==============================] - 17s 312us/step - loss: 0.7166 - val_loss: 0.7050\n",
            "Epoch 8/1000\n",
            "54000/54000 [==============================] - 17s 312us/step - loss: 0.6907 - val_loss: 0.6635\n",
            "Epoch 9/1000\n",
            "54000/54000 [==============================] - 17s 313us/step - loss: 0.6662 - val_loss: 0.6732\n",
            "Epoch 10/1000\n",
            "54000/54000 [==============================] - 17s 313us/step - loss: 0.6473 - val_loss: 0.6387\n",
            "Epoch 11/1000\n",
            "54000/54000 [==============================] - 17s 312us/step - loss: 0.6367 - val_loss: 0.6193\n",
            "Epoch 12/1000\n",
            "54000/54000 [==============================] - 17s 313us/step - loss: 0.6225 - val_loss: 0.5845\n",
            "Epoch 13/1000\n",
            "54000/54000 [==============================] - 17s 311us/step - loss: 0.6115 - val_loss: 0.5978\n",
            "Epoch 14/1000\n",
            "54000/54000 [==============================] - 17s 313us/step - loss: 0.6023 - val_loss: 0.6051\n",
            "Epoch 15/1000\n",
            "54000/54000 [==============================] - 17s 316us/step - loss: 0.5967 - val_loss: 0.6134\n",
            "Epoch 16/1000\n",
            "54000/54000 [==============================] - 17s 313us/step - loss: 0.5917 - val_loss: 0.5679\n",
            "Epoch 17/1000\n",
            "54000/54000 [==============================] - 17s 313us/step - loss: 0.5863 - val_loss: 0.5679\n",
            "Epoch 18/1000\n",
            "54000/54000 [==============================] - 17s 313us/step - loss: 0.5801 - val_loss: 0.5843\n",
            "Epoch 19/1000\n",
            "54000/54000 [==============================] - 17s 312us/step - loss: 0.5759 - val_loss: 0.5680\n",
            "Epoch 20/1000\n",
            "54000/54000 [==============================] - 17s 312us/step - loss: 0.5717 - val_loss: 0.5763\n",
            "Epoch 21/1000\n",
            "54000/54000 [==============================] - 17s 316us/step - loss: 0.5678 - val_loss: 0.5748\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FW0gMXy0cqRk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "outputId": "00356b23-6344-40cf-a294-719fe4fa0ad2"
      },
      "source": [
        "# Plot training results.\n",
        "loss = history.history['loss'] # Training loss.\n",
        "val_loss = history.history['val_loss'] # Validation loss.\n",
        "num_epochs = range(1, 1 + len(history.history['loss'])) # Number of training epochs.\n",
        "\n",
        "plt.figure(figsize=(16,9))\n",
        "plt.plot(num_epochs, loss, label='Training loss') # Plot training loss.\n",
        "plt.plot(num_epochs, val_loss, label='Validation loss') # Plot validation loss.\n",
        "\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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d6R/YlqqqprsUAACAGU3gHKee7lq27hzKhq07p7sUAACAGU3gHKfdszgH7OME\nAADYH4FznBrdo7M47eMEAADYL4FznHp2dTidVAsAALBfAuc4LV00P51zilmcAAAAByBwjlPHnJIj\nF9fM4gQAADgAgXMCGvVa+nQ4AQAA9kvgnIBGd5cOJwAAwAEInBPQU6/lkQ3bMjxcTXcpAAAAM5bA\nOQGNei07hoazfvOO6S4FAABgxhI4J6DRPTIaxSxOAACAfRM4J2DXLE77OAEAAPZN4JyARnctSczi\nBAAA2A+BcwKWLJiXeZ1zdDgBAAD2Q+CcgFLKyCxOgRMAAGCfBM4JatRrHqkFAADYD4FzgnrqXR6p\nBQAA2A+Bc4Ia3bU8snFbhoar6S4FAABgRhI4J6hR78rQcJW1m7ZPdykAAAAzUudYLiqlPJBkU5Kh\nJINVVa1sZVEHg57R0Sh9G7bmqHptmqsBAACYecYUOEddUFXVupZVcpBp1LuSJP0D25KnTHMxAAAA\nM5BHaieoZ1fg3OCkWgAAgL0Za+CskvzfUspNpZQ37O2CUsobSik3llJuXLt2bfMqnKEWd3XmkHkd\n6RtwUi0AAMDejDVwPquqqhVJnp/kTaWU5zz5gqqqPlZV1cqqqlYuXbq0qUXORKWUkVmcOpwAAAB7\nNabAWVXVw6OfH0vy2STntLKog0VPd1f6zOIEAADYqwMGzlLKglLKol1fJ3luku+3urCDQaNeS/+A\nDicAAMDejOWU2iOTfLaUsuv6T1RV9S8treog0ah3Ze0T27NjcDjzOp2/BAAAsKcDBs6qqu5LcuYU\n1HLQ6emupaqSRzduy7GHHTLd5QAAAMwo2nKTsHsWp32cAAAAP0XgnISe7loSszgBAAD2RuCchF0d\nTrM4AQAAfprAOQkL5ndmca1ThxMAAGAvBM5J6unu0uEEAADYC4Fzkhr1mg4nAADAXgick9To7nJK\nLQAAwF4InJPUU6/lx5t3ZNvOoekuBQAAYEYROCfJLE4AAIC9EzgnqbFrFueAfZwAAAB7EjgnqWfX\nLE4dTgAAgJ8gcE7SUXUdTgAAgL0ROCepNrcjSxbM0+EEAAB4EoGzCRrdZnECAAA8mcDZBI16V/oH\ndDgBAAD2JHA2QU+9lj4dTgAAgJ8gcDZBo7srm7YN5ontg9NdCgAAwIwhcDZBw0m1AAAAP0XgbIKe\nbrM4AQAAnkzgbAIdTgAAgJ8mcDbBkYtrKUWHEwAAYE8CZxPM7ZiTIxbN1+EEAADYg8DZJI16V/p1\nOAEAAHYTOJukp9ssTgAAgD0JnE3SqHelf2Bbqqqa7lIAAABmBIGzSRr1WrbuHMqGrTunuxQAAIAZ\nQeBskt2zOAfs4wQAAEgEzqaVd9lfAAAgAElEQVQ5atcsTvs4AQAAkgicTdNTH+1wOqkWAAAgicDZ\nNEsXzU/nnGIWJwAAwCiBs0k65pQcubhmFicAAMAogbOJGvVa+nQ4AQAAkgicTdXo7tLhBAAAGCVw\nNlFPvZZHNmzL8HA13aUAAABMO4GziRr1WnYMDWf95h3TXQoAAMC0EzibqNE9MhrFLE4AAACBs6l2\nz+IcsI8TAABA4GyiRnctiQ4nAABAInA21ZIF8zKvc46TagEAACJwNlUpxSxOAACAUQJnkzXqNR1O\nAACACJxN11PvSr8OJwAAgMDZbI3uWh7dtD1Dw9V0lwIAADCtBM4ma9S7MjRc5bFNHqsFAADam8DZ\nZD2jo1HM4gQAANqdwNlkjXpXErM4AQAABM4m69kVOHU4AQCANidwNtnirs4cMq8jfTqcAABAmxM4\nm6yUMjKLU4cTAABocwJnC/R0d9nDCQAAtD2BswUa9Vr6NuhwAgAA7U3gbIFGvSvrntieHYPD010K\nAADAtBE4W6Cnu5aqSh7dqMsJAAC0L4GzBXbN4uwbsI8TAABoX2MOnKWUjlLKd0sp/9zKgmaDnu5a\nkqTfPk4AAKCNjafD+ZYkd7aqkNlkd4fTSbUAAEAbG1PgLKUck+QFSf66teXMDgvmd2ZxrdMsTgAA\noK2NtcN5eZL/L8k+j10tpbyhlHJjKeXGtWvXNqW4g5lZnAAAQLs7YOAspbwwyWNVVd20v+uqqvpY\nVVUrq6pauXTp0qYVeLBq1Gvp0+EEAADa2Fg6nOcmeXEp5YEkVyW5sJRyRUurmgUaOpwAAECbO2Dg\nrKrqv1ZVdUxVVccluSTJ16qqurTllR3keuq1PL5lZ7buGJruUgAAAKaFOZwtsuukWl1OAACgXY0r\ncFZV9fWqql7YqmJmk4ZZnAAAQJvT4WyRnl2zOAd0OAEAgPYkcLbIUXUdTgAAoL0JnC1Sm9uRJQvm\nCZwAAEDbEjhbqNFdc2gQAADQtgTOFmrUu9I/oMMJAAC0J4GzhXrqtfTpcAIAAG1K4GyhRndXNm0b\nzBPbB6e7FAAAgCkncLZQY9dJtUajAAAAbUjgbKGe7tFZnE6qBQAA2pDA2UI6nAAAQDsTOFvoyMW1\nlKLDCQAAtCeBs4XmdszJEYvm63ACAABtSeBssUa9K/06nAAAQBsSOFusp9ssTgAAoD0JnC3WqHel\nf2Bbqqqa7lIAAACmlMDZYo16LVt3DmXD1p3TXQoAAMCUEjhbbPcszgH7OAEAgPYicLbY7lmc9nEC\nAABtRuBssd0dTifVAgAAbUbgbLHDF85P55xiFicAANB2BM4W65hTcuTimlmcAABA2xE4p0BPdy19\nOpwAAECbETinQKPepcMJAAC0HYFzCjS6a3lkw7YMD1fTXQoAAMCUETinQE+9KzuGhrN+847pLgUA\nAGDKtF/g/PqfJR9/wZTe0ixOAACgHbVf4Oyclzx4fbKxf8puuXsW54B9nAAAQPtov8DZu3rk8z1f\nnbJb6nACAADtqP0C55GnJosaUxo4D1swL/M65zipFgAAaCvtFzhLSXovSu67NhkanKJbljTqZnEC\nAADtpf0CZ5L0rkq2bUgevnHKbtmo13Q4AQCAttKegfOEC5LSkdz9lSm7ZU+9K/06nAAAQBtpz8DZ\n1Z0c8zNTe3BQdy2PbtqeoeFqyu4JAAAwndozcCbJiauS/luSJ9ZOye0a9a4MDVd5bJPHagEAgPbQ\nvoGzd9XI53uvmZLb9XSPjEYxixMAAGgX7Rs4jzozWbB0yh6rbdS7kpjFCQAAtI/2DZxz5iRPuyi5\n55pkeKjlt+vZFTh1OAEAgDbRvoEzGXmsduuPk75bWn6rxV2dOWReR/p0OAEAgDbR3oHzaRcmKck9\nrR+PUkoZmcWpwwkAALSJ9g6cC5YkR6+Ysn2cPd1d9nACAABto70DZ5L0rk7W3Jhs+XHLb9Wo19K3\nQYcTAABoDwJn76okVXLv11p+q0a9K+ue2J4dg8MtvxcAAMB0EziPXpF0HTolj9X2dNdSVcmjG3U5\nAQCA2U/gnNMxcnjQPdckw63tPO6axdk3YB8nAAAw+wmcycg+zs2PJY/c2tLb9HTXkiT99nECAABt\nQOBMkt6LRj63+LHa3R1OJ9UCAABtQOBMkoVHJI0zWx44F8zvzOJap1mcAABAWxA4d+ldlTz07WTr\nQEtvYxYnAADQLgTOXXpXJ9VQct/XW3qbRr2WPh1OAACgDQicuxzzM8n8euv3cepwAgAAbULg3KWj\nMznhvJHxKFXVstv01Gt5fMvObN0x1LJ7AAAAzAQC555OXJ1s6kseu6Nlt9h1Uq0uJwAAMNsJnHt6\nWuvHozTM4gQAANqEwLmn+tHJEacmd3+lZbfo2TWLc0CHEwAAmN0OGDhLKbVSyrdLKd8rpdxeSnnn\nVBQ2bXovSn7078n2TS1Z/qi6DicAANAextLh3J7kwqqqzkyyPMnzSinPbG1Z0+jE1cnwzuT+61qy\nfG1uR5YsmGcPJwAAMOsdMHBWI54Y/efc0Y/WHeM63Y59ZjJvYcv3cZrFCQAAzHZj2sNZSukopdyS\n5LEkX6mq6obWljWNOuclx5+X3P3Vlo1HadTN4gQAAGa/MQXOqqqGqqpanuSYJOeUUk578jWllDeU\nUm4spdy4du3aZtc5tXovSjb8KFl3d0uW76nX0q/DCQAAzHLjOqW2qqqBJNcmed5efvaxqqpWVlW1\ncunSpc2qb3r0rhr5fE9rTqttdHdl0/bBbNq2syXrAwAAzARjOaV2aSmle/TrriSrk/yg1YVNq0Of\nmhz+9Jbt42w4qRYAAGgDY+lwNpJcW0q5Ncl3MrKH859bW9YM0Ls6eeBfkx1bmr50T7dZnAAAwOw3\nllNqb62q6qyqqs6oquq0qqreNRWFTbvei5Kh7ckD1zd9aR1OAACgHYxrD2dbeeq5SWdXS/ZxHrm4\nllKSfh1OAABgFhM492VuLTn+2S3Zxzm3Y06OWDQ/fTqcAADALCZw7k/vquTH9yXr72360mZxAgAA\ns53AuT+7x6Nc0/Sle7rN4gQAAGY3gXN/ljwtOfT4luzjbNS70rdha6qqavraAAAAM4HAeSAnrk7u\n/2ays7ndyEa9lm07hzOwZWdT1wUAAJgpBM4D6V2VDG5NfvStpi67exanfZwAAMAsJXAeyHHPSjrm\nJ3c397Ta3bM47eMEAABmKYHzQOYtSJ76c00fj7Krw+mkWgAAYLYSOMfixNXJuruSgR81bcnDF85P\n55xiFicAADBrCZxjsXs8SvO6nB1zSo5cXEv/gA4nAAAwOwmcY3H405P6U5q+j7Onu6bDCQAAzFoC\n51iUkvRelNz/jWRwR9OWbdS77OEEAABmLYFzrE5cnex4Inno35u2ZKO7lkc2bMvwcNW0NQEAAGYK\ngXOsjn9OMmduU/dx9tS7snOoyrrN25u2JgAAwEwhcI7V/EXJU57Z1H2cZnECAACzmcA5Hr2rksdu\nTzb2NWU5szgBAIDZTOAcjyaPR9nV4ezT4QQAAGYhgXM8jjw1WdRoWuA8bMG8zO+co8MJAADMSgLn\neOwaj3Lv15OhwSYsV9Kom8UJAADMTgLnePWuTrZvSNZ8pynLNepdeUTgBAAAZiGBc7xOOD8pHck9\nX2nKco3uWvoHPFILAADMPgLneHV1J8ee07R9nD31rjy6aXuGhqumrAcAADBTCJwT0XtR0v+95InH\nJr1Uo7uWoeEqj23yWC0AADC7CJwT0bt65PM910x6qZ76yCxOo1EAAIDZRuCciKPOSBYsbcpjtY3u\nkVmcRqMAAACzjcA5EXPmJL2rknuvSYaHJrVUY7TD2a/DCQAAzDIC50T1rkq2Pp70fXdSyyyudWbB\nvI706XACAACzjMA5UU+7MElJ7p7ceJRSShrdXTqcAADArCNwTtQhhyVHn92cfZz1mj2cAADArCNw\nTsaJq5OHb0o2r5/UMj31rvRt0OEEAABmF4FzMnpXJamS+66d1DJH1WtZ98T27Bgcbk5dAAAAM4DA\nORk9ZyVdh016H2dPdy1VlTy6UZcTAACYPQTOyZjTMXJ40L3XJMMT707uGo3SN2AfJwAAMHsInJPV\nuyrZvDZ55HsTXqKnu5Yk6bePEwAAmEUEzsnqvWjk8yROq93d4XRSLQAAMIsInJO18IikcWZy98QD\n54L5nVlc6zSLEwAAmFUEzmboXZ2s+Xay9fEJL9HT3WUWJwAAMKsInM3QuyqphpP7vj7hJRr1Wvp0\nOAEAgFlE4GyGY34mmV+f3D5OHU4AAGCWETiboaMzedr5yT3XJFU1oSV66rU8vmVntu4Yam5tAAAA\n00TgbJbe1cmm/uTR2yf08l0n1epyAgAAs4XA2SyTHI/SMIsTAACYZQTOZlnckxx52oQDZ8+uWZwD\nOpwAAMDsIHA2U+9FyY/+Ldm+adwvPaquwwkAAMwuAmcz9a5OhgeT+74x7pfW5nZkyYJ59nACAACz\nhsDZTMc+I5m3cFL7OM3iBAAAZguBs5k65yUnnD8SOCcwHqVRN4sTAACYPQTOZuu9KNnwULLuh+N+\naU+9ln4dTgAAYJYQOJutd9XI57u/Mu6XNrq7smn7YDZt29nkogAAAKaewNls3U9JDj9pQvs4G06q\nBQAAZhGBsxV6VyUP/muyY/O4XtbTbRYnAAAwewicrXDiqmRoR/LA9eN6mQ4nAAAwmwicrfCUn0vm\nHjLufZxHLq6llKRfhxMAAJgFBM5WmFtLjnv2uPdxzu2YkyMWzU+fDicAADALHDBwllKOLaVcW0q5\no5RyeynlLVNR2EGvd1Xy+P3J+nvH9TKzOAEAgNliLB3OwSS/W1XVsiTPTPKmUsqy1pY1C5w4Oh5l\nnF3Onm6zOAEAgNnhgIGzqqr+qqpuHv16U5I7kxzd6sIOeoedMPIxzn2cjXpX+jZsTVVVLSoMAABg\naoxrD2cp5bgkZyW5YS8/e0Mp5cZSyo1r165tTnUHu97VIyfV7hz7I7KNei3bdg5nYMvOFhYGAADQ\nemMOnKWUhUk+k+S3qqra+OSfV1X1saqqVlZVtXLp0qXNrPHg1bsqGdyaPPitMb9k9yxO+zgBAICD\n3JgCZyllbkbC5pVVVf1ja0uaRY57VtIxf1z7OHfP4rSPEwAAOMiN5ZTakuRvktxZVdX7W1/SLDLv\nkOS4c8cVOHU4AQCA2WIsHc5zk7wmyYWllFtGP36+xXXNHr2rk3U/TB5/cEyXL104Pz31Wv7m+vuz\nYat9nAAAwMFrLKfUXl9VVamq6oyqqpaPfnxpKoqbFXrHNx5lzpySD73yrDz8+Nb87qduyfCw02oB\nAICD07hOqWUCDj8xqT9lXI/VrjzusPz+C07JV+98LB/9xr0tLA4AAKB1BM5WKyU5cVVy3zeSwR1j\nftlrf+64vPjMnvz5/70r37zbmBkAAODgI3BOhd5Vyc7NyY/+bcwvKaXkv7309PQesTBv/uR38/CA\nQ4QAAICDi8A5FY5/TjJn7rgeq02SBfM78xeXnp2dQ1V+44qbsn1wqEUFAgAANJ/AORXmL0qe8sxx\nB84kOWHpwrzvF8/M99ZsyDu/cEcLigMAAGgNgXOqnLg6eeyOZMPD437p8047Kr923gn5xA0/yv+5\n8aEWFAcAANB8AudUGed4lCf7L889KT97wpK8/Z++n9v7NjSxMAAAgNYQOKfKEcuSRT0TDpydHXPy\n4VedlUMPmZdfv+KmbNiys8kFAgAANJfAOVVKSXovSu77ejI0sbB4+ML5+cirV+SRDdvyW//w3QwP\nV82tEQAAoIkEzql04upk+8ZkzXcmvMTZTz00f/DCZbn2rrX5H9fe08TiAAAAmkvgnErHn5eUjuTu\nr0xqmdc886m5+Kyj84Gv/jDf+OHaJhUHAADQXALnVOrqTo59xoT3ce5SSsmfXnx6TjpyUd5y1Xfz\n0I+3NKlAAACA5hE4p1rvRckjtyabHp3UMl3zOvIXl56doeEqv3Hlzdm2c6hJBQIAADSHwDnVTlw9\n8vneaya91HGHL8j7X748tz28Ie/4/O2TXg8AAKCZBM6pduTpyYIjJv1Y7S6rlx2ZN13wtFz1nYfy\nD9/5UVPWBAAAaAaBc6rNmTPyWO29X0uGm/MY7O+sPinP6j08f/C523Pbmg1NWRMAAGCyBM7p0Lsq\n2fp48vDNTVmuY07JBy9ZnsMXzMsbr7wpj2/e0ZR1AQAAJkPgnA5PuzApc5J7JjceZU9LFs7P/7z0\n7Dy2cXt+6x9uydBw1bS1AQAAJkLgnA6HHJYcfXbT9nHusvzY7vzRi5flGz9cmw9dc3dT1wYAABgv\ngXO69K4aeaR287qmLvuqc56SX1hxTD54zd259gePNXVtAACA8RA4p0vv6iRVcu+1TV22lJJ3X3xa\nljUW5y1XfTc/Wr+lqesDAACMlcA5XXqWJ12HNXUf5y61uR35i0vPTpL8+hU3ZdvO5pyGCwAAMB4C\n53SZ0zEyHuWea5Lh4aYv/5Qlh+TyS5bnjv6Nefs/fT9V5RAhAABgagmc06l3VbJlXdJ/S0uWv/Dk\nI/PmC3vz6ZvW5JPffqgl9wAAANgXgXM6Pe2ikc/3XNOyW7xl1dPznKcvzTs+f3u+99BAy+4DAADw\nZALndFq4NGksb8k+zl065pR88BXLs3TR/Lzxipvy4807WnYvAACAPQmc0+3E1cma7yRbH2/ZLQ5d\nMC8fvXRF1m3ekbdc9d0MDdvPCQAAtJ7AOd16VyXVcNPHozzZGcd0510vPjXfvHtdPvCVH7b0XgAA\nAInAOf2OXpnU6i3dx7nLJec8Ja9YeWz+x7X35Kt3PNry+wEAAO1N4JxuHZ3JCRck93w1mYLRJe98\nyak57ejF+e1P3ZIH1m1u+f0AAID2JXDOBCeuTp54JHn0+y2/VW1uRz766rPTMafk16+4KVt3DLX8\nngAAQHsSOGeC3eNRvjoltzv2sENy+SuW565HN+X3P3tbqinorAIAAO1H4JwJFjeSI09L7p6awJkk\n5590RH7roqfnH7/7cK644UdTdl8AAKB9CJwzRe+q5KF/T7ZtnLJb/uaFvbngpKV51xduz80/at1Y\nFgAAoD0JnDNF76pkeDC5/xtTdss5c0o+8IrlOapey5uuvDnrntg+ZfcGAABmP4Fzpjj2Gcm8RVO2\nj3OX7kPm5aOvPjs/3rwjb/7kdzM4NDyl9wcAAGYvgXOm6JyXnHDeyD7OKT7E57Sj6/mT/3havnXv\n+vz5V344pfcGAABmL4FzJuldlWxck6y9a8pv/Ysrj80rz3lKPvr1e3P17Y9M+f0BAIDZR+CcSXpX\njXy+5yvTcvt3vHhZzjymnrd+6nu5b+0T01IDAAAwewicM0n3scnSk6d8H+cu8zs78j8vPTudHSVv\nvOLmbNkxOC11AAAAs4PAOdP0rkoe/FayfXo6jEd3d+WDl5yVHz62Kf/1H29LNcX7SQEAgNlD4Jxp\nelclQzuS7396yg8P2uU5T1+a31399Hzulr787bcemJYaAACAg5/AOdM89eeS7qckX3hL8uGzk2++\nP9nYP+Vl/Mb5vVl1yhH5ky/emZse/PGU3x8AADj4CZwzTef85I3/lrzkfyYLj0yueWfygWXJlb+Y\n3PG5ZHDHlJQxZ07Jn798eY4+tCu/ceXNWbtp+5TcFwAAmD1KK/borVy5srrxxhubvm5bWn9vcsuV\nyS2fSDb1J4csSU5/eXLWpclRp7X89nf0bcxLP/qvOfOY7lz5+meks8P/UQAAQDsrpdxUVdXKsVwr\nPcx0S56WXPSHyW/fnrz608lxz06+89fJX5yb/OV5ybf/Ktn6eMtuv6xncd79H0/PDff/OO+9eurn\ngwIAAAevzukugDGa05GcuHrkY/P65Lb/k3z3iuRLb02u/v3klBeOdD2PP2/k2ib6hbOPyXcfejwf\nu+6+nHVsd55/eqOp6wMAALOTR2oPdv3fGwmet34q2TaQLD4mWf6qkY/Djm/abbYPDuUVf/nvufvR\nTfncf35Weo9Y2LS1AQCAg8d4HqkVOGeLnduSu740Ej7v/VqSauTx27MuTU55cTLvkEnfom9ga174\n4etz2IJ5+dybzs2C+RrkAADQbgTOdrdhTfK9TybfvTJ5/P5k3qLktJcmZ70mOWZlUsqEl/7Xe9bl\nNX9zQy465cj81+efnBOW6nQCAEA7ETgZUVXJg98a6Xre8U/Jzi3J4SeNdD3PvCRZeMSElv2r6+7L\nu790Z5Lk+MMX5MKTj8hFJx+RlccdlnmdzqECAIDZTODkp23bmNz+2ZERKw/dkJSO5On/YSR8nvjc\npGPuuJZ76Mdb8rUfPJZrfvBY/v3e9dkxNJxF8zvznKcvzYUnH5HzT1qaJQvnt+jNAAAA00XgZP/W\n/jC55Yrke1clTzyaLFianPGKkfB5xCnjXm7z9sFcf8+6fO3Ox/K1ux7L2k3bU0py1rHdueiUI/P/\nt3f3QXLk913H39/unpndnVmtdJJW9t3pdI+2DhvjxMaOcWLfQ2wc44oJUGATwOGhjIFQ5A+KxyqS\nMv+Eh1AF/AEF2FVJKhiHgIkLkmDHd36CsvGzHd/pTncnyfdkraTTPszuzkw/fPnj1/OwoxlptdrZ\nXWk/r1JX/7r7Nz092+rZ/uzv192PnJzn5KtmsRvoyisiIiIiInuDAqdsTp7BM78P3/p1ePr3oMjg\njjeF4Pn6Pw1Tc9e9yqJwvv/SMp87dZ7HTi3w3ReWALh9bopHHpzn0ZPHeNt9h5mqbO+jW0RERERE\nZGdsa+A0s48D7wMW3P31m1mpAudNqHkBvveb4XrPhScgmYI/9H5448+Gu91GW7s2c2G5xeNPLfC5\nJxf40umLrKc5U5WIH7//CI+cDK2fr5qb2uYPIyIiIiIik7LdgfMdQBP4NQXOfcAdXvpWCJ7f+y1o\nL8HBu+CNfwHe+MFQ3qJWmvPVM6/w2JPn+f0nF3hxcR2A191+gEdPzvPIg8d4wx1zRJG63oqIiIiI\n7FXb3qXWzO4G/qcC5z6TrsOp/xW63D73hTDv3neGx6uc/BNQmd7yqt2d0wtNPvfkAo+dOs83zl2m\ncDjSqPLwa+d59MF5fvyBozT0rE8RERERkT1lVwKnmX0Y+DDAXXfd9aZz585tamPlJrH4A/j2J8LN\nhhZ/EJ7tec9PwL0Pw30Pw+H7b+j5npdXO3zh6Qt87tQCn39qgZVWRiU2fuzewzxycp5HTs5z4nB9\nGz+QiIiIiIhshVo4ZXKKAs5+KTxi5bnH4fLZMP/AnXDfQyGA3vsQ1I9s+S3SvOAb5y6Hx648eZ5n\nL6wCcP98g0dPzvPwyXnefOIQSaxnfoqIiIiI7DQFTtk5r5wJwfPZx+HMF6AV7krLq94QWj7vfRju\nehtUtn5joLMXV3ns1AKPnVrgq2cukebOgamEd752np98cJ53vuYoB2eq2/SBRERERETkahQ4ZXcU\nObz0bXj2sRBCn/9/UKThjrcn/li/++3867Z819uVVsqXT1/kc6cWePzUApdWO0QGbzpxiEdOHuPR\nB+d5YL6hZ36KiIiIiEzIdt+l9hPAQ8AR4Dzwi+7+sau9RoFTAGg34dz/Ca2fzz0OF06F+fWjodtt\nN4AeuH1Lqy8K5zsvLJZdbxd44uVlAO48NM2jJ+f5o/fcxmuOzXL34TrVRN1vRURERES2w7a3cF4v\nBU4ZafkleO7zZQD9PKwuhPlHXtvvfnv326E2u6XVv7y0zuOnLvDYqfN8+ZmLtNICgCQy7j5S5zXH\nGtw/P8sD8w1ec2yWe44oiIqIiIiIXC8FTtn73OH89/vXf577v5CtQ5TAnW/pB9DbfwTi6380SivN\nefZCk9Pnm5xeWOHp802eWWhy7tIqRflfPo6Muw/P8MD8bAijx8L4niN1akm8zR9YREREROTWoMAp\nN5+0Bc9/tQygj8HL3wUcanNw7zv63W9vu/eG3qaV5jx3YZXTCyucPt/k6fMrPLPQ5OxQED1xeKbX\nEnr/QIvoVEVBVERERET2NwVOufmtXoIzn+93v116Psw/eKLf+nnPO2Dmtm15u1aac+biai+APn1+\nhdMLTc5dWiMvk2hkcPfhei+APnCswQPzs9x7VEFURERERPYPBU65tbjDpWf73W/Pfgnay4CFLrfd\nAHr8rZBs7+NR2lk3iDZ5pgyhT59f4exQED3RC6IhhN4/3+D++YaCqIiIiIjcchQ45daWZ/DiN/oB\n9IWvgedQmYETb+8H0PkHYUKPR+lkBWcurg5cHxrGZy+ukpVB1Azuui1cI/rAsX4Yve9og+mqgqiI\niIiI3JwUOGV/aS3D2S/3n/956ZkwP5mCuTth7ngYH7xroHwcDtwBcWVbN6WTFZy9tLrh+tCnz69w\nZiiIHj80w/3zDY4dmOLobI2jjWoYz9Y40gjjmer13yxJRERERGTSFDhlf1t8Plz3efGpUF56HpZe\ngOb5jfUsgtlX90PpwTKMzt1Vlo9DrbEtm5TmBWcvrva65J5eaPLsQpOLzTaXVjuMOgzr1ZgjszWO\nNvohdDCQhnKVI42auu6KiIiIyI5R4BQZJW3B8oshgHaDaC+QPg9LL0KRbnzN1MEyfN7VbxnthdPj\nUD96w912s7zglbUOF1baXFhpc7HZKcftgXltLjTbLK6lI9cxO5WULaW1Xkg9OjDuhtTDjSqVWM8e\nFREREZGtu57AqT57sn9UpuDwfWEYpchDK+jSC7D4g37L6OLzcPkMnPkidFY2vqbXbbfbSnr93XaT\nOGJ+dor52alrfoROVnBptT0ikPYD65MvLfPFlTYr7WzkOg7NVDa2lg6F0u74tnqVOJrMNbAiIiIi\nsj8ocIp0RTEcuD0Mx99y5XJ3aC1d2TLaLZ/+zJXddrHQbXdDy2jZbXfuDqjNQqUO1ZkQXq/RWlpN\nIl49N82r56av+XFaaR5CaLPNxXI8HFS/9YNFLqy0WU/zK38cBgdnqjRqCbNT3aHCbK1fbgzOn0rK\nZZVe/Xo1IVJoFREREXLlwFYAABMbSURBVNm3FDhFNssMpg+G4VV/eHSdbrfdxR+E1tHBQPri1+GJ\n376y225v/VG4025lJgTQaqNf7obSygxU6/1xrzxYJ4ynqnWO1+ocn52B4wfHhll3Z7WT90LpYDi9\nvNZhpZWx0spotjKef2UtlNsZK62U4ho98s2gUQ3hszEYTKcqNGoJB8pg2qhtXDYYcBu1hGqibsAi\nIiIiNyMFTpHttNluu4vPw8pL0G5Cugad1XK8Bp1mv5yuhvHqxYE6q2HgOq6/Hg6zlTKsVmewSp1G\ndYZGZYa7BwPsXAMO18Di0PobJWE9Zdktop0ba5mxnjmrmbOWGquph6EDzdRZSds0Oy1W2gUrHVhZ\ndn54oWC5XbDYdloZ5ETkRBREZMTkREA/INeSiNmpCgd6wTVhthZaWOvVmKlqzHQlZqYcT1eTchwx\nXUmYHlg+VYl70+oyLCIiIjJZCpwiO2mw2+6NcIestTGUdsed1YF5kwuzBkyVw5YljP0WcozCYtxi\nCiLyIqZYi8jWQjjNPAwrTLPkMywWMyx5nWXqXPA6y3Snw3iJBss+wxJ1WlQBo5pEG4LqVLfcC65D\n4255OMBWYmaqCdPVqFxHUq4vwib0LFgRERGRm4ECp8jNyAwq02Hg8PauezDMZi3wPLTMegFFVpbz\nslzO69bplUfNL4bq5EPryjfMN8+Je/PHvXcaWolbi/j6Zbz1Q1hfJEpXr/oRc0toJQdoxQ3WojCs\nWIOVrM5yOsNis87lYoZXihkuZVOcyWY4n05xuZhhhRkKNt/FtxtSp5Ko1xLbDbf91tZoxLww1Mpl\n09VRryvrJJGulRUREZE9SYFTRDbaEGZvHsZAJ9w8Czd4ai2WwxKs98vx+iL11hL11iKHe8vOheXr\niyHUDq+82p8sqrMUtTmy6gHSygE6lVla8QFaySzrUYOmNWhanRVr0CyqZFlGmqVkaYc8S8nTlHw9\nJc8yijwjz1KKIqOTZbSLjCY5CQVxd2w5CTkxBTFFr9wbW07VCqpRQTVyKuZUooKqFVQsp2IFCQWJ\nhdckFtYdUxB7ThHXyCoNikqDotrAa7N4dRarNaB2gHh6lqg2RzxzgGR6lmRmjurMHPHUgXDjq2vc\niVlERET2LwVOEbn1xAnUD4fhermHbsUbgurShumotUTUWiRZX2SqtQStF6H1RKh7jdbVq293OYxQ\nRBXcYtwiCksorIyfFqJjuA42JqPb5TiU0yKi7RFNj0iLhI5HYSjCNbM5EVVSGqwzay/TYJ26tZhl\nnVlb39Rmt6iwxgxrFoZWFIZ2PEMnbtCJ66RJnaxSJ09myavdcDsLtQbUZrHaAeJanWolppbEVJOI\nWhJRLYdKFBFHRhIbSWQkUUTcK185HUem7swiIiJ7gAKniMggszIENcIjbK5XnpYBtQyr6Vr/pku9\n8eAwMG/wBk0bhug6OvFujrvTzgrWOzntrKCd5XSyglZWsFROt9OMvNWkaC3jrRW8vYy1V6DdJEqb\nRJ0V4k6TJGuSpKtU8lWqeZNavsaB/CJTnTWmijVmfI0qY+7OPPijc2OVKZpM0/Tp3vgyU7So0vEK\nbfrDtaYzq5JalSKqkkUV8qhGFtXwqEoeVymiGlEch5AaG3EU9cJqZVPTZdAtX1+NIypxdEW5EkdU\nx5TDsLmyArSIiNyMFDhFRLZTXIH6kTDsYWbWux50R2TtcL1tZwXaYfDWMnlrhWx9mXx9iaK1DK0V\n6u0wHGuvEHWaWLpIlHewvE2Ut4mLNlHeIfLs+rahKIfBzSIhtSqZVehYldQqdKiSdkOsVWh7hU43\nyHqFtie0qNDycigSml7lYjHFik+zwsyGwNxkinVqDN55eSuSyEYG2quF28HgOhyse63F8WCQLgN0\nt6W4+7pe/TF1N5TLerGNrdudn6glemvch657L6+NtwiqsxDpUVIisncocIqIyOQltTAMdHM2rnqj\n4msr8hBks1YY5+2N09ngdAvyzsCyFmQdkqxFcsU6NtYJ49XR6/CMXvPzVbK7W0RRCd2J80qDrNIg\nS+qkSYM0niFN6rTjBu1ohnZcZz2qs24ztOLQTXnVp1mLplllmnYekRUFaeakeUFaOGlWbCh3soLV\nTt6fnxdkhZMXTpo7eRGmszzMy4rims/V3V7euw55KuoOThJ5uPY4orwOGZLIScyplMtic6qRh2uS\nLVyznEQFCd6r212WmJfXQRdUzIl61zM7EaFebEV5fXSYjih610vHeJgu50de9B/i5AWR5xj5QLnA\nPA/lsq4Vw/PDMivLeIEV2cD8cFO0sCy8nm7dXrkY/5O1CKYOYtOHoDvM3NYvbxhuK58vfQim5kIP\nCxGRbabAKSIiN6coDs+Mrc7s3jYUeQie6Tq0l3utt/1hGdpNrL1CXA4b6q39sF/uNDf3npWZcLOm\nwaE6C/XZK+fXyhs7VeuhBSzPw92d8064uVavnFLkKUWW4lmHPO/gWUqRdfA8DUPWgSKUyTp4kYUu\n5HmKFf31WZGGADVUjoqMyLvjDBv3+CUH8nLYZbkbORHevTaa/rXP/WunI1Lf+CzhMmpuqJN7TIie\n3ecNJ2V5aJ0e9V8z9HziDfV94zbkREQUzNkqB9NVDjabHLRVDvIsh+w7zNHkgK2N/awFxgp1Vmiw\nZLPhzt02y0oUyk2bpRnN0owOlONZ1qJZ1qIGHiXEkRFZ6D0RGURmRGZYr0w5PbA86tYP8+Kxy/vr\n660rGr3u+JrLu8sG1zu0HdGV64rMiMvtibt1I8r5G7cxLt87jjZuc2862vh5B392MQVR0Q7HSd4h\n9pSo6GB5Gv4glqehhTupQjwwJLXQwyaulfN0ii97h/43ioiIbFUUhzBXrd94N+oiD6HzitA6KsSu\nbKy7eG5jkC2us7sxoaG221hbgXBNcVyBqFKeyI4qJ+HktjJwotudH1X6J769ciVcl7yhXC6L4v51\nzBvGUegqesWy6Mq6FpX1x61nuG4Yh5tx9cNe5kbhEZmHFuDcQ2uwO+TuFO64O4VDXnSnoSjrF86G\n5d1y4WFduGMFmDuxh7G5EzkkA+vovk9ecPX3LMvhveCSOxcH3rO7PV6kVNMmtWyJ6WyZqXSJqWyJ\nqWw5TGfLTOfLzGTL3J4vM52fZyZfYbpoEl3l+cyrVh8IoiGchqA6y7J1xw2WvUGbStgW6G1fVv5c\nHacoym31she8A73P2P/MYRyyl+O9z3mjDKdKRoWcqqVUyKh2h4HpME6p2tA0WTkvDeugfI111zOw\njrJePLDOxMa3Xl+PnIiUhIyEDgkZFVJLSElIrRKmqZBZQmaV8vKCSlkemG8JmVXJh8aZJeRWJY8S\n8vJ1RdQfxzixOZHn/Z4EXt4h3bzsQZAP9CjIw2sG5kXlndR75W5vAgs9Cnr1vPvnmILIs14PhJhu\nT4Nunf50t5dAr0v/hrFhGG6hBFb+M9zCnH69ja+1si5YOdv6dTdMh/c2M3xgPYPrjl7zLube8ue3\n5f/DblPgFBER2QuiOHRrnJq7sfV0n6Xbbm4MpxYPBcBxIbIc76PrAI2r3iRairy8EdrlkUN9/TL1\ntVc41pv3QjleDM9R3k4bnoG1NxRRhSKq4uU4TIchj8KNy/JohsIq5FGlF9RaVmXVErJodKDLhgJg\nNygW7iFY5W1iz4iKDlGREhcdoiIj9nLaU+IiLcuhtTTpzveUWpFS95TYmySeEnvWW14p6ySebuqm\nbzup2/NgY4+Cod4CHm/oJdAZ6klQhPgI9P87hekr542qt/E1wIh5w/WGe3WMX3eY92zrGA8rcIqI\niMieM/gs3cbR3d4auRVEcbgOdOa263tdUUB7IKiuXQ5duPe6bgt80u2eOqLL6kCX1shs2+8kvqe4\nh14Teae81n2ge2/W7nXLD/M64dr3bm+D4Tuwd3sbXDE/Gqoz1EthYH5sNvaPQ4O9CQZbw0Mr+MD8\nIrSyd+s7Zb1uHUId2LiuMMt7reuDy4brdtffb40f/V7j6p44XJ/4rt0pCpwiIiIisv2iqH+DIrl5\nmfV7QVT3dgjqXqcb77Vm8H3ulv6DjIiIiIiIiOweBU4RERERERGZCAVOERERERERmQgFThERERER\nEZkIBU4RERERERGZCAVOERERERERmQgFThEREREREZkIBU4RERERERGZCAVOERERERERmQgFThER\nEREREZkIBU4RERERERGZCAVOERERERERmQgFThEREREREZkIBU4RERERERGZCAVOERERERERmQgF\nThEREREREZkIBU4RERERERGZCAVOERERERERmQgFThEREREREZkIBU4RERERERGZCHP37V+p2Qrw\n1LavWG7EEeDibm+EbKB9sjdpv+w92id7k/bL3qN9sjdpv+w92ic37oS7H91MxWRCG/CUu795QuuW\nLTCzr2uf7C3aJ3uT9sveo32yN2m/7D3aJ3uT9sveo32ys9SlVkRERERERCZCgVNEREREREQmYlKB\n8z9MaL2yddone4/2yd6k/bL3aJ/sTdove4/2yd6k/bL3aJ/soIncNEhEREREREREXWpFRERERERk\nIrYcOM3sPWb2lJk9Y2b/YMTympl9slz+VTO7+0Y2VK7NzI6b2eNm9oSZfd/M/s6IOg+Z2ZKZfbsc\n/slubOt+YmZnzex75c/76yOWm5n9m/JY+a6Z/ehubOd+YmavHTgGvm1my2b2C0N1dKxMmJl93MwW\nzOwPBubdZmafNbPT5fjQmNd+qKxz2sw+tHNbfesbs1/+hZmdKr+jPmVmB8e89qrfd7I1Y/bJL5nZ\niwPfUe8d89qrnq/J1o3ZL58c2CdnzezbY16rY2UCxp0L63fL7tpSl1ozi4GngXcBLwBfAz7o7k8M\n1PmbwBvc/SNm9gHgZ9z9z23PZssoZvZq4NXu/k0zmwW+AfzJof3yEPB33f19u7SZ+46ZnQXe7O4j\nn/dUniT8beC9wFuBf+3ub925Ldzfyu+zF4G3uvu5gfkPoWNloszsHUAT+DV3f305758Dr7j7L5cn\nx4fc/e8Pve424OvAmwEnfNe9yd0v7+gHuEWN2S/vBh5z98zM/hnA8H4p653lKt93sjVj9skvAU13\n/5dXed01z9dk60btl6HlvwIsuftHRyw7i46VbTfuXBj4OfS7ZddstYXzLcAz7v6cu3eA/wK8f6jO\n+4FfLcu/BTxqZrbF95NNcPeX3f2bZXkFeBK4Y3e3Sjbh/YRfVu7uXwEOll+YsjMeBZ4dDJuyM9z9\ni8ArQ7MHf3f8KuFEYdgfBz7r7q+UJwKfBd4zsQ3dZ0btF3f/jLtn5eRXgDt3fMP2sTHHymZs5nxN\ntuhq+6U85/2zwCd2dKP2uaucC+t3yy7aauC8A3h+YPoFrgw2vTrlL6kl4PAW30+uk4UuzD8CfHXE\n4reZ2XfM7HfN7HU7umH7kwOfMbNvmNmHRyzfzPEkk/MBxp8Q6FjZecfc/eWy/EPg2Ig6OmZ2118B\nfnfMsmt938n2+vmym/PHx3QR1LGye34COO/up8cs17EyYUPnwvrdsot006BbkJk1gP8G/IK7Lw8t\n/iZwwt3/CPBvgf+x09u3D/24u/8o8FPA3yq74MgeYGZV4KeB/zpisY6VXebhmg/dSn0PMbN/DGTA\nb4ypou+7nfPvgPuANwIvA7+yu5sjQz7I1Vs3daxM0NXOhfW7ZedtNXC+CBwfmL6znDeyjpklwBxw\naYvvJ5tkZhXCAfYb7v7fh5e7+7K7N8vy7wAVMzuyw5u5r7j7i+V4AfgUoYvToM0cTzIZPwV8093P\nDy/QsbJrzne7lJfjhRF1dMzsAjP7OeB9wM/6mBtAbOL7TraJu59399zdC+A/MvpnrWNlF5TnvX8K\n+OS4OjpWJmfMubB+t+yirQbOrwEPmNk9ZQvBB4BPD9X5NNC9u9OfIdxsQH9NmKDyeoGPAU+6+78a\nU+dV3WtpzewthP8D+kPAhJhZvbxoHTOrA+8G/mCo2qeBv2TBjxFuMPAyshPG/gVax8quGfzd8SHg\nt0fU+d/Au83sUNmN8N3lPJkQM3sP8PeAn3b3tTF1NvN9J9tk6Fr/n2H0z3oz52uy/X4SOOXuL4xa\nqGNlcq5yLqzfLbso2cqLyrvU/TxhJ8TAx939+2b2UeDr7v5pws7+dTN7hnBB9Qe2a6NlrLcDfxH4\n3sBtuP8RcBeAu/97Qvj/G2aWAevAB/SHgIk6BnyqzC0J8J/d/ffM7CPQ2ye/Q7hD7TPAGvCXd2lb\n95Xyl/y7gL8+MG9wv+hYmTAz+wTwEHDEzF4AfhH4ZeA3zeyvAucIN93AzN4MfMTd/5q7v2Jm/5Rw\nMg3wUXffyg1VZIQx++UfAjXgs+X32VfKu9DfDvwnd38vY77vduEj3HLG7JOHzOyNhK6BZym/ywb3\nybjztV34CLekUfvF3T/GiHsD6FjZMePOhfW7ZRdt6bEoIiIiIiIiIteimwaJiIiIiIjIRChwioiI\niIiIyEQocIqIiIiIiMhEKHCKiIiIiIjIRChwioiIiIiIyEQocIqIiIiIiMhEKHCKiIiIiIjIRChw\nioiIiIiIyET8fz/kW3gcgFQzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNrVUUrEGcYB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "outputId": "21283226-2fee-4723-fd84-52ea5dbf5b1d"
      },
      "source": [
        "# Show original reconstruction.\n",
        "n_rows = 5\n",
        "n_cols = 8 # Must be divisible by 2.\n",
        "samples_per_col = int(n_cols / 2)\n",
        "sample_offset = np.random.randint(0, len(x_test) - n_rows * n_cols - 1)\n",
        "#sample_offset = 0\n",
        "\n",
        "img_idx = 0\n",
        "plt.figure(figsize=(n_cols * 2, n_rows * 2))\n",
        "for i in range(1, n_rows + 1):\n",
        "    for j in range(1, n_cols + 1, 2):\n",
        "        idx = n_cols * (i - 1) + j\n",
        "\n",
        "        # Display original.\n",
        "        ax = plt.subplot(n_rows, n_cols, idx)\n",
        "        ax.title.set_text('({:d}) Label: {:s} ->'.format(\n",
        "            img_idx,\n",
        "            str(target_dict[np.argmax(y_test[img_idx + sample_offset])])))\n",
        "        ax.imshow(x_test[img_idx + sample_offset].reshape(28, 28),\n",
        "                  cmap='gray_r',\n",
        "                  clim=(0, 1))\n",
        "        plt.gray()\n",
        "        ax.get_xaxis().set_visible(False)\n",
        "        ax.get_yaxis().set_visible(False)\n",
        "\n",
        "        # Display reconstruction.\n",
        "        ax = plt.subplot(n_rows, n_cols, idx + 1)\n",
        "        ax.title.set_text('({:d}) Reconstruction'.format(img_idx))\n",
        "        ax.imshow(vqvae.predict(\n",
        "            x_test[img_idx + sample_offset].reshape(-1, 28, 28, 1)).reshape(28, 28),\n",
        "            cmap='gray_r',\n",
        "            clim=(0, 1))\n",
        "        ax.get_xaxis().set_visible(False)\n",
        "        ax.get_yaxis().set_visible(False)\n",
        "\n",
        "        img_idx += 1\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RvrPLLrtY+8gjj3TKpNRd+jDgno+qMP1Ppuy5556hy7fffru1fem0ZPDgwck3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Jajn23Dhg0jYUkmd3GsWbMmcnznnXd6efz48V6OM30FojoYttO6XLXZX1Je8zWji3MxqY26effd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\nnX5SPghJQ6bu5Wu2E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zzxRKeNp3hKs6+ZUCpxlQ84HmfMmJF6vXvuucfq3/72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\nbGcCaGQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x720 with 40 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6PrI_FcGcqLY"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}